Analyzing the High Frequency of False-Positive Carcinoembryonic Antigen Elevations in Postoperative Pancreatic Ductal Adenocarcinoma

Analyzing the High Frequency of False-Positive Carcinoembryonic Antigen Elevations in Postoperative Pancreatic Ductal Adenocarcinoma | 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 Research Article Analyzing the High Frequency of False-Positive Carcinoembryonic Antigen Elevations in Postoperative Pancreatic Ductal Adenocarcinoma Haruka Tanaka, Yoshihiro Mise, Atsushi Takahashi, Fumihiro Kawano, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5271407/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Dec, 2024 Read the published version in Langenbeck's Archives of Surgery → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose The dynamics of postoperative carcinoembryonic antigen (CEA) in pancreatic ductal adenocarcinoma (PDAC) patients have not been well assessed. This study investigated the correlation between postoperative CEA elevations and tumor recurrence. Methods Medical records were retrospectively analyzed for 84 patients who received curative resection for PDAC from January 2019 to December 2020. Postoperative CEA levels were monitored for a minimum of 12 months. False-positive CEA elevation was defined as a CEA level exceeding 5 ng/mL without evidence of recurrence in imaging studies. Results Of the examined patients, 59 (70%) exhibited CEA > 5 ng/mL within the observation period. The sensitivity and specificity of elevated CEA levels for detecting recurrence were 84% and 41%, respectively. CEA elevations without tumor recurrence were observed in 27 patients, indicating a false-positive rate of 59%. More than half of these patients demonstrated peak CEA levels between 5 and 10 ng/mL, while only true-positive patients exhibited CEA levels exceeding 40.0 ng/mL. Conclusion CEA may rise in more than half of postoperative PDAC patients without recurrence. CEA alone is not a robust postoperative marker. Carcinoembryonic antigen CEA pancreatic ductal adenocarcinoma false-positive rate tumor recurrence Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Pancreatic ductal adenocarcinoma (PDAC) ranks as one of the most aggressive malignancies, with a five-year survival rate of approximately 12% [1–6]. According to the GLOBOCAN 2020 estimate, pancreatic cancer is the 12th most common malignancy and the 7th leading cause of cancer mortality worldwide [7]. Notably, its mortality rate nearly equates to its incidence rate, and the latter is increasing in most countries [8]. Less than 20% of patients are deemed eligible for surgery at the time of diagnosis. However, surgical resection with radical intent is critical for improving prognosis, underscoring the need for well-coordinated multidisciplinary treatment strategies [9]. Serum carcinoembryonic antigen (CEA) and carbohydrate antigen 19 − 9 (CA19-9) are biomarkers monitored in patients with PDAC to evaluate response to treatment and detect tumor recurrence [10]. A systematic review by Daamen et al. conducted in 2018 presented the diagnostic precision of these biomarkers, specifically in detecting tumor recurrence among postoperative PDAC patients. Their sensitivity and specificity were recorded as 50% and 65% for CEA, and 73% and 83% for CA19-9, respectively [11]. The National Comprehensive Cancer Network guidelines recommend only CA19-9 for pancreatic cancer follow-up due to its superior sensitivity and specificity. However, it must be noted that 5–10% of individuals either cannot synthesize or have a deficient secretion of the antigen due to their Lewis antigen-negative blood type [12]. Furthermore, benign conditions such as obstructive jaundice, hepatitis, and pulmonary diseases can result in elevations of CA19-9, thereby necessitating a careful analysis of laboratory results [13]. To compensate for these limitations, CEA is proposed as a potential alternative biomarker. CEA was first introduced by Gold and Freedman in 1965, who identified it as a glycoprotein primarily found in fetal colon tissues and colonic carcinomas [14, 15]. Further research expanded our understanding of CEA, revealing that CEA can elevate in a variety of conditions, both benign and malignant. Benign conditions include acute and chronic liver diseases, biliary obstruction, pancreatitis, inflammatory bowel disease, chronic lung disease, smoking, and diabetes [16–20]. Other studies suggest that monitoring serum CEA along with other tumor markers can effectively predict the prognosis in PDAC patients [21–25]. Nevertheless, the correlation between serum CEA elevation and tumor recurrence following radical resection has not been thoroughly evaluated. This study aims to delve deep into the dynamics of postoperative serum CEA levels, investigating the precision and clinical implications of CEA elevation in patients with PDAC who underwent curative surgery. 2. Methods 2.1 Study Population Medical records of 92 patients who received curative resection for PDAC at Juntendo University Hospital from January 2019 to December 2020 were studied retrospectively. Eighty-four patients were selected as the study population according to the following exclusion criteria: incomplete perioperative data of tumor markers, lost to follow-up within one year of surgery, and concurrent presence of other malignancies. All the clinical and pathological data was thoroughly reviewed using a prospectively maintained institutional database, with due approval obtained from the ethics committees of the institution (E22-0356). 2.2 Follow-up after Surgery The surgical procedures involved in the treatment have been described in prior publications. Patients were closely monitored through clinical examinations and laboratory tests during the regularly scheduled medical appointments. For each patient, the levels of serum CEA were observed for a minimum of 12 months after surgery. Imaging follow-up was conducted by contrast-enhanced computed tomography (CT) scan every 3 to 4 months, supplemented selectively by magnetic resonance imaging (MRI) or positron emission tomography (PET) scans. 2.3 Definitions Tumor recurrence was diagnosed primarily by imaging exams without histological confirmation via biopsy. A false-positive CEA elevation was defined as any elevation of CEA above 5 ng/mL (CEA > 5 ng/mL) without the evidence of recurrence in imaging studies for the subsequent 6 months. 2.4 Statistical Analysis The continuous variables were expressed as medians and interquartile ranges and compared using either the Student’s t-test or Mann-Whitney U test, depending on the requirements of the data distribution. Categorical variables were expressed as absolute numbers and frequencies and compared using a chi-square test. A receiver operating characteristic (ROC) curve analysis with the Youden Index was used to calculate the optimal cutoff value of serum CEA for predicting tumor recurrence. Logistic regression analysis was conducted to identify predictive factors that could lead to false-positive CEA elevations. Factors with a p-value lesser than 0.1 in the logistic regression analysis were further explored using multivariate analysis. All statistical analyses were conducted using IBM SPSS Statistics software, version 29.0 (IBM, Armonk, New York). A two-sided P-value less than 0.05 was recognized as the statistical significance in the study. 3. Results 3.1 Statistics A total of 92 PDAC patients underwent curative resection during the observation period. Among these patients, eight were excluded due to following reasons: insufficient data on tumor markers (n = 3), lost to follow-up within one year of surgery (n = 4), and concurrent with other malignancies (n = 1). Thus, 84 patients were included in this study, with their clinicopathological features summarized in Table 1. The median period of observation was 18 months. 3.2 Correlation between Postoperative CEA Elevations and Tumor Recurrence In the study cohort, CEA > 5 ng/mL was observed in 59 patients, accounting for approximately 70% of the studied population. 32 patients indicated CEA > 5 ng/mL with tumor recurrence, while 27 patients showed no signs of recurrence for the subsequent 6 months, implying a false-positive rate of 59%. This resulted in a sensitivity of 84% and a specificity of 41%, respectively. Figures 1 and 2 present the postoperative CEA trends in patients with CEA > 5 ng/mL. The CEA values for true-positive patients are plotted until the initiation of subsequent therapeutic chemotherapy or within a month of recurrence diagnosis. A general trend of continual increase was seen in a portion of these patients but with dispersion. The false-positive patients, on the other hand, tend to show a pattern resembling a chevron arch, indicating the periodic increase and decrease in CEA value. Among the 65 patients with CEA > 5 ng/mL, Fig. 3 demonstrates the highest CEA levels recorded within one year of resection. About 56% of the patients with false-positive CEA elevations had their peak CEA level between 5 and 10 ng/mL. This range also presented more false-positive cases than true-positive cases. However, it is noteworthy to highlight that peak CEA levels crossing the threshold of 40.0 ng/mL were exclusively recorded among true-positive patients with tumor recurrence. 3.3 Determination of Cutoff Value of Serum CEA for Tumor Recurrence Prediction Considering that CEA's sensitivity and specificity for predicting postoperative PDAC recurrence are lower compared to CA19-9, and its biomarker status is acknowledged but not fully comprehended, additional investigation was deemed necessary. In comparison to the conventional cutoff value of 5 ng/mL, ROC curve analysis identified an optimal cutoff value of 8.5 ng/mL for predicting tumor recurrence (Fig. 4 ). The area under the curve was calculated to be 0.697 (95% confidence interval [CI]: 0.58–0.81). The accuracy of recurrence prediction using CEA cutoffs of 5.0 ng/mL and 8.5 ng/mL are contrasted for comparison in Table 2. Raising the cutoff to 8.5 ng/mL decreases the false-positive rate from 59–28%, yet it also leads to a decrease in sensitivity from 84–63%. 3.4 Factors Associated with False-Positive CEA Elevations The study proceeded to explore the clinicopathological factors which are potentially associated with false-positive CEA elevations (Table 3). Logistic regression analysis resulted in the identification of postoperative elevation of hepatobiliary enzymes as an independent predictive factor (Odds ratio [OR]: 3.84, 95% CI: 1.25–11.89, p = 0.02). 4. Discussion The current study investigated the clinical implications of postoperative serum CEA levels in predicting tumor recurrence among patients who had undergone surgery for PDAC. More than half of the patients with CEA elevations over 5 ng/mL presented no signs of recurrence within the following 6 months. The results derived from this study suggest that CEA alone does not serve as a reliable recurrence indicator in PDAC patients. Despite the fact that false elevations of CEA are widely acknowledged to occur in patients with pancreatic cancer, their frequency and characteristics have not been thoroughly studied before. This study aimed to examine the accuracy of CEA and elucidate the traits of false positivity of CEA. A report by Litvak et al. discussed the characteristics of false elevations of CEA among patients who underwent surgery for colorectal cancer. They noted that over 90% of their patients with false elevations recorded their highest CEA levels between 5 and 10 ng/mL, recommending the repetition of the test before initiating further workup [26]. The present study, focusing on PDAC patients, observed a false-positive rate of 59% with over half of these patients recording their highest CEA levels between 5 and 10 ng/mL. Additionally, within this range, the frequency of false-positive cases overpowered that of true-positive cases. Contrarily, CEA levels exceeding 40 ng/mL were recorded exclusively among true-positive patients. Therefore, this study also recommends repeating the laboratory test to observe the serum CEA levels before proceeding to more extensive examinations, especially when CEA levels are within the range of 5 to 10 ng/mL. While the traditional cutoff value for detecting tumor recurrence using CEA stands at 5 ng/mL, results from the present study imply an optimal cutoff at 8.5 ng/mL. The establishment of 8.5 ng/mL as the cutoff, instead of the conventional 5 ng/mL, reduces the rate of false positivity by over half – from 59–28%. However, this adjustment comes with a drop in sensitivity from 84–63%, concurrently increasing the risk of failing to identify early recurrence. The present study displayed a significant correlation between false-positive CEA and increased hepatobiliary enzymes after surgery. The perioperative elevations of hepatobiliary enzymes in pancreatic cancer can be attributed to conditions such as liver dysfunction, cholangitis, and biliary obstruction, also recognized as benign causes of CEA elevations [16, 17]. Several studies have attempted to unravel the mechanisms leading to CEA elevations in benign liver and pancreas diseases. For example, Khoo and MacKay hypothesized that liver regeneration, which is associated with CEA production, may become accelerated in the presence of liver disease, leading to increased CEA levels. Liver dysfunction, on the other hand, could result in a decreased excretion of normally produced CEA [27]. Another explanation for benign CEA elevations lies in the existence of glycoproteins which exhibit CEA-like activity, such as nonspecific cross-reacting antigen 2 (NCA-2), which may increase during liver inflammation [27]. The rise of this CEA-like glycoprotein during the inflammatory process may result in benign CEA elevations [28]. NCA-2 is a structurally similar antigen to CEA within the CEA gene family and produced in normal tissues. The presence of this antigen in normal serum, alongside its associations with cancerous tissues, requires further investigation. Yet several studies have demonstrated the cross-reactivity with CEA in various laboratory reagents, potentially leading to seemingly increased CEA levels [29, 30]. Further examinations should be conducted using a reagent exclusively reacting to CEA for comparison. The findings presented in this study hold strong practical implications for the follow-up of postoperative PDAC patients. Not only do the findings underline the necessity for a meticulous follow-up protocol, but they also emphasize the importance of cautious interpretation of laboratory results. However, the outcomes presented in this study stand upon the foundation of retrospective data, necessitating further prospective evaluations before elevating them into clinical practice. The culmination of this study does not aim to conclude the function of CEA as an exclusive tumor marker for pancreatic cancer. Tumor recurrence should be diagnosed using imaging exams and, if required, a biopsy before initiating the therapeutic chemotherapy. 5. Conclusion Observing postoperative CEA elevations between 5 and 10 ng/mL in PDAC patients implies nearly equal probabilities of being either true-positive or false-positive. Therefore, repeating the test and observing the trend of CEA elevation are prudent steps to take before escalating to further investigations. While CEA exhibits potential as a monitoring test rather than a diagnostic test with a single absolute concentration, imaging studies should always be performed to confirm the diagnosis of recurrence before deciding on the subsequent therapeutic treatment. Further investigations on multidisciplinary treatment strategies and patient care protocols are indispensable for prognosis improvement in PDAC. Declarations The authors have nothing to disclose. Author Contribution Study conception and design: H.T., Y.M., A. S.Acquisition of data: H.T., A. T., F. K., Y.T., H.I., H. I., R. Y.Analysis and interpretation of data: H. T., Y.M., A.S.Drafting of manuscript: H.T. and Y. M.Critical revision of manuscript: Y.M. and A. S.All authors reviewed the manuscript. Acknowledgments: We would like to express our gratitude to the members of the Department of Hepatobiliary-Pancreatic Surgery at Juntendo University Graduate School of Medicine for their advice and support during this research. References Chu LC, Goggins MG, Fishman EK. Diagnosis and Detection of Pancreatic Cancer. Cancer J. 2017;23(6):333-42. Ciernikova S, Earl J, García Bermejo ML, Stevurkova V, Carrato A, Smolkova B. Epigenetic Landscape in Pancreatic Ductal Adenocarcinoma: On the Way to Overcoming Drug Resistance? Int J Mol Sci. 2020;21(11). Torphy RJ, Fujiwara Y, Schulick RD. Pancreatic cancer treatment: better, but a long way to go. Surg Today. 2020;50(10):1117-25. Kannan S, Shaik Syed Ali P, Sheeza A. Short report - Lethal and aggressive pancreatic cancer: molecular pathogenesis, cellular heterogeneity, and biomarkers of pancreatic ductal adenocarcinoma. Eur Rev Med Pharmacol Sci. 2022;26(3):1017-9. Halbrook CJ, Lyssiotis CA, Pasca di Magliano M, Maitra A. Pancreatic cancer: Advances and challenges. Cell. 2023;186(8):1729-54. Yasuda S, Nagai M, Terai T, Kohara Y, Sho M. Essential updates 2021/2022: Surgical outcomes of oligometastasis in pancreatic ductal adenocarcinoma. Ann Gastroenterol Surg. 2023;7(3):358-66. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49. Howlader N NA, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). SEER Cancer Statistics Review, 1975-2018 National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2018/, based on November 2020 SEER data submission, posted to the SEER web site, April 2021.2021 [updated April 2021]. Kleeff J, Korc M, Apte M, La Vecchia C, Johnson CD, Biankin AV, et al. Pancreatic cancer. Nat Rev Dis Primers. 2016;2:16022. Meng Q, Shi S, Liang C, Liang D, Xu W, Ji S, et al. Diagnostic and prognostic value of carcinoembryonic antigen in pancreatic cancer: a systematic review and meta-analysis. Onco Targets Ther. 2017;10:4591-8. Daamen LA, Groot VP, Heerkens HD, Intven MPW, van Santvoort HC, Molenaar IQ. Systematic review on the role of serum tumor markers in the detection of recurrent pancreatic cancer. HPB (Oxford). 2018;20(4):297-304. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology. Pancreatic Adenocarcinoma. NCCN [Internet] 2023 Dec 13 [cited 2024 Feb 14];(1). Available from: http://www.nccn.org/professionals/physician_gls/pdf/pancreatic.pdf 2023 [ Kim S, Park BK, Seo JH, Choi J, Choi JW, Lee CK, et al. Carbohydrate antigen 19-9 elevation without evidence of malignant or pancreatobiliary diseases. Sci Rep. 2020;10(1):8820. Gold P, Freedman SO. DEMONSTRATION OF TUMOR-SPECIFIC ANTIGENS IN HUMAN COLONIC CARCINOMATA BY IMMUNOLOGICAL TOLERANCE AND ABSORPTION TECHNIQUES. J Exp Med. 1965;121(3):439-62. Gold P, Freedman SO. Specific carcinoembryonic antigens of the human digestive system. J Exp Med. 1965;122(3):467-81. Loewenstein MS, Zamcheck N. Carcinoembryonic antigen (CEA) levels in benign gastrointestinal disease states. Cancer. 1978;42(3 Suppl):1412-8. Begent RH. The value of carcinoembryonic antigen measurement in clinical practice. Ann Clin Biochem. 1984;21 ( Pt 4):231-8. Hao C, Zhang G, Zhang L. Serum CEA levels in 49 different types of cancer and noncancer diseases. Prog Mol Biol Transl Sci. 2019;162:213-27. Yang Y, Xu M, Huang H, Jiang X, Gong K, Liu Y, et al. Serum carcinoembryonic antigen elevation in benign lung diseases. Sci Rep. 2021;11(1):19044. Zhang D. Correlation Between Blood Glucose Control and Levels of Carbohydrate Antigen 19-9 and Carcinoembryonic Antigen in Patients with Type-2 Diabetes Mellitus. Diabetes Metab Syndr Obes. 2022;15:2489-95. Reitz D, Gerger A, Seidel J, Kornprat P, Samonigg H, Stotz M, et al. Combination of tumour markers CEA and CA19-9 improves the prognostic prediction in patients with pancreatic cancer. J Clin Pathol. 2015;68(6):427-33. Zhou G, Liu X, Wang X, Jin D, Chen Y, Li G, et al. Combination of preoperative CEA and CA19-9 improves prediction outcomes in patients with resectable pancreatic adenocarcinoma: results from a large follow-up cohort. Onco Targets Ther. 2017;10:1199-206. Esen E, Aslan M, Morkavuk SB, Azili C, Ersoz S, Bahcecioglu IB, et al. Can combined use of tumor markers in pancreatic cancer be a solution to short- and long-term consequences?: A retrospective study. Medicine (Baltimore). 2023;102(11):e33325. Li X, Li S, Liu L, Hong J, Zhao T, Gao C. Effect of Perioperative CEA and CA24-2 on Prognosis of Early Resectable Pancreatic Ductal Adenocarcinoma. J Cancer. 2020;11(1):9-15. Chang JC, Kundranda M. Novel Diagnostic and Predictive Biomarkers in Pancreatic Adenocarcinoma. Int J Mol Sci. 2017;18(3). Litvak A, Cercek A, Segal N, Reidy-Lagunes D, Stadler ZK, Yaeger RD, et al. False-positive elevations of carcinoembryonic antigen in patients with a history of resected colorectal cancer. J Natl Compr Canc Netw. 2014;12(6):907-13. Khoo SK, Mackay IR. Carcinoembryonic antigen in serum in diseases of the liver and pancreas. J Clin Pathol. 1973;26(7):470-5. Lurie BB, Loewenstein MS, Zamcheck N. Elevated carcinoembryonic antigen levels and biliary tract obstruction. Jama. 1975;233(4):326-30. Burtin P, Chavanel G, Hirsch-Marie H. Characterization of a second normal antigen that cross-reacts with CEA. J Immunol. 1973;111(6):1926-8. Börmer OP. Major disagreements between immunoassays of carcinoembryonic antigen may be caused by nonspecific cross-reacting antigen 2 (NCA-2). Clin Chem. 1991;37(10 Pt 1):1736-9. Tables Table 1. Patient Backgrounds (n=84) Observation period (mo.) 18 [12 – 26] Age (y) 69 [62 - 75] Sex Male Female 46 38 (55%) (45%) Location Head Body Tail 51 20 13 (61%) (24%) (15%) Resectability R BR UR 52 21 11 (62%) (25%) (13%) Tumor size (mm) 25.5 [18 – 39.25] pT T0 T1 T2 T3 1 12 4 67 (1%) (14%) (5%) (80%) pN N0 N1 N2 40 28 16 (48%) (33%) (19%) pM M0 M1 82 2 (98%) (2%) Resection margin R0 R1 80 4 (95%) (5%) Neoadjuvant chemotherapy Yes No 54 30 (64%) (36%) Adjuvant chemotherapy Yes No 71 13 (85%) (15%) Smoking Yes No 40 44 (48%) (52%) HbA1c ≧6.5% ＜6.5% 29 55 (35%) (65%) Preoperative hepatobiliary enzymes Elevated Normal 22 62 (26%) (74%) Postoperative hepatobiliary enzymes Elevated Normal 40 44 (48%) (52%) Abbreviations: mo, months; y, years; R, resectable; BR, borderline resectable; UR, unresectable. Table 2. The accuracy of carcinoembryonic antigen (CEA) elevation to predict tumor recurrence compared between cutoffs 5.0 ng/mL and 8.5 ng/mL Cutoff > 5 ng/mL ( n =59 ) Cutoff > 8.5 ng/mL ( n = 37 ) True positive CEA elevation 32 24 False positive CEA elevation 27 (FPR = 59%) 13 (FPR = 28%) Sensitivity 84% 63% Specificity 41% 71% Abbreviations: FPR, false-positive rate. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Dec, 2024 Read the published version in Langenbeck's Archives of Surgery → Version 1 posted Editorial decision: Revision requested 05 Nov, 2024 Reviews received at journal 03 Nov, 2024 Reviews received at journal 28 Oct, 2024 Reviewers agreed at journal 23 Oct, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers invited by journal 19 Oct, 2024 Editor assigned by journal 17 Oct, 2024 Submission checks completed at journal 17 Oct, 2024 First submitted to journal 15 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5271407","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368051420,"identity":"e5ec3af0-27ce-4330-9e5e-abd1200cf687","order_by":0,"name":"Haruka Tanaka","email":"","orcid":"","institution":"Juntendo University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haruka","middleName":"","lastName":"Tanaka","suffix":""},{"id":368051422,"identity":"4107288a-4a91-4b5c-85bd-f1bcbb7babb8","order_by":1,"name":"Yoshihiro 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recurrence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5271407/v1/59349f6caad8f701c81d4447.png"},{"id":67367483,"identity":"f1135bbb-deda-480f-a6a2-235920079963","added_by":"auto","created_at":"2024-10-24 07:34:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":282157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePostoperative carcinoembryonic antigen (CEA) levels observed for 12 months after resection for false-positive CEA\u0026gt;5 ng/mL patients without tumor recurrence.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5271407/v1/e8871d4015a81bece8da71e9.png"},{"id":67367487,"identity":"5a084a4d-40b8-463c-b17e-4eb22efb8766","added_by":"auto","created_at":"2024-10-24 07:34:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe highest levels of carcinoembryonic antigen (CEA) observed within 1 year of resection compared between false-positive patients without tumor recurrence and true-positive patients with tumor recurrence.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5271407/v1/55be603b5ab9cfcc8f87df21.png"},{"id":67367484,"identity":"574dfc71-4797-4e2e-96cd-0fc2777e0e64","added_by":"auto","created_at":"2024-10-24 07:34:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":675292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe optimal cutoff value of carcinoembryonic antigen (CEA) to predict tumor recurrence calculated using the receiver operating characteristic (ROC) curve analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5271407/v1/f2e2addb7eefa39a5e4c2c3f.png"},{"id":71552437,"identity":"7e77e32d-0056-40f8-8b6b-f412677249eb","added_by":"auto","created_at":"2024-12-16 16:06:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2158119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5271407/v1/4a094a34-a344-41ea-aa3b-8e219c52f80e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the High Frequency of False-Positive Carcinoembryonic Antigen Elevations in Postoperative Pancreatic Ductal Adenocarcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) ranks as one of the most aggressive malignancies, with a five-year survival rate of approximately 12% [1\u0026ndash;6]. According to the GLOBOCAN 2020 estimate, pancreatic cancer is the 12th most common malignancy and the 7th leading cause of cancer mortality worldwide [7]. Notably, its mortality rate nearly equates to its incidence rate, and the latter is increasing in most countries [8]. Less than 20% of patients are deemed eligible for surgery at the time of diagnosis. However, surgical resection with radical intent is critical for improving prognosis, underscoring the need for well-coordinated multidisciplinary treatment strategies [9].\u003c/p\u003e \u003cp\u003eSerum carcinoembryonic antigen (CEA) and carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9) are biomarkers monitored in patients with PDAC to evaluate response to treatment and detect tumor recurrence [10]. A systematic review by Daamen et al. conducted in 2018 presented the diagnostic precision of these biomarkers, specifically in detecting tumor recurrence among postoperative PDAC patients. Their sensitivity and specificity were recorded as 50% and 65% for CEA, and 73% and 83% for CA19-9, respectively [11]. The National Comprehensive Cancer Network guidelines recommend only CA19-9 for pancreatic cancer follow-up due to its superior sensitivity and specificity. However, it must be noted that 5\u0026ndash;10% of individuals either cannot synthesize or have a deficient secretion of the antigen due to their Lewis antigen-negative blood type [12]. Furthermore, benign conditions such as obstructive jaundice, hepatitis, and pulmonary diseases can result in elevations of CA19-9, thereby necessitating a careful analysis of laboratory results [13].\u003c/p\u003e \u003cp\u003eTo compensate for these limitations, CEA is proposed as a potential alternative biomarker. CEA was first introduced by Gold and Freedman in 1965, who identified it as a glycoprotein primarily found in fetal colon tissues and colonic carcinomas [14, 15]. Further research expanded our understanding of CEA, revealing that CEA can elevate in a variety of conditions, both benign and malignant. Benign conditions include acute and chronic liver diseases, biliary obstruction, pancreatitis, inflammatory bowel disease, chronic lung disease, smoking, and diabetes [16\u0026ndash;20]. Other studies suggest that monitoring serum CEA along with other tumor markers can effectively predict the prognosis in PDAC patients [21\u0026ndash;25]. Nevertheless, the correlation between serum CEA elevation and tumor recurrence following radical resection has not been thoroughly evaluated. This study aims to delve deep into the dynamics of postoperative serum CEA levels, investigating the precision and clinical implications of CEA elevation in patients with PDAC who underwent curative surgery.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eMedical records of 92 patients who received curative resection for PDAC at Juntendo University Hospital from January 2019 to December 2020 were studied retrospectively. Eighty-four patients were selected as the study population according to the following exclusion criteria: incomplete perioperative data of tumor markers, lost to follow-up within one year of surgery, and concurrent presence of other malignancies. All the clinical and pathological data was thoroughly reviewed using a prospectively maintained institutional database, with due approval obtained from the ethics committees of the institution (E22-0356).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Follow-up after Surgery\u003c/h2\u003e \u003cp\u003eThe surgical procedures involved in the treatment have been described in prior publications. Patients were closely monitored through clinical examinations and laboratory tests during the regularly scheduled medical appointments. For each patient, the levels of serum CEA were observed for a minimum of 12 months after surgery. Imaging follow-up was conducted by contrast-enhanced computed tomography (CT) scan every 3 to 4 months, supplemented selectively by magnetic resonance imaging (MRI) or positron emission tomography (PET) scans.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definitions\u003c/h2\u003e \u003cp\u003eTumor recurrence was diagnosed primarily by imaging exams without histological confirmation via biopsy. A false-positive CEA elevation was defined as any elevation of CEA above 5 ng/mL (CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL) without the evidence of recurrence in imaging studies for the subsequent 6 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe continuous variables were expressed as medians and interquartile ranges and compared using either the Student\u0026rsquo;s t-test or Mann-Whitney U test, depending on the requirements of the data distribution. Categorical variables were expressed as absolute numbers and frequencies and compared using a chi-square test. A receiver operating characteristic (ROC) curve analysis with the Youden Index was used to calculate the optimal cutoff value of serum CEA for predicting tumor recurrence. Logistic regression analysis was conducted to identify predictive factors that could lead to false-positive CEA elevations. Factors with a p-value lesser than 0.1 in the logistic regression analysis were further explored using multivariate analysis. All statistical analyses were conducted using IBM SPSS Statistics software, version 29.0 (IBM, Armonk, New York). A two-sided P-value less than 0.05 was recognized as the statistical significance in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Statistics\u003c/h2\u003e \u003cp\u003eA total of 92 PDAC patients underwent curative resection during the observation period. Among these patients, eight were excluded due to following reasons: insufficient data on tumor markers (n\u0026thinsp;=\u0026thinsp;3), lost to follow-up within one year of surgery (n\u0026thinsp;=\u0026thinsp;4), and concurrent with other malignancies (n\u0026thinsp;=\u0026thinsp;1). Thus, 84 patients were included in this study, with their clinicopathological features summarized in Table\u0026nbsp;1. The median period of observation was 18 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation between Postoperative CEA Elevations and Tumor Recurrence\u003c/h2\u003e \u003cp\u003eIn the study cohort, CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL was observed in 59 patients, accounting for approximately 70% of the studied population. 32 patients indicated CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL with tumor recurrence, while 27 patients showed no signs of recurrence for the subsequent 6 months, implying a false-positive rate of 59%. This resulted in a sensitivity of 84% and a specificity of 41%, respectively.\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2 present the postoperative CEA trends in patients with CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL. The CEA values for true-positive patients are plotted until the initiation of subsequent therapeutic chemotherapy or within a month of recurrence diagnosis. A general trend of continual increase was seen in a portion of these patients but with dispersion. The false-positive patients, on the other hand, tend to show a pattern resembling a chevron arch, indicating the periodic increase and decrease in CEA value. Among the 65 patients with CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the highest CEA levels recorded within one year of resection. About 56% of the patients with false-positive CEA elevations had their peak CEA level between 5 and 10 ng/mL. This range also presented more false-positive cases than true-positive cases. However, it is noteworthy to highlight that peak CEA levels crossing the threshold of 40.0 ng/mL were exclusively recorded among true-positive patients with tumor recurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Determination of Cutoff Value of Serum CEA for Tumor Recurrence Prediction\u003c/h2\u003e \u003cp\u003eConsidering that CEA's sensitivity and specificity for predicting postoperative PDAC recurrence are lower compared to CA19-9, and its biomarker status is acknowledged but not fully comprehended, additional investigation was deemed necessary. In comparison to the conventional cutoff value of 5 ng/mL, ROC curve analysis identified an optimal cutoff value of 8.5 ng/mL for predicting tumor recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The area under the curve was calculated to be 0.697 (95% confidence interval [CI]: 0.58\u0026ndash;0.81). The accuracy of recurrence prediction using CEA cutoffs of 5.0 ng/mL and 8.5 ng/mL are contrasted for comparison in Table\u0026nbsp;2. Raising the cutoff to 8.5 ng/mL decreases the false-positive rate from 59\u0026ndash;28%, yet it also leads to a decrease in sensitivity from 84\u0026ndash;63%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Factors Associated with False-Positive CEA Elevations\u003c/h2\u003e \u003cp\u003eThe study proceeded to explore the clinicopathological factors which are potentially associated with false-positive CEA elevations (Table\u0026nbsp;3). Logistic regression analysis resulted in the identification of postoperative elevation of hepatobiliary enzymes as an independent predictive factor (Odds ratio [OR]: 3.84, 95% CI: 1.25\u0026ndash;11.89, p\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe current study investigated the clinical implications of postoperative serum CEA levels in predicting tumor recurrence among patients who had undergone surgery for PDAC. More than half of the patients with CEA elevations over 5 ng/mL presented no signs of recurrence within the following 6 months. The results derived from this study suggest that CEA alone does not serve as a reliable recurrence indicator in PDAC patients.\u003c/p\u003e \u003cp\u003eDespite the fact that false elevations of CEA are widely acknowledged to occur in patients with pancreatic cancer, their frequency and characteristics have not been thoroughly studied before. This study aimed to examine the accuracy of CEA and elucidate the traits of false positivity of CEA. A report by Litvak et al. discussed the characteristics of false elevations of CEA among patients who underwent surgery for colorectal cancer. They noted that over 90% of their patients with false elevations recorded their highest CEA levels between 5 and 10 ng/mL, recommending the repetition of the test before initiating further workup [26]. The present study, focusing on PDAC patients, observed a false-positive rate of 59% with over half of these patients recording their highest CEA levels between 5 and 10 ng/mL. Additionally, within this range, the frequency of false-positive cases overpowered that of true-positive cases. Contrarily, CEA levels exceeding 40 ng/mL were recorded exclusively among true-positive patients. Therefore, this study also recommends repeating the laboratory test to observe the serum CEA levels before proceeding to more extensive examinations, especially when CEA levels are within the range of 5 to 10 ng/mL.\u003c/p\u003e \u003cp\u003eWhile the traditional cutoff value for detecting tumor recurrence using CEA stands at 5 ng/mL, results from the present study imply an optimal cutoff at 8.5 ng/mL. The establishment of 8.5 ng/mL as the cutoff, instead of the conventional 5 ng/mL, reduces the rate of false positivity by over half \u0026ndash; from 59\u0026ndash;28%. However, this adjustment comes with a drop in sensitivity from 84\u0026ndash;63%, concurrently increasing the risk of failing to identify early recurrence.\u003c/p\u003e \u003cp\u003eThe present study displayed a significant correlation between false-positive CEA and increased hepatobiliary enzymes after surgery. The perioperative elevations of hepatobiliary enzymes in pancreatic cancer can be attributed to conditions such as liver dysfunction, cholangitis, and biliary obstruction, also recognized as benign causes of CEA elevations [16, 17]. Several studies have attempted to unravel the mechanisms leading to CEA elevations in benign liver and pancreas diseases. For example, Khoo and MacKay hypothesized that liver regeneration, which is associated with CEA production, may become accelerated in the presence of liver disease, leading to increased CEA levels. Liver dysfunction, on the other hand, could result in a decreased excretion of normally produced CEA [27].\u003c/p\u003e \u003cp\u003eAnother explanation for benign CEA elevations lies in the existence of glycoproteins which exhibit CEA-like activity, such as nonspecific cross-reacting antigen 2 (NCA-2), which may increase during liver inflammation [27]. The rise of this CEA-like glycoprotein during the inflammatory process may result in benign CEA elevations [28]. NCA-2 is a structurally similar antigen to CEA within the CEA gene family and produced in normal tissues. The presence of this antigen in normal serum, alongside its associations with cancerous tissues, requires further investigation. Yet several studies have demonstrated the cross-reactivity with CEA in various laboratory reagents, potentially leading to seemingly increased CEA levels [29, 30]. Further examinations should be conducted using a reagent exclusively reacting to CEA for comparison.\u003c/p\u003e \u003cp\u003eThe findings presented in this study hold strong practical implications for the follow-up of postoperative PDAC patients. Not only do the findings underline the necessity for a meticulous follow-up protocol, but they also emphasize the importance of cautious interpretation of laboratory results. However, the outcomes presented in this study stand upon the foundation of retrospective data, necessitating further prospective evaluations before elevating them into clinical practice. The culmination of this study does not aim to conclude the function of CEA as an exclusive tumor marker for pancreatic cancer. Tumor recurrence should be diagnosed using imaging exams and, if required, a biopsy before initiating the therapeutic chemotherapy.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eObserving postoperative CEA elevations between 5 and 10 ng/mL in PDAC patients implies nearly equal probabilities of being either true-positive or false-positive. Therefore, repeating the test and observing the trend of CEA elevation are prudent steps to take before escalating to further investigations. While CEA exhibits potential as a monitoring test rather than a diagnostic test with a single absolute concentration, imaging studies should always be performed to confirm the diagnosis of recurrence before deciding on the subsequent therapeutic treatment. Further investigations on multidisciplinary treatment strategies and patient care protocols are indispensable for prognosis improvement in PDAC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy conception and design: H.T., Y.M., A. S.Acquisition of data: H.T., A. T., F. K., Y.T., H.I., H. I., R. Y.Analysis and interpretation of data: H. T., Y.M., A.S.Drafting of manuscript: H.T. and Y. M.Critical revision of manuscript: Y.M. and A. S.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eWe would like to express our gratitude to the members of the Department of Hepatobiliary-Pancreatic Surgery at Juntendo University Graduate School of Medicine for their advice and support during this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChu LC, Goggins MG, Fishman EK. Diagnosis and Detection of Pancreatic Cancer. Cancer J. 2017;23(6):333-42.\u003c/li\u003e\n\u003cli\u003eCiernikova S, Earl J, Garc\u0026iacute;a Bermejo ML, Stevurkova V, Carrato A, Smolkova B. Epigenetic Landscape in Pancreatic Ductal Adenocarcinoma: On the Way to Overcoming Drug Resistance? Int J Mol Sci. 2020;21(11).\u003c/li\u003e\n\u003cli\u003eTorphy RJ, Fujiwara Y, Schulick RD. Pancreatic cancer treatment: better, but a long way to go. Surg Today. 2020;50(10):1117-25.\u003c/li\u003e\n\u003cli\u003eKannan S, Shaik Syed Ali P, Sheeza A. Short report - Lethal and aggressive pancreatic cancer: molecular pathogenesis, cellular heterogeneity, and biomarkers of pancreatic ductal adenocarcinoma. Eur Rev Med Pharmacol Sci. 2022;26(3):1017-9.\u003c/li\u003e\n\u003cli\u003eHalbrook CJ, Lyssiotis CA, Pasca di Magliano M, Maitra A. Pancreatic cancer: Advances and challenges. Cell. 2023;186(8):1729-54.\u003c/li\u003e\n\u003cli\u003eYasuda S, Nagai M, Terai T, Kohara Y, Sho M. Essential updates 2021/2022: Surgical outcomes of oligometastasis in pancreatic ductal adenocarcinoma. Ann Gastroenterol Surg. 2023;7(3):358-66.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49.\u003c/li\u003e\n\u003cli\u003eHowlader N NA, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). SEER Cancer Statistics Review, 1975-2018 National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2018/, based on November 2020 SEER data submission, posted to the SEER web site, April 2021.2021 [updated April 2021].\u003c/li\u003e\n\u003cli\u003eKleeff J, Korc M, Apte M, La Vecchia C, Johnson CD, Biankin AV, et al. Pancreatic cancer. Nat Rev Dis Primers. 2016;2:16022.\u003c/li\u003e\n\u003cli\u003eMeng Q, Shi S, Liang C, Liang D, Xu W, Ji S, et al. Diagnostic and prognostic value of carcinoembryonic antigen in pancreatic cancer: a systematic review and meta-analysis. Onco Targets Ther. 2017;10:4591-8.\u003c/li\u003e\n\u003cli\u003eDaamen LA, Groot VP, Heerkens HD, Intven MPW, van Santvoort HC, Molenaar IQ. Systematic review on the role of serum tumor markers in the detection of recurrent pancreatic cancer. HPB (Oxford). 2018;20(4):297-304.\u003c/li\u003e\n\u003cli\u003eNational Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology. Pancreatic Adenocarcinoma. NCCN [Internet] 2023 Dec 13 [cited 2024 Feb 14];(1). Available from: http://www.nccn.org/professionals/physician_gls/pdf/pancreatic.pdf 2023 [\u003c/li\u003e\n\u003cli\u003eKim S, Park BK, Seo JH, Choi J, Choi JW, Lee CK, et al. Carbohydrate antigen 19-9 elevation without evidence of malignant or pancreatobiliary diseases. Sci Rep. 2020;10(1):8820.\u003c/li\u003e\n\u003cli\u003eGold P, Freedman SO. DEMONSTRATION OF TUMOR-SPECIFIC ANTIGENS IN HUMAN COLONIC CARCINOMATA BY IMMUNOLOGICAL TOLERANCE AND ABSORPTION TECHNIQUES. J Exp Med. 1965;121(3):439-62.\u003c/li\u003e\n\u003cli\u003eGold P, Freedman SO. Specific carcinoembryonic antigens of the human digestive system. J Exp Med. 1965;122(3):467-81.\u003c/li\u003e\n\u003cli\u003eLoewenstein MS, Zamcheck N. Carcinoembryonic antigen (CEA) levels in benign gastrointestinal disease states. Cancer. 1978;42(3 Suppl):1412-8.\u003c/li\u003e\n\u003cli\u003eBegent RH. The value of carcinoembryonic antigen measurement in clinical practice. Ann Clin Biochem. 1984;21 ( Pt 4):231-8.\u003c/li\u003e\n\u003cli\u003eHao C, Zhang G, Zhang L. Serum CEA levels in 49 different types of cancer and noncancer diseases. Prog Mol Biol Transl Sci. 2019;162:213-27.\u003c/li\u003e\n\u003cli\u003eYang Y, Xu M, Huang H, Jiang X, Gong K, Liu Y, et al. Serum carcinoembryonic antigen elevation in benign lung diseases. Sci Rep. 2021;11(1):19044.\u003c/li\u003e\n\u003cli\u003eZhang D. Correlation Between Blood Glucose Control and Levels of Carbohydrate Antigen 19-9 and Carcinoembryonic Antigen in Patients with Type-2 Diabetes Mellitus. Diabetes Metab Syndr Obes. 2022;15:2489-95.\u003c/li\u003e\n\u003cli\u003eReitz D, Gerger A, Seidel J, Kornprat P, Samonigg H, Stotz M, et al. Combination of tumour markers CEA and CA19-9 improves the prognostic prediction in patients with pancreatic cancer. J Clin Pathol. 2015;68(6):427-33.\u003c/li\u003e\n\u003cli\u003eZhou G, Liu X, Wang X, Jin D, Chen Y, Li G, et al. Combination of preoperative CEA and CA19-9 improves prediction outcomes in patients with resectable pancreatic adenocarcinoma: results from a large follow-up cohort. Onco Targets Ther. 2017;10:1199-206.\u003c/li\u003e\n\u003cli\u003eEsen E, Aslan M, Morkavuk SB, Azili C, Ersoz S, Bahcecioglu IB, et al. Can combined use of tumor markers in pancreatic cancer be a solution to short- and long-term consequences?: A retrospective study. Medicine (Baltimore). 2023;102(11):e33325.\u003c/li\u003e\n\u003cli\u003eLi X, Li S, Liu L, Hong J, Zhao T, Gao C. Effect of Perioperative CEA and CA24-2 on Prognosis of Early Resectable Pancreatic Ductal Adenocarcinoma. J Cancer. 2020;11(1):9-15.\u003c/li\u003e\n\u003cli\u003eChang JC, Kundranda M. Novel Diagnostic and Predictive Biomarkers in Pancreatic Adenocarcinoma. Int J Mol Sci. 2017;18(3).\u003c/li\u003e\n\u003cli\u003eLitvak A, Cercek A, Segal N, Reidy-Lagunes D, Stadler ZK, Yaeger RD, et al. False-positive elevations of carcinoembryonic antigen in patients with a history of resected colorectal cancer. J Natl Compr Canc Netw. 2014;12(6):907-13.\u003c/li\u003e\n\u003cli\u003eKhoo SK, Mackay IR. Carcinoembryonic antigen in serum in diseases of the liver and pancreas. J Clin Pathol. 1973;26(7):470-5.\u003c/li\u003e\n\u003cli\u003eLurie BB, Loewenstein MS, Zamcheck N. Elevated carcinoembryonic antigen levels and biliary tract obstruction. Jama. 1975;233(4):326-30.\u003c/li\u003e\n\u003cli\u003eBurtin P, Chavanel G, Hirsch-Marie H. Characterization of a second normal antigen that cross-reacts with CEA. J Immunol. 1973;111(6):1926-8.\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;rmer OP. Major disagreements between immunoassays of carcinoembryonic antigen may be caused by nonspecific cross-reacting antigen 2 (NCA-2). Clin Chem. 1991;37(10 Pt 1):1736-9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 71.9577%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Patient Backgrounds (n=84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eObservation period (mo.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e[12 \u0026ndash; 26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e[62 - 75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eSex \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(55%)\u003c/p\u003e\n \u003cp\u003e(45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003cp\u003eHead\u003c/p\u003e\n \u003cp\u003eBody\u003c/p\u003e\n \u003cp\u003eTail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(61%)\u003c/p\u003e\n \u003cp\u003e(24%)\u003c/p\u003e\n \u003cp\u003e(15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eResectability\u003c/p\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003cp\u003eBR\u003c/p\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(62%)\u003c/p\u003e\n \u003cp\u003e(25%)\u003c/p\u003e\n \u003cp\u003e(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eTumor size (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e[18 \u0026ndash; 39.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003epT \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eT0\u003c/p\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1%)\u003c/p\u003e\n \u003cp\u003e(14%)\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003cp\u003e(80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003epN\u003c/p\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(48%)\u003c/p\u003e\n \u003cp\u003e(33%)\u003c/p\u003e\n \u003cp\u003e(19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003epM \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(98%)\u003c/p\u003e\n \u003cp\u003e(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eResection margin\u003c/p\u003e\n \u003cp\u003eR0\u003c/p\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95%)\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eNeoadjuvant chemotherapy\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(64%)\u003c/p\u003e\n \u003cp\u003e(36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(85%)\u003c/p\u003e\n \u003cp\u003e(15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(48%)\u003c/p\u003e\n \u003cp\u003e(52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003cp\u003e≧6.5%\u003c/p\u003e\n \u003cp\u003e＜6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(35%)\u003c/p\u003e\n \u003cp\u003e(65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003ePreoperative hepatobiliary enzymes\u003c/p\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(26%)\u003c/p\u003e\n \u003cp\u003e(74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.6745%;\"\u003e\n \u003cp\u003ePostoperative hepatobiliary enzymes\u003c/p\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(48%)\u003c/p\u003e\n \u003cp\u003e(52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.0423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eAbbreviations: mo, months; y, years; R, resectable; BR, borderline resectable; UR, unresectable.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The accuracy of carcinoembryonic antigen (CEA) elevation to predict tumor recurrence compared between cutoffs 5.0 ng/mL and 8.5 ng/mL\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCutoff \u0026gt; 5 ng/mL\u003c/p\u003e\n \u003cp\u003e( n =59 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCutoff \u0026gt; 8.5 ng/mL\u003c/p\u003e\n \u003cp\u003e( n = 37 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eTrue positive CEA elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eFalse positive CEA elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;27 (FPR = 59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13 (FPR = 28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;63%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eAbbreviations: FPR, false-positive rate.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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CcrzI488Yn9TBpAbtEcYril7yFXuv8622+aR3UkwDgYP6NDWoLOlg/dPWQo6ySGHHGLf+UHeUhdpI7gX6kh4t5RlDFK0WeQrXvPJNprtQW8gj4N+8ntUT3j2UG/MQzGqAiVLlLTyjzccacWicxKbD5ZF7x0dmGelfpOflPcA99CxY0fzoqJcoLOiX1NWOBfPTru+4447Wp1KvnOuR3tKm839U4eTcK9BT7A6WbaMxc8kT8jzaltWW6XeBd2P50XeU9ZS23IhhBAbjow2nkjw5cuaxBxKJRKMFgeEy/Bh7nkqkdD1bdq0sThBzD9n/nbUqfFXXHGFj5Qn2ycSbrlxMNgWdQRtPnukLNnniCOOsPgQgWuvvTb3msznD8ybP8937tzZYgcRD4kYSJGSVqC4R4FIMfPHHXecnfviSy7JjdWxpjBfP9wjcU+iDqelc++XXXaZ/R0JWB8py/Y38/wjxctHAt9HirDFK4o6pavEKGD/SNmyOCNR587m7UdKgM235167dOniixQukntt4jRECovv0aOHHR8JcR8pGBaPhbgC5HWkLPhIAbPtwPz+SMGwdxEpevYszL2PFCaLcXPllVfauzu7/dm+VOlSFj+AmAPs37179/gs6Yk6Xv6UU06x2AStWrWy5+Aap556qo+UEtuHd9CzZ0+LR0B+UHY4d6S05ubXAw88YDEMuM/ddtvNzrF/y/1tG3Au8iRS6Gx7kyZNLC/uvffeeI+VRMqY3Q/vqX79+vaMBxxwgI8UbD9s+DCL4VGqZClL79atW3xUzrMQlyr1WbjPqFNg+5Cv3Ht4H1HH1OJ0kYcQdcIt3hXPQnwdyj15mYyT8+ecP/2JJ55odYF3STmhbEfKmp0zUurjPfNC/WQbeUBecm9nnXWWPS/PSRngs2X1Lf3wESMsHhLXrrbllv7ll1/xl156aW48MeotMYcixdu3aNEi93koe5GybmWJ2DbAPbKtbHRMhw4drBxzr1wrv/IRdU7tmtxrOD/X5/0FuAZxJSi3lDvylnu7++674z2idiDKe2IycTz3Eu6LmGkcRxp5Td3hnSZjNxCjZetaW1u7wb7c+8MPPxxvzcyjjz5q7zXcD/F/iMtGneE+eAaun4RrkXdb1djK2iuOoz2lXAUipd8f9b+jrHzss88+FhuH98E9RZ1FK3dck7ylfSUPo469lfWQh7QlxKGgXaF95JnYnxgakeCw+6JMhXJCnhM3BJ588knLJ8732GOPWRpx1Y4+5mgfdajt+rQfxLuIOkG2PRNRJ9rqA3WAOFLUF65JnaLOw9ixY+3dNGzY0M5NnnD/1JfQFlMvjz32WDuW52BfYu1wnzwvZZF8iDo11gZwf1HHPLdN5Tlov046+SRL45mJZ8JzzY/aR+pIyei8paJ85Zznn3++5QllmLxOjTtGHBzi4pQsVdLufbvttrP6yftNQntPe05e0q5Rxnj/Q4cOte20uzwL1+DZjj/+eKtroT6wb9RJt7Y46jha2aX+cX+cO3Daaafmvvv99tvPYttxTvbl/n7++Wfbj+8DDzzQrkXbwLaXXn7JV43OzbH8Dm0o98A3ab/88ou1BbSRtEm8+1/jeE2ffPqJ33WXXS3PKY/VqlWz98F7Bd4L8o/zcU9bbLGF3T95Qv5yH506dbJ9lyxd4s/tcK6Vd94T5/zyyy9tG9AWcQ/hGhxPW7y6GGOffPKJ1aXUMnb11Vfn5uNzzz1nZYL75NOuXTvbP8hAYudEnWHb9/7777d7DHlOXaUtuPjii20bZYhnpQzddddddgz5wHNyHHWT98DzWXsbfShDI6I2GWjH0ZeoNzwv5YDyE9qTBx980MoDdYFrn3b66db2hPJx+OGHrxKLLJU333zT3gHPx7MHaGNoG8KzHXnkkXbvmSBPKBsBZEo4lvv56KOPLJ3YRbfffrv9TbuQLL9JfvrpJ5O/yfaM9xDkazrQDdiPdj55L+mgzepwXgfLt0iBtrJEGQ/HoXdSTzkf74YyFxg/YbzJIsoJ8SQpQ+jTtC3PP/+8tW+8Ez684xBLk3JGmUPWf/zxx5Y2btw4e7dcizofyvx1111nOg/nvOCCC3JlPh/eOTpL79697ZyUj3At3idw7GOPP2bPhexAZlLOaFuS7XXXrl3teMop5Ys2kHIbrocc5z0BeX/W2Wfl6n6UVY674YYbbLsQQoiNg0w2ng1qHKJjg3LKtiA4CGDIb5QWDCKQNA7xQQGhQ4dCFNIQToFMxiGUZtK4JnBNfiO481MmgM7JJ59+6jtGiitKMPd3aceO/s+//or3WDOSxiGELAEXUeIQ0sE4FEDJI519UWxRSFBe+Y2SOXHixHhP71988UVTAFBIMICE66BIoNT9+OOPln/h2nQGUci+/+EHO/6aSIkgHUVh5syZ/otJX+ReG6UqQGDhcA6UMZ4BRZ7f3P+QqEODskdn668oj1CmMMCgEGeC8oIxhXOE94mCHK5z3333WdqUKD9QnkibMGGC//PPP03BxcgIdMaC8vvMM8+Y0oJBBoU5KK7cB8dj7ODdfzzmY1MwOaZ///62D2C85Nl433RgKJPhfoIhCINbSEMJA64TyiwdKEiW1z59+ljaN998488+++zcdJQuFEGeAegskI5SCOQBv3mW2bNnW9plUR0ljfdOx/+DDz4ww2A4ZybjEPBuUODCvsHY+MQTT+SmYWCjww109IIBh/KFEYl9yJ/JkydbJwPjEu+ddMrESy+9ZAZYrgXBOMSHa6O8ouDzG8U1U5B0FFnKL8bLoJTyXoNxl44KHV22YbQAjC7sh3LPu4RU41AIznnVVVdZGoox1yJgKorye++9Z9vZj9+UE9ofjDQovSj5YZ9McL5kWea6lAGU+2AgOuHEE6wOAO0Byj3pwWAZ6i3GcMo89O3b19LoSCyNyvnYzz4zZRzjKdBe0KFmH8oM7+zPv/60sks7RjrtH/fP++MdYYginTr22muv2XkoV3T0SefdhjaHTgn7B2Mm9x+M59QFTLV09Pid2q6lEso27QTnxSDMb/KXMkUehuDjof6ceeaZ9ps8DEYUwEhAOh+eE0MebSK/6cySfxga+U2bQxuBAYHftGcElsXIQd0njfdO2QOO5b2Tzof8I7/D+XkXlEUgPzEEkk7nEjDc0c5QBpLlhg4jz4FRjnykLefd0RaHayfbXd4H5Yc2PrxL0mgLGSAIdYprTfxipYxIGocw0GB04v2FcoJxPrST3F/YFxmCES3UX4797bffrLMc9kHGBp566ikzxpCPgIGI9pb9QjuJ4ZvfvPOQZ7yLIEv40EZgNKRu85vOKe0+0IEN5+T+Q9tBOxTuiwUgMOwFIxr1h/KVDo4LnX3kMWXujDPOsN/kMfUgcO6551o6n4qVKtq7OaRNzrvmQ7sGlKVQ1vggF1gMgrYfo0eQRbynMAhDeQr7P/vss6YDhYDCtPMYlUKZoC6TTvuwaNEif8edd9pvyl3osIcyyAeDAfeKASukDRw40PbLRJBxtH3BkAeUnXAOPhdedFG8pWCkGofIc4xCtD28t9WBIZZne/zxx3PPQzmg/GeCNg15wL6Uv/zA4MJ+yAQI74q2CZ0QGZzOOIRuSztCOrKI+hTaF+oFBH2UDwafIM9ph5Grr7zyiv2m/gQ5FhZYQdfhN2UyLBpAm0ydJJ12BDmNbKSOIr+DnKf9xugPw6LyTHkiHWMj7Q7tFL8xeAa9GJ0zWc8ZkKO9DnWP9jDIUe6btKD7fRXpfjtF9Ts/PUQIIcT6Z70EpF5TcDvHxRciJdS+cSmGSPCaGzhESoN9Q6TgmUtzJOhsnn6kqFo6waFxz80EruG420Ik5Owbl+VIkTC3X+IJ5UekxLlHH3nE3XvPPeYOzv3ddeed7qYbb3SRAhnv9c/ApZe4Nczbx/U2Euzxlhxw6SUdcDGPFDRzbQbcfHG/hkgQ23QxXP0jZcKmhJBPkSJk9xt14M3NOOqA2/4QdWTM/XjrKC1Szt1DDz9s6cSPKVOmjKuxVY3cWDJMBwp5HClv9g3Tpk1zUYfHgkESmwPX5h9/+MHcsXFf59miDru5f3MvmcBlmykAwJQDiDpcue7KkTJs379H+cWzArFUuD4u+wQMB1zxcaPm+qQz7YJrM02GfJg6dap77LHHbF9c+SPlyNXeprZNS+AYYrRESpFtjxQgy/9I0bMpfuwTphGSn1ChQvk8ZRR4FlzAId2zMN0HCJCZdJdn6g/5TRr7hPtkahawJHCFChXM/Rs3cN7/Aw88YNtwGY8UfvswVaog8D7C+4EXBwwwN/Tk83wz/Rs38s2c6TDU2TA1tEiRwrlTuYB6EHUgLU8jpdXSqK+RUmzTKFLfPS7zBENmaibPBeQ77y8d3BPlN7xnoCxzbqDsUJe4jzGffGJpoT2ZO3euvZN0hGcNZY+pGZHi7o466ijXoUMHSwPigFH+KQdWZ6JzRx1VuyZTNPKDa1AfA1Fnw6YgcJ1Iwba05597PrcuM+2LMszUAcotBNdPgsJSpiHUCY47u317t1XULlDmQxvCNcPz8U35Ll+uvE3/COU36ohb2aFc0x6Gd5eEMkWQc6BOvxRPI6GNZipR27an2O9Ro0a5F1980f5mf65MGwNMB2RqQjoo62F6CVNsuI+WLVvmlkvulevQjgPPTzkNZYG/Q32DIpttZlOZgPb6gAMOMPdZgtKywADnIVgu0FaSX2HqJdckr0hL117RXoT7YpoPbQRtd2gXqJPhvTAFhKkXQB4C9YNyTHmkTAHlnvviOWizo86WnY/9qHNh2k7yfv73v/9Z+TnuuONy5QH7k0bQdd4p8M6/nZ6+TkWdXZOH3D9lApDFTIGFotF7CGWJfCT4O1NFaCN79Ohh8vfCCy/MzQ8CD/P8MHbsp1aHqlWrZr8f6NvX2moIdXaXXXaxb6ZNPfroo/Y31wtlc7Pob2QKsj7sO3/+fMsTYD/a31TQB3iOqINq02iop5Qn4JhQJ1KhjCEfgDKGvAixppYsWWLTiwJJGci0cKZrt9x/ZYwu5AbQpifbLN4R7R1tP/dCmxkI+Rhi6NC+Igs2i8pzaAeAd8V5aCuR+dCqVSuruw3ickibFO43vEOIlEG710MSU6qYFpkJ3nsoz5wH/SNAG5WkSmLbP+G6665zV155pcl3njk/aEteffVVd26Hc61+Bz2F90Q68iodFStWzJ2qic6TCWIyMr0NqJMQ6jBtCfIima9JaMvIe2QEOgNtBvomoCsBMiC0/WM/+8x98umn9veECROislHbFlsA6kzQF96P7glqxXKNd8M0T+A6QR+mXNWLpy+SRrlItuuh/N8R1WP0RdpbyiTtDroO0OYE3YV3Ecop5yHGIm0GdQv+/vtvkwsQ9HZ+n3HGGW6z6F5uu/XWfHU/IYQQGw85msgGAgXnsssuMyUFZfbd994140cAwZcKylNQGhBk20SKF6AQoGBmAiUqKAKfRB1HYgA8++yz9hvFPAi0TKAE3BIJOO4vCE/gHEEo/lOYr41h5Zabb7E52RiikiC0UY5Z3WP//fd3TzzR3xQXQPFHgQUUtZXCPEe5ZlU0Ort0zI488khL45hA0rBFfKJgdEHx5Zn5oNgAHePQaUjC/aGc0cnAMITSikISFBBWXwsryF1zzTWWlg6OC0ornZaxn401BSXcIx0noMMclDvumf1R5G+44QZLwwgTFFgUNDooKGS8cxQcOrDcC6B8U56SnWI6+0GpJt8AQwjXxzDAu+KZeFZI5meAZ0EJz32WsWMt1kfqs0B4f5A8F2Uy5DeKKPeP0YBjKe90PkhfGv0NdCLCM1A3CgqdS8odvBl1ZqkfGEzpFFHfFi5Y6D54/wN7F3R0Qmc/51ZXfXbKb3gOvtPlD3CvlaN3yTVCZ5C8QFnNj2SZTf6NAQ1DE+X80DZt7B1RPgLp2hMI94cyDZQNOr5HHHGEKfD77LOP5XXoQNGpx1BKeQtGRIwz1DPeOW1a+NBxDobvJKEzD+G6gNGC+CDB0EEeBaU6lHng/UA4D/nWv39/q4ehw54fyXzj2EzvKICRifYnvKchr71m9YTOEx2EHXfM6bgSSyaci3pCPoXnx0AXjF+pEMcHgyeEtuuUU06x9pZyR+eMTh2dR4xOlNm3337LDRiwcnWi1HYzaoDsi/xr27atyRvqIuWE2CkY3KnXfIC2CcMbgwh06pYupV6lz5fQttE2YoBIlmHyNtxL8t2HNom6GWQYHT/ug3IU3imyilWbMPqSZ5CuE0tdBNrocG3uhfYRQlsAdN7SEYwRGE84FijrvI9c4mfFyEN94H1TtkOHmXpCxxR4nhCbZcKEie7Y6D0F3hu1Mi8wGgP3GNqqINMg5C/bK0X5lpq/tH+BZNkNx9F5B44JaeQnZQkDYaZOPfUplLFgCAvGTq6zShmLCe8CORJYumxlG588Lln3MhHuj/OFshLOTX0NOgdyJZQRZD9GO3QSnpnrhHYk5AGQnxDOC5naRqBsBkMX+ZmULcVL5DUkY1z7N/R9oK975NFHTE4n5WI6aBsoR/vsvY/J26OPPjrekhPTMdOAH2Uq5C9tWKa2j7aYuknZe+fddyyWEjKc3xim0LdC/Ukl6GHUOwZ4brzxxtz2bfLkyVZ+Mf5i0IP5UR4PjM6N7EHnOOSQNrkDL+hX6Bqs3HbyySe5N0a+4d56+23bBsl3lyxbqc+VWk8WLV6UWyfR90J5CIMY6IJBD0qWH8ohOhYE3RA4H9BOhP3JL56RfMhP9xNCCLHxsEGNQygZBDmkI4ZC0/aUtrmdrYKAosLILaBIpPMcCkIq6TXAfigVKLl4lPBBAOcH5ykXCU1GHxF4oVOG0oSC8W/YrMhm1uG+/IrLbTQmKYgBpZ3OwmmnneauvLKT69q1W7wlLzxjUAAmT84ZYSePUOIR0JkU4gDPEowWKFAoPkmFgneTrnOHkhMU9wDXY5WfAB5idPbCKHo6OAfPyEgxwX4PO/QwGxVLVcDIKxSN8DwoaZQjVqsCtpOPQZlG4WKkq0dsPOI5gxIVOlDJ52Tkm84RCk0Yffzmm+m5BiU6Qhggkp4gqXDPeOSgrGOgoLOR7lnyI1mu6AxQXzAkoGhSZunkEvw5vDOUu1B2UhXD/EAxpOMP1CM6R4xUEiQ7jESibOORUlCPpIJg97gG97k6MB5wj7x7DDcYagrSEQt5xQh06OQCZRWjwo9ReUFRDu8DxZ4PxprOnTvbOz755JOtc0vZZdWk8GFUn7KcSvK+uO8AHhF09sK1KC/pymjo9PE+8CAJoMxjOFmdJ+Q/AQMvXi9ApxTjDwHhWW0pEDxBgTyjTrdp08bypXv37rmdilSS7XNou6jftCO0X+QD58KrgLr/5JNPRO/m1Kh+Zl7FKED+Be+PQMjf5ctXuGnTcgwvvM/DDz8810OjIEUzuUt4P3yHv5NGltCpTr7HID+o36Ee08HmnjE4dOnSxeohBphU0nWeKVcFKfOBcC/kdWhPSUvndUH+BCNIEu71/AvOj385W2GKgNVVN6/q6icMrvPm5sh22uV0soh8CF5HgWReJcmUHtq/cB7OGTwrKHvUlaTnbCq0oZSxq6++2vXv/7jJo9UZqyGt0Sj9LeZLeK5Qp5Ft4VmCR+UWUZscvEpoL0K5QT7z/JRf6huf4NmRJFwjmYeZ8hNo64JexvmDXIWqVarm8XxK1uN/wuZVN3enn3a6GXqSRg+MNGHgCrifJ554wv7G44yV4DBMJmX+iy8OSPteeMdBX8ELLZPOGdoI6tPcv+aazoj3D/lKndx2u20taHsS8oe8DMY7zs09YKCmrUYmIF/NpTKC1cOCHoERcvBrg+2dJtt06t1VV11lsqbfg/3c2WedndHYW1C4TxajWL4spw2hbQp1J1kWgsdY2BZI1/b4FTnHIScY7A0URPcTQgix8bBBjUOAWz3ChFFTvDvWpPOJEAvCHwV156aZDTyhgwWMbDCCzOgnRoVOnTrlcdleHbgCMyUEUIzKJ9zakyOaBYVVSQIYn/AASQrohZEicF08fWDOnD9t9DNMM0qSVNLodATBXlDopAalKXQw+A55XDRSqlgSNRVGxFOVBxQpOuhJIw4dDgwbaRXpmOBlhLEQRYqOfqpBhd/kPwphGF3jPjGgodChuLBULZ0UPH0CLzz/vHnFJEe2ks8ZFB4UTFZdKhEpTOHadNjzc0FPB0ZEnoUpAuFZUvMpP5JlluekrGLwwIuMMosbPeUknJP8Dc+zpgTjEDBajpEEBXX7ujlu7xgjMKriubaxQicJAwTGCDrY5Hu6epKJ7bbdzj39zNNmIAhgbHks6nwkO7SUbTwoGM3lXdCOMKrLdCim3GEoCh+8Dxo2WulpmI5lcecOMDqjqCcNj+Gdhk4ghHKPYQ9PCKa/hbJKh5LOC6xJeVsdGArD1DLuibpMmxM8SCA5kowhiTwKH+pkOuMCkKcBOsHpDP3Q94EHzBPwnXfetRH5dgVY+pt6lDw/ULcD1Ov82qR/Q/DGgdC+cK3wTktF91Eiur9kXaeskV+hruNNGTwM1hW0I+H+aM9rbb3qe+LdBvmQykEHHpQ7dQpjKF6wLfdrmevtRjkJZYNnD8/Pd658iZ477P9PCXKTcwXCaqkFpU/fvlbG3ntvlHnjYeRf32BwpWxjyMCYQHlAJjI4cO999+WWK/I01HumqQW9hnKDvAjT9/4N1J0wSED+Jo026EJ4VgYYVMDzMRMcH959OsI25CbeQAHa9TAlEZg2zLVoYzBIcU28/ZjuG3j22efyGKsD6GihzLESZabpTkkjGGUbr9SgM1Int6m1Ta5xJQltbqgnXAvZwHvhHfJezj//fFesaM65mXIdDH2029dcfY21p0kjOnnG9GumFeMpjFEseIH/UzgnZSjIh6SxJ9kWhqm5oV4VBMoLsgEDa5CZGOuSuh/n+ye6shBCiHXPOjcO5dc5QYkkPgIjYwhJpkAlFY90iihCJSgQCJcwurNVja1yFdp018QgEAQVUx+S000Y/X49Q8wQOtxMDUg1tISpZShGFSvkjPwjtImLQKcwj1t+GpL3WCgMI0UwMsUoS3L70CFDXK/bbrO8oVOKISs5shv2RannOQFjBsaSAHn8VUK5CoRjydcw5xwQ5igMdEjDKFW5qCNaP45nkCTVeAMYeTCOcA8otSH2CyNqYf58KnQKMSZh2MKoQ0ebewiKSygPKEi4eKNkDRs+LFcB5p4ZdcO7CQ8SOubEHggxplAiv/zqS8vjUBYYOeTZydvwnChN20VKLzkTYgIACmrYh3whNgCkK2/cByN9KK3hWZKdwmTZTuZfUnFmqlfo1DJlEu+dAPGGBkfllmcP7uA8dxjlTip46d5PKky5CTFKkp5ESaMRxqJkZzfnsVd99kzPk1RAYdUjC0a6/AbeNUYRnv2gqC2h45AcYc3UsQ3nQwH/Y/YfbsCAAa5bt265+frxmI/tXQRvQTocA14cYH8DnRbqPoYopv1h5A6fJ5980jrO+TEj9mwA2hXqcPB0oZxRriFMH4Hg6cg1aM/6Rh1aPOfClCKmPdA2Jp85U76FdN5Vch/+Tj2GDmvoKDKaT0yJ5PsOcWuA+5k3P2dUnndPHoU6kwrPTLkD2gHeRYBnwXsLQ13nqH3gueg8hukKgVWeL+7QrJIeQcyOkDfUpTB1CMw4FbWXOc+VNz/yI2znO/xNPKNAeH+03aF+1tpmG7dF/Oxh2hnGquT9MB0lxB7LdA8FvbdUaPuAewt5iQGvzrZ17G8jn3wM0MaFtoLn26zoZm7/lnkNyfXr17dvnj20U3wHo+e2derk1rnVkbyXdPe1fSLGD4MEIVYVYOhenKFjSjvbJWq36ajTGWewJp2sTSWkZ9qerk1MbQ+T0N4gp5A/6BSUj0cfe9S99fZbZrgKkKdBlhFDK2kIY4pkUmaksro8DGCoDsYK7jlZ5xhQQl8JegNtEUuYp4Oyhpd40pMy9brhNwNhwQDC1KehQ4fmMZjcd++9tp32Hi9avjE+oG8EQyueRhj3UkHWhzKH51MmQnsPtBFMBwug03w6dqzlfbq8C0Zw3jX7Br2WvEN3mR+36YDRKcCgBt5ESZiixkAbcoCYR8iZpDdbujY+qSeHdj31PhlgCAZd6n8oj0lPqtpR+xRIHh/+TpeG3of+d/PNN7tnnn0m11MP3Y+p8uTjwa1bu+bNm9sUOSGEEBsXq+81/gsQSskOTVLJAgwoIdYPnQaMKiFoNKAU80kKIARkMC7QSaNjhoDudEWnXAUsqbwEJQAvixCQEjdzRn+Ys97voX4mrDMFUnwg6uAgjBFkCDyEPM+EVwId5Wu7XmuKER0YvKBQPFEG6KjmRzJfeKYgyFFsgldAIMz7BqZynHrqqblKH4o28XboqKE8JadyERwUd2TmsuORhbIIoXMH3C8KFJ0PlFG8LgBlGsMJAh1DB7AteFgl32U6t3vu6+abe5oiiJEmeFqRZ5m8B+j0hWCgGFVQOukkhg4MMV/w/OH3448/5l6IOvHN9miWawTDkBLit/AOCEqKWz1GHUak6TjXqV3HlO0Q3Je85f65djAA4jkSOjKM9AXwbiMANu+Wkd0777rL0hctWpx7j6HTl3wWjF08y72RQhv2Y1uIMVKm7MrRcuoE982HjkHw1EDRpczyDnmvnbt0duXLlTNPntAhx+CJ6zYKegi2C1w/kydGgDLBCCc0bNQod/rQ8ccd78qVy/FiIV+SiuiKFT7Puw91rVTpUrkKOtdl6huKYhj5DWWdukR+kGdhFBEFNV15SkLZC/nINcPfKJ3BkDJi+HAr83hBBYg1ReeJ6wdFmOPDffNO6GRwL0wtCXVhl6a7WJ3EIBG4+6677X0So4n4PtQTOnA8N+1B8pPOKEWwWOrVlKlTLP4N0MZgRMUbAGMi5+K8ocOHURTwEKBOAW0dRkgMv3QqguclQabpYCbbQv4OeU89DO+I8kGMFgxikMyb0KkJ0MaEa+yyS9M8XkPQ5tA2uQZq2pb2Z7d3o6LOIp3CJ554Io8nXxKMYklDJNPx6OzRzh53/PFWV2jvw/vFYM/UhRDQGShnXBNWRM+wOC5H4XmSMBARgstS3nh2YtJg0DqlbVu7FvIkPD9lLJQTyC3DUZ6GMhy283c4julgYWpPiDnCuekEcn6eGc8FDEMstAAcj9cQbRj1nSlbwXCWrBvh3VJew7X5Dn+HvIJMdSp0eClbwTOSshfa6OWJ50qXj0l41tCBpywngzADneBgXA7Tk7/7Pke24FlE+xYI11oS5SPvkt+hjSDvQ/7y999/5zxb8h3RToc4asgSvNaC0Zf7SNVFArQBId+Qq5SxECgdmE45Kc6zZJ6Gv5P1JZQRSLbx6C/cC3KX5woyg/sP75TyjcxkyjZlgftovldzV6J4iTz3vm+LfXPLFwNAyH/apAf79bOBt6SRIhA8FdOVpXTQftHmAMekxmdk6i2eMehhXAePEe4hnB9jA/oEz8MzJ/WbpCGC50f+kSfofDwn06wI9s17DYZvYrthgNorHsxIQrnbfY+cwM9A+5D0OALqXtC/ksbsVBj4C8ZddA/KJ4Ya4qg9/MjDpjPyvIsW5eQd9x/KKEb00L6i01LuaMOYukz9L11ypScj1wjeQ0ceecQq9Yb9MVYCsp12Imk8RicKsQmD9zjlkPrMoAG6IvcW6hR5Gd4NZQt4D2HaYvD8Qh8M01k5NlnnwvHJshjKMd89e95k93XsMcfmLupA3Ue+4f35RiRvkHuPPPxIxroohBBiAxEJjXyJhEf815rx9ttv+0s7Xpq7/DGfSDjYErZhKdSo0+N32XUX21akcBHfunVrW34zUpYtLVIG/AsDBthyslGHxEcdfN90l11sid7OXbrY38WKFbMl6SOBZ+dkyeOaW9fMvWajRo181GG2be9/8L4tyx228alYsaItd56JqCOeu9RnJHhtaX6WLt63RQs/OF7mGSKFMnep6VKlSvnnX3gh3pKXSCn2N/W8Kc9SvXzandnOvzbkNVvaNpVIqchdGjzqbPr27dvbMrohb+vVq+fHjBlj+/744w+2lHSkXOWeO1JSfNSBtWV6IVKAc5dZZb9IAbBluSFSKHyrVq1sG0v4Hn/88fZ31Imx5YCB9xeWZeXD8uNRR8a2BaJOjS0pvs0229hSy5ECZPn3QoZ8gd9++y13GVU+UcfTRx3E3CWiy5Yt6yPFwsoNZYQ8iRQ2f+ihh9q1br31VjtP1Om25+O5o06C3XukuNpyzZGCY/tEnaLcpVnZJ7w7lt2eOXOm7QOR0uw7dbrC7j3cV6QsW1n99ddfLd+3j5dz5cPfr776qi39euBBB+amh2epXr26/eZZwhLlkeKcu8ws75f85z3AuM/H2RLk4Tx8uBeW/A2MHDkyd5ln6kP9+vVtOe2wP39HHfN478xEnSFbqjgsEw6RUmh1b6eddsotI0A6zxOuwSfqVOYu5Xv33Xfn1mPKB/tGirMtHUz+hWOijprVMa4b0igDP/30k50nSdRR9W+8MdLv2HhlHY4U6dx8jDpUvtY2tSydOsuS0bzzcD32HT5ihN0D+R/OcdT/jrL3de2119pvlgOm3aOO7rzzzj7qXNj5IwXdR52v3OP4UH/OP//83GX686NHjx52TOHChXykJNu5a9TMWSqd5aVZHj8QKd/WrnHvjRs3tjpEu8J7jjpH8V7eygHHR50c37lzZ2sLNt98c2t/yUPyINwrdYR7ANqCk08+OXcbZeb111+39xbaGj6UxR/jJbMD48aNs22803TwPrmHcA4+tMNRZyDeIz2TJ0+K3v3KOsOHNj/q5FrZob7xfKSTL7TFXbt2tWWvSaMM0b5QZ5PLjFMneOe84yTkEXkb9uNTrVo1/9hjj9n1OnbsmJtO3vGeKd/cT/IYlvmOOqG55Z3P0UcfbeUFyFfaBe6D8rjrrrtaubnuuuts6ejA9G+n+4MOXtn+8aHdohwAy1JzbNhWs0YN/9JLL1lZDWWca1x11VX+0Ucf9ZUqr3yPLSJ5RbsPJ5xwvI866D7q/Fl7hEwObeFll11mdRu4f2RJOAfPR12LOta2PR3IC+oebVI6WGKca/K5/IorfK1atXzVqlX9vffdZ9vJ9169euXRG5BdyBf0gZB2YNRG0M6ntkG036TDQw/18yx7n9yO/E/Ws1SijnFu20meHn744fZ+kzrJK6+84t9+5x2793DeZs2a+aFDh+YuOc4HPSUsfR91vHPlKu+I9zFp0iSTk7RL4RjKN/UrLB3PPSBDkWfce/PmzS2PR8RLkQNLiAd5Hj6U4xdffNG2v/zyy3n0DdrXEVE7uMMOO+Sm8cyjR4+2/dMxceJEa6PYl7KeCjoa+U35pMzy/si7s88+256b52rTpo0t4Q+zZ8+25eeD3Asf3hfyGp2kSZMmuen77bef1WuuHWTFAS1b2hL8tJWA3H4g0g9CGxE+yIv3Rr1n+wBLsJNOvvK+82PY8GG5sjV8KLtBp7zq6qty06nTyIcgu7r36G7tV/LYltE9p5Ntffv2te3or6mMHz/e2k+2k6/kDW0HugJpvOtBgwfbvhwf9BF0W2Qy7R51ijpPOh/0F/Qt9FbqPLoSZfi8884z/YJ2kTIeuOeee/LIzHbt2lmdpO6GNNoQZMXg6F7Ii6D7USbIhwGRLg+9eve27RyD/hbaGyGEEOuXTDaeQvwXNdIZYW41IyVrCp5AjErizcEIKTD6wMgKUyJsdCS68tSvproxH4+x1VJIZ3QIF15GLxg1Iy0SJDZSwogTsVBwRf183Oc2msroL59IoNk1JoyfYCPtJUrmxDdgtINrhalHjCJ99PFH7puvv7ERW0ZsmNKWCUbU8Mh4/4P3LSghox+cjyk4yTnxMO3rr839Gfd4Vg3CayAVPHwY9YqEfB4XekYrGZ3DqyXkV4CRGEZhGEFi5AWvDvKRkShGwbiXZMBVRpm43y8mfmEj0nhNcd5IKY33yBmNYuSmeIkSrkmUN0lvHo7Hy4LtxYoXs+vtu8++uXFQGJFipJmRQuBe8DxJehEELx/mrI+NzsOxkfJgcVl4n5lgJJnAvhXKV4zefRNz7Wa0jPOR74yyMerHiCIrXY3/fLyt1sUoH9fH64JRMLx7KC+MWDKiynUjJTXPO8Hbi+dkNLh0mdJu112i54zuMUzlCvBuuAfyg3dDmeQ+KD+cH28FzstzMaJGzCjyO/dZKkTPEr0DnoWyRPnkWdgnjC7iRcL5OQdxE5JlixE9yuxXX35l98ZzhilgQBWePGWyjWrOmjnLPN24T+6L6XGbV61q9St4dGRiwcKFNi3qf0cdlcelnntmpB/vpwCjhwSdZRQy6sDYPZDGaCPXoswSFJlRcp4leGlwj+RnKPu0CXgb4LVFeQrn4X3h/ZKEdLwvyKuQb5Q96j6eIJyLEVTKJh4qIX/xEKTMUA94N9QbfofySx2nvvIuGbnmOpyHOsVqc8m6gTcdHgXkLW1L012aWt2gLKwOpgYwgsw7xqOPMku54vyMeBNIPbVuhHpIe9h4p8bmKcd+AcruV9O+ivKzqPss+pt6yPunTaOtCVM4KA/kD/ne+pDWrkjhnKXhuf5fUV40atjQzsu7xlspmTfcZ7LsMNKLVwvvlDY6FfKP9p9zcd9b19ralvhOtlGZwCuBZ6aOBJnAe6OMAe+WvKcdpqxRH3hG2jM8KHhu7o93Hp6B56btI49T39O0r6dZe/zjDz/a/VFm8BqkXOOhQL7x4Zm4B+6H65MvodzzziireGCFMsyHesp9Am0m5Y58YQo03mjka+r75vlHfzTaTfpikt0zz4iM4h6ogzw/1+U4ngt5SZtDuSSdNOKlsZQ/ZTzcI/dP3jDFA1lKe0x+kdeUATwraVPwRKQNBbwV8XALv6nT7Md7T20jA0zhog3B8yUp3wLcH/lH20CekOe0d3iksj/5zrG04ck2gjaEtiTZRiCXyA/qIc9JGttC28Hvz8Z9ZuXwzzl/2jV4v8G7KRO8IzxKkZ3kP89PGaMd5hyUAfbhfYQyBtQ93h/3zfuhjNSrVy/Xs4fywXl4HuQHbRF1m+cKspljKO+Uf4Iy076lA48XZJzpURGUf8oxeVqpciXXYt8WpvNQbmgjkm0m+YWuQVsX8ph8p9ynq88BPCTx6MNzBc/WVMh76jttJx5YXJPnarRjI4vnhu4Wgu/zfil3yOZQvoAyFp6ZdxryhffP8bxLpofzLOQVcop3xHugvFKuqP/hOOA3+cXxPDtev08++aQtUkGbvDq+mPSFvbfvv/veygSyAk9H7hV9D0IbwXun/OG1xrV4Rsog05VpX5FTwaMtCXmF9yZhA5KeVcB5kU2fjv00yoearmlU/shH6iZ5wb1QLrkHoH5RL6jjtHnIR/QQ8jqUV/KuxX4tLAA4bQfl0OpJtE/jqNw0i+okZQR4r9RJvIApL9wP5+RcyIpQhuyckezhHORXJt2P90S+0T7hmUQ7L4QQYv2TycazzoxD/0XIqlRlXohsQGX/3xOMQ8BUSOJrCbEpg6H3lYEDXfWog8e0beLA0Vkl+L745zAdlul9GAow2tARx4CDgSkMujFdKUw7Xh9g+MJYzjdxZZKDQekIquXGJDcwWmAwwSDJkvHBWCWEEEJkG5lsPOs05tB/DXWORbaisr92YdRZiE0dPDBObdvWvEpY2AGPl3+7kpLIUdjw1MCgTL5ijOEbTxS8YPBSCh5J6wu8dPAYwhOE5dy5v/xAZmxMcgOvJuKzYbwk9pEMQ0IIIcSqyDgkhBDrGKZF4pof+PDDD2wqHS76QmyqYChgWhBTrAj6zEpaqdOtxZqDhw5tA0Hv77zzTlswgqDuBH9esHCBLbKQnO66vsCwgscNAbZ536txPN9oYKp8p06dLM9eeOGFAk1xFUIIIbIRTSsTQoh1DCsdEgcHwhQ9pjew6li62GRCbAoQVwWPFso2MYuYCqXy/O8hjtSYT8a4Dz/40Fa+JA4PsZJCXMINbdwgjg/T27iXTcGrlHh6xPXZLsq3EHtJCCGEyGYUc0gIITYQwSAU4DefEERUCCEykdp+CCGEEEL8GxRzSAghNhCpHTt+yzAkhCgIMgwJIYQQYn2g3okQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEEIIIUQWI+OQEEIIIYQQQgghRBYj45AQQgghhBBCCCFEFiPjkBBCCCGEEGK9472P/xJCCLGhkXFICJFV/Pbbb27cuHHuq6++ss+XX37pJk6c6H755Zd4D+eWLFnipkyZ4iZNmmT78M1+S5cujfdYPcuWLXMDXnzR3XzzzW7u3Llx6vpj1u+/u969e7unn346ThFCCCHy8uuvv5oMRMbxmT17drwlM3///bf75ptvbP/JkyebnFyxYkW8dfUsWLDAvfvuu65Hjx5uzJgxcWpmkKf333+/e+ihh+zY9c3rr7/uunfv7n766ac4RQgh/pvIOCSEyCowDj3xxBPuuOOOc0cffbR9P/jgg3mMQxiBPvjgA3f55Ze7I4880l199dVuwoQJpqAWFBTsO++4w3Xu3Nl9//33cer649NPPnHXXXedu+mmm9bovoUQQmQPP/zwg3vkkUfcMccc4/73v/+5iy++ON6SmQEDBphsZP8rr7zSjR07tsAeQBiWbrzxRnfyySebjMK4tDrmzZvnLrvsMhtswSi1Plm8eLHr06ePu/76621gSQgh/ssUiRq76+O/04K1/KCDDop/CSHEpk2VKlXcrrvu6oYPG+Y+/vhjt+eee7obbrjB1atXzxUqVMj2KVasmGvQoIH9PWjQINetWzdThIsXL25pBaFUqVKuUuVKbrddd3OtWrVao2PXBlWqVnEVK1Z0Rx11lGvUqFGcWjBQ3gcPHuwqVarkypQpE6cKIYT4r1GtWjW3ww47uJEjR7pPP/3Uff31126vvfZytWvXjvfIy5w5c8yo89FHH7nChQub988RRxzhNttss3iP/OEY5C1eR3jlMkiz0047xVudGX+QzbVq1XJFihSxtKJFi9p97rfffm6//fdzRTcraunrA55r8y02d3W3r+sOPfRQV7Zs2XhLwRg9erT78ccfXc2aNeMUIYTY8GSy8chzSAiRVaBsVq1a1e3YuLH9xgiEEorCmqREiRKmsGIg2WWXXQqs+AY439H/O9pdccUVa6xMrg0qVqhoI63HHntsnFJwvpj0hbv11ls3iPu+EEKI9Qcyceutt3aNY5nI4ECfvn3t73S899577q233rK/K1SoYMdhvCko4XpbbbVVnJKXAS8OcC+88EL8Kwfk77nnnuvOOussV7JEyTh1/dFy/5auS5cubsstt4xTCsYKv8LdfffdZkgTQohNARmHhBBZSRiRDN5C6QgGo9R9UJ6D4YQ4C/Pnz3fLly+336n8+eefGbcVhEWLFrmZM2dmPAfu9ri9w8KFC/Psh5v/H3/8Ef9aCdPmOCfnTuXnn392HS/taM+XSeHnmXhmIYQQmz7IOuRF06ZNXfny5d2777xjRqBUkH2PPfaYDaqw37+RbUEGJ8HodNutt5mnbeqADdcift+/uSZy6/fff88YHwm5hzxlO/I0SVLWJiFGIfI03fTtvn36updfftmVLl06TlkVpqCnXksIITYUMg4JIUQBwdDSt29f1/qQQ0xBxvUdl/jmzZu7008/PdcQg5LNtvMvON+1bNnS3PBRSrt27epat25tU9T4MHULpk+f7i644ALbRrwHFE3279TpCjv+kOh6++67r7vnnntyFdDvvvvO3XDjDbbttddes3M3a9bMYjmgwL/44ovusMMPs/MGOPa+++5zBxxwgLnH77f//ubZxPXgiy++cCeeeKLFWyJOUrt27dzxxx9vSjF8+OGHFmOCaXLcT4cOHSyYqRBCiE0XZBaGjzZt2pgcmTVrlhk1UiHmDsGriRdUsmTJPHGGWPwA+XD44Ye77t1zIlawL7IROdW+fXuTdYHUQZdnnnnGnXHGGSYvhw4d6g479DB3fXQervHmyDfdKaecYjKHe3v77bdtKhsfroccC/K3X79+Np36sMMOs/MA1+X6QZ4eHMnaJ5980rYB93n5ZZfZFIv333/fXXTRRW733Xe3+IRTp051V119ldtnn31sGlwAg06PHt1tqhv5xnbkbxh0YTo63kYMxiB3ke/PPf+cbQOmrJOGPOXYa6+9VoMuQogNT9To5svll18e/yWEEP8dImUSrdZ37tw5TlmVzz//3NerV8//8MMP9jtS3nz9+vXtuD322MM+kXLna9SoYWmXXHKJ7Rcpmj5STH2kPFv6Tz/9ZOnffvut32233SwtUph9pERaOjz88MO+atWqPlJM7fc555zjy5Yt65977jn/5Zdf+r322suOe/bZZ21727Ztfc2aNS2tefPmfrvttvMlSpSw8w8YMMA3btzYtjVt2tT2h0gZtrRIafW//vqrjxRg+3311Vfb9pkzZ/rXXnvNV65c2W+11Vb+oYce8iNGjLBtpNeuXdtHyrLlR6TE27EHHnigjxRa20cIIcSmx/Lly3379u199+7d/dChQ61tr169uv/iiy/iPXI45phj/Mknn+w//PBDX6FCBV+nTh3/3Xff2bYlS5b4+++/347dZ599LG3p0qV+/Pjxvnjx4r5atWp+zJgxlg6dOnWyffv372+/p0yZ4u+++25LO+igg/yLL77ox0+YYDIIOUN6kyZN/PTp023/22+/3RcqVMgXK1bMrrFixQpLR97uuuuu/txzzzUZO2/ePH/00UfbPXz66ad+9OjRJsdLly7tP/roI//111+bPGN70aJFTWY2aNDAFylSxJ9wwgn+tNNO8+XKlbPrv/fee3YN6NKlix3z4IMP2j21adPG9rnrrrts+yeffOLPO+88S+NeBg0a5L/66ivb1rdvX7uHIUOG+KlTp5q+wH5nnX127nMIIcS6JJONR55DQghRQAhcTfBN+Ouvv2yVs2HDhrmnnnrKYhQRVwC3dAJA41m0xx572L6BbbbZxt1yyy3294wZM/KMEkbKrYuUUPNCYgT0nXfesQCcjIDWrVs31wOIYKHAqOdJJ51kfxNAm0Cib775pnv44YctztCdd95psY6idt72AeI4EJA7UrTdFltsYaO0uO+z0gyjncRiYgSUEWECUTOiyUgq93PbbbdZDCaOjRRld+mll1rMCO4zeEAJIYTYdGHKFu0+3kPIKDxQA8go5AzeO3j9JGULMA0ZGVG5cuVcryBiBSEPQ8Dp1KliSerXr29eOEAwbLyCGu+4o8mggQMHmpxLcs4555i8YloXQazDNfE84l7w/kG+ITOZIofHLPfBsx188MEmq5F92267rS2Rj0cRXj4777yzLa//+ogRtjrao48+atsgXINjOSdy/4QTTrD7Dau8saw/sPBFeO4mTZqYh9P2229vK6MS0BvvWz41atRw50Z5isx9on9/C9QthBAbChmHhBAiA6nKLxBnAVj2Nyh+DRs2NIMJ07lC7ACU03SBqDH+oJy+8cYbpmgDLu/8HYw9BMG++Zab3R133GHKLW7uzz2X446OIhzAiANt27a1+2KFmRBUFEUTZTr5DJ27dLHgmATYRoHt3/9xO18yVgKxFsIx4funn34yRZhYDUxf69ixo7vrrrtsNTQCdCpeghBCbPrQ5iO7zjvvPPv9wAMP5E47ZjCkTp06NgUKI0o6MP4EA0oSzlkQQiyg8B1AliELkyDjMCABgyIBDFrEREIuw/Z167p777vPpnnxfEw1Gz36Q9sWZB/3HVbmRJ4SI6jlAQfkrtgW5H6A7Z27dHb9Hupn25Df/fv3t23JvEn3PMhz4vYxRY1FIy655BIzChGkm+thPBJCiA2FjENCiKwkjEJiHEkaUJKE2AHJEctChXMU36RBhdHRoBCHc/E7nZKMgsvIKxBjAV5/43UbPQzL5wMrnaGA7r///q5nz56rKKcQ7iUYiZKku36zPfZwu+22mzv77LPdmWeeGT3fYrufTM8fjg9xhY477jjznkLJZuSTZTA/+eQTiwUhhBBi0ybIAuQOsoK2n1hC06ZNs/YeQ8Y/IZOMWRPSrRjKIM12221nXrsYXZYtX2aerMRECpQuVcqdcPzx7ocffnB777OPPU/p0mVW8WIK8i5doOx0Hk+tD27t6tSuY3GDrr766rRyOB0MzKBTENMPT2RiDfXu3ds8f4n3xwCSEEJsKGQcEkJkJWGUkClTmUZBGd1DqS2XxjDzb2jRooWNaj7//PPu888/d8OHDbcR0KBc4tpP8GmCRuPiT+BKAkVnoqCK9/Dhw+18GLaGDBliHkB4N6WO0qYSVoeho8BUN6bH8cFriHvGjV8IIcR/g5o1a9piC8CUKzxFmYqM0Sg/gixKNzCyLmAKG9O6kOPItwEvDDDPWwJPB/5euNBd2amTO+/8893ZZ51lxiFkMPf6T41WHMfUbqbBMW0OGU3A64KwIpKnTClnanqQp3gN8TeDNQpKLYTYkMg4JITISnCPB+IosDxuOog7UKFCBVeyRIk4Zc2U3kz7ogxiDGJ1mPMjhZX4BRhtAqx0xogiMQtY7STdiOmawrWYKoDyef3115snUjAKpVOS+R22V69e3b4feeQRW9EsyYABL5piLIQQYtMEWRU+AWLXESNnypQprk+fPmb8YPo0pO4bQFYxhWzR4hyv2wD7JmXK6kgnkyDdddudeaYNUrz00kvm0YpXbJI333rL9b79dnfSiSea12ySdNcoCEwJQ44Sdw+vn1KlSuV7ruS2uvXq2fezzz5r3kxJiEmYKmOFEGJ9IuOQECIrIe4PRhoCUhL/JxUMQ48//vgqHjvBiyapvAaFlW1huhlKcIgPFO1p30kIpMlI4ejRoy1WEH8H8NBh9BCjFYYjPq+++qptI74PQaCTpDNu4Q3F/YR7DCOVfAdPH9zYWRYYwxFBOydMmGAu9Sj47Pvbb7+Zuz7PQQBPYk8QjwG3fZb3JSbSPffkxDASQgixaRLkVZBZwHQtloMHlnUnoHMAmYF84ZM8hkEHPsgTYtQBCzbgIYvsQq4Egscu5wqEKdzEuUM2jRgxwjx4mdbFdTgmOaUbakdynCnPyCS2M80rSVjiHnnGscjXDz/80GQj98MAUdL7Nfk8wH7hHoMs59m4L2Tz7NmzbQp6kNEcz1L7xCAMz/Pdd9/ZMyG7mTa2da1aFqfo1NNOszQMcNdcc4159O7UpIkdI4QQG4So0csXLWUvhPivwrLwW2yxhd9ss81syVmWjWdZ2uOPP95vueWWtlx8pNDGe3tb8vass85CO/SRsmzL4LL8e58+fXypUqVsaV+W8mXp3FdeecWW7mXf2267zZaOT+XQQw/1lSpVsqVsk/zyyy+5S9GzpDzL8u633372myV1OW7kyJG5y/uylP2EiRNtKWGIFF1bnp5t3APL0EfKrT/ppJMsrUqVKnb/LHvP8vnhHGPHjrXjjzn2GEtjKePDDz/cL47Oy/XC0vnhwzP37t3bjhFCCLHpgWx6+umnfY0aNWypeOTF7NmzbRtLr5csWdLfeOON9nvZsmX+448/zpUlxYsX91dddZUtQw9///23Lf8e5Aeya//99/e1atWytGbNmpmcZen5Fi1aWBoy5ptvvrHjZ8yY4WvXrm3p9erV8127dvVz5871zzzzjC0tj8y5995788hleOutt+yYHj16xCkr4VpBdtWtW9fuAflauHBhe7YLL7zQDxs+3JblZx9k/KRJk+xYnnfUqFF+u+22s20dO3b0P/74o90Tz0UaMpTnbNmypS2tzxL5++67r+kBb7zxhi9dprTtt/POO/unn3nazkt+ly9f3tLDB9nOcvdCCLE+yGTjKXI9fpH5QAA6lpEUQoj/GsQKIGbA5lts4SZPnmwrcjH6WLZcOXfuOee4K664Ijc2ETDCFymGtloLnj6MGjJNK1IebQoYy9UyEsk0LNJYIhevILx12J+4AkmYskb68ccfH6fkwDVxV8c9n9Hb008/3XXu3NlGIrkOU84YmcTLZ++997Yl6BdG98LzMFI5ceJE99lnn7k999zTglwzwkmMI7ylGDklngRL0uMOz9L2xA7q1KlT7tL7rHjGSDJL+kaKv6sW5Q/T8HgW7ol7xluIY1hOWAghxKYJHqN46CAratWqZR4ueNUSYwgZxd/Ev2N1SjxomA6Fhw5ykKDVyCW8ZZAbyB/Og6cPshGZwoIKeNawnWnUTKEmPhAgv8KqnjvssIN5HSE3Oc/BUd+D1byQufRFkDl85s6b5+rXq2exhQKcA+9dAlQjn5IQlwgZx/0wTY4l55lizT2xvPxpeO9E8pR9uB/AI6pp06b2bK+88orbfPPNbRvpyGdkLfKc50Q2IsORy8hNZO2NN95o50MX4Fju9dRTT3XHHXucye0dd9zRngV5yn7IVo4JS+YLIcS6JpONpxAWovjvtNA5ogMhhBD/ZTD04PaOUoryl27FkrUNiiTGmuB6nkponlNjLPwb/u05OT7kU0GXJxZCCJE9ICcwvoRFFpCv/J1u1a+1BQYrzp9JdiNv18X1edakPE39nR/sS94Qs2hd5o0QQqSSycajlkgIISJYNh4PHEYu14dhCFAGMxmGAAVzbRqG4N+ek2PJKxmGhBBCpAM5EQxDgMxY18YPZFJ+sntdXT9Vnq6JfGVfBqNkGBJCbCyoNRJCCCGEEEIIIYTIYmQcEkIIIYQQQgghhMhiZBwSQgghhBBCCCGEyGJkHBJCCCGEEEIIIYTIYmQcEkIIIYQQQgghhMhi1upS9pdccokbPXq0K168eJwihBBCCCGEEEIIIf4JmGywsQwdOnSt2Foy2XjWqnGobdu27u2333YlSpSIU4QQQgghhBBCCCHEPwGTDTaWcePGbTrGISGEEEIIIYQQQgixcZLJxqOYQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFmMjENCCCGEEEIIIYQQWYyMQ0IIIYQQQgghhBBZjIxDQgghhBBCCCGEEFnMejcOLV++3C1evNi+V6xYYd/Lli3L/ZDGx3sfHyGEECIdtJV///23mzdvnrWr6dpN0pYsWZKnfQ3tr1gzZs6c6WbNmhX/yoG8nT9/vlu6dGmckpmQ/5neAb85F+fMD97lggUL4l9rBuUhlBmuxX1AuKdQXniecK9BPv8WPf/vv/9u+4ucPCOvwrtM5uGGhvc3YcIEN2nSpDhFiH8P5Yq2hw9tQjqoAxtrvdgU+fPPP90vv/ySJ//I+4LICgjvIPkuknBe3ueiRYvilPRwHPtxjjWFa3D+IHeCvAz3w/ZUuUM6v+dEz//zzz/b/iKzTpcsHxsK7mPatGluzJgxcYrYFCkUFaZ8S9MVV1zhevfuHf/693zzzTfuxRdfdF9++aX9Lly4sNtss83sbwr4woULrdA3adLEnX/++a5ChQq2TQghRA4oBa+/8YZ7+aWXzGDx++zZrnLFSq5u3bqu7alt3c477xzv6UwZe/rpp924ceNM0aLJR4CXKlXKFStWzNWsWdPtt99+brfddouPEOkY8frr7t577nGdOnVyLVq0sHfwyiuvuMGDB7sff/zRFS1a1LVr184df/zxrkiRIvFRK7nvvvvcJ598YtuC2D3ppJPcQQcdZH/PmDHD3XLLLe7TTz91ZcuWdVdddZVr2bKlbUvlik5XuIULFto5kaEFAYMQ53788cfdt99+a3K2ZMmSrlatWu7YY4917777rtt1113dwQcf7J5//nm7V/bhXnm2PfbYw1WuXNk9++yz7vJIL9hd5cX98MMPlh9fffWV/aZMULd4x9WrV3fNmjWzd8j7XF/wvt56+23X67Zb3YgRr7urr77a3XzzzfFWIf4ZM2fNck89+aS1C/y9LJIl1bao5vbeZ2938sknW9sQ+Pzzz93LL79s7SLtE3KHOkF7U6ZMGdegQQPXunVrV61atfgIkYlPojb75p49rY0mn+GNN0ZaG/3dd99G7c0yd8wxx7qzzjrL8jcV9hs2bFiunKB9OvDAA13btm3tN/pBr1693NtRm1GoUCF34YUXmgxLxz2R/Bs1apR75JFHXLly5eLU/KFNHD9+vHsyKjsTJ040mYLeseWWW7qjjz7aff3111YmzjzzTJOlyCH6gbRj9A3r1atnsueuu+5y7c85x7WO5FO2M2fOHPfUU09ZfgYDEd+846pVq5oud8ABB9jf65Oxn30WyZ3brMwdddRRph+JjZuMNp6oAubL5ZdfHv+1dogUVD9hwgQfNQpox75Ro0b+7rvv9g8//LDv06eP79Gjh99pp518kyZNfKTAxketPaZPn+5/+eWX+JcQQmxaRB1Sf8EFF/iow+k7dOjghw8f7kePHu2feOIJv9dee/ktttjC9+vXz9paiJRBHylg/qqrrvKR8mDHcTztbqRQ+ooVK9rnlltu8YsXL7ZjRF4iRcw3bNjQDxo0yH5Hypi/7777fPXq1U1ukf+nnHKKjzpA/tZbb7V9kkyZMsVvvvnmJvPCJ1KO/ZgxY2w7+X7qqaf6SBH2I0aM8EcccYTffvvt/bRp02x7kkjh8o0bN/ZRByxOWT1//fWX73hZR1+iRAk794ABA/y4ceP8+++/72+66SZfu3ZtuyfKBHz33Xe+Z8+euffauXNnk50Qdfrs+pECaL+zmUWLFtl7iDpTlk81atSwehYpXD7qAFsa2yZPnhwfse5ZFNX7oUOH+qgDaNfnfoT4N4wcOdLvuuuuvk6dOv6ee+7xo0aN8m+++abp65UrV/YtW7b0n376aby39/Pnz/dRJ9/vu+++VgZ33HFHaxdpU/bbbz9fqFAhv/vuu9s+IjNvvfWmyR3keeCll17ytWrV8t26dfNjx471l156qS9atKg///zz4z1WMmPGDOtL8Q7CBxnw2muvxXt436VLF5NFL774oj/vvPN81apV7f2mwvvlXmhbCsrSpUt9z1tu9uXLl/fNmzc3HYV7/uijj/y9995rcoR7op+JTKVvxrOWKlXK0tu1a5fbdiKrKIN33HGH/c5mli1b5r/88kt/0UUXWT6hv1144YX2Lps1a2Zp1En0kvXF8uie3o/KTZCF6Bli4yeTjWe9G4cCzzzzjBWg//3vf37evHlxag4ozIcccsg6UajOPPNMP2TIkPiXEEJsOmDwwaBD24kisGTJknhLDr/++qspgxiBHnnkkTg1h0mTJvnSpUv77bbbzn/xxRdxqjdlv1ixYvbBCCLy8uGHH0bK+Nb+wQcfjFO8n/jFRF+2TFnfsWNHU2ph9uzZZtzBOPfxxx9bWgBj3HHHHRcp+2/5119/PdegF/j+hx/snXbq1Ml+YwDi92OPPWa/A7/+9ptv2nRnf//998UpBePcc8+185199tm5RsMk3BcdBMpU4LfoWhzD84wfPz5OzaFXr162P3kjvB84cKDlVevWrf0ff/xhaRMnTvT169e3dPQcOszrk4ceesiuLeOQ+DcgN6jrNWvW9G+//XacuhKMChgnaPswRiS57bbbrAxidEi2k9QT0nfbbTcN1mbgq6++8jvu2Mhfc801cYr3v//+u99qq638scce4xcsyGlPMFCTn8j8MHgR6H17bzMSIG8w8DHwwDvEaAOLI/2hXLlyNljPIBID97yXa6+91rYHGLxo1apVJJ+uiFMKBgMPnI/7mzVrVpy6EtpIDE4MrHB9wPCBzClevLgfPHiwpQVeeOEFX6lSJStzIqevzIAURrZvvvnG0n788Ue///77W75jTEMnXJ/Qv+aeDj/88DhFbMxksvFssIDUuJgCrnDRfdjfgaigu86dO6/1KWUDBgxwkRKX1uVfCCE2dnAlxqV7m222cVdeeaVN90kSKVUuUuzMzZgpSmH6LrAvbuO4lfMJHHPMMS7qxJq7Ny7dYiV//fWX5eeiRYvdaaedFqc6N3TIUDdv/jy35157WZ5CpLSa2/9vv/3mnnvuOUsDpvMNHTrUnX7G6S5S2syln6lbTDkKTP/mG/sOMo9zAdOwk1x91VVu2223c+ecc26csnqY9vTggw/aO44UAVeiRIl4y0q4r3POOcf98ccfcUpEzmO5SEnPnfodOPfcc13FihVdz5493dy5c+PU7CXUw+DiD40aNXJHHHGE/c1UnC+++ML+Xl+ktg1CrCnEl7n00kst3s2JJ51o049TadOmjU1DQtZ069YtTs0htBthOjPQtp199tn2N/ViRnRukRfy69Zbb43ajEkWXiMwcuRIi73TeKcmrlSp0pZG+0z+0+7QzgfY76knn3L/+9//TN4wzYgpzLzD8F6+/+47a7+Z9sqUpCB3mC5LDMMA01KZ6tW1a973mx9vv/OOu+2222y6IVOkq1SpEm9ZCW3kZZddZrGIaDuNSO746B/tV7Fieduw4447zjVt2tTddNNNNmUx20mn09WoUSN3WuCUKVPchx9+aH+vLyhbQScSmy4b3WplKNI//fST23vvvW1OKjC/8sknn7C5cSi3/fv3NwU8FSoC81Ivuugi6xgxVxdoNB966CFrhFB+UdT5/e1339p2GsGPPvrI3XHHHXaNfv36ualTp9q2AAHhOI5Gkvt76aWX3I033mgCEaE3YsQIm9tPI9inTx+bcymlWQixtqA9eeKJJ+xv4gVkmvOP8aFe/XoWFJD4bquD9iso7iiaYiXEYcBg1r59+1yjCnmFrIEVCSMb7LTTTvb9XaR0o0wDxhli/JzT/hx34okn2iBFUvGGsvG7DAre8hU536VL53QA4JlnnrEgj8i2VGNNJpB9jz32mP2NTMVAlAmMX03i+zdyx2xsEDLnzxg6E8SLeO2119a78rkxEupPKhhxgfed+s6TkIePPvqolRVic6BrcE7Kw+uvv25p6D3Tp0+3/WkLnnnmaTMQo9fwjolZlR90xLkG5YhyDRyDwZnzv/rqq3mNgxHEF+nRo4cZCO67/z6LDyKyhzfffNPKJkaDI484Mk7NC3FuDmlziCtUuJDFGfrss8/iLZkJBlQ6kUUKGDMtm8CQ/MILL7hTTz01T1ymyZMn23fIvwAxnJDdGPH4wKBBgyz20/XXX29GagaVUvskmeQO8QjDIDryj3aJuERrEjvthagPxODKjjvumNaoGDjssMPcAS1brnymhLhJ16wSp49+IvGJRHqId0ccJ4yMxBrMBIsVBLmDHKD+hqDk1HtkDjEKyW9gAHHgwFdcly5d3MUXX+weeOCB3Hh7mUBXQj4R95LYV5Q1+tOUb86PjpoabJxyi2GRvvztt99uMavE+mWDt8pYqwMUGgpCUrigUFEIsVgjoGjcCPpJ8LVkocdYQ0P6ZVRQCbCJAn3YoYfmFsbZs2fndqYYjR07dmx0rnn2m31R/tmnUuVKZgA6NDo2ND4U/ksuucSdcPzxrnv37ubVhCLdtWtXqzgDosKNUQkhWb58eWtE7777bht1EUKItcHceXNz28b8OvkoifXr5WynnQuKXwCFPDmyg7ECZRTFL3g6iBxFCOMH+XfIIYfEqTn5F+QWRp8kYeSVwYuwKgsd7nr16lngcAYNkF0dO3bMI79q1qhhXl90vrle6ATs2LixfTMIwUgysqdOnTqWVhB4r0F5Q0nPD87b7swz41+rJwQwR/ZmMo5kO9Q/2HrrrV3t2rXt77REZQoFGs8zDD6siGf1NPq3wq8wTy2MOgRyBQxCV111tXUA0JHwxDjjjDNW6fwlwaD47nvvuVNOOcVdd911lsY1KJenn36669ChgxmUgfd5//3322AXnUTOe8XlV9j9SVHPHiZNnmR6LHptMHSmo2aNmq5qlapmDCBIbipJPZ99GEAFglKjr4u80PdAfrRq1SrPQEDIxxlRZzppIMKLk7aBgfSwmiaGZDxzeH+c74ILLrB2gvoeqFqliskFOufU8bCy4Q4NGth1WZkS4xKDUXvttZdtKwg///SzdfAhuThGOjbffHN3dtT/KujAFMGpYciQIZZHYlWQ++geeGs1bNgwTl2VwlHbjhGRdh0PNdr/4HHKN17TOE0EQyGewh06nGcGJPrTDBow4BUGLdLBsVOmTrUydOmll9h9kRa8Eum345wRQJ/gXmgnuIcbbrjBBqJYZEGsRyIlIF/WVcwhgllyeYJ0HnrooRa8iuDUUYNk8/cD77zzju13ySWX2O+owPjDDjvM0qZOnWppBOZiPvRpp51mvyEqULZPpPTkBlklYBtpyWBsBNck7eabb45TvM3bZR4uQd+4BscTRI1A2ezbqtUB/oMPPrC5wG+/87bfZ999LEYSc2Vh2PDhvk2bNv6nn36y30II8W8hACjtD5/3Rr0Xp64K7VBo6whqHGLMEMOAdo34EcQjoe2LBLYFqCYI5eOPP277iRyIn9GgwQ6Wj19/83WcmsNdd91l6cR3IoZGgJgIpBPnIVLCLY0YNMTvGfvZWJuHz3Y+UQfdtgeIL1SjZg2L97DttttaHAbi1BAfIlLeLF5eiDHFO406ArkyJxMED+WdR4q33duaEGIObb11TT9p0soYVYH33nvPgpsSA4O4F9kMOgV5RUD4SDG3+BrEWylZsqTlPcFXVwcxFgnESn4mA5FTb3fYYYfcNIK5ci3iR8HChQv9sccea2kEGQ9Qn0lLxhzinREcdu+9945TcoKrE7MiGSvrgw8/tHYhGYOsXVT+ON9JJ50Up4j/MpQL2hzeOXFhlscxYdJBmSeWHfsSvD5A8GDSaPeGDRtm9YA4Q1Hn0AJTr4u4ops69DdatMgJ5E2MoCQhFh19k2TeEUCatpi4T6FfhHygDZ8ydYrFKSTPOfaMM86w7QFiEdWM2vjLLrvMYtQQr+bnn3+2bbQdxAviXMC98XdqrMNUCNK/zTbb2PXuvOPOOLVgINPoF5YpU8ZiJaXCYglVqlSxtpK/sxna+6JFi1rdo23n3fTv3z938Ytk258J4ljRfya/iT0YQIbR5+W8wAJRyDPqLTIHCIbOdZIxoCiz9OOTMYeIh7T11lvbfaIPBej3Ix8Jsg7fff+9r1atmu/evbv9hu49etg1CGAf+vJi7bHRxRwKYDVmLizzlln6LlKM8ljEWWYZd/hI2bbfzDNlRBewQALukli+zzvvPPsNjKLhcXTkkUfmjraF0fLkKMbDDz9sy/3tu+++cYpzUaG2399//715BnE8bpth5KRbt+vMio4Vdb8W+7nChQq7999/36a0MUq8x+672/WDBVYIIf4teUYQozYnE7RveBRAqtcQIza0UUwjwRsSV2JivI35ZIx5D4iV4Nnz/fc/mDdQ8WJ5RzUZyWLEldFRPD2YfkOe4goNyLEQP4hRXeRc052bWiwiRuN4R8iWZEwhvFFffullc+lHthA/gmll7Dd5ymQ7DpnywQcf2GjbCSec4K655hr366+/xmdYFZ4B1/Kkp9jagvvEo4Dr/zFnTpya3TAdHVd4lp3mXe+zzz72zfLQq6Nu3bqmN6DLEFskQDlAH4kUa/tN2eI3XgWQjA8SdKJMoMukG6HnPULQjV5+6SUrO3iBMHKMJ/Qfs2fbNq6Xblq/+G9Bm4G8COTXhuA1H/TsVJkDtJN4PtJWMr2R2ERvvvWm22GHHeI9RID8+/rrbyw/k9OK4aCDD7b4cPRN8OqLOvPmaYF3Bd4c9GXCNDTkD3IHL2JCZVCHqee0R7yDAP2vEcNHWLuC9yDTfJiW9MYbb9jfzJbgXLRteB4hd2jPvv4m8xRT+miLF+dMTwqx69YW5AseMUzbnhl7SWUz6IVMD0YXIH4k/VC8hQid0vPmnvFemSEmFO8T2UF4lADlhD5viI1IniNz6KtT35FTYer86mbJcI/oCz7Fw5jymOyPD3r1VdMneB6egzJLPEbeOR5xqZ7aYt2xwY1DKDw0OLhN08Dhcp8sLLi6o3jTcKF0MZUrdY4j8R0wKNEQBgjKRcEiGFsgtWBSGYjhgPIfpgMEQoUI7nJBMKKco+wn4Z6pJEwtY/4sjTXussn7EUKIf0OtSFAHgzPKYSYI7Bg6bygJSaMShgIM7nQ4MWYQRwfF/ZMxK5VFkQMu6yg9KNyphn7yEOXr2OOOtY4808Teeecdk0VA7KF0gwMoSMFNGpf9ELsIkHtM1ULJI6AkHQPcsZlq3a1rN5uWxPmJuUAHH1n4RP/+dr4l0XtNB1PVUMDoOKzteDHcH+emM/NbPgaqbGLXXXe1esVUTTpuKNsMegWYCoEizsAVHzpjxG3AuENHHH0FRZhpN5avUT1+77338hhu0W2IA8LUUsod5SVMQ0zVcQpK8jj+plzSQaSjiW4UYkzxbBgpUzut4r/JVlFZAwyF+RmhiSGCPo2ejJEzFQZchw8fbro+MF135m8rpzeJldCXIK/pZ6TWs1JRp5z24syzzrQpYEzLYVpQ6DQzYBEMvUmQLUwro71BFoTprgGMdExVpS9Dff81aneYwkwbtfvuu9sUHwYk0DuQNwxQXHrJpZaeDoxJVapUtb/DVLW1Be0jBg2Q3MnR6bbddlvT6ZA76CPECqOuhUHED0d/6C7teKn1oXmnTNsifEqYhvy/SEZR3p544gmLWYU8QnYRADzoMQw4UtYY+EAGUPbCdLCCyJ10+5CWTGc6HO+W/n6QOxhDiYvEIEW6oOZi3bDBjUOpoww0Xi1atIh/5UBjiIJFR4Yg0BQWoMGjYgSrZXIubSDdKEYA6yQNcY6VO2+wSAonhIIbjEPpCjjzNR/v/7jbbtvt3OjRo23VFypOpoZTCCHWlDKRorht7D2Q3xxvjEMEzQdiDiSNQ0CbSLtJO4uQp/3s1KlTvufMRlgxBcirdO0+sQ8eefgRU8ZQmlDG2G+bbWq5ww47NN4rPcR2wsCTHAhJBbnE6mQE8wyDHIzm8m4ZvTOP2gMOMIPDtJQBkwBGrKBIfzM978pn6Uj3nJng3kPZSueNko0wSIWegkExXScNQw+xfO699177oNC/8soruUFA6YgRUB6PHbyRCUaNgQbvviR4k9Hhpi7TWWM1ooLA+83vHaPn0H5guOQ5MAjhzXTmmWeaZxtxIxj4Cp6J4r8N8YAoExjKg0xJB+UFfZfOXDrjEG0ZbQSLttBu4rlC3KvkLAERE3U1yBfqYbr+C/l77z33miyg7ceDCE8O8rdt21PivdLDamV0sPOTO9Dzppus3QmzMejXTJgwwTxHaKNoezAQMbieDgbGMS4DcWBXxz+VOxiKRE55IS+Co0PorwbGfTbO3Xfvfe6+++4zudO3b1/zGg/Godp16tigE4YhZA7vFWNvckYNYDCiDKArYjwkft3ahD487/eggw40eYPcYWCE62ADCLqMWPdscONQKihVwR0fCOBJA0XhINAz07sYfQUqBFbNUGCItJ4Eww/BpkP0/lQwAKEA4R7340/pl0Xcfvvt7TsIsdRKR6NGVPe2p7R1H4/52IK3lSldxiydBMMWQoi1Ae3ciSecYH/TLjKamw68S/C2pC095thj4tS8BGUMLwYMD4wIMqqU6ZzZSOlSpc2bA4UlTGVOhaCMeL+iDKN4sajBpZd2dPXigOCZYDCC95lfkGiUODpkSddwRnwZTQ7eq8gw3l2maT4EQmbaAIz9dGy+BsC/IkXRPFYK2GFDvuIxgAzGm0WsHjrHBP0Mn6++/MoGlsqVz1ksg/KE9zGgSwwcONDqZ9KDgFXF2rVrZ1Pm77nnHvMOLKgnD4o3n9TOGOWcND68T4wCeGgzAp1kzpw5ZtBCZxL/fQ468EAzTDKlIz99Fh0Y41CL/Vq4vZqnD1yMDo3BAD2e9gsdOaykKFZSskRJyx/qWqbpOvSBmFWB8f+55541ww3eIHvt1TzeIz0MdGBECosJpIOZDwx49OrdK9drhCD0tA3oFIDcoe3PNM0HwzhGZPg6auc+/vhj+zsdyFYcAOYWMLg0+yNnoebWNe1b5A+GFha1yJU7UduOJ18w4GFsw+CHkZGp8XgQNW/e3MpXAIMRsog+MdsJNI68SoU+cmo/OV0apBopKdO0NUOGDI1TcsBQmm4VcbHu2GDGoWBsgVRFJQmu2RBWNEAAMecdULBpPMOoGi7PWNKB81PIcc/EkgqhIHIOrKJ8M0pHY8Oc26SVnqkWNHAh1hFQuGlck/cOeDN9+umndh1GQ67vfr2lhykGQgixNsDtm5FX3G9ZBjQVhCixHWjTiCm0S9Nd4i0r21m+QxtGG3fddd1sbjnLZ+NqnNq+ZSuMgjN1B1mRyTgUwDCEwQ4vi4suvjhOzZmLzzKwyVWkkDssHYsRIKl8JUERwzuE2EMVK6ycxoxyjpziPQPGPBT4VO+wJEw9YrU0ZNrll19uz5MK93fN1Vfb+cLS0snykk5E402Ako4BSp5DOVB38tNn6PRhTAyf7etu77astqWtShZghJRyxzRFdJ2kDgJhGfqgE1HGgq5BuQge1KEeJ+szRiTK9c8zfs69T6akcTzHhnIeVgTCQDVo8GDzbMI7hKmUXH/LLbe07eK/DZ1HvEvhhRdeWCWkA2CYQPembPe8qWeeeHihjCV1a+Jwde16rZW3Hj16mFeKWAltOX0a6i0G+Px48aWX3O2332EeQdd37x6n5hiBaO+pswHSWPGQkBmpnogBYroic5AZYcVTYCCD/hPngDCIlJ/nDtPQ8HplcJ6ZFOk8zzgf1yP2Xul40D+v3Fm1LaUt4pyUNwbis5mQP5SVZDufCm0+U89y5c7225vukTTO4IlM3UQe4JWWnA4NGIB57xxHGeXaGJyAv5Nyh9/J+0E/oH/8y6+/5OoulAeO53eYuYNBCvCuffiRR0y2oS8R/widSYNQ65HoJebLulitLGoQbPUxLk90/UgxibesyoMPPmj71a1b13fr1s0fffTRFqmeNKLqR8LF/zX3L4tkThqRzlkpjNU4okpgq50Ful3XzfaJFB9b2YxI+FO//NIi/0eNnK3gA5Fi7qNC6Lt27Zq7QsOff/3ld9llFzueldaS7Lzzzv7ggw/2v/76q/2+v8/9FtU9RHkXQoi1BasVNd6psa9QoYJ/4IEHclcjY0WI9u3b2+oVna68Ms+qEMBqWJFQt1U+ImEbp+bw9NNP22ombLvmmmt8pCTGW7IX5FSHDh2szY86xHFqXubPn++jDrSvVLGirfYSKTPxlhwiZdxkEqtZ9uvXz1blOPzww32keOWuAJNKpPz6Vge2sneYysiRI+1+7r77bvuNTGJFkenTp9vvTESKna1Ew7GsEMLKQaxGQpl5Knr3xxx7rL/44ovzrDrGCiTsT3li1bNUhgwZYtvDSqLZCuWEVWHIC1Zk+eyzz+It/5wrrrjCzpdc+SnwxBNP2LY6derYynbHH3987uo0rCRz9dVX237XXtvF0s5IrE4UdRZtH9JZwYgV81q0aGFtCWnHHXecjzoG/vvvv8/Vd6KOhe2DnoPuxaqJIrtg5T1WM4o6b7ZyL9BWvP7667aSGStTvfrqq5Ye+PPPP608UYZoe2hvAhx74okn2jb6AKxQTFsqcmD1ZPLmkUceiVPyQv49+OADfvOqVW0lS1YlS/Luu+/aClT77ruvv/POO20F5lNPPdXqfn6y4vQzTrf2ZEnUpiVhZTTkwHnnnWe/6Yex6mlY3TATrJzGamc8C32yAQMGWD/phx9+sNVSTzv9dLuvmfHKnsBK0LQ5HHP//ffHqSthdWq2HXPMMavI22zjvvvus7yg/R48eHCc+s8J56PepoLuUaJECV+5cmWzC7Rr187KGPvvueeetrIh9OuX02fnfQd9gm9WDg/v7cYbb/StD2ltq+SSdsABB5i+wcqvrF5OGn1y2ptmzZr50qVL+yeffNLOJdYumWw86904NH78eH/kkUf62rVr25KtGGFY2pJCmY7fo8JCg0ZjgRHn4Ycf9n369LHKgEGG5fXgm6jBa9PmEFu2l0YMw1DqMog0cBiGKlWqZMrc3LlzLX3MJ59YQeZ+dtxxRxN2KGUofcA1uGcqAvuwrCzCMoAxqnr16magOuigg2w7S3auWLEi3kMIIdYeKNoXXXSRtTUYilC+abdoSzFALEsscY4Rgn1Z1j60uQ0aNLDl7pNtFG09wpp2jo7hs88+G2/JXgZGHR4MaqlLwiLH6HSTTwcddKAfOmxYrrxIMmHCBL/d9tvZYAEyYp999rHlnek4ZeLGm270e++zd54l8gNhYIV3fdJJJ5pRoF88qLE6MARdeOGFtkw+spQBFz6UGQZGFsbL02JAYIlaOm2UFz5cr2PHjrn7AAoeShyDKdkKHRUU6aQ+06RJE9+7d+94j38GnapGjRqZITiVufPmmZ6BrsP10J0effRR02sYMOM9M6hGfacDhy509tln5747OmV05lH0uQYdcxRyygSGYTpuwD2E6xQvXtwMX6kGAJE9YHCgE1d/h/q+adOmZiysX7++GXmmTZsW75UDA6MtW7a0MkMZRabQycM4EKB80R5SbjF00mnMb6A4mxj3+efW58HwsySxbDxG2wsuuMA643vv3dwGdRbGg0NJMMDssusuJneQEfR7GOyeMWNGvMeqPPHkE9bOhz5VKr169TLZ0bbtKdauXBudryBg+OHa6CqUB9olZEvjxo39rbfemivnGOS66aab7B4oM8hLyhfGo6Qs5JmRO6kDXNkEecpAIG17kDvkG4MK/wb0Suo2MiEVNEXeRfny5a1MdenSxQaZKAvIPIzGGCGRKbw75Amy8eeff7bjMfrxzjH6UN8ZrOQZGDxDF6V/DpTRU045xfr4yB2u1bdvX9sm1j6ZbDyF+C+qaBlhBa5I0Yl/bRhwdcZ9vVLllUsKsxwzUyKSS23ixoZbJG7zuM6lc3UnnsL8efPSBovkWKahsSrMmkRFxyWfOcAcjxtnVCm0UpkQYp2DSy6BhmfNnGVTUbSaw9oFN2oCNTLXHTd9YtQBbu3E+mE6HiuCpZtPH2BqALEZImUnd6GDTPjo38MPPeR22WVXFylpcWpecMFmyhErSjH9J+ooxFsKBiKf2EMsyICcqrNtHVd0s1VXVssPppQ136u5a9iooXvkkUdy4wCKtQPTcJhqsd322+WZphNYunSp6RoVK1V0JYrnlEl0ImJA5DfFMIDOgr5CjAfiTODez5RFXP9TYcoZ7v1MSwjlX2Qvc+fNdVMmT7GyQAxQprWKtQttNKuHMXWc4PRBbhCHiHa/Zs2aNuUv2f9JhX4T7TzviT5JfjIKnn/hOVelctXcGHWp0L9iahFx71jdjKlsawpTiZCFTAnbdrttLb7SmtL64NZu4d8LbZl99bPWPkwdrV2ndlqdgDLA9LEyZcvkTulj2hd97YLIBvQpyiRT0Znqxt/oDgQ/TwUdi/hD9MfZV6wbMtl4NgnjkBBCCLEh+OyzzywQIwG7kYfCuT59+9iKOay0hVFSCCHE2gOjLHJnr732shgswllMWRYoevrppy0+jhDi35HJxrPqkJQQQgghDDx4WE3lueefs9HKbIcVhlgWlw6LDENCCLH2wSsVr0w8dVjwAK+NbGbY8GHuyiuvtADWMgwJsW6RcUgIIYTIh0MOOcRWy2A554/yWZb3vw5LHLO8OUay1FW0hBBCrD0YmBg8eLBNa3510KA4NftgddYnnnjCBmnatm0bpwoh1hWaViaEEEIUAGI+MILL0r7ZyMxZM91mRTZLG5tGCCHE2oc4qsSuIy7Y6mIH/Rf5868/3aK/F6WNTSOE+OdoWpkQQgjxLyCQZrYahmDzqpvLMCSEEOuRMmXKWPDlbDQMQYXyFWQYEmI9slY9h1q3bu1GjBiRdpUwIYQQQgghhBBCCFFwMNmwwigrq6+N1UMz2XjWqnHo1ltvdePHj3fFihWLU4QQQgghhBBCCCHEPwGTDTaWPn36uKJFi8ap/5z1YhwSQgghhBBCCCGEEBsnmWw8ijkkhBBCCCGEEEIIkcXIOCSEEEIIIYQQQgiRxcg4JIQQQgghhBBCCJHFyDgkhBBCCCGEEEIIkcXIOCSEEEIIIYQQQgiRxcg4JIQQQgghhBBCCJHFyDgkhBBCCCGEEEIIkcXIOCSEEEIIIYQQQgiRxcg4JIQQQgghhBBCCJHFyDgkhBBCCCGEEEIIkcXIOCSEEEIIIYQQQgiRxcg4JIQQQgghhBBCCJHFyDgkhBBCCCGEEEIIkcXIOCSEEEIIIYQQQgiRxcg4JIQQQgghhBBCCJHFyDgkhBBCCCGEEEIIkcXIOCSEEEIIIYQQQgiRxcg4JIQQQgghhBBCCJHFyDgkhBBCCCGEEEIIkcXIOCSEEEIIIYQQQgiRxcg4JIQQQgghhBBCCJHFyDgkhBBCCCGEEEIIkcXIOCSEEEIUgBUrVrilS5fGv8T6YMGCBfFfQgghhBBiXVLk+oj477S8/vrr7qCDDop/rTuWLVvm+vfv70qUKOGqVq0apwohhIBffvnFPfDAA1EbWdxVr149Tl09S5YscQMHDnT33XefmzRpktt8881d5cqV4615wfgxfPhwN2TIENesWbM4VcCcOXMc4rJ48eKudu3alkbePv30065fv35uypQpbptttnFly5a1batj8eLFbuTIke7hhx92I0aMsLRatWq5woUzj9kgJ9966y338ssv23W33377jO9ybYEx7JVXXnHvvz/K7bLLrnHq6lm+fLl76aWXXJ8+fdyECRNcjRo1XMWKFeOt6fn777/dhx9+6AYMGGAfjpk4caJ7ZeArrmnTpq5o0aLxnhsfGLGee+4517dvXzd9+nSro+XLl4+3Zmby5Mlu2LBhVjd51vAZP368+/XXX92WW26Z57m995avY8aMcQ0aNHBFihSJtwghhBBCFIxMNp6NxnPo66+/dtd26WJKjxBCiBx+++0317NnT7fzzju7zp07R23lN/GW1UPH/uKLL3YXXnihK1WqlBs3bpw78sgj3YejR8d7rASjw6GHHuoOO+ww6+SKlXz11VeuTZs2rlChQm733Xe3tL/++su1a9fO3gkGoQ8++MAdfPDBZghZHRiVrrjiCnfVVVeaceeHH753Rx99dPT7KjMapeP99983g93ll19uRrzTTjvNjEnrCq4xaNAgd8ABB7jjjjvODR78Wrxl9SxcuNBddNFFVu4Y8CFPyBsMP5kYHZXJli1bunPPPdcMICeeeKLbbrvt3D777ON+/ulnd9JJJ0X59EO898bF3LlzrSx07drV3icKF+8Tw09+zJs3z8rP6aef7k455RTXtm1b+/BuTz31VHfHHXeYwQwwDGJMbBm/Dwy48mITQgghxNpkozEOoUzN+OUX9+STT5jSLYQQIsezAG8SjDt02PPzLEnl9ttvd4888oh5vPTu3ds9+OCD5r1x4QUXmNEpgLHis88+M2PDml7jv87vv//uzjmnvdtll13czTff7EqXLm3pdNyfeeYZ1/eBB9wtt9ziHn30UcvHDh06uIVxhz4Tt956q3vqqSftuE6dOrmnn34m1xjwwgsvxHut5MUXX3RHHHGEq1ChgnnUXHnllW6PPfYwL6Z1xfz5880bqmbNmvZ7TcrEQw89ZB40t/XqZWUQ76gqVaqYAeXPNPKd58N4xPNgkOL5MIRhWCpTpozlV6ko3zGa4MG1sXHdddfZfd99993upptuMg+/RYsWmXEMQ1kmvvjiC/f222+bUZZ9L7r4InfxJRe7dme2s+077bRTrmfYzJkzbRBt2zp17DfvA2OlEEIIIcRaw6+Gyy+/PP5r3bFgwQLfsGFDz+3wiZTjeIsQQgiIOtZr1D7+8MMPvk6dOr5WrVr+t99+i1O9v//+++08jz76aJyykg8++MBXrVrV77777nFKdrN8+XJ/wQUX+HLlyvnvv/8+TvX+q6++8ltssYVv0KCBnzNnTpyaIy8322wz379//zhlVX797Ve/3Xbb+R122MEvX7Y8TvX+/fff9xUrVrRt8+fPj1O9nzBhgq9evbrfaaed/I8//hinrj8GDRrkS5Uq5Q877LA4JX8oa5S5bWrXtjIY6Nmzpy9cuLC/884745Qcxo0b57faaitft149//U338Spq8J+NWvW9JdddlmcsnEwZcoUX7ZsWd+sWTM/d+7cONX7zp07Wz0bMmRInLIq11xzjb/++uv9kiVL4pQcRo4cafVw2LBhccpKuB7nPe6440x3EkIIIYRYUzLZeDaK4eHXXnvNRsCOOuoo+82I4+pgVPOTTz5x7777rnkcEd+AEW8+SeZF+3366ae2HyPA0TPHW4QQYtMBL4o1gTbvu+++c1tvvbXFGQowVQevAzxdUttLYpvwUTuZA3Fd8GppfXBry8fAp2PHmufVnnvumetJBEyBYvoPnrB4EaVjyuQp5gWCl0zhIitFMHGM6tWr57755hs3bdo0S2OK2aWXXur+/PNP16tXL4vBs74pVqyY22yzzQpcJj7//HP3/fffu12aNnWVKlWKU53lFeWN+EohyDRewtdfd737+eef3X333pvrFZOOJk2a2Ofxxx6zmDwbC0OHDrXpYby/ZLypunXr2jdeU+kgPy+44AKbSpgaS4m6SZ1t0aJFnLKSjTnukhBCCCE2bTYK4xAu2LiUX3PNNeZCPTZSvHG1zsTgwYPNpf7aa691d951pymhJ598sjvzzDPtHLNmzbL9iF902KGHuo4dO5rLPsE7740UUCGE2NRY0ykkTFmhM54avLpixQpRx7OqTRlKNQ5pmkpe6PhjBDqj3RlxSg4fvP++fdOBT3bWQwwgjB0YdNLBwAaxYph2lFyJiyljyD+MBlOnTrU05BWxoAjGTOBhDFUEh8aAtL4MeGtaJkaNGmXfTCNLTnvDuEZekZ9BRjOF7tVBr7odd9zRpo89/8LzFjydGE/paNWqlftjzhybzrex8OnYT+17iy22sO8AC2uULl0q912mQr5utdVWqxh9yRsGtHjWkiVLxqkrUR0VQgghxLpigxuH3nnnHZtHj/JNoE+MPgR3zBSYmrgYZ5xxho2gMsd/wAsDbHQNJZNglShbjHQSvPOSSy5x55xzjimrzz77rCnepDGqK4QQ/2VC7LYQsyRQvHiJqENa0s2dN9e8KUV6yJs33njD/g6rkwEGtV9/+cX+xqCTJHiOYABBjqUjrGKFAYkg1gE8jkKAYf4Ggg7DTz/9lBPD57bbLNAxgyl43G6MhLzhGfE4CmD8wcBFvmI4Ix8xBAGeVMQVur337e7444+31TMwgqWyww472DeB1fHW2RgI95G6EhuGnWLFituzzptf8HvFGPjbzN8sALcQQgghxPpkgxuHGAHcddddXaOGjew3hh94880304644VH0xx9/uNatW9uoJCORrKbCNAlc8lmZB6X0hhtusJE7RnaZGoBSGty8N6ZRRyGEWNvgVYJnCpQrV86+k+B9sGL5CmtLRXrw6mFJcjw7Uj04QpDh1GXrg1fH/PnzcvM/lZ2aNHG77babGRXuueceN3v2bEvHEIW3F7Jq2223dd9++60ZhZBxBCu+//77bcnzLl26mOcQab/EhpiNieANhTEoCXmDnF64cIGtwEX+MDUcWLmNYOlMOSMYNdPSWMUrVQfAGwmDE3nz448/xqkbDqYOLl2SY9DLVM8w+P05J70XWTqYDspzsjqhEEIIIcT6ZIMahyZNmmRLsxKLYNznn1schy233NJGY1EK33r7rXjPlRBbKPkNuHOjdDJNApZEythHH31kCjyjj/369bMVezAWsQRsw4YNbT8hhPgvQqc0xMJJnTq2NOrQLlm82IwQGNJFejBy4NGCp09yehR5Wz72GErN22A0KlmylHmwpqNIJKuIH4Tcw0sEj1liFWEMmTFjRvROypmM4toYkJCHhx12mA104AXWvn17G1DBUxb5mQr3wNLxTE1iinamD9vXhXGpQuxBky5vkNt4rWFww7DCFCqeqU2bNibH8b7B+FWnTh3TAVKXvicPMA7hjZPJM2t9wjsO08JSn5c6hmGI+01nOEoHRjG8npkmn6n8CCGEEEKsKzaocYhRQhRGpnldcfnl7qorr3Tdu3fP9fB5+qmnV4nbsO+++5qy9eqrr1rsBiA4JUonS/3CzNilnylqffr0sYCQLK3bv39/m37G0sFCCPFfJgQDTl36e9HiRW5B1O6yNH4yYLDIC94tgOdO8AgC/g5xnFINFEFe4fmR6jmTBC/X4cOHm0zCU6Znz57uxBNPtG0nnHCCGRNypiUVs4GPpKGAuEYsqw94NqWCgenGG280OUew40yfyyOZm19sv38KU7shddoX0xyR0xi7glESwwqGt+T0M567ZcuW9nfqtEfyBaMm0+7C1LsNTZhOlqqrUMfwjipTpmyBjbB4jhFv6dA2h8YpQgghhBDrjw1mHMKV/qmnnjLFGFd54gfxIcYCsRXotDBqyAhnEgw+PXr0cKNHj3Zt27Z155x7jrmjd+jQwZ177rm2T+kypU2ZHj9+vLnfJ2H1F64jhBD/ZYjPQkc6rHwVmD37D+vINmvWLNfrQaxK8LxCVoVYQAG8fYDpTUkjBfHzoEGDBqsEAk8FQweerMgtPGWCd2v79ufYduIc8RsPplQPnxDrKDWeFHCuxx57zBZuIKZPpg+yluuvbZDRgGdTMuA2hizyitXyttlmGzO6EYgaY1YY6AmEVdlSDWyUW86BQWZj8XpjBTVINdT9+uuvVm723LNZnJI/TAVloIzg40wrFEIIIYRY32ww4xDxFRhVw9uHUUMUcT4YdRo1auT+d/T/bD88f5Iwmjt58mSLt3DJpZeYcosbNvEYgjKP0rh93e3NtR6PoeSSwhik0rniCyHExgweK+GTCh1LvDCTRgyCFtMRx0BO8ONAMGAQ+DfduTJdI9vAs4ppznivpBqHMKyRtyyokPSQIYYOnj5MEwtwLO8m0+piy5Yvd1de2cmMJD1uuCE31gzxjBo0bGiGk9RBkmBM2Xvvve07CddHFmJYWd0HA01ByK9MIF95vgDGjcaNG9sAToinBCwmASwgwT3iIdS8eXNLY1p5EgwrGM/q168fp+SAkYzr4Zm1sXi9HXPMMWasw+Mn6aU3/dtv7Tt4hAGDU8EjLRUMXwMGDLBg3DxfJsJ7UD0VQgghxNpmgxiHcC+/6aab3E477WQKdioYiPZunqP04nqfNOa8+dabZuBBGZ39++xcYxGxG5ivD4ULFXYdL+1of7MCCh5GLJd/3nnnua5du9p8fiGE2JRgChMGBjqYqdhUlEMPdddee21u55PO88WXXGwrH7EEOuDN8eijj1qnPEzDTYLBnk/S4yNbwStnr732sr9Tpwwxtev88883r6wgn7777jvzyGEJcgwGgJy65ppr3OGHHZ5rlEvCeS+5+OLouFddr969XPuzzo635ND9+uvNQIUnEOeHL7/80q7J4g3I0HXNwr8XWplLVybw4rn66qvdUUcd5T6PDTyUu44dO5qxC69gYPU2ViBlOly7du0sDQMRMhkD0PXXXZfrHUWgaWIFHnvssW6//faztABGI+oAnkfky8YAXk6sgsr0dqbKAwNTLw4YYLGiwhQ58pB8YUALnSUVYkBR95KGxXQEQxz1PDnwJYQQQgjxr4kUrXy5/PLL47/WDqNHj/b7778/Q6g+UvB87969fdTpibfm8M477/io82L78Klatarv2bOnj5Qh/8EHH/gqVarkbkt+GjZs6Ae/9pqdI1Jk/WWXX+ZLlSqVuz1S9n3fvn1tuxBCbAp8+eWX/rrrrvOVKleydmz33Xf3t956q1+xYkW8h/dDhw61bTvuuKOPOuJxag5R593XrVvXn3vuuT7qePpmzZr5zz77LN66kmeeeca3adPGzlOsWDHfvn17/9FHH8Vbs5M+ffpYftx7771xykqiTrqPOvt+++239x06dPBNmzb1Bx54oJ86dWq8h/czZszwDRo0sHO88cYblrZo0SI/ZMgQ37lzZ3/AAS19y5b7+5deftm2pWPgwIG+Ro0afs899/Tdu3ePjjnAn3raqX7mrJnxHuuGpUuX+kceecTvu+++dv/lypXzF154oZ80aVK8h/dz5szxO+ywg21//PHH41TvlyxZ4q+//npfp04df84559i9I9PTlbtBgwb56tWrW9ns1q2bPd9pp53mf/7553iPldx4442+ZMmS/sknn4xTNg4WL15s9QUd5Pzzz/e77bablYWvv/463sP7WbNm+a233toXLVrUv/DCC3HqSk466SSrm/PmzYtT8kK5efbZZ32rVq0svytWrOjPPvtsP27cuHgPIYQQQoiCkcnGU4j/IkUjIywx27t37/jXv4fpDYx84rKPuz3BKJmznwy4yQgpsRzYBxhxwwWdFVrGfDLGXd7xcnfmWWea2zlLMTOiyfcrL7/satepY0vV45LPCBujebizc35bMr9RI4vDIYQQmwK0bWElRqbg4l1Au0lslzCtBK+iVwcOdNtE7d/ezZvnmW5COxt1IN3HH39s01Wizret+pQKq0cyDYi2ErGAZwJeHauLnfNfBm/UI4880mLcpAvejOyZEMmYjz/6yNWsWdOmTCWnBK2I8nHUqFHuh+g8//vf/2wqF3Lp5UhW/fXXn2777evZNKwqVVaNHRRgFSw8lDgPAZ0bNGzgdmq8k8m/dQnXxQOGskV549rIYuIEJZ+RqXUsuX/44YfniQNkXr1RuX0/um9WIsMLqFq1avHWvEz7+ms36r337PzIaKbWkVepcA68h/BS2tjiZfG8TP9jaiGeTdxrCFYdYDr971Edw6OobMrzMQWefErn0QfkP7oTXljoRvymbhPcPFO+CiGEEEKkI5ONZ70bh/4NuODjct26dWtbCjgVXPqJUfTcc89pFR4hhBD/GlYUYzoQcoV4MGsDxK7ixawZBNBm4QmmiDOFUgghhBBC/DMy2Xg26FL2awoj2yz1ykglS9njYTRr1iyLUcBvDEPEKZBhSAghxNrg9NNPt9g4119/vfs6ZfXLf4oMQ2sGHsA9e97kLr74YhmGhBBCCCHWEZuUcQh39pNOOslcyk855RR3yCGHmBcR3917dLdA0wTpFEIIIdYWPXr0sKnMXTp3dlOmTo1TxfqAKZGdOnVyp7Q91YxDQgghhBBi3bBJTSsD5tgTB+G9994zzyHm9Dds2NA137u5q75l9sbGEEIIsW4Z++lYV7ZcWVe3bt04RaxriOHD0vwNGjSIU4QQQgghxL/hPxFzSAghhBBCCCGEEEL8M/4TMYeEEEIIIYQQQgghxNpFxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILEbGISGEEEIIIYQQQogsRsYhIYQQQgghhBBCiCxGxiEhhBBCCCGEEEKILGaDGocWL17svv76a/fRRx+5KVOmuLlz57oVK1a46dOnO+99vJcQQmQ3y5Ytc19++aUbO3asmzVrVpy6ev7++2/322+/pf3MnDkz3isvf/zxh/vmm2/iXyKATBo9erT76quv4pQcfvnlF/fpp5+aLFu+fHmcunrY98cff3QTJkxw06ZNc/PmzYu3rArvf/bs2e7XX3/N8w7Xl5ykrHz33Xfxr4KBLP/pp5/cJ598YnmzdOnSeMuqUKY///xz25frcGzgs88+c5MnT45/bdxQFsaMGeO+/fZbe2drwoIFC/K8W86VqUz8/vvv7vvvv8+TT0IIIYQQ/5ZCkXKZr3Z5xRVXuN69e8e/1h4omzfeeKN77733XOnSpU3JqVixotthhx1MGXzjjTfcZpttFu8thBDZCcaaO++80w0cONBtuWU1axcvuOBCd+ihh8Z7ZKZnz55u8ODBrkiRInHKSipVquReeeWV3G1//fWXXeOZZ54xo8Prr79u6SLHOHNH9A5GRfLqhhtucE2aNLF08u/WW291xYsXt3xs0aKFu+aaa1yxYsVseyY4X+/be7snn3jSBkMqV67s9t5nH3f11Ve7JjvtFO+1krfffttdf/31ZlAqVKiQfe8U7XfvvfeuUzmJ0ebll192Tz/9tKtevbp77rnn4i35g6Hjvvvudx98MCqS7d4MGeRN165d3RZbbBHvlcOwYcNMxyhatKgrWbKkGcCOPPJId8kll9hvBo969e7ljjj8CHfyySfbfhsb1JcBAwZYPeX++BxwwAH2DOg3BeGiiy4y4y/lCH2Ic1x++eXu8MMPj/fIeR9Dhg51Dz/0kNtuu+1cnz59XKlSpeKtQgghhBAFI6ONB+NQfkTKSfzX2iNSfPyxxx7rN998c9+/f3//ww8/+HHjxvnLLrsMQ5WvW7euj5TneG8hhMhOFi9ebG1w1FH0UefT//Lrr/7cDh18tWrV/HvvvRfvlZ6ffvrJ169f39rUMmXK+MqVK9uHv0k76KCDrC2GxUsW+0svvdTvtttutm3PPfe0dJHD9ddf53fddVc/ceLEOMX7oUOH+nLlyvlzzjnHf//9937gwIG+UqVKvtOVV8Z7ZKZXr16Wzzs13slkYdmyZe13kyZN/I8//RjvlcPChQv9ySefbNsLFSpkH/6+44474j3WDX/+9ac/99xzfaNGjex6RxxxRLwlf5Dd1113na9evbof+OqrftasWf7OO+/0pUqV8kcffbRfvnx5vKf3H48ZY2WSMv7tt9/6mTNn2nMVL17c33LLLfFe3n/44YeW/7fddlucsnExaNAgX7p0ad8hqpu//PqLf/75533FihX9DTfckFvH8uOdd97x5cuXz33HfG+77bb+66+/jvfw/ueff/Zdu3bNrdPHHXecX7BgQbxVCCGEEKLgZLLxbBDj0EcffeSLFCnizzjjjDhlJVddfXWk/NTzS5ctjVOEECI7eefdd60jSCd96dKcNnHatGlmHDr44IPtdybuuusu61CPHz/ez5kzx//xxx9maHjzrTd9hQoV/OOPPx7vmWOwnzdvnh8y5DVfrlxZv8cee8RbxLPPPmsGDIxBgd9//93vv//+ZtTBYBfA+LHFFluYjMvEL7/8YgaXhx9+2N4HxpTRo0ebMYV3/cADD8R75jDyzZF+t91385MmTbJ3hLEFI0ooD+sK7mv+/Pk2gINx8vDDD4+35M8Xk76w57jiiivilBwwLhUuXNjOF+jRo4ft+/nnn8cp3i9ZusQMK61atTLjaIDjyG8MMRsT8xcsMIMNg1pfffWVpXHf6DfFihXz30yfbmn5gYGwW7du9n4pW7xf6mySJUuW+L/++su/+eabMg4JIYQQ4l+RycazQWIO/fDDD+YWT+yG33/PGz/jrDPPdKVLl0k7lz66X3NXxyVfCCH+6zzz9NP2vc8+++ROH2I6yTbb1HYffvhhvlO/Nt98c/fwww+7xo0buwoVKti0Xabp0O7S/jZv3jze09lUpTJlyrgqVapmbH+zEWLm3HbbbTYV6uDWB8epzk384gub6tWyZUtXvly5ONW51q1bW7yYFwYMiFNWZcaMGe7UU0+1D++DaUTNmjVz559/vm0nXk2SXrf1cuXKlrN3wnS1KlWquKpVq67zadfcF1OiKEclSpQw+VsQ3nj9DfuuU6eOfQeOPvpoewamOQZCOSMvAwsX/m0ynjKbnJ534kkn2bTzzp07u/nz58epG56hQ4a4qVOnuu23394+wH03bbqLW7Jkiev34IOWlok333zTvfXWW26rGlvZczHFkPfL8ydhmlm5qKzVqlUrThFCCCGEWLtsEOMQnZXy5ctb56Z9+/amWAVq167tzjrrLFekcN4YGUOHDnWnnHKKO+yww9xxxx3n+vfvb+l0dC699FLXoUMHd+GFF9q8fZTr559/3pTtc845x2IBCCHEpgQd5OHDh7vChQtbBz1JgwY7WLBaghln4qSoM01cmiQLFy50A18Z6A455BBXo0aNOHUlXAtDkciBjvu4cePcueee6woXWiku33v3XfvGAEK8oUCDBg3se+qUKe7PP/+0v1Np1KiRxZJJjUu0ZfXq9h3OARgN3nnnHbsPDE/HHnusGzVqVLx1/RDKREGNQyGY+d+LFtl3oG7duvaNcYy4QrD77rvbN/GUXhjwgv195x13OBcVwXZntrPfgWJFi5pBk8UrChr7aH1A3ERIrU81amxl7zhp+EqFRTmI50RcsUsuvsQdfPDBrlu3brY4RyZUP4UQQgixrtggxqF69eqZAQgGDnzVRl8JiERAVEZDzzvvvNwgqXD//ffbaOFRRx3lOnbsaCufnHnmme6uu+4yY9L+Lfe3oJnsh2LKqFuV6NO3b19TaPfcc8/4TEIIsWlAe0gnumzZsqsEnaWNAwLUrgm/zfzNDA4HHnSQeYOIzODFMWjQIPt77332tm9ApkyLVyzDGwvjSQCvHsB7KFMHH4NBUr4FvowHSXbbbTf7Bs7BYAhGPsoCXjfIwcceeyzeY+MD7xb46ssv83j5UoaR7xjNKNuA7GcQh9/ndTjPHXjggTaY8/hjj7s2h7SxfZLsu+++5vWGsWxj4ecZM+wbj58k1Fs8w1h1LJMnHqsJEuib4PJ47mHsvemmmywINWVICCGEEGJ9skGMQ8BKZZdddpkpUChPnTp1cvvtt58ZeZKKFCu5YBhi9RKU5COOOMJ1jI7z0T64+6NoHnnEkTb6RmeHpXA/+OCDSLl8zLVt29bdc889aUfIhRBiYwZvAjrCdBrpZCYJhh2WtC6oRwe8Nvg1t9VWW7ndEwYIkR4MFiyvDhUrVLRvIL95N5A69Sd4EfFe1mTqE940zz77rHm/Bg8bwBD05JNP2n2MGDHC7bzzznbtLl262KqeGyN77rWXfb/00ku5+Qd4CGMswnhEmQby6/bbbzevKJ5r5MiR9oytWrWy7akwvQ/PmS+//HKNl9ZfF7A8/8IFC+zvdNPAMIYtWrQoo6GHY9CFXnvtNfN4vuqqqyxv8EZi5TohhBBCiPXJBjMO0dlhCeDnX3jeXKkBRfKEE04wF/PQ4UFZZPoES7ziis8UshHDh7utatQww9KPP/5o+zHiiGKFUs4UM4xKN9988yqdKiGE2BQIMWX4TvU0WfT33/a9ptPAMEDghcLUXpE/eHUgX6pVq7bKFLCi8W8MAEmYJgRr+l7uvvtui+/D0ufpvIqApdEZPMGrhAGVIUOGxFs2Llruv787/vjjTRZfcMEFrlevXu7KK6+0ZeyB/ExOk/xs3GfmFcWUcQxHDPQwjY/jU8EzizxnWzDQbUh4z+F9pZaFpUuWmDGMcpD0LssERiG8hvB4Rrd544033BdffBFvFUIIIYRY92wQ41Aw/NDpOaT1IdZhIXAqgRZRpphiFmIKTJo0yVz3CWZ5+umnW9whFM1hw4aZy3/9+vVtPyDe0EEHHWQjik2aNJHHkBBik2XzLbbI9TwgsG0SYgdBmMJTEKZNm2bTbjEyiNWD5xCeIZUqVcrT8aejjwcLsEBCkvCb91LQgQk8bJB3DzzwgAUbz49tttnGXXfddTaFiQETPMtS4T3jZYsnLu8604cpWk899VR81NqD6WN9+vQxoxAebkwB436DsXPPPZvl5iceQKefdrrFYXrxxRfdnXfd5SpWqGD5weBOKiEQNwNGqXm/IcAwVKZsWfs71MkAhkLKD89KGSoInO/kk082PWfOnDn2LoUQQggh1hcbxDiEUvvY4ytjJmD8IQYRrtU77rijjdgOHzHctjFCjns++zDiTTyGXXbZxZRJgoEmR2eTHSgUTQJ5CiHEpkjxYsXMGEAnMXWK0nfff2/fa7Jy0SuvvGKGDaboitUTvIUwRCRj50CY+oUHS3JaH6ubAbHwUmPQpINFGTD23HLLLa5FixZxav5svfXWFrQag0M6jxQ8cxgoYTo2U5MyfZiatvfeK2MprU149iuuuMI8fwmqTiBp4mNx73gHB/B+Iobg+RdcYFPMzmzXzvW2gNSFbNvkyZPjPXPAGEZ+Y4DaWLyCt65Z075DkO3A77Nnm8Fo2223XcWraHVQFvAeQu8RQgghhFhfbBDjEHGC0q0ghsGHKWHw98KcaRP1d9jBjD7PPPPMKh0kXLAZeQSURlb5qBkpaj169HAzZ840N/ZUhU0IITYF8JDAi4D2L6wABZgi6FDjRVHQYPt0qAlETRtLB12sHgwQfJAleIAkwSuH9zNmzJg8HiNhGhBxc+jc5wfTqDHQYMhhZbkABidiEGUCOcg+GHbSTV3jungF4UXLdOtMH6ZzY3xcl2AU4X7vvPNOu2dkc61aOdfEswbjGAYQvIUCGIgObNXKjKKpK74xnY76wPSyjcVwwjQ/DImphqxgKPzf//5n32sC0xl5Rk3/FEIIIcT6ZIMYh3CdHvPxGNevX784ZSVhhZcddtjBvvfff39zTWfUm3hDuFkzbQwlc8Trr7t68bQy3NgnTpxo8YoYMW3Tpo17//33LZZDppVChBBiY4aOZdmyZSwYMdPL4KPRoy3ALW1jWNmKDviDDz5oy2YnPVkCBC9mii5B+vMjHKs207ny5cubJytGjBBLKIB8OuaYYyy/MVgEhg4datOZmQYN5CfTqpB1SSMSq1KdccYZNi2aYMzff/+9GQAx4LVv3z53OhGeN+/Gy+YHHn30UZvWRoye9cHqysTrkRxmWnhYgSwJxzI97NVXXzVvpdNOOy3ekuOZhVcNRiAMcElYwQsPqNSp4ZR7BoJ4/rBi34Zmn332cc2aNXNfffWVGzdunKWRF6Ojeso9hrIAeEM9/vjjuQNdv/72q3vhhRfyDGJh+CXuEgNl6aajpavfQgghhBBrhUjRyJfLL788/mvtESnAvmTJkr5MmTI+UoDs9/Tp0/19993nI2XI77nnnj5SFm3fSBH0Xbp0QRuyT+nSpe3DscOGDbN9nnjiCV+uXDl/00032W8Y8OKA3GPuuOMOHymg8RYhhNh0eLDfg9Ze9n3gAT/u88/9fvvt5+vVq+e/+eabeA/vBw0aZG1d48aNc9vOJLfffrsvEZ1jxowZccqq0Ebe3+d+X7hIYV+hQgU/duxYP3fu3HhrdnLlVVdZvg4ePDhOWcnkyZN9w4YN/RFHHOGnTJniu3XrZvnWr1+/eA/vZ/wyw/bhHEFeffzxx37rrbe2tOLFi5ssK1WqlL3jQoUK+erVq/vFS5b4n376yd4n++23/36+R48e/oQTTvDNmzf3n332mZ1rXbJixQo/+4/ZvnuP7nYPtWvX9pMmTfILFiyI9/D+999/9/Xr17f77t+/v6Utie79119/9ePHj/dnnHGG37JaNX/3PXebLE9lwoQJVpabNm1qz/Ttt9/6Rx991PIHfSCVRx55xO6FvN6YmDJ1iq9Zs6a9ny+//NJ0lrJly/onn3wy3sNbnrBPsWLF/IsvvmhpDz/8sD1PtS2rmS7UtWtXv9tuu/krr7zS8j8J+Tdr1iz/QNQOcEyjRo38qFGj/MKFC+M9hBBCCCEKRiYbT5HrcbXJB0YFcU9fm+Bejkv+sccea274jJwxbYwpYoyUR0ph7qggbvO4zzOSyMgrK3owatv79t7ukEMOsVE2gmriYs4UAOIR8f3QQw+5Lbfc0kZmWSKW+ARMqRBCiE0JgusTw+bFl150L734krV/BC8m5loArw5iuRGrhOlCyRgnTInCo2XHqP1jafRMq2Hh3RJ1Nt32229v18PzEs8N/s5WShQvbl4vxPY54ogj4tQckFF77LGHeWThOROmTSW9swpF//AKAuLqEYtn6LChFn+H94rnDO+Rb4JRk/fnnHOO23333S2oNe+VqVV4LhGAGU8xVvlk/3UNcZbuvedeN2H8BJOjePJQJurVq2eyFYgTxPPjZXXiiSe6rbbayjxoWJ6dqeMcc++997rDDzs87RQ4PIDQL/CaGjhwoK3QxfmYbpf0MgowPQ0vmzvuuMOmXW0scC9MNfzk00/cY489Zu/stttuM++yAHmFdzMrtbGaG3nD8zOFkDqK1zT7MM2QMpCaX5QBPIqI4dSwYUPzKmJqInmuqaJCCCGEWBMy2XgKYSGK/04LQSVZPWxtwvQIXMNZupfL41JOEGqUJgw7mUA5wl27QoUKqywtLIQQ/2VoM5mOQkc8HbShdCiZhpsEAwPGCNIzHSsyw7LqTAfC+BZWKUtC/vJeyN90cgl5h5z7pwGUOTfGA+ReOgPLhoZyieEqrJzH/c6ePduMHxg7CgrnQMbznOkCbX/73beu9cGtbco4RqKNFQxDxH1KZ4QlrxjkSsajIo2pdQx8pdZdIYQQQoh1QSYbzwYxDgkhhBCbAniqHHrooRZX5v77749TxfoEA0qIOYincTBECSGEEEKINSeTjWeDBKQWQgghNgXwgGHa8/c/fO963twzT2Bpse7BW5hg1lOnTnV9+/aVYUgIIYQQYh0h45AQQgiRD8TZefKJJ93yZcstboxYf3z88cdmEMJAl83xr4QQQggh1jUyDgkhhBCrgUUPrrrqKte4ceM4RawPCPp97bXXbjRL1wshhBBC/FeRcUgIIYQoAASc/qeBpcU/gyDqG2MgbiGEEEKI/xoyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg4JIYQQQgghhBBCZDEyDgkhhBBCCCGEEEJkMTIOCSGEEEIIIYQQQmQxMg6l4L2P/xJCCCHyIhmxflmxYkX8lxBCCCGEWJcUuT4i/jstr7/+ujvooIPiX2uX+fPnu3HjxrlPPvnEzZo1yxUtWtSVLFnSfTbuM1etWjVXqFCheM91z6+//uqGjxjuBr06yG233XaubNmy8ZZV+fLLL929995r97v11lvHqUIIsW74+++/rZ2cMGGi22yzIq58+fL/qH1cvny5mzZtmvv000/d999/72rXrp32PEuWLHEjRoywNq5ChQpxanazbNky99prr1nebLHFFnGqc1OnTnUfffSR+/PPP93mm2/uihQpEm/JH87zxaQv3ITxE0z+lChRwpUuXTreuhKuO3nyZPf555/bfsjIdPutS7755hsrL1tuuWWckj9z58113377rfvjjz/c7Nmzcz+///67yf1SpUrlyacZM2a4jz/+2H399deWnizfH374ofvhhx82elmLEeurr76y++UZK1asaPWnIJAvY8eONd2Cus6x6crR4sWL3ZQpU6wtIG8rV65c4GsIIYQQQgQy2nj8arj88svjv9YukeLojzvuOL/DDjv4Vq1a+WbNmtnnuOOP93vssYdfunRpvOe6J1K2/EknneQLFy7sa9Wq5cePHx9vSc/NN9/M0LE/77zz4hQhhFg3RB1n3759e19/h/r+iCMO93vv3dz3798/3lpwRo0a5Y899lh/yCGH+E6dOvnnnnsu3rIqd911l7VxfAvvow67v+yyy/z//vc/P3Xq1DjV+379+pkMi4Sr33fffU0mzJ07N96amQULFviLL7nYb1VjK5M7FSpU8C1btvRvv/NOvMdKbrzpJl+zZk1fqFAhX758eX/YYYf5iRMnxlvXLd99952//vrrfYMGDfwxxxwdp+bPsmXL/E3RPW+77bZ2HPnDh7/r1q3rj49k/M8//xzv7f3gwYMt70477VST/TvttJN/6qmn4q3eT5gwwcptr169/MKFC+PUjYvly5f7+++/3zds2DC3LFxyySV+zpw58R6ZmTRpkm8d1cmSJUv6IkWKWL6RfytWrIj3yIFzUQZbtz7Y8rBevbqWL9OnT4/3EEIIIYQoGJlsPBvEOITh54ADDvBbb721Hz58uP/zzz/9jz/+6G/rdZsvWrSo377u9qZgri9Q5j/99FO/5ZZb2j2tTvH++uuvfbdu3fxHH30UpwghxNoHI8IZZ5zhS5Qo4d99912/8O+F/uprrjYjAZ3qgvLggw/6ihUr+v32289//vnnftGiRfGWVfnwww99pUqVzDh0++23x6nZzcUXX2Qd/h9++CFO8WZcK1asmO/SpYvJsPfff99XrVrVn3XWWfEemencubMZhZCDGJSQPeT3dtttZ/IlMGDAADOsXHfddfYuMBKx3ymnnLLODSUYIzBKNm3a1K55xBFHxFvy5+cZM3y1atVMlnO/GDvCh/Ng0AiGD8p0mTJlzACFHEYP2GuvvXypUqX8a6+9ZvvA5MmT/Z577mlGzVSjycYAxtrNNtvMX3vttWZIHPnmSF+5cmXfsWPHfHUZys3JJ59sBiUMjaeeeqoZAcm74SNGxHt5v3jxYn/++eebfvJBVD+pvxjQypUrZ0bF/OqzEEIIIUQqG5VxCIUQxZhOTyo9e/Y0ZXh9eg4Ftt9+e1+jRo31NiorhBD5MWzYMOtQ4zGAdwLQgd5qq6383nvvbZ3G1fHqoFetvT388MP9/AUL4tT0zJw10wxIGJ+47h133BFvyV769u1rhjU8rwK//PKL32233XyVqlX9rFmz4lRvRhsMa2+99Vacsip44zRq1MgPHDgwTvHmjYT8Ic/vvvtuS8P4c+GFF5oxL4ABin0w2FAO1iXBCIOBCiMYHksF4Z577jGDBd7BEMotcpVy1adPH/uNEYVzbrPNNnm8X0aOHGnldf/99/d//fVXnOr9K6+8Yoakxx9/PE7ZOJj9xx/mccw7DcZDnrlDhw72rr744gtLSwflBEPhkiVL7DeGpNNPP92Ow3soMOaTMZbWrWvXOCWHE044wdJDngohhBBCFIRMNp4NEpCa+fXMzx8/frzFEkhyysmnWDyFFT5vEMp58+ZZjAZgTv6iRYvs7wDnmzt37mqDhbIP+/5buBfuKRXiToR7iBTbfK+1dOlSN3PmTPsWQohUnnnmGfvefffdXdRhtr9r1Kjh6tSpYzFKiAuUH99995279JJL3bbbbutuvfVWV7pUqXjLqtBu9bqtlytVupSLBEacmt0Qa+e+++6zOHR77713nOrchIkTLe7L/vvt58qUKROnOteqVSuLBfPyyy/HKauCDLr00kvdoYceGqc4V69ePXfOOefY37/99pt9Fy9e3PXq1cs1btzYfkP9+vUtxswOO+zgqlSpEqeuG0LMH+LvEQ+poJBXDz74oNtmm23sdyi35Em5cuXcIYccYr//mvuXGzZsmKtVq1aePDzggANc9erV3WeffWaxsQJHHnmka9Kkibvxxhvd77Nnx6kbniGvvWbxmKiTNWvWtDSeObw38iITzfdu7q677rrcuEHEGSIOGFDnA2+9+ZZ9V99qK/sOHH744fb95ptvWjwxIYQQQoh/wwYxDu28884W5JRg1KeccooFogxU36q6u+KKK9xmRTaz3xiQUKQPPPBAC6R6//33u3333dfts88+piTCyDdHuiOOPMI1b97cHXvssRYIMwkBPfv372/bOA/HE4d7zpw58R7peeSRR9xhhx1mCtiJJ57o3nrrLVP8uQeuz3bAUDR48GB33HHHuQ7ndXA/z5jhzj//fLtOmzZtLEBlkgULFlhH7eCDD3ZnnXWWO/CgA93NN9/sXnjhBffkk0+awUgIkd1gNH7jjTfcZpttZoFnk2AcwEhOcNr86Ny5s7WHJ5xwgh3zyy+/ZDRG01EfNGiQu6HHDevc8LCpMHLkSDdp0iR37rnnxik5vPfuu/ZNkGSMOAGMN0BgZWRFOho0aODatWtn7zUJQYghGBUwMGCUSQYMn/jFF1YWOH5NDDb/hnD91Q28BDD+YCBKwiAJZZlnD0Yj5C8GDeRhapmsW7euDa5QXgPcB3IXg90zTz8dp254PvjgA/veKsVwg4GLshG2p6NY0WKuWLFi8a+cgPEEOD/iiCNMfwhgfALyKsn2229v3wT0lt4ghBBCiH/LBjEOMTLWqVMnGyV7//33XcuWLe03ChBpdGRQjFm5AyMKI28YkDp27OiGDBniGjZs6KZPn26jqh06dHBdr+3qypYpayOcjE7SIcIgFODcV119lY08vjroVRsB7t69uzv55JNNAc0E98L1GNm89bZb3U477eT69etnhpwxY8a4n3/+2fYbPny4u+mmm9yLL77oRr4x0p3Tvr0ZsoCRfQxRoaOA8nf11VfbB6MXxqBtam1j93zBBRe422+/3Z5NCJHdzPlzjrUbtGus7pQkGIvy6xDSRr3zzjvWjmGg3muvvVyjRo2sPcPAnWwjWQXryquudBdeeKFr2rSprYqU7eDhg7EMdt1tV/sGjCShjcagkzTeVKpUyb7JT45PB+8jeNMEgictBh/yPwnXw0sVWYlsYbACmbkpgfwmzxgMCpQrW86+kZV4EycJnjSp3rl77rmn5cd7771XYGPVuua3mTmeXqkGXLyhWFkOT7Cly/L3DsY4Rpnp0aOH1dmrrroqj9GoatWq9v3FF1/kqZvkE+WJspbOk1kIIYQQYk3YIMYhwDsIgwpLAi9cuND17t3b3Mn79u1rI+KAq/3DDz/s9t9/f/u9yy672FLCjz/+uBmFUIhGRQrzQ9E+zz77rHkHMeL46quv2jnh7bffNoPO0Ucf7U499VRXbYtqrmvXrublg1Hn7rvvtv0AVRNFnxFdroNn0kMPPWTTCmptXcuUP4w6GJsgKPiM8t177z02UrhgwULzBmKUdPTo0a52ndq2BDFL3AJu8kwVoZPGCCseVEzh4Jo8Hx26Zs2a2b5CiOzlzzl/mjGZabZ0MpOEjiPGo0ydZNoaPC8wLu23337unnvusTYXT0eMQE899ZTtxzXojDbesbF5PIockC94DUH5cuXtG8hvpg9D6jL/yfeS6uWRHwyM4DnKAEjwBgnwvpBDxxxzjBlSkB/dunVzC2IZtykw6v1Rtrw93rIBlqunXDJAw/MFKLN4XkFq/mIkQe6SDxvDIArv5u+FOfoKz5MEmY7hhn1m/pa/Vw8egOg0GIcwElE/MYAF8JgCBr8++ugj+xuo49Rf8okpe0IIIYQQ/4YNZhxCicbIMvi1wbmjibiL4z2DsSSMaqNcBaXr+OOPzzXIEHsAdo6+GzVsaH8zasuoOMalEOtn6NCh9nvXXVaO/NLZYroY4LkTKFyokB2HMQmvnp49e7qzzz473rqSVIUVSpYsZaO+W9eqaYYoYLS/UcNGZqgKo310OOg44IIenqVe/Xp2Tzx/8DgSQmQ3TEnBWE07kfROgWBApwOaui0QpuS0aNHCDOK77rqrdUAv7djR0u+44w77pg3ECIInZmiTwnc2Qx7/+OOP1lYnvTggTCVDPiUJ76Vo0RzDQEG55ZZbbIoa8i8VzsNAwpNPPeXOPPNMMzoxcPH6auJNbSzMnz/fjXpvlDv4oINzPasAmdelSxcbIGIAhqnbl1xyiclPZCFTG6tVqxbvncPmm29u3jJ/zJ7t/lzNtPD1AfcSpgeG7wBGIfQY6lJq+UmFMnbNNde4hx95xKZ/MuUe/Yi8A4xDxKTCkMY3+snpp59uHtCw5ZZb5noXCSGEEEL8UzZIDwCFCTdqlKbddt3NYvcQsyfEWsBb6NFHH7W/Q+colWQAy0By30LRNyO8dHr4TiqlgBEJTyCULWIEQZFIuWOqGNPYGMkNwSVTSXc/XDtdJw2lkOuH0X2UQOIxEGwzTDv4Y/Yflh8odyi/QghBW0CHk+D7dDSTzJ+fM4Uk1VshSTCQp3YaD2jZ0jreBKumHWIqK23yDTfcYMZwOp/BqwiPTAwW2TjVFdlAHpLHyY4/7T+dcQid90DwFipXrvwq3l6ZwGuGeHYMSqTGrQGujcHgoAMPNCNK+/btLT0Zqy8JMWtat25t09N22223jB8GWIKcXZfgBYQH73HHH7eKAYUA3sh+PKaYFoWhCA9a2HHHHc2gmaRylcp2jvlRPi/4e8N7TiHz8cyD1LKQU28XmwEpVf9IBV1kjz32cGedeaaVh1A/Q0wxjJF33nmn6/9Ef8sXpuFh9A1xqvCuXhNjpBBCCCFEOjaIcQhX6OQKHig+ePIQ34G4GBhKRo0aZdvSGVwgGFsywXF464ROVVgBJoDCj2cP164YewItjzpIGIQYueR4RmmDe/s/JfU+UfIvvvhiU4T5JiYRLuQoeYyaavRPCAEYlplaO3v27FxDcmD69Jyg+6yQlIkQ+De17cPohHGD6Si0tXRumSbFNFsCMDMlNngw8k0btbrg/f9FSpTMCfiMkSgZnwmIewdMAUq28SFwMAMABWnL8Wxluh9Gn+ANmx8YnJgSDcH4lwrvHYMfgy4YGjJ9HnvssTwrpq0riJWEfNu5yc5xSl4wVOHFRny/yy67LNcQedJJJ60yCLRk8RIrt5TZMqVXHSDaELASIBAUOsmsWbPc338vsiDca2K4YWENDECp7xd95bRTT7PYhhhvqcOUN87PlEMhhBBCiH/LBjEOMcL2yiuvrKL84M1zxhln2N9Lok4LJI1DmQxF6fDRuZnmFUZik/P0Afd/DEd4K4Vgryj5jApjpDnttNNsBBbX7hBMem3AMxBviA+jg6wQxAo3xJsgELcQQgAdyrZt21pn+KtpOTHLAEMFHWg6h3QkM4FhiSmwGLgxAgVo52h7mcZEJxQjwsCBAy1WGx/irRE3DfjGOIRRO9soVbKUGSEI+p3qucU0H+QGciUZW2jixIn2jfdLkCuZePfdd21xA+IHJWPxMJUtdcXNJCVKljQ5wgIJ6UDuYbxiVVAMTpk+bMdTZ11CvuF91vKAlqusYJYO4glipGQKOdOmUgmr7eFpE7xmNjQsdFGyREk3efLkPEZE3iOEaeZrAoNXlaJnJIZiOjAY33nnHTbIRBla1+9RCCGEENnBBjEO4RY+duxYU2pSPWt++ukn+24aKa5AxyjE6+HvVJLH83f4HTpDxDHAOwi3faZQBD4f/7mNqCdjCnEsxzFiT4BsXNrpNLFiWvLa4X6Sxi2283vF8rwGr3DOcD+MLrIyGcpcpyuvdN2uv85179HdPKbSPZ8QIns58sgjrK0YPGhwbpB9jAq0XW3atMmdiotnD9PCCFgb2qXme+9t++D9Qwc9gKGJ6bbELcGAgeEHYwHGBj4YFjAcAcZ1PCNWZ+j4L4JhDQMKbTdThJJg0Ee2sEx5MOSQ7xjXWI0zePfQpuPpQYwgPJACeNO0a9fO8p38ZqUyZOKAAQMsBl9YCRODQ/gbuMbLL71k74lgzusDngE5lkk+Pf/88xafL93KeRhIWJChxb4t0k7HToLHGgs+MF2KhSLSxenBU4v7IBbRxuJli+fTAa0OsMGkTz75xNIw3lBP8eI66qijLA2I73XbbbflDjhRbydMmJBbt4G6SXk4/bTT0k4bxeBGPr37zrvmccWCGEIIIYQQa4VI6cuXyy+/PP5r7fHWW2/5MmXK+NKlS/tIcfJPPfWUj5Rl37lzZ1+2bFkfKTt+/vz5ftmyZf7NN9/0UQcFi4/v3r27jxRxHymc/qyzzrK0XXfd1Y8aNer/7J0HwE7VH8ePlREqlNE2SjupqLRoqGigHVGUtFM0tP2baGrLqCi0kSKVlJkooxDKahhlNcj538/vved1PZ7n9Sqz9/vR0/s8565zzz33jN/5DR8NvH00Acre99FHH/XRYNWud8cdd/gCBQrYvm+99Zbv2bOn33vvvf0999xj28OxpUqV8tGA1EcDeb9s2TLfvXt3OxefK664wkeDNj9t2jTLH2nR4NxHA3r/2+LF/n/33ecLFizoixQp4t977z3L4/Dhw32FChVs35vatPHRQNBPnjw5uvZe2ecNn6JFi/oTTjjBD/10qOVJCCGgT9++fvvttvN33XWX79evn69evbqvWbOmjybK8R7ev/3229aO7LPPPmukz50719o6Pm9Gbd/gwYN9jRo1rP2kjc3Eww8/bOd74IEH4pS8yZ133mnl0KdPnzhlNd9//70/6qijfK1atfygqFwvu+wya+/7Rs8rMGfOHL9X3N7379/f0t5//31funRpS0v3qVSpku03ZswYv8suu/j8+fPb83r++ed98+bN7ZojR460fTYmf//9t58yZYq/9tprLV/c2wcffOAXLFgQ7+H9/PnzfdWqVW17ly5d4tTV0GdXqVLFf/fdd3HKalatWmX95IiRI6zPpe/m/hYuXBjvsTZPP/20lceWVi9nR895//339yeffLL/MBrftGjRwu+0007ZzxzmzZvnd911VxsnvPrqq5Z2yy23WNntu+++9s516tTJH3300f6GG27wK1assH1g+fLlftKkSTa2qFevnt9rr738q71fi7cKIYQQQqwfmWQ8BaIJx13R4CQj0WDQnXTSSfGvDQMrqJhUsXKKrwv8XGBehXo+ZmWEW8bXACrTb7z5pttt113NfILtqMyzeoitPWmsbLNiWa5cWTdo0GALA3zEEUfYNaKBtW3HBAD1bPx2sDqJmQXXwfEqq5Os/OLviFV4VgExOcMvB6vFmG5wPlZsWbFjxR6zOK7NPZBWrGhRWwkmBD1OQNkHcwRMDsgD+7L6zEo01yBELdoAOJFkVR51e1ZCWXl8b8B7rkHDhq5k7ORSCJG3wacIGiaff/65+/jjj01jBJ8ySVMS2rHZs2a5U0491R0XtSvB8S/t0Iknnuh+i9q+T6JjMUfDv9uNN96Y1ql/APMYtIVwbIy5b16FMsAXDu18qnkQWh1ofC6O+qk333jD+iZCkaOtFeA5LI/6E0ygGjdubMd8MfYL8x10zDHHWJ+R/ND/oM3KM+f50O/wN0S5RGMJLR1MBjc2QesJLRj6QDTMpkyZYtcOgRO4D3zr0M+dd/75bqcUbZ5gNo2ZWCr0iT169DBtNzRn2rVr566++uoctdTQuiE/aBal06rZXNBfM07C7O3tt96yeyCvtWvXjvfIKivGKvT5lAemcTxbNJGLFI7GNT/9aM8bX4dEFQzvMBC9Db9UOCHHZJHIgkcdmdmkVAghhBAiJzLJePIhIYq/p4VJBCZWGxIGhVw2qI0jBGKAVLJkybSq5BsKBl4ImDAz25jXyQl8KuDjo2fPnub8mrLgw0Ac9XsmBvgfyuRPQgiRNwkCaoQQ6Qj+TpKTyiQIvWn71mXeI9aEPhAhxqhRo7KdfCehL6MNx0dUOsfD63ou64K+kWePcGFLhPunXlK3UmHxh742ncCHe0LgxbacBEKBSZMnuXqn1bOFHfw0banwnvGOpvORmK6sKIdwTCbH1SxUMU5CyPhP65EQQgghRCCTjGezzBII7ZoUzrC6jRbOxhbYMCniWptLMMRAGd8TrHwyKGZQSFmEwfHUaVNtVTaESRZCiADtVybBEDBpzGniiHBBgqH158477zTH3fiKC4ESkiAEoE/JNLFf13NZFwgStlTBEHD/6QRDgLZsJsEPdZF+PzeCIYQn9993vzv88MPN386WDM8qnWAI0pUV5bDttttmrD/Ae4+PJQmGhBBCCLEx0UxhE8IEArOzb7/91kxDcGh6/fXXuyuvvNJMP15+6WV3W7vbslX2hRBCbF5YUMCRcKFtCrl2Ufu8MA+G9d+cYEZ+zTXXmHbWI488stkWd4QQQggh/utIOLQJYYX06aefsg9+KYjkMnz4cDd//nz7/fLLL7sah9eI9xZCCLElUKFCBdf5yc5uj933cNO/+y5OFZsConfhZ+e5556TVq0QQgghxEZks/gcElk+BEL4WpxSajVUCCG2bPANF8yBxaYBk7It2axOCCGEEGJrY4vyOSSyfAiUKlXKPhIMCSHElg9+YSQY2rRIMCSEEEIIsWmQcEgIIYQQQgghhBAiDyPhkBBCCCGEEEIIIUQeRsIhIYQQQgghhBBCiDyMhENCCCGEEEIIIYQQeRgJh4QQQgghhBBCCCHyMBIOCSGEEEIIIYQQQuRhJBwSQgghhBBCCCGEyMNIOCSEEEIIIYQQQgiRh5FwSAghhBBCCCGEECIPI+GQEEIIIYQQQgghRB5GwiEhhBBCCCGEEEKIPIyEQ0IIIYQQQgghhBB5GAmHhBBCCCGEEEIIIfIwEg4JIYQQQgghhBBC5GEkHBJCCCGEEEIIIYTIw0g4JIQQQgghhBBCCJGHkXBICCGEEEIIIYQQIg8j4ZAQQgghhBBCCCFEHkbCISGEEEIIIYQQQog8jIRDQgghhBBCCCGEEHkYCYeEEEIIIYQQQggh8jASDgkhhBBCCCGEEELkYSQcEkIIIYQQQgghhMjDSDgkhBBCCCGEEEIIkYeRcEgIIYQQQgghhBAiD7NFCYf+/vtvt2rVqvhX7vHeu/kL5rvPP//c/fnnn3GqczNnznTjxo2z8+ZFuO+JEye6b775Jk7ZeuFeJkyY8J+4FyH+CStWrHDLly//R21kgLbyr7/+yrFNZJ/k599c77/GL7/84ubPnx//yoKy5LnwfP4pHEtZ5wR9G9dZ134bA+rAypXrf38rV650y5YtW2fZcH7qZbpymDVrllu6bGn8a8vmn9YF7pljKIN1vW/sR5n+8ccfm6UuCCGEEOK/yxYjHFqyZIlr2bKlGzLkozgld8yZM8fdcsstrkrlKu6KK65wCxYssPSFCxe6Sy+91FWrVs19/fXXlpaX+PDDD92ZZ57pDjjgANejR484detk8ODB7owzznAHHnige+mll+JUIfIGTAb79u3rTjzpRHfqqae6a6+91n377bfx1vWDdmHPPfe086XCRLN3797u4osvdi1atLBPkyZN7P0Tzg2KyqFZs2YmpA5MnjzZyuvEE0905557rnvrrbfiLevGr1rlZs+e7Z577jl3yimn2LnSwfN/5pln3EknneTq1avnbr75Zuv3NgUIK0aPHu1atWrlrr76mjh13VCXBgwY4C6//HJ3+umn2/117do1rdCE85/V4CxXuUplV6tWLffYY4+ZgCUwfvx4d1mLy9zYL8bGKVsmjDOaNm3qjq99vLvoogvdu+++myvB6qJFi9wDDz7gah5R0+27777u7LPPdgPfHxhvXQ3PgvfznHPOsb69/un13a233mrCMyGEEEKIDcEWIxz66quvXI/u3aPBz6vrtVKNUIn9WVVllTJQpEgRd+ihh7pjjjnGbbfddnHq1sGYMWPWqwzSgZCsWLFiW+XKInmmDALcy7bbbmvpW+P9CPFveOGFF0wAUevoWu7e9ve6adOmuQsuuMBN++67eI/cMXfuXNe6dWv7+9tvv8Wpq0FQgeABAWyXLl3s06dPH2tL8zq9evV01193nQnMjjvuOEujz0JoTbnddddd7vDDD7cFCcotN3wwaJC77LLLbFFk7NixNvlPx+23325CAAQst912mxs2bJg9f7SYNibLf1/u/ve//1nde/bZZ63e5JaePXvafVWpUsU98MADrmLFilZ2d955Z7xHFgjEECDNmzvP7VN1Hzdp0kR3/fXXu3vvvTfew5lAjHtveUVL984778SpWxZoKCOwmffjPPfQgw+5/fbb3+73td694z3S8/vvv7urr77a3XrLre735b/bGOaNN96I6tWZJkxLQr1i3+rVq7uHH37YXXftde61115zF154ofslRZtNCCGEEOIfEU22cySaTMTfNh6rVq3yN9xwA7N+X7JkST9r1qx4S+749ddf/S677OKrVq3q58yZE6d6v2TJEr9w4cL419ZBNMj00YTDr1ixIk755/Tr18/K9JZbbolTtg4+/fRTf/bZZ8e/snj33Xe3ynsR4t/w9YQJfocddvD169f3y5cvt7Thw4f7UqVK+WbNmtnv3PD333/7Sy+91N4hPs8991y8ZTXt27f3derU8R999JF///33ff/+/f1nn33mowlsvEfehPLeY4/d/TPPPBOneHsW559/vnf58vlJkyfHqd4fe+yxvmKlSn769OlxSnro8yZNmuQHDx7sDzjgAF+8eHH/5ZdfxltX8+GHH9rzurRFizjF+4EDB1ragw89FKdsHHjuI0aM8Pfdd5/PF90ndTA3/DDrB7/jjjv6iy++2P/555+Wtjjqi4844gi/7bbb2j0B57+iVSv/8MMP+0WLFlmZPvjgg3atnXfeea16d8899/gKFSr48ePHxylbBquiT50TTvDly5f3I0eOtLTffvvNn3baaXYf8+fPt7R08I7tueee/pVXXvEzZ870E6L3vWHDhvZ89913X//zzz/bfpxjv/328w0aNPDLli2zNLj//vt9kSJFfPfu3eIUIYQQQoh1k0nGs0VoDv3404+2Ws0K9eLFi1337t3jLbmD4woUKLCWVkk04HbRxCr+teWDKn002HNTpkyx+/m3RIPs+NvWAyr2rC7PmzcvTslia7wXIf4tr/bqZe8EpjlFixa1tJo1a7pddtnFRRNLM8nJDd26dXPRRN80W9KBFgr7nH/++aYZgwkTJmxHHnlkntYcQsOqXbt2Udv8u2tycZM41blJkye7XtGzOT4qq1133TVOda5+/fpu+nffuVd69oxT0kN7ts8++7g6deq4smXLZvQB9cQTT7j8+fO74449Nk5xbu+99zZz6YcefNAtWLgwTt3w8Nxr1KjhDj74YOtLU/vXTHzw/gdWnw477DC3zTbbWFqJ6PjzzjvPfOW8+OKLlkYfd9VVV5mm0Pbbb2/1+6yzzjJtI+45f4E1hyeYjZcqVcrd0Lp1nLJlMOTDD93QTz4xkzC0x6BkyZLu2OiZYf6Xkyk0pmg33nijaYLtvvvubr/99jNtLd7vH3/8Mdt8FP9CnItP0uSOcqMc8+XbotxHCiGEEGIrZYsYUTz/3PNu//33d23btrXfqKQvWbrEvqcD1euhQ4e69u3bu5tuusl86jBgYkAZwNxs0KBBpu7P5IrtqKQzUMPfxowZM2w/JkwIo1DPHjlypKn2cxz7YVLB3y+++ML2ZaLw9ttvu1deecXOwXnh008/tQEdvkAeffTRNSZsDKh/+OEHO88zzz5j+Rjw3gDLF9f9+eefbT/8BqCGTz4QkKFC3q9fPxtMZ4Jz4wfjnnvvNXORF7q8sIb/gSBQKViwoA3WOefdd99tvhBSB/qUF/4y8N/Ec8C3wdKlq52AIqwhb88//7z79ddf7brcw5OdO7sZM2faPt9+86178oknTbjz0ccfub9Xrj3hoYzxKdGmTRvLb9LPxtSpU82nx5AhQ7Inq++//75tCxOFQoUK5epe3nzzTbsXzGR4juFeeL74sOA5cZ1JkyZZPcI/BuexMh30gbvnnnvseT711FNr+KzCfHF4VGceeeQRE2pi/oaK/4PRRG1EdG8B6hX1kmtTX7hWOD/1hTpOnUAQKEQ6aOdobyApgIADDtjf2g7q2bqg/lJfeV+YwKaDdxJzNd6XRo0ameAjk5lTXuKTaNLPBxOhokWyhHPwadT/AP1W0YTw7JBDDrG/X0TtAu3Qv+H777+39rFEiRJu7732ilOdCZPwG4W57ahEm7OxyBI+5Furnc0EQRAg1TQan3GAgIO805bvu88+ayyEkE5bjdBom0JZgqVAmTJlTPjy2bBh2e/FlgA+ufClhHAnCb8Zk2TyQ0V5IqzFtDAJwj8WtRDO8awB0/ijjz7a+k8WkOiHGBswDtl9991MmCuEEEII8a+JBig5srHNyhYvXuwPOuggH01O/IwZM3ylSpV8NKDy3bt3j/dYk2jC4jt06OD32GMPf+utt5oaesWKFRm1mnr+vHnzzLTsoosu8tGAytKjQbapqEcTdR8N2CwtmvTb+TAZwIyLNNS5MediP1TESYsG+3706NG2L2Zqd955py9WrJhdG/Xu3r17277nnXeef+CBB3y5cuXsg7kADBgwwNeqVcvuqXKVyr71jTf6nSvs7AsWLGjnb9Omje2HSUE0KTOzOvIdDRj9Qw89ZOr26fjjjz8sDwcffJCZg1x++eV2zpNPPjnbpIFrcw1MtKLBo48mmH6bbbbxhQsXtnsMYHrXvHlzU4Pnmscff7wdd8EFF9j2jz/+2I4nLRrg+0suucTKGpV50jBFiQbr9hx5LqRh9oLKfACzlnvvvddXqVLF8o2pQjTwtd+Yv8HQoUN9vXr1TE2ebS1btvRPPPGEbRvwXta9nHPOOeu8l0uje+E8yXtp3LixbevYsaPfM84j+a5WrZp95y915/nnn/c77bSTmeCQX55H5cqV/dixY81UoMnFTSxv0YTGnh3fOZ5P6dKlzfwNBg0aZGVEOmXxQfQbUxKgznAcJiiYlgiRjt8W/2b1u0SJEvZuJLnxxtZWt2677bY4JT20Z/Xq1/OtWrWy35ihcFzSrGz578v9Fa2u8HvttZcvFL1TbM+XL5+9f7wTeRVMomgXKY9hnw2LU7NMwpo1a2bpd9xxR5yaxcSJEy0dEyr6odxwwgkn+KJFi65lVoZ5H20H7dGUKVPi1Cxog7lO6Mc2JpgY0g7SP+QGxgzk7aabbopTssBkmnYT86jU+wH6V0z16Lvp39Lx0ksv2bl5LlsKjc5ulPZ+339/oL27e1bcM07JHZiSYWp2+OGHxylZvPXWW1YfuFaTJk1806ZNrd/66KMh8R5CCCGEELkjk4xnswuHunfrZgKF+Quy7PJbtGhhgx8ENQiCUnnvvfdsO8KFAPb6DDqx0Z87d65NiD7//HN/2GGH2b4InQKtokkQaU8//XScknVOjj/zzDPjFO+7du1q+51++ulrDFTxO1G3bl3LG4PZGjVqmE+QH374wbYz6eK4tm3b2m/8IT3//HPRILG4L1SokL+pzU1+xswZ5leHid/ee+/tv/nmG9uXwTODQnwnrVy5MluYkI5nn33WJhRBGME9B2FEEKwF4RADe/bHbwHCFtJOOeUU2wc6dexoQjOuD/g+OPTQQ20/ygEBHn4iENowaUQQhcBt8uTJ/sgjj7T9GKS+8847JoBhwkIaApbgc+LVV1+14++++277DZ988ontx7XDZIG0MmXK+KOPPtp+hzJA0MS+6e7l1FNPtX2gI4LD6HzBL0XyXjjul19+McEbv/Ff8Vrv1/zjjz9uwkmeIeXPJDn4iaCesS/bgXqFUIc09nv99df9mDFjLL+kHXXUUf6nn36yfXk2TA4QZC1dutTSAEETx1IHhMjEd999ZwJfhM9BQB1ASE19a3HZal806UAYRDtIvYdQn5PCIdoahNDUWwQUCJPZhw8C0Jzaof8y9CX0KZTDtGnT4tQsQXf9evUs/ZFHHolTs6CvIZ0FC9rH3JBJOITAnXTaj9CmBK64IqsfC4sLG5P1FQ7R/5A3+rbQL8IHH3xg6Sy40C4HVv690vfq1cuEIeE4BGPpCH0Gwjeez+aGfpe+lDzxriVhUYUFA4R7wXdQbnjttdes30AQlsqLL75odYLrsUj15ptvxluEEEIIIXJPJhnPZjUriwZWrnefPq5x48audKnSlnbNtddaZKpocGgq1Ekws8CUCZV6osQEosG1qV9HkxhT1caMChV29kulePESpiIfTK4A9e1owBX/ygLfG5UqVbJ8DB8+3NJ+//138/PBNlTiySf+P4imgtkHZmfBlCD4BUAd/OCDq7mSJbdze+21l0Uy2WP3Pew4oqn99PNPZkYGqNeHvIXv6cCsCrMk1Nbr1q1radwz6ub4ZcDXQ5LmzZubWUQ0ULW8RgN9M4njupTpY48/br4cUI/v1KmTRUsJfP7552bWQNQ3ypdIKUTO2W233VzVqlXNHwUQcQZ/G6jDn3zyyZaGz4RgmoJ5GH4TKLsA/iz4jfkEZiyAGn7y+aSWAfeBGn64F/LGvVDu1Ce7l2gbpoHhXqgTgHkgpgnBvKFBgwbunLPPsQgw11xzjdWho446ynytcH5MHIJZX/iLWQPlDpyfc1Am+AahDn322WfZZnbkj7qJqV/wswGYC1J2wQRFiHTgh4a6i98W2psk4b1auWJldv1OBdNb3qvOnTtbvQfalVRIw3dJNIm195m2BTNZrkmktGAmlNegvcckmLaxcOHCcWoWq+IyT+03aIOAdjWTH6HcwnPlQ5tIH5WE80OoB1sS9aJ+gLYdfzmYD2NGi3ntfffdZ9vLlSuXXR9h1d+rrI7jR4k+l+OI/EVUtlQ4jnqJmXMwyd6chGcEqXWBZxTGJLl9TgsXLrTIcPj9wg9RKtTFHXfc0XwSMcbArD51nCSEEEII8U/ZrMIhBA8MHL/55hvzc8NABz8zf0cDKgZJ7733XrxnFkzWGQjtvPPOaziaRkjDBCcM0gChAoPqVJL7JElNZzLQqlUrEzogEIJRo0bZ7xDKmGvgu+bxxx83nx0IZpKClUAQdhTaZvUEj98IaRgYM4CETHlLBaELPpMoh+Q9nnbaaeYjB0enSbgXrgeEt+cYJi58lixdasIZhCFMDskTAhd8PuATCcFdgOPCJ8CkEpKTV54F+zA45i8+JvDzw3cmBgHyhR8FSIauz6kcgoNTYDDOtZL3wmQu9V4Q/nAvF110kR0XyoKBdhLOjR8g/K4QfvnKK690H3zwgW1L5il8p/wDlStXzq4XM6ZPt7+A0Inr4a8pCJuYKOFgmGchRCaYBFK/mVgGYUBg+fIsYSV1PNTnJNQ1fAztuGMZm0jjgwufa7yHQOh06jYC7VQQNPMO0J7RDs+O3t+8CG09/m94Dsn2jfKmfQEESEmCEJnnkiosWF9YWKCNoG3Dx0yA9if8pq1LhfY2+OOjX830ad26tfvwww/jozYcpaK+GQEHwg0WVuhLEIh/9913tv2AAw6wPjtA2SJkf+aZZ6zPR7BEvaTOphKexZIli6Nn8+98Om0IyEu4FxY/kvCMeHfZJ7WvycSj0TiCOodAN9nPAuVxySWX2HNjnMHiAn7Cmrdobn+FEEIIIf4tm004xACXAQ4DRTRoGAgj8NmpTBnXonlz2+fll192P/30k32H4EQ6rMYFkt/XxfrsywSeSQCChbnz5przYzRH0JoJfDP5G3fKKafYhL9p06YWeSQd6SZw/xQmJExCKJsgWEqSfY9pLkk++IR9FsXRbvbYYw8TBKFlxOfiiy92TZo0yRbeZGJd98UAF82euXPn2u/kJAfQ1oH1eS6BMHjmWD65vZec8jxm9BjT9sEJLcIhJsiZSJY9K/tBU40JfcAiP514omkUvf/B++6ToZ9YvhHkCZETTDrRlEBIETQRAz/+mNUuhvcnFdrKr776KpqcjzChKPX/wgsvtHYK0GTjd2hTU6GOogkIG7Lt2pqIWhX7izZQsn2iPNAUBZzzJwnaLOXLl7c+7d+AIAThNm1mUohHXQi/EUqnQvvDcQiocvqQv1SNpA3FQQcdZAKiwYMGWb95ww03mPCdxYSzzz473mttiFSGdiigIZtKWBwoUKCgfbYEeE6AQDYJz4h+mnc0N4JCAhi83revLTaF+hVAy5eFKJ437y3awSxkoIE64esJrmu3bvGeQgghhBD/nM0mHELDg0EjwhRUzzFV4tOmbVtbtSakK/uw2h0oXaa0DczRdEmqlJMWJjDrEjKEiXvqYJ/JUOqxFStWdOeee64NUjs83MG0llB3D2AudP4F57vp06fbqh4RQ8JAcX0IeYd15R8Y2HOdCRMmZEdSC3w+fLhpX0H+dYS35Vo7ld3JygSzr3HjxsVbsuD+HnroofjXapL5zQn24xpMVMgvwhQmrEnCuY444gj7m/H+c3HJsuXK2nMcOHBgtoZEINO9JEGDrUHDBva9d+/eJkzKacU39bkFkxLqTZKbovoNL3Z50bW/t73Vk6QGlRDpQOOA9gZNgjlz19Teoa7yXqWakAYQkHbs2NEi6aEFRzQ9vgeTTzQ1OnToYOYpmaA+o5myaw77/JfZtliWRip9TapZ0PHHH29/ac+SGiMh+iLCkXRaPesDfSDCAIQMyaiOCCHQDqKtI1x6KrQtRFq8/fbb3W233Zbxc8cdd5gZ7caC8PeVq1SxxZRu3bqa5hBanJjh5gTbqdvBfDcJz4J6idA0qT28OTnxxBOtL5j8zepnBGFBhMWjdUH/S0TBJ5980jSnAggC6Vt+/uVnMxNlW7hvIg9i2gwzMwh5hRBCCCHWh80mHOratauZ5YRBdpIKFSrY5AVQNQ8Dc1bTmHgzMH711VezBQmsrCJ44JNqfpEKg0qOS67yYWLBBCxVLdw0PE49zYQxDNxYAQxCDCAMOgNeVomDUCiEJmewiFAp5NH+psg9OH/Id/jNZCSsCiMcQ+smFfP3s09V+377HbebsAxmz57tHnv0UfsOK/9euyyCQIP8cF0mQEwwEJ4wmcC/CINvNAoQ1gVhWiAcFwj3lyoogfA8yC8aVxAEVwEmuYBPI+B6lENYkad8MatId50A29iHe0FTh3tp167dGvdCWHvMZZIEYU6AesCAnnrJ5ATwfwFclzpDHQl5SK7ms0JMWHG04FIn3Nx7vXqnmQkHYcWbNWsWbxEiZ8477zz7O/SToVbHgTqJaSlmJWFyTxuJdlow3aGdQ1sI09jwadmypatVq5Ztr127tmnUsR8TUIQctIEBzse7esaZZ9oEPy+CcIOw4pRLaluBxitlOGTIkDW0hz7++GObvOO3LIDJDybUqQKm3MAz5Ljg9w5o51kYQINkSxAyUx9zuj80Yp5++hnzE3jrbbfFqVlwTKo2Ke0omjZo7qaCiSTPgv723wrfNhT4/aOeTJ0yNfv9455YoKA/O+eccywNGDOwyJSsT5its0iGdhXvM5qC1ClM7Do8/LD7LfoefF7RByXLmfeZfi2YdwshhBBC/CuiiXWObIxoZUR3iibfFro9E0TlIHv58+e3kOLRgMjSCTVOOpGvosGUhXclpCv7RYN5C4MbTXQswhhhcdl3RiJaGZFXSCM8Oee98qqrLDpKoUKFLIQ84eiTYXYJgU80GY4hMlUSooxxbL58+SwC1vXXX29hetmXv4TIJwoQ0dC23XZbv8suu9j5gPwdfPDBtm8I5f7DDz9kR1hrdkkzu79M4ZA5hvyyLxF1zmpwlv0llD3XhM6dO9t2wrivXLnS0igbyp77J+wyDBw40CKfsC+h5bnfvfba2/IXonZFg14rI6J5hUhg0DwO9ZyMQsbzDfkKobCJtkRUsm222SY7Uhz7cb62bdta1Bcgwk+VylUsIgvnvvnmmy0qHMek3gv54JlzLyEkPGW99r3s5atVW30vnJPthMWPJtyWBuSxbNmydu2rr77aX3nllXZu9q1evbqPJuo+GpxbGGHSqL/RxNGOfezxxy3iXTQRst+pvPZab6sn9evXj1OEWDe8F7QDvLNEt6PdIIw57z7RkAJEdyLtpJNOsoiBmWjfvr3VXaIuBvr27et32203X6dOHXvPiJLFu3HiiSeu0XbmNSh7wvlTXsmyDtBulilTxtqvVatW+TfeeMOeQTKkOW0O5RpN3tNGJ2Q7UbpoO4j2mAr9xbnnnmvtEO0d0S8bNGhgbWlovzc2tGm0XbSByfYSfvvtN1+7dm2LyJWaf/L+6KOPWsQu6mxqvSQCG2Vz5llnWR2cNWuWf6XnKxat86GHHor3WhOimvE8ror67S0JomnSx7dr185+9+zZ0/qh22+/PTvaH30HUdZ23nlnP2jQIEsj2hh9Du83UTCPOeYYi3zJfiVLlrT6B9RFxhjUkzAOoV8k+iDRDEeOHGlpQgghhBC5IZOMZ5MLhwjvjdCAwWSlSpVMAJA6aGRgxT6EGmfgw1/2Q+jBQKtTp06+XLlyJhhAuHDdddeZkAYhAINK9mNgymSegWRygvPnn39auHwGcgzYL7jgAguFTzj9mjVrWhjZ5cuXx3tnwUSKwVoQUCVhsrXDDjvY4I7rjRgxwga3DN45F5MKhCzhPhAokI6Qg9/cB6HWEXIBoZGZcHAOQtHnBMcQMhlhRsGCBU3wEO61w8MP2zk4P3m56uqrbVBZo0YNywsD1OOOOy471HCfPn1s/8KFC9u5GKiGUMyEb2cCwLk47vjjj7fJKOGUeYYMbpm8UPaEMWayE/Y9/fTT/ezZs+08TG4oY65/4IEH+n322ccmUkHAEmBATZkSHp9B72PRBCP7XqLndNU119hkguuEeyFP6e4FgRb3gsCPATYCQZ51KBfCdjPhClx3/fU2KOf6DMaHDh1qE2fuj8kgk6MgHKoS1TfqBZNo6l+XLl3WmjwFECRSP8NzFiK3UD8RECFwPuCAA+wdQsidZNiwYVbXk8LhdDzxxBMmwKCNDYwbN87Ci/Pe0y4feeSR/o477rDJbF6H95UJOQLldPTu3dvab9ozhNEIyeljApQh7QNCcfqGJEOGDLF2lVD1tGP777+/LU6EBYTAL7/8YgsgbKddo90eNWpUvHXjQX/HfdMO016ST649fPjweI8sAdBRRx1lfVZY5KCtRahBX4Owg/YYoWYq9Pv0jbTT9IV8Jyw8Av5MILSnbU4naNvc8E4hQOM+eE4IbpLPkn6Obdzrhx9+aH0FZUvZ8fxLlSpl98aHd5S+NfmeI4ijHeDc9KuUO+VLeH8hhBBCiPUhk4wnH/+LJroZQd0Z3xQbCi4XzHIg9XdgXfuhdj1z5kwzQcNpNOY6hMElugtgKoH5TjS4N59AOyciS3GuYIqFbw7MgzDTwGQNPx+pYDKEiRj7poNtmEIFNXf2jwZ+Zo4GybyH7+nSAuRn2+LbuoK5cLiJivnUqVPtvslfpnOm/obUtHAunJRyLlTiIdO50qUHQnrqPoBZBKaBmJtFg+I4dU14viVKlnAF8mdFoUt3/SSpaeFeME/Yfffd13kvAX7j6JsyCKr6qPnzfHEQjJkcEWNwBjp69GgzP+N5RwP2HKOPER4cfxLUR+qrEOsL7wTvDe1cqiNhTDh5r6iP0eQyTs09mMFg/sTxmO+mvl95lWhyb6Z9mE7hky2dA+cVK1ZY2fFepzN1IuIbpmnpyjVdW5YJTKpwTFy5cuW1zH03FuQPQh7T5Zd6uWjRomyH/PSHlBftLv0ubWcmqHf0xbTX9DmY8mWCfhETaM779ttvx6lbFtQF+h1M0DPVBfoT7uGfsnTZUjf9u+nmUBwT5k1VF4QQQgjx3yGTjGeTC4c2FUzYidKDDyDC5ec2lKwQOZEUDuGjqFq1avGWtWEyw8CdY4hOxgc/TkKIrYcxX4xxDRs0NCfPN9xwQ5wqNjU4X6bd7du3rwlIhRBCCCHEPyOTjGezOaTeGIz9cqw5BCYq1Zy5cy3CC047JRgSGwpWuNEmAFbxM4G2GlFqDj74YItchCPr888/P94qhNhaOLT6oe6pp55yr/R8xb3xxhtxqthUsH5FAIvu3bubgEiCISGEEEKIjcN/Sji0ZPESMzW777773Dlnn20hZq+44op4qxD/DswnMA0jshPmN489/pgbNmxYvHVNMKXAxABTH8x8Hn/88WyzCyHE1gVaf7169nLvvvuuGzFyZJwqNgVEefzggw9cr1690kY3FUIIIYQQG4b/lFkZfjcIFUvocvwX7LvvvrLHFxuM+fPnW8hm/FLhdwN/Gfh8qF69erzHmqA9RGhjTM8IGS6E2LrBZwxd5pYSRj0vMGvWLPPnFMK5CyGEEEKIf0ee8zkkhBBCCCGEEEIIIVaTJ3wOCSGEEEIIIYQQQoj1Q8IhIYQQQgghhBBCiDyMhENCCCGEEEIIIYQQeRgJh4QQQgghhBBCCCHyMBIOCSGEEEIIIYQQQuRhJBwSQgghhBBCCCGEyMNIOCSEEEIIIYQQQgiRh5FwSAghhBBCCCGEECIPI+GQEEIIIYQQQgghRB5GwiEhhBBCCCGEEEKIPIyEQ0IIIYQQQgghhBB5GAmHhBBCCCGEEEIIIfIwEg4JIYQQQgghhBBC5GEkHBJCCCGEEEIIIYTIw0g4JIQQQgghhBBCCJGHkXBICCGEEEIIIYQQIg8j4ZAQQgghhBBCCCFEHkbCISGEEEIIIYQQQog8jIRDQgghhBBCCCGEEHkYCYeEEEIIIYQQQggh8jASDgkhhBBCCCGEEELkYSQcEkIIIYQQQgghhMjDSDgkhBBCCCGEEEIIkYeRcEgIIYQQQgghhBAiDyPhkBBCCCGEEEIIIUQeRsIhIYQQQgghhBBCiDyMhENCCCGEEEIIIYQQeZhNLhz666+/3K+//uqWLVvmli9fbh++//777+7vv/+O9xKZGDt2rPvkk0/cihUr4pRND8/wl19+cd9//72bNWuWPc+QzvNctWqVW7JkiVu8eHH2M166dKmlhU94/hyTE957t2jRIvu7Kfjjjz+y8xw+K1eujLduer788kv32Wef/av7p6789ttva7xzPBvS9M5tHfDMfv75Zzd//vx1vjM5wfP+8ccfrb1Nx59//unmzJlj19lU79zWxNSpU93s2bPjX1nQtpG2cOHC9Sqz8C6m++TU5vA+0/7OnDkz43PcEFBX0uWNT26vm3oO2iDqWE6k3t+UKVPcvB/nxVu3bLhH6kLoE9cX7pe2OTf1iDpCOXFNIYQQQogNQb5oEJLjKOTGG290HTp0iH/9e2bMmOFeeeUVm/QiRAhCjnz58rny5cu7GjVquDPPPNOVLl3a0sVq/lrxl9t9t91twP3666+7o48+Ot6yaeBZffrpp+6ll16yAXuhQoVcwYIFXbFixVydOnVsQlmkSBF3/fXXu7feessNHjzYJkw8W9LZF1ZFVW5ZNKFaumSJO7luXXfTTTdZejpGjhxp299+5x23w/bbx6kbh7lz57o777zTJs8FChSwct5uu+3cQw895CpUqBDvtemgvC+66CJ32OGHuxtbt45T158ffvjB9ezZ040ZM8buiU+ZMmXcIYcc4s477zz7LrZMaCMHDRrkHnvsMTdq1ChXokQJV7t2bdeqVStXvXr1eK/c06dPH9esWTP39NNPu8aNG8epWXw1/iv3v/v/57777jtXulSp6J0+wV1xxRV2zbwO3eQr0TvUM+q72rVr54488khLR3Dbvn17EwaULFnSNWzY0LVo0cLavJyg/bz11luz+78ktKvXXHONO+aYY+KULBBcD/5wsHv7rbdNQJg/f347R5UqVeI9NiydOnVyH3/88Vr3QlmULVvWPf/883FKZh588EE3bNgwa085jnNdfPHF7qyzzor3WA1Cow8++MC9++67dq+FCxd2111/nVu4YKHVf8r1xBNPtPveEqGs6CsWLFgQtamlXYMGDe0d22abbeI9MoOg/vPhw91TnTu7ihUruocffnidx/Xq1cvdcMMN7oEHHrAyFUIIIYTILRllPAiHcqJ169bxtw1DNAD03377rY8mvQil/N577+2jgZ+PBkP+4IMPtrRTTz3Vf/nll/EReZexY8f6OXPmxL+8jyb1vk2bNv6yyy7zs2bNilM3DdFkxEeTIl+sWDFfp04d//bbb/toguMnT57sX3/9dR9NWO3ZRQN4H014/Pz5833Xrl19NLH0RYsW9bfffrvv16+ff+ONN3zfvn19t27d/OGHH+7r1asXXyE91193nZ23S5cX4pSciSbTfuXKlfGv9SOayNi1kh/ulXLfHEycONHXqFHDjxo1Kk75Z/Dspk2b5lu2bGn3tN122/lXXnnFz549+x+Xldg0RBNOX6pUKb/TTjv5448/3pcpU8ae4T777GPt6PowdepUH0087fhnn302Ts3im2++8VWrVvVHHHmEHzV6lL2nXOu2226z+pPXefLJJ/yBBx7oP/nkkzjF+2HDhvly5cr5s846y3/xxRf++eef99tvv721I+vi+uuvt+eQL18+v03hbXzhIoV9kaJFfP78+X2RIkXW6v9++uknf+WVV1pbeu211/px48Zt1Ocyb948v+eee1oeCxQoYPnjU7BQQUurW7duvGdmvvrqK1++fHnbP3zKli3rx4wZE++xmt9++83ub4cddrD+bfz48f7333+Pt3r/7rvv+kMPPdS/+OKLccqWxdChQ+0dbXR2Iz9hwoSovjzpS5cu7Z966ql4j8zMnTvX33333dnl3aRJE//HH3/EW9MzZeoUawPYP/VdFkIIIYRYF5lkPJtcOBR49dVXbWBzxhln+MWLF1saE9gw4DnzzDP9kiVLLD0v8ssvv/izzz7bf/rpp3FKFkzmly9fHv/adDB45bkwKfj111/j1NUwmTj22GN9/fr1swe2CIh22WUXG/B/+OGHlpaECVCDBg3iX2vDZJYJGddFSJMbQcbMmTP9+eefb8LGBQsWxKnrhuNq1qzpu3fvbkIvJjZMUH788cd4j03PCy+8YMKzDfW833//fStL7pNnI7ZsEEoiKG/arJkJg2knEU7su+++9hyvu+66eM91wzvZsGFDO47Pc889F2/J4oILLvDbbrut/+CDD+w3Qlba/oIFC/qR/1I4ubXDe4NQA4FqAGHG6aef7gtts42fMWNGnOr9ySef7HfddVcTEGQCoex+++3n77rrLj9p0iQTzCHo4y/H0+ZR/gGEJAjdeW4IAjaFsPqRRx7x+++/v7XbLACQN9rIl19+2QT+PXr0iPfMTKtWrfzFF19sx3/99dfWnqYTaK6K/jVt2tSEUB06dox+pefRxx4zQWlqn7i5YTGEhY499tjD7hGWLVtm/TfCQoQ/ObF06VKrQ9QvnjFllpNwCKEg7yuCxXTvshBCCCHEusgk49ls+tnBxAi7+Sgf9r1SpUqubt269h2zM1TvA5jChP3C31Q417rATCO35GbfZP5T4fic8sw9ZbrGCy+84Pr162eq9UlQz8dEK9M1cwvXzW1ZfPTRR6bWj4nVzTffbH9TKVeunIsqman8B58SfMeMINO1MIdALT7TvWBO8/3M7+2e8bXE73Wx0047uaOOOsq99tprLhqwW37HjRtnZZ0TmMHhCymaEFk9POCAA1w0STPzidzA/a3PM8GcJKfyZzvPH9OSokWLxqlrvgeZrpkpH5QjYK6wpZpmiNUs//13F00u3YMPPOB22WUXM++iPlx++eW2/cuoXueWJzs/aeaFmCulMnr0aDdkyBCr68cee6yl8d5iKkr79vxzz61X3f4vgU+Xu+66yxUuUtidc+45capzEydNcu+8846rU7u2tTmBevXqmR+2nr16xSlrg9+ia6+91szT9tlnH7f33nu7vfbay7ZNnz7dTEkp/0D37t3NhOvqq692l1xyySZ5d2lbXnzxRTNhrFq1quVx9913N/Mnth1//PHxnumhvbbyieoQx9Gu0p6G+0zy7DPPmqkyplGto/4gk0Fe80svdbvttpv1Myty0ddvKgYOHGgmu5QR9wiYWmN6iLlhly5dLC0T2267rdtjjz3cQQcdFKfkzJNPPunmzpnjWlx2WZwihBBCCLFh2GwzxEyTjeDbJUzmGVz1iAbHDAinTZtmvjKuuuoqG4wBE/quXbu6S6OBY9OmTc3HAZOgJAxm8V1z33332T7Y2DFwTTrVxInk08887Tp07GAOWRnQ4W/jtttucxMmTIj3Wg1OoRFsMJBv06aN+RsILFu+zL3z7juW5xEjRrjX33jdtbqylXv7rbds+1dfjbfzNmlysWvZsqXr3Lmz3Qcw+L799tvtg4CAfOD7AT8g8+bNc88884xNKsgvPPHEE3bPHTt2tL9cD3Bey3H4LmCfv2IntjiRvv/++6NrN7Frc/51ObSkrPAdxCQhTB7Twbazzz47e/KS04SSSRBlhiAnOREK4IS6R48e7tbbbrUJF78HDBgQb80MgpQrr7zS/B3dc889blI0iSNP+FnheByipvLTzz9ZvZo4caL5wuCZcnxuwL8E/nzwi8Q94V+D+sVEbujQofFeq+Ead9xxh/llYh8mYF988YV9wjMF/B9R30866ST7jR+kbt262aSS7++//769B9ddd5177733bB/Ofc+995hvjkcffdQcmyZJPo+8OtnfmmCCyUQwKXwAJtvU80oVK8YpOUOb0LVLV/e///3PJtepUPeoU7vuuusafk523HFHV7p0KfMDsy4nwv9VEIwPHz7ctby8pStYIGtBA4ZG7T8gEEBYHwjCga+/+mqt9y9w3HHH2TsahLUBngN918knnxynOBNs33zLLSZUob1mUSUnofKGgr7tsMMOi39lQR+FPyDaY+pGJmhbEM7TniGMZP/HH388u49LghCJ/gjBJ/0X95ZJkI8Q5YgjjrAy6dunT5y6+aGOkG/enyS77rqLPS+ER7khXT+YCnWRNqF99C5X3HPPOFUIIYQQYgMRDeRyJJPK0b+lT58+zE7NDCmYlUUDLDMnI/2EE04wfxuYCEUTFl+4cGEzQcP0ge3RBNxU+xs3bmzpvXr18s2aNbNtqHgnzWZQu65UqZKPBrzmH+e0006z83EOronq/GGHH2bHVqlSxcwvUHHnNx+O/e677+Kzef/MM8/46tWrm68kPpwL9fF33nnHVP4vvPBCM0Pg2EaNGmX7CYkG2/7bKVP8AQfs76NBrvneOffcc20bZgNAvh966CHzxRQNFn3z5s19NLA2dX7yzb6or+OPCEjnN+mYJKD6D6il33vvvWYW8vTTT1saplJHH320v/rqq33Pnj2tnDjuiiuusO3pwKwqmszYfi0vvzxOzQz3H8CsCxMLfNwEc5WwPZqo5li33nvvPSt3zC6CORT3iXnD+oBJFmYIlC/H47cFvxVJfxaYPGC+s3fVqubzg2thCtexY8ccTTjwB4XZV/Hixe1D/cNcBF8TnCOa0JlJRSAa2Nv22rVr+8EfDrb6R92OJuw+mvD7W2+9Nd7T+yc7d/YnnHSSfacMeC/wRcJ5L2/Z0uofJpjUEeosvi2OPPJIMzkK16eMkwwaNMjSjznmmPUyuRNbFrQHhQoV8i+99FKckplly5fZ8775llvsdzQRtzqQNEXBlxhpmMEkwecVPopoQxYtWhSn5h1oI4JvvJGjRsapWf0UfmFIxzQsCe8q6bSt6+MXDlMhzkn7lDTbveSSS7LPR7tdq1Ytf9RRR/kHHnjAzJE2Jd98+43VO0xvc2LJkiVm8oi/NPpG8s9fzPCS7Q7l2LZtW9terVo18zlE/8Q90nelM6cN5uiYXm0pNGyUZa558803xylZfPDB+75kyZJ+9z12j1Nyhvct3Fs6szLGBtQP/IABZcT+MisTQgghxPqSaR6+2YVDCElw7ImPhquuusomuzh2ZDsg7GCQyb5nnnmG/3bKt75Tp07mAJQJcYUKFWzSDUxgGICyLw6QAYEIwgkEPvg2APzjMGFiPwa6DFKHDBnid9xxR0tD4PTDDz+YUKFy5cqWxsQfyCcCDwZmAfLBPgza8RVEPsKg/qCDDjLfOjgr7d+/v+/QoYOl9+7d247Frw5CBBybhsEwEwV88TAQHz16tKUxWGSyQR4RJOCQNMBkkXMiaEuCn6BLL73UvuMIHD8F5HHhwoWWhn8Eyg8n0wi20oGPCPLH+ZP3nBuYCATBFc+Qe0KQh2NRhG8IRzJRv1697LwvW7rMypHzPPHEE5b2T8D3w3333ed33nlnK2+EfDz7v1f+beWLn4jPPvvMJipci0kxgqlMIDiinuBrif1POukkPyUqr8XR5Ig6RFqXLl2y9+X+SRs4cKCl/Rk9Z4SA5AXhWVJghaAPwSPgawk/SJQbxzOJnPfjj5bOMyWNSTwOv2HIRx/5MqVL233iDyMg4dDWD/WU9xx/MMt/X7cvqptuuskmlEGQQP2nDiQnlAgdSMNheRLe/eADLicfOv9VEP6Gti+5OMC7XC8W1D/66KNxahb4jiGddg9BUW6hbaKfYmEgQF8SgjTQ/uEHCF9qoU1F4J2T8HpD8+hjj9q1EabnBG0q9ZT+bPac2eZgOgiJLk8sMCAEox6TTj9Lv45AHmE5afShK1aubr+AsQJjBBZaaHs3N/TV+OEjvwjskrC4haCe8UxufNetSziE8IlFsyA8lHBICCGEEP+UTDKeze54BDMnwqhhlhNNRsynAmHaGzVqZNvxg4E/G2jb9ma3V5W9zCSHMNyYY0WDTlN1x1QHUxrM0FDXx5wI3njjDVNnx0dCvtibAec755ws/xEcgzo3/m8wh8J0itCwqIjXqlXLTISgf//+9hffNPiUwOyHaxL6HP9ImHlgEhBNwsxPSPBVg3+QaIDvmjdv7k499VRT1efaNWvWNPV7VOQ5hu9JM7dUuE/8jqBan8rpp5/u9t13XzN1++qrrywNMzDU2TGpgpkzZ7q+ffuaCQT3jNkaqv+YkUQTDBdNamy/VNi2cmVWuOXwd32IJgpWNuQRM6orWl7hrr3uWivbTHAP+PTArA+KbVss21cKvjeiSbF9X1/wn4I5BB/8JlHe3F/+AvmtfIOfiA8+eN9CvLM/pg6ZoL5QTzhXgfwFrEyr7LWXK1G8uIsG8bbP/Pnz7S8mgdQbyhs/I7BNoUJWDzDpwdwtmKdgxohJBmUG1Ofy5cvbB7hOuah+kR7Kkbp12mmn2fcqlSu7StHnp59+2iQmKGLT8fbbb1tIe8xIixZZ7YsqHbRZ+Ol66qmnstsN6kwqtH+QNI8C3g1MWyEv1iPaB9p02gvahzXIUGarze9s4SX+vm4IiU8Ie9qfAG3AggULLEQ+fR0mpPRHzz77rKXhlw5/UZuKV15+xR12+OHmPygnqE+UF+3+zhV2trw/8sgjVgfffPPNbDNtTJ0xnaVNfPXVV61fx5wNc2LKlf5p8KA1zXtLly5t5YTZdGhbNyfca3h/UusI7w7vEPzb92fg+wPNlJhQ+fQ3STBdE0IIIYTYEGx24RDCEvyuMBBEyMOAN53ggAFQUjCybPlym2wzYT7mmGPcoYceagKjW265xXyw4NMHxo8fbwOznXfe2X4H8BWBEAchDwPw4CeHQSn+DwI4NcbHBxOFOdG+TPIB/zrkvXr16jaJxy8PjkOzHRjH7gOYWCQhr6+88or52znjjDPcyy+/bD5/uH5uJhMMjFMhfyeeeKL5uGBgDfgJKRINzk+I0uGXaCDNYByfGExAyHuNGjVsokG5B0FEKkxCypbNEs599fXX9nd9oOwZNCMEwdfGyXVPdhddeJE9p2Q5J6E+8FwYCJ9/wQXuosaNza8PfB3l4eOPVvt3yg344rnxpptc/fr1rbwvu+wyG2gz2Uo3sC5TZkcTCiHEmfzNN+6PP/6It6THJgf8F08SgIkRhMkB9QDfMWFCFAjbCxRcPWlHqIdT7FQfFqGOJq+T6o8GuCc+1KfcT0/Flg71Br9prVvfkO2LKhO0U+3bt4/auO3MDxa+yp577jn7DvhJeemll6xu71CqlKXRDiVZGU1uEXZQ38qUKROn5h1oTykf7j3Z7vIehnY+tczC7xIlSma3AbmBZ0HbTD8WoM+h/BFaB198gJ82Fjt4vxEqpcJCAH7Xzj//fPOflumDABxhY27AiTb177gcfM5lgrYIQX/jqB3Hpx7tMayM2j7Kl7Yx2RfQt+JbCIE5C0ZJaO84H8+GRZXNDXkpXry4fU9d3PkrenYrovaeulMqfsf+CSx43dj6RutH8dHEu4z/OfwoAn09/X4YmwghhBBC/FM2u3AI7QxWsxkcrrU6G5OcDAeWRINDVuYYmDFQRrjBh2hnCEqqVatmg0ccGQOD0iQM1sKANDnwtwl1QkjDSuUOO+xgeSgY5TMM/oksgrCBa+JwE00RBD9hJTloKaWuGKLRg1NsnErzFw2APffcM3uF/p+CQ27KD0fKONQmWs55557rCsQChcXx/SNMw+FpKC8mmeQ9naNa4P4RPsHUKVPtb05wH8l7ptwoz9RyOL52bXfV1Vfbd+pAKHM0mLgHVsgp37pRXk+M8ocDV8qZ/RgcrwsmHQjgzo3KgPN8F01s0AhDiMdEJdxTJigPtLF45qxs/1s4B5MxJnoIvRB+oemFJhr3VeuoLIEowiPSeTZJLY907wAk6+qGgGeB4FVsWdCONW/R3B19zNHu+htuiFMzQ/1CK3P06DGmjYFmZuvWrbOd46KdSTptBVpogAZbkqXLlprgnDYDQXpeI7x/QcMwCW020J4nCRN0BLv0G7kBDRgm+vQfyT4wlDttatIhOIskFWNn5OkEJAj0jz76aGvXiRaW6cP2cJ51QX2hv0RD8Z9Cv0x/HYRmRaJ7ZdEGIRDtThIWLyB535Avf1Y7SDltU/jft8sbgqDZTGS7JIt+XWRtKRq/mcY2uYH3kveZhS60q3iX0aRFKAR9+/Zxt956qwWtEEIIIYT4N2x24dA/hZVbBsGshKMFkoSBFJopDETDijfmW0kY7PNBQ4PzpAovAkzW+XAurhkG/JhmJWGSjvAhdYCWnNRzPcIiozlElLAGDRpY/tKZemSa9IfzpQoLCL2OYIwBJPc+/+efTTASCCZJaOUwIUzy4YcfWno6EHYhqECAxoo0WkaZYBJFVJr50YRyXWBSVSoqS6LOEW0uCMc4P+XBAJjVbcIb8+FeEBAxMfr888+zo7KlwmSiV69eZmKFEIhVaUwZ+FA+POvcwrnQHAsaO/+E5HNiJZ9JGSYRV7S6wjQ70GJCoyPUK54f5mBoB6SSSUCUE5mOyHQuIuGEqHpiywDBUKtWrVyVylXcY48+lh01i0k1dSkdtGtoYXbt2s3ebd4xNBsbNmxo2xFMo22ARgpafUzSMfdJCql//vkXE4YjvEw1n8oLIMhFqMv7SB+QpHbt2vYXk+Kk9lAwZ8aUOLcCNdo83sczzzwzTslijz32sPaLhY0pU6bEqVkEgfV+++1nf2MmR/oAAOI5SURBVJOw8IEgmgWDZs2aZfxg6ky/kRsQKiIsJ0//FO4DkygWboA6RTmhGbMgRcgWBEhJjSn46cesZ0G/WWqHf66NsyFBYEafNXny5Dgli9mzs/pZ+s9/A8Il3l3e1/Au08eF+sKzRAMYk3ghhBBCiH/DZhMOBWEMApPcaD+wsphcvWUwfcqpp9jAHN8/CIiYLDFAY3UtqHojEAAEIKykBwg7zqD/ggsuiFOyzonGSVLLCHV69iX0O9SoWcMGtZgBYHrEaiErv3xH5Tt1QsD5AuSVcMUQtJZCPlBP51xh9Z6VRiZqDIS5r6CKHzRzkmURYAIJ5O2kk09ewzcBq5uHVD/EffPNN2ZOxflQzadcCCWcanaXhEEoPqBYpUb7gAlRKuQTYQf53yEuA55rTs+WsiHcPOXHZIf7R3B24YUXphXioOWEvwu0GTBDTAeTZTSPWKXu16+fCTvQ8soJzCUQNiUnxpQLzx4/Sf+EIFBKrogjOMNHCPeI/w7MH9EcC4I7+Ozzz201P2gmBCjH8MxTNbMgUzljuhEIxy1fvvZKPfBcyVvStEVsXnhH8VtGm4SpLIIi2gvMbQhnHYS1CGYxEQrvJu0Qk9azz25k7y4fhNFMxoFnfMopp9gknDRMTXl3ELwC9enTTz+1+oXfslDP8hK00QhfKNtU4RDpCM2GDBli7VGAtgfTp6RAgH4BjcVkX5AEwQsCgHTt1AVRW8i7mlwAIS/0G7TpaAhtbGizWPRI9pVJ2E49DEKyxUsWm9liUpOGNh7hBnUpmMtS96iT8FK0LdmGUQ8RRmFelgTNLMoDodmWYuqIoJBnhwAvCAdDX89iRhDIAqHoBwwYkLYuhPtPbcsZy6D9Gt5jPix+BMFejRo17feWUh5CCCGE2IqJBiI5sqGjlUUTXD9/wXzftGlTRkA+GhT7aCC4RlSlJLPnzPGE/2ZfonIk95s0ebJF22IbocerVq1qkZ+I7BJNoGwfIkI1Ovts2+e8886zCDTTpk2z6FmEkV+2bJntRySwaNBq+91xxx0WLWTuvHkWHaRypUp2HKxYudI3bNTI9itcuLCFWyeCSzQw86+99prt89vi3yycPfsQceWvOM/R5NzC6ZNOyHGiuBCtLRpA2rkIV9w7jmLWpk0b2480QkwPHTrUItpEg2KLLkYY/FSIQsb5OBeRT1Lp2q2bRUDjvOXLl7dIWdEk0qJfkbeciCaOFrGNY0NkGcK0c50XX3zRosGxPZoQ2P48Z6JnbbfddnYMoY1HjBhh0WaiCZTv1auXrxOVLc9/1KhRFnmLCHPs273H2qGSyR/n5HmwD+X90ksvWbSYJPwmlPL6cNZZZ/loIuijybJ/9tlnLcrbgQce6Dt37hzvkRnqTYimR0Q6oO5wDtKIJkZ+yDuR2kgjAhSh6A+Lnm3NmjUtXDblsnTpUgupT/S7VKLJoN+76t52fDS5sPNRJnfeeaelUUc4HihP6mU0obdIfpTtol8XWTRA9i1YsKDVcZ7Byy+/bM+PqFaUKdcP74TYvCz69VeLTMYzo53jfeW5EpmOdoC2jnYBBg4caPtRn6KJuKWlo3379rbf008/HadkMe277+y89erVs3f4rbfesnbp+uuvX+sdyyvwjl177bVWXkQTTIV+i/aLqIpEqKTtiCbyFhEuQD902GGH2btIu5dKiAb54IMPxilrsjLqO4hyyHl5X3mXeV+pDz179oz32rg8+OADftvo+vQBqdDnEEWxaNGi2f3fq6+9anWHfphQ//TbderUsf43tW1evnx5dsRFIpTSv9KuE+WrR48e8V6rYRv75hTpcnNAXWAMQjj+xdE9ElWTMiHCXIDyo98vWbKkf/fdd+PUrL6Nd4znyb0Rzp+od+vqk0PbT9RNIYQQQoj1IZOMp0A0eLsrGmBkBLv2dTk/XR9YWcNkCA0WVMZZWWP1ldXA4GcggBnTja1b26o5+7IqjvYKq9ywY5ky5uSYiDKoprPCixkQ5hTBhw7q3qyqYSKECRrq2UTtqlGzpvl+CZo+rNBzXDQgc0Q3Q227V8+epqLftVu3bHV6NELQWOJarOCzQsg50BwiChmrumgu4TgZjRBWOmfPmmX3hsYRGjqUAav05I1IbZgUodXEqiqmAGgRlY/u95vJk+2+WLFlpfrmm2827RZWCHEOTYS13RJOi1mJ5T7ZjvZN0pcSoCFQuXJl8+tDvqPJj5k6derUaZ1+dXhO+CqiXDHbw59PNFA3XxRoIaFdRP4oC1ZF0YhBBZ7Vd8qByDtETuLD82YF9ZeoDNDswrQKH0xEV2JfopXxnHGYHUB7ishwmL5QhmgWBYecyXpDma7rXlLhmbPKi7kd56cc0dJghTYnWA1v27atlQf1k2fOKi8mbGgtsbKPVgGr/NwnK+ncN/WAOoYfKHyWUH7UywMPOtCN/WKslQcr4wHq2U3RdRb/mvUeBO0z6jN1metQfzFJo77efffddk+YQZJGnSDKGyv8lB3paEtRfnxI5zdlh6YUTtbF5gdtE94JnjkmTrQ9vNPUbzQLaftCFD+eP/vilB9H92xPB+8+Wke8r0kzFEw8OR/vNdp2aH7g1BgT1bxoUgZoS1HmtHG0N0ELNYAGDG0qGoBoBaIhRHnR/gfo19CCoR3HjIt3NQkOpdEcQnszmJYm4fp1TqgTtQE/ux5RG8EznjDha3fvvfeuoZGysaDtQJsQ5/z0b0EjMkBfRf7pS+i/0HgsWaKkaWLS5lPXaL8wC0bDl3qchPpMAIrFUXv4ctSfvPnGG9Ye/u9//7PzpfLEE09YX0nfndS43NxQF2g30d7j/aEf4X5py0OZ0b7y/LhnyjJoUNEX0wejWUXbTJ3B7xxjjky+AIF3Ge1W3neZlAkhhBBifcgk48mHhCj+nhYcAyPA2NJhIMogjAFjJhMIBA4IaxBgJCffwPEM7lDvp7CYbDHBYmKWCYRWTP6ZcGeajKWDATfCJcy+Ql7JWzCFCyBoYkDJJ7eQJwbqOUVHYfCJQI3JSLoJSW5gssM5EP4wyGWSsDWD8AmBHc9yfYVLuQFzu0suucTVP72+a35pczMPog7woQ61adPGJuf4VWJCkDoJE2JdUI9oh1LbkfXlx59+tDD5qSGz8yK0lZgUI+zAb106c1faW95v+hWE6KkgCKYtx7l/KjwzhMYI+tfVziNIpr2gjdpUcG8IY2jnU/vMAHmiv0SwEeB+6Ws5Lqe+KAllwXGcJ137h5AJn2300wjTt0SIDzl3zlzrV1MFYYAgiL4+XZRJIYQQQohNRSYZz39GOPRvQWuEQSfCpaFDh9qgVogNBZoBRJRhkpnUiAImDGhysMq8Lm0lIcSmZcrUKa5+vfqm/YKPNLF5QHMUn05ocqVqYAkhhBBCiNyTScYj9YQYtEYw2WIVFHMhITYkrMADjmqvvfZaiziDuUb7/7V3tevUNuerOB0VQmxZ7FVlL9NUGfLREPfc88+5v1etHQxAbDzQtH344YfdwIHvuSc7PynBkBBCCCHERkLCoYjxX403XwZoCzGJ79Cxg5s+Y3q8VYh/T8uWLc2PBpHI8EuBTyOiu3380cfmNwbJ7fqYJgohNh2E++/Tp4+bNHGSGzVqdJwqNgX4wcL/YJ8+fV21g7PC4AshhBBCiA2PzMoiGHjiPwdHxBQH2kM44JRfALGhQSsNE0acduM4HJ9W1DshxJYPvslYQEjne0hsHNDqpa2UHzYhhBBCiA2DfA4JIYQQQgghhBBC5GHkc0gIIYQQQgghhBBCrIWEQ0IIIYQQQgghhBB5GAmHhBBCCCGEEEIIIfIwEg4JIYQQQgghhBBC5GEkHBJCCCGEEEIIIYTIw0g4JIQQQgghhBBCCJGHkXBICCGEEEIIIYQQIg8j4ZAQQgghhBBCCCFEHkbCISGEEEIIIYQQQog8jIRDQgghhBBCCCGEEHkYCYeEEEIIIYQQQggh8jASDgkhhBBCCCGEEELkYSQcEkIIIYQQQgghhMjDSDgkhBBCCCGEEEIIkYeRcEgIIYQQQgghhBAiDyPhkBBCCCGEEEIIIUQeRsIhIYQQQgghhBBCiDyMhENCCCGEEEIIIYQQeRgJh4QQQgghhBBCCCHyMBIOCSGEEEIIIYQQQuRhJBwSQgghhBBCCCGEyMNIOCSEEEIIIYQQQgiRh5FwSAghhBBCCCGEECIPI+GQEEIIIYQQQgghRB5GwiEhhBBCCCGEEEKIPIyEQ0IIIYQQQgghhBB5GAmHhBBCCCGEEEIIIfIwEg4JIYQQQgghhBBC5GEkHBJCCCGEEEIIIYTIw0g4JIQQQgghhBBCCJGHkXBICCGEEEIIIYQQIg8j4ZAQQgghhBBCCCFEHkbCISGEEEIIIYQQQog8zCYXDq1cudL9/vvvbsWKFfadD9///vvveA+RE1OmTHFffPGFW7VqVZyyZeC9dz///LN7//337fluaVBe48aNc998802csmWzbNky9+WXX7r33nvPzZ07N04VQgghhBBCCCE2PPmiSb2Pv6flxhtvdB06dIh//XumT5/uevXqlT1JRzBEFvLnz+923nlnd8QRR7iTTjrJlShRwraL1VBWu+22mwk63nzzTXfkkUfGWzYvCxYudI8/9ph74YXn3dy580xItOOOO8Zbtwx++OEHt/vuu7vq1atb2e26667xli2PESNGuPvvv98tWrTITZgwwdKee+4516hRI/suhBBCCCGEEEL8EzLJeDa55hACoPr165t2ycsvv+xGjx7t9ttvP1exYkU3YMAAmwC3aNFiq9Hw2JhMnjzZ/fTTT/Gv6GHlz+/OOussd8IJJ7gKFSrEqZufP6JnWbx4cZcv35ZhpYhwaurUqWtoVxUvUdydeeaZ7thjj3UlS5aMU7c8ZsyYYfX/oIMOckOHDnX33XefW758uQmHhBBCCCGEEEKIjcEmn80XLlzYHXjggdlaEHy/4YYb3P/+9z/32muvucqVK9vfu+66a4s0T9pU/Prrr+7+B+5306ZNi1OcK1CggOvUqZN7+umn3R577BGnbn4Q+F1yySVun332iVM2L2+8+Ybr0aNH/CuLUjuUMmHkvffe67bbbrs4dctj5MiRpi104okn2m/KtV+/fu7BBx+030IIIYQQQgghxIZms6l6FCpUyP5iKhU0PA444AB3yimn2Hcmyd9++619B0zP1mEBt87tG4N/e81MxyPceOP1N9w222wTp2RRpEiRDaL5kpvyXB/y5ctnwquc2BTPB42zRzo94v766y/TtEqy7bbbumLFisW/1h+/auPnf/qM6faXvALPH02xatWq2W8hhBBCCCGEEGJDU+AuVHRy4IMPPjAfQBuaSZMmuT59+ri99trLNWzY0DSKAFMqrsnk+Nxzz3Xbb7+969+/v2kTBdMzvu+00072weTm3XffdS+88IJ755133Jw5c9wuu+xiZk4BhBJoY7z++uuue/fubuzYsSaQwjQrCDQwReL4zz//3HzTvDdwoOvWrZsb/9VXrmy5cq7UDjvYfoExY8a4Ll26uFdeecV9Fe1TtGhR06CBP/74w306bJjrEV2rZImS7uuJX7suL3axfHAPmDx17drVBECffvqp++WXX0xjCoHZ0qVL3aOPPupuvfVWc9aN7yXuqVSpUua0mzxSHpyHbdwPpnnkgb8IRMqXL+8WLFhgvp2416+//tpM97jXefPmWZ65t48//tgtXrzYrr0uwQ7l9cYbb7gXX3zR8vD9D9+7CuUrZAuq0PKifL/77jt30003ZQs3gLIln9zzJ5984pYtX+b22H0Pux/yQR5xFk2ZIrzBXxH3zHPGKTNCQgSHMGbMaHvWPXv2svvFcXOVKlVMOMX9XHXVVdnPY+HChW7JkiWuUqVKbubMma53794mdKxatWp2fQN8/FBWr776qtW/ggULZj9LnhnHcm+cH023/gP6W76//eZb2y83wrr58+e7t99+28ph8IeD7Tf+oxD2wcSJEy1/+EOaNWuW1Xvum3KnPgshhBBCCCGEEP+WTDKeze4kJlW7Y9SoUfYXoRGCklatWrkLL7zQPfDAA+7mm292l112mZmgIVhBMIAgAmHBnhX3NCHE5Zdf7i644AL322+/2XmAif95553nxo8f7w4++GD34YcfutNPP93OCZ8M/cQ1OruRHYdgpnXr1u78aP+HH37Y3XTjje6sM8+0CXsA8ySEEKv8KhMO3HPPPXa+QYMGmTDhyiuvdBecf76ZxnV6pJNr0riJu/+++127224zQUPjxo0tTzhHRkjG/d0WbQOELNwX50U4NHv2bItWhRCJe2ffZ555JtsX0fJo/7vvvtvMjxBuIBQBhA7Tvptm5UG5IHhC2MB+lPE+++5j+T377LNdu3bt7JhMIIDD9O+ZZ7LM2bj2la2udC2vuMIcPecEZnHkGcHPvvvuaxXx/PPOdw9FZQs4Xb7lllvcpZdeaoK/oNmzbfFt3egxo+35Ux6AYOmcc851I0aMtLJDSNagQQP37LPP2nYELgjGgPNybYRfAwcOdBdffLGVBWUUzgc44jo/elYIkTCLe+bZZ1y9+vVMiAOU0UUXXeQuv+xy9/gTj7vborK65upr3KOPPOquvvpqq4tov+UEAjPqB+fcu2pVq5tNmza1ehAikWFGiNCQfAPliqAKIZ8QQgghhBBCCLFR8eugdevW8bcNS58+fbDR8cccc4z/+uuvfTQZ9u3atfMFCxb0JUuW9NFE2v/9999+4sSJ/pBDDrF969Y92Y8eM9rfeeed/rPPPvNdu3b15cqV859++qmdM5pI+1NOOcX2bd++vaV98803vkyZMv60007zf/31l6V9//33/vDDD7f9evfu7X///Xf/9ttv236kNWzY0E+YMMH379/f77bbbpbWqlUrO3bKlCm+YsWKltfAo48+avvUrl3b8jBr1ix/0UUXWdp+++3nP/nkE9+xY0f/2muv+U6dOll6z5497djJkyf7SpUq+fLly1s+gHw2aNDAFypUyI8ePdrSlixZ4idNmuRLly7t99hjDz9u3DhLh4cfftjOyTFJHnjgAcsHrFy50l9yySX+sMMO8/Pnz7e0L774wpctW9aXKFHCDxw40NLS8cILL/jChQtH1/zSfpPPWrVq2TXvvfdeS1u4cKE/+eSTLe3nn3+2NDjzzDP9odWr+2XLltrvMdE1S5UqZftxfbjpppvs9+WXX26/A9S9a6+91r6vWrXKN23adI3jhgwZ4gsUKGDXDQwaNMj2adOmjR0D5I1nQPpxxx3nf/rpJ0sfMGCAL1asmF0/wPMoWrSo5fHzzz/3K1as8K+++qrfYYft7Vptb27r586da+VPPU19Fukgf9SZ5H5XXXWV5Sc8H+C5Uwakh+ce7kEIIYQQQgghhPi3ZJLxbHbNIXzEoKmDZsfgwYNd/dNPd3379nVNmjQxrSLMp0LY8dtua+cOrX6oaeQQxv2RRx4x8yXMfdACQoMILRf44osv7C/mQGhjRBP0bD9HmPOgLQIPPfSQadmgiYI5ERpLnBczrFNPPdXCvAHnCX8Jx49WE9fEUTAaH5hloemBpgpmQCHPaPscc8wxpnlzzjnn2HnxIVOzZk3bjiZR8LsU8p4OzOTQbMGULHpucWoWROEi30OGDMmO8oYmCo6MMc0D8tyzZ0+3ww47WDkRKh1NHXzaoEmTKToc94M5FGX0zjv9LHrWY489lu0nCq0YNJxSNcAAk7DPPvvMFYjK9amnnrbyeiMqP+4BQph2NLYwJUOrKGgiff/992748OGm2QSYjVFmaOBQhpQV+1IWlB95CPuFv+E79xzM0tg/pKNBxnnOPifrGoDJWfPmzc0kjfKiTqDxtN1221tdfOD+B0w7CfOyww4/zDR7KKNMoK1FvWZ/IpAF0EYqW7asPYOgLUe+QjnmC3/jvAohhBBCCCGEEBuLzS4cYsKPmRYCIUx4EB6ESE1JmKQno0xhfoUAAV88e++9twlk8CGEGRLnuv76620//Nng2ybVbwsCBs6HuRjCIybhCA4wa8LfSwAhFIIehAg//fhjthkQx5OO+ddRRx1l/mKIIlamTBnb7uI5fWrIeWz7MHPCxw7mVk899ZQJchAKpAp90hEEXEnwGVSnTh07D+Z2gKCIcwZbQkyu8IWEgCMI3Pbcc0+LfoZvH4Rn6UBIQjlzjd133838MXHP1157rd0zploIxoKwKAmCI66JwIXr8QwQtGDKxTOibAFTPwRohHHHrxFQRgiMwj7Atcgr/n6aNWtmpnnhuqHskmWYWp7BrxJ/KSt8FkG5suXsb+C4446zv5ghBsEX9aNAwdV+mfhdongJu35Ozw1zQOof9TQJ90w5Ur4IFQPp7kMIIYQQQgghhNiYbHbhEBNnnAMzeU46kU6STnsCvy1//vmnCWNwaI0mBh+0c/h99NFHmx+Z4LNl6bKl9jfA9YIWTvL8/E5OzDl/mNivjPLKOeH444834Q7XRAsJ3zdnnHFG9j3ki6VDqUITjsdXzTXXXGMCHTRxENYEzZd/Cj57EBwhYMPHEAK3Ro0aZUc7C75sEKShjUO+yT/78EGAkw6EO4ujskZoht+mcM+UM+dBuBcEa6kggOF4hFBoMCWvyTNC4BRAqAcIhRCYIDzi/Mlng/8lyvnJJ5+0c6FBhuZYOsFUKpwnnIu/aEsFP1IIGpOUK5clLApCIUjmY31A4AXU1STU+VBXgraQEEIIIYQQQgixOdhqZ6VEKiteoriZLWG2kwQtmTvvvNOEP5gUARGskgQhEA6W0RTKJGBAeIXgBuEImh7bb5+lvfTEE0/Y3yQdO3Y0bZkkSaEC18B5NRpGaAzhHBqNGrSiUoUr6YQtkElYgVkcwibM6bh3on0hiAmUK58l8CBiGMKjJGi3oI2TDsqwXPnytg9mXkkwmXr++edNuBK0cpKgNYXggwhcwYF2gIhnRJkLIMw7/PDD3UcffWQOtjknAqQApls4AX/vvffMtK9+/fpmlpVaDslyS90WfrNPeJ6A+VuSVfE5atSoYfeV6VnkBjStAEEUQq8kmMMheEyamwVS8y6EEEIIIYQQQmwsNptwKAhj+JubyTcCmqQAB6HDiSecaEIDNEgQXqCdgQ+fNm3aZE+u8e8DHw7+MNskDDCVIoQ8GjBJOAf+hAJEvOKcaM3AoYceZho6CFmCSRh56Ny5s4XBD/50An/99Vf8zZm2CvtAMDdDaIAwCwER2lB8BzR+EB6ED36NgHKgvNIJs4iSxjaimdWuXXsNUyZMp/C7g5Csbdu2JsRCAEMYd/wPJfdNghbN/rG/Hkz1Phk61PJDmRDNbU5UpkRIQ4iWmidMpyrsvLOZZ7W9+WY3fcYMOxYhE76eSgcTvAjK9IorrrDyevzxxy1CXAjzDjw7QvITNh6hEEyZMiVbMIXACy0lyhHQlMIUkOcXtHZCHeIvQsNjjz3W0gnPn6yDE6LrAAIrYJttT6mm1DHOl+5ZBKh/pUuXNr9CCDID5BdhEX6kMFEMhHz8HeVRCCGEEEIIIYTYJEST0RzZGNHKokm7v7JVK2bBfs899/RfffVVvGVtFixc6KtVq2b7vvzyy3FqFl+OG5cdYYxoX0cccYRF/qpadW+LGAbLomudeuqptk/z5s39okWL/Lwf51nUqnr16vlff/3V9mP/XXbZxfZ78MEHLe3X337z9U8/3e+y885+5syZlvb7H39Y5DP223bbbS2S2sEHH2z56Natm+1D1KlmzZrZPldeeaWlwcqVK7OjVNWoUcO3bdvWH3vssRYdiw95InoaUO7sV6dOHX/xxRf7jz/+2PKz0047+eLFi/t+/frZfkmIQla9enWLcjZ+/Pg4dTVPP/OMz58/v513r7328jVr1rRoZWef3ciicmVi+PDhvkJUBhxHtLSjjjrK77777n7//fe36G0wbdo0v88+VW0f9g88+thjlsancuXK/sgjj7QoX0RO+/PPP+O9spg+fbrfe++97f7C8wssjOpBiERH+V933XX+0EMPtd9ErKOciHpHRLD8BfL7HXfc0Tdu3Njfdddddvz4qI6xb8WKFS0iGUydOjW7bt19993+j+jZco4qVar4q6++Ojt/A99/3/JcpnQZqz/AvpQ1x/bs1cvSMhGiyXEtngv14JZbbrFIeCEqGRDpjmfCvkTAg1WKViaEEEIIIYQQYgORScZTIJo83xVNRjNCBKng1HhDQIQqfO4MHzHCtDfQvuAaaJRgVpQE7RS0YYhKxb4jR4407RscQANaLfi8wbwJzZJ58+aZT50XX+zqogm+7YNGyimnnuoWLlhg1+nevbv542E/om7h9BjQ/unSpYtprsyNztP7tddMO4j8cQz7A5opJ518kmn4kL8ff/zRNEfat29vTpI5Hg0YNITQcsHnDL5yMFFCw6ZslGccIaPRgobSvffea9pG3EPdunVdixYtTGsIrZoxY8bYvZ911llmeoQTaLRNMInCFGrvqlUt8lqAdM7JdckL956kWrVqprGEA2Q0V7gHon89+WRnOzYTOJI+6sgjTXOH61PW+EnCFA0H05wLky/KAhO9YcOG2V+21YieKZo+XBNNLT5oYaF1hL+gJDxjzo0vIvZJ+uKh7PDTg4NxNIY49tFHH7Wy5ZyY6GFqtv1227nFS5a48ePGWVSxW265xZx/39y2rT1Lzkn54+i6UqVK5oibSG79+/e354yT7VNOOcX973//s2vyHNvGmmiFixTOjiyGWSER3rjPr8aPt79JDaAkRxxxhJmw4SSc82OKR1lxjuBwm2dN1D7qFOWAhhdpmD0GH0hCCCGEEEIIIcS/IZOMJx8Sovh7WgjlTnSpLRmEMwhhmPgjLEnn/wYw22LyjQkVvn6SIGQ49NBDbRJPeHEcWSMcYHKOYCAdQdiBX5lUQUdOYAqFY2oEU+HcmEEhFEiCmVT+AvndNoWynErnBoQ3K1aucGV3yjK9SgfnRSCCuVMw0coNmI5hjkeVwcl0UnizLjDxouzXdU0ETUQFK12qdJyyJgjxECRyHiBPCMSSkeyANAReuc0jgjLqAAKl9SmT9YF8U+6h/BB2CSGEEEIIIYQQm4pMMp7/hHBoQ4ADZ5w6o6UxdOhQ074RQgghhBBCCCGE+K+QScaz1UYr29DgWBpHxZiFoZ0ihBBCCCGEEEIIkReQcChi4sSJ5r8GHz1Lly01KRrmU0IIIYQQQgghhBD/dSQcisABdIMGDVzPnj1d927d3cknnewKbbOmM2chhBBCCCGEEEKI/yISDkUQ2YwIVccff7x9Tjn1FFehfIV4qxBCbH5wvL8OF3EZ4TgconOO3JDb/fIaOPznk8r6lG0qHItj/dzwb+rAP4VrYm79T64b6l1ujmU/TLtTIYLlH3/+Ef/aslnf9yyVf3OsEBsS6vK/rYucIzfvfoB28M8//0zbDuRlCGBD8JvUsqScctt3pBLcaKzrGbPf5nweuamD61vPAhyzPnU89Rq4IJm/YH78a/NBvtZnHJFKbuoR16C+5LY/F1s3Eg4JIcQWzLRp01zz5s1d1X2quiOOOMJ17NjRBou5ZdasWe766693xx57rDv33HPd66+/nnYgwCDpm2++cbfffrtr1qxZnCoCnw4bZuUybty4OGX1s6ldu7a74IIL3MCBA+Mt6+aHH35wd999l2vUqJEdT7n/+OOP8dbVMBBjYtDr1Vfdaaed5r7++ut4y8bnvYHvucsua2GLJ6effrp74403cj1RGDJkiGvV6go7tl69eq5Xr15p6x33fM89d7vzzjvPygEHidTZwJgvxrjLWlzmJkyYEKdsmXz77beuRYsW9p41adLEQsTmFqKttmzZ0u6fcujXr1+8ZTUD3x8YPYvL3JVXXrnG54orrnAPPPCARdwUYkPw5ptvumOPO9btu+++7owzznAffvhhvCV3EBmY+o9GPv3NuqD+P/HEE1afL7roIvfOO+/EW8SEiROtj6FvCdGN582b52666SZ33HHHWf+B1UNuBQME36GvqV+/vjut3mnu/PPPd++9995a7TrP8NFHH3EXXnhB1C7VsfaJ8cGmgD5v6tSp7t57743qw4UZ742+o1u3blEdPd3a39zy008/2bkPO+wwd/DBB9u9ffHFF/HWNUFYOXbsWHf55ZdH5dYuTs2CqMi33nqr69qtqwlONgcsnoS6cM4557jXXnst13000aFvueUWO7Zhw4auR48eax3LsyBIE2Mf+nHGIFyPsY/4DxM9+Bxp3bp1/E0IIcSmJBr8+Giy6atWreqjiaMvUaIESzY+GszEe+TM999/7w8//HB/4EEH+rffeds/3OFhX6ZMGf/cc8/Fe2QRDQj8I4884g855BA7/xFHHBFvEdC3b18fTZR8NPCKU7yfMGGCpR155JH+3Xff9XfddZcvXbq0jwZY8R6ZiSZD/uijj/Ynn3yy/+STT3w0CPc77LCDpUUD13ivLNh+5pln+gIFCvhtt93Wjxw5Mt6ycXnmmWd82bJl/cMPP+w//vhjH03a/DbbbOMfe+wxvyreJxO9e/f2u+22m7/zzrv8p59+6ps0aWz5p4ySRBMVq9/HH3+8HzJkiH/++ed9+fLl/eGHHeanT58e7+X9Cy+84GvUqOGjSUycsmVBXdh777390ccc7d9//30fTRh8uXLlrN6si2hS4w866CAfTVT822+/7du3b2/1iLIILFy40OoZ7+Yan3xZf0866ST/66+/xnsL8c/h3a1QoYK9lwceeKDVr5IlS/r+/fvHe+QM70I0kfZFixa1Y3l3c6JPnz6+YsWKvnr16r5Xr14+muzGW8Rnn33m999//6h/eNT//fffljZv3jwfTeatvaE/euKJJ6ydpt1YFz//8osde+ihh/o33njDjxo1yl973XXWrvd46aV4r6z2pkmTJnbtfv36+Z49e9oz4jN27Nh4r43DqlWr/JNPPmntPfXn4IMPzr73JAMGDPBnnXWW7bP99tv7r776Kt6SM4sXL7b2kv5or7328qXLlLZz7Lzzzv6jjz6K98qCNrVdu3Z+zz33tH2aNm0ab1nNlClTfJ06dWyuvHTp0jh100D/WatWLb/PPvvY83zs8ces73j88cfjPTLzS1QXKIdKlSrZ83366aet77399tvjPbJ4b+BAaw8uu+wy//nnn1v/Rv2h7GZG40uxdZNJxrPZhEO///67nzx5sv/zzz/jlP8GNA4TJ02MBs85D59p7L777ju/fPnyOGU1U6dO9fPnz49/ZTWWs2bN0uBPiDwE7z2TzJtuuskGzMuWLfVPPdXZBt18FixYEO+ZmWbNmvnixYub8AL++OMPf8EFF5iAaNq0aZYGK1asMCEEgwoGijVr1oy3iDFjxtgAikF4gHJs3LixDRi/jiZDAQZqlStXNqFcTjDwZiA2fvz4OMX7Bx96yM6XOjj7+uuvTSjC5KlQoUKbRDg0ceJEv9NOO5lAKICgEiEGE0UmgJlgwMq9XXjhhdbPA30XQkrqIvUscPPNN1td/vLLL+MU7zt27GjlcMUVV8QpWdx+xx0mcCJvWxqnnHKKTdAYPMOiRYt83bp1/e67724TrUz89ddfvnnz5na/Y774Ik71/oQTTrB7ZYwAPH/KFAEu3/kMjAbtL7/8stWJO6KyEeLf8s033/gGDRr4t956y//222827jzjjNOtfl5yySXxXjmDUJd3/MQTT7TjunTpEm9Zm5defskXLlzYn3baaX7uvHlxqgDKkTY/dQ522223Wbn2Swjr6OdZOBg3blyckh4E/rTBb775ZpyS1V7vt99+/ogjaprgBDp06GDjAARDAa7Hdc8777y085YNBXOjoUOH+meffdYXK1bM+px0wqEvovaSNnDPPffw2223Xa6FQwie6KPfeecdE+wM/XSo9dvcG202Y6EA5UFdvvHG1rY9nXAIJk6aZP3lAw88EKdsGq688kpfpEiR7EUI8s57ykLTuvrJe+65x+6p7+uvxynet2rVyt7H0aNH22/KnfcYQR31JMACGNdt3/7eOEVsrWwxwiGklZ06dfJVqlSxlQk6n/8CTN7uvfdeGwyywh8GxZlgNaVgwYL+5ltutt9MBGno6tWrZyuOrydeWCT1vIjnnHNONEFcFqcKIf7LoM3Du7880ZbQQaPVQwe+cFHmSSdwLBNWVhiT7RETSgYF6SaUI0aMsPYHLQbh/ZIlS0y7B2Ha0mWrVwUp23z58vljjjnG9gkwOKRs77///jhlbRiQskp5wAEHxClZIChihZJBPv1kKmeccYbPnz//JhEO3X3X3XYfrCYm6dCxg6Vfd911ccradO3a1fZJCtOAfp90BGOA0GTHMjuaRgyCp8DiJYttcLvrrrvaBCDAPmhqMYDfkmC1mYkU2k/JSUyoC0xGMjEpmlSwgk0ZIFAKIATi2DvvvNN+33fffaZhkcqrr75qq7poGAjxb0ErZcrUqWssbSJIoC62aNEiTskdN9xwgx2XSTiEIIP2DsG7BENrQt+PcJzymzFzRpyaNX9CuEM7iPZp4JVXXrH+aF0CPITxnLNz585xireFaIQj9PnMLxDkowlDu5Scb7AYFYQom6IPon4gIEd7KZ1wCBBSkVfKJLfCIRY8EAwl+ejjj+0czLOWppljodFGG3/xxRfHKWvzv//9zxY6NrZmVYAFGsqHT1JjCQ0inhGLipngGdPHov3DeCRAH8O8lAVEQNiEthj7ocwRQGjIIlGqJrDY+sgk49nkPoewUyR0PPakOPPCz8WWBva85G19+O6778wXBSHwly5ZanaaORE1RC4a1Lntt9vefmO7is0r5YMdbdJ+lRD70WTC7VBqh2ybYyHEf5tocOaqVavmihYpEqdktRM4RMb3yA7b7xCnpieaMJpt/e677+6iQU+c6twuu+zitt12W9e3b9+1bPm5Zv78ckUX+Pjjj100+XeXXnKp27bYtnGqc8OGDbM2/sADD3TRgDBOde7QQw+1v6NGjbJnlY7p06e7aHC2VjmXL1/eRQM9Fw3IXTQQi1NXsynb/pnfz7S/qXncfbfd7e+IESPsbzoy+UQ66KCD7C/+hPBnYU5W5/+y1jVKFC/hogmj7TdzZlY+oGzZsuYj4pNPPnH9+/ePUzc/gwYNsv56jz32WONewm/es0x8+umn9g7ut99+9k4Gqlevbn9Hjx5tZYVfCHyLpIJ/iZ122sl8kQnxbylXrpyrUrmyS7Y0+DQpU6aM+atbH6JJZvxtbWgbb7vtNvM19NRTT7ny0XXFaiZNmmQ+2vAHtHOFneNU5z7//HMrO4LolCpVKk512f0QcxCeVyYOP/xwi8784IMPuuHDh1va2LFj3ciRI+35FitWzOYfc+bMsbFAchZTokQJ80EFEzaB/zfaTvKQE/SJOdWzdOCfDT94SXbbdVdrr+ljCqT0R0A+1tX/4msOZ834zlrX/G9DwDPDbyFjhmTfwbyyZMmS5h8Qf0npoP9etGiR9bO824H999/f5qbjx4+3/peyrVOnjpsyZYr73//+Z30R9Iv6X8rj7LPPtt/iv8fab8FGpmbNmu6JJ5+wyrspXqD15ffff3cPd3jYBFjrQ61atdx9993nSpcu7f5etW7HcI3ObuQGDx7srr76avtNw4tjs6OPPtp+J6FBxiHg/ffdv8ZERAiRd8BR4DPPPGMd+h133BGnZmbGzBn2l8ljEjr/7bffzjr/FStXxKkiFQZ67777rk3869WvF6dmwSAcdthhhzUGsEyuAKFcTgsMDKwY5LNfgAEezwYQIG0JsIiThPzRByWFNpmgfiWhb6SscK6NgDMIUtgv1cF6qLOpTpZx2EwfzXPZUpgSl1FykA3bb7+9PdMfZv0Qp6xNsh4lJzmhHuEwNAzIU2FizTgFR+HrmrgI8U/AyS8OfxHkMEncUDCeRcDLhBxBOg6BmzZt6jp16pRRqJ6XoH1jAeHkk0+2xeHAl19+aX+32267NRZ8aC8R+jDhz8kx/QknnOBOPfVUEypcfPHF7qGHHnJt2rRxl1xySXYQijAvY7wx/bvv7DuQD9o0YDF8a4WF9lSBEn01fTHRqgsXLhynrh8IaVjMQ6i3YMGCOHXjMSNewEGglYQ+p1SpHdzs2bMzOskO9Yh5Z7Ie7bjjjnb/1D36HuC9ZEHx5Zdfdpdeeqm7+eab3ccffeQee/yxbGGh+O+xyYVDUKhgoS12MDNgwADXo3uPtVYzcwONZ27vq0D+Aib9R1KfJJ0UnLyw+s8AUgiR93j//fdtEvjII49YxBA0CtbF4t+yhBNMypMwiCxcuIhNsn+ct3Z0LJEFA7zPP//MvqPVE0Dbdf4vv9j31DY5rODlJByqWLGiPRMm9zzXABpDYWK0uRcB9txzT/uL1lRyskH+qDc55e+AAw6wv0z+kqvYlCdaMvST9HNMNBh0om2LlluScE3qahImk8DKNSvcmxvuZ3n03CC1LjDoJv9oEmeasCEoA8oiOXagHtHvsz2TcAgNKoR0RDcTYkOCsBbtEqIT0degZYI2yYaiT58+9pf6jSAcYQfamK1btzahRaZJbV5gxYq/3ODBg+x7EBIHQr/DpD7ZNjKPoF1dl3CI41hgQgiC4L9t27amgfTwww9nayIhbKBdpp9D0BFgsYTzw39tkXrMmDFWbhc3vfgfz02pw5TdH3/8YXV5Y7P416x+Id34rkiRoqY1lEmLLFmPksIh6hF9M31OEA4deeSRrnPnzrb4QeTBDh06uMtbXu4ubnKxbRf/TTaLcChf/qyXj8EPLyIqlN27d7dwisuXL7dtSRg89u7d2yolA85QaYEB+GfRi4j6P4NW1C5ffPFF+506OKfDGfvlWPf000+7xx57zA1JDHzZRsVnBYOOEckqanvJhpbrMlh+8skn3VtvvWWD+6RZXGhUeLk4HyF4u3TpYqskyZVRJPO8tG+8+cZag+J0QikG1YQF5YM0P8BkgvvkXmjw6cST12H7qOgeXn31VfuOphKqhqwG0XixYoN6IceF89JRU4aou69r9ZprcW+PPvqoPb9Jkye5v1dmaU1x/6giEmKTiRKDAMrslVdeWWNFmXPQMHMf4cO1yRsDEga/4Tt/yXdYtaDswz0wUOZcPC/qCOVKHj6O0p9//nk3LPpNow3ki07v1ddedT/+tPYEg3rE+VAP5fmN/2q8dYwBJkgfDPrAVrk4D2YFqGEKsTGgnlP/GLChNcR7edZZZ60zbHowGQsCiwDpYRt1XaSHvmjmzO9t0p+6mrgyLr9U4X5oJ/gbyjgVFgUIOctzZeWWVWLaMb7TFjK4q1y5crz35qFx48Zm6oSpMyZNmB682+9d9/jjj9v2nPJXr359W0FFqwVtWM5Bm4w6PzDhYSKCFtK1115raaxGEr565KiR7u677zaNGiYgqdo4/GYSRP+Z1LraXPAMw3NOrQv0qWynv8+k3h/63dTJFn0XUI+SfX6AcxJeGEEcwkYhNiTUO7T8qlat6rYrWdLG302aNMkWZv5bvvrqK/uLqQrjdQRRXAPtC8aoXbt2te15kZXRGHrq1GnWzgVN0kDod5ITeuB50Sb8/ffK7D4oE7RTCP8RKnAexro8hzA+pr877/zz7fk/++yz9nyYD9H2B43Nvfbay/7+F2DOQN90zTXXuKNrrW25kVtow7GIgU1hdpfT+C70GeGZppKsR0lhWKg7WedYXY9YHKPf5sO2jh06rrGwJf6DRA1Kjmxoh9Sw8u+V5gwLR2M33nijOaeOXix0Gc2JVjSgivf0/p133zVHj42bNPYNGza0fXCKRqSdwR9+aE46OZbwjITPjBo024e/V199tUVcCOAYEq/2eJzH4370YliUERytRQNNixqCx3mOx1k2zsdGx17bP/74Y7tG/fr1/TXXXmOhA6PJmu/evXt2fnEQV7ZsWduGF3mcxnKu/Pnz+7PPPtvyEr18dgxRCML9JuE40nv16mW/cZxGuEHSiCKxLHaWRrQc7pf8XnXVVeZRnnvmfFwD52xEiylRvLjfcccdzTkg+eA87E8owpC3Rx591M4HUeNvTrVxQkaoyExEg3OL1nPqqadauXHffF7p2dM8/FNvcCjKs8ErPmUXng2RkEI0H8qVukAI50aNGpnTbco+5JXoSdHAwZy08pvQqkRqAco9mkhY+imnnuIff+Jxqyv8Pvfccy3cN/dBHjgfDkJx4LnX3nvZNdmPfEUDHjsf4CSVcqU8+YuzN8JkPvXUU+akkbIlmhAhv7lH6k/UiStajNgkzJw50yIZUXeD08BM4DSY/a6//vo4JYtRo0b58uXKWfv0888/x6lZ0N7h4FYOqbMcYlJ+OPROOkym3aFvYBshhpOEY4j+kowGlwp9Ac5Bo8mX32OPPawdIZABx9IfLk0TEpdw9rRj63IGSttMtBnaJBxGZvrQjkUTg/iotWEb7Tv9HO0dEYVo08kj588JIrzRX+F4m37+/PPPt+8cS58fwDEmeWGfXXbZ2Zx/H3XUUbYfbX2I1hWgvtKeRxMbP2zYsDh18xLGJfQvSQb072/9Fu9T6LdTITxwumNxVE16qiPQABFNqTc4CBdiY4JD6hIlSpijWsJY55Y2bdpYHU7nkHrnqE3hfMnAK4DTa44JTuvzIvPnz7exMuPpZBRHoM2mfJjbJCFQBWNa5gM5tYv0K4yNaW9po2l7OV++fPn8gw89GO/lLYo0Y2/CyNPmE3ghtHM4IqZ9SgWH+gQwII/Jfib1Q7Q15jUrV66Mj0wPcxgclpPXvzM4pP7999+tzyi+Hg6pk9CXM+ei70328an07t3bxks5OaSG4EQ89flsDELUutSonl+NH+93i54Zz3RWhoBP7du3t2NTncwzD6belSpVyg8ZMsTSGBPyHLgnypj5GcfSn38xdnXACLF1ssU4pA4gFUe6iokEqwSsGrAChhZMUF9FY+j6665z0WDRTL1wvtiqVSvTHrn//vtdubJl3UEHH2yr32i/cNybb73l2t1+u0nR0fx4++237VxcC9VJrvHCCy+Yxgf2vEg/0X7BZrdjx47mEwnNn+jlsXzhYJRVyubNm9vqX9duXd1jjz5m9pdIXdE0YgUvwLE4E0XlnRUQtGpQGUeNFo2jaHBvjjmjCYftn5TapoP8nnjiifY96jCy9+e+sAWvX7++3SdOL5Huch00sXBKFjXormixYrbaM3PmDPf8C8+bCnrzFi1MCwv7UvLbpHHj7JXxGjVq2MrBTTfdtJbTtiSs9PLsXnrpJdPMoUzR1Lrm6qvtOeAbgjzwbMhTs2bNTHuHMkfTJ0id0cLhOmg3oVXEPbGqETXa7qSTTnIXXnih2UTzgcqVKpndNFAWXIf9OzzcwdU+vrapQAJaaKxOs9KBphjS9ajTcj169DCNHzTAosmWaSolJeA8d7bx7NHGeqVnT8sL5fFN9FxRxX2y85MumnhbGfIcghaAEBsbzEtpc3hvaZdyIqgbp9q/o0X4W/Susj34EBBrQx8CrMKF70C7wwo3YJufJKyss8KGmnkmWGG89957Xb9+/dy777xj7SeaOoD/h9TVwPWFFcB1fdBOSd5XKvQfaH6ioUb/TNvIMfQPDRo0iPdKDw6VWXF+Pzo2mlya+QIr1pRJ0oklv/FnQjn069ff+oBDDjnEtmH6QP+XBK0qyp9+lM+WQNBuwo9SEt4xtM/oZ1O1igKhHqWajgXt6GignvYdRfOCfY479rg4RYiNA+MkNFVpB5Oa6f+GnaOxIW1PavuDw2Sgj8rLMJ5EQ4NPEvp/4DnQhgfo42mbGfPS3mQCtxl8mM/QRuO7EE1NngNj6J/idod29qqrrrLxLW0z8yXaY6DtT22XA+SXfKT2Nakf7m9dc59NQadHOpnmVOenOq/lu+efwLwWGJ9tbML4LmndAsuiPufXqD+hLvBJR+h3mH8ltVqpR/ymzwnlwZwcGHeiqcqcDx+7zPPQIBL/UaJGIUc2luYQ2hisyIZVsahBsdUCsjR8+HBLa9eunWn3PPTQQxZ+GW2aEB4TjQ7CGH766acmyUa6/NviLC0hpMysqrMfmihIqKd9952tSN59V1boPdII+xg1UKYVAuQB6TiaIEFjCKKXw1Y50GBJgkYM1yCUIqA5hJYOq8ZIYAMcH/IciAbOlsbqSpJUzSH4+uuvLQ3tI+4ZBrz3nt1z37597TdS+2rVqlnIZcoE0DJgxZf7CeEKo4bZ/gKaNZyXEPyBt956y1Zsg2ZPOpAe8/zQaIomDRZGkxX0aEJk5wurQRc1vsh+Ex4xEMJos4IArEB8/vnn9h26vNjFyprViuTqBGXLKgYr5xMnToxTvT/v3HPXCN9JSF/OT7hKniewCoLkG2n4Rx99ZGnwzDPPrJEXVvq5bo0aNfxrr71meX322WdNk4r9okbRVluokzzzb6d8a8exwt6jRw/7LsTGhnqKNgIakDkxePBgW/GlDUjyWvTOUp+bNWsWp6xGmkOroc2kvaEMU9vDDz/80MoQbUdWLwOEbyedfmp9oA3kOMqdcNLpyK3m0MYik5ZLbkCjlWNT+7tUaL9ZuaS9pv9KhWdCf4Z27jfffBOnbl7oK7i3008/PU7JomPHjpbetm3bOGVtWOVnHzSgk9pi9Dukp64KA2MXtIJZMUfzSoiNDRohvHdBmyA35KQ51KpVK9v2xJNPxilZMJ4knbYmr0I7wPiatj5VsxONQcoH64mkpksIX452Z05ccukldl7GsYElS5ZYP8bxY1M0lQJoJqGVhLbIpup/NrbmEGN2LCjQ9g2sWrVqDWuTAPVyXZpD5DHMYZnzbWyGDh1qGrRoHychzDx5QFs3U7kxnqEeMNdJahdxLPMvNKlg2fJlNt+pV6+e1ZPAiBEjbN5dPSq/FevQABNbNluc5lB0bVtNCyukSFzD6lrSNwxSTH7jZwOtDSShRAVDcwPNncBuu+3qSpbIsveMKr2FegZCw7MqV6liRdPwaRGlo+nSvPml5oMnX74sv0cB8pX8C/geYtUEfx9JkLyTfzR1WOngPBxHvlgZDqDpgp8JnLkFrSi0gHIL95PKKXXrmh1w1HC6Bx580LVsebmVV3LfcF+s9KLFA0mJNo7/KE+0dYIWwnPPPeeOr318tmQ5HdzvihUrXdGixax88QHESgY2uzyb1FWFIOGGIMlm9QCiQX52GF40iu64/Q7L92OPP27bAjgiJZQvKw5o7AA+n0aPGWO28KmwIh3KglUQfqMdlbThDtpSIS/ULyTp7Mt9Uefwa3H55ZfbqjnaXtQBtJXQNqt1VC3Xrl070zrDR4cQGxpW4pIrO0CocNIaJTQwqMP4q0HbMkBo1YMPPsjNnj0r28cDjP3iC/ubU2jiZPuXV6GtwLdC6iotEDqY8o0mS2v4tkMTkVW3pNYlftN4NqnnCPDMaL9pR2jb0DpKR2jPk/3VpgKfcvhfo61lBTEJ7SR+hegj04FW6JNPPmFao3fedVecujZoXdGeUrfR2gyr5EnmzZtn5cjqeKZV0U1NNCGL+vfK1n/QJwGr5yGkP5qlAfah3+a9BrTFGB+grRqcvQJjFeofDoFTQVuN1f/jjjvO+iohNiS8f6ma0GjrH16jho03A7yHvPehzq8P9U8/3f4OicZRjLkCaN1T74molVdhLoSGBs8AzfsktIn02/ioC3MJYI7CnCKplYn1AulJP66Ftyls5022NZT3YYcdZt+32271vCWAFgpRldGMRNs1aHdtKnIzFsm0D+nUqdAWB5jz4OOPcT1lTRnRruLXlTlMJnLKC/U4lGvqXHFjgEXNoYdWtz6R9zDAWAPod8IcCD+v1JkwliRi20UXXWTHEbkugAUO9SMEOcgfzY+ZJ3FvybqIhQl9cPFo/ltwPeayYisiquw5srE0h6JBjWmCJFdkw2pC0O5AIozNfk6rY0jWkWDisyEJq4rRi+GjF99seIFVOrSH8CfDyi+aM9j2JjWH8JPACgl+OQBpafBjk9TmAfZhlR1tIa7BvfAdf0JBwweQ8LMiXKxYsWwfCvgd4py50RxCU4a0pOYQIPlGSwkNArStWE1Iag5FL73lhTyxGpEO7I85d6dOnSxvFStWXENrKh3YIm+zzTb2NyfwScS5KesAfoxIw142Cb5+jjjiCNuGzXLUQMVbVoPGBCsJSMu//fZbHzXids/JfcMKPCuurLAG0J5Asyk8V+jatesaeUFTCCn5nXfeab8zwfMI5cYn6ghy9M8kxD+Bd516xqoeK1e0I/h8OPSwQ/21114b75UFq0i856wcokUYIB1fBLQrQHvJ70svvXQNjZdANDiyNpf3LPghy6vQH1wW+8BIXcEF/LOhjRj8jdEGULbXXHON/Qb82dE3seqa1JAMjB49yto9fMvwrDKBHzdW88gLq8SbCuoAq6D0sfTPSY1YIF9169a1FeX+/fvHqVlQfs8++4zVSzRdUv1bJUE7Fv9v62pL8RlHGaTTqNmc4JeF/h1tYkB7Npp0mdZQ6J/o4/DdwbuV1MDgnabfbhNrGA0aNMh+4/MuXT9IW1C+fHnTshJiQ4K2KeNd/NVFE0cbQ95///2+cuXKa/kbov1j/ItfsqRWQSBo76dq3ANjM9pJtgfNIrQCaQfRzsik8ZBXaB/7hGGMmgrjc/yNhXLiN+N2NBejyb/ts2LFCtNiQQMJzcYAbQZtOeME2iNAQ58+iPF60LYPzJw5I5p3NLL+CwuBTQnzNfLPuD1d/QL6HywdKKt0/SdjdeZA1Ksw18RqA791tKGUA/NMrCDwpUP/fW/71ZYUgaARnDrPTIJ21ZFHHmmaxpm0fzc0aPDQv2I9QV3gN30HlhNhzkcfTv/LvIk5TgCNKd5r/O/+FdUXfmPNQ3kkx4bMh7j3ME/m3aUucJ2kVYjYOtniNIdyIsqX/S1eorhp5GDvmgoaLkh7g2Q03WIqEtBoIGara1HFN0kqWiScD+2PdCuP4dphdRapOh/Ax04SJPVcn5VetGO4XjqQuKL9xIoy+dkQ9OjRw7SjuA/KAl9JUYMXb12TnFaao4phf7EtJrLMvvvua36WciJqjGzViNXLIIkOsOJJtLH1hYg4RCQjXDd5Is+sRCdX25HGszKCXSwrGPgVatq0aY73tz5QH3ie3BfaAkmQuqMlwOoJWmGUPxF48I+B5hRaU8kVMCH+LWj5zfz+e/MnduWVV5oPLnytXXXlVeYfLRU0L2gTQxsGRx99tPnP4t3ivW7RooVpubFqltS8BFbMaAPCSlGdOnXsHcursIJ7Ut261r4QkTAVtDqI4ELbeUj16ra6ipYhzyjAs+CZBB8ywG/8xPE8mzdvYX7S0GLlWaWDyIsNGza0FVDaKJ4RK56cZ2OBdgt1Dq1OfOehbYv/PLSbklBHaY/RngptNe0h/irQrOrWrbvVVfoo+o0k7I8/uFNPO9VWw1nBZeU2qXWVCvWY/jypjbMlgF8Wwv3yrvKe0Z/dcMMN5tMj9E/UJ/xDJMsKqAcc+8H771t/gmYWWr2UW7q+DR9ORJGizxdiQ7IiGnON+eILa5/Q1Ma/DG0BbRz1NAl1mLrMmCipkU6UQnxFMhZkzIs/NX4nx4qMnaNJp/m+ueuuu8yv5sXRWO6MM86wa4dxfV6lbtQGMp5HozBVI5P2hfaU+QjWC+eff75pEaLZyfwmwLwDbZjk8fQ1tOezZ882f6X4kqItRXOI9p02Cq3G3r17W5t2+ulnuGLFtrV2+YILLojPsvGh7aP9pB5Qz6h75DsJvpDIO5ozWEfQX9HeJmFOxliG/pfyJKow90n9o3wY04c5C2N86nEyRDvHXn/99dZPUZc5Hg1YrCdSIR/UfeYkmbR/NzRo8DAXQTMcjS40e3lv8asarHK4J6xn+CT7HXzf0q9//8MP9vw5jvOhJZwcG9KX8Y5yTuoM98/zoRw5RvxHiQavObIxNIdWrFxhmkP4yGFlIhC0Zli9AHzh8BvpZli5RTqKVg2+gZCIoiXDuYh2lYTVN44N/hGil8B+JzVWgmd5JKJI3IkognYOaWGVF8k0GiqkIRVO2qOycsIqezRYt9/4xUGaGr2ka/j2YcWF/a67bvVqf9AcSo1WFrSnevbsGaes1hxCgyBIdPEXQhqriED+uS7SYSTo/EZSjk0t0vBMknfKM6kFQxSBdcFKAyvmaFj9LyqbIKHm/omkhMQfiDDHOZOrpAMHDrS05HPA9w8aXNjOBh9UgP1r7z5Z9xfgnrhHzsEKbOpqdNAcQisMCXcAzSFWCpK21kFzKDw/fFywIkMaq7iLYo01VgTwCcFzJLoa9TRoVcyeM9sfUv0Q0zhipU2IDQkrebwT1FvqOu9rOkhHsy4ZeS8J58EmP9nepkKbZe3Wqizbe47JdL28Am0bK+P4uKFNTccfUTpRZTKtFvLc0MoMZUnbwQru2LFjM7bLSTiOZxG0SLKf00aEdpY+lzZxXT4F0Jr9NvZpB2i40eZzz8k2OBXuAY1X2s3fon52XSxesti0jeueUjdO2fKgf+a5Jv2BJEHzamqGKHZ/Rs+Y1Vv6m5xAU4A+WIiNAe8z7yTjTsbEOYEGd9DMD6T2HbQB/M7E7Ki+07+l8/WSV6HsmAswzs7Ur7APfXo632xAm8rzSVf2PBPGC7QlaLcmYRv9GW1z6rZNRbqxSGpfEurVuvpFxjzBrw77hnqZW5LXTu2Lk6BdxDzgy3Hp/TZtTLhvtG8z1QW0yRlHrp3r6J6ie5kwYYLN39LdV4CxEH0b7UKY/4itn0wyns0iHCLULirXqPpR0YAXLjjzCqpvdBoMyknDUTDbEZCUL1/OVAMBQQiqhyW3K+mHj8hyZI1A57hjjzN1Qyo8BIdtqF/i7BHhBOqKpKFGi9NrOrmg6tq8eXMzb0LIhFkbZmH58+c3tXFeIF4OBEkIHULDM336dDsnKphBxZ6GHefFDGq570AQOCHESIL6H+kdO3aMU1Y7WUYAFjpQ1NVJQxWyW7duFpKQhgmnaQh7evToYaZ1CNbYj4F6JhjII+ihrHPbQYcw2ZiXoWrJ9Slbng/PkkYUgQr7BAEWBIfUIYQiwhxCJZPWs1cvm4gsjSZM86LBdePoeT+Z4rAQCIXM/jyfVN5++23bhvPW0IDxvKhr1JOkiRvnZt+kk7ngZBxhFfUC1X6EVieddJKdjw4TVWrKFzg32/bdd18NboT4D/L58M/NbGpdZrRi40L5H3jAAVuMI2ohhNhYYGaH24jrr78+ThFbKgigDth/f/9Ip05xihBbB1uMcGjUyJHmOwFhBHafRx9zjP/4k0/MRhLbSQQw+B0I9q14xkcww4SdD8egDRRWcVndRLpesGBBm8QjsEGIglZS0r8MK3YIMTgHQhSiIXTo0MG+I3gKwpxBgweb9k++/PlMch8EDJOjASk+ERBq4eEdvx8IZpIe8hGKPPfccxaVjHPgXwABFVpORBkAhAlEfmCykS9fPovMcvPNN5sACoGVXTtK5xwIsRCsoP1DuaB9dGHjxiaEYIWxSpUqdj8cg1AjRHLDDw9CGPLL/XEs5ZHJVwVlg9ZRMmrZuiAPV111lZV7eDb4nkAAtnz5cvNpgkYX10Yww/NEAIcAiTSeNfsEYRj5xO6ZsuWDryjSEXylMmr0aNOGCr6VAghs9t1nHzs/1+b5IQyi/Kk31DmEeWhHYUeP5hFlTf3Bbwirvn9GzxtNIoRs4b7QGKOjBmx6qQMIADkvgiGEaggRhRD/TfAhQxuKNqPY9NB/HHDA/v69gQPjFCGE+G/DwjBj1hdeeCFOEVsaaOswD7i85eXmu0eIrYlMMp58/C+aAGfkxhtvzI4OtSH466+/zI4T21ZYsWKF2UbiPyGa1JstKPsUKVJkjehl2Ndi24ytffny5bNt8bEXxU9NzZo13BNPPGkRu/BtsP8BB7hiKT548F9EZAV8xhC9C1tMolKVKVPG7EkDRKhauXKlK1u27Bq21KRxPPam+F44+OCD0/r54f7wD0G0ACJc4Wco3C+wnXNxbv5y3/g1Ip3HQRlQLhzDNu4fW+JVq1aZPTD5Zx/KAy/0lAf3TLn98MMPbpdddjG/Idgbcw7K6s8//7TyTJdfbMqbNWtmNsvYMOcW8oPt8rfffmt52Gvvvdw2hbLyiS061w33Qn7IO/dLnvjOfnznnkMew3dgG3lOPgN48KEH3WfDPjP/Dkkba+oQ5+A4zs2HekR6KAfKiDIgT+SB4/nONsqVv+Rh7rx57pvoGeJLquo+VV2Rwlk2uNQh/BFxDM+Y/akHqf40hBD/LSZMmOAefPBBd9XVV7samzhiS14Gf0xEL7utXTtXLWprhRAirzB7zmx39113u9Pq1XNnnnFGnCq2BPAhd9ttt1kUv1ZXXukK5HFfWWLrI5OMZ5MLhzY0CIdwklWr1lGuf/8BcapYF0EQg/Dkkksusd8vvfTSFusIEAEZgiac7p540omu9Q2tLd9CCLGpYDCI0DnVMbPYeMycOdMWatItbAghxH8dFltZPE4ujIvND6HrmTttKgfUQmxoMsl4tnoxJ5oofBAeiNyB9kurVq1c3bp1zfP8wIEDLWrElioYIkIAEWzOPIvoCadbZILzzj8/3iqEEJsGtEwlGNq07LHHHhIMCSHyLMHaQYKhLQvmIhIMif8iW7VwiPB9L7/8soUk/PrriRaCD80SkTMI08aOHes++eQTC+F49913u+OPPz7euuXx22+/uWHDhrkB/bNCzD/77LNrmQwKIYQQQgghhBDin7FVC4d+/vlnV6tWLde1a1f34IMPmOYLgiKRM9ttt5177bXXzIxszJgxrkWLFvGWLROeMdpNvXr1ciNGjnT77btfvEUIIYQQQgghhBD/lq3e55AQQgghhBBCCCGEWDf/WZ9DQgghhBBCCCGEEOKfI+GQEEIIIYQQQgghRB5GwiEhhBBCCCGEEEKIPIyEQ0IIIYQQQgghhBB5GAmHhBBCCCGEEEIIIfIwEg4JIYQQQgghhBBC5GEkHBJCCCGEEEIIIYTIw0g4JIQQQgghhBBCCJGHkXBICCGEEEIIIYQQIg8j4ZAQQgghhBBCCCFEHkbCISGEEEIIIYQQQog8jIRDQgghhBBCCCGEEHkYCYeEEEIIIYQQQggh8jASDgkhhBBCCCGEEELkYSQcEkIIIYQQQgghhMjDSDgkhBBCCCGEEEIIkYeRcEgIIYQQQgghhBAiDyPhkBBCCCGEEEIIIUQeRsIhIYQQQgghhBBCiDyMhENCCCGEEEIIIYQQeRgJh4QQQgghhBBCCCHyMBIOCSGEEEIIIYQQQuRhJBwSQgghhBBCCCGEyMNIOCSEEEIIIYQQQgiRh5FwSAghhBBCCCGEECIPI+GQEEIIIYQQQgghRB5GwiEhhBBCCCGEEEKIPIyEQ0IIIYQQQgghhBB5GAmHhBBCCCGEEEIIIfIwEg4JIYQQQgghhBBC5GEkHPqPsWjRIvf999+7VatWxSlbDt57N2nSJPfbb7/FKVsOK1eudNOnT3fLli2LU7Z8Fixc6MaPH+8WL14cpwghhBBCCCGEEOvPJhcOMQHv1KmTu/nmm92tt97q2rRp41q3bu1uueUW17lzZzd27Fj3999/x3uL9QHBxuWXX+723HNPN/bLsXHq5uePP/5wPXv2dEceeaQ79dRTTaCxpfHOO++4SpUqubZt28YpWy7z5893zZs3d/VOO83VqVPHHXTQQe7NN9+MtwohhBBCCCGEEOvHJhcOlS1b1lWrVs2NGjXK3X///a579+5u/oL5btp30+x39erVTXA0b968+IgNy88//2zaNf8Fpk6duoYgrUCBAq58+fJur732ckUKF4lTNz8IM8jrjBkzrOw3t/Dvl19+cQsXLox/ZVG8RHETDu24045xypbJ8uXLXcuWLe0ePh32qbvzzjvdzJkz3XPPPScNIiGEEEIIIYQQ/4hNLhzadttt3fHHH+8uueQS+33EEUe4zk92dn1693HdunVzFSpUcB06dHD33HOPW7Fihe2zIUEAhWBqa2fGzBnujjvucH/++Wec4lyRIkWs3AYNGuT23XffOHXzs8suu5gQY++99zbzrc3NQw895D777LP4Vxa1j6/tBg8e7G666aY4ZcuEfH/00UdRfo93BQsUdFdddZV7seuL9j4VK1Ys3ksIIYQQQgghhMg9m83nEIIMQFgQ/OOccMIJ7rTTTrPvQ4YMcVOmTLHvG4oPP/zQ9erVa6s3W8N3z+OPPW5CroIFC8apWWy33XZu1113dfnzb3nupFLzujn49NNP3SuvvLKWkIq87bHHHq5Y0S1bwDJx4kS3dOlSVzJ6zpAvXz7XrGkzd/bZZ28R5SuEEEIIIYQQYuujwF0R8fe0fPDBB+6kk06Kf204cEzcp08fM4Fq2LChK1y4sKVPmzbNvf/++65QoUKuQYMGpnUCmAGNHDnSfMMEodGOO+64hhAER8fvvvuu69evn5mlsR+mVqVLl7bjrr32WvfDDz+4ffbZx/zgFC9e3D7BGTHCI+73119/dSVKlLBtATR08JUzZswYV65cOfftt9+6gQMH2jUQyJQsWTLeM4sff/zRNHjeeustcxBdbNtibocddrDJPL6Bxn/9lRv6ySeuTJkybvxX493HH39s5+Wa3MfHn3xsfmS+/PJLE55xDcqIvLVv3978NhUtWtTuZcGCBXYemDBxgpXBnnvsaZpXCEPmzJnjZs2aZeZH5JPjZs+e7b744gvLG6ZemKMBxwwbNsy9/fbbbvTo0c5H/yg/nkdO/P77727o0KGW53Hjx7n8BfK7MqXLWPkHEMpw3QsuuMD8IgXIH+XONWfNnmVlQFlR5uSROjF37lwzTeN5b7/99lYObOO58ay5f/I4Pro2908dos6gTUPZwYABA6wOYN6GFhP3ynaeNXlAcwizQ4REAQRxXBuNHc7Lfgg2OSfPEjDnGh3VC+on90V++/fv77777jurv6Fu5wTmYhMmTLA8cp6//vrLlSpVym2zzTa2nWdE3evbt6/7+uuvXeXKlU0YRLlz7yEvQgghhBBCCCFEJjLKeKLJb460bt06/rZh6dOnj+fyp512mv/tt9/iVO8vvfRSS69Ro4afNWuWpU2cONGfcMIJvm7dur5Nmzb+iCOO8NHk3N9///3+jz/+sH2iSbI///zz/THHHOPb3X67j27WztOrVy/bfuONN/po0m9pRx11lG9wVgM/ctRI2/bUU0/5Qw891De7pJlveUVLX7ZsWfs9bNgw2/7VV1/5yy67zJcuVcpXqlTRymT77be3c/E59thj/TfffGP7wpdffhnd16n+vPPO9U2bNrW8RpN5//HHH/sZM2f4Jhc38SVLlvS77rqrb9GihS9RooSd5+abb7bjL7jgAl+hQgW7zoknnugLFCjgb731Vv/333/buU8++WQ7pmjRov6ss87yt9xyi6XfcMMNlnfONWXKFP/LL7/4c8891xcqVMjny5fPzjt37ly7xoQJE/xhhx/mt912W//SSy9Z2pIlS/z111/v69Sp7a+++mq/zz77+CJFivh77rnHyjcT8+fP982bN4+ez8n+qquu8nvuuafd32OPPeZXrlwZ7+XtXrjekCFD4hTvR4wYYfdz4YUX+osuusgXL17crvvZZ5/55cuX+44dO9ox3BN1YtSoUXYc98E1Cxcu7G+77Ta/bNky/9prr1mZnnHGGb5Vq1a+WLFi/pBDDvGTJ0/2q1atsvKtVKmSnYs6RNlxHe6/Zo2alk7+kwwYMMDqS8OGDf0NrW/we+21l99tt938K6+8YtvHjRvnzznnHL/NNtv4/fff37dr18724Vx8qM8rVqywfTPBc7rmmmv84Ycf7q+77jp/9tlnW7k3btzYz5w50/aZOGmib9SokdUjzst9NWjQwD/77LN2b0IIIYQQQgghxLrIJOPZ7MIhBANMjhF8dOnSxYQKpD/88MO2H5N+BD777ruv//bbby3t559/NuEN+3Xu3NnSELwgMHn99dft98KFC32dOnX8iy++aL/h8ssvt2PefPPN7An74MGDff78+X2btm3sN3Tv3t0EL/vtt5+fPXu2//HHH33f6LzkDSHAwQcf7Hv37u1f7NrV77LLLnbOSy65xI79888/TYiBsCNc47HHH7d9EB788MMPdv3dd9/d0hCOdevWzTdp0sQP/vBDEzoULFjQX3vttXYs97HjjjuawGHSpEmWhrBq7733tnMsXrzYrvPrr7/6fv36+QMOOMDOG/ZF4FOrVi1LQwiW5LzzzvPt27ePf3l/3333+fLly/upU6fa70mTJ/ty5cqZcOqDDz6wtHQgUKpSpYqfM2eO/R4+YoQJxBBwfPHFF5YGqcIh8n7QQQeZ4C0IOO67/37La/Xq1bMFf3fccYelIfBJ0qFDBys/jl0QlRN5qFixYnY+EBpx3ONR+QfIK2kIkig3jh0/fry/6aabLB3hTGD69OlWHiedfJKVIwwfPtyEjAixEFT99NNP/uWXX84WFp5++ulWF/v172/PiDSEUzlx9913237v9nvXfv/1118mnCMNAVgoBwjpCIXIP/sKIYQQQgghhBC5IZOMZ7M7psGE5swzz3RHH320mUvhLwfH1NffcL1tHzdunJkr1a9f30zQAHMyHPEC+2IKFE2UXTSBN7MbzKcwSyKsO/sGgk8WzILC96efftrMlE47NcvXEVx44YUWdh3/Li+88IJFWDuhTh3LGyZCRIbCx0uzpk0tshp8/vnnZvb0ySef2D2xH2ZzmFL9OG+emS9hzvTTTz/Z/ZI/0riPiy++2HXp0sXVqV3bFS9Rwu2///7u5JNPtvNiWoVZF/eGORpwbsyr+IR7wcypdnQ8ZmZJMNG64oor7DtmbsGB9YgRI8xE6txzz7XfmE69+uqrZmJF/inX4dE9UTb4aMLELB2YTr3++ut2P5jGEX3uy7Fj7ThM9zCFygRmeZjqkX+uTbj7BfPnmykVzxDTPWh5RUu7D0y1Qhplwe9LL73UTKqKFS3qKlasaM8Np+aYW5EHoOwC4blThnzn2AMPPNAdddRRlp6E/BAV7Mwzzsw2MaxZs6aVGX5/7rn3XrfTTjvZb/KMqdcDDzzgjj32WHdK3brmeB0o50ywjTpyyCGHuFpH1bI0zOPatGljJoAvvfSSG5Uo+2DeF/K/LnM/IYQQQgghhBBiXWx24VCtWrVsAoxQAV83X331lQlLCuTP8lWDY2ooV76c/Q2ceOKJNnlGiICPF86D8AiH03Xq1HH/+9//LCx+vXr14iOc+Y9J/kV4gS8ZfLYkhUj4yUFYBd988439Db6N2C+570WNG5sQAh9DCH7wB4NPH/bHvw4+jsjn3Xff7e67777sY1etWmXpCJwgCC1Or1/fBDH4quGYq6++2vwChesHwj2kkvTxE0AYVXHPPc2nUojUhmDlgAMOMIEGkE9CziPsIN/48eF+iIL14IMPphWeAGWP8AXhEMchZMIfEEIvooIddNBB8Z5rg+CP6HWUBcdyn5QPQkJcYeFzB8ruVNaEQGzHPhJCvQjPCSEZwqYnnnjC6hLlRr2CZFml1oFAKLdk+vDhw014tPvuu8cpWRx22GEmDPpm8mQTUuFfiePIA/cDPK8gUEJwmQnuG79VPAeOD+B/ikh+CPOmTZ0apwohhBBCCCGEEBuezS4cYlKNZgyTYzQ+UoUgOOmFFX+tOcFm8sxkGmfSOO/lO8IAHCuhodOuXTvXqFEj04LJBE6acSaMZkxq9KogtFl7Yo/wYLUAYbuSJU2gEYQK8+bONUEPAhnCot9yyy2mXXTjjTe6a665Zg1BA8ckhREBtJXQTEI407ZtW7fffvtlR3TLiUxOidFQannFFXav7733nmnDoOF0+umnx3s49+uiRSbkQWDENck3H+4BLRa0YdKBpg9aOocffrjtF47jfjm2Ro0a8Z5rM3fOHBOycK+hrLg23xEuBWfk1IkGDRu4wkWytLYQyOB0HCFgcMQNaBXhxPz55593devWzdaY+icg4EIjjeeTWgcQoCH4CXXPyj0u+nTPMydwqA1cI/mMEVaFOhi0vYQQQgghhBBCiI3BZhcOrQtMugCtkeTEG4EBE2oECESXIsIX2jFvvf2269mrl6tSpYpF+kL7BhOgdDDJxzxn3o/zTOMlSdDkCVG1Vk/ckQKsKYQhXwgpOF+58hVMyDJ48KB462qIxoUZVU489dRTJkRCyNG5c2d36KGHmuArndAhkzAoHWeddZYJ4Xr06OFefPFF03A54YQT4q3Obb/DDpY2YEB/E4okQZuLSF0I0VIpHd03goz+/fvZM0iC6VrQ9EnHLrvualG4ghZQkjfeeMPM+gLVDq7mTql7igkLH3/8cdO2OfXUU+OtWfWDqHcI/DDnQzCIsDET6yo7zPQoDwRA307JMmULcL/UP+oeGlO5EdxlgjoD33z7jQnZkoQ6GLS7hBBCCCGEEEKIjcFmEw7lVrARfO9gMoRGUAAhAGZhaNUgHCIM+X333++KFinizjv3XAtdz+QdE63gcyZoJRE2HI0PhB2Y7ixftnwtAcXkyZNNO+SYY46JU7LyjNZKUpPkp59/MhOsqlWrmiCpevVDLL1Hj5dc127d7DrsTz4wz9ohNpVCvoTAJ1WwgN8e9se3TbheELqwP/dMOveCJhDfCWWPACynMiVv5513nvm4ueeee9y555yzhglapUqVrLy+//4H0+DBTA74+9DDD5u2UXL/wL777uvKlivnxo790t0dnZfw8cDz4Xn8kdB6oby5hyDoClpFaAP1evVVu2/KC1O/jp06mTArgC8kTATJw6233mr5rVatWrw1y+cTJoAIBUNYfkzlgGMoRwh1gPLietxXOihL/BfB4EGDs48HzsvxwWQxX0JYmHwG4Xs6wV4AwQ/PZvKkyWtouSGUwkQRf0hJ4VCoL37V+mkoCSGEEEIIIYQQmdhswqHx48bZX3wGpWqcJMHJMhohOD7GZw+TcibOnTp1Ms0QTJmASTNOjYOQB40OtD8wPQq+a0puV9L+ojmDgAH/O2gWofWDKdJbb71l26dMnWLaJ40bN84WTgFCBky9EFQFOnboaOlo++Ac+MijjjItHYQkV115pQlkmjZtaoIETLN223XXLAHTXyvsvoNZUSAINh599FEzL2vZsqVpyWDm1KFDB9e7d28TlHB/+Ai64847XceOHe075YKgClKFHuTxtNNOMxMztFUaRGWaBNO45i1a2H6UzxlnnOGaN29uvp1mzphhZlrpwPSJ/eCxKM+Y0+EfiHJf8ddf7thYuIYoY2l032jHUIbAs617yimW18uia19wwQXmb4pr49AZ08EkOCXfe++9TZOKMk6CuR6+n/AThBAOn1NoXsH777/vrrvuOhPSUCcAv0S33Xab+bmCINTCB1AQ5mDahs8k6tSTTz5paQgau/fobmZ03CcgQFu5YqUJHRHYAYIwnhnk5JCafJM3BEnkh2cN/fr3My2p1q1bm/AzEMpu2vRp9lcIIYQQQgghhPjXRBPhHNnQoewnTpzozz77bF+mTBlfuHBhX6xYMX/IIYf4p59+Ot5jbX755RfftGlTCyNfrVo1f9hhh9nfjz76KN7D+7feessXKlTIQss3atTI16xZ00cTeP/111/He3g/evRoC3UeTcR9s2bN7Lww4L33LAz6Tjvt5I888kgLYR9N/LPDlwOh4gm7zrG77rqrr1u3roVR53yEn08ye/ZsywP7UsR8WrZs6VeuXOmjyb8dW7x4cbsfQtS/9NJL8ZHewqNzTo4hjDoh/Qlrz29C88+YMcP2u/+BB+x+S5UqZaHwFy1a5C+66CILt086ee3bt6/tG1i+fLmvXbu2hW1PB/l75JFHfIkSJbLzTdh+Qr3nBKHW77zzTnuW4bijjz7aT5s2zbb//PPPFq4+5K1q1aq+V69etm3mzJm+fv362cflz5/fws3/+eeftj2VNm3a+COOOMLymgp1hHNQrg0bNrT755qlS5f23bt3t33GjRuXHWKe8iJvXbp08XtHz4G87bDDDnb98OwnTJhg98J5qHcHVzvYnl+4N85HmVKXt912W1+rVi0/dOhQ36RJE1+2bFk7J6Hvn33uWds/HZQfYfm5RqVKlfxRtY6yPCZD8M+dO9efeeaZdi8FCxb0FSpUsDo2duzYeA8hhBBCCCGEECJnMsl48vG/aKKcERwLo7GyoUC7Bc0ZNFT4cHlMifD9Q5SvTKAZhC8c/N+g/YK5TTSZjrdmhTZftnyZ+3357xZSnvNhdhQiRgXQ/Fj0669u5woV7PoB8oApGZojVfbay1WuVCnbBAnQ8iFiF9ohr/V+za6Dtsf+BxzgdohDpidBcwStKHzhVKxU0e22224uf7782fdfqFAhOx7TJr4n7x0/PPhAQosIrSccEqMRhKYUDpyB8pg7b67d3/bbbW+/0arinsg352XfZAQswFSJ8yRNtlKZv2CBmzxpkp17n333cUUKr3mOTKDVgmlXqdKlTMNnm0Kr88o9ky8+lAH3HPK2MiorNKjmRGVfqXIlt+suu2abZKWClhFaUqkh+4Hzch6ePSZylAXliO+e4NsHKCc0hSgHtqHNxLF8J6/USfwNhTyQxnnR6uGZYMoYngPHUSeCfyB+c1+Y/3Gv5CHdM04HdZMIbhx7QFS/y8X+toD6FN4bzstvPuQzXFsIIYQQQgghhMiJTDKeTS4c2loJwqHff19uIeH32CPLUbUQQgghhBBCCCHE1kAmGc9m8zm0tYEMDQ2e4MhaCCGEEEIIIYQQ4r+AhEO5gOhUzz77rJkozZ+/wL3wQhc3bZocAgshhBBCCCGEEGLrR8KhXIBJGZGzCLlOVDPC1uMfRgghhBBCCCGEEGJrR8KhXHDAAQdYiPULL7zQXXTRRfYdZ9dCCCHyDjiLxwG92LJgsQaTbyGE+K9BUBMCkqzDRazYxDAW0HhA/BeRQ2ohhNiCIUIfWouffvqp22677VyjRo3cWWedlasodUOHDnUjRoyIf60JUfWuuuoqi34HgwYNci+//LJFA0Q7EiE4USFFFtNnzHD33HWXu+DCC91JJ51kaXSfCxctcs9Hz4comq1atcp19EBMkzFXnjBhgitbtqw744wzXP3T67uCBdY+nqiX7777bnbUR65LXbj00ks3arRCrrNgwQL3yiuvuF9//dXdeeed8ZY1YYD8wQcf2Oe+++5z26eJ4JkJokA+8cQTZr5ds2ZN17RpU4sKmYSIkt27d3f9+vWz+z3llFNssSZEIyWK6ZOdn3StrmjlDj74YEvb1PDePPPMM/a+EU2zcePGuX5/KNunn37ajRw50iKbNmvWbK0FqFV+lRvQf4B77bXX7FosWjVt1tTtu8++8R5CbBhGjRrlnnrqKYtAW6VKFdeyZUu37765q2cIMnr37u3ef/99iwy78847uyZNmrjDDjss3iM9L7zwguvTp499kpGI8zpED7476neqV69ubSPQLi+P2sRXov56zpw5rk2bNha5Njf89PNP7pmnn3Fjxoyx9vOkk09y5513nitaZO1oukRO5nnwN/Q7RAMmH/Q/GxME/m+++ab78ssvbQ6ajC4dYEHg888/t/rWunVrV6lSpXjLuiGwEG3pL/N/caV2KGVjqlNPPTV7PBQYPWa069unr/VTe+yxh937QQcdZNvosx7u8LA74/Qz3AknnGBpmxr63hdffNH63l133dVdcMEFFjwpN3AsY7733nvP7bjjjnbsscceu1a06M8++8z16N7Dzfx+pkWiPv+C890RNY+It4qtmYwynuhlz5FMMfCFEEJsXKJBmT/jjDN86dKlfeXKlX00QEKY76NJerxHZlasXOFr1apl+6f7RIN9v3LlStv3gQce8NGgyEeDch8NMm17NMH3ffv2te15nWHDhvn99tvPP/LII9lltnjxYh8NynzlKlWsvFq0aOGjwaptWxcjRozwO+ywg48G2vZso8GYneOaa67xf/z+e7zXanje4bmFD9dbtWpVvMeGh3t59tln7b65Xr169eItaxJNAn3duqfYPtHg1M+fPz/esm6igb2PBty+du3avlu3bva3Zs2a/vvvv4/38FbeV111pdXHaGBqZca1okG6/z1RVu+++66vVq2a79mzZ5yy6Zg9e7aPBuS+6j5VrczIW8WKFf0nQ4fGe2Rm7ty5/sQTT/R77rmnf/zxx32rVq18+fLlff/+/eM9srjrrrvsvqNJmS9RooR9375UKf/OO+/Eewjx74kmzX7nnStY/S1btqzVs5133tlHE8R4j8zwPl5yySX2Tt9///1Wh88//3yrrzm9l5y7TJky1g7+8ssvcar49ttv/WGHH+6jCZyPJvKW9ueff/o33njDH3rYYfZsTjrpJBsn5IapU6f6vfbay/p6yjuMJxo2apS23X755Zdte/Jz6qmnWt+3MenTp4+1p1zvwAMPyO5zk4wcOdJfeOGFtg9t4rhx4+It6+bhhx/2O+20k7/++uv9e++9Z/Nc+pfbbrst3iML6ixt8a233uq7d+/uD4vKfMcdd/T9+/WL98jKx+HRM+rYsWPafG5MeA6NGjbyFSpUsOu3adPG3tlXX3st3iMzS5Ys8RdffLHdD+8q916uXDn/zDPPxHt4G18wxuG9LF68uJUR5V20WDH/1FNPxXuJrZlMMp7NJhxi4Dlnzhy/YsWKOOW/AY0DA8VV0b+c4KWjE0zXmPz4449rNb6//vqrX7ZsWfxLCJEXQGjTuHFjP378OGtX2rVr5wsWLGidNG1CTgwYMMDvsssu/sorrzShxhNPPOGffvpp3/bmttbR33HHHbbf6NGjbWLaOhqADvrgAz/gvQE24GQQwGBo1qxZtl9eZdq0ab7aIdXWEsjRfzGIZYBJWV122WW5Eg7R59WpU8fXrVvX9+7d2wRP9LMM2PkMfH9gvGcWdv1q1ew5Pvjgg/6+++7z99xzj00cNiZLli7xPXr08Ndee43d3+mnnx5vWZPX33jdP/bYY75kyRImHFqwYEG8JWeov4cccojfZ599soVBo0aNsonoBRdcYL/hlVd6+tNOO81//vlnft68eTZ4ZbLJgHXChAnxXlmEwTwD/k0FfTnvKBOUQYMGWdpPP/3kjzzySH/wwQfnOJH6+++//U033WTlOzialAOTP45lEsd54IuxY61s2fejjz7y/aLJyTHHHGPHHXjggX7Rr7mbHAqREz/88INv2LCh9RV8HzNmTPYCQ8uWLeO9MvPxxx/bvl26dIlTvF+4aKHff//9fZUqVfwff/wRp64GwQb1neOKFCki4VDMzz//7E8++WTftOnFaywCUF5vvvmmv/fee63M6KvXNRYAztG8eXMTZNCu0+8wvihatKidJ3Wyj/CA50Lb9tBDD1m/g4CahY2Nycq/V1o73r79vb5QoUL+oIMOsnYylQ+isQr1rHz5ctb2jh8/Pt6SM/TbjH/orwN/r/rbN23W1Mph5syZlkb/WqpUKd+sWTP7DRMnTjQBCnU5tM0QhJvPP/98nLLx4Xkyhttmm21MgAP0Hby/u+22m40bMsGxnTt3tvt94YUXstOaNGlii4NhbDE36m8rVapkC1EDBw70gwcPtvNzHEKlGTNn2H5i62WLEQ4tXbrUBsQ0aA0aNLBVs/8Cv/32m0mWjzvuOH/22Wen7QSTvP766/bSdejYIU7x/ssvv/RXX321DZgZ/AUmT55sA2hWlZMrpUKI/y4IjumQk4NlJtGHHnqoCYhyGkTT0d9+++2+U6dOccpqmDgzoBgxcqT9ZoBIu5McgE2ZOtUGQUzAh2/kweCWDIMtVr7RqMqkEYNwjcFSboVDCEAQDCX7Ptp1rsN5kquXPMfb2t3mzzrrLPu+OWD1n8E0AppMIEBkAsgKZm6FQz179bL7TY4xuEcmRGgHUa4wZMiQNTSJmLQwdqBujh07Nk7NggWUc845xzSM6JM3BaOjCTQTLCbRyX4fAR7398orr8Qpa/Pdd99Z3WIClCy3//3vf3Zshw5Z4wP+XnfddfY9QPlQ3hzPpFyIf8uMGTP8p8OGrbFo++qrr1pdRMtgXbD4wL6p2ge8GyxUpGq4sK31ja2t7UBogSaLhENZ5cJCEGX59YSv49Q1of+gDUTrMDfCoenTp5sWMgK/JG3btrXrIASivwt0ebGLjTWYs20O0ARCII7majrhEKBNhbYp/VNuhUNBgIl2Z4DyRjuI9LDgcOstt9o4C43WAPtdd/11tl/q2ArtLrR2NvaiTYC+gwWEZJ9L/sL7ijAvEzzT3Xff3YRI4X459qWXXjKBXMsrrrA0znXeeeet0R7QRlSvXt3qXq+oDxdbN5lkPJvcIXU0oHFRBTT7yKiSbZFOJLF1jRrJ+FfuwFdAly5dXNTwmH+IqDGLt6Qn6gDt/n/+6Wf7TUS0Hj16uLfffttFA177HVi6bKmLGgI3a/asdZ5XCPHfABv7aKLsypQpE6c4e//x6YDfodJlSsepaxN13O7mm292V199dZySBcfjuwa/LJUqVrS0aDLt7rjjjjVs7XfdZRcXDQztPNsUKhSn5j2wtR8wYID5ZChdOn15p/OFkBPRoMx8SBEBM4D/p+DHKOnLAX8SnZ/sbP0KPqD69u1r/dOmBB8/67pH6k40qDSfFLmB/fr07m3fU/1E4K+H/vett96y38cff7z54QlEg1jzlXDkkUe6ipWy6nAAv0+UYzRANx9Fm4KB771nPpHwR4E/jkC4L/r1TAwfPtwtXrzYHXLIIdn+kwDfS4DPMPy2NGzYcC1/T/gg4ZqQPFaIfwr1qdZRR63hxwwn/LRJ9erVi1MyE3xsPfLII+6rr7+27xMnTrQx8XHHH7eWL7LXX3/d9Xu3n+vcubO1r7zbIssfXbdu3VzdunVdlcpV4tQ1Wd9+p1SpUvZcUn2ynX7GGfa3RIkS2eekTXr0kUfduPHjXJOLm1he8HO2KSEvyXqYDhufbLNNrvsdwHcW7SX9yxtvvGFpCxcuNF9xBx18kKtcubKdb+KkiW7lypXWpwS4Xnge9MlJ6tevbz668CO4sSF/9B1Tp061vpFnC+SP3+QZP4GZ5rHMMXkn8S0ZxiEciy8ljv08GvdwL0cffbR7/PHH13gOjF9CHUqOVcR/i00uHDruuOPcy6+8bA0RFZwKuSVBY/DYY4+58ePHxym548QTTzQHfjvssINNwNZ1Xxc3vdgNGzbMtW3b1n7zknXq1MnOA8mJ2sEHHWwNwWOPPpZrp3NCiP8effr2tQH2/fff7/JF/3KCTj51cMUgCCePOF4MQqc999xzDQEU0IYhwGawgLPkvAgTFRwgI6g/99xz49R/z0477WSOI1P7CJ4N1KhRw/4CTjPpGxA+sKiCIK958+Y2KNzawdEoZVAy4XyW3zvvXMG+z5o1y/6mMvD9961u4kRxu5JrD073339/V6FChU02SGfyCzj0TMKAvUSJ4m76jOlxyppwLItKwMQY4VqA/AMD9F9//dUm7akTawRkfDhWjuPFxuDHH390r/TsaY72WZBYF7x7OItHONukcWP3Wu/XbJGC+tv+3vbxXllMnz7dPfDAA+7Gm26yfoZFD5EFi+ezZ882J8lJgfO/gX6Evj5VqDQ/aksBAXXYxnyDMUChgoXcG6+/YQ7y6QNZ3N/aYaxDH0q7euWVV7pnn3vW3XXXXVbeTz7xZHZ5r4zuH2bOnGl/A7Tr9FMs2CdBKIPQBCUBzr2xmfbdNOtDkotMgOBrxx3LWL5XrFz7neIYHM4DfW9S+MVYj4WqRYsWuXnz5lk/lNqvcTwLVJTBupzMi62XTS4cgmJFi63lEX5LAYENq7o0jOsLL1lupflFChdxRxxxRLbEN4AUPBUmeDTcTCiEEHmP4SOGRwP0K90dt99umgRBkLC+sGJEx47WRU6gsfLFF1/YahjRZvIi8+f/4j7++CMbBO2yyy5x6saBZzpw4EDrE/bbb7841VkEFFY3Eei1uKyFDdaJzIJGGFHEtlYYPLOqyUCUhaIk226bpQWTulLNSm67du1cy8svN+EQA9h0MFimX2VldH0XedYXBso8O0jty7m3QoW2cYt/W7yGJnCSn3/O0hxm4kY9CzDA5zf3melYog3xnhIJKilYEuLfsvz35a7HSy+ZxuSgDz4wISTaJOuCySaRB4mWxLt33rnn2fvx6quvmsZBAIuBO++607Q4mjVtKo2hBAjJ+vfvZ983dt9L+4W1AlqOyQhXzDfQPGFxpE3bNvZchwwZ8p9YmKBdveeee9xFF11kws+Wl7c0bWoWX2rVqhXvlaU9DURDQ2kgwLtAuaXOYVEM4HnRXgeh/8bkt1+z+oVUjWbmkEWjOTb5/OnHn+LUNWHRAeh7k8JH5rD0JdxD2CcVoraNGzfOonGmLiqK/w6bRUKTL3/WIIiXlBeMgSJhmidNmpRWKEPnMvbLL00NkH1Qcw2wP+EEJ0+ebN+R/n700UcmNU2+0MAL/fMvP7vBgwdbozcj2ieYtdE5Mdi6PBp48lIwMGXgxoptgMEspmAM4gmdS1jhJGFwFxoNQkYO+WiISZhTV0W4BzpP7idJOqEZx4YXMlk+5Jn77N+/v4Wh/jHKd/Ke2T537lwzjQjnYLWWQTWDSu6Pe0VCHDpnngW/2b4u6TfnnD59hpXlp8M+zRqsx9qdlDWDCa4dJgKU2fARI9zSRLlxDvLBKjHPkQ/PkDQabs7BPZAn0vgeJgVcg3TugeN5lpyPlVzqA/fE82L1ne3sD+QHyTmfdGqXlPH33/9g4R0/iiaH8xfMzz4W2E5Z0qmykoIKcOrqghAbCqvH0Xs2bdpU67gR2px22mnrPQChDhO2FC2DnFZ82K99+/Zm3kOo9LzKsmXLo/Zjpk3UmehvTBh4mybpzTevMdhjsMlAHfMCzMtY5WQQSpho2p+tFdpd2tGsgeyaIZRDW8s+yf53zuw51rbTz9JPYGqFWUoqDHjRsGXcMGHChDh140B/wwdSTbvCfXCfmUwBw1gm3bHcJ/efro+inyd8MXXlhtat41QhNgzLli5zE77+2sbBjEnRpmdsnBuT1ooVK7rDaxye/V4zrkbQkIR+aOqUqdbPsKBKfU839s2L0F58++0UK4/UdmFDw5yCcO5XXXWVmRgF0BZBC6Z27dru/vvuN+Eei9OM4TeVue7GhD6idp3a2Vqr9Cdo/IT6TduL0B1tTeY3CDyZd3w57ksrL6CeJ2GMEM7HPGtjE+auqYsrvEuh78m0sBD6FN7R5HsX+h3OnZxnB9hOXeC87W6/PU4V/0mih50jG9ohNeCNnmg7OGEk/B4Ox6IOwiK1EGIwyZgvvvD169c3R881atSwfXD4/NNPP5kDZyIobL/ddubEEu/9wfM+DldxyBW9BPGZvDnCxnnX8ccfbw7yihUrZp7oowGYOXfDWVsI40wEEEI24oUecAp97rnn2nE45sS5XtR4mvf2AI66CI/IfUWdnjlJC3m54YYbzFlp1PCbs2nyyzacmCUhIg3pwdEXoX4J14iDTrzEL4/DWUYTRn/33Xfb9cgn1+R6ofzIC88Oj/JEbyHaQNR42bkpMxx2UpaU1y233JJ9XqIE4ASOiDE4I8tE1Ij6m2++2cIOE3mHa+Ng+8MhQ8wpJ8/1gAMOsPLEKz5553pcn/L7JXY6SMQEnLix77HHHmvPGSd4PBv25TzcB+ePJsaW75CvaIBsjnQ57xFHHOG7de9mz6hgwYLmbI76gNM1fnN8165dzRkdkX9CPaEuLU5EkyHvOAOlrp1wwgkWbrpCVH5vvPlmvIf3Tz75pIUBP/Oss8x5OM+XZyHExgZnkjgDpO4SMnh9WLxksb3X62rTiYTCe0NI47xMNBC2csbp47x58+LUtcEpMvvl1iF1KkShwyFrajS0TNA3cD36LvqTVBYuXGjPEEfI7JvpQ5ji1Ghf6SBU/XZRH5uTQ+o5c+ZYu0pfkxuH1L9H7Sx1kXaZqHpJHo76Ku6Pfj8d9G2nRnlhHyJ2pcL4Ihr42/ZH0jhk35DQD+Mcm2vRxybB8Tv3R5nQX6bCsbzDHEsUuiSMN0ivXLmyRchJhWdHfzt8+PA4RYgND+1Z56eesjEUY6bkeDcdK1ettHaMd5tgCldccYWNj6jLwbk6bQ79C+MznPfy+5NPPrExF/sRjW/mzJkZnRD/11m0aJGNdRnbfxHNfzLBHCiayOfaIXUq0QTfxu5EqMpNWdPWME8jmBDHprJ06VJ75rRN6fqb8GE74d/Xdc2vv/7aoqjm5JCauRvBHZhn5NYhNbz62qs2N3r22Wd9x6iPKFmypNW91D6VKGBVq1a1/o85Fk7TmROwL3OTJLTn1He2ETxoY8K1bokdaDNnTMK4hfeLeRHvUSph7sixyYhtQJ2iv+J+Q+TNJEOHDjUn1n379o1TxNbOFuOQOsAKOKt677zzjtkkRxNu88Vw3333ZauzoVLd/NJLTWKN9gfaMdg99+nTxz388MMmvcQu9NfoL6sTaPREld4cdyL5RHUQZ6KAdgfbkOyy6opNLyr8wTl29EK4Z555xnwisZLB+ZGQRo2BSYybNm1qq5YvvfySSZKRMqO1cuGFF5r0PcB9oRGE9hLXixoJMwvDnxArfdGLmb1SAtELbH8zgfQW1T0kvcl9uVfyGDXupjmEWiSSa0zigtYMKoJ/rfh/e2cCb1O1x/GFSHmvQQORiuZEqdCoRK+oaNAgqkeRNKBBSWl6zZTSaEjRHKFIEZXSpNKgSVGSeSghs/3W93/3uu17nHNd3MvV/X37nNyz9j57r3mt/3/913+tsHwcOXKEu+qqq9yxxx7rzm3a1PZ6c514sFqNBhlwgMu+U1YScvOzQdp4J3mEJRaadcz4W7dqZVp4VoR5NtZV+H6oVq2aOebEYoH4YpUDlCGOb5977jnbPsFKMD43MIkknPLGt4TvtG2F9pAaNWzFGMhHyoV6w8pWpV0rWZpYVeUdWGxRptQDwjp06ODuufseexbaf5ytUZeoVwEc9vXq1dP17fukhfft29fN9+Xf9tJLzQqJeoS/l3bt2rlBPr6s+OMnKqweC1GQ4ISWPgWoj+vCiDdHWDtoHDugTMenn33qbrrpJtf5xhts1bAoQ15B6KsLAqwY6WuxDurUqVMcmjtsCazh+8FMlp30qYyXjFP0g5k+9N/0Z5uCLf34wKos41vSApe8nv9HlmVoqrl8AP8l13XsaNu40m1xKF6suCtVMmt7dsGV3N+E7WSp5YHlEukjnf9KYwHACm3Yrpi6ZSdsG2XsZ8tZEvyBYHHBuBYcVwtREDCfvbRNG3NGjRVbsNrOxLcTvrW5YSs/D2Queb+fT10eH4rQvXt3s0hnzs8c8fHHHzNn8ziQZ64ZrC3ws4MvzqTVYFGCcYcPfWFBjT3IR2zRpQ9CNlmbHAJsd2auS1+1ystdqVBe7ABhXEk33oQP5Y/sV5Djam7QL7dv195kOyyjr/Jywb333msWODjeTjqaRr5AhnzhhefNpyz3BQuhs846K77rb8IWrY2Rth3icSd1ezljDhZQjDmpW50DWCQDeUFdCDCG8Z30pW4ZY6xt3769+WkKMpj4B+Mrca4UlOUQ2lc0kGiQAQ1wOMqXo36BI5bRCKPdZWUBreUtt9xi96BR5jeE+0Zt33+ZkqUlRYONdpv7wvGbrL5VqVIlh5bVT8rtHo7fBFZJmjRpYlr7cIwuoD1m9YN3J7n22mvt91jFQLAcYkXPNyQLg3AkJVYvgd69e1tYx44d45AsUi2HAA06YVi5BAsfrKB8w4969epl3wGttm/Qke+g7TtaY1ZzWV0gn9EYcxwz//rBJzrvvPPsuVj2BDg2GE35t99+G4esiRco7D2s7GLZRBkQXzTOPM8PDnZf8+bN7XvyWNOQbo75BSwhXh/+98rx0GFDrTy38Z+QDmAllXz9l68PUxLHGre55BI7ujhAfHh+o0aNslc3WLVFk84qLivqAdKdjAur39QRrMOwGGMFyw9kkRdG7L4XX3zR6sUWJbewuvrHgqzVmsGDB0cPPfSQ/S1EQfP9999bW+OI0bxCm7/wvxdaH5TpQHT6C+p+qvVmXlYV/4mwqk27Z6zywkwcuibrazmEVSsrjBxTn7rym1ues7p35JFHZrRWDP17Xj7cuzYKwnKI95J28u2B7g/EoVnhZ59zjoWHcTkds2fPNqtVVnNTYV6ARRHPyM36Nb8IY1qTs5rEIVk81KOHhWOpmwlWZ7kHi1fiHeD4ZMJZyU4yceJEs7BIjtlAvuWlLIVYH8Jcib4gN7C65r7kPGvxX4ujE/x8HOsjLA6wFu/QoYNZffNp165d1KpVK7NY57dYfXtB3Ky4iyKLFy+2OTByCDJPJjbEcggrR6z0kVmS5DbuIHsg69AnMXakg9+njjHpPnnpqwrKcoj5OnWxc+fOcUgWHa/raPUPeS0TYcwKMkMS0nTRRRfZ9VQr0vyGd2GlRT1JjoGEvzlihMWB8XpF4gj6JMwhuYdxMlhF2299+0bWPeqoo3LMZahrDRo0iK677ro4JAt+U1Tnh/8UCp3lkK9UtjIYTuVAGxtWyMJKIhY4/M0+VyxUnn/+ebMqYlXCC/9mkYP1CFrv/fbb1+2+W5bDO99RmJUI4NOH1Q4c37Eyccutt7rRb7/trrqqgzn5TNWYEy/weWP/AsfJok3lGUlwXka8OQIRyx608PwOnxB4rg9g/cJqZ/CPA6zI5JV0Wn2eiSb3lFNPtROMsIYhn5L3Eh8+aIDR+vM3ec6/5BsWVuQV1jWsNgNWPqxM428kE5QL2mlWELD4oWzII8oEh3WhHHkPeIHU/oVQ3mHPK5YQDU5qYH9zwkXHazua1vve++7L4RyO/dBo6vFXxNGKgA+ikW+9ZdZbqZD/pA3QoLN6y8pA0qk3VkYQ4oLFFfmAFRB1DcevWDixAsYKA1p4fn/E4UfY9dq1apsFGfmVemS4EPlFsi8CfHL5ATnH0cJ8JzyTNQnXvhj/ha34/O329m+wsqMdYYnoBwsL45n4+MJCgRWmogb9w+677255mptlYOjnwr+p8HvyP4wtQJ+DxSx9HiuS9JlcJ59ZvcT/EPDb0DcH8DnFOMKpQOkI/XtePpnivD7k9izqMHUpOJnmXlZlgXoZ+mDSOuWXX8znRbJ+p7YBnsOKKVYGqWB1w9iENWxyHC4oTjvtNLM8xocKYzxQlj98/7393bRpU/sXiDP5ENLjJ/ZmTctqdWi7XMM6COvbcHopMD4xvuIkntOjuI/3MA9g3A7vFmJDSG1rfPfCpNXVpF8awunXkocjhHac7As4gOaE+vVtHkabxPEx1irM4/hgUYQ1NvNEfodF/KWXXppvp3RtbtAvkxeMOVjQZyLkcaZ+F9kJC63k2EWZYR2PxTy7NZBL6EOYzw/wcgTWpsAcPPXQC+YIzP056ZQ4pgP5Izm+ZPpkinMqeb0vN9g5gf/SAHWUfEh9dp1j6pickGqpGSDPqK/IXlj7pkJ+ByseHHwXJMSdXSC0ScqYsSHwtZcDAWs8ZGRgPOS+MAfBXxLjCHOJ5Kmg7IKhvjD2BhmVvMPfGHOh22+/3cKYGzLePPbYY5u9g3KRnk2mHAIaaKiskKoEQalTxg8od99zj02gw4etUwwoVHwqKc9JhU4P6Fy5B5iUsT0Ik/QDD6xmghCNLN3vA/yeRpUOtjPRmaA4YlIbOhueF94J3IeChkZHpwvr0uklnxsgjIZ89llnuT69e5uDWjqKZH5yT27vYeLJ1qqvv/7aFCEoW3DSjBf/3KBTWLlyhXU+bCcL5YKSqVevXvZMI351Mi8yweCEKTHKPJR/6RzhcnIGyiUUUpQlW8PIf5y1BkJ6U9/JgJRa31LzFYGL8j711FPWSFdvn8ecHMSWO4Rl0ohgx2SdbTp0skLkJ7SJzp0724cTM5YsWeK++vord9fdd9n2V9pDgL6AdsDW0XSKnE8+/tgG8XSm0N99/50777zz7Bm0AbatYTqMAMokgRPOiuJEHUV66MvoGzMRtlrwL2NBkgV/LrAtqChy6GeBMYAtZPQx9EuYqjPZ5IOCrqMfn3bz49e8+fOsLHCM+fwLz2f30zfffLOFb6xjZP9c+KfVvdStT0mYcDMGMkFO19+P+3ScO+P0M0zoC/WTrcZtL2tr2wzCEcn06QggKM7ClisESRZ72A5O3uH8/44777BTydJN0lGycB99NYsiBQ31pEuXLnZIAYdmAMo9ygoFGIsHQLrbtGljyiS2wQNKMNos8WXBAdhygZDGmB7M9wnjdBj+ZT5C+ZN28hMF2VujRpmCSogNgXpJnXrkkUfMTQBtnnqJ83vrlxLK1jHvjbHxgfrLWAXUWcYK2nEI4xkcTEJbTJ6IJdKDUpitdoBiIxNh3KHvSFUi0QffcustNmfF7QOsXLnSdevWzcYe3sHCJn0IC5uMMcy7y/q+DDnltttvs7lCnyf72KI6fS/30nelU8gXBKRp4aKFlr5MMhrpnP/7fBufUsdeYOv9BRdeYAqdoCBi7oSig+1iQTGCXIBCHgUmi9xJyA8cqLdu3drmSeQhBgCpMPahUCNv2SZe0NDOOEmOPGBBCZjjIbOwoE1bBMqdOQNhwc0Kip9rrrnGFsBIG7DlEwUYjsgZp0JYy5YtzfUK6bv22mutHjA3JE9xu1HQJ7mKTYRvdLlSUNvKMFPHoXNyi5CvcPQAtrUJMLfnux+c7HsSzFb94GNbj3Am1rBhg/hKFmwJ8BNvMzn0E1fbCsX7MMHDNBtwxFy8eHEzYQXM6HAumdxW5hueOXwmHl4os7AA2wkwhWUbFmCiiZMzHMb6js3CwE/m7N1cIy7Qr18/e2ZetpWxxYsw3xiznzts2DDbwsaWKt8hWRjmpX6imr0di/cSF97rJ6YWlopv9PZsP8hHV7a70px1+44yvpoe4l6sWNY2t1QwBQ1O9M6/4Hx7dtKJIc5HCUs16QzbBdnWMn369Dh0TTA/5j4cfFO2992X03wTp3lcxzEc5qsBHEd7YSJ7yyIE0/0bbrjBvrOFDKeLdeset4Y5JvWJrXnkox+UIz/Q2FYyzF55Bk60qY9C5Bd/LVliWympX9RdthLRPnDk6QXg+K4sQjumvbPlJgnt2Q/o1gd5AT4OzYItQJgf89t0n2LFim2UrTmFldBHpDoMBj9pNfNxzN65hzENs3u2ygboJ8h38pFtqsB26WQep34Y98BPxsyUmzA/EbRxBif5jA0bo6+hD7zzjjttzCQO9I1eOMiRPiA+bOMuXiLrwAFM1cm3JJjy83u299J/BtjmjENU8pD+HAfMjAWMxUDdJc08l+19bKmiDWDez1a2dDA2soU49OsbA+YJbPPDeTllxvZNtrSHgxeAcZotyhwMkdx2QxofeOABa+vk4957723bzEL6mDMEZ73pPtQNL+jZvUJsCAMGDsyuVxy6QVtmXskWltR5IY6F6ddwph/ml9zz8oABVpc5YIQtldRdxhgc5WaCfoD3MB/n76LOGD+HpwyYx9K3JKFvxNUFbiS4h36VfiPpQJh7gmuN4HrCC/62nSqUb+qHbbp/xoezBFkMGYo+mXrAQSyZ5Ij8BlcU9PPEgTp24okn2kE+SZjzIBOV2jLL4Tlzn9RxmrEK5+jIdJMnT45Do+ijjz6y9HIQDjIXW+zYSjXs9WHxHZEdeMShO9Tjw2rWjLp37572YIEA9Zt5Gu46UsusoKC9sa2ZcYfyJj3Iqz8m3Jowfhx//PGWj8gsAX7L3I6xiuu0Y8bacAAC15EvQ/1I/fC8vB6iIQovmXQ8m0Q5tGr1KptI46MABUYgKEbCKTlhDyiCfbKysw+XSTgdFfuXeRZKjSRjPxhrvw0TRDpTvicrMwoGKnhQDrF/ledyH0qGAPssCaPhhEkr0DnxbjpSQDmEL56aviNJ3scJWfgHatasWRySWTkUOuWkQEZjJQxlDHGEcMrJQD+YB4455pg1lEMoLQjL1KkjAJxwQtbkmw/xWht0uCjk2OebFAJ4B3EcEQ9SF1x4gT0zKPuASQZhyX29r7zyip1ORscaTocDhCkm+kkmTJhg72USwSliv/32W3wli9yUQ/jDSPqSCoJfUFTh14hT6BgQH3zwwezJ0FLfuba86KJo8JAhlrc33XRT9jX2OSO0MUCjLBQiP5k1e7b1hwiT1C+UDamKS2ACQL+IEjMV7ucZTIhCvQ1wbdasWbbvnGenfghP9mVFDRRtTBrpR1NhAkjescBBXiHM83dyYQDY30/ZhIUBlATcm5rXfMjvZF/NZBSfbkyMKdtU5V5BQl0hPowjxA2lPWMcSqskpId0cy8f7kn1O0Q9e8+P1fTfqZAv9L2MZSzcpE6s//BpJv9oA/Tv5F0m5RhxZmxBCEp3UktBsmrlqmjKr1OiQYMH2QJJKO8kjOWMIctT2jBpJn+H+DGGBYxk+kgT41xqXQkf6mhRbqMi/1jh50zf+rbIQhlzNdpj6kJEYJmvc8xt6d+SUF/pE6jnzO0Yt9amVKD+8xvq88YSrAszjCEoGZizJpXpQP7Sv9K/kV/0G/wdFDuBX6dOtXE/KDS4nqkfod9Oji3IGSyG0h/RZ1M2vHdjgVI9dUxJHftID/eQftIQ8iMJcaYvpk9NjT91kmuMO8gd8+bnHLNY6EYO49rcPPjRCzLdu2PejUM2DqSLdKPYIq6p8w8g/1ikZ8ExCW2NuoScxTwjtZ2G/A/1JPWjBfHNn0KlHJr/+3xzpLX//vtnW/FQwS+++GJrXGi44Xs/GUZYJ4yVRVbmUNSw+hiOxWTSuN1225l1zA8TswQjJqIoeXAuHCajHNHOc1ASoGHHyTXPIQztaM+ePa1DurRNGwvr0qWLaa8ZIJmMsprHih+/A+J7SZtLzKk2E1uYNGmSHctOWFhdZSKN82LSkVRwcQwi72EFNEmLWOmDlj7wWezwFOVUGAD4HWFozskD8gYFBUoWnP2Rh+QteYwihYacCRxI8yysoPK6atPs/CyrIMoRR6ysoLNChAY+CA8oiriHASZAR0wYikAg71ihIowyADosOh3qXo+He1hYkuD0rX379mtMJIKDT5z1Jifn1B8UdHSAAVZUuPeSSy6JQ3y+tmtnYeTl+T6NlBMrB6zSMDghnLCSQr0InHzKyVa+qRYbQojNH/pHlP6MIaJwE8aT5PgphBCbGyxkMifHybco3KBgqVmzlsmnUm6KzYlCoxzC9A6lARY7eEVvHJupo9DAcgTBvHr16tknXr0y6BUTxgnnw4ogJxwELTLCPkI/1zArJL48n+0+SaUEGuZghsl7MY9HwcLz+P3TTz9t93GaAooB7sO0O6yavP3OO2Z2h2k7ChlOw8LyKWkVg1IEqxKsalA8NT//fLNoYuUZCydAqYQ2GsUV76DzR7GCdh9FRIUKFSwcZRSnYw0fPjyqV6+ehaGcate+vWmGUQihECMc6yUUNOG0N+KJ4uOMM880KxjCeAaWTulAM4zSLNUTfW5MnTo1aty4sT07fCg3VkZRymARFcqFfKI8MX/E7JEwVkQw2QyKHuKJ6THb2/hgBUV48jS2wPtjx1pepKaH8g7m9+QJZYG1BFZEoUxR9GCRhJKPfCIMyyoshYg3nXw4ZS18MJHmFDTgX0xUUQCinGrRooXVNUyshRD/THr06BHVOKRG9EnC8lAULrBwYmvKlVdeIUsaIcRmD9txD6haNRqaYkEvCg9YVbGQfMYZa546KkRhJ5OOpxj/8wJwRnBa1bVr1/jbhoOzyJ9//tmcdvHqFStWuEq77eamT5tm13EyvXTpUnMmWbFiRbuHExFwiuoboTm1w5GlF/bt/jHxKVkH1zjY3dj5Rjfxx4luh7I7uNqHH+722D3r9LIAzsc40WubbbaxZ3CaFY4jceS411572buJD44/ly1bZiczJB2P4ZSU30/w13faeWdXp06dHKdfwerVq93kyZPNidv06dPt1AEch+K0MjBlyhRzJkce8D7SUrlyZcsXHKvhLIz3ly1b1u6ZOXOmOR9bFTv65Jnc891337mffvrJ8unAAw80B244RibeOGqeOHGi/a5YsWJ2Dcdh5cuXt2ck4SQKTlTBQWryhLC1sdg/k3RyKgvPrV27tjnexgEazpqJb7I8SRPlT3r95N0ckOMQjeshjqE68p204zhu25TTA3AO/cILL7hXXnnFyjIwzdchHOryfPKVd+MMnPAS8QkJxAGn1osXLzZHiZw2R1z4DScMcB/34JwX59iUAekK+UYdnOTLd5mPM/kPtWrVctWrV7fnCyH+mbw54k3Xu1dv1+mGG9whNWrEoaIwwBwBx7j01TjUZVwRQojNnU8/+9R1va+ru+DCC13DDCdUik0D8gwOvjkJ+QY/L0DuEmJzIpOOZ6Mrh/IblEMcyXfMMUe7oUOHxaFibaDEAhQ0nBKGgogTJlCWFEZCNV24cKGrW7eundzSvn17CxNCiI0Bin2UwBvjiHSRd1j4QcmPgl8IIf5JsDDNYib9mxYhCw+cJjdv3jy3995728KyEJsbmXQ8m/Qo+/wAJQcfLEVE3sBS54477rCjGTtc1cH179/fjtwtrIqhSZMmWVyvvuZq1/KilmaRtLbj9oUQIr/BklGKocIHFrxSDAkh/olgAc/uBimGChc77bST7dSQYkj809islUOs4g4ZMsQtWrTIffHFl27o0KFuwYIF8VWRCbZv9evXz/Xp08d1f6C7u+SSS1zjxo3jq4UPttuxleyB+x9wo94a5e6//37bviaEEEIIIYQQQogNZ7NWDuHvBl8+t912m2vbtq3535k7d258VWQCPzpPP/20WQ+NHj3adenSxbaXFVbw7YQy675773Wfff65O/744+MrQgghhBBCCCGE2FA2e59DQgghhBBCCCGEEGLt/GN9DgkhhBBCCCGEEEKI9UfKISGEEEIIIYQQQogijJRDQgghhBBCCCGEEEUYKYeEEEIIIYQQQgghijBSDgkhhBBCCCGEEEIUYaQcEkIIIYQQQgghhCjCSDkkhBBCCCGEEEIIUYSRckgIIYQQQgghhBCiCCPlkBBCCCGEEEIIIUQRRsohIYQQQgghhBBCiCKMlENCCCGEEEIIIYQQRRgph4QQQgghhBBCCCGKMFIOCSGEEEIIIYQQQhRhpBwSQgghhBBCCCGEKMJIOSSEEEIIIYQQQghRhJFySAghhBBCCCGEEKIII+WQEEIIIYQQQgghRBFGyiEhhBBCCCGEEEKIIoyUQ0IIIYQQQgghhBBFGCmHhBBCCCGEEEIIIYowUg4JIYQQQgghhBBCFGGkHBJCCCGEEEIIIYQowkg5JIQQQgghhBBCCFGEkXJICCGEEEIIIYQQogiz0ZVDURTFf4mCYN78+W7y5Mlu9erVcUjBkq48CQvhayvvhQsXuq+//totXbo0DhH5AeW/atWq+Fv+kNe2m9f7xLqzIXmrctlwFixY4P7444/4298obwuOmbNmuhUrV8TfCj8bMvauTz0qiL5eCCGEEEWTYn4ykuts5JprrnFdu3aNv204KC4GDhzoZsyY4YoXL+5WrlxpE5tSpUq5XXfd1R1xxBHu0EMPdSVLlox/IfLK4sWL3QUXXOBeeeUV98m4T1zNw2rGVwoGqs7rr7/u3n//fbd8+XJXrFgxC6tYsaI788wz3e67727XP/roI7do0SIrbyayu5Qv7+ocd5x7/733XI8ePdxOO+3kBg0aZOW/LqBUuvfee13fp55yW5QoEYdm5rfffrP3fPjhh65Pnz5uq622iq/kP9Rr8qNEHuKVX1D+lMXo0aMtrZUqVXJ33nmn5XtuDBkyxL377rvZ99Eej/Pl07hxY/v+w8SJ7p6773Y//vijO+igg9xNN93kypUrZ9eSIDS3bdvWnXjiie7CCy+MQ8WGQpt66623rK2gRK1Zs6a79NJL89xexo4d655//jn3008/+Tq/tfURp556qttiiy3iO5x744033MiRI+NvOaFvvvXWW+3fosy4cePcXXfdZXW8fv36FjZlyhR33333uW+++cbKo2XLlq5u3bp2bW2MHz/edevWzX355ZfWVs877zx39jnnuFJpxr4ffvjB9e/f3/3111/WTqkTO++8s7v66qtzlGN+Qn/yzDPPuBdeeMH9+eefrkaNGu7iiy92tWrVWmufMmr0KDdk8JA1xnH6FtJK/7Djjjta2Hfffefuuece99nnn7ny5cq7Zs2a2Sf8lv5p8JDBrtP1ndw+++xjYYUR5jb333+/++qrr9xee+1l7Yx+NC+8/fbbbsCAAdbH/vvf/3YtWrRwJ598so0h6Vi2bJnl8Zh3x9h7999/f3f99dcX6JgmhBBCiH8OGXU8fpKZK37yGf+VP/gJZzRmzJjIT65RSkW77LJL1KpVq6hZ82aRnzRa2JVXXhlNnTo1/kX+Mn369GjevHnxt80bP6mOVq5cGX+LoiVLlkRXXXVVVK1atcgLK3FowfLbb79FTz75ZLT11ltb2Z155pnRxx9/HHkhxq7PnDkz6t27d1ShQgW77gWn6LPPPotGjx4d3XDDDRZWtWrV6Ndff7X714Vrr702Kl16y2j48NfjkMw89fRT0THHHGPv23vvva0eFiSvv/561PG6jlbXQ14UJJMmTYqaNGkSbb/99lGnTp0iL3hGy1csj69mZvbs2VZfyJfwoSxfffVVuz5nzpzo4IMPjk466SQLK1u2rLXVZL0L/O9//4vq1KljZS7yj379+kVeYIzaXtY2Gjp0aHTsscdGRx55ZDTVt7218dJLL0Xly5ePbrzxxmjkyJFR8+bNIy90R926dYu8oG73eEHTnpmsA8mPFzytbynKvPbaq5YPlEXg+++/jw466KDokEMOiV5++WVrdzvuuGP04osvxndk5seffor22msva1u1atWKihcvbp8ePXrEd/zN6tWro+uuu26NcmnXrl18R/6zfPny6KKLLrL3MC6TLv6m/b/yyivxXen5448/cq1P9erVi+bPn2/3/uTzoWbNmtG+++5r+bDlllvaPbfffrtdDzz66KNR7dq1o1GjRsUhhYsffvjBxjHiOHjwYJs3MeYNifvR3HjhhRd8Hu8a3XLLLdGIESOixo0bR1tssUX04IMPxnfkhHeddtpp0U477RTddNNN0ZdffmltWAghhBAir2TS8Wx05VDgmWeesUkgE6GFCxda2DvvvBNVrFjRwlEYMUHNby6//PJo+PDh8bfNF5QBZ5999hpKjgULFpgCDIFiY8HEFCGHcnvttdfi0L/5888/bTLL9SeeeCIOjaK5c+daeSN0ratyiPsRynhm8+bN4tD0rPb/IVSgpNpmm3+bcqigFTa9evWyuCFMNWzYMHr44YejGTNmxFfzl2nTp0WHHnpotMMOO0TDhg2LQ/NG125do6OPPjp6+umnrU0i/CLohrb33HPPmaBCeuCcc86JdtmlfPTJJx/b98DYsWNN2KUNi/zjey8IotxBmb5o0SILI49RAl566aX2PROTf54cVa5S2epfAKGcdrPNNttE48aNszDqzO677x7ddttt0YABA0z4RxGIooL3IIAWZb744gtTXtx///1xSBQtXbo0atmyZeSKFYs+Hz/ewlC2HXHEEdE+++yTq+IORdv5559vCrpp06ZFs2bNylb+HHfccdYvJvnm229MCXXnnXeaIqF///7RU089VWALKICCY9dKlaLeffpY+j/44ANTEBPHww8/PFu5k4633norKleunMV34MCBVp8GDRpkiwgoJlGiAXmIgquLr1/kA89Eick7UEilKjz4XeXKlU0pV5hgrD21USNToL333nsWxgIUbXbPPfeMfv/9dwtLx+TJky2vWrT4b3afO9PXh/38mLjzzjvbQkoS7kcBRZ/w5ptvxqFCCCGEEOtGoVMOIYAyCTz55JNNoRFo3bq1hSPAf/3113Fo/vDGG2/YhAurjs0ZrDZQclWpUqVQrBgyqUUootywbEgFoRaLIq4jIAQQBlAorI9yqI8XWkqUKGHPRGBInUSnY+LEiSa4kW8FrRxCaYd11IUXXmCWOChYqHv//e9/o48++sgEo/wCQbOYF1JR8KwLWH2hVOrbt28csibt27ePypQpY8IdBGuv8B0QflAwsfIt8hcUNuT3448/HodkCaMHHnigWSZgNZAJLA/47b333huHZMGzCG/btq19v++++3IobQMoVBHmUfwVVVi4OOWUU0zJGxYx4Auf77Rp6n0yHIVIujxPQtunD1ixYkUcktUWyesGDRqY5U2AsqavP/300zeawp/3YJX50EMPxSFZTJgwwRQg9DWTJk+OQ9cEC0IUWKmgJMJSOCiQyQesTJNWaSh+GBO4L3Vs+23atOiAAw6ITj311DikcIBCqHTp0qbYC9Z4cNddd63RdlN57LHH7J7UPvjWW2+18CuuuCIOycqvCy64wMLT5a8QQgghRF7JpOMpdKeV7bfffvYvjorxeRCYO3euGzFihPl36NW7l/nUwX9BEi+kmo8EL1Bl+0rwk027xn7+yy67zM2ePdt84Lz22mtu+ozpds1P0u0+fnP33Xe7wYMHm8+WJH4C6z788CN7zoIFf7hx4z51XqAyPxC//PJLfNffTJs2zT3//PPmqwP/NvhV8Plt10jb2A8+cF6Yt/vwEdO7d2/7G+bPn++GvDrE+Um2+TDAH0xwgjpv3jzzLfDoo4+a/xHSMWbMGPNFsXz5cjfu03Hu4YcfdvPnzbffDBs2zHkhz3yW4FOE3wO+MvAz8uabb5ovjQDPIJ/x5cM+RN6dLIdMhLStjXT3BV9F+ALyk2X34osvmk+qTJAuyop8OOSQQ9zPP/9s6Vgb+MnAB1BuccXh7NChQ823SPfu3d3Hn6xZz/LC1ltvbb5HnnrqactrL6i7atWq2bMPP/xwd9RRR1laqXfk+foS6iD+LfisC6+++qr77LPP3E1dbjIfF8SNOpWEdEDIM3wpFS9RPIefE9rMlltu6a7vdH0cIvID8hofWbSP3XbbLQ7N4sADD3TTp093XjCNQ3JCeYV+KfhuCeALjGfii4i67QcH17p16/hqFqtXr7a+7rDDDjP/KUUV8pe+s8V/W7h//etfcahzY99/38qnevXqrnTp0nGoM39QEPyspYO2Urt27RxtaLHvvxmH8DOz7bbbxqHO/EQ9+eST1r81aNDA+vZZs2bFVwsG6saV7dq5iy66KA7JYpdddnEHHHCA+cTZMhf/U506dXLNmzePv2VBfWQ83GGHHVydOnUsjHzAf1Ey/xgb6YNatWq1ho+rCv79+CNkPMtLf78xIF2Ml8SZdpX0xVS5cmXLy5deeikOyQm/xT8RFEvx4UQ6gbEt1KOn/HyhX79+rmnTpmvkrxBCCCFEfrDJlUNMnpKEydIee+yR7XAVx8NnnXWWe+ihh2yi1L9ff1e/Xn1zthuEWZQ3CDh9+/b1wmsJUwwxifriiy/s+ueff54t4KLwePbZZ71wlaWAQNGCA0yc+TLxxuHoKaec4t555x27zjMuv/xy17BhA3f99de5Ll1udiecUN+1adPGBHImat9++63dC59++qlNrIcPH25KDhw+MenH6SSTvTaXtnEnnXiiOfbt0qWLOf5lMkz6AGezbS9ta2liIlyvXj1TViCMoEBCoYCggpKEiSfCy8SJE921117rTm54srviiivc3HlzTcAjHxAq/vOf/5gSAQEEeDaKK/J10qRJFobTUQRFnKTOmTPHJqI4F0ZRxP3rC2WcyYEp1yhD4nL88cdb3p977rnmsJQ0pQOFBgqXs88+28oFcMKdm0IpL/A+6tCDvhxQiKG8qXvccfbvhoDjVdKFc26UfrfccosJ7JQT5YLSEsXMuiqhSC9KJ0DAoj1Q73h+UIpmAoUUTnRpZ7NmznJPPfWUOcSlDiWFWgQ5yj4oLmkLe+y+h/0OENJIF+/estSWFibyh798vqNULlOmTLaSLlChwi72b26KgtDf4eQ2Cc/bZpttrC+izqX2wUBfgGKKvgfHx0UR+tugMG3UuFEcmpWvjCdQtmzZHEqe8uXL27+UC3mYF+hr7rzjDhtHcPicBIV5lSpV7B20NfoM2il9YEGya8WKa9Q58gGFJMrtpAIrlXR9/a+//uomTJhgjtDT1Tegn+nZs6cdSkE/lAq/oz6yEMICTmEBZ+FAP59ku+22s3GasSoTjNEw1edPEpRoEOoRC1/dYqeR9L3MGy655BL7Nzn3EEIIIYTYIPxEN1cKelvZCSecYL5Y/MTTnE76yZSZrT/wwAN2H1uSjjrqKHOai/NKwCcD++75fXDa+Pbbb5vT1uAsE9N8L3jn2MbUpk0b+82QIUOyzb/9hNved0PnG+w74GeFrUD77bdf5Cd25pT31ddei/yE2Ez/2YrDM5719+222272zAsvvNB+u8Sng+uYf3vhwsIefuQRuydsn2Jbm5/gWVijRo3Mj4QXCszcHnNxtiuwnQfYcodPArbZBSfTmPfjAwM/IeQP78GvD2nBNwXP9RNGu5ftU8E56CM+HknwWXT33XfH36LotttvjzDnx68BTPzxR/tO/uTmp4ltZfih4B1+wmpm9GxJ4IOvHbatVK9e3a6zHSzAtjLymHC2Coz2ZUjeky7CWrRoEd+Zk3N8vM+/4Hz7m7oTnF3jJyM3qD+UAdvQUreVecEk8kKXvRufTYD/FZ6LPyUvSFtYfkEdZpsjdSW8Y10dpb/40ovZjsDZ1texY0fb2sB36stXX38V37km1P9p06ZFP/oyfnfMu7Z1ht/xYUtJgHyhnhzun3/HHXdYG+jSJcsHze++jeFMlvINUBeT22XE+kM7pL/Bv8gnn3wSh2Zt+/FCoZXVJW0uiUPXBB853MMWNC9kxqGROb3luWxzpO2mg76Esv4n+GdbX+hbqlU70Po/2kmAOt7Y99vkLX6DklBmhLM16vu1+MZh2xQOw0ObpQ/92vftSegT6Hvw+4M/HrYvce+RRx6Z3U9tLPAlxLvxd7Su4M8MZ9PptkHSF9F3Mxfg+fjE+vTTT+OrOWGM5B628yXr9KaCusDWeOJE/5iEOQnbEXcut3M0d15OP1KBsPXzsMMOy9H/M0cgnHLGwT/P4jsf8un666/PHnMZW5P9gxBCCCHE2ii028qwssF6BauRBx54wI6qZctQu3bt7DqWCmx/wPJmzz33tDBW1bC2AKxhWF1jBRyTdEzXWSlnZZOVtbCSC+FYcaw2wurm448/7rbffnt30okn2Xc455xznJ98mvUFW8JYEaxzzDFmybT1Vlu7nr16ukaNGrnzmjZ1N3TqZL9hGwGWJ+++846t6rLKiXUSFhlTfvnFVuunTp3q/ETPLHmIH2Gkg/exPejYY49125cta1uluAdYNWQFEmsOVkwhxJ8PpvekC1P/Y3wcw7a8AEfbYokEwfwdPvjgA7M8adKkiX3HMmTAyy/bc7CY6tWrlxv11lsWT19P8rxSzXY8VjLJOz5YPrCymmkV3U+uzfyessdKB2svtnORf8SR3ydhm8Un48a5i1pmbXmgfClnoCzDSuy6Qh177rnnbGWe7RPQsGFD1759e6t7HLefH7A6Tn5g0cW2OCwQsMxgO0pye0VemDxpstUJVtrZMshx0Fg6YFFFG7i5y83Z1iOpUHcqVKhgW4bqHFPHLNCwZKNusXqPVREQJ7a1tPV1CEs1/r755lvsWufOne1Ie/IMBg4caHWZD+1QbBhY+VGf2X5DuSSh3cDyZcsz1vkzzjjDHXzwwWaxcd1111mZ0rZpazyb/jT0iUmoM/369bO+mO2PRRXa6i+/TLE+kDJIsjy2wEy1rmEcAizzQhllguuMWWX9eEY/xhjS0Pf7wRIFsEzCUuSggw5yt99+u231Y4shfSPWkhuLlStXuC5dbjIr1zPPPDMOzRvUJ7Y+77vvvmmPog9jN/lMf0KfSJ/L3CAVxmLaAhZMjKWbGuIeyjy1LtAuSXu0OnJLl+Tcrhs47bTTLE9IK9vhGe8Yp9lODtQL5idffvmlfceimXkFlsRYDGOJhbU11r4hHkIIIYQQ68smVw6hhGGywwf/Dvi/wWwe5QCwrQrKlS9n/wbq169vWyMw2WZbFM/Zf//93csvv2xbdW6++Wbz8RKULJAqKDP5Z5LNhDNpEo7gHISisLUpKJN22HEHV3b7svY3nNesmSlvUFDhzwhBjEkiH0zB8ZXEBI9tN2zPYvILxAVFTMWKFe17eP7JDRvaVgKUEQjfHTp0MKVSuB5ITUsg9T5AkYUSgLz8+OOPLYytQAd5wTEo3DD7J64oRlAC4HsHhRRb5xAmUVzlBRRRbHV65JFH7IOPDL4HXxzpQAGR9C+BAIIijvwkX5OguEDRRF0hP+/3cfsp3haH76Yvv8qaRK8rbLGBpG8XFGukHd9PKPI2BPy/9H+mv237ws8GWw3ZcoDAR7xJV6pwsTYWxb6g2HYS4se/vAOhH+EhL/6igLQinLCdD0EtqQzkmeeff77lNwoH6hhbjlAeopBCcEagYUsMysmjjjzKttHhO0qsP/RJlCNKhFTBL5QrfUi6Ng/4QGGr6kknnWRbklC84h8qbEOhD033W9oXytIjjzzSnl9UoR3woc9OKufIs7DVbtmyZfZvIFkua1P20t5btmzpBg4YYMpZxi/6ehS9mUBpQp9KHFAmpVMMojRhqzRtEx99mT7UBcaavNC9+4O+Xiy0PiDpeykv0Lcy9jQ9r2naPCFv2VLH2I0fPBRh9PvpfPUwLrLFjvGJstnUEPfQ96b6jqNuEEZ8UfCkg/GGhaETTjjBvfD887ZQxlgQtsNXrXqAjY1s8wbG6zBX4b30s8C8Y/Lkyfa3EEIIIcT6kl6q2IgwuUW5wuook/BUYSVYMKxYnrVSG+BeJpoITqyCI6CiYMLaA4URgi4rnCh/MsFEnAkmz0hd5a1UqZL9mxrusiy7s/70IAQgKARlzcwZMywNxAPrJ5QAV111lbvyyitNcZJUPtiqYholD0oVrD9Qztx4441+glg1o3VAkqBQSyVYD/E8/CChdMHvUqNTT43vcG7BH39YXiCgoJAi3nxIA9YzKN/WF9KYl/gHSAdKKgTi5O8QehAeUPghwJKO6dOmuUq77mrWVkzEez7RM7573QiT7+CwOz8gPviZwo8RQl3LFi3Nsgf/VihX+KAApHzXh7KxwJFah6hje++9t1kVrasAhSKRFfzcVqF//uVnU5hRr3FQC9RZ2iPKpXbt21kZ9nnySbsm1g/6FoRKyjBYDQZCfV2bPyCsCbHiwjE9/SPKTsoGxUQmC5C3Rr5l70NQLcqsWp3VBtK1hTA+oKRIgoIdGJ/WRbFGOV1xZZYFXvDvlQn6OhyFByvQVIgv9YP+MrcPlqN5UR4PGDjAFBg9evRIa/mzNhjDsfTBQf/awNItOEcnfqlsWXpLq7+MsalzhU1F8A/EQRJJaLcsQKHMyU3xf9xxx5nVKm0UhRgWpYzZtG0siwArT0idMxx51FF2L+9ifBdCCCGE2BAKx+wqF8KkiNXHpKKAiSFKISbpKJaYmKFQGDR4sG1vQcmBI2scHWcSkLHoQak0Y+aMNSbkYeLJlif4+90oYHIqYZiMs6LJZG4XH18maazUp0K8xo8fH39LDyv9KGewnsLihlVU4phOiZROGZQuDLDGIY5sF2FlEqsrnHsGtvNCKCuRTFBTJ+XEGQeguSkM8huELKyHksq0IUOG2GosQgor2JymxueOO+7I3lqGBcv6rKCGekY6UxVZlBtKnnWB7TucZIRDb06lY/WXFX+2jARrjqS12vqANRgKmVTn0wgLlCX/ZlqxzgSr3PwWIS0dKEvvvONOsxBCEQQowVDIkkYEJaxdqF+zvACaeuqfyDuUBUpFFKEzZ+XcQkOZU04oCtYGyvcaNWqYJSXtHys2tgLyPRX6mTfefMMUhFgOFWXKbF3G+lMULYw1AcLCiVtYYSWVNKEtkrdrU9ylsteee9m4g8VXbnAPccAaM52CBGtULGdRBLLdKNOH8QXrsdygH7vrzrvsWSgxAqnKykzQl6IEP/KoI92+++wbh+YO22SxTgoHUiSZO2eulQX9zLr2bQUB5RDyJbUfnu7HUdKPVdDaYCw49LDDbN7CtnoWrrDEPOSQQ+067ReYByUVemW23tr6W/KDLYhCCCGEEBvCplMOxUqM1JWwVBCuAYuRsPUHEHBQFHCkM5NphHfM5EuVLOlOP/10UySgWPh8/Odu0eKsFbUwkWZyxYSeSeYxRx/tlvy1xLbgJGHSz8pvcjsVE0F+mxQUUCzxYVLH9p5wBC0TPJQwvAfhmaPrmZBnCww++aQ7VRHBiWkoYcJkEMUW27xCPjEpJx6khVVr/kZ44T7+zpSXKLk4BQzFD1vc8AuTFCxQNFTy+cWklJXLINTz77333WdxSOefJJCajlQyxYvQ4j7exD2AiTwrzQg/Ydsbyj/89GB1g7CbSvDdgGIoN18cyfckIX8QSFDqocwJZcx2CMoynaCSG/j8odzZ4kXdoh6iwEKBidCfH2A9gJKGtpD0z4H1E8pOLD/CNg6+45sipAslD9sxk4pTypCtHSgcUEqmg5VttrNwSk7ww4JSiO0VyfrMe6hf6YRXkTeoq9R3+GDsB9l5Sz+I5QcKiGDRR36jDKfdZOLVV181IR8/JSig05UN7Z0thVguZmorRQX6AyzwUM4l+3ygfbD1mC2hwWKD+o9vHcaN5HZm2h5lk7RCZexK7RN/mTLFlfHvTPp54repZco7iBPWqQUJaUOJyAdfN/RnjH/UDyxaV8X1MZxEtqaVbZZl1WuvvWbjaLp+m7Eu9Xf410GxnS595AVlwUJHsNjZ1JA3jB/0+eFkMuI4wZc5MO4G6KuZW2RaaHn2uWfNSou008cGqIfMdT4YO9byO0B7pS7RZ69NqSiEEEIIsVb8BDVXCuq0shs6dWJmbKdHeWEnDl2TpUuXRn5yZfeef/75dgqZn6RGl19+ebTPPvtEXuC1+1588cWoXPly0fA3sk7X8cKTPZsTqPzkycI6d+5sz+G0jyuvvNJO4vl8/Hg7tWfbbbe1k2Pgu+++sxOtuId3Ae/1AoH9nlOAYNXqVVGH9h2ibbbZJho1apSFcX+Ts86y+/wE104yOeOMMyI/kY1uuinrlCc/sbdTurxwnX26WoBTyvjtMcccY6eLnXPOOXYfn9NPPz3q27evpc0LhXbfVb58OKXKTzrt5LI6depYOKebpMLpWGXKlImqVKkSLVy4MA79m969e9tJafy+Ro0akRdM7VQ0njljxoz4rjWZOnWqnajE77p37x6H/s2kyZPsBBquE9cAp5VxqtbWW28d+QmxhXECjResLJzT0gLkBb8fOXJkHLImdevWtXuqVq2a9rQbTsopv0v5aPvtt1/jZDA/mY9atmxpv/fCndU5vnOq3O233x7flXe8UGcn0xU0XuC3OFNP/opPYPPCnJ265oUQ+86JY82bN48qVqwYDRw40MLefuft6IADDrCTyLxAEr377rtR27ZtrV5NnDjR7kll0qRJdvLVs88+G4f8DafU7bjjjtG4ceOsftImGjVuFF8V68uSpUuiVq1bRWW3L2t12guVUbt27exUx2HDhsV3RdH7779v5UsfQV+VxAvfdsKUF6ijpk2b2gmMmRg0eFC0ZenSGU+LKkow9lx00UV2Wtl7770Xh/4NfTfjxv/iU6o4zYvTqS699FI7UQ7o4ziRctddd40+/vhjC/v+++/tNMtWrVpZP83JhZzeSdvq3v2B7N/S5/JbTqXq2q2rtdEePXrYyYb9+vWzewoKTqvbZZddou222y4688wzrV6ddtppNp5Rz2jvwFhGHBkvGV9SGTRokJ22SdxTmTNnjvU/nNTJCXrkw9ChQ6Nq1atlj5WpMEbT37Vu3ToOKRy88OILNo7ddddd9p1+mbrRoUOH7JNRf//996h+/fo2b+HUtSSMP5zySRulr549e3Z85W840Y13MDZxPx/6bE7G4zQ7IYQQQoi8kknHs9GVQxMmTDBlCQoZFBUIkSgiOMY+EwjyTAZRtlSrVs0UDRyhjUAU4Gh5rleqVMkmsVznGNggIMPnn38e7bPvPqYA4XlBQfDWqFEmKBOnWrVq2d8ce7948WK7DkE5VLx4cTu+nkneSSedFO219942sU/CpB7FSsmSJW0iy28Q6Jgk/vDDD3bEPhNHJnooX55++un4l3Ec/eSR3yFQcNTtNddcYwIK7wsKh65du1p6iTPCOhNPFGEoPggnrqnxQknAuzkGNx3EDyUMAg7vIw7kB2WWFi/DPPrYo1H1g6pbWfIh/xF8EICgV69eVr4IGaQXgaNJkybZ5fLaa6+ZsEO8Oc4XxU7jxo2jb77NOrYfUNIhYJT2Qiv3BkVSgLSjVEM5QRx4DxNmlF0B4kl9QPGDYF27du2of//+8dUsZs6aZXEP5UYedL7xxmhprFxcFxCuOXoaheTaPp06dYoee+wxK5/1gTKjTnKc/X9OPNHKOAiigMKS47JJTziGmqO5K+1WydJJ2VBG5CEKoHQgiKCAuvDCC+OQnFAvOVIZ4ZFj8VEWplNQinUHhSmKSvol2gh9YFBkB8hryhLlXujXvvnmm+iqq66yNkx979mzZ44+LRWUEtxf09+PUkNE0YuxMuKWW26JQ3LSp08fKxfGGxQkKANQmAQQ8mmXSQUTiw8o6Hkuihb6ahYsUKTQzgIsapzbNGthhMUBxgrGNhYigsKhIGCxhnpUokQJ60sZv5Ifxs+PPv7I7qU+0SfTNwfFc4D6xDhIv4uCMpU5c+danpE++gv6IBT8zz//fLaCLAlhzEeYM4wYOSIOLTzQhzN+0dZY3Lj22mtzLMIsWLDAwhn7OaoeqAuXXXaZ5RG/6+v754WLFtm1VCjzJ3o+YWMh9zMfOPbYY6PPx38e3yGEEEIIkTcy6XiK8T8/OcsITonx65Jf+AmebbVii5KfaFqYnxDb97BNJR1Ek+0ymOf7CattqeDfANuteC4fzLt5Fj5fkvfAX0uWmPNl/A35CXscmmXejj8YzPjZYoUfo+R1zOMx91+y5C83YMBAM+WGqgce6P6d5vQW4ssWLczMK1ep7CpWqGjP4z38lq1FfMeknrQnT+tiqw/bv4gjvlsw52frGE5Ok1uSONGlTJkytv2B94V8Dc/lXrb7JOGoXJ6bm78G0sp9PHefffdxpUr+HbdUSAtpCu/hb+JC/lO+xD3EJaSfOsB14gorV620Y9nJL8zzyftQNwCnnvyW+6krPCuZX7yPeHBPpjoV4hHiyXf+Tj4HeBZm+8SFbYLBF9G6wta0jh075lqnA8SLbQE4DPaCQxy6bvzu6/T3vsx23nln216QrCeADyfqFPWVbXzAdhi2ONBGqPPElTxMB9shiF/rS1q7nXZMf6w/Wz7w2cQWQE4180JzfEXkB9OmT3NTf51q+Uq/kIR2Rb+H77Dyvp8A6jB9WsVdd3VVfLtaW13kGWzp4T7ekakuFCXYvtWkSROr2+RN6LOSsNWKbVVsGU36SAvMmDnTxhy2BoXfL/hzgfvpx5+s7dPPsOUqta8G+kr6Yt5BG+W+ZN9YEPBO4pVb+Yf+HBiH6F8OqFo1xRufs9PQ6F/YhpcOTlz8ceJEG7vo+9kqli4fgDEex92MXanbwAsL9KkTvplg431wWp6EesT4yjZ0YL6Bw276bMa9vIwXoa9nPsDv0tVJIYQQQojcyKTj2ejKoc2VpHIIPzR77JHlqFqIdKAgQTGYF0GOJogiDuFIE30hChf4+MFvDD5g2rRpE4eKjc3jjz9uhxHg92x9T3gUQgghhBCZdTwFuwT5DyJYPC1dusytXLnxTu0SmyecHFOjRg1bMV/bh5PBsAqQYkiIwgcnk3GYAI6CR741Mg4VGxNOjHzkkUfcXXffLcWQEEIIIUQBIeVQHmBLDauWnBDEqV2cQsZ2DSGEEP98mjZt6nr36e36PtnXffzJJ3Go2BhwEmn//v0t/xudemocKoQQQggh8hsph/IA/h7Y2//kk0+6vn37mrUHVkRCCCGKBocecqjr3r272z2NXyFRcDD2PvPMM652rdpxiBBCCCGEKAjkc0gIIYQQQgghhBCiCCCfQ0IIIYQQQgghhBBiDfLVcuiTTz5xs2fPlmNdIYQQQgghhBBCiHyAU7Dr16+fL7qWTDqefFUO1atXz40ePTr+JoQQQgghhBBCCCE2hNKlS7v58+e7rbbaKg5ZfzaKcmjChAnu999/N62WEEIIIYQQQgghhNgw0LHUrl07X3QtG0U5JIQQQgghhBBCCCEKJ5l0PDLxEUIIIYQQQgghhCjCSDkkhBBCCCGEEEIIUYSRckgIIYQQQgghhBCiCCPlkBBCCCGEEEIIIUQRRsohIYQQQgghhBBCiCKMlENCCCGEEEIIIYQQRZjNUjm0fPlyt2LFivhbZriHewsLURS5lStXxt/yh/x+nlg/KNulS5fG3zIT7uPfwgj1qbDG7Z8C+Zvsl/ie13bMfZtrm1+XdG6ukMa89ANCCCGEEEIUNkrc4on/TsuIESPcf/7zn/hbwbF69Wr3zjvvuC222MJtu+22cWhO5s+f74YNG+buv/9+V6ZMGbfXXnvFV3Lyxx9/uDfeeMN1u7+bK1GihNtvv/3iK5sOhIb//e9/bvjw4e7AAw90//73v+Mr688nn3ziOnfubGncd99949DNh4kTJ7r333/fjRw50k2YMMGEKvKldOnS8R1ZfP/99+7rr79206dPd1OnTnW//vqr/Ttv3jz3119/uS233NKVKlUqvnvjsmrVKvfll1+6xx5/zL399tuuVq1aFp9UqN+koWevnlYHateuvUY6NzVjx451Xbp0cVtvvXXGtlUQfPfdd1YHRowc4T779DO3YMECt8suu9i/1PE99tjDjRs3zupLqAO//PKL/bvNNtsUunzMBHVgzJgx7uGHH3Z9+vSxelOhQgX30EMPuXfffddVr17d8j4d06ZNcwMGDHBPPPGEK1eunNt1113jK5sHtO0bbrjByvOggw7abMosryxbtszaz8MP9/BpHOeOO+64+IoQQgghhBCFi4w6nmgtXH311fFfBYsX9qID9t8/8oJTHJKTJUuWRF64iLzQiFlD9Oijj8ZXcuIn6dGtt94aeeHJ7rvvvvviK5uWpcuWWtx32GGH6J133olDN4x77rnH0njppZfGIZsHf/zxR3TzzTdH++67b3TKKadErVq3js46++zogAMOiI6vVy/6+JNP4juz8AJl1KZNm2iPPfaIypcvH1WuXDk65phjooMPPjjac889o+OPPz4aOXJkfPfG5e23346OPPJIK4datWpFs2bNiq/k5IMPP4jq1Klj91WrVi367bff4iuFh1CfLrvssjikYJk+fXrU2pd9KM/LL7/c3k15kqe1a9eOWrZsafe+/vrr0YUXXhiVK1fO6sChhx5qfcC8efPs+ubAgAEDoqOPPjp6/InHo3PPPdfymjpRsmTJaLfddovGjRsX35kT+sarrroqKlWqlN3LczY35syZE5UoUSLaa6+9ogkTJsSh/xxefvnlqGrVqlamxx57bBwqhBBCCCFE4SOTjqfQKIf69++fLWD/9ddfcejfrF69OlqwYEHUuXNnu++xxx6Lr+SE+/7888/of//7n93XtWvX+MqmZ9SoUSZELFm6JA7JOwMGDojee++9+FsWKCJ69+4d/fDDD3FI4WfmzJkm/BcvXjx64IEHrKxXLF9un3GfjosOOuigaOutt7b6kGTGjBnRiSeeaGV6++23WxnPmj07uvHGGy1syy23jPr27RvfvfFAGYlQX6ZMmeiwww6LZvs4pWO5T98XX3xhykGESBQjm5Kff/45evDBB+NvWaCw6tOnTzRp8qQ4pOD47rvvTMFDPSAe1INVq1bZZ/78+aY8pFzPOuus+BdR9M0330T77LOPhT/99NN27+YC9WL77bePOnbsaN9///336LzzzrO2MHDgwOjVV1+1OpKOFStWRAsXLrT6j3Jo0KBB8ZXNh1WrV0WvvfZaNHTY0GjFyhVx6Lrx/PPPRx9++GH8rXCxcuVK67Mon7p168ahQgghhBBCFD4y6XgKhc8hthz07NnT/mbbwQcffGB/JylWrJhtIVnbdgruY2tSpUqV4pDCgxcEXZMmTVzpLddtSwXbZ+65+x7bRpVk5513dhdddJHzAnMcUrhhWw3blkaPHu2uuuoq165dO7fVVlu5LUqWtM9hhx7m7r33XucFLNehQwfbhhXYYYcd3E477WR/Uw8o4539d7bVkads67jnnnvy5IsqP2E7W5UqVda6rY00Va5c2bac+XYXh246yOdPP/00/pZFxYoVXcuWLV2VylXikIJh4cKF7pprrnGfffaZbbW88sorrR4UL17cPttvv71jt2uLFi3c4sWL4185t91227myZcva3+Q5924uTPl1ivv999/dzuV2tu+k5amnnjKTzjPOOMOdeuqpVkfSwVZbttGyBa0w1J31oXix4u6UU05xJzc82W1RYos4NO/Mmj3Lde3a1f32229xSOGCrb2MTfQDm2sZCSGEEEKIok2hkK5QBjHpP/zww+37o48+av+mIwiEKIEAQXPWrFlrTMjD9XA/QubMmTPTTtwXLVrk5syZY38vXLTQ/fnnn/Z3ktmzZ5ufE3zMrC/8dsaMGabISIU0TJo0aY1r+NTp1KmT+VxJ58vmz4V/Zsc9FZzekq/p3gc4h8WXCUobIH8yOVNFMTV58mT7zdy5c3MoYfi9lYH/LzfwA9WrVy/zJ3P22Wdnl1GSE044wZRovCNV2ZPufnyXHHbYYfY3SrRVcVrWF+oHfm3wd7M28IFFneA3uSkq8IH1888/Wz6lu49wyon38jfvXpvj3iVLllid5N2L/1psiocAYZTHTz/9lPY5ffv2tXJA6ZAKaeKTDsqCPM5Un6g7c+amr4tJ8BvGBwXPBRdcEIeuCQpEyjedU/l0dWFdII+m+LKbPWd2HJIe8pVyyQT9T2h/5DX38uxUvv3mW/u3VMm/lYgog1AqkK/0C+nSSRj9Av1AuvIKUG+mTJli5Z4K8SGO9HNAnFMVzalQzjNmzkiblgBxxv9XXiEt/CZVgUsY6QPiSL1OQnxv7Hyj+/zzzzP6KqJOUt/T9d2h36V8eHem/nJt8Fv6wEz1gTq5ofVSCCGEEEKITUWhUA493ONhV7duXbMWwCHre++957744ov4anoQBrgfq5mqVavaqjRCVCoIJHfddZfdhyNoHC/h4BimTZ9mCgiUC1gw3HfffW6vPfdyJ9Svn/2sz8ePd6effrqrf0J9d+yxx7qaNWu6N998055722232TNxJHvooYc6nMwCwk2bNm0sXlgF4Gx5yJAh7sQTT7RnIcQEiMspp57i6tWrZ9dw1nrrrbfaNYR0LIOeffZZ+46wjPIEawOElOuuu87tu8++7vHHH7frAYSf66+/3p6JRQLxwDojKBAQoIhrnTp13DnnnOO+/fZbd9ppp9l91apVcwMHDrT7AIGffKZ8mjZtannFB0VSACsUHGJj3ZQbQ4cONWGT92RyeIxwFZSECIPpyjQJ9QDHvnDAAQe4LbywvT7wHJz9kr/kGY6lTz755Oy6koTyO+uss1x9X0/OPPNMd9JJJ5mwjaCfBGG9WbNmlnfhfoTUpJCPoHnO2WfbOxv691FPsahCIE4HdQsHxjVr1TSl4SOPPGJ1gLr51VdfWb0g3qSjcePGVi7U66DUpK60b9/eBGUUNMccc4y74447rI5effXVVrZYtCShrKlPONmlnVFHsfxB6RWgXM877zxX9YCqbuRbb8Wh6endp7f9S74Ea7B0UEdol6n5uiGgREE5duSRR7pGPn9qHlbTLM+w7EmCwhoFJmVLXh588MHu+eefj69mtduOHTtae8HqcdDgwe6QGjXc/vvv7xo1apRt4TLe9x+UAxZu0L17d3s3ZYyygWfSTmlbKGQCS5ctdbfffrvdS75SH2k/qQoiVDcvvPCCO/roo60foR/i/bQbygQn/+eee679/v2xY63Pov3hpJ/yJj+SvPzyS1nP8uV8eO3DrS599/138dUsyBvyhLw56qijrL3iWD4TKDuffvppq6P//e9/sxVYxI20UQexJqQekn98qF/0sbTLSy65xD3zzDOWnmuvvdbS8lZcx1AqUUeIB3GmD6CfRHnKNfIXZXPz5s3tGfTfxLd169ZW/yg/6vP5559vCmno16+fO+SQQyw8tAXiT95SV/gd7xs1apRdE0IIIYQQ4h+Bn3DnSkH7HPLCkzkaxp+OF5bMMS3R8oJvfEdOvABv1/mNF5wiL1Sbg1rCcGA7Z84cu89P8C2sUqVK0RFHHGH3VaxY0cKqV69uPj/wr+IFXgvbfffd7d1c49k4TZ04caI5PL733nvtmfi8wG8IPnHwH+OFl8gLO/Z7L0RZ/ANvvPGGPfPTTz+NPv/8c3Om6wU7ezZpBnwoeUEu2mqrrexdM2bMyHZaPGTIEPOpgp8V7iEM3yxeKI28EGmOu4P/FS8w2fPACzjmm4R3f/3119HSpUujW269xe7zwpz5PhkzZozFt0SJ4tE222wT1a9XL2rRokV05pln2n34gvn111/teaTZC/DRRx99ZN95L+n3wqd9By/o2u+8QBeHpMcLVHYfznhzg3fivJa4eSHQwshrL8DZ772AbT4+pkyZEt11110WtuOOO5rflvXBC8jm7Jx08W7Ax1PZsmXNsXlIO5DuAw88MKpfv77lNf5xzjjjDIsD9Y/8BcrIC5hWpyhXfCThY4b7qGPTY59DF1xwgfmdoT7iXPkCX0+ok/ydjueeey7ygrk9p0KFCvb8GjVqWDxfHz7c/J1UqVIl+uqrr6Jp06ZZ3efe4LAbn0933nmn1UXqAHVk8uTJ0S233GLP4d7bbrvN7gXiRbnhC4pnLlu+LLruuuvsvoYNG0aLFy+2+6irvKtYsWLmQycTy3w54neJ3+MAe10gPV6wt9++//77cei6gbN6HMMPHTo0WuXL/aoOHex5lEEAJ+P//ve/o4svvjhatGiROYTGyTD33X///ZZm2sHee+9lYThZPuWUU6IrrrjCnKQThm80WLhwofUV9KOE41j6iy+/tDwfO3Zs1KxZMwvHGTttG8jjdu3bWf0fMWKEhfXs1dPKDN9aSZ9DPXv2NGfW9J+0dZ7P86hDn332mZXVtttuY+2p5mGHRU2aNDHn7vSZxYsXNz9AgYceesjaEXUM8O3Fsw7zv+PZMHr0aPstPp9ok/ShlDn9JPUjHaTh7LPPtmfRt9BuyVecjdM/E46vOfroS+K48czgePvHH3+MmjZtaveR/6Tr9z/+sD6AZxx99FG+vf1q9YM6zX3kA47sTz/99Kh06dJWnowX1B/6M5yZU87kJ306/uAC9Ac4Dr/yyivt+7vvvms+xegf6eOH+3bGO2gXvDPAQQP/+te/LO+FEEIIIYQorGTS8Wxy5RAOWlGIMCEHhC4m3ggkCBGpBOUQQg7COeCklAk/4Y8//riFBeUQAgcCMaCoCQoihAz40gtqfEdI+fbbb+2ZhCEENG/e3AT5jz/+2IR9hBSEQO4PJzoRhuDAyVs42Q2gbOAUMYTmAO9AkENYhJ9++snCUNpwGhsgpPP8u+++275Dq1atLAyhJMkjjzxi4cHJLeCUmTAcVQeIQ1DMPNC9u4Wh/NpzzyqmmHpzxJsWBjUOqWFC6ajRWcISDp8ReBAKA40bN86RVhQdCHJBoZQO8hUFA3EgPbmB0Ea8uPfFF1+0sKRyCCUGQh9lg+BHOaFECnVoXUExx3MvuuiiHE6BUXYRfqavayEcgZF3JhVG/E1YzZo1s5WTCOWlSpaM3k7kG0rCbbfd1hxSozCCvfbey8om1FEE37Zt22Z/TwfKCuJFnafuo3iizqK4QghGORjo1q2b3fvUU0/FIVknK3HyFQrBJKE+oRQIIEQjqCcdwKMQQwnFvTgVD6DI5FQxFCKZoE2TB/w2k1P5TGyocghFGPUq2V5QIKDY4LkBhHsUSLTtAApV2sV2221n9QUeeOB+iwuKwlBe773/XlSyVEkr02XLllkYkFbuRQGTCvmx//77ZzuWR7GMIqhLly72HajbxBGHx0E5RF6iqETBiLKJevHBBx9YvaB8KWc466wm9u7rO12f3R+hACTsmmuzFLr8HsUQypYAbZw2huKKtPCh/9t7773tfj7kBfdkSlsA5RJxQjmEM/QASnN+26FDh2wFFMpfwoKSkrS3b9/ewlDqBagD5BNKRvogyoA4oPSin//tt98svfS5hKHI4jsO5FHM81zGEZ6bPP2S9kxbps8HxgriHsYW+gIU8yjgkwoxKYeEEEIIIcTmQCYdzybdVuaFGzd8+HAz6V+6bJn5m2B7DU5ncZbL9rJMNGjQwJwUgxeanBcy7G8v0Nm/bE8Ctj+UK1fO/mZbAA53wQti9m/w48OWBxwG80wv7NjviRvbe9i6wxYutld5Acauh9+x9YUtWTwvbDNgS4kX2m3LUdLHDM5Kk3jhwnkBznkB2y1bvtw980x/N2TIYLvmhRj7F7wQY//68rJ/A14Qif/Kgm0qXrCxvw859BD7F4gD29PgrZEj3Qr/Lnx3lCq1pTm1rntcXbsGbFFiK8eypVl+ZdimQblQRmz7YKsb+UFeBfAhRFor5eIEnHxlyyAknQynwwtfln7u9wJ5HPo3lBVbcfB3wr1s8+B7KPN1JWyJ2W///XI4BWYrCuU8/rPPrHyJN1vPqEeUXYCtYDhUpnyIA/mH36xqvp5U2XPP+K6suoJjYe4LZVm7Vm03duxY16BhA/fSSy9ZHKhnwfFyOkI9ot6zBQfn3NTJPfbYw7aKPfjgg7aVhy0xbJEB6kYgvD/EIeAF7fivv2E7JGWw9957xyHOHEazPRNee+01+xeoK7TL1HqZhDINbWJtfm/ym5G+7tOW2EoUYPsQ20RffPFF+/7dd99ZH0L+J7c+1qhRw7ZhspWObZ1QpkxWOmlroY8pu31ZV2GXCuYLLJm/4e+cOZ5FKM9Qf1999VWr//V935WE/E0+c6rvZ9gOSD968803uxtuuME9/PDDFm+2joV8Jp48u97x9bLDdtttNytv2jawxYu+jq1UAeo15c92NuJIvkycONH6QBzL33jjjfbe0GeSt6l1KsD7U/s/7t2i5BZ2jS1hoU+lTkOIG2TnX+L5Y8aMse+UK/2zH+Rsexrtk7xiGy3PJl74t2L7Guln6yR1mmtXXH65PWvQoEHZ224HDx5s9+AAHC7wY8vrr79u2wzpB2699ZZsX0uhbxZCCCGEEGJzZ5Mqh0aNHmX+W/ChcoQXdI866ihTtAQlAr40Fi3+W0BIglIgSe3ate3fIHAy8YdM9yEIMLEvXiIrC/BrEn4D+HbhWUcccYR78sknzYEvigEEJRRX3bp1i+90rm3btvYvAiYCzQcffmBpwDdGklTfKQhnCFUINEf7tL///lgvhO9j8cgkZOUGCgFOe4OtSm9l/wbwMQIINSjieEcyvYHUk4RQAqAkwVcOwiDCI0qwVEEvL1SKFSrkazrHsQH88FBuKJ2CgJYE4feyyy4z3yKU4d13321C8vry7rvv2r+pp8ihEKEc5/r4EmecT6P42XbbbXM4xk3mI3/jfBpBm/tQGgXS5Tl+a8jTL8Z/Yf6fTqh/gr0n08lVUKx41jOoT0HYB/7GRw2CLIIwylWUGnkl+SygjdA+qaehTQb23HNPu586xyev7LjjjpYvgKCNomFj8cMP3+dQugJ5SFpQlgB+m/CJkyxfQOEV6iLlC7407d8knMpl+bgOzTco5agb+ILCTxGUTnFAH8on1KEZsWNkfPLgRwnfR3xQ9ODAHn9n9COpdQ6y+7s4nj/99KO1ueS9vA+FY1AE0yfOnTvXfBrhs4x30S+iRMc/GH6U0r0rkNr/AXlIHEl3IN19qfCbib7+7LTTTu6mm26y9NM/P/fcc6ZsReGHP7gAaUn33IN9+6h73HHmw4g+hH4OxT6KoJDfO5Qta8ol0oePpJIlS23WJ8cJIYQQQgiRjpzS4EYE5UC/p/s5nOMyMUfpgtUDgi3KGIQzhI7vvlvTIXA6wkQeYQEyTdyzJ/w77GB/R6vT34fAxgdBHQfPCMdYfYTV7eQx4FgioERBGGc1e9Arg8yCAouO3ECBQPpxEoviBWfBQaG0voJHWMmemXAYDSEuWLyQt5men3riGEduEy+ELyxVsIrCCilYWqwLx9WpY/9igYC1QyaCogfLBQT3VIKAj9NaHObyPCwZyM/1IeRZ6ilGlDl1hLpCvQoKLaynwulKEPIy/BssHlCuJK2k0uU5SjvqOQIugjjOfhFAk07LcyP5TOJ38cUXm5N1rLwQ4FFuri88G2GfNKSegoXlBQosrIiCsievYM0HOBLPTbHE+6nHScfXGwIWNCghgnVhkm+++Sa7/vBeyhCnxkmCog9FQSZS28+6Ql0M701azkAo6/BvUCqhmKGe0j/xQXFLXc5rHYKtttranpvOATthKEdL+veV8O/hfZR9eB99InHlfanKt4KEfoz6gSNvFD/EhTIibijBUWYFSFvItyTU4/OaNbNrKLpQFFOfsRwKfPzxx2bZRN+Hs376GvqE0G8IIYQQQgjxT2CTKYdY2Z42bZqd4sTWpl133dU+5cuXN+sHJuMIGr39hD0dQckTQKFQvHgxV7XqAfY9CAKp9yEEIvCGVeVMwhyTf1aHEdbZ+pUES5/kiU4oXs5t2tT+7tChgwlJWEClI7myzhYwVt85pYxTcALphBhIDU9dpce6IaSLY+OTLIwFTSynEJ4yvSMQnj1gwABThHHkONstsNhBwA4nTq0L4dSlyZMnmxIwHSjjUJYgcHLCGgIfpKYVEAS7du1qVh9sC2FLzfrANhSgPFD8BP7yQjqCOgoqsy7ZPcu6BMuOpIKBOhbih8DIvQjuX3/9dQ5hO9RF8p4P93IiFQo4Tqh75ZVXbOsS9Qfrj0wk8yL5N/nGqUoNGza07ZkQBNh0+bc2QZ6tVRUrVjTrnqQyFFDY8Mx1sUwKYGlHXcVK54meT8Sha0IeozgN1kWZ0p1XgvUc27aoZwEUGyjnKGsUkvRHwYIkyeJYIchpXkaaKKwtXumuJtsibRPlE2GpbYRnU56hTLGsg969e5vCJsB1+icsaPhNpjglr5FuYMsc9TaANRHbHCnvXXxd2HGnndxHPl/CCYoB+kgU/Hmx+skRn1yyK128k/V5/6pV7W/Sn1TsUo4ocJJtOZnWVGhzbMMjTShWk1sjKQe2/XKiIyelocDN1G4yPV8IIYQQQojNgU2iHGKCf+edd9qWqnTCJQoBJujw0ksv5dgyFISDsH0MsGpAQKhb93h3xhlnWlgQuJICAr9hdRgLGBQVECb6WHkkt6AhyHPcMeDPAn8eWAXhz4Vj6jmWPsmxXsDguGt8crClB6E6FYRc3sHWJAgWAggebKXiXwQsQHHG9i3ShnUGoDTgiH/8gECwSglpYCWd7RAoVrAaIS6Bl154wYTJkK/4oLHPyhU5hFMUP+ZzKI4jSjyOgF4drTa/KiiJeM7222XFCbD8at68mSnNcoMVeY69R5l2//33ryF8Y/mCsoR8QDHEUdkB4hOsdZKWJAj8CHRYSyDEUl+SdYQyZNsN+ZsJFHNYVCEYk5bAaz6fqQcoMMnTnXbcybbUEBfeNeXXXy0PySPKEisY4o7Sii095CXp4Vhx/kbBgiKCD75tSA8KHRRwQFu4uNXF9nfqNq4kobx5X3JbFvlHWaJ8o96gCOUob8C6Amsv8ob2hSDL9igsLLAKA+IISWsnygFQXCXb4Zsj3jSrixYtWth30o8V3AXnn+8mprHMScI2OvKPPL3t1tvMMi3pE4n8RQGHDxm2mqIwBtpOqANJqxriy5YfjkmfNHlSHLompzY61RTQtKFWrVqZ8mT8F+Nds2bNLL9RClEGWDaRl+RL6BNQJo2LLUiCAjbk/coVf2+Jomz4EKdkuwpWZ8m8DZBe3sPzKBeOvgf8VtEfcH3O3Dm2DY/yw+ouKLKwnKM9UN/oA99++213xRVXWDtA6UEcQj8T6g1Q1nwP+Uhb22///a2PadPmEut7JnwzwfKU36OArVK5sjvuuONMnX755ZdbGdKG8Tt033335bC2SYV4kA4+ybIOcQp1D0K+8d5wHaUZYG2GjzDS2dD3ZeV83UCRSp7h/4r8Ii/Ik6AMRAFFvQllkArbR+nPyVvihrI+CXEGFHBYl2E9RNsinDaBzyMgvqQjmRb6ceraY489liNcCCGEEEKIQoefiOdKfp9Wxuk2nArFqzm9iuPH/aQ6vpoFp+Scc845dg8fP8m3U6v8xN1OpeKUmBIlStjJURzlzDHTXhCy08YCnC7Ekd5euLdTw3r16mVHgHPcNic7gZ/s2+k8vONf//pX1LVbt+zTpmDevHl2MhfvCnHxAm10bXzCTyrklRdi7CSwJDyTk6C8UG7PaOPjM9uH/TZ1avYJXhybzlHoxJF38ByOqPcCY/TCCy/YPcVLFLcTizgVirhzNDPhHJlOPnrhxk7S4aSzLbbYwo7Y5hQeTk6rXLly9hHjs2bNitp36GB5w6dHjx526g7H53NiEc88t2lTSwdHcvP9kksusWOvW7dubcemhxPXIJz4wxHZeWHMe2OiI486Ktpxhx3s+f3797cTijiWvUKFCnZinRcK47uzTpTjhClOiuI9lCvlGU788kJX9nH6Xsi3U828kGh1hTA+aztmn/s5Oa5UqVLRHXfcYadwcYJUp06d4juyGD9+vJ1MxzM5EalBgwZ2DDd1hN/+5z//sfrrBXk7SYr7OJqb+yhfypVPvXr17BQ8Tn7ieteuXe10KcJ5BnUvHZQJR6bz3K222spOtwv5wGlelX3ecI0Tq4gXp7rxnbpHewFOi6JuEF6ufDk7lpyj0HkvYaRv1OjRlq9wU5culq+cNMWpW1dfc7Wd1jRs2DC7DpyyVrp0aft96qlvmej/TH9ry9RB0s0paaTHC/d2lHi/fv3iO7NOsAun9vGhnVBfFyxYYHU3hPPuZN1JhbZEfof7+SRPpgL+5sQ3rp177rl2AiCndHEKVTiV7CNfdrV8eXIP7e/111+3o+hps4QVK1YsesL3TfR3Q30+Uc6EV69e3U7N4mQ52iEnYVEfqD9X+zpKuZMm2hv5Qp5yMhx93M4772zPoF6Fk94ot1DPwof0DRjwsuXD8z691AXCOdXsww8/tLIi/wjbw/cLw4cPt/IiDZzslXwWz6Y+BzghjdPZkvdQ7zkVLxPk55133ml5wr2cgEjan3nmmex8oZ+lbdFvhzpLPeQeePa5Zy0vyBP6QE4cA07ho+9Mxoc8po7zDtpy6Hf/26JF9olwqXBCGfnEqWmpUF7Em/hTX8k7ypxn7rHHHpZ/pDEc17/LLrvYuLRo0SLrMwmjLwl1RwghhBBCiE1JJh1PMf7nJ68Zwa8LW3fyC7aSsNLKSj2rtFjFcNJV0lICp8r478HCAViRZVWfFV3uY7UWCw+exfYdVvux7sDZbRKsM7gPSwFW5LHs4b5wshCWA/iT4JlhhZrtYEmrH7YrsCKNJQPvClvegv+RJMQHR6hYPHghJg51tkUFf0qsNBO+YuVKW/XGsTLpxGIFqx9WrwnD6od8YTWc7W2semP1MXvObHd83eOdF2bNSof38TusDogz9xNHipRtZaQPiwf85ZAuTvkCLA/YhuUFHvvOVhBObcJqgPQSzjN5D6vd5BEWWKzG8z7Kga015Cm8O+ZdN3DAQLNayt5ysxbw34F/D9KA9Qpbq9jagbUDFmVJ2O7F9j7eTf4RN95NmsIJaVgGkY9YZlBfsATC6oQVe34L1IUQ53RQxjwD6x/eRVxwEk2eJmGrGPmHhQ75Tn2gfLBA8IJztk8a8hOLG7ZHYf3CVi9+R5zxUYWjX6xTsN7Asoe48zwcgCdPg0uC9REWbNRZLB2ot7w/3E8ayVeun3jSiW63SruZRQn1CKsvrHWA+jP2g7HuwKoHWr3DGoN0UfbU0z38805u2NDSjkUL91NXqU+7+vg3btTI2kKo51hUYFVHXlNPsODI7cS1AG2TbUxYYpBPWJdRD7Bw48SpAG0Qy5lg9UScSBOWM6SJPop6ggUgViXp2ifQNkg37WPe3HlmfYPlWOpJe1idUXbEDwst/Iphbch9PIP8wIqFfKY+Ur5sJyT9oQ1Sh2hDpA3/N8STvOVf6haWV/QLwaJklS/PRr6OUHfJw8FDBrtPx31q7ZPT+IgTbeWEE06w55aMrWmCxSFlQ55jBUQ7pN8gDbTd0KY5CYz4EXfiRx+MJSBbEUkL9WvosKFu5oyZVo+xwqFPSkJa2EZGfSEtxx53rGtwUoM12kmAdkK7or4CA04DH0fKCwfXxI3yxPKINhCs74gv/SAnJdKmX/Ft52f/LOor6bdn+Xym7jKm0Hex7Ys+nriTH9Qb6igf8p58SWfhxHNwbH/a6ae5/ffLOi0tQPvEEov6GZ6P1R15Tp+KNRVxJg6khTZJvjCuUbZYVXFy4XlNm2bMIyGEEEIIITYWmXQ8G105lJ8wCUdQDAJqJvJ6X24EwWZtzyA7c1NAbEyIC2lHuMyPOCFI8qx0eUD+rE/+EkeEQATdDSmf3GjRsoUrVbKUnWaUF9alXpEfQN4EYT2VvNxH/iFIIlwWZohjbgJu586dTYmD/xYE5LwS6gHPDnm1rlxz7TVu6q9T7cSqvDwjWS6ZoFz4bCqhPtnvoDwq7utlprZMergvP9p6XvImP9+XH6ytbq6NvKR5fQhDbGHJJyGEEEIIUbTJpOMpGGl8I8FEPi8Khbzelxv8Pi/PKEwCAHFBWMqvOOWmwFnf/CVuKBE2tHzSgcUEPn+m/zbddbyuYxy6dtalXgUyKYYgL/fxvsKuGIJMwjfWFd27dzcrM3x0rYtiCEI9SOZVXsFqqVu3bu6L8V+4Tjd0yvMz8nIf5bKpFEPA+0Nd5KSw3Noy6cmvtp6XvMnP9+UHG1pOea036wp5VJjySQghhBBCiHTkv0QuRCGBrWtY6vTq3cvtWWXNI/FF/sGR4ijjevTokb3lZ2OB823ezRbCgw86OA4VQgghhBBCCJFXNuttZULkxvpudRPrzqbMa5WzEEIIIYQQQuSNf+S2MiFyQwqDjcemzGuVsxBCCCGEEEJsGJKqhBBCCCGEEEIIIYowUg4JIYQQQgghhBBCFGGkHBJCCCGEEEIIIYQowkg5JIQQQgghhBBCCFGEkXJICCGEEEIIIYQQoggj5ZAQQgghhBBCCCFEEUbKISGEEEIIIYQQQogiTLHIE/+dlmuuuSb+SwghhBBCCCGEEEJsznTt2jX+62/WqhwSQgghhBBCCCGEEP9ctK1MCCGEEEIIIYQQoggj5ZAQQgghhBBCCCFEEUbKISGEEEIIIYQQQogijJRDQgghhBBCCCGEEEUYKYeEEEIIIYQQQgghijBSDgkhhBBCCCGEEEIUWZz7P/Tp0LWrQi/vAAAAAElFTkSuQmCC\" width=\"1159\" height=\"658\"\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carcinoembryonic antigen, CEA, pancreatic ductal adenocarcinoma, false-positive rate, tumor recurrence","lastPublishedDoi":"10.21203/rs.3.rs-5271407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5271407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe dynamics of postoperative carcinoembryonic antigen (CEA) in pancreatic ductal adenocarcinoma (PDAC) patients have not been well assessed. This study investigated the correlation between postoperative CEA elevations and tumor recurrence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMedical records were retrospectively analyzed for 84 patients who received curative resection for PDAC from January 2019 to December 2020. Postoperative CEA levels were monitored for a minimum of 12 months. False-positive CEA elevation was defined as a CEA level exceeding 5 ng/mL without evidence of recurrence in imaging studies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the examined patients, 59 (70%) exhibited CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 ng/mL within the observation period. The sensitivity and specificity of elevated CEA levels for detecting recurrence were 84% and 41%, respectively. CEA elevations without tumor recurrence were observed in 27 patients, indicating a false-positive rate of 59%. More than half of these patients demonstrated peak CEA levels between 5 and 10 ng/mL, while only true-positive patients exhibited CEA levels exceeding 40.0 ng/mL.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCEA may rise in more than half of postoperative PDAC patients without recurrence. CEA alone is not a robust postoperative marker.\u003c/p\u003e","manuscriptTitle":"Analyzing the High Frequency of False-Positive Carcinoembryonic Antigen Elevations in Postoperative Pancreatic Ductal Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-24 07:34:09","doi":"10.21203/rs.3.rs-5271407/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-05T05:46:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-03T12:46:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-28T13:40:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272150206630546897248588267432035018031","date":"2024-10-23T09:17:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174714965990038358277722234128153391491","date":"2024-10-22T02:09:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-20T00:15:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-17T13:40:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-17T08:18:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Langenbeck's Archives of Surgery","date":"2024-10-15T22:01:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b726956c-f0fe-45fd-92b3-5917cbed2c45","owner":[],"postedDate":"October 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-16T16:00:47+00:00","versionOfRecord":{"articleIdentity":"rs-5271407","link":"https://doi.org/10.1007/s00423-024-03577-5","journal":{"identity":"langenbecks-archives-of-surgery","isVorOnly":false,"title":"Langenbeck's Archives of Surgery"},"publishedOn":"2024-12-14 15:57:30","publishedOnDateReadable":"December 14th, 2024"},"versionCreatedAt":"2024-10-24 07:34:09","video":"","vorDoi":"10.1007/s00423-024-03577-5","vorDoiUrl":"https://doi.org/10.1007/s00423-024-03577-5","workflowStages":[]},"version":"v1","identity":"rs-5271407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5271407","identity":"rs-5271407","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}