Dual-energy CT iodine mapping and lymph node characteristic parameters for distinguishing metastatic lymph nodes in papillary thyroid carcinoma

Dual-energy CT iodine mapping and lymph node characteristic parameters for distinguishing metastatic lymph nodes in papillary thyroid carcinoma | 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 Dual-energy CT iodine mapping and lymph node characteristic parameters for distinguishing metastatic lymph nodes in papillary thyroid carcinoma Bo Gao, Huizhi Mi, Haiqiao Sun, Jinbin Zhang, Jingfan Zhang, Weibo Gao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8628721/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background : To evaluate the predictive value of dual-energy computed tomography (DECT) iodine quantification combined with lymph node morphological characteristics for identifying metastatic lymph nodes in patients with papillary thyroid carcinoma (PTC). Methods : This retrospective study included 123 histologically confirmed PTC patients who underwent DECT between 2021 and 2023 as the derivation cohort. Among them, 78 patients were randomly selected for internal validation. An additional 47 patients scanned between 2023 and 2024 composed the external validation cohort. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of lymph node metastasis(LNM). A predictive model was then developed and validated using both internal and external datasets. Results : Multivariate analysis revealed that the arterial phase iodine concentration (≥ 2.6 mg/mL), marked arterial enhancement, heterogeneous enhancement pattern, irregular shape, indistinct margins, and incomplete capsule of the primary thyroid nodule were independent predictors of LNM. A nomogram incorporating DECT-derived iodine metrics and CT-based morphological features was developed. In the internal validation cohort, the model achieved an area under the curve (AUC) of 0.992 (95% CI: 0.956–0.984), with a cutoff value of 0.2, sensitivity of 98%, and specificity of 95%. In the external validation cohort, the AUC was 0.950 (95% CI: 0.893–0.884), with a cutoff value of 0.486, sensitivity of 89%, and specificity of 88%. Conclusion : A predictive model combining DECT iodine concentration with CT-based morphological features provides high diagnostic accuracy for preoperative identification of metastatic lymph nodes in patients with PTC. Nomograms DECT Thyroid Lymph node metastasis Papillary thyroid carcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In China, the incidence of PTC has risen sharply, particularly since the early 2000s [ 1 ] . PTC accounts for approximately 90% of all thyroid cancer cases and shows a strong tendency for LNM [ 2 , 3 ] . Despite its favorable prognosis and low mortality, PTC is associated with a relatively high rate of locoregional recurrence [ 4 ] . Such recurrences often require secondary surgery or radioactive iodine therapy, which can significantly affect patients’ quality of life [ 5 , 6 ] . Accurate preoperative evaluation of LNM is therefore of critical clinical importance. Ultrasound offers a comprehensive assessment of lymph node characteristics, including size, morphology, margin clarity, cystic changes, calcification patterns, and vascular architecture. Although ultrasound has high sensitivity for detecting lymph nodes, it has relatively low specificity [ 7 , 8 ] . Individual sonographic parameters show limited diagnostic value in distinguishing benign from malignant lymph nodes. Moreover, the accuracy of ultrasound is operatordependent, with interpretation variability influenced by examiner experience, and may be further compromised by acoustic shadowing artifacts from adjacent anatomical structures, particularly the trachea and clavicular region [ 9 , 10 ] .Contrast-enhanced CT can compensate for the limitations of ultrasound [ 11 , 12 ] . Typical CT features of metastatic lymph nodes include necrotic, cystic changes, calcification, and heterogeneous enhancement [ 13 ] . However, these characteristic features are not always present. Furthermore, relying solely on quantitative enhancement analysis is insufficient for accurately identifying metastatic nodes [ 11 , 14 ] .Quantitative parameters derived from dual-energy computed tomography (DECT), including iodine concentration and spectral curve slope, have shown promising ability to differentiate metastatic lymph nodes in PTC [ 15 , 16 ] . Predictive models have been increasingly used to assess the probability of cervical LNM in patients with PTC [ 13 , 17 – 21 ] . However, few studies have integrated both the morphological characteristics and quantitative parameters of lymph nodes in these models. We hypothesize that combining morphological features with quantitative DECT parameters can improve the accuracy of metastatic lymph node identification. Materials and methods Patient Patients clinically suspected of PTC were retrospectively enrolled between 2021 and 2024. All enrolled patients (n = 180)underwent contrast-enhanced DECT within seven days before surgery. The details of patient inclusion and exclusion are summarized in flowchart (Figure 1). Inclusion criteria (1).Histopathologically confirmed diagnosis of papillary thyroid carcinoma (PTC); (2). Preoperative contrast-enhanced dual-energy CT (DECT); (3). No prior history of other tumors or malignancies. Exclusion criteria Exclusion criteria included motion artifacts (e.g., swallowing) or contrast-related artifacts on DECT images that compromised diagnostic interpretation. CT Scanning Protocol All scans were performed via a third-generation dual-source CT scanner (SOMATOM Force, Siemens Healthineers, Germany).The Scanning parameters included automatic tube current modulation with a tube current range of 220–500 mAs. The slice thickness was 5 mm, and the collimation was 2 mm × 192 × 0.6 mm. Tube A was operated at 90 kV and tube B at Sn150 kV. An iodinated contrast agent (370 mg/mL, 50–60 mL) was injected at 3 mL/s, followed by 30 mL of saline. The arterial and venous phases were acquired at 27 s and 35 s delays, respectively. Correlation between Imaging and Pathology Lymph nodes were identified and annotated by two senior radiologists via the imaging–pathology zonal correlation method proposed by Reza et al [22] . Before surgery, the clinical team delineated the planned lymph node dissection regions. Following surgery, the pathological status of each lymph node was determined based on the final histopathological reports. DECT Data Processing All imaging data were transferred to a Siemens syngo.via workstation for standardized processing. Arterial-phase iodine maps were generated via the virtual non-contrast (VNC) technique. A region of interest (ROI) was manually delineated on the most intensely enhanced area of the lymph node on the largest axial slice, carefully avoiding regions of necrosis and calcification. The Arterial phase iodine concentration (APIC) was measured three times, and the average value was recorded. Additional morphological characteristics were recorded, including nodal shape, short-axis diameter, margin definition, enhancement intensity, presence of cystic changes or calcifications, arterial-phase enhancement pattern, and capsule integrity of the thyroid nodule. Statistical analysis Data analyses were performed using SPSS and R software. For non-normally distributed variables, the Mann-Whitney U test was applied to compare differences between groups. The Kolmogorov-Smirnov test was used to assess the normality of the data distribution. Pearson’s chi-square test was used to evaluate the associations between categorical variables. Variables with p < 0.05 in univariate analysis were entered into multivariate logistic regression with stepwise elimination to identify independent predictors of LNM，A nomogram was then constructed, and its performance was evaluated by AUC, Youden index, and calibration curves,Multivariate logistic regression was used to calculate odds ratios (ORs) with 95% confidence intervals (CIs), Model performance was evaluated by ROC analysis and calibration plots (rms package, R), and clinical utility by decision curve analysis (rmda package). Results A total of 170 patients with pathologically confirmed PTC were enrolled in the study. The patients were divided into two cohorts: 123 patients formed the derivation cohort, including 127 metastatic and 136 non-metastatic lymph nodes; 78 of these were used for internal validation. An additional 47 patients were included in the external validation cohort, comprising 53 metastatic and 75 non-metastatic lymph nodes. Comparisons of morphological parameters between the derivation and external validation cohorts are presented in Tables 1 and 2. Model construction Univariate analysis revealed that APIC, shape, SD, margin, APEP, APED, and NCI were significantly associated with lymph node metastasis. Multivariate logistic regression identified APIC, shape, margin, APEP, APED, and NCI as independent predictors of metastatic lymph nodes. The odds ratios (ORs) and 95% confidence intervals (CIs) of these factors are presented in Table 3. All variance inflation factors (VIFs) were less than 10, and tolerance values exceeded 0.1, indicating the absence of multicollinearity among variables. A nomogram was constructed based on these independent predictors (Figure 2). In the derivation cohort, the AUC was 0.992 (95% CI: 0.95–0.98) (Figure 3A, E). The calibration curve showed a mean absolute error of 0.011, and the Hosmer–Lemeshow test showed good calibration (p = 0.95). In the external validation cohort, the nomogram achieved an AUC of 0.950 (95% CI: 0.89–0.98) (Figure 3B, F), with a comparable mean absolute error of 0.04 and a Hosmer–Lemeshow p-value of 0.73, indicating excellent model fit. Model Validation Model validation was conducted using internal and external cohorts. In the DCA, the x-axis represents the threshold probability at which a clinician would consider intervention. The DCA showed that when the threshold probability was set above 1%, the nomogram provided a greater net clinical benefit compared to the "treat-all" or "treat-none" strategies (Figure 3C, D). In the derivation cohort, using a cutoff value of 0.20, the model achieved a sensitivity of 98%, specificity of 95%, positive predictive value (PPV) of 95%, and negative predictive value (NPV) of 98%. In the external validation cohort, using a cutoff of 0.48, the model achieved an AUC of 0.95 (95% CI: 0.89–0.98), with 89% sensitivity, 85% specificity, 91% PPV, and 85% NPV. Two representative clinical application examples of the nomogram are illustrated in Figures 4 and 5. Discussion This study aimed to predict LNM by analyzing DECT image features and quantitative parameters, particularly arterial phase iodine concentration (APIC), to support surgeons in formulating appropriate surgical strategies. Our findings identified six independent risk factors for predicting LNM: APIC, shape, margin, APEP, APED, and NCI. A nomogram including these six features showed excellent predictive performance. Common CT imaging features of lymph nodes include shape, short-axis diameter, margin definition, arterial phase enhancement pattern and intensity, presence of cystic changes, and calcification. These imaging features are widely employed to distinguish metastatic from non-metastatic lymph nodes [ 13 , 21 ] . In this study, statistically significant differences in imaging characteristics were observed between metastatic and non-metastatic lymph nodes. Lymph nodes with irregular shapes, ill-defined margins, and a short-axis diameters ≥ 10 mm were more likely to be metastatic. Our findings align with Yan Zhou et al., who used lymph node imaging to distinguish metastatic from non-metastatic nodes. [ 13 ] 。 Metastatic lymph nodes typically demonstrate marked heterogeneous enhancement, which is attributed to the increased density and irregular distribution of intranodal vascular beds [ 23 ] . The proliferation of neovascular structures within metastatic lymph nodes contributes to the elevated iodine concentration observed during the arterial phase. Metastatic lymph nodes exhibited significantly higher APIC than non-metastatic nodes. Given the observed interpatient variability in iodine concentration among metastatic nodes, this variation may reflect differences in the extent of tumor infiltration within the lymph nodes.In PTC, the presence of LNM has been shown to correlate with the integrity of the tumor capsule [ 24 ] . The metastatic pattern of PTC is predominantly locoregional, with a tendency to spread to regional lymph nodes [ 24 – 30 ] . In this study, PTC nodules with incomplete capsules were more frequently associated with LNM in adjacent cervical regions. Traditional logistic regression models predict disease risk based on Ors and 95% CIs [ 16 , 31 , 32 ] , whereas nomograms provide a more intuitive graphical tool that visualizes the contribution of individual variables to the probability of a specific outcome [ 17 , 33 – 36 ] . To date, the prediction of LNM has largely depended on indirect assessments. In this study, the metastatic potential of lymph nodes was evaluated by combining morphological features with arterial-phase iodine concentration derived from contrast-enhanced CT, offering clinicians valuable insights to guide surgical planning. Limitations This single-center study with a small sample size may have introduced selection bias. Future research will aim to address this limitation through larger, multicenter investigations and the inclusion of smaller lymph nodes. Conclusions The logistic regression model developed in this study enables direct differentiation between metastatic and non-metastatic lymph nodes, thereby assisting clinicians in preoperative surgical planning. Furthermore, all parameters were derived from noninvasive imaging, making the model practical for routine clinical use and potentially reducing the risk of missed diagnoses. Overall, this predictive tool can contribute to more precise surgical decision-making and optimized postoperative management in patients with papillary thyroid carcinoma. Abbreviations APIC Arterial phase iodine concentration SD Short diameter APEP Arterial phase enhancement pattern APED Arterial phase enhancement degree NCI Nodular capsule integrity CD Cystic degeneration DECT dual-energy computed tomography PTC papillary thyroid carcinoma LNM lymph node metastasis AUC area under the curve VNC virtual non-contrast Cis confidence intervals Ors odds ratios ROI region of interest AUC area under the curve VIFs variance inflation factors Declarations Acknowledgements The authors would like to thank Jiaxing Wu for her selfless and valuable assistance. Author contributions Bo Gao: Writing-Original Draft, Methodology, Conceptualization, Supervision.Huizhi Mi:Writing–review&editing,Visualization,Software,Formalanalysis,Validation,Methodology,Datacuration.HaiqiaoSun:Visualization,Software,Validation,Methodology,Datacuration.Jinbin Zhang: Methodology, Data curation, Supervision, Investigation. Jingfan Zhang: Visualization, Investigation, Formal analysis,Data curation.Weibo Gao:Data curation, Validation, Formal analysis, Funding acquisition.Ying Xiang: Validation, Methodology, Visualization. Jiaxing Wu: Writing – review & editing,Software,Investigation,Supervision, Methodology. Xiaohui Li: Writing – review & editing, Conceptualization, Supervision, Methodology, Funding acquisition. Funding This study was funded by Shaanxi Province Natural Science Basic Research Programme Project, Contract grant number: 2024JC-YBQN-0893; IIT Clinical Research Fund of The Second Afliated Hospital of Xi'an Jiaotong University, Contract grant number: M019;Shaanxi Province ‘Dual-Chain’ Integrated National Medical Center Medical-Engineering Cross-disciplinary Project – Validation of Micro-dose CT Algorithm Application (2021LL-JB-06). Data availability All data generated or analysed during this study are included in this published article. Ethics Declarations Ethical approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Xi‘an Jiaotong University. The requirement for informed consent was waived. Consent for publication NA- not applicable. Competing interests The authors declare no competing interests. References Miranda-Filho A, Lortet-Tieulent J, Bray F, et al. Thyroid cancer incidence trends by histology in 25 countries: a population-based study[J]. The Lancet Diabetes & Endocrinology, 2021, 9 (4): 225-234. Cho SJ, Suh CH, Baek JH, et al. Diagnostic performance of CT in detection of metastatic cervical lymph nodes in patients with thyroid cancer: a systematic review and meta-analysis[J]. European Radiology, 2019, 29 (9): 4635-4647. Fritze D, Doherty GM. Surgical Management of Cervical Lymph Nodes in Differentiated Thyroid Cancer[J]. 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Nomograms to predict ipsilateral and contralateral central lymph node metastasis in clinically lymph node-negative patients with solitary isthmic classic papillary thyroid carcinoma[J]. Surgery, 2021, 170 (6): 1670-1679. Thompson AM, Turner RM, Hayen A, et al. A preoperative nomogram for the prediction of ipsilateral central compartment lymph node metastases in papillary thyroid cancer[J]. Thyroid, 2014, 24 (4): 675-682. Tables Table 1. Comparison of clinical characteristics and DECT quantitative parameters between metastatic and non-metastatic lymph nodes in the internal validation cohort. Variable Category Non-metastatic (n=127) Metaststaic (n=136) Total=（263） p value APIC <2.6mg/ml 113(88.98%) 15(11.03%) 128(48.67%) <0.001* ≥2.6mg/ml 14(11.02%) 121(88.97%) 135(51.33%) Shape Regular 41(32.28%) 133(97.79%) 174(66.16%) <0.001* Irregular 86(67.72%) 3(2.21%) 89(33.84%) SD <10mm 80(62.99%) 109(80.15%) 189(71.86%) 0.002* ≥10mm 47(37.01%) 27(19.85%) 74(28.14%) Margin Clear 86(67.72%) 1(0.74%) 87(33.08%) <0.001* Unclear 41(32.28%) 135(99.26%) 176(66.92%) APEP Homogeneous 107(84.25%) 4(2.94%) 111(42.21%) <0.001* Heterogeneous 20(15.75%) 132(97.06%) 152(57.79%) APED Mild/Moderate 105(82.68%) 3 (2.21%) 108(41.06%) <0.001* Strong 22(17.32%) 133(97.79%) 155(58.94%) NCI Complete 120(94.49%) 53(38.97%) 173(65.78%) <0.001* Incomplete 7(5.51%) 83(61.03%) 90(34.22%) CD Negative 136(100%) 106(83.46%) 242(92.02%) <0.001* Postive 0(0.00%) 21(16.54%) 21(7.98%) Calcification Negative 136(100%) 111(87.40%) 247(93.92%) <0.001* Postive 0(0.0%) 16(12.60%) 16(6.08%) Table 2. Comparison of clinical characteristics and DECT quantitative parameters between metastatic and non-metastatic lymph nodes in the external validation cohort. Variable Category Non-metastatic (n=53) Metaststaic (n=75) Total=（128） p value APIC <2.6mg/ml 40(75.4%) 10(13.33%) 50(39.06%) <0.001* ≥2.6mg/ml 13(24.53%) 65(86.67%) 78(60.94%) Shape Regular 4(7.55%) 53(70.67%) 57(44.53%) <0.001* Irregular 49(92.45%) 22(29.33%) 71(55.47%) SD <10mm 47(88.68%) 48(64.00%) 95(74.22%) 0.002* ≥10mm 6(11.32%) 27(36.00%) 33(25.78%) Margin Clear 49(92.4%) 22(29.33%) 71(55.47%) <0.001* Unclear 4(7.55%) 53(70.67%) 57(44.53%) APEP Homogeneous 47(88.68%) 14(18.6%) 61(47.66%) <0.001* Heterogeneous 6(11.32%) 61(81.33%) 67(52.34%) APED Mild/Moderate 45(84.91%) 24(32.00%) 69(53.91%) <0.001* Strong 8(15.09%) 51(68.00%) 59(46.09%) NCI Complete 31(58.49%) 8(10.67%) 39(30.47%) <0.001* Incomplete 22(41.51%) 67(89.33%) 89(69.53%) CD Negative 53(100%) 65(86.67%) 118(92.19%) 0.006* Postive 0(0.00%) 10(13.3%) 10(7.81%) Calcification Negative 53(100%) 74(98.67%) 127(99.22%) 0.399 Postive 0(0.0%) 1(1.33%) 1(0.78%) Table 3. Multivariate logistic regression analysis of independent predictors of LNM in PTC. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 08 Feb, 2026 Editor assigned by journal 08 Feb, 2026 Editor invited by journal 22 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 21 Jan, 2026 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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01:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8628721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8628721/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102529939,"identity":"1a95a2c8-6d82-44b7-8855-ba7fd278c668","added_by":"auto","created_at":"2026-02-12 16:12:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139796,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the inclusion and exclusion criteria for the internal validation cohort of patients with PTC.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8628721/v1/0919470786980ad960cf67ec.png"},{"id":102529940,"identity":"fa97972b-c295-4087-8951-420742b61818","added_by":"auto","created_at":"2026-02-12 16:12:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78903,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram integrating DECT iodine parameters and morphological features for predicting the probability of lymph node metastasis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8628721/v1/cb9929d95cc52c26b2e42267.png"},{"id":102962443,"identity":"f26c78a7-c486-4cfa-99e6-7837bfaa1e3b","added_by":"auto","created_at":"2026-02-19 04:08:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":356647,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B): ROC curves demonstrating the diagnostic performance of the predictive model in the internal (A) and external (B) validation cohorts.(C-D) Calibration curves illustrating the agreement between the predicted and observed probabilities of metastasis in the internal (C) and external (D) validation cohorts. (E-F) DCA showing the net clinical benefit across threshold probabilities for the internal (E) and external (F) validation cohorts.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8628721/v1/c6a3fd4777c4bb72e715d198.png"},{"id":102529941,"identity":"62988439-777e-46da-9f14-83c663152e1a","added_by":"auto","created_at":"2026-02-12 16:12:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":469554,"visible":true,"origin":"","legend":"\u003cp\u003eA 41‑year‑old patient with PTC presented with an incompletely encapsulated nodule in the right thyroid lobe. A right level VI lymph node (arrow) showed an arterial‑phase iodine concentration of 4.9 mg/mL, with regular morphology, well‑defined margins, and homogeneous enhancement. These characteristics corresponded to a nomogram score of 162.5, and postoperative pathology confirmed a non‑metastatic lymph node.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8628721/v1/98aa1f9e7ad342b19d27651f.png"},{"id":102529943,"identity":"d21ad2d4-de7a-4ee7-95cd-b4fefd2bf1d1","added_by":"auto","created_at":"2026-02-12 16:12:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":530369,"visible":true,"origin":"","legend":"\u003cp\u003eA 40‑year‑old patient with PTC presented with an incompletely encapsulated nodule in the right thyroid lobe. A level IV lymph node (arrow) demonstrated an arterial‑phase iodine concentration of 3.8 mg/mL, a regular shape, and markedly strong heterogeneous enhancement. These characteristics correspond to a nomogram score of 300. postoperative pathology confirmed a metastatic lymph node.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8628721/v1/49e3f908efb9b20c633f48e3.png"},{"id":102964969,"identity":"68769a9a-9e29-4721-8431-90826cfe3232","added_by":"auto","created_at":"2026-02-19 04:29:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2291609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8628721/v1/f8b0ef63-c6bd-4392-aee1-dd14525c30ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-energy CT iodine mapping and lymph node characteristic parameters for distinguishing metastatic lymph nodes in papillary thyroid carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn China, the incidence of PTC has risen sharply, particularly since the early 2000s\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. PTC accounts for approximately 90% of all thyroid cancer cases and shows a strong tendency for LNM\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Despite its favorable prognosis and low mortality, PTC is associated with a relatively high rate of locoregional recurrence\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Such recurrences often require secondary surgery or radioactive iodine therapy, which can significantly affect patients\u0026rsquo; quality of life\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Accurate preoperative evaluation of LNM is therefore of critical clinical importance.\u003c/p\u003e \u003cp\u003eUltrasound offers a comprehensive assessment of lymph node characteristics, including size, morphology, margin clarity, cystic changes, calcification patterns, and vascular architecture. Although ultrasound has high sensitivity for detecting lymph nodes, it has relatively low specificity\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Individual sonographic parameters show limited diagnostic value in distinguishing benign from malignant lymph nodes. Moreover, the accuracy of ultrasound is operatordependent, with interpretation variability influenced by examiner experience, and may be further compromised by acoustic shadowing artifacts from adjacent anatomical structures, particularly the trachea and clavicular region\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.Contrast-enhanced CT can compensate for the limitations of ultrasound\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Typical CT features of metastatic lymph nodes include necrotic, cystic changes, calcification, and heterogeneous enhancement\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, these characteristic features are not always present. Furthermore, relying solely on quantitative enhancement analysis is insufficient for accurately identifying metastatic nodes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.Quantitative parameters derived from dual-energy computed tomography (DECT), including iodine concentration and spectral curve slope, have shown promising ability to differentiate metastatic lymph nodes in PTC\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePredictive models have been increasingly used to assess the probability of cervical LNM in patients with PTC\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, few studies have integrated both the morphological characteristics and quantitative parameters of lymph nodes in these models. We hypothesize that combining morphological features with quantitative DECT parameters can improve the accuracy of metastatic lymph node identification.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePatient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Patients clinically suspected of PTC were retrospectively enrolled between 2021 and 2024. All enrolled patients (n = 180)underwent contrast-enhanced DECT within seven days before surgery.\u0026nbsp;The details of patient inclusion and exclusion are summarized in flowchart (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1).Histopathologically confirmed diagnosis of papillary thyroid carcinoma (PTC);\u003c/p\u003e\n\u003cp\u003e(2). Preoperative contrast-enhanced dual-energy CT (DECT);\u003c/p\u003e\n\u003cp\u003e(3). No prior history of other tumors or malignancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExclusion criteria included motion artifacts (e.g., swallowing) or contrast-related artifacts on DECT images that compromised diagnostic interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCT Scanning Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;All scans were performed via a third-generation dual-source CT scanner (SOMATOM Force, Siemens Healthineers, Germany).The Scanning parameters included automatic tube current modulation with a tube current range of 220–500 mAs. The slice thickness was 5 mm, and the collimation was 2 mm × 192 × 0.6 mm. Tube A was operated at 90 kV and tube B at Sn150 kV. An iodinated contrast agent (370 mg/mL, 50–60 mL) was injected at 3 mL/s, followed by 30 mL of saline. The arterial and venous phases were acquired at 27 s and 35 s delays, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between Imaging and Pathology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Lymph nodes were identified and annotated by two senior radiologists via the imaging–pathology zonal correlation method proposed by Reza et al\u003csup\u003e[22]\u003c/sup\u003e. Before surgery, the clinical team delineated the planned lymph node dissection regions. Following surgery, the pathological status of each lymph node was determined based on the final histopathological reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECT Data Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;All imaging data were transferred to a Siemens syngo.via workstation for standardized processing. Arterial-phase iodine maps were generated via the virtual non-contrast (VNC) technique. A region of interest (ROI) was manually delineated on the most intensely enhanced area of the lymph node on the largest axial slice, carefully avoiding regions of necrosis and calcification. The Arterial phase iodine concentration (APIC) was measured three times, and the average value was recorded. Additional morphological characteristics were recorded, including nodal shape, short-axis diameter, margin definition, enhancement intensity, presence of cystic changes or calcifications, arterial-phase enhancement pattern, and capsule integrity of the thyroid nodule.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Data analyses were performed using SPSS and R software. For non-normally distributed variables, the Mann-Whitney U test was applied to compare differences between groups. The Kolmogorov-Smirnov test was used to assess the normality of the data distribution. Pearson’s chi-square test was used to evaluate the associations between categorical variables. Variables with \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 in univariate analysis were entered into multivariate logistic regression with stepwise elimination to identify independent predictors of LNM，A nomogram was then constructed, and its performance was evaluated by AUC, Youden index, and calibration curves,Multivariate logistic regression was used to calculate odds ratios (ORs) with 95% confidence intervals (CIs), Model performance was evaluated by ROC analysis and calibration plots (rms package, R), and clinical utility by decision curve analysis (rmda package).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 170 patients with pathologically confirmed PTC were enrolled in the study. The patients were divided into two cohorts: 123 patients formed the derivation cohort, including 127 metastatic and 136 non-metastatic lymph nodes; 78 of these were used for internal validation. An additional 47 patients were included in the external validation cohort, comprising 53 metastatic and 75 non-metastatic lymph nodes. Comparisons of morphological parameters between the derivation and external validation cohorts are presented in Tables 1 and 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis revealed that APIC, shape, SD, margin, APEP, APED, and NCI were significantly associated with lymph node metastasis. Multivariate logistic regression identified APIC, shape, margin, APEP, APED, and NCI as independent predictors of metastatic lymph nodes. The odds ratios (ORs) and 95% confidence intervals (CIs) of these factors are presented in Table 3. All variance inflation factors (VIFs) were less than 10, and tolerance values exceeded 0.1, indicating the absence of multicollinearity among variables.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;A nomogram was constructed based on these independent predictors (Figure 2). In the derivation cohort, the AUC was 0.992 (95% CI: 0.95–0.98) (Figure 3A, E). The calibration curve showed a mean absolute error of 0.011, and the Hosmer–Lemeshow test showed good calibration (p = 0.95). In the external validation cohort, the nomogram achieved an AUC of 0.950 (95% CI: 0.89–0.98) (Figure 3B, F), with a comparable mean absolute error of 0.04 and a Hosmer–Lemeshow p-value of 0.73, indicating excellent model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Model validation was conducted using internal and external cohorts. In the DCA, the x-axis represents the threshold probability at which a clinician would consider intervention. The DCA showed that when the threshold probability was set above 1%, the nomogram provided a greater net clinical benefit compared to the \"treat-all\" or \"treat-none\" strategies (Figure 3C, D).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;In the derivation cohort, using a cutoff value of 0.20, the model achieved a sensitivity of 98%, specificity of 95%, positive predictive value (PPV) of 95%, and negative predictive value (NPV) of 98%. In the external validation cohort, using a cutoff of 0.48, the model achieved an AUC of 0.95 (95% CI: 0.89–0.98), with 89% sensitivity, 85% specificity, 91% PPV, and 85% NPV. Two representative clinical application examples of the nomogram are illustrated in Figures 4 and 5.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to predict LNM by analyzing DECT image features and quantitative parameters, particularly arterial phase iodine concentration (APIC), to support surgeons in formulating appropriate surgical strategies. Our findings identified six independent risk factors for predicting LNM: APIC, shape, margin, APEP, APED, and NCI. A nomogram including these six features showed excellent predictive performance.\u003c/p\u003e \u003cp\u003eCommon CT imaging features of lymph nodes include shape, short-axis diameter, margin definition, arterial phase enhancement pattern and intensity, presence of cystic changes, and calcification. These imaging features are widely employed to distinguish metastatic from non-metastatic lymph nodes\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this study, statistically significant differences in imaging characteristics were observed between metastatic and non-metastatic lymph nodes. Lymph nodes with irregular shapes, ill-defined margins, and a short-axis diameters\u0026thinsp;\u0026ge;\u0026thinsp;10 mm were more likely to be metastatic. Our findings align with Yan Zhou et al., who used lymph node imaging to distinguish metastatic from non-metastatic nodes. \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e。\u003c/p\u003e \u003cp\u003eMetastatic lymph nodes typically demonstrate marked heterogeneous enhancement, which is attributed to the increased density and irregular distribution of intranodal vascular beds\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The proliferation of neovascular structures within metastatic lymph nodes contributes to the elevated iodine concentration observed during the arterial phase.\u003c/p\u003e \u003cp\u003eMetastatic lymph nodes exhibited significantly higher APIC than non-metastatic nodes. Given the observed interpatient variability in iodine concentration among metastatic nodes, this variation may reflect differences in the extent of tumor infiltration within the lymph nodes.In PTC, the presence of LNM has been shown to correlate with the integrity of the tumor capsule\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The metastatic pattern of PTC is predominantly locoregional, with a tendency to spread to regional lymph nodes\u003csup\u003e[\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In this study, PTC nodules with incomplete capsules were more frequently associated with LNM in adjacent cervical regions.\u003c/p\u003e \u003cp\u003eTraditional logistic regression models predict disease risk based on Ors and 95% CIs\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, whereas nomograms provide a more intuitive graphical tool that visualizes the contribution of individual variables to the probability of a specific outcome\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. To date, the prediction of LNM has largely depended on indirect assessments.\u003c/p\u003e \u003cp\u003eIn this study, the metastatic potential of lymph nodes was evaluated by combining morphological features with arterial-phase iodine concentration derived from contrast-enhanced CT, offering clinicians valuable insights to guide surgical planning.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis single-center study with a small sample size may have introduced selection bias. Future research will aim to address this limitation through larger, multicenter investigations and the inclusion of smaller lymph nodes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe logistic regression model developed in this study enables direct differentiation between metastatic and non-metastatic lymph nodes, thereby assisting clinicians in preoperative surgical planning. Furthermore, all parameters were derived from noninvasive imaging, making the model practical for routine clinical use and potentially reducing the risk of missed diagnoses. Overall, this predictive tool can contribute to more precise surgical decision-making and optimized postoperative management in patients with papillary thyroid carcinoma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPIC \u0026nbsp; Arterial phase iodine concentration\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; Short diameter\u003c/p\u003e\n\u003cp\u003eAPEP \u0026nbsp;Arterial phase enhancement pattern\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPED \u0026nbsp;Arterial phase enhancement degree \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNCI \u0026nbsp; \u0026nbsp;\u0026nbsp;Nodular capsule integrity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCD \u0026nbsp; Cystic degeneration\u003c/p\u003e\n\u003cp\u003eDECT\u0026nbsp;\u0026nbsp; dual-energy computed tomography\u003c/p\u003e\n\u003cp\u003ePTC\u0026nbsp; \u0026nbsp; \u0026nbsp;papillary thyroid carcinoma\u003c/p\u003e\n\u003cp\u003eLNM\u0026nbsp; \u0026nbsp;lymph node metastasis\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp;\u0026nbsp;area under the curve\u003c/p\u003e\n\u003cp\u003eVNC\u0026nbsp; \u0026nbsp;\u0026nbsp;virtual non-contrast\u003c/p\u003e\n\u003cp\u003eCis\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;confidence intervals\u003c/p\u003e\n\u003cp\u003eOrs\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;odds ratios\u003c/p\u003e\n\u003cp\u003eROI\u0026nbsp; \u0026nbsp; \u0026nbsp;region of interest\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp;\u0026nbsp;area under the curve\u003c/p\u003e\n\u003cp\u003eVIFs \u0026nbsp; \u0026nbsp;variance inflation factors\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Jiaxing Wu for her selfless and valuable assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBo Gao: Writing-Original Draft, Methodology, Conceptualization, Supervision.Huizhi Mi:Writing\u0026ndash;review\u0026amp;editing,Visualization,Software,Formalanalysis,Validation,Methodology,Datacuration.HaiqiaoSun:Visualization,Software,Validation,Methodology,Datacuration.Jinbin Zhang: Methodology, Data curation, Supervision, Investigation. Jingfan Zhang: Visualization, Investigation, Formal analysis,Data curation.Weibo Gao:Data curation, Validation, Formal analysis, Funding acquisition.Ying Xiang: Validation, Methodology, Visualization. Jiaxing Wu: Writing \u0026ndash; review \u0026amp; editing,Software,Investigation,Supervision, Methodology. Xiaohui Li: Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Supervision, Methodology, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Shaanxi Province Natural Science Basic Research Programme Project, Contract grant number: 2024JC-YBQN-0893; IIT Clinical Research Fund of The Second Afliated Hospital of Xi\u0026apos;an Jiaotong University, Contract grant number: M019;Shaanxi Province \u0026lsquo;Dual-Chain\u0026rsquo; Integrated National Medical Center Medical-Engineering Cross-disciplinary Project \u0026ndash; Validation of Micro-dose CT Algorithm Application (2021LL-JB-06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Xi\u0026lsquo;an Jiaotong University. The requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA- not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMiranda-Filho A, Lortet-Tieulent J, Bray F, et al. Thyroid cancer incidence trends by histology in 25 countries: a population-based study[J]. The Lancet Diabetes \u0026amp; Endocrinology, 2021, 9 (4): 225-234.\u003c/li\u003e\n \u003cli\u003eCho SJ, Suh CH, Baek JH, et al. Diagnostic performance of CT in detection of metastatic cervical lymph nodes in patients with thyroid cancer: a systematic review and meta-analysis[J]. European Radiology, 2019, 29 (9): 4635-4647.\u003c/li\u003e\n \u003cli\u003eFritze D, Doherty GM. 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Oral Oncology, 2016, 62: 109-113.\u003c/li\u003e\n \u003cli\u003eLanger JE, Mandel SJ. Sonographic Imaging of Cervical Lymph Nodes in Patients with Thyroid Cancer[J]. Neuroimaging Clinics of North America, 2008, 18 (3): 479-489.\u003c/li\u003e\n \u003cli\u003eZhang A, Wu S, You Z, et al. Application of Preoperative Ultrasonography in the Diagnosis of Cervical Lymph Node Metastasis in Thyroid Papillary Carcinoma[J]. Frontiers in Surgery, 2022, 9.\u003c/li\u003e\n \u003cli\u003eHaugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer[J]. Thyroid\u0026reg;, 2015, 26 (1): 1-133.\u003c/li\u003e\n \u003cli\u003eZhao W, Shen S, Ke T, et al. Clinical value of dual-energy CT for predicting occult metastasis in central neck lymph nodes of papillary thyroid carcinoma[J]. European Radiology, 2024, 34 (1): 16-25.\u003c/li\u003e\n \u003cli\u003eZhu J, Tian M, Zhang T, et al. Diagnostic value of CT enhancement degree in lymph node metastasis of papillary thyroid cancer: A comparison of enhancement, ratio, and difference[J]. Front Endocrinol (Lausanne), 2023, 14: 1103434.\u003c/li\u003e\n \u003cli\u003ePark JE, Lee JH, Ryu KH, et al. Improved Diagnostic Accuracy Using Arterial Phase CT for Lateral Cervical Lymph Node Metastasis from Papillary Thyroid Cancer[J]. American Journal of Neuroradiology, 2017, 38 (4): 782-788.\u003c/li\u003e\n \u003cli\u003eZhou Y, Su G-Y, Hu H, et al. Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer[J]. European Radiology, 2020, 30 (11): 6251-6262.\u003c/li\u003e\n \u003cli\u003eZou Y, Sun S, Liu Q, et al. A new prediction model for lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma: Based on dual-energy CT[J]. European Journal of Radiology, 2021, 145.\u003c/li\u003e\n \u003cli\u003eLiu X, Ouyang D, Li H, et al. Papillary Thyroid Cancer: Dual-Energy Spectral CT Quantitative Parameters for Preoperative Diagnosis of Metastasis to the Cervical Lymph Nodes[J]. Radiology, 2015, 275 (1): 167-176.\u003c/li\u003e\n \u003cli\u003eZou Y, Zhang H, Li W, et al. Prediction of ipsilateral lateral cervical lymph node metastasis in papillary thyroid carcinoma: a combined dual-energy CT and thyroid function indicators study[J]. BMC Cancer, 2021, 21 (1): 221.\u003c/li\u003e\n \u003cli\u003eLai L, Guan Q, Liang Y, et al. A computed tomography-based radiomic nomogram for predicting lymph node metastasis in patients with early-stage papillary thyroid carcinoma[J]. Acta Radiologica, 2022, 63 (9): 1187-1195.\u003c/li\u003e\n \u003cli\u003eLu W, Zhong L, Dong D, et al. Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma[J]. European Journal of Radiology, 2019, 118: 231-238.\u003c/li\u003e\n \u003cli\u003eReinert CP, Krieg E, Esser M, et al. Role of computed tomography texture analysis using dual-energy-based bone marrow imaging for multiple myeloma characterization: comparison with histology and established serologic parameters[J]. European Radiology, 2021, 31 (4): 2357-2367.\u003c/li\u003e\n \u003cli\u003eWang J, Zhu Y, Li Q, et al. Spectral CT-based nomogram for evaluation of neoadjuvant chemotherapy response in esophageal squamous cell carcinoma[J]. European Radiology, 2025, 35 (7): 3800-3811.\u003c/li\u003e\n \u003cli\u003eMasuda T, Nakaura T, Funama Y, et al. Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies[J]. Radiography, 2021, 27 (3): 920-926.\u003c/li\u003e\n \u003cli\u003eForghani R, Yu E, Levental M, et al. Imaging evaluation of lymphadenopathy and patterns of lymph node spread in head and neck cancer[J]. Expert Review of Anticancer Therapy, 2015, 15 (2): 207-224.\u003c/li\u003e\n \u003cli\u003eChasen NN, Wang JR, Gan Q, et al. Imaging of Cervical Lymph Nodes in Thyroid Cancer: Ultrasound and Computed Tomography[J]. Neuroimaging Clinics of North America, 2021, 31 (3): 313-326.\u003c/li\u003e\n \u003cli\u003eChung SR, Baek JH, Choi YJ, et al. Risk factors for metastasis in indeterminate lymph nodes in preoperative patients with thyroid cancer[J]. Eur Radiol, 2022, 32 (6): 3863-3868.\u003c/li\u003e\n \u003cli\u003eY\u0026uuml;ce İ, \u0026Ccedil;ağlı S, Bayram A, et al. Regional metastatic pattern of papillary thyroid carcinoma[J]. European Archives of Oto-Rhino-Laryngology, 2010, 267 (3): 437-441.\u003c/li\u003e\n \u003cli\u003eBack K, Kim JS, Kim J-H, et al. Superior Located Papillary Thyroid Microcarcinoma is a Risk Factor for Lateral Lymph Node Metastasis[J]. Annals of Surgical Oncology, 2019, 26 (12): 3992-4001.\u003c/li\u003e\n \u003cli\u003eMao Y, Xing M. Recent incidences and differential trends of thyroid cancer in the USA[J]. Endocrine-Related Cancer, 2016, 23 (4): 313-322.\u003c/li\u003e\n \u003cli\u003eAlzahrani AS, Xing M. Impact of lymph node metastases identified on central neck dissection (CND) on the recurrence of papillary thyroid cancer: potential role of BRAFV600E mutation in defining CND[J]. Endocrine-Related Cancer, 2013, 20 (1): 13-22.\u003c/li\u003e\n \u003cli\u003eKim K, Zheng X, Kim JK, et al. The contributing factors for lateral neck lymph node metastasis in papillary thyroid microcarcinoma (PTMC)[J]. Endocrine, 2020, 69 (1): 149-156.\u003c/li\u003e\n \u003cli\u003eMasui T, Adachi S, Uemura H, et al. Clinical Study on the Risk Factors for the Recurrence of Papillary Thyroid Carcinoma[J]. ORL, 2023, 85 (2): 104-108.\u003c/li\u003e\n \u003cli\u003eLiu W, Wang S, Ye Z, et al. Prediction of lung metastases in thyroid cancer using machine learning based on SEER database[J]. Cancer Medicine, 2022, 11 (12): 2503-2515.\u003c/li\u003e\n \u003cli\u003eYou Y, Wang Y, Yu X, et al. Prediction of lymph node metastasis in advanced gastric adenocarcinoma based on dual-energy CT radiomics: focus on the features of lymph nodes with a short axis diameter \u0026ge;6 mm[J]. Frontiers in Oncology, 2024, 14.\u003c/li\u003e\n \u003cli\u003eChen B, Zhong L, Dong D, et al. Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma[J]. Frontiers in Oncology, 2019, 9.\u003c/li\u003e\n \u003cli\u003eQiao D, Deng X, Liang R, et al. Nomogram to predict central lymph node metastasis in papillary thyroid carcinoma[J]. Clinical \u0026amp; Experimental Metastasis, 2024.\u003c/li\u003e\n \u003cli\u003eFeng J-W, Qu Z, Ye J, et al. Nomograms to predict ipsilateral and contralateral central lymph node metastasis in clinically lymph node-negative patients with solitary isthmic classic papillary thyroid carcinoma[J]. Surgery, 2021, 170 (6): 1670-1679.\u003c/li\u003e\n \u003cli\u003eThompson AM, Turner RM, Hayen A, et al. A preoperative nomogram for the prediction of ipsilateral central compartment lymph node metastases in papillary thyroid cancer[J]. Thyroid, 2014, 24 (4): 675-682.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. \u0026nbsp;Comparison of clinical characteristics and DECT quantitative parameters between metastatic and non-metastatic lymph nodes in the internal validation cohort.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNon-metastatic\u003c/p\u003e\n \u003cp\u003e(n=127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMetaststaic (n=136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal=（263）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAPIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;2.6mg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e113(88.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15(11.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e128(48.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;2.6mg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14(11.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e121(88.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e135(51.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eShape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e41(32.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e133(97.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e174(66.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e86(67.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3(2.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e89(33.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e80(62.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e109(80.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e189(71.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e47(37.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27(19.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e74(28.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMargin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eClear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e86(67.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1(0.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e87(33.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e41(32.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e135(99.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e176(66.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAPEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHomogeneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e107(84.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4(2.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e111(42.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHeterogeneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e20(15.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e132(97.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e152(57.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAPED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMild/Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e105(82.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3 (2.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e108(41.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eStrong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22(17.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e133(97.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e155(58.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eComplete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e120(94.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e53(38.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e173(65.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIncomplete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7(5.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e83(61.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e90(34.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e136(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e106(83.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e242(92.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePostive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e21(16.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e21(7.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCalcification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e136(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e111(87.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e247(93.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePostive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16(12.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16(6.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2. Comparison of clinical characteristics and DECT quantitative parameters between metastatic and non-metastatic lymph nodes in the external validation cohort.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNon-metastatic\u003c/p\u003e\n \u003cp\u003e(n=53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMetaststaic (n=75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal=（128）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAPIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;2.6mg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e40(75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10(13.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e50(39.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;2.6mg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13(24.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e65(86.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e78(60.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eShape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4(7.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53(70.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e57(44.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e49(92.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e22(29.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e71(55.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e47(88.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e48(64.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e95(74.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6(11.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e27(36.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e33(25.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMargin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eClear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e49(92.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e22(29.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e71(55.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4(7.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53(70.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e57(44.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAPEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHomogeneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e47(88.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e14(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e61(47.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHeterogeneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6(11.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e61(81.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e67(52.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAPED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMild/Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e45(84.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e24(32.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e69(53.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eStrong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8(15.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e51(68.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e59(46.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eComplete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e31(58.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8(10.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e39(30.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIncomplete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22(41.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e67(89.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e89(69.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e53(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e65(86.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e118(92.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePostive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10(7.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCalcification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e53(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e74(98.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e127(99.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePostive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1(1.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1(0.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;Table 3. Multivariate logistic regression analysis of independent predictors of LNM in PTC.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAysAAAH7CAYAAADM9gBaAAAQAElEQVR4AezdCbzmU/0H8POlQotola3RviAVpUWRklZKRUXRqki0SCK0i/5pT8pa2SJaaKHQrpRW2mmhPbQR8r/v8zgzv/vMc2fuzNyZuc/zfO9rzvx+v7P9zvmc7/lu55zfs8IN+ZcIJAKJQCKQCCQCiUAikAgkAonALERghZJ/iUAiMIMIZFWJQCKQCCQCiUAikAgkAjOFQBorM4Vk1pMIJAKJQCIw8whkjYlAIpAIJAJjjUAaK2M9/Nn5RCARSAQSgUQgERgnBLKvicCwIZDGyrCNWLY3EUgEEoFEIBFIBBKBRCARGBMEZrmxMiajkN1MBBKBRCARSAQSgUQgEUgEEoH5EEhjZT5IMiIRGGEEsmuJQCKQCCQCiUAikAgMEQJprAzRYGVTE4FEIBFIBGYXAtmaRCARSAQSgaWLQBorSxffrD0RSAQSgUQgEUgEEoFEYHoIZK5EYD4E0liZD5KMSAQSgUQgEUgEEoFEIBFIBBKB2YBAGitLMgpZNhFIBBKBRCARSAQSgUQgEUgElhoCaawsNWiz4kQgEVhUBDJ/IpAIJAKJQCKQCCQCXQTSWOmikfeJQCKQCCQCicDoIJA9SQQSgURg6BFIY2XohzA7kAgkAolAIpAIJAKJQCKw9BHINywPBBbbWLnuuuvKtddemyExSBpIGkgaSBpIGkgaSBpIGkgaWA40cMMNNywP+2GZvnOxjJWrr766HHLIIWWbbbbJMIsxyPFJ+kwaSBpIGkgaSBpIGkgaGE0a2H777csll1yyTA2H5fGyxTJWrr/++nLBBReUM888M0NikDSQNDAuNJD9TFpPGkgaSBpIGpg1NHDGGWeUq666annYD8v0nYtlrKy44oplo402KltttVWGxCBpIGkgaSBpIGkgaWAxaCB1iNSjkgaWhAa23HLLcstb3nKZGg7L42WLZaystNJKZbfdditHHnlkhsQgaSBpIGkgaSBpIGkgaSBpIGlgGdPAEUccUdZdd9159sOI3i2WsRIRZfXVVy9rrbVWhsQgaSBpIGkgaSBpIGkgaSBpIGlgGdPAmmuuWW5605uOqIkyr1uLZazMK553icBiI5AFE4FEIBFIBBKBRCARSAQSgQUikMbKAuHJxEQgEUgEhgWBbGcikAgkAolAIjB6CKSxMnpjmj1KBBKBRCARSAQSgSVFIMsnAonArEAgjZVZMQzZiEQgEUgEEoFEIBFIBBKBRGB0EVjcnqWxsrjIZblEIBFIBBKBRCARSAQSgUQgEViqCKSxslThzcqHF4FseSKQCCQCiUAikAgkAonA8kYgjZXlPQL5/kQgEUgExgGB7GMikAgkAolAIrAYCKSxshigZZFEIBFIBBKBRCARSASWJwL57kRgXBBIY2VcRjr7mQgkAolAIpAIJAKJQCKQCAwZAsvIWBkyVLK5iUAikAgkAolAIpAIJAKJQCKw3BFIY2W5D0E2IBFYDASySCKQCCQCiUAikAgkAmOAQBorYzDI2cVEIBFIBBKBBSOQqYlAIpAIJAKzE4E0VmbnuGSrEoFEIBFIBBKBRCARGFYEst2JwIwhkMbKjEGZFSUC8yPwv//9rwgt5frrr5/0fMMNN8x9lia0vAu7qveSSy4pf/vb3xaWdYnStfGPf/xj+fWvf13cL1Fls6gw/C677LJy+eWXzx2DxW3ef//733LxxReXK664YnGryHKJwNAhgB8sCs9qHVRG2fa8sOt1111XLr300vKHP/xhyqzqM5fxKnN7yowzkHDVVVfV+W7ez0B1s6aKf//73+VXv/pVufrqq5e4Tf/4xz+qzLjmmmsWWte1115bfvrTn5Yrr7xyoXkzw3gikMaKcc+QCCwlBCjDp556ajn00EPL2972tnLGGWcUwrS97i9/+Uv5/Oc/X/7v//6vnHDCCeWXv/zltA2Cf/7zn+UVr3hF+eQnP1kI/1bnTF8pCh/60Ifqu6YjeGb6/Uujvn/9618Vt1e96lXlU5/61BLjx5Dbddddy2c+85ml0dysMxGYlQj8/e9/rzwNfzvkkEMWaExQgN///vdXXveFL3yhKDudTnHGnHjiiWWvvfYqZ5555pRF8Kn3vve95cgjjyyU7ikzzkDC2WefXXbbbbfyi1/8YgZqmx1V/O53vyvve9/7yn777TdJRi1u684777yy//77V4NlYXX89re/LfjnF7/4xYVlzfQxRSCNlTEd+Oz2skFg1VVXLbe4xS3KscceWyj8d7nLXcqtbnWruS+XJg+D5ba3vW253e1uNzdtYTcrr7xyechDHlLmzJlTVlhh6U1ldd/3vvctm266abnJTW6ysGbVdF5Oqz71YRb+p09rrrlmIVAJSu1dkmbe5ja3qfist956S1JNlk0EhgqBVVZZpayxxhrl4x//eDnooIPKJz7xiSmdLeeee2557WtfWz772c9WPqfsdDp705vetPLQfkePOfvnP/95rqPBnN5ggw3KPe95z6JMWYp/d77zncuDHvSgsvrqq0/7LRxXDKppF1jGGVeekCf/+c9/yje/+c0yE06pddddtzzgAQ8ot771rRfak9VWW63KMmUWmjkzjCUCS0/DGUs4s9OJwGQEGCL3uc99yi1vectyhzvcoVD63bdcN7/5zQsD5h73uEd58IMfXCi9EdGSF3i92c1uVvbYY4/yyEc+skRMr8wCK5wiccUVVyxPetKTystf/vJpGysXXnhh+ehHPzpFjcs/mqK0ySab1DGZidbc/va3L69//eurwTIT9WUdiUAfArPy0Txaf/31y93vfvey0korlVNOOaX8/ve/n6+tlN/3vOc91bDgJLjrXe9alJ0v44AIzh1OmX5HCUPl+OOPr3Uqhk9tt912lVdpi7ilFTbaaKO6AsFQm847bG+y+jSbjRXOMsYFo2U6fVpYHrLupS99aZkORuSeFZ0HPvCBC6s208cUgTRWxnTgs9vLDgEev4gojItBb+UhlCZfS7fnmjD+zW9+U+yLtt3gxz/+ceH56ub561//WrdTEIKW8SkKgj3V8ilrn7c09+r905/+VNT1ne98p9iGJp+gHdKEK664ovzwhz8s9h2Lt+VMW5SXV7Cd7Uc/+lH5/ve/X7RDnHb85Cc/KQcffHD5+c9/XqyutLZIt++cIWNftHrFLShos7zKaLPnlp8CpE3aaKvId7/73cJ72a1Xflvrvve975WLLrqoaF8rL19Ez8izjc4KC+wEdcpnL7V+eo+6YAMXfZNfessnTTs8C7aiwMd79dvWM/EZEoFRQsA8ssLwkpe8pPzsZz8r55xzzlwDovXzc5/7XKEMW/XA5yJ68878wZ/MOQq9usw9/MpcNufU0S1jrjof9upXv7p84xvfKOahMsriW3gRPmXeSlO3OPWY/42vKiNOGXMU//BeZcULtqpphy1s+I+y4j2Lx4M8C3gk3ofXqEecuvCON77xjeVLX/pSNeS0RVsFbcQ/vR8WyiwsTNVe9Wmvczsw8l68qst/1Y1Haaf+6oNy4iNiktOLrFGHPDBsY6Gf4vRRHvwVn7MVFj76rD4Y4Ymunr1H2+TFP5Vvdeo7WYQG5F02Id8yTAisMEyNzbYmAsOIACa9KO2m1Nry8MpXvrLYg/3pT3+6vP3tby+77757+eAHP1j3YxN4xx13XCGwbSHD7BkfyvBgEgTeS1A4k3HyySdXQ4fS/4EPfKAQqKeffno58MAD6zkZ73T2RX1HHHFEsbf8hS98YV0doRDId9BBBxXvUa+4N7/5zYXgUc49pYPwsY1AOsGkTeIpCfJ9+ctfLvZ7W4Ww8tIE2yB8vOdjH/tY0Xbvectb3lL3ozcB+a53vat6N/UPLtq4zz77FEJYfd5p/7r3/uAHP6h9VZ82Sle/a0RU5eorX/lKXT2CH2EsTX9tWznqqKMKAe9ckbbYc7/33nvXA6EUjmOOOabsueeetW/KURA+/OEPlwsuuKAG+DhAKi1DIjBKCJhH5vEOO+xQ1lprrXpui0Lc+kgBNYde/OIXz7eaYi7iEfjBSSedVItQuG0re81rXlM4O0R6h6ugjDmO71Gi8Ru8xnkW28zwC0YEBw+ewVBg3CiLJ5jn+AUeSsHG684///xq+LzpTW8q2qM/tojieXgwXutMhbOF3u18Dl7b+MSll15a3vCGNxR143FWCeTzPu/GL/QLX2QAMCbc4+2ub33rW+tZHoaIdk4Vuu391re+VbRX27X3q1/9at2Kh387O8dhhB86h2IMYMiY9IwvaavyjCVp3XdGRMHD8D0r6uojS+RhVOg/GaWv+DnehgfKb3zwW9hrQ8OI08nKG6MPHvvuu291lknHK50f/PrXv+4VGRKB+RBIY2U+SGZfRLZovBCw3/pOd7pTIQh4JCnnu+yyS92LzdDghVt55ZWrYiCdYaKMrRK8hYSP7WURUbdtUbh5NG0/Y/QQeI9+9KOLOgl8RostFLZyfO1rXyvqtCd78803L5bweUR56AhxQo1AbULocY97XHnUox5VHIxUj+0XtrPZFmX/8SMe8Yiy9tprF8KJUJb/Oc95TtE/yr73TTW6BCAlX94nP/nJZcMNNywMCfH2OPO2MlTU6zyN+iguDuPCjEB+xzveUbfZbbvttmXOnDnl3e9+d/2qUPed+gQ/2+ngxxhpW1RsPdFf21YoLIT+E5/4xLrVRDwjD9byEd4MM3XDg/L0lKc8pWy99dZ1ex8vo7QMicCoIUBZNuef8YxnlHPPPbeuYrY+mpPOLdg6RXlv8a7mmS2w8nCgRES54x3vWMxvToau0SO/YBV6q622KraTCVtuuWXlheuss05dDWakaI9tSOqxouGqLP5kznqn9lKe8Y+HPexhxVylOHNIWBXF+/DW0047rZ6BscUMX1TOCsI555xTv5qFf+ArRx99dMHfYIBHU+i909kWvBX/xXfvdre71dUgvBzPYeQ99rGPLXgdI019yvUH2PlYC6NEe/E07dBefbLNmNEEN7xmp512qluEP/KRj9QVL+VtmyMfNttss9pfKxzOGTVDpL1TG6yW2eLHGIKH85XS8ToYb7rppnXbHyy135h4r/fAiLHDoMKLlWOQuH/84x9f+Sdeqt1wwUPxS4aQvBkSgX4E0ljpRySfE4HljABhTNDyUhLA22yzTT18+KxnPat6uwhUgoMQtNdXcynuhPzzn//8qowTsuJ581wpCgwSQk599oGrg5JuZUCaw6mEMcH6hCc8oX7JhWLu0LjgHeoi8BlG3qU8QaM8QenqnA6BtvKEQUWAEnyEpH7xuFndoTyoj+BV56BAwaFAMH6kq4sxwUhgQBGW3kHwEvoPf/jDy/3vf/+i74SijxU89alPrYc8lYcPAXnllVd6nBQioio89ljzfNo+IgPvK4HqfIvtDryIPITq5hkkaGEJX/1URuDppQQxxmD0vOc9rxpN0jIkAqOGANpnIFDGzQ1eds/m67e//e2qxJvv/f0WRynGLyKiJuMv5rfVhxrRVSLc7wAAEABJREFU9x8e4x3KyEsxxss4ZNbrfOACj3HWzvzjbFGN+a9N+AVDSRk8Qn3qVQ+FmYNHXZwUDCL98vWvnXfeueDJ97vf/ernziOibp3CO/EOPMb78ED8EAbaoZ3aq634BQMD35WHMRAR9cMrHCz4jbb2B1gxDJ72tKfVDxRERF2p0l4GQjv7iIczCPDHLbbYohpa+JHy5Ir+NsxhwODR3+77jCd+zRGlDg4s7xDP2HnoQx9aOKPwcitQDBM8kKEGRw4m8kn+iKhVM3DOmTDwrJbBwSoYLGClrVONdy2c/409AmmsjD0JJABLG4GIqAKNQMC8p/s+TJ8wYQwoQzGOiCokPROEBJD7FjbeeOO6amHFhHDhOSOYCAd5dtxxx+q5tApCANkuERGSavBORgIjgOAmeLU5Yl4ebbJFTL28eLYT2Mcc0csjv8ralXFBQFlhIcwZHTyQDJjtt99e1oFBXgoCI0F7baeI6L1DAW2lABB47sURfN4rWEnRTislVmCsKMmzoAA/5azOEJ4ME/1lONrGQtlheNjiQAmBwaD6KDcOq9pCYXuYeigRg/JmXCIw/AiU+hUwyj0FF28x79C9bUE+MjJVH/Gx/jTzV1zEvPnuuRvkEbrl+/khY8ScxmvwX44IvI1xIq+2cnBwQmgz50T/O8xzxgZeqKx0ZV1bwMes/truZoWA8o43SdfGFvAU7WCU4Ml4nMCIwHN8it67lOsP3qm9+EprL0Ojmy8i6pfTmszQBsEY4JFWQGBipVvAmyOiyqduPe1e3xk3tm7hoQwpht/mm29eDSXGoO28HFcwxicbRt7b6nG1om7bHv5phZ88g4E0fXPNkAhMhUAaK1Mhk/GJwAwhgGkTFAQY46C/WsKLMJOvP82zNFchIlzmhm6aSIKPQLMliSDj1bR9iqCVbo+0ZX/eQYKmKffSphv0wXYE2wso4AQXoTZVef0jLBk0lBaCmWfSKg8P6lTlrArZL85I4XkTpso7KN6WLNszGEreRcgPyteNgx+vJEHuvbye2stwY4C8853vrF/8siWMIcWz2C3f7q1O2Re/yy671K0p9rfDvX+8Wv68JgKjgAAF1Gos5dPcs3J6r3vdq64ELEr/Inp8rmuITFU+opd3UDpnwmMe85h6Ro+BwLtvRcB8VrftZ0cffXRVvG1jspoyqJ4FxZnT+kkBZ2jgE1YW1C+tWzYiqlGHh1oRsVKCvwhWq/HTbv7uvfpsq+q2l3HYzbOge+U5lhgXHFL6C4v+NnbriIhi5ZpB54wgQ0V+77Xy4qMKVpiNtzNGtt3qV7cO98rAxRfR1HX44YfX381h0ErPMACBjJqEQBork+DIh0Rg5hFgEPA42b41iJGLJ+QYNN23RwwWwhGD45VVzxYTS/+2QRGeVld40qQxlsRZOSAYbSHzToImYuo6le0GKx2EDeGjHl5KxpB6uvnas/4LDn7qK6PMexkTPITdMt17njwrRFZh7PNWh/SIXlsjeldx3RAR1VPoMD3hzCijhFBQ5IsIl4FB2whxxhfh60Bq286gLc7gOLBrZcUee0YbQdxfGSORQWhVxTkbSpPDucalP28+JwLDjEBEbz6ZBxFRt6yaMw7Mn3XWWfXZvNLHiKhz030LeEFE1PMfLY5i7b5dI8LjpNDe142MmJwPD8IPefCdCXGWxDkM8ZwoeBKeaV5znLi3+qHuVm9EzNfmiGjJ9SMbzvBZUbBygddT5Bkk3iNjtz68Ej/iCLGFCjYCXm1VhmNHmf4gnoNIee3Fd71TuYaTMhHz2ua5BedS/NYXQ4UBgv9rp7Ld9kXEpP7iXbvuumv9UAhnDYNKnPNEHEFW632kwOoKnt0MkIhor667AZzBtP0O73SeR/+tmMsU0csb0buKy5AIdBFIY6WLRt4vDgJZZiEIWJJ/5jOfWT9dfNhhhxVKun3TthQ5LO4AJ+FOcKiK4CCAHGokoAhVQZwro8OVkBEnj6uygpUU36t3oNEqC+Em3hK+A5C2QliOp2gzHtz7co2VDwJWu7xbGW3pvocg13bpyjjb4UCtPdHqtdKgjD5bmbANzTsdTtXv5z73ucV+dlsefKmGEeI9g4I6tQVGlAw4aZ8tCdIIX20Tp/+eXT27l0d57VDGFg3xzs1Ig6H8+iS0NjDmbH1wSN4+bEpMRBTbHBhXFATnfghtgl9/1aO8d7vnxbWy4l4djDoGUBsLeTMkAsOOgHmD5s0zhrg5ZXXlBS94Qf1MOLo3T8TjL+Yf3oEXKav/8jsXgp9whHACOBRvbuMf8uJx5hne4145zge8y1wzv9UtTTvcyyOo++lPf3r9Uh8F3zZU8erDm2wPxbsoz9rAoWQFQb+00Tu1XX7l9EX9nqV5H8Vd27WHg0S7YaIeW620Vb3q11bbxhgyVlw5Myj5nCPibDv1nv4AD6vlrb0cIj56YusaA6G1F14w1nbtVA4fku6cjK15+uvLW9oHA1fpDT/9Vb61wUcMGEjKMvbwMf3mEFKXFekXvehF9UcytR9G0tWnLvUwXskKsqHxT1t4vce74Um2aav8GRKBLgJprHTRyPtEYCkgEBHFVgSfpyTQePh4yBgTgm0SvH8EldcTMIQawUoxJkgIQQe2Kb4UbUYCoUgRIBQJDMxeeYLRwXQrCpbcxQm8iwSKunm2GEe+KENQqMMWA4qDehyiJOQIEoJX22wZ0AYrBsoRPJb15dd+gpDQpgxQ9glNWxYiojDWHKgkvHjhfPVGHtvCtG1QkE4wwoih4qtasIIFI4ugdrDVIVXvpgQQdPqgzTx+Dp16n/3o2sw4Ul6fCWtCllAlhPVDO/SVh9RWB1vCxEVEgR/hrC4eQQJ8hx12qJ5V7+atpJAYH15L+XxtyJYyWPtMKCGvvgWHTE0EhgMByqXVAXOHIW8u4hkUW2cT8CHzyZw455xzCt5gFQEPwnP00mqGLZXmBoWdBx6fE8xzdfpQhY9ceN8ll1xSPzWOl+FR5pg68EmrNOq0IuoqRERxKN1WUKvB2iPe+3wxC++w4myLmC1sEVF/u4oBQNGmXOOHeJd+ahN+65A5x4d3Ms4o6AcccEBRjw+U6Cs+ZO57P97jU+r4onM0jBQ8/thjjy22WFltwXda+7SxG9TjIyuMCys5nDjeIz+Dxbu0l7HAQQNfOHC+uNpWy1kET/3VDzICz4SrdG0nU/BWRlZ7PzmEnzI+8UXx8GM8cTwZM/zO6gqe7R0cTPrk3fitlXGflbZCjX/qK1zIDLwYnsYCrcDZOzIkAg2BNFYaEnlNBJYiApj9s5/97GJPLy8fJd0ZCkKOl42Hvr2eELCX2ffwLZlj+JbdHeCkePviCm+/8pb1CXqCMaK3hB4RhVC2XYmAa/VSEvbaa6/6my177LFHzeP3ACjRBIXtYlZ+Xve615V73/vedSsAQehgvDjv0m7vUo+83s2oOPDAA+vvBKiHsCQUtdUBd8JN/7xTHEEpELTa1NrXf4UBga5uig+DT3nvdCbEe51poRCpXzspC/vvv39tv3Mx8vsNB4c7vU9+mNtTro8w1i7e14geftrBqDROtnR4FvTZ55EJf2XV6536awXL1jh7uK2gaCsFhMKlDoqV1TP1ZEgERgUBtI8PUZ5tn6R84xkcJgcddNDcL+CZExR088/vkXCiMALgEBHVmWMu+iAFxwbPu2fbj8w3fAX/MQfxH+8w/80x/EAejgm8zBxlGKi7hfXWW68aBPK1OEYGZVkdVjjc41e2OuGf+K7fndI3K9WNV+mL+Yy36DMlHL/TXu3TLts/PeM5+smx4VPFDtDjCYwrq814BocP55F3c7S09vVfvV8ZZ4HUgz/hZdqL33Am6Qd88Wnv5UTCpxkaxga28msnvolfkUmMS/1QJ5z1S/luGxgdHEbGVjyZ5F36yJGjDsafPpBPMFK3M43kkDbjtdqJf1pZxz/V531Wohk78I2Yx4u9K0MikMZK0kAisIwQIKAIVJ/YJbwwdwo5oRAxjzkTSjz+BC5Fn5AhSAhacZi9Z4zeM6HqudsNz97VjXNPiBC8lH0GFCVdHQQGpdq9d3p/RBRKAaGkLmmUfMaUNvmksJUOZbWJAFJnmfizvM9Y4KXTn4moop+2qFmB0G8CTPxUwbu9V3v95oH83qHN+qff2uTdBCQlRtsFwtN79ZMyJU15B1nVZSxaf9VDQPIw8jbyCvIUMnC0obXPe7yfsiHAMiKKPnfHQt/hw/jTX+9XlnLU6sprIjAKCDTaN+fwJ06DiB4vMz8azZuveIH5Kq/5omzDwNxkzFj9wGPwH/PeCiW+0Z2r7R0MJV58c8tcN6/VLZh/rW5X7TB3XT23YK7iU+rBQ7QZT3OPZ6hLm71f2Ygo+oJniLfKrB94oHluvuMl3u9e3oioW4AZEPge3uP9rurAD7vGm7SpQmuvut1rr365h5X2CrDQLu3zDu3VFth5Fz6ozfgkPuUezm2MXJW3OtNWPqzcaGtrmzFTt9VtfW91SocB3i3dWBsfdcLA+7v803uNobxoSF/UkWF2I7CsW7fCsn5hvi8RGHcEKMAEnxDRE+z9mEREXdmImN61v7zniHCZL0RErbslRES9jYgaH9G71siJ/yJ6zxG960RU/Rcx7zli3n1NnPgvYnCc/kfERI6F/4uI2qaWMyLabY2PiHoVGdG7j+hdu3HuhYhwqSEiatmI3pWXkxfQyhWlgoCvGW/8L6KXT/uFG6PrJaKXFhH12X8RUY09eSPmxZf8SwRGCIGImDSPpupaxILzRUSdL2XiL6KXd+K2/ovoPUf0rjVy4r+Iec8RvfuI3nUiedK/iJj03B4iora/++w+ohcf0buKEyJ6zxG9qzghYv5n8S1ETE4XHxG1zxFRpvsXEdNub0Qvb0TMrT4iBpaXIaKXFhHF9tiXvexlBU+0Pc89g0e+FiJ6+fE4oT8+opcuPqJ3L58groWIXlpEtKi8JgKTEEhjZRIc+TB6CGSPEoHpIWCbli0NtlY4i9IvUKdXS+ZKBBKBRGD4EbCCbjucrV+24i7qp+OHH4HswWxCII2V2TQa2ZZEIBFYbgjYnuCcj+1ftnEst4bM9hdn+xKBRGDkEWCsWGV2Ns/qip0AI9/p7OCsRSCNlVk7NNmwRCARWJYI2Fdtr7q95BG5HWFZYp/vSgTGGYHZ2nfnVvDENFRm6wiNT7vSWBmfsc6eJgKJQCKQCCQCiUAikAgkAkOFwCIaK0PVt2xsIpAIJAKJQCKQCCQCiUAikAgMMQJprAzx4GXTRwCB7EIikAgkAolAIpAIJAKJwJQIpLEyJTSZkAgkAolAIjBsCGR7E4FEIBFIBEYLgTRWRms8szeJQCKQCCQCiUAikAjMFAJZTyKw3BFIY2W5D0E2IBFIBBKBRCARSAQSgUQgEUgEBiEwWsbKoB5mXCKQCCQCiUAikAgkAolAIpAIDCUCaawM5bAtnUZfdtll5YQTTsiQGMylgaSHnA/DTgMnnnhiwdtmkuQ4ltcAABAASURBVGuef/75OUeSTyYNjBkNHH/88WW2hZNPPrlcccUVM8neZmVdaazMymFZPo26+OKLy2tf+9oMiUHSQNLAyNDAfvvtVy666KIZZaqnnnrq4uKT5XJuJQ0MKQ3su+++ZbaFAw44oFx++eUzyt9mY2VprMzGUVlObdp4443L6aefniExSBpIGhgZGmBYbLLJJjPKVXffffeRwSd5fsq84aaB8R6/k046qcyZM2dG+dtsrCyNldk4KsupTauuumrZcMMNMyQGSQNJAyNDAxtssEHB22aSra699tojg0/y/JR5SQPDSwPrr79+WWWVVWaSvc3KutJYWYbDkq9KBBKBRCARSAQSgUQgEUgEEoHpI5DGyvSxypyJQCIwuxDI1iQCiUAikAgkAonAiCOQxsqID3B2LxFIBBKBRCARmB4CmSsRSAQSgdmHQBors29MskWJQCKQCCQCiUAikAgkAsOOQLZ/RhBIY2VGYMxKEoFEIBFIBBKBRCARSAQSgURgphFIY2WmER3e+rLliUAikAgkAolAIpAIJAKJwKxCII2VWTUc2ZhEIBEYHQSyJ4lAIpAIJAKJQCKwpAiksbKkCGb5RCARSAQSgUQgEVj6COQbEoFEYCwRSGNlLIc9O50IJAKJQCKQCCQCiUAiMM4IDEvf01gZlpHKdiYCiUAikAgkAolAIpAIJAJjhkAaK2M24MPb3Wx5IpAIJAKJQCKQCCQCicC4IZDGyriNePY3EUgEEgEIZEgEEoFEIBFIBIYAgTRWhmCQsomJQCKQCCQCiUAiMLsRyNYlAonA0kEgjZWlg2vWuhgI/Oc//yl/+ctfyh//+Mfy5z//ufz73/+erxZxV1xxRbnyyivL3//+93r9xz/+Ua655pr58mbEkiEAa+Pwt7/9rVx77bUDK7v++uvLVVddVcfBmBiL//3vf3PzejamV1999dy47o3y//znP0u3TDe9/14+dWoXOrligha0Tf3q+u9//1vEaUu7iuuvJ5+XDgI33HBDnb8f/ehHy6te9apiTi+dN41HrfCE4V//+tfyhz/8ofJHz+K7CJgXXZrHG81Lc8v86ObN+wUjAFvY/elPf6q85LrrrhtYQD68CO4t4EEtM+zxvn/9619F3hbfruKUX1D92oHXddtC1hlvVzyuG3KsG7rL5mrszLVf/vKXqYMsHcjn1prGylwo8mZ5IYDB//CHPywf/vCHy1FHHVWOOeaYcvjhh5f3vOc95Tvf+U7BEFrbfvKTn5SPfOQjZd999y1vectbynHHHVfLfPCDHyxf/vKXC6W15c3r4iPwi1/8ohx99NHlE5/4RDnyyCPLKaecUgjd/hqNh7F45StfWQTjQJmS7+c//3nxfPzxx5ePfexjA43Piy66qJx55pnTUmoZT5/73Ofq+KtP204++eRy+umnV9oh0C+//PJy4oknlgMOOKDSiPzitSfD0kfAXP7Nb35TPvShD5VTTz110txd+m8frTdQZn/wgx9U/mYu4nXmE2y///3vT+osxfWrX/1qede73lVe+9rXlne+853lpJNOquOAp1544YWT8ufDYAQo+2QOYxvPI5POOuusgYooZ8l+++1X+R7e96Y3vak6bdT8u9/9rrzvfe8r+BN5Jq/4bmB8SqfsduPdM34++9nPliOOOKLyTvmM52mnnVaOPfbY6sy77LLLygknnFB5HX6nncnroLdsgvmJ17397W8vz3nOc6rzdNm8eTzfksbKeI77rOk15eYrX/lKOeigg6pA2HLLLcsznvGM8rjHPa62cZ999qnKLCEi4m53u1u51a1uVT75yU/W/BtttFER993vfrfsvvvuhZDpGjfKLJMwQi8h8N7xjneUiChPeMITyoMf/ODy8Y9/vCpN/d0kRDFswpXQveUtb1ludrOb1ZUYAlRdD3/4w6vidPbZZ08qbsWD8F1zzTXLKqusMimt/0HdlAHCe9111y1PfOITy9Oe9rTatksvvbR84AMfKLzPd7jDHcpd73rX4l3nnHNO2Xjjjcvtb3/7/urG5plA5eGl2DAMeWSNF2UX/jMNxIorrljWX3/9st5661UamOn6x6m+c889t/JFToJHPepR5ZnPfGbZaqutqlJESf7Sl740Fw5z7kEPelA1+inZa6+9drnvfe9b1lhjjepoePnLX17SYJkL15Q3nDR4zFprrVWe8pSnlDvf+c6Vt3z729+erwxDQX68z+rGyiuvXG5+85vXVeL3v//9hUwybowffLJbARnFCXTb2962rLbaat2kuoL25je/uRqad7/73cuTnvSk8tSnPrXyOnOXwYrX3fGOd6y87rzzzqvvwqdvd7vbTapr3B44yhiGP/3pTwveZ2Xre9/73lwjcqbxIO840X71q1/VcZ/p+rO+eQiksTIPi7xbDgj8/ve/L69//esLxvuiF72oMD4IiPvf//7lZS97WRW4b3zjG8vFF19cW7faaquVOXPmlJve9Kblnve8Z3noQx9aHvvYx9YtJ5jUu9/97vLrX/+65N/iIYDBU5II2h122KGss846FWOCkLHws5/9rLQ/q2E3uclN5q6Cvfe97y3Pfe5z69hQjq1q3O9+9ysELiHeL7CtnhHWm2yySVlhhalZEcFOQB922GHlhS98YXnyk59chTSa2WCDDcouu+xSHvKQh1QljtFzl7vcpay00kqVTtp9a/O4XQlvzoCXvvSlVfHlfYXlbrvtVo17gnYmMYmIOv6U54hYoqrHuTBlC61zzLjiiwwQxje+SDF+yUteMpcvMhLNJYa8e/nMWQb905/+9PLjH/+4vO1tb6sK3DjjuqC+c5x9+tOfrgYfucLQ4zy79a1vXVesGPqtPBlj6w/Dxg4A4dWvfnUxLgyXz3/+89Vox3/wqU996lOtaL1ytnEWMD7Jsho58Z+dAYwYK8dWyDiL1HGnO92p4HU77rhj2WyzzeoqN5663oRTYNVVV628Dn3gexPVjOU/48cg33///etq19e+9rW6ugXHF7zgBVU+zCQwEVEdYfe4xz0KOTiTdWdd8yMwtYYwf96MSQRmHAEKrO1CvEcEM6U1Iqryihlvv/32hefclpK2ulJu/CM85CecCWlMmwfeGYsbs+RlERGwnQTDV4ziHxEFvhtuuGFl9lYrysQfQcv7RwkSIqIQ7sZsIrlu+VKXOpRfecLraGykMYgIEgYo4UuxFT9VYBTxYlqhIdwJhoieIhwR5Ta3uU159rOfPV/xrhIwX+KYRMD93ve+d7EthVeWl37XXXetBp/tKd/85jcnIWGMbDN5wxveUJVbYyuDsoxFHmNb7YT/+7//K2iAMkD5avPT+CojuOfZlPetb31roch5hy18Bx54YF01NY/lNW/N84MmVlltq2FkoTNp4xQY5wz/Sy65pK4yc9BE9Ogdv2OU4IuUZVtiG0YRMdcYobjJa25Rds1LjgZ1jxOWi9JX9PuNb3yjrtybN8qSKZRRtMixJg6GjBR86YILLqgriFZ05ZWO7wm87sZAXVZfpJkPts5+61vfqs44/FF8C3ii7V6PfvSjy6abblp5b8S8sfceqyyNt0X00jxH9O5bXQOuIx0Fa2PFAWPFy2qH7Vn4nS2SttV1ATAW+BFeZkse3ijd+OJF4oyjueTckHGzOu2+8ayIqLpKufFPfunKoif5nDvybDXMO2V19Q5tNC/di88wNQJprEyNTaYsZQQIWUoOJddWoIj5ma3leGm2PPA6dZtECfaMuXz961+vh09tfWC4iM+w6AhgrrzxFEqMt9VgjAgDSitGS3BjvgxNHt499tijGIOW35hZIaNwEQK2gxG+0n/729/WbVo8xAxUcVMFgsI2CgYrg5ahMiivlRXe50Fp4xxnzAhPW8Ee+chH1m1E5gcDhjAlSLv43OIWtygUIsaEs0TmX5n4s5WO0QF/4++cktU3XnvjbHXU+YqJrJP+RUT1+lIeePbRFSXuPve5T12R8w40h5YOPvjguo3G9ht5GKBW57xvUqUj/mCOUa4YGLzpg7ornhJszllR7uaJiLmeXoobxdictnXS+HXz5v1kBOBFgSWbpMCLcQg/Br84iiUFF19jVFv5ogg3Ol199dULAxHNk1nGx06BMvGn7Be/+MXqLOjfsmUemGOM0Ic97GF1G+5Ekfn+2e5HKZ8vYcwjIqLgc/B+wAMeUDG28oQ/GUfYdyEiWxihZJeVZ7JMujo4bPArco5j7XWve13h2LFtee+99y4MF3m7ISLqx0UYsnYYGH90hC96B+eMZ2XwTg44zj/ny6zKyS8tw2AEZqexMritGTtiCBAKlFAKK8E8qHs8gzznPBBd5VleXijL9rZ+OQi5zTbbFAzBsrv0DIuOALwxeEqlDxa0GjBwwjgiqhCFMe8v7C2x/+hHPyoYPsasDC+jffXGCFMmvJ/1rGfV7Qu2IlmpsT0M8yYQCHXl+oN0Y0+JcBalP709U9zQUXvOaw8BCpDzKfCx9YsxIoVgJsCd9/LcgvF/xCMeUQRj14yZduXV5SSQzxkK5TkIjI86Wz3dq5UBxo4xRkO8wGiMV5nBJK9tMvbeu+eFpGAwap1BoyiKH5dA2eKJx/faePX3HZ54Jh7KGO2mw5liZdX60EMPLRSy5z//+XV+RkQ3a953EGDYo2eKJGNEkvkDT/ct2BaGtzlAv+eeexZf9KLAwlkeW7GcnzQueB+ap7xSjhmhDHHKtHjbZZVXTjpDxTs5FMQNCuaysR+UNu5xnGOMEgYkvgMPW/bwEGfpPLeAj3GgkVWMxIY5OWJebb755kUdDBfyz1la2/LII9v8Wj3tajw56WzH42AzrmjhgQ98YNEmOxaMLWeEFWo7BdRpd4Ezlhw+2tnqy+tkBNJYmYxHPi1DBDBnIWJqAUqAUIzkwwy6zaPomPiHHHJI3TvqULh92sp08+V9KdPFgALr4wY8gVZM7MW2XYgxaAWrMXTCknePB5DXiFKEifvykLGKiLqF5RWveEWRx2F7Z41sK3LokTJsnHy9SN286hTe/nZi7gwWY48O+tPzecEIEH4MSGPQVkkowlYsHv/4x9dzX/01GFsClLJl5VO6lU3CmzdYcH7JSo299RwGxs64y9sfIqJulYiYPM+Nf0Rv6xJFjxJovDklvMNXr3baaadatozRHwzQO3ym6ra5QNmSV+jmU9ZcZbBwJjh3Qbm2OtrNl/eTEWBEO6eHB/oggY+KOFfna4Pi8C8lKKCcL87acdSQO8bK1ymNRUQUmJsj+CivubyMUGPymMc8pjBYGDs+DGKLJN5p3BhGEVE/UuJdGaaPAP7DaeZ8DweKkniJM5jOtzJMxLUQEQWf4YDBv5pzjvPMqhk5aJys9BpDzhVOPGmMj1ZP94pOlImI6tSLiHqOCU+NiJqVU8bKDOOFQ4FMdDaUodWcQjVj/jcJgRUmPeVDIrAMEeA55KE3+THpQa/GgHg1TGZCopvHVhaeCYdIeesto0b0GEI3X94vGgJOkXf7AAAQAElEQVQEMUFqmxbDAu6YOWWSEG61RURlyJg4AczTzivJuJAH4zZGBDZvE0+TMwny2irhc6y2Adnuw5vuYCSBrWwLlDLbktSlLS0+r9NDgMHhsLZDuRFRPyVsy4qx8MlNCtqgmngDGTNWPHgm7avedttta1ZjRMBS6Ahm+Xgga+IU/ykzRVJtEyVAuq8nOZfkoxk+nLD11lvXjyVIm2VhqTUHX6TcUIim4ovmo3nGC2ybUrcxPO+cNhQsWyN5gdtKQTdf3k9GICLqOREGivnCEWZli+GHLsmqViKix/vwJ9uyGPcUZWMmD0OSM8cKCn5qrBgl+CdZdsYZZxTPzh7hrwybiCg882Re8jooLlowV3y1zUddrJbgOeeff37hrGGsm1P9NUZE/YAMY4bMo4sow7AxLsZ3iy22qD+JwCHnjFJE9Fcz33PE/HkienHoCt9EC3id1RqGsbNK/XN5vorHOCKNlTEe/OXddYzAQUKCVMBc+tvECyyNF5cQ7qZH9DwjviJm24gfoeOh6ubJ+0VHgJfQFq0999yzWBmxmoLB2uYF50E1MiZ8gYgRonx/HsoVYUB5st9eulUY3l5bgmy/4GVimEprQb0MHgocw4bnsqV1r5SBFPBdRHr3tlLxFEZEMTfsl7cVhSJrjHu55v+fEbPXXnsV3j7KGwXL2Mppe5g93IQtY4ISRzkbNO7yCwQ/JazN8Yion/o0bsaYkkbRYFjJK6jPdgpKiDrGJcDaHDEXYD2o33BicDIqzaH+PLAzLrYn2WpkhWXccOzHZDrP6J6iSpa8+MUvLowV47CgFT5Y42sUZDKt/z1o3HYwfNSKMh72mc98pm4xwvcoys5FOLeF15lnDvr319OejSO52J5H57pkPbEqwTBhCDhfBEMrw87VwX2q2jladt5552K7LMPeCpiVMPmtTNuKyrHGsPAlSs6zxsfkGRS66RFRnXqMXvECvkyfsdotmPN4nt0ig+rLuFLSWEkqWG4IREThWbIUi0lg6v2NwdR5D3l1CRLpEeFSGUBEFAyeUm1lxWeOeZNrhvxviRDgiSJY/ajjve51r+LMA6V0UKUUUV/HIRj6BTbmbHzV1Q75ijNOGHZEb6mcsCEcuvVHRP19AZ8SRQuW9Lvp7r2bsLc64zmiRx/e53mcg60GVsRgy2NoBYMX2BaHiB5OU+FjTzXvsP3U3f3eBK2zZpQ0wtVqGuWJkLcKoz7jy0spj2cCnrA2R7XFti/paECbeBfF84BazWGk8Dzbw8+YUce4BAqyPffGzYcO+ucEZdUPn1JyrD5Z2WzYwLvRfUTULZi+iGQrk0/itnx5nRoBPI7CC2fnFfCsBSm78Laq4lwKWdVfMyOcw4DB03ijMxHGLSLqyiFnDuOT0WNVh5JtvvbXZVuT+sy1lhbRM/zb87hejYG5AmP8g6OFM8VZSXNqKlwioh7G5zCxLVl5Oon8jB7yz+qZc5bkFT4mSMfnIibz0cav8Dd50BGDl34TEfXjCxxrHAh4njz4p5UVeT1nmB+BFeaPypiFIZDpM4cABsDLa1mc5x3jVzumbDIfd9xxhXeQBzGixxQwdUyJt19ezIHX4zWveU3hnbd32NenpGVYfARg7EslmKzDfzy1asOo7dO2X9vBRAooJk95YlRG9MZJXsHnHglY+SkB4ihVm2++eeH1ZWxQgG2X4FWU3g0UXVvEHLB3jobwaILAlWC3+maFRjkCANOnOLuKG9fAuIPzdtttV2xlMT48xxSlhWHC02eLpS93NWyV4Q22rY/x6rOgxpAQ9xldqzfowdXY2AdOEbOCyvCRHx1QJMxbh/LRBsPIvLWfm5fTPnL12A7WnBTePQ4hIoqVZHOKoQhXc1Hf0bY5yWtsxctqpniB4kQJpvi07UiUNAYNT7HVMKuZ4z4nYLWwgPZsg2U82O7I+64M+cRQfN7znleMAaOd1x2meFNEyDY34EvmgI+/NN6G99kaaw7gfeaPlWNnLfBY8hDPsz2WAkvRVSEaYMjL31bTzC00gX/KM87B9lZONbhZ9fX1SHynjd2CsGGcGFPGCTnU+CNZZ2w5yqxIO6NHrliFsaXZmBgfDhd6iXdYQcMP6S74mSunjHK+/mWLpvEnQxm4ZCsHkpU5QR0Z5kcgjZX5McmYZYgALwZFigC25cHWE6sjDA/PDnhTXChOmkWx4S022QkCB4UJZwoNYYHh8MYq5xCbMhkWDQFeO9uFKEuUVIfjKZoRPUFM2Npba/+uFS1GJa8URZRy1H0bRk5hpigTwN00Y0yQHH744XW7kU9FGsduHvcRURi1DFHKNm8nz7NtabzvylBqvZtAoBwQOJbW7QUeV0FupYmhaA83ZQj+VsuMH1ynE2x7sCVG+ZYftrz0hC2DxVj4SIIv3DCE5PXBBIfy0Y33EeCnnHJK8Zss5qkVOHvAbbHgSeaRphxyWojnuPAbLr561d47TleGnFVnOFCcrTjBxZViBGtnv2ALF0qrbZKUZ4YfxYpnnjIMQ79dY3USvzR/mjGjbIYeAow9soMSykiwEoL24dfLUepqPp5lTHy22G8Hwdgc6Fc0xfu0NNqnRJcb/yKiqNtc+eAHP1jwUVtuOXKMp3MulGLKtnlmm7N55kMy5CXHnPmireYYx4Fn40rZvvE1Y3VhsJFZjHcGnzGDCTynC4SD9OacVeSInqzzUQVjBVfOAVv28C4yx3sYjhyjbW7huZw7Pjpj7M1ThqWVGZ/X1x5GqQ8rqMd2zi984Qv1gwz0IKt6023vuOVLY2XcRnwW9pcXgyeEZ5XCi2lQaPwwHUWmGSqajulj3L5QhXnzUlBSpVHEGDqYCkUWoxGfYdEQMB7OM9g+gnkzRCJ6zFtNGK4PGzAeGDRWvngOCXHp3UDpckCRYI6YV4c8zr8Yc8KBEiaf+KmC/H7fgwLH0EEjjFPCm/GkHEHAs+ZLUrzIlG1KurRxCby/tp3ov7ljvCizi9N/Hkdf1ukKUfcEMpwJYONOQRbMRYoCpUGa1TBtiIj6i948ibbUKEM4U8woeRG9rYCEPi+jbWFtTBen3aNQps0dKyJWo2zdw/v22WefQvmJmDefYGyO+XQ7T+5uu+1Wv/Rm7GFhvKwAHHbYYQV/hbn4DJMRgAtHGOMDDVJ4uzkiolipZBhQRBnfPixCIe3mcx8Rxdkj42Z8xLUgP4eaHQOMEfy0pRkz/NdYegf+itehAbzOCk1E7zC+d+N1jB7vMV9bPeNwZWDaUsURYgWEDsB5KX5R+8+5xQDFv1pZstBKr9V8DhgYM2A5Z8xBhqJn9GC7GUcNg9I8JR854DgVOPyskFoxtepidZqzx++2MEzRgLa39+Z1fgTSWJkfk4xZDggQzBgtY4Rg5ZH1jHF3m0N48Ghg9gLGrWzLY583hVXIyd9QWbQrjHkDGSlTYSgewyWMMWxMfdBbCGljyWvYnx4RxcqNz+oac96q/jz9z5QJnn0rLYwcW8+67yYICBu0IbjXhv56Rv3ZWRIrKoQkI48Bszh9jogpPx1svNr8ZMBE9JRncRFRvdDd+DLxZyzETdwO/DxrRNR4dcgz7gFvQ8P4orN55hKFqB8fmDLu0LyAR+KFEVEhlF89+KKremtC/jcXgYgoMDRfFoQRGuYIM7/kxdsiejjPrWziBuaMdXJs4nG+f/gr4xwvY+T3Z2i8zns4DOTv8jr3HDHGW9Bm/K+/nlF/tlXLOHBecpJ4Xpw+tzlk3Lrlm6xbZ5116vkiOge88T94tzGAP0NFWeNJPqETeRhC8rmP6NGKeWw+oxHvUC7D1AisMHVSpiQCyx6BiKjKUURvQpf8W+YIREQdgzKNv4gFj1NELLCuiCiYfr+AKAv5i4iqDC8k21gmw5LwdrjUVhLeWwrVWIIxIp2OiDqPIqLk39JDwNwRpvOGiIWPBQU4Yup8eJ88C3pfRCSvmwKgiCgMc6td2267bf2gBIMzIqYokdHDikAaK8M6clO3O1MSgURgzBGIiKrcUryEkn+JQCKQCIwgAhExiddFpKFSRvAvjZURHNTsUiKQCMwkAllXIpAIJAKJQCKQCCwvBNJYWV7I53sTgUQgEUgEEoFxRCD7nAgkAonAIiCQxsoigJVZE4FEIBFIBBKBRCARSAQSgdmEwKi3JY2VUR/h7F8ikAgkAolAIpAIJAKJQCIwpAiksTKkAze8zc6WJwKJQCKQCCQCiUAikAgkAtNDII2V6eGUuRKBRCARmJ0IZKsSgUQgEUgEEoERRiCNlREe3OxaIpAIJAKJQCKQCCwaApk7EUgEZhcCaazMrvHI1iQCiUAikAgkAolAIpAIJAKjgsAS92OxjZX/3XBDuf5/GRKDpIGkgaSBpIGkgaSBpIGkgaSB5UEDN5QbltgYmO0VLJax8t/rri+nfvNH5Y2fOLu88eQMicEI0UDSc87ppIGkgaSBpIGkgaSBIaCBgz95TvnTlf+c7bbGErdvsYyVFVeIcvc73bY85B7rZkgMkgaSBpIGkgampIGUEyknkwaSBpIGlg4NPOju65Sb3+xmS2wMzPYKVlicBq64wgrlfnPWLI/d6B7lsffPkBgkDSQNJA0kDSQNJA0kDSwDGki9K/XOuTTw6A3vVm61ykqLo8oPVZnFMlaGqofZ2EQgEUgEEoFEIBFIBBKBRCARGEoElq6xMpSQZKMTgUQgEUgEEoFEIBFIBBKBRGA2IJDGymwYhWxDIjBNBDJbIpAIJAKJQCKQCCQC44RAGivjNNrZ10QgEUgEEoEuAnmfCCQCiUAiMMsRSGNllg9QNi8RSAQSgUQgEUgEEoHhQCBbmQjMPAJprMw8plljIpAIJAKJQCKQCCQCiUAikAjMAAJjbazMAH5ZRSKQCCQCiUAikAgkAolAIpAILCUE0lhZSsBmtYnAGCKQXU4EEoFEIBFIBBKBRGBGEUhjZUbhzMoSgUQgEUgEEoGZQiDrSQQSgUQgEUhjJWkgEUgEEoFEIBFIBBKBRGD0EcgeDiUCaawM5bBloxOBRCARSAQSgUQgEUgEEoHRRyCNldk7xtmyRCARSAQSgUQgEUgEEoFEYKwRSGNlrIc/O58IjBMC2ddEIBFIBBKBRCARGDYE0lgZthEb0vbecMMNQ9rybHYikAgkAsOFwDLjt8MFy9i3dmF0IV0Ye6CWMQCJ+cIBHwtj5dJLLy2/+MUvyiCC+PGPf1w+/elPl89+9rPltNNOKx//+MfLJz/5yfKrX/2q/O9//6sIXn311eWb3/xmTT/jjDPKpz71qfLlL3+5iK8Zbvzve9/7Xvm///u/ctBBB9Xr17/+9fKf//yn/PCHPyzXXHPNjbnG63LhhReWI444osDt2muvndT573znO+Uzn/lMxd4YXHLJJZPSBz3861//Kp///OfL73//+0HJNe6yyy6rY/rf//63Pvf/Z2zf9773lYMPPrj8+te/7k9eLs9/+ctfynvf+97y5je/uZx11lnz0VZrFBzf9ra3Vdyu5kkV5gAAEABJREFUv/76Fj3lFY2eeeaZRZlPfOITA/OZG4cffng57LDDysknn1z++c9/TsoHR5i/6U1vKh/+8IfLJZdcMil9ph/MV/NxYf0zp8y3Qe35yle+Usf3yCOPLH/9618X2kR1HX300bXM8ccfX6644or5ymgXjA499NDyjW98YyA/ue666+p8137hc5/7XFFuvspmUQQ+94UvfKHSyMc+9rHyj3/8Y1qta2UG0Uyr4NRTT631oukW173+7W9/K8cee2zNc8EFF3ST5t5r3zvf+c5izs6NHNIbtGgO6/eCumDe7r///uWiiy5aULZJacqcdNJJ5aijjpo0R73zuOOOK+avsRpE2//+97/LRz7ykUr/xqyfB0x60TJ6IK9/9KMfVVlq3pGvC3v1n//85yofzL1u+OpXvzq3qHrPPvvs8pa3vKV86EMfmoTV3EwTN/L9/Oc/L1/60pem5McT2ZboH76Pt3bb6l57vb9V/t3vfre85z3vKe9617vKF7/4xUXSJ6666qqq0+Dx3/72t8sgvqodaOSYY46ZT1f64x//WPAx7SKvzz///EImtLbN9ivefuKJJ1YeY+5Nt+3mCh0BnxtUxnzDu+QxPv040HfgJZ3chHF/Hs/qefWrX11OP/10jyX/G4zAyBsriOyUU04pJirDoR+GNdZYoyAixIIhXn755ZU5PfOZzyy77bZb8XzTm960rLXWWgVBPu95z6sKnXI3uclNqsLyu9/9rrz+9a8vBOrtbne7svXWW5f73e9+5dxzzy3bbbddwQC0o//do/qMyTIm9tlnn6qI3Ote9yobb7xxgVfrMyHw0pe+tOy5557l5S9/eTnggAMKQ6Sl918pgQxE+L/uda8baKzIQyF/znOeU4zlIMx/8IMfVIF8hzvcodzjHvcor33ta8u3vvWtuYZp/3uX9jOsfvnLX1Yj5ZxzzqmG8K677lpppitUMDSGDEN5k002KU3Yip+qjZglupf3QQ96UPna175WKEBdnCmIhOA666xTHvvYxxaGHsOG0FcvpUX6gQceWCgxrsaAoant8sxUMF4f/ehHy0477VSVCErqVHWby+iAIUyYtnzq4HAQHvnIR1bDS3/QY8vTf1UereIDW2yxRblwwsDed999y5VXXlmzageF5VnPelbRPpi6Z1x6X810438MYXWhacGYwfTG5Fl3geMHP/jBQhnBty6dcOy84x3vWKCBh+YYibB42MMeVppCSSloHUQ3e+yxR6HYoD1KdD9elOb99tuvGob3v//9C9oyf81j9cCdkwh/NUe957e//W1Bs9otz7AE2Ji7O+64Y2Hs6vtUbZeXUoqH6e9U+Vo8Q8Pc3GWXXaoS+/CHP7yQT9J/85vf1DkPe3PiFa94RXnVq141iX/Cd6+99qqOtUc84hGFoY8fq1cdyyPgLWhy1wleyICGxVOe8pQqT/C1MuAPv2SoKWPutfDiF7+4cEAook/4Gd7BKfmGN7yhkBeUeOktMPDM8+23374q+v3zvMzAHzo/5phjyqD2UoIjohoWp004URmSD3zgA4uxJQcZ7vq7oGaYP4wevOxPf/pTedSjHlVl3gorrDC3mHlkbuJZK620UsEz6ToRUfNI9270AU/0gxcu7N218Cz4j1OAkf7Tn/60bDHB28+d0MkYDvo1VfPMzVe+8pVzeRcjjdPLeLUy8DRu5OSGG25Y3vrWt1Y6abio/41vfGPVC/FJ+iFdkA6JttWDFjm91EH2c7rRi/C57rvkzVDKPKodUTQoIKxbVium3N/N29zmNuU+97lPMYHvfve7lxe+8IVVgX3Sk55UTjjhhIJpIzwTeLPNNqsKzF3vetdyt7vdrSrff//73yuhUnAYPE972tMKZRLTR8wYBOKfisH2t2d4nge31ETkqWcEYPCY26abblrueMc7loiohcRjeC94wQvKu9/97kIQUX5gWjNM8d+cOXOq0cNT5D2DsjGM1l133YIR9KeL400z5hSzxz/+8UV+jB/D6M+/LJ4xJQbxNttsU7EgIO9973tXpRhO2qCvGCYv4Ite9KIqUCg9FHXx8gwKGDNh+OQnP7mgxz0nDEMKgFWsxlQJotVWW61sueWWFYutttqqfP/736+rA+rEPNE/Q5wiSbhT/LVT2+WZybDBBhuUNddcs3oy9XtQ3eYSDxnGT7Hr5iOUCHrjax6axxQ2DgvCu78+dekX3vD85z+/UAgYS4zaD3zgA9WIJZgYeuY3Dx3c8QpYwKpbJ+Xcu5uyyVjRp26e2XRP8afowonQ3XbbbQsPNjoZ1E5Ym7uUPwL9IQ95SHXI8D5S/pRBW+iOcm6O45ucP/CiCMuDdigRxubpT396VaSe+9znVodPW01A/xRLyv2tb33rctvb3rY6GigOzZBU1zCEiCjm9T3vec9qQMNxULthh9YomYzCqfK1sngajzgFDG946lOfWmXTKqusUmnXHCHfYGacn/3sZ9dVbqutZeLP/KFMwRr+DEv0T3kyjxb2/okqlso/DgR8ilFBmcZvVl999bq6dvHFFw98J5ogGzhkDjvssLoK0eaffilENjGkyRvyXTrej8a6/IFjjSHO+QgjZWc6aCvdgMF+2I3tJZ/MQzLT++DAyNx8882LPmy00UbFvfm1MCeI1UyOmgc/+MGF0UW+rrbaanPlsDmIduCALvCA9dZbr9z85jf36ho40jhr1aONwg477FBWXnnlmj6b/zOXrFrhcdrMYYo341PmxaC2wwSPN/9e9rKXFbzriU98Ynn/+99fV9OVQQ9WSzh2zBWyk4w1jj/5yU9kKcp7P5lNvjAGGSGe0Z9M6M54kyd3vvOd62oVA0f7vEOeDPMQGHljhQf4Zje7WfUanTbhoZjX9d5dRBSTccUVVyzyrbrqqlVZwvRvcYtb1CVXigxjxgSNiEIQyF8m/jAEihOrmVAw0eVVJ+bKA/uABzygUIomso/8P1tIKP+uvDFrr712xbfbcQyQx9TKxkMf+tCCgWAKPDvdfN17wsOEJuwjekZPN929PAxJ74yYPw/vLEUA07rVrW5VvI+nilfNdsDlIZjRyvrrr1/gQElHQ4QL5Syi1wdCDQOdM2dOIcj0UxlGGSVFuv53A2bHELzlLW9ZeK2VkR8+DBxCUn4rARRGSk9EVAPcykvDAs0zwAnKu9zlLoV3kwFOAGDG6pipYM4wArRzQXUyJBhRBHB/Poo04wqO5uid7nSnos/w06/+/HA477zzir7BXBltQGe80Zwd+vmEJzyhCOpCqwwbjgoCqNXJqKGgUwp4KNE1hccYtDwLvC6HRLRgjM1FtGjurDGx2kxB1O/+JhG0FAC8jRGGruClzNvf/vaqiMPBFgp0DFfjas7BgWJMCbf9kqEsXll5CH0C3EogpcH8xEOMCwWVocNggb1rf9tm8zPZAgvK4ILaSdYwFimk8kX0eID7/mCO2mp89NFHF8omhwSZFTGvDCfbM57xjOqQ45ixiqANjW7RN8UKveOJ6N/c0VYKPQOg/73L4hmN6NNjHvOYOn/RBmcLRwklcao2cOIoZ+5ZKcYnraJzXMALPmT7phMONHMZP3v0ox9dV9fRXKuXHoCu4dDiZvoaEXXctLnb3tvf/vZF+7wPLzOf0L+5ZnzoEvAxX+UZFPDIvffeu9A9OMK6RkrLj+9ZiYIBPkWvaWmu3oOfwoWRpI2cWeRwxDwak3c2BhjR+chNcgB2xhSO+NugNsOaPkfO2m0gr77D2go0LMgezm8rNWjL3DZfzSUGNr4JS8YO55e5xWlDJjCW6Ubeje+hac/o2oqZ95JrysuTYR4CI22s8JQgVtuNCFYW7CDFDhwYmWsLJjcBijhbXP8VUbKUCW4KocnQn8eKAmVPff1po/jMOKQA8dLBj/DtYosBYgaYBa8ERm0pFDNYGB5Lmgcj0RYMpr2LckZRZ6yglxa/rK5oBm1geN7Jw8ngtcLX4hgTP/vZz6pCjXnKR5HDeMVT/MR1A68bJUT/KInSIqIow3P9hz/8QVShDFCweV7R82kTBr25gkHLIH9TZD1rG0URhhEzK7AiosAjYup6bc+wZxvjN7dK3x/nAUWDUiIJTjAg7AcpOZQB3kt9l19QhkJnxYsxR6jY1omepWujOiOirhiKE6wCEGK2ney88871vIG80mZjMBdgiX8ZV210ZTQz4G2hENcN8LeaBPs2HwlWBqZ4tGh1C9Zop+WhCFP8nN+DN6cBRbjh6B3mAcNEHkYR3otn4ykUbQqV1UKe4Kn4uHpma2hYwH1QG/Ef9AMn2A3K041DsxxDaJXiBHOKT8vjfeZxd67C2MoyhVg++Slo+LJnAf1T5PGdSy65RNQyD+jCql3jd676idbQyKAG6RvFUPvlN/fQI35gDkdEobh2+Zn60D96bvNb3RFRd1vAsCylP/3oby+8zRXt8VrjhPY5TqymmJOcBbbiUsDl6Q94mm1J5hcl2Vwxn7r5PPP2m+ucT+QFB1c3DxmBpx1yyCHFyoFVFfIpYmr+3C2/vO85kzhFyasmS2HumWHAmOlvo22X+BdDJaLXT/OF0Y9ncaZYlWGYoMdW3ljIh1eZU5x7jebkwVfVSS6hOXF4qXH1TmNMxlj9pB/1j4X84x5G2ljhiSMAeAN4BUxI20GmEhaYv4mNECltiI4yx5MwiFAoMwQ0hoJQB+XBMAkLDHRQ+qjFmWyUjAsvvLBua9pll13KS17ykoIBwB3z5/HipbC8SjFhTDJwjNXSwkPdBBchhmG09xBQxobAlqfFL+urd6MnAgkzRHMRPWZJQBEuTYC1thGymBqm1+LaVV2YMcYc0atHmr6rr60yWB63HY5A4oFluBFiyskPL2PmXlAOVrx1sBPXH8wjAhMd9KehAen98e1Zervvv0o755xzirmGsffXQ+GiYFkpaG2LiKLP8hK+/XUSYgQYeoVlSzdv3cPdveBZ0A7zXhso0OIE2yg4L3iBKfswYgh6t/TZFvQNJvBq/YuIQrnDBwnm/jYbV/RGMeymoUU0jMdKR0e20HTzoF/xVrM4MaT11+NZebQLZ2PDcEeD2milhTI3WzHVp8UJ+mpFCe7oZzp12HLCO47ubdmy/YljzKpWo+U2D1p9xtQ4UVDFGXvjbdVZPeIEWGsTGvG8rEM/3/F+iiDjy2qJ5/6ARiLm8To0YpXK9jt0JL9+yedewM/QrB0QEfPKSluUgNepyzv7y8F7ULx2RMx7pzrwoW570ToPvT7bdmmrn3QOEeX73+WZM4oDkIJNHjsvgcczPMw/eSje3qUOq51WYchl+lGjHXORXPBedMQAcs/ZoI7ZHsgp9AvDiB7O+sF5R04Yr/4+MPDMQX1vaWjGM32Q7DSHzBU8r+VBr2gMb/NOcqWlucIULdIn5RMnMJ7pP+S1FXn6kjaae9JnYVhuTRpZY4Wws9fTkjCGbLKybE1MFvcgxCmzPI3KWV7nvSAAmiXcXwZhm/wIE4H1p4/bM6bM82kCPtp8vQYAABAASURBVO5xjyuWOH2kAGPkATQmJr5tBpRk+2B59Bl7zkTAc2lhpm3t/f3jGRGFoFha755OvZgZnKyGMLIJGIqkshij9lHSPbcQEQVTGyQI0aW0LmNULiLqoU14eGZI2xZB8bbkz3jEcFu6PN1gBQqtcwAQdN009zBWj+1p5pE+mW/aIh3Dt4w+qM3SFxRsXbGSxMjlqdKOiKirMWXiD05wNN8Jj4mo+i8i6j5tXsEa0fmPENp8883rKohtSYwQhgbBglZ5xzrZ6y1F2zY4+927+Fryp/Q4oI7eGUm20vCc1YKz7D9jZRzMB31tzYvo0RU8W1y7okPxBH6Lc40Il0pb0tEPbGvkjf+hF/He6d2iYeTagjzS0YvxdWYBLzGexs9WPAd9mzHdyg37lTPHOSHyivGgv/rUHRfP3UBGRURhMNve5UyQFS5ngdBxN6972JuPtufd9773FVXMf6uUHCS2k9k+5NyVZ/hLrxmX839WjayaOgfaT1dTNY0yyqDD2wbhiMbwFPzCysFU9SwsHr+0Iobn2W6KN+C/rZy2U0bb81TX1l5bgdr4R/RWw8lLcpVRqc346FT16DOFm/GBt5tDMHAeghMWHZAz3oGP2+Fw0EEH1TNheBqjEDbmOF7LQaBftl+iUSs75uhU758t8W0M8Dd8RbsiosoCfAwO4lrQZ/JDmr63eNeIHk/Ub3mUZQRJayEi6jkxaS2uXTmt8CzHBVpbOG8OPPDAulVPG73fPLYrxXMrm9ceAiNrrGDWmIbzEfZlUqIRF2UMk+91f/L/iAcTtyfdBHdgCoOYnGveE6GC0SFuRDwvZTzvmtfVNgYGCUHHg7fFFlsUTI7SChmTlSCk6NlnvfvuuxcKoC030mck9FVCWBkvDKHLTNxjPpTfiOgrtewe0RGljLCwhYhQslStBdIYxJRAzy3AW7+kt7h2hW1EzGeEUfisJBFU8hJsFBiGJYFlzlB27KGV3g1XXHFFPZzLezvVvGCImHu86oSFrXcOIx599NHlnHPOqR+tYIRFLBrWvHkHTjB248Rg4lTwHhjAyjyHESZvLhrn1nZ9Nkf7BZB02NkjzHuJRxAqMIALb2Z/P72P0mRPObpWRwvGAl0zKikK+s1rSxFteWbTFT+EmTnQxYshERFFen979Q9mMO6mGWvPjA/p6Ks/D/oRj/5a3f15eCVb+Ygo6NiYGh/eZOXNY1h73ygEnlrny8gfe9rRNq+3MUGL6B399vcVbcHHFmTbUGzdYuxQZK249OdngHDI+dhBwx+2FCTY+kiCFW6GOpplfNuO1V/Psn6meDsITmEnmyOmxzsYXjC0xWZQmxkHeKwVUI7MQXkWFod+6RP4ky1VdmVQ7B2c1maYMmTMqYXVhecYZysnLa+5acVNXzhAbOsiS9WPr7V87aq/vP9kBSNWv8hj/F1dthiZq4wnc8kYox0Y+YAIvo+fakdE7wwjnou2fEVOfvQxnf60Ni2vq5UPfdRvuGiH8dJ2sgAvE9dCRNSPCzSe2OKVhRm9Be+Ch3v1dvOgJ3UKLd7V/CVbOCC7zi88zPzFz8hehqH6tS1iejSu/nEJI2msmGgYvm0tltYwEZOWRw6htgnbP8gYuD2klppNXkQUMTXREC7qJmxsXeivb9yeTTTMweRufae8MFwoIbBv8e1qYjsECEcMocXP9DUi6pkPzIrC2er3TkzHkjkG1OKX9TUi6nYlgoV3noe0eeR5EjHILo3B2DPFjlHY315xsG0GYkv3LA2TFEcAUlgYlbbrveY1r6mfRbY6Ahd5BAadLQE+SEBhwGjF9wcYYrrmHk+clTWeW0oSwwU9EHjy9Zdd0DMlzry1Gke5EqxgWOr3xT4eQ3UaR0IcD1Cf8abswE+auP5AWOMN6uPVQosUPngQHN38vI7SeCwJtW5a9x72DBZ8R/5u2my5Rzv6bj7ASbvgxqjA23gCxbXgCkfx3T6hRXQFDxhLR1PilBHQEkUOj2XMNSUYX5AuaIOxstKqbeJacIaju0e8xY/ClZOGvLK6jK4Fcw0e6N2KHz7V31cYt7iInpyCvzE1L1qaK+++LYrom0yM6OWPiILX2AbknbzslDyKl09PG2/ll1cwl817bWRkobHptsUX68hy9DaojPMC5DyHGbk1KM/C4swX9M7hgSfYsrjrrrvWrwpyjDKG6BT4wMLqco7Tqor6Wl4GqXNbzu84B4FPcRYxiNTd8rVrRNQP2kREPXMTEXUlgVxhcJiDMG044uMRvTyMJO8mV9Be6fuTRk6QBYPS+7Iv90d8hLxBy3iUBtFB8By8pJ+3S6fzCV3+pr92feg/J5x64SdOGcH8VC/ZiseJExixHACcCGgxIkRPCuSWce8aMpMy5ENFYCSNFV5WghLj5f30tQ/7gDEUZ1fs53TgtiJw438RPSKKiDq5I+LGlPkvEb00BIsx8UbwWg2awCYHj4n2zF/TaMVg+L5qRVCauHoXEXWbDmHTncSl8wcjygsh2qIpN43BtDjXiKjj436qEDE4j6+eGCP7VVtZ95gWRo1ptPjlecUQBQdBtcP5FUIVjRGO4iiYVjF4/6WLg5f+uaewoH0e0i7jpfTrK2UUE+a5xSQZmhRsApdxgabVpy7lbZ8kLG0bMM7KDqJpbRYIQfUR0sr4hDWFSP1T0YF3RYTLfMGKJ4+idgh+e8H2BILD9hfL6xFRzG/OA0qvSrRTX+BEkROnXw0nzxFRv/CHRgk3wkVfKUfSBfl5u3kdvYvgEu893uFeva4tGCPjSNFucbPtanssgWqMtY1gpyD5ehKhLU6/zEf3+sO5gMeKF2euU4ZhhhbRF2XAdqKWB61QypU1ZhRESjE+3PLgG9phy4pxUHcLEbHQed/yzvZrRExqornNC4+uBbTtbIA55PyYcwrNaDAODS+8Fu0ZrxaHh5FLjZeK5/RwjsUKtnkUEfWrbcZNQyJ69G8VUXmri7bbcTpIX17BnLNqYR45D4afiDPnXLVL/2Divhso5bbTkv0Rk/FG41Z6yRz14n1WItCo+rr1REwu201zD2s0zSAyXgxIhoFtZcYO36N7eIf8fWHuo9UQ3nd5I+a905zAv8yriCgcV3QOSjHe3iqAR2s74wiPJR9aekTPiMGP0RLjR351d/OQhXCBtXh5XAX3+oiG+p0J0mdb0BeONfRvnmgfRwxjjNFFjolDP/rm3hzgXLUa4lmQ386PzTffvOBZtlHig1b2pQtwRD/4ZuNd6JTTj5Hiq4cRUX8Hybxr71NWiJg35p4zzI/AyBkrvAaWTSlgjYGYYIIJSNEgaO27bARMARQQVz8RdSEjSKVjnghcGgXYhOC18u16k0G8fJQADNO2F0xM/KgHnjuM10SGEY8DJYXwo/wQFHCieMOTscB45IXGAOADX3l41NQhrgVCRTBeLa57hbs0Y9tflhKE4RgTDEMeW5MICApDt55ldY9eYSXol2cKMS8YutIOTNcWCMKJcaGPluLhTHA1wUHYUUrgSojyjPL+WA1QxsFLy/8EtDrNB4zZORBlvCsi6t5lCjbFBaP2eyPe59m2Br9b5HcPvE+ZblCnfN04c4+QZSAtbB4YM4LX+Lhv9ShPwSVkBIYYA0R9lIUmzMXru/bqM0WOsPLVOQqDOnk8fVEKLbb6XeHpnA268ENqMBLvmeeb8kEQMVoYNL6Hz+ttzsOJkum9ntG8jyU4UEl5V89sDOadOcqhon2MDjgw1CJ62wgZHQ7qok94Mwj1t9EiukI/zvcZf4YcT6J5hmZhbo88/G1xpCQwHK26UUaNkTxW3tCd+dhPQ9o2CgFtw1F/W3+sclKA0LWAhn2RLyLqjwtTPmFmTlgtQL/KU4AonTBssssKJj5qBRT9m9u+4sQhZB7gqbYl+R0gClZrgys55aA+ZZZjwXiJXx7BnNNOclU/9NGcM8esoqJZGJD1FEL5u+3EmxjFFOtuPNrGu9AjeaQ8fmb1wtW7uvnV611kSje+3Uf0jICIycomnkf/0Ab8qOWf6qo96ABP6+Yxl9SB57S2mRv0GyvU8uI3Vlpg5Bk/RENohbEjjuFCmeYsMq7msO2D6AHtyOMsjPcxgrXfl7Skm5/mO7nkiqdNp0/qXJ6BXGRwc9DRM9ALXqXtVqe0zfiiAfqGdGOmDJlpLolzhtHYOAgPewaN34ZSxpxBGxxpnHRka0QU8bZ2GhO8sclNRrL61Ov9GaaPwPSMlenXt9xzUj4ZIoiF4O02yETjcaE0IVAEhHgJXROYIkfAYmjdchRIwhxDsUWEIkKAi8c0CGleRQLAWRdL9xQ8zC8i6gEq9XfrHNV7QtIKFiZHqFAKKX22NmESBK5D9ZaznY3gSZROaaLowEUek5oAaQzalReWUk9hpBRiovK3gAHY22vsI6IQWMa8pRsD3njj5jCkdmDKlKbVVlutZVumV4YZjPbcc89i3yrhTMGwFQTj1JiIqJ8YlocHlhLM+4nurILII8DSF18IJc/Gwh5nQl45irWPRlCIMF2BQRMR9SyJbQXGzZI0wyAi6hkVP2rFoHQGw15pwVdj+gWrdy5JoMgxYhn57s1lgrjVGRHVux7Ru1KqKL3GNSJqNkae7WfmMTy0c9NNNy0EUETvAKT9/PpCSKMZdORdaI7gsXcbBrXCif/MfQqOeU9Z0v8WGDzGSV2EE+81uqY8WN0iFNH9RDWz8p8xNDcJW4YansWx0OgKHvipDwUQ7BFRfwMCXfmiH1pEM+iVgqSTEVEoRWjNHJbHXLTNiYIkD9qD1UYbbVTQJewpCPinVRl5RimgZ7yLAoTPUVi6vCkiJtE2o5AB7hoRFQpzAd1SgNAt+rcCY1zMb/zW+TPjaR7gLbBnaBtXX3NqdMuIVL928RpT+BkqeCv6p3jVly6n//AbRhYlHX20dqOniN5KENrUZnPNPGxNZQyiWVtR4dfiYUbu42f4jN8FavU618pARJfyq08bKPvN4QNnaTMd9MMcMmfg362fvkFJJtf001hSkjlfePHl1UZzi6PKM8XaPLJC4scM9Rd9kHOcdRFRGLRoiSFm7inLOLSNrTlX0KhtwZw05AqZizegLe+Z7cFYknWcH/g2/sYAw5daH40z+Utfo2PQBxgczjHh5+YPIw0GeKU+cxzAks6nHNohs9ET3mXeeZ96zUdj0+jM2NE1vEddGaaPwMgZKzxzvnpBYaFEdKFgaVsW5iUkfHmVLeshZkQpzjKpfN1yCEtdGASBwOskn3gTAhHbN2/PL68DRs+jY6JgDogzoidwuvWO4r3lZ4qMT/Dx0lD6HN60NB7R89o7KO2wHuWGMocB8h41PIyJX8C3vQfG3fgddtihYAS2FvUz9ogolFcCnCFitaR/LClHGDDvE8WJYOa5jFi249P6pA+8zYwonlKBsuGKtrr5GCu2hGCIyljFoqi3PLYcoEEGtDhKMsZsOyTFxJzwzDiULsABHnAh2L3X+xn00h3SpCBgyM63EH4Co8qYyjPaYdPdAAAQAElEQVRTIaK3f55g8E5zWR+mqp8HUD7j3PIYb6udBDxBzwhGj42+0JMtGvqAPgkoygJMbIkirPEF+Vqd5jpDkuHTxcC7rWxRPuWhVKE9Xkt0TeCZ+62e2XhFf8bbfEU32267bWF4tHbDXzreiD70QRn5ORzQlb7ijXCQLihPYVIWnTj/Y1wIeumCrSzG2lzGMxnfjOQu9vKNQoiIggbhgE4oOmh1qr4xsK3cmZ8tj/LoluENo4goFFa4mb/o2YoWfoovGA/jQlFlbDfaNXd9yMP70T/l3ioDesUfzLsu72nvX5ZX9EBZpPDpcwsMZPMXHQlkMYeL9nfbhz7Jim5cRBQrWE3BbHiom8JOVrX86kbblExGnvbAvKXP9NWcM1b99RpDPIw8nDNnTv2sOLkpr7kpP95jfuJ5niOi4ImHHnpodXLh5bDA29ocjuhh4dA3ObjGGmsUsgW2rV6rqxRt81u8OmC0NHHQ/pkM5oQ5h/fQDchCdI72vQfdkKtdXUMZeoG5RNaiJQZMt9/qosf4KA6s0SHeZd6gHXOQcYSuGp2hO2NCN/TuDIuGwMgZK4jLhKJw8B504SBkKaY8KPLwEJikGFGLU57HqlsOkcqnTvkQGwYhXr6I3l5SAsNEIPARrroQrjzjFOCMAVJ0CVPjENEzBuABP8yPUgf7frzlIaSNUcMtIuoP8PGIGAPleJBaumtEz1hp40Toaou0FoyZswlWHSgEFICIXttanmV51Vc0iRnCRJsocZhefzsoIDCxPQwOynbz6DcFvgkbaYST7W+tjDrEtwAPKwC2FDBUjA0G3tIpmsp7r6uAzrV5UBtbucW5qs+80gZjbH7200a3XuNPUPTn0UfxHAXa2u1PRM8gMv5w0n9GDXpVhtESMZke0K/+a5P+t6BuadpkLKTzjnKIoD11S5vtAX7abjXFtTtnjInzD+iy2x/CXl5lOGvQWX8/1Wseoz14qKs/DycQBRKvMC8H5ekvM8XzrI7WLwqOOQo3mHZx7m88mpUPPbY0dI1uecwjejRqTMwZ8x6G6kXXylB0va/Ra/eqjDzo1j36N6e1KaJXt/TlFcxJ88u867YbbzDvtQumaA9O+iFO0G/b4xjMnluI6M39bn3uvUe96mt5Yc04UL/gvvuOlm8mrurVXnNhUH3G2PvNEzzaOJl/La+5Z46RiS0uIupnqdGFgA7g0tJd9Ve96EYe/FZbpAl4G+cruWQee45Y/rShbYsS0As9hE4GI2Pbyusv+kH/Lc7VvIN1K9OPnTzGy5iYk3hXRA8beckStCugsRa681MdGaaPwMgZK9Pv+tLJGRFzv8JRxvwPI4joTeB+KCIGx/fnW1rPGPXSqntx6o2ISjdlGn+L0/aFlZFuvKbx+qHJQshPp7H6Pd2806lvWPMsDgboZkH9jYiF0nVE1C1QJf8WGwHjsDjj54VJ/1BYviEiFtqAiKgfqymL8BcRCy0zNe2UkfpbnPkBmwWBEBHJuxYE0AympbEyg2BmVYlAIpAIJAKJQCKQCCQCiUAiMHMIjISxMnNwZE2JQCKQCCQCiUAikAgkAolAIjBbEEhjZbaMRLYjEZg9CGRLEoFEIBFIBBKBRCARmBUIpLEyK4YhG5EIJAKJQCIwughkzxKBRCARSAQWF4E0VhYXuSyXCCQCiUAikAgkAolAIrDsEcg3jhUCaayM1XBnZxOBRCARSAQSgUQgEUgEEoHhQSCNlaU/VvmGRCARSAQSgUQgEUgEEoFEIBFYDATSWFkM0LJIIpAILE8E8t2JQCKQCCQCiUAiMC4IpLEyLiOd/UwEEoFEIBFIBAYhkHGJQCKQCMxiBNJYmcWDk01LBBKBRCARSAQSgUQgERguBLK1M4tAGiszi2fWlggkAolAIpAIJAKJQCKQCCQCM4RAGiszBOTwVpMtTwQSgUQgEUgEEoFEIBFIBGYnAmmszM5xyVYlAonAsCKQ7U4EEoFEIBFIBBKBGUMgjZUZgzIrSgQSgUQgEUgEEoGZRiDrSwQSgfFGII2V8R7/7H0ikAgkAolAIpAIJAKJwPggMHQ9TWNl6IYsG5wIJAKJQCKQCCQCiUAikAiMBwJprIzHOA9vL7PliUAikAgkAolAIpAIJAJji0AaK2M79NnxRCARGEcEss+JQCKQCCQCicAwIZDGyjCNVrY1EUgEEoFEIBFIBGYTAtmWRCARWMoIpLGylAHO6hOBRCARSAQSgUQgEUgEEoFEYDoIzJ8njZX5McmYRCARSAQSgUQgEUgEEoFEIBGYBQiksTILBiGbMLwIZMsTgUQgEUgEEoFEIBFIBJYeAmmsLD1ss+ZEIBFIBBKBRUMgcycCiUAikAgkApMQSGNlEhz5kAgkAolAIpAIJAKJwKggkP1IBIYfgTRWhn8MsweJQCKQCCQCiUAikAgkAonASCIwq4yVkUQ4O5UIJAKJQCKQCCQCiUAikAgkAouFwMgZKzfccEO56qqryu9///vyu9/9rlx55ZXlmmuuqQFC//3vf8sf/vCH8otf/KJcd911ojIsAwRg/cc//rFi/69//WtabzSWf/rTn2qZ//znP1OWufrqq+uYyz9Vpn/+85/lH//4x1TJQxuvz/qGpv/85z/392PKZ3Pk8ssvL3/5y1/K//73v4H5/va3vxV5zCHv6c/kveaZMbr22mv7k2f0udve66+/flp1G+/LLrus6MegMviC/sFuQfTlZfoPB33Vb8/iRyU0fP/6179OSQ/9fVWm4TsVDf3973+vNCRvf3nP+AIaNA7//ve/RQ0Mjc4GJg5xJN6F/tDVVBj2dw+tKiPArz+9+6x+fKF/XMUbO7irr1tmtt+T4WTJorQdf4KXcvo+qI/4gTzwGoSr8ZEGN7xgUB0zGafNV1xxRRnEuxb0Hm037/S1f06pC1/UfsG9uP76zFd8rj9+GJ/xajIAveiz5+n0o5WBxaAy8EEL5i7MF1QnjNWHfvrnG7r69a9/XYz1guoY97SRM1Z+/OMfl8MPP7x89KMfLR//+MfLEUccUd7//veX888/vwrhn/70p+XNb35z2XfffQtiK/m31BEwOT/3uc/VMTn99NPLCSecUA2QBb2YQPrsZz9bjjnmmHLqqaeW448/vmDA3TKY+fe///3ywQ9+sHzsYx8rnrvp7hlGX//618shhxxS1CdulMJvfvObctxxx5XTTjutfOQjHynnnXfeAoUbpvvLX/6yloGHefK1r31tEnaY53e+8506jz796U/XvBdffHFRtmHH2H/3u99dXvnKV5Y99tijnHTSScWYtfSZunrnr371q0oH2muc+9s76F3aZ97DRR+/+c1vTmo/AXPkkUeW1772tWX33XcvH/jAB6pjY1BdBNwXv/jFSrfw/e1vf1t5yaC8wxYHX2N79NFHlzPPPLPO0W9961sLdeRcdNFF5X3ve18xn9EfeunvO5774Q9/uHzyk58ssDaO/XnOPvvsSl/y4NuEfzeP+UsRePvb317e9a53VdrGT9BoN9+yu5+5N1GC8LVPfOITFfdPfepTC51DDLsTTzyx8kTy7TOf+UyB0aBW4Y3y4r0/+MEP5tI/hdx477PPPmW33Xar85wSNaiO2RZHZuuzfpnbZMnC2q4MGQKvU045pQjmdLdvFEmY4GN4zBlnnDFpjjNwvGvPPfcsL3jBC8r+++9fdQrzp1vPTNzjoz/5yU/qnHnve987n9xb0Dv0A882n77xjW8UBkvLr60XXnhhOfDAA6v+87rXva4cddRRk/QguJx77rnlLW95S/nCF77Qig7tFe/49re/XftpTMkC/G5B/ANOeOCHPvShKlePPfbY8vOf/3wSBmToe97znvKKV7yivPzlLy/okbE7KdOND4wkNIcGySFz2Du0gTGJtvbaa6/KJ7XXs/Qbi+flRgRGylhBBCaivm277bblKU95Sll33XWrovuzn/2sMutb3/rW9WrVBbHIm2HpIQDjL3/5y9WYePjDH14e85jHFF4EijWmPNWbKU6HHnpoecQjHlG22mqrgsn+3//93yQBwiuE8RD43/ve9yaltXp5KzArCpX3tvhRuMLvbW97W+E13XrrrctDHvKQ4nmQ4tj6Ky+lj2H3+Mc/vtzjHvcomDLh2PIw+N/61reW+9znPuVxj3tcjT7ssMPmCjXKAUF2+9vfvqhDXQcccEDB4GvmGfyvtRcT9y7t5YDotrf/dRReDokVV1yxPPGJTyxrr7120f42/gQEBQ5+aOt2t7tdVYTRSH9dDGRCiQJw3/vet2y55Zblrne9a1F3f95hfKa4mlcrrLBCecITnlDmzJlTlVf8cqr+MJAPOuigcotb3KKWueMd71je8Y53FLi3Mj/84Q+rIrzWWmvVMcAHCHW00/IwOmELV+/Gk9VDiZeH0OZUosyjsZVXXrm4R+PdeuQdtgAPDhSOFDzxkY98ZDEOFPGp+qIMvml+PvrRjy7CySefXBjS0lo5ig56pYyKx0Mf/OAHV5pl2FBGzSeYr7nmmsUYUPxb+dl61ZfPf/7zRd/wOjwPnTEuGLCD2q2flET9e+xjH1u22GKLAnOGS8tPycQfvvrVrxZ1PvShD608sY0FPBmSnCX3u9/9Kl/0jJ5/9KMftWpm7EqumQsMfbITn5pO5QwVfBsf1k70sc4668wtqh4Ku/nLgPOeVVZZpay00ko1j36qAz4MN6uZNWFp/7cU6+ecNq/WW2+9KstudrObVXonV6Z6LSN/v/32q3yezLEq8sY3vnHuzgx6JnoiN8gPc0r+r3zlK/NVeemll1aZjE432WSTYp7f6U53KhFRd/9w9nGA4W9kCocN5ysZNV9lYx4xUsaKQTdRn/SkJ1Ul7G53u1vZZpttyote9KIqWI01Qrnzne9cicVzhqWLAGWPV/Uud7lL2WijjQqmQXDyYPV7K1pLeCJMWJP7QQ96UGUam2++efV+G9+Wb9VVVy2bbbZZufvd7z7QUJGPQq3sbW5zG48jFQhhigqGOWfOnGrY3epWt6qeasJoUGfNER5vmFBU4EsJ4DUk2Al9ChBFkXAn7Aj47373u9Wbq075xO2yyy5lxx13rN6lm9zkJmVBRpJyixMoEMZ8iwklQ3vRBKHKQ0aI9NepLxSaCy64oDzvec+rzgp9pQDzFspPOVl//fXLrrvuWnbYYYeyz4SHmVLdlBN5WmAYnXXWWeW5z31uocSsvvrqhcBr6cN+PeeccwrDjxHG6EAPlBgOgEF9gzllkTHDw8wZ9KhHPaoaKgcffHAtQimy6kYAP/WpT61jwHH0pS99qa5OyYTOeBPb/MST0ZOVGkaMPGjKHOe1tBJ2ySWXVNqmXAnyDGswBynInAEM8Ac84AEFbfOkd42+bv94c3l50SGex8i75z3vWVcdu8YbBffVr351lX3PetazCmxvfvOb16rM3Xvf+951bjz96U8v8qlLe2qGWfyfeWu1FF6UcTyPAwytWu0c1HTKIvkDM86XDF1ntAAAEABJREFUe93rXgXWDDRyRhkrEJTznXbaqcoS4+AdVl0ptRR4Cj4jmXL5hje8oeKGxzBg8CP1zFRA8+TahhtuuNAVzvZOc+1Vr3pVoWi/8IUvrH285S1vWcyhlocBZN6QrWiPEq/PnADyRETBY/EC+SJC9NAGfMgKE2zgqW+MXKuMnKGDOoYvmWPkCJ5l7pCDDBHxyjBOxJEvO++8c3nNa15TyMvGt+QR5EMrjJuXvOQlxXga2zYm5mRE1BU0ziJGImOSDG951JOhh8BIGSuIklKCEF110aRDoISq5xYQB8GLmfFU8TAiUOmuCIxAxpB4ZK+55hpJ9eyLvAQKwpaHd0X+LtNSBwFLIZfuvpteKxuD/+ALQ4KVkhcRhZDhRaD0DILAeNhmwiMYEdWw3GCDDSrj5tGArXImNO+uej0PCtLk8b5B6cMaR3BbwrdSyADUj4ioQspKEsEkrj/AnIJ4hzvcoSZhjAxJKw28OYQzpkuZh69MViYYe4S+OaYsJUm6eaSONdZYo67syD+TgTLAg6XN6sXstZdBQhiI6wbznmBhZMkrjYFB+WDcwQVmG220UfUotvbLw7EhfwsMJcYKQwX9tvhRujLQCHEY69dqq61WlVv0gMbEdQPnA1zmTBjHhK00tEHhZfDadmK7FwOTItkUITRkDAhjq514gkDpNEfVow78Wv2Evzn7tKc9rQp5dKlexhRvJ0VMmWEM+FdToigwET0exyC3fZEyNahf8CVDKFARUdAuBZzXliFXJv4o1QxF2D/nOc+peSai5/4zV9GysVOeZ9183nTTTefmma03+JqVdE5INBMR1RC2dYnB0d9uWDHE0Y35HtHDGe/izLE6gp9x4FBsN9544yprIqJwqClHgUTDz3jGMwqegiZhR5F1xW/637ukzze96U2LcfHe6dTFALU6bi4zPvt1HXXoJwMNbze/OCTM2/53mH/kJdpQbpgD+UBOWAlv/IJDhiwx/+h//f1j9DLcH/jAB8419PAugVGsTo4tcwgtwAnPxD85bVp9aI9DjZOPs/y2t71tS5p7JZ+e+cxnVrrC38gmq2FWX9DA3Ix5UxEYKWOFUUIg8hi86U1vqttSTGTESiggrNrrif8wJ/sH7VXfc889C69EMygoyryE0nmLeV55sBG37Ua2JljCtzqAOfBOiGvLpoQRIjUhMDtLe+rAaCdePVb/GGsEAwWxdZzAxOgJiRbXrjAmJCg0mGmLN9kxcApOvxKFMbR8g64LSx9UZrbHURrRIlwbI9ZmjBgdUho9dwO6xIjh34QUhgtnTJoRTvgaM8w3ImpxioF6ee0wVWXaXMK8zZEnP/nJ5f73v3/NP/3/FpzTuLX2Gnu5vRstYOyCuG6guGi/PBG99kf0PIatb9ouKIcPeAfjjOAQJ+jXgQceWAUWQ8b2I+enKD/aJc+wBwqM+WQuNuForCmv5iB+2N9H81IammlpjFZKAO8+x47AG82AbXnUT9nj5FEv2oVjtx7vJviVh7/22RamXuNDmUfbtsegw1b3sF317cILLyzmIcWwtZ8Rgn71Hx9s8a6e8cumTIqLiGJe4q/GBJ4UUbKHsmVFkIPB6hnHmjLo3hxyr06GEcOTp1jcbA4MYO3v8jt8wbN+9Lcdv7MahY7QVUt3j9bgaSzwC3OA8tjyoGe6A0NQGtptaa5kUERUQzqix2fEz2QwntOpj6FqeyAjzlykJFuNxNNbeXRl7uDxzlnw9NNpYNTytKv3Cu15WK8cyMYWfaAb/TDfjL/5xzknrhvITTh1+ZIyeCJakg7jiN6Ym3sMQCv0XYNfvne+8511C7K5Z1Wmf0zkoUei4fXWW69uUaRzMoqU6bYr70sZKWOF9esgpknLI+rgLKPDci+CjYg65hFRD61hONtvv32hpGCECAWDMokJcXuJeVAohAwP3j6EaxIwQjB9k97eXx5D3g0vsJpAuXnYwx5Wtttuu/LsZz+7brWw73EUmIA+TjeYkBRjDD+ihz/cMA+KSz8eBChFB0MwZu09yqiD4mIcWvy4XineBC1FmuBtOFBm0DXPWT+2mDPaxRwpj8pERF1hINApkfJgwLCP6I2XsYK993WVRHRuvlEoeTYxeXUOCowC+4eNX386gem9/fHG2fu0Fz20dH1EJ2irxbWr98DGnG1xrupw1UdXQT5z1lK99lNMxAuMEnPctlHPgq0ilvwHKUbShy1QYGBgbM2v1n5YoSHzsMW1KyGqTD++FG/0pox05Rm8rZwrWhWPvhodDKoH7RpHbbI64AMOlCxK2d577114udGnOocx6D/s4YE3tj7ok37DEJYt3hVu6B3tGx9xgnksrzLm0VlnnVW3KcLOs5V/NGt7nfwtmAe2VDnX4gwLRaylzdYreaH/ZHBrI74AA9i0uHaFC34nv3ItXpmIKGgKrsbDWMCs5TEuyuM/rhE9XtjSrTxyzvCEt7j+q/oZnt7Rn4bf4bn98YvzTFexosZRi2eao/gyR2rjyeaZbWxW3Wy/ZMhw0uJ7i/POYSiDx8AYbyLDtNkY43fSYCWuBeOMdxkbBk2Lj4hibhpHvKnFk3/0S0YJ54vnlmYrJn7VjF56pTExBuhYPmNCR7WVmrwxXzm9OQ66Ml3eDGW0jBUEaesQBcThN8oRA+FlL3tZsVrSBhxRspzltTxsPytPLGJDzJtvvnlxYJjXCrMiWHiyERPvFwK0FOhsDMKy992yII+GyUHpsWRtOxrPFg8Orw0GYSK0dozDNSKqd5pA6e8vrCOiP7o+y98VMMaMgmqMhZppjP+L6OGG8Ub07sGBRl0XhBFG2E1vZYyHshFRFR73LcDefcvjnoBnkFvRdODeIUNMWVo3EAAYNY+e/dTyYc7GVD5MnaOgtUOcoI3yoAX34gT5hG5bxLegTJd2xLf2U1Q8CxQSHmgKB17B8UCISfNMcXSmwKFbe/8JJp5rB3pHYR7DNKI31u71WzCG8O1iJV6IiLpVBnaeW8D33BuTiF4eSqK4Fgj7iKhbkyKi+Osq3p7V09qiLvvnKWG2WNh3bpXctb9uZYcpNBpF263dcHev3679IWJ+XqoeQRljhm7tIkC3nGhkH6PRweuuokX5cnbDeTfeZ3NyttN0RK//+FfDRt/1exCtyhMR1RnTaEqcMq4wcxXQU0SPJj03fiFPxLx4aQwD/I5XHLbi+gMDED+B/4tf/OL61bw2vvIyHjl/3C9J0BcGkbqcIcPLnv/85xdnKbzD+UN50Jm5Yxul9ji3QqfhgDEvl6QNs7VsRFRehV4iojYTFo3HRPTiasKN/0X04rp8SRJZ4NqlIzokXmRFha7HKWAc5GO8kEG2FtMRjQ090VZOK57oixHFIW5Vxc4EW2LNXWemjJd6MsxDYIV5t8N/h9kiRnufeeDsV/Q1IAqGA2U8KK2XiAGDi4i5wlNZxOgAIuOCF8KhUAyrdP7kw9xaecaLvcO8Wzw8vBmEgVUZXkDeQecLLAP2T4JOtSN5aysI5tAmu05i2iarlTDP3QBTTEAeHqKWJj9GAGtMoMWP6xWjY6gwBNBjw4G3iCKJfiN6jLelyc8ol4eAF6+sZ7jDFn2ibXHSBXnNLeW7wtk7MFuKEFq3BxztK9MNHAUEpfnkk5/qYLxwKNgiadnbOyMmt5eQ0V60ow3qbO01J7urHtIEZXisurQjHu24Ws53FeQlaGzR5G3UTl5Kaa08pwQ8vc8+dsYNL3RX8ZN/GAOc8Clj3cXXWJtjXe9i6x8s0F7Dp8Wjw4goyqAL5bv8Vj78Ubyx5ggS118PXLXJ2EiPiMqfnRvaYYcdir+IyXQibpgC/GCPJtF2a7u+6zcnADnU4l3hDjP5BXGCsYuIokx7dm8M1GH7kj3wFFrOOHkEc5mjgYJlHjpXaRVG2mwNzlaRJV3lmpyAR3det/ZHRME/YER+tHj50Tt+B1f0Bnu8peVRJqKHa8Q8epPPuT/OC8Zey9+9ymNrLEepL1rSQegiPiDh2TZ1TlDv7ZZbnHttNofwb7yKXmOO2dGhfvxMf1vdEVHk4WDi1UcXjJaWPkpXugcs0Auc9A294G/owtiLayEi6tZMc0OeFo92zFVzE6Yt3jwm/+wsYJQ4K9m2JstPZqAxeAucB/ij4wDoWD0RUY1pq2Cc5BFRDSxpGSYjsMLkx+F+4p3lJdILAtGE5P1AUFY3CFRpQiNe9y1ERD3EbRuYz7ta5mVsULRannZFwK0ORImIETkhIZ7S5tmEETAS8cq1Omb2OjtrmzNnTsEUMET910orVSYzr4TnbiBgrV4ZP8pNS+PxxnQJCEyjxY/rlbLDEwMXWDYcbO9gBFJsWly7whZ+lEjCWDzmTXAaJ0YE+nWAXVwbL8LQdgoHUL1XuRYievvmefTEmQuu3eDAJw+ueeT9vEyMFmUIEyucPEoR0S1Wmbb85m23vdqivebVpAITD9rvPfK09lNMbAUlKPRtItvcfxFRvxRIkGg7jCQyhCKifhbasxAR9Wt28kRMbqv0YQv6i4YoV+aW9ruiKVtpCXRx3QBf81OeFk/wojs0x0tIQUYnPNAtj/Gz/QGuFEsfzIiISb8DQUHAG4yt+d/KuuKlDGn3wx70xRkc/M1cbP2xwohG9R+NtXhXz3YAwLGVQd/GgdIE94golCPpxkQ5YwxvPNi9uG4gtyhJHETe0U2bbfccBeQnGmltw/vwJ/TU4tpVf9CqZzzEVSCL0Ll+oynYOYuF/qQL+AUDwPzwLHg35yWseMsjBvMAODNQjLGvj/Ga22pqpQXP0iYfjlC/epckRPT4r77i2a0uBitaiIjKR1t8u0ZEsavEfMaDW/woXWEAa+Nt7PTN3EA/dDvzUFw3mCt4FKdzi1fGPOMEl97iXSOioAfHCciZiBBdP/zg2btqxMR/3mf80cfE46R/5mHqNZMgme9hpIwVDMayZ+tlRNQvLWDyLFsTWlpEFIzevRARcyc05ue8iUlsOQ4RYfwRIevccl1rXbp3U7oIDsIG43B4X30KIn4/DISAPY9LwCwoorzWFGP9th0O/rbXeBYIHHHuMXkKp6V0z4IvhEVE/RxnG0fxgmchojdG4rohohc/iEl08w3TPeWDp46SyDvW2m75mVDnhRSHSaNV94Ktiwxp9OgZI1aeMYGZMlgY+Q7pKisPJROztrXAPBLXHzBl9N/PzOUjNDF09xFR9/8aX1tVMHnvozRI7w+EvvfrpzTzqdteceiK8uHe/DPfeYlbv7WNE8OnWgkL+foDZYaRZzunNHN/zoShzUPacBBPobEtCX/wPOwBvryBFGd9gYP5aW42JaaLLwWAR553ULwyFD1jwtuMfhiEtptw+rQ86M2KlC95wY4RKtgu2/Bl3Miv/vZu9Y9awKusROJ5VjRa/3hmYddVvBknjS9aWSI/HKZXRjw6x1/xWfXaLmcFBc3LI5jjlGZOA8/9gbFKEaO096fNpmf0oo3mMjrRf7xstdVWKxwpra3mPZqKiLe+V+QAABAASURBVEKW6Let2S1deTSIxvEzRgtcfUCk5XGGlcGOjluclWN1K4fPKGPbnfnS8rjiZfB0L1BCzQsrs4wUcw5PlLagYDwjYq5u0s2LD3qOiKKNMOjKS9jIY+VYH+UV59oC5y4eDIsW5xrRe6f3ex7WQJ7ZRmqM8G394MSim1kJaf0zpnQ46RwtaMn4exbodvgX/qZOcf1BvYy/JnfRiHHvzm/0YizswpHWX0c+LxiBkTJWEJ/P89mHSRDoOiJz2IlwIGgRDIFM+cLQ5EGslB1E6RnhEhzK+QKQbWSEs/2GGH9E1B8pVLf8mBwB4otiEVEZJ4K35GtlxvfaX/rSl1Zre5SFMCz6A+8sIQtLExr29mxuu+229bdw5Pe5Tt8st5rlmWfDsx/p422kSPnAgV9bpojL04IxxIgIZ2Pb4rtX40u4GfNu/DDfR0TxGxYUFdsS0C86hZev0zGy9Q+GO++889xf4LUlBEM2HrBzWJyywmjg6SNobSFgIDh8qd6zzz67UOIxeHXaWuBHAQk7dXj2RS2/YzKICZuXEaHopIBxe5/3TkroPPgEK0WMR9P4ai8a8lEMxqf22WKp/YSQuijElANn1whoczki6vfwlaE0+1pgUz5g5msttrM1YUOZsLxPaPnaiyYx4Mx5c5qxKG7Ygy9s4YtN0fGVHGNqzkZE/VS7g9ieKT/GjKcYH3MeD75oiRC3nc5YG1N79eHswyPGDR9lAPrYSETU7Ra2AsJU8E4/5GkVnNEbMT+9DDvW3fZTIq0wwsX8c4YLj/MxAfxPXvMLn/Q1L88+uWsF0OFu/A4t2jHggK5VLdibo1alfMZWGYe8jSn5I55i7ZCvdGPHQPR5cGfJKODKLM2wJHVrH16mz/pFTpvD+BUjRt0UU/yPw9EzxyE+6ZwZw4+Mh6dVDrsdIqJ+ch3PQH9kBR5j3sMJH8FjbGG1bYrD0ZYdY+c9zvpI964WIqJuXSx9f3gPvmHuRCycvo2xurW7W5X2MXp8REi8VQLt8Rs9eJk4ugpM5IuIgpe5x8sYs2St9jQ+qkwLdCfzkbxuccN4xavMH44uOhodgCzgEGBM6BOMnU92nkSf8THjinc5l4TGyNeNNtqoiI+IQi4ad/NTnRxa5hB6aXKXkWwu+6kFjhzvUh/Zy6FgrorLMH0ERspYwcx4mxEaZcqXFTAg240wJ0wCMzP5eWkIWUyP154yJh7zVgeB4cA8jwVGwJsqDsPH5HkjMEQKEQ+ifb9tyZjgoAxhrBiDCcLCJ4inPzSjkRNDJAgIVF/NsNoES8yhq9hiCnDVaxOZgDF+jE/KEm85Y5AyKo/ASPHFJmNkPIwnwS+tBQyXMoR5YMKYtWtLH+YrpZCBQMDa7kgBIZQYdBHzhCFsI3rPVj4oleheXgaOZ/g0LDBm9Iu+7a82L2zbMr/kwaAxaufCGJCMGfOr0b88MxW0l7GvvYx/7bXPvnmfI6IQMvrY3kkJgQchYf4T7rZhMHrkQWecD9J8A59BR8nj0Ijo4SQfryOhRCGirLjiK12s5BvmACv4chgwHqx0GFeKceuX8e7iC0e0QwGAIe++KwOvlUELFCZzEo2a03BuK2zyMUr8yB4FkKHI++9z0U3gyzOqgSJljnE2oFXntvBHxkjEPBo0t9ErHPBSCjOFBz3ipeYd/gpfeShi6kX78pBhZI96pasL7RsTcs6YWFVF69Jnc9BH81R/9R1vYvRxbDECtF3/KPjuBThTMhnYaItjw/ZT8ki6gI9Ko/TDjtFoTtjqJR19MyY5IM0DThEONMY7Z4oVWflmKnCImjcUZ/OQI8jqZbd+dBHRoxN99GUvMhUuDBEKOmMG/1QOXTBkfP0NH6XLKNOda7DjpLI6b3XTO/ED71LHsIWI3qelyTdOE/TCOPFMR9MffUYvZEhED090AENGCp4ozRxtWOGHDBZ80nwk/9yTu+oUzFWykbHEed4MSfKkjYl8MxDGpoqRMlZMMMTgsKyJiyFRbDD0JkhtP+EZoQRjWryKmLUJbCKb1ISob/kTyJgjr62vZ8iPYaIOh/B5G5XFLDF7TENaRNRVA5Y2BYdXh+HC0JE+bsEkhxVvK0+Ow7Ld7Thz5swpBKuvmDRsCBBCBkOV30cKjFVLd2V8GnOKKOFLcGHw0lqwd55wM76vf/3r67YA5Vr6sF8ZfgxlHmtf2yI8uwYdryNah7G+ol8KkjliW5R5YhtWt4x7HnfGonGj0HtPxDxmzkg3R8wv48ZrHtFL956ZCtrLONA37fUlm257tRVNEcyNPiJ6QorHDA9Qpts+WBA+FDU0Id3c7hrP2m8+80bCirJD+KAxgkj6KAT4ErLw1Vf0oI9w1T+Y4G/Gu80tZeBJwWv4cv7I34I8MEUf5jHsGDkt3RWOnBL4gvmNjrt8QZ5RDuiVsgNfKx+2B8G79ZnShEbRe4tjXJJnMDVWyjRFXR6Y2hZk7tryaW4wwpujQXlyyYoOWWlFhTHTxlYdszlwTOm7OYvnubd639pMvnNuNeNMvDIcjnBDZ+Q5uSCtBfoBhVM+/E75NgdsByNj8BjGAF1A8HEQvG+m5QnZZ+wd0Kcsc3KSoa2tjHrjhze3OOOLlswzCjLagEVLp5/4UVx9YIihDbi0dNeIqB8kMCfJY/m8Cx+UPoxB29E3/kZ+GFu6G/6kP5yc5gqcW5xxh7l4NKYMvVB+wfxi9Db5h2bww4jJ8g++5pd08xT9deWoujJMH4GRMlYQB+LjvaOQIEoC0kSP6BESg4FVLciH0RAangXpBIbtIDwmGIeAmREKEVEIhIgolojVz0uhTOn8RUTBEBG5PMMiDDpdmNHbVVZZpZ4f4hXsjoeXYA7wNnaeW4C3/IIyLb5djUN37IynuJbuilkRZsaWR8OYNKYkfdiDvjQ61L/+/hNi6BPGra/yKGOOKNNNa3ngphzahbH3tDS0bkwwXlsN4KvOlj7TV3V326tt7R0RvXmmrd02utcufTT23T66l1/7CXQY6FOrs3tFgxQ8OKhHW7rpo3APDxgYS3303PoVEQVfhVdEj4dKg6+8Dd9BuIhjfMAODSnXHxq+zhWg1f70UX/Gj2AI3y5d6zfZ1GSQZwHuysAUz5RHfDfA3XiqF+3CuKVLMybOVFDC5ZuK9luZ2XYlS9ELHkRed9sHQ7j0ywv5YAZP5SPm0XIrT/7IYyy6mMAPTgI9oBv639PqWpKrMTLnvM8cI9e6c1Lb9L3/3foIF6F/vumDvhl3+NBpBrURPeGb3iuYk2huUN5hiUMTxtR8gCV8W9v1jWyBdYtzhbcyMFNGPvECjGBMfuCZ8OrWKY8QEfV8JppTjzGNmJ/u5M2wcARGyliJmEwIEZOfFw7HgnPYe+1simAp3XYzS4QLLpWpXQQiZnZMunWP833EouMasfAyEQvPs6xwj1h+bYlYfu+eCt+Zjo8Y/T7ONGbLq76IhY9VxMLzLK/253uXHgIROe6D0I1YdFwiFr1M/7sjYuBHEvrz5fOCERgpY2XBXV3yVMaK/aS+CmHZ3vkIcUtec9aQCCQCiUAikAgkAssRgXx1IpAIzFIE0lhZhIGxNOiQnb2i9jK6t8S4CFVk1kQgEUgEEoFEIBFIBBKBRGDEEZi57qWxsohY2q9ov6c9oEJELGINmT0RSAQSgUQgEUgEEoFEIBFIBKaDQBor00Ep84w8AtnBRCARSAQSgUQgEUgEEoHZh0AaK7NvTLJFiUAikAgMOwLZ/kQgEUgEEoFEYEYQSGNlRmDMShKBRCARSAQSgUQgEVhaCGS9icD4IpDGyviOffY8EUgEEoFEIBFIBBKBRCARmNUILBVjZVb3OBuXCCQCiUAikAgkAolAIpAIJAJDgUAaK0MxTNnIMUcgu58IJAKJQCKQCCQCicBYIpDGylgOe3Y6EUgEEoFxRiD7nggkAolAIjAsCKSxMiwjle1MBBKBRCARSAQSgURgNiKQbUoEliICaawsRXCz6kQgEUgEEoFEIBFIBBKBRCARWHwExtFYWXy0smQikAgkAolAIpAIJAKJQCKQCCwzBNJYWWZQ54sSgVFFIPuVCCQCiUAikAgkAonA0kEgjZWlg2vWmggkAolAIpAILB4CWSoRSAQSgURgLgJprMyFIm8SgUQgEUgEEoFEIBFIBEYNgezPcCOQxspwj1+2PhFIBBKBRCARSAQSgUQgERhZBNJYmXVDmw1KBBKBRCARSAQSgUQgEUgEEgEIpLEChQyJQCIwughkzxKBRCARSAQSgURgaBFIY2Vohy4bnggkAolAIpAILHsE8o2JQCKQCCxLBNJYWZZo57sSgUQgEUgEEoFEIBFIBBKBeQjk3UIQSGNlIQBlciKQCCQCiUAikAgkAolAIpAILB8E0lhZPrgP71uz5YlAIpAIJAKJQCKQCCQCicAyQiCNlWUEdL4mEUgEEoFBCGRcIpAIJAKJQCKQCEyNQBorU2OTKYlAIpAIJAKJQCIwXAhkaxOBRGDEEEhjZcQGNLuTCCQCicAoIXDDDTeMUneyL4lAIpAIDBkCy7+5Y22sXH/99eWzn/1sOeKII8onPvGJ8oc//KH85je/KX/+85/LNddcUy644IJy5plnlrPPPrucccYZ9f7rX/96ufjii8s//vGP5T96Q9SCH/3oR+Xoo48un/nMZ8q///3vRWr5L37xi4r9tddeO6ncn/70p3LyySeX448/vvz85z8vg5SaX/3qV+XjH/94+eQnP1kuv/zySeVH4QEN/+AHPyjHHHNMpeVBGPT38y9/+Us5/fTTa5kLL7yw/O9//+vPMvdZ2ne+853yrW99a758P/nJT8pxxx1X6zJn5hZaCjff/e53y5FHHlk+97nPLZR+zM1vfvObdd6auy184QtfKFdcccXc1qEHtHPiiSeW3/3ud3Pjp7o55ZRTCiymSh/WeDTz7W9/u3z4wx8uZ511Vrn66qun1RVYHHXUUeWcc86Zssy5555b5/33vve9gXVedNFF5WMf+1j51Kc+Vf7+97/Pl+eXv/xlOfbYYwemzZc5IxKBRCARSARGEoGxNVYIxhe84AXl05/+dFlppZUKpfZZz3pWEXfppZeWFVdcscZTjnbaaady8MEHV+Xny1/+cnnzm99cttlmm3LAAQdMUn5K/s2HAEWIsnjYYYeVVVZZpSp773jHO8pVV101X95BERTP9773veUDH/hA+e9//zs3y/e///3ypCc9qeyxxx5l1113Ldtuu201hOZmmLihKD3xiU8se+65Z9l5553Lk5/85EKJnUgamX8URdiusMIK1aA78MADJ+HU31EK+ktf+tIivOQlLylPf/rTy4c+9KEplc3f//735XWve101hBgurT4KPjxf+cpXluc+97nlmc98Zp1DLX2mrt7JwH3f+95Xbn3rW5fzzjuvoId//vOfU77iq1/9ah3v7bbbrrSw/fbbl+c973nVEaEg+nn2s59d5/LLXvayIv3Xv/61pIEB3ewMJdRLAAAQAElEQVS+++7F/B+YYUgj4cuQP/zww8vtbne7aqygh//85z8L7BEnAf53i1vcopx22mnl6KOPni//O9/5zmpo3OxmNyuHHHLIfPPTe9GQ+fmc5zynzuEf/vCHtZ6//e1vlY7xYs4kBrY4TqSaIf9LBBKBRCARGBsExtJY4Y3mReRNPeigg8oznvGM8uIXv7i8/OUvL7z10m9yk5uUe9/73mWLLbaoqyg77rhjec1rXlMV4ze+8Y2FAcMj+LSnPa3w+hL6Y0M1i9DRSy65pLzrXe8qm222WTXwKIjwouDAeUFVMXS++MUv1lWtrreXockTS/lsiiyD0/jxxKqTkv3Rj3607LffflVJMmZWzSjeFHZ5hj2g39e//vUVW0aHvn3pS18qDIlB2MITXve///2LVQJjsPbaaxdKpVXEfjxg/pGPfKQamN1VrZ/97GeV5hmdVhz33Xff8tOf/rRQ5mdamVQvQ+Vxj3tcNU4ZRYwRdKE//W1mxFjFY8i+/e1vLy085jGPKeuuu24NV1xxRV0pesMb3lBXhd761rfWFdVDDz20v7r6/Nvf/rZQ5q0eDcK1ZhrS/xgHjBPG/uMf//jKC61AnXPOOa1Hk64w/8Y3vlGM/d57713nNEPP6sdXvvKVmlce9MVA2W233cpTn/rUagzisYxEmayWnnDCCQX2HEbqsvpinuLBxmarrbYql044jtCr+b755ptXY0r5DIlAIpAIJALjg8BYGiv/+te/qgLG48dbu/LKK1evLWG9yy67zB19qytWA25605uW1VZbrXoeb3vb25Y5c+YUqzD7779/Vdre/e53z/XYzi2cNxUB20ooeQ94wAMKnNdaa61yt7vdrSrUC9sORnnh0X7Qgx5UIqLW5z8K6SMe8YjCgHzgAx9YFSyGy2WXXVbHQ56//vWvhbeWMan8C1/4wjpmlN9m0Mg3rIFxTIHmbX7Ywx5Wsb3Tne5UmmLHE93fN4r2He5wh2pwP/jBDy6PfvSj66qJcbAdR52tTFM4GR/3uc995uIv3goXo+EJT3hC2XjjjauhT7Gk4M60IUhx1r773e9+xXw19xhYtvZ1DajWbn18+MMfXh0LVnwEbb3lLW9Z6YFRqx5tf+hDH1ruete7ViNovfXWK4NWE9CaNtzznvcsHBgR0V41ElfbYI3pBhtsUPGFw+1vf/u6tbJLD62zMGJcwBHduSqLLzI0GLhokpG70UYbFeNm3puv3nP00UcX44Yn7LXXXtXYQYscDVbBbC1krBi3hzzkIdXRYM5ycJjD5nJrS14TgeFEIFudCCQCi4rAWBorBOwd73jHuiWI94+SCzhGiW1DFGrPAgHr2i+41cGb/ahHPapuDXFuoOWVP0Mp8HDm51a3ulU1BmECtzkTxh7v94IUW2kUnqc85Snlzne+s6JzA4WbgmMLikjKZ1MmGZfiKKGbbrppVcA8i6fI2y4lv7hhDrbHOXey+uqrF7Tc+nKPe9yjwNbKUotrV33nnVYmIopnY0E5hUnEPEWckciDbtVx1VVXLe0vIqqCz4ChvItn8MNbfcZX3EyFz3/+84Ui3NpA8bVCcv755xdKcf970BoDypYmbRHMb/RkdUZ+tLDmmmvW/numYKMxW0A9d4OzOrCGG3rupg37vf5YoYIV3PTHnML/vva1rw3cqglzhoM8bfyVQUdWvKxeWnlzhmzDDTes22nVu9pqq1W6MW4MlfXXX79ssskmBc+NiKIO48ZBpF5b8vBUtMUIZzR775VXXqm6DIlAIpAIJAJjhMACjZVRxYEC8/znP7+uljh/svPOO9ftBbyyd7nLXQpFZjp9v/nNb1623nrr8sc//rHwTCs/nXLjkoeCQWmhHMJcvykjFBdnVgYp1PJcd9119dAtQ5CBQakS34K6KK3t2dVWnTXWWKNsOmGgeKb8UITct8BDa1XnXve6V4sa2iulzRY7Sjx8W0codxRKK0strl0ZE9LbsytF3HhQHiNCVKVnH5xwpgWm/Ya69zFuauaJ/6TD39arruE0kbRE/9RrXplnxlxl+qDPtnLZIiSuG6T3j7uPCJjTFGx5I2LuSpFV1lNPPbVYHWAAS2+hpT32sY8t3tniR+VqlcRZPePJQNAv85Mxi6cJ4rrBShPDT54WHxF11Vl9jBUfKkFXjOBuHvjbwmnVj3HkXS3d1UoqOpTP4X3vsDIqr61k5q/2ypshEUgEEoFEYHwQGEtjxfBSTuyZtvXLPmoGy5577ll86atfOZZ/qnCb29ymUK4ph5SrqfKNYzyPNQWGstGUTThQKGHFmPHcDeJ5aCmclMSIqB7wiChT/VF+eIId+OWFH5SPYutw9D777LM8Fc9BTVusOIo045ih0VX6IqKIh/vC6BjWPiCx+eabl2bAGRNb93i5W1xE1DEoU/xZyaHYOj9jbPuzaQ9POaPAuQZjoX0tn21eVj/62ysPGmKYdvvY3kEhbnVMdYUTDz0jtluH/Axm53WcX3vPe95TD+7boiRNOY4M29us2rV3RkxNh8oNU2B42NLHsO9iw3AxFsalvz/wMSb9xpv5Ki/6kY4n9s9FPAAteKe83cAQsZLngxm27L3qVa8qxkQZ2zZt/3IOxha/brm8TwQSgUQgERh9BMbWWKF8UMgIRIc5KWbHHHNM4U1mvBDW0xl+wlldBGzE6Cgy0+n7wvJQYHjgKcUNT1cKT0TUr6311+HQuDMCVrh4620p4Y2lOLX7bhmKzznnnFPPwTgkHDH/GFBqKTrOLmyxxRbd4kN7T4FHd/0dgK143vKI+bHo5mfg8YL76IHVC2kO2l944YXFuRB4UxRhbwysklFE5WsBtvCH/d3vfvcWPenKOIE/r7svw/maly88GWfOAcaRK9roFkQ7+tmNc6+PaIqh5nlBgSGrj5wTEZPxoKQ7G/G2t72taPtb3vKWos/q95ECxoyVOHH6oH3q0w95FvTeYUjTfxjrV7e9eJpnPM21Gxgyg8YEjciH7qTL118vPMUzQORtwUqgT0j7spwzaBFROIGsrNz3vvctr371q8saE6umtoM1Om1lS8m7RCARSAQSgVFHYIVR7+Cg/lG4bKHh5bM9hMJy2GGH1QO49qg7vCvPoLL9cX6fwNYayg5B3J8+zs8UC/vhecibckfJ5NGl1AzaMsRQpNg6S+RLQg7eOgT84x//uFj5OvfccydByqt/ySWX1K8NDVKumjFDMTPODKhJFQzpA5rjubYdqqsUWhGQtjBFHs4OSr/oRS8qDlU3GJwREW+VCv6+9AVjYwB/BmTL68rQWHnllYuzXlPRPyX1kY98ZP0suDpt7TEejBy/nUL5b2de1NmCsWI0oR10I15f9VF5c1fcgoKvTlGOnanoz2c1YZ111ilbb711/QS21VH5YYoG/aYST782w8NqgdUhqzGMlv76hu25Oz/xQu13NV4MBQaDuG4wb6UxUlu8MbEt0fib02gPTTBwWx7jBzPjJr3F4w2MVuPjoyXqaGmu6nM4XznPGRKBRGApI5DVJwKzEIGxNFYoO5Qs3sCIqF9SsleaZ9WVwiLPwsZLPp/s9MUbX7uKmOy5XVj5UU+PiOKTsZSWptxQPG35caDZ3vR+DODIcORN9bUgn8O1BUR+9xtttNHcIrzdJ510Uv2iEIU7IuqhfgpXy2SVwIqAg/pt6wrFSWh5hvFK4bNK5PwAJbv1wcoUxZ8nusX1X+Hmk8O+rsRzbSVGHpgwJHzCF/aCQ+dz5sypX/2Cf/ccAmPDiogyXcWW8qq+FnwC3NkjxqTyzoa0bZc+Z2tlx6H5lr971Udfh2r0w/i0UqLdjLVu3kH3DA54dNvXn4+C7DA4HLTPtkWfgbbiykATfFmOceMLWD6swSDsr2fYniOifprdygajQfs5aeCLD6Ixcd0Ac6vQDMwWrwy6MwdhbdscHG3tannwU2WsVHVp04ccGIfmJwNHfnTYaCgiRvIrbPqZIRFIBBKBRGB6CAyzsTK9Hg7IRSmxImJLCoOlZSEgeQ4pxs2TJ06QhxLMu2plwO8T+DE5gpcywwMsT4bJCDgTxEBh2MGRYuKQLIUPxuIoRzz9FFEGDE83T73gE7NWrXwBTF0UZ2MgP2WS1zUiiu1KFGdb+XxtSF3OR/hUqq0lvMXaYNz9ZoMxnNzS4XpCw37YERYMMjjqo68tbbnllgVd6hFsndmgUHp2dsTKoa+GeYaJlZPjjjuu/p6Q7VIwh73A2IQ9BdTXtCiw6nLWxSF86ZRd9TgUbTuP8VZ3C1Y2GATt2ZXir17KLU99xGBDHy3ol/qVY7hYSfOVMisv+u1DDfrY/17t1CZfnfI+5QXxVoi0m2Ls2Yoqw4jRsvLESpHP5jYcfDhAWkQUX0HzyWOrEuoa9mBMYYou9MXZI86EHXbYoX7JCz6MDPPNPYPTOR4GLwNFGemMZgYe7NCeA/EMYnNRHjTGacHoM262mnEYWVXhgJDPGDMuxRkT5TIkAolAIpAIJAJjaaxERD2EbAuKlRHC0fYXP15I0PrNDsoNpZqyQ7g69O0Xl30u1o8NOutiG4otIYQ35THJaX4EeGEZJhQTB9xtMWLYUWYosJRtuPLy2yaihoioX2uKCI/1bAsjMqK3cuIMgcPPPPvwd85I2GWXXYrtYrzeDuv6EU/vfNOb3lTPIsljXzzFaBSUTasV+sxw8Anao446qvCI+5E+NAs82L7iFa8olFErMFZNGHTol7Gz66671t9JoYyuvPLKiszFPiLqPeydbYiI+jEJRqBVSFfbxNQBW2PIeGjvrpUt4X+Mp2222aZQfM8777xiO5pVFYaEOcdAsbrm/Qzh7uvMWc8wcW2BsaPdgrMpp512WqEoH3zwwYUyLl9Er+8R4bHiwFCbyb7Vipfzfwx5/IsBbzzh7CwfI1HTGP14pFU1qy8RvdUYRo4fhuTwwRP9Zo8VNmWMiy2XjDq/BWTu21ZnPtoOyOhRziF67/N7VY2GGDxWYRi46sqQCCQCiUAikAiMpbFC8SJMKSsUZ55F3kTeZoptU24IaoLbjz5uscUWVZGxGuDwt+0xDuaKV1+S0mAEGAU77bRTPRdgXzu8nQGweqUEo9C2IIpz26YlvgUGDWXV2ZWGszop5MaKst4ChconqeVjsDhv4AtVLd1VHEVLve0dw3rVB/3VHyuEVqB87cw5jNanTTfdtOi3vf8MCfTq/IWzKuIZ5nCzLQturVy7NhwdoKeEincOyRh6l3LqUJdnKy0RPQVf3iUNjAd1Wy2yEmIbEcOEo0Dd6MfWLNvJ5BXXAiNLXis3Lc7Vio5VOjTI2IENGrWqIn1QcLjbhwGaEj8ozzDGGV9GphVKxqyVI4YDTPQHjaEZ9NLog9HGAPZpcdvzGM1WmfFGZQR4MYxhyvhgXFqBNibSbSdjvKAZ49toyDzfbLPNFvj1OeWHJmRDE4FEIBFIBJYYgbE0VghMn8BsWz14/n0pigLMYKEAEcy2vlBOxNtT7d7ndClkfXEmoQAAEABJREFUzlHYl02YL/EojHAFEVF/1I9iY1sRDCmJTfF1dU6F8divbILFWDgTxCML64gotoJZmbFVxbi14ICulZxWxrj159luu+3ql67UPQqBIo4eYctoocRHzDMWKJJwcWaDkaH//Zh4ZqBHzCvXsDFXKLLGIKJ3fsC2HduwlGvYu4ozJ1rZmbhGRFEn7z+F15XBi27Ub6xt82JsaKu4Fhhq5mvE5H7BQtv33nvv4ryMTzejqVZnK9+9UurxCcp8N37Y7yOiWE2GE3ytkHRpyJyzQrLjjjvWH3DU34goDD40Z4ucMs24kS5ERGE0yyPAuBkzcMY/jYGAdlrgmFC3OjIkAolAItCPQD6PJwJjaaxERN3WYcgjom4zWnnllefGiRcievki5r9KzzB9BCKiKjsRMbBQxOD4ljlicnpE1PGKmHzt5o+YnBbRe255RuUaEQs8hBwRc7saEQvEbW7Gzk1EdJ7KwPIRUePLUvqLiCnpJ2LwuxkyETGwRRFRz2QsKE/p+4sYXFdftqF8jFh0fCOiYhgxNS4Mk0EYR0Sll4j5ryX/EoFEIBFIBBKBDgIrdO7zdkYRyMoSgUQgEUgEEoFEIBFIBBKBRGBJEEhjZUnQy7KJQCKw7BDINyUCiUAikAgkAonA2CGQxsrYDXl2OBFIBBKBRCARKCUxSAQSgURgGBBIY2UYRinbmAgkAolAIpAIJAKJQCIwmxHIti0lBNJYWUrAZrWJQCKQCCQCiUAikAgkAolAIrBkCKSxsmT4DW/pbHkikAgkAolAIpAIJAKJQCIwyxFIY2WWD1A2LxFIBIYDgWxlIpAIJAKJQCKQCMw8AmmszDymWWMikAgkAonA/7N3F3B2HdUfwOf0jxQPXjy4Q3GHFC1e3CHFneIOLe4Udyju7p7iTnGH4g7FHf77ndeT3L15m2yS3c3bzcknk3vv2J35zfGZ+1IIFAKFQCFQCBQCS4BAOStLAGJ1UQgUAoVAIVAIFAKFwHIiUH0XAnsqAuWs7KkrX/MuBAqBQqAQKAQKgUKgECgEZhyBZXJWZnzWNbxCoBAoBAqBQqAQKAQKgUKgEJh5BMpZmfklqgEWAq21AqEQKAQKgUKgECgECoE9EIFyVvbARa8pFwKFQCGwpyNQ8y8ECoFCoBBYHQiUs7I61qlGWQgUAoVAIVAIFAKFwKwiUOMqBJYNgXJWlg3a6rgQKAQKgUKgECgECoFCoBAoBHYFgT3TWdkVxKptIVAIFAKFQCFQCBQChUAhUAisCALlrKwIzPWSQmBtI1CzKwQKgUKgECgECoFCYDkQKGdlOVCtPguBQqAQKAQKgZ1HoFoWAoVAIVAIHI1AOStHA1GXQqAQKAQKgUKgECgECoG1iEDNaTUjUM7Kal69GnshUAgUAoVAIVAIFAKFQCGwhhEoZ2UGF7eGVAgUAoVAIVAIFAKFQCFQCBQCrZWzUlRQCBQCax2Bml8hUAgUAoVAIVAIrFIEyllZpQtXwy4ECoFCoBAoBHYPAvXWQqAQKARWDoFyVlYO63pTIVAIFAKFQCFQCBQChUAhMB+BetomAuWsbBOeKiwECoFCoBAoBAqBQqAQKAQKgd2FQDkruwv51fveGnkhUAgUAoVAIVAIFAKFQCGwIgiUs7IiMNdLCoFCoBBYCIHKLwQKgUKgECgECoGFEFiVzsq///3v9vnPf74997nPbU94whPaW97ylvbXv/516hz/97//ta9+9avt0EMPbU972tPahz70ofaXv/xlat2lyDziiCPada973fa5z31uKbqb6T7++9//9nkefPDB7W53u1t7znOe03784x83mOfA//Wvf/W1eulLX9pe9KIXtcMOO6x95StfafKzzlFHHdXe8IY3tLvc5S7t/ve/f1+jv//971k89erd2n32s59th831+bOf/WxePeX6fMxjHtOGCb18+MMfbso1MI6f/vSn7XWve117/etfL2sm0p///Of2nve8p93nPvdp9773vfv9P/7xj3lj+9vf/tY2bdrUXvCCF7QXvvCF7WUve1n7yU9+Mg//eQ3mHn7zm9+0V7/61X297ne/+7X3ve99zbvmiqb+PfLII9ujH/3o9q1vfWtqORw//vGPt0MOOaT985//nFpnW5nm8P73v789+clP7umLX/ziPNr4z3/+0775zW+2F7/4xb3cXL/zne80+dP6Nd53vvOd7ZWvfGWno4XqZVuy5Ec/+lF7xzve0V7+8pe3ww8/vCXOrp6f/vSntyc96UntzW9+c0Nz2XZ4JVPIGdiipR/+8IfD4pm+RxOvec1r2uMe97hORz/4wQ8288dCA9cGXvjJ2sB9XBdfoUt1Xvva17bf//734yrta1/7Wpcb6rztbW9rf/rTn+bVsT5o7KEPfWh77GMf22lb3lDGzGswow+//e1vO98ddNBB7UEPelD7zGc+MxXjX/ziF+15z3ten6v50nE///nPFzWrX//6150PjzjiiM31YQXjJz7xie22t71te+Yzn9nQJkyzEh7B396Lzq3nd7/73Sxekavx4J9HPvKRXQ/Q1ca5vZf/8Y9/bG9/+9u7bNDm61//ejPnNqWhd7z1rW/t8hK/ZhUyiAwzf/T6ile8opExWb4cV/zzkY98pHkXuk+5gq6Nzfvp04997GNbvd6cyat73eteTSI/t6cv//CHP3Q7iS6R4KCfrTo/OsN46Af20tFZ/eI9mzZt6rYUWoGbvF44g//Ak3y3ro9//OPbq171qvbLX/6yy5FtDZfsev7zn9/tS7LxV7/61bzq+iXPzF/f8B1WUE5fDG0P9/jwU5/6VNdf6qBV9Ldx48ZGRqJRecO+6n6CwKp0Vvbaa6922tOetp3whCfcLNQJqcmU5v+L8RnJD3/4w9v3vve9dtaznrUd61jHml9pCZ+M6YIXvGA70YlOtIS9zmZXn/jEJxoBQBgwkh/xiEd043qoZI444ohuaJ761KduV7jCFdrJTnay9oxnPKM7kJiVk8ko/9KXvtROcpKTNIzMQP/0pz/dlC80c4bxl7/85S5MXvKSl2xlRBoTZU+Qf/KTn+zGAQOBYGH8Zr9HHXVUV3aHzBnb3pn5u/NK+MOEojjOcY7TvvGNb3SlxLHIcTEwKDoO1gUucIG2YcOG7rAz6hYybghBvEC54wuC9s53vnMXkt6ZfedVfQrzsMMOa7/73e8ye96VccVIeNOb3rSgkTCvwejB+An1i1/84u085zlPX090lWuPZ5/1rGc1OFztaldrBDvnSf6oq/aBD3ygG9wcVzx4kYtcpP3f//3fuNq8ZxhzlMzDGLQjH7yfkoIxfC9xiUt0B+jQQw/dKthB+cGJcjvxiU/cLnWpS7VTnepU894zqw8ULgMWnte61rX6MM1R0KE/TPmH4Y3v0eWVr3zlTnecDbyU1bW/+93v3p0PfI9XH/awh/XnrPPtb3+7rxdZiX4FnWCYtCiQgF7RKePBWMkHBsfwXdnfrF4ZMuhckGbvvffuDjG+G+ssNIeGObyuZBeMt0fD5k2OcjY5kOhRnvT973+/O9n4HV3jJUYmLJVLxoWnTnnKU7YrXvGK3Zg7eC4AhSeUr0QS3EMfxsswfOpTn9rudKc7tW05TWSgAI3g02Uve9l2hjOcocsPtAbL8bjN07zIebSlXB/vete7ugzE5/vtt18PlsCD86fOUif6R//GzR655CUv2U5wghP01xgPDDhObBZOSy84+h/y74Mf/GDXk9qgj/ve975t06ZNR9fY+oKf6EhY4RsBoXvc4x5NEEZ/4xbyyDO68si5YFWWM6TRJP6D1YUvfOFGN6BVuiLrzdKVHCKb4Ep/sFOe/exnt205auyXBz7wgQ1uV7rSlXqw7ClPeUrXPTk380WznA88h/+yzNV7HvWoR/VgYNof9Bq8rG9ENHLUOgsmwJmceO9739s4r96tn1lKu3ssq9ZZOcUpTtEufelLdwMXYWAYBuwYUMQqgqFs3333bac5zWnaMY95zHG1JXsmMO95z3u2M53pTEvW5yx2JBrPeL7rXe/aDQ7KReROHoYj2IybYSxCfbGLXawrk4te9KI9ks/oUAdT7rPPPj2a9oAHPKBHBjH+F77whR590Me0dIxjHKOd4xznaAxMwnVYR7+cnzve8Y6N4WVsksjGSU960kbQcni1Of7xj9/7IPi1k7e7k/kbJzqy0/SQhzyk06ydohybCDQj+yxnOUufD3q70Y1u1ChiQjTrDa8ilxHRDp4zRBiblBXnUURnmoPDWGdQwWqa8te3MTEkI8LjDiXtjGHfOb5EH5e//OXb6U9/+kaxijzqTP/Wl9I4+9nP3q597Wt3x9TuxXC93v3udze0eKELXajd8IY3bOpa07aNP+SCSO45z3nOdv3rX7+d+cxn7kZDRDTKA06MII6K8dn5o6gZz9ktpccAlMe4MgeOOcMw68zy1bgZTVe5ylU6PzFWOfoir9PGDXOONIOJwX2+852vXf3qV2/4jXGjDUVOCTMOb3azmzXra00YS3a91CGP7TCQDdpbtwMPPLC99KUv7Tux6qA5fTAu3MvjGKNJxofn1ZAYIWSVSLU5i4ZzKOis4fjJVA4cOUVuSaLgeHRYb9o9PsEz8EysXMnX/fffvwc7GE/Xuc51GrpXVz+cGM4Rh5Exd97znreRI8YBa+ut3nIm/H344Yd354TDxTDEawIJ+Nw8pr2fI2MHlR0gyEBGHPvYx+67FeTjsA3DkAFO9w9pB//SUevXr29okBy93OUu1+gfRuawj6W4ZwiTKxHR6CfvtL7pkJK1pzvd6Ro5YmzjuVtPTiV8kp60pXcXGp+1RBfWn9xHV+gRX3F2xu3Qh91VbYZl6NhOn7b0OLlIzgooLBTMGrZf6XtyiOFPnqHtc5/73G2/OWcUrU3bsTI+c+Z8mP+tbnWrlvJNMI5Tq44E83Od61xNn9rIy2TNOCa3v/3tG6wl9schcwFR9AU/60wGkr9k5clPfvJ+KgW+xquP7K+uEwRWpbNi6BHRI+8WHtOIXIkaKBsmREYQrVu3rjFwEcmwHJMR2MO8vM/oy1hgYwLCflo7RCx6ln24Eo6Se31J7ldzMgcGHsHOcYSxHZHjHve4/ZhBzpfAJRBF6SKiOyDWyQ5URDTrIhK9z5zDcrzjHa+d7Wxna/rS53ithnhZS3UYhsP81lqLiMa4vPWtb93Of/7zNwb9Gc94xiaisW7dui6A2tF/ROz1YdxHZ+32C8OBkSw6z5miECg0wjEHR5gxbghFwhJWoqUcHTtUWW94NUeYwJhwpChvectb9kiqvoZ1KWsOAIGNpodlec9gJdRFzr0/8xd7taOC/xiz1pMhYd0YCZwF/TjuwHHjeHiHNSfwKSGKWx3RR4qYIjrwwAP7rqa6yhZKHCV10YY5oothG0pEoEO5sUmMInTNuIe5vkU0GYqMB04PJyVixx03fe2OxHlAWwwk84eve3NK+TccF3627na2JW1c8RfjFsmtJ24AABAASURBVG8LHlHsGzZsaPpDP7ARKOIYo1EywU6K3S/0ro57Chy+3g1LEejLXOYy/biYqC6nigNvzMNxzfI9+YKf0Ri5x8BBz0N+Nn4Ooqg3PqXXGEqcdxgrXyjZMRad5TwO60REgztD3nvt+uE1a+Id6tJjjlSTufC2Dt7p/Xa58Kd6y5nIsmtc4xqNs8EQR3/klHHahVA+7f10Pj6Eo3GT5XZn0ckw+EJX2c23BnYDhn0p4yyhaX1ERN/9Iw/J32HdXb1H0xx6O0d2Hc2VXBn2a63RCRk3zM9768hRMDb60s4M/iPTs874Sn7e5ja3aXDCN+jq5je/eeN84NVhfTusdMpNbnKTbl8NsUeb6Ew/bBz0Am/5AgjDfmbh3rrSH2Q4DGCLtmEoEDdtjOiG/KEbUy7Rv3Qqp4J80k5fbBYpYr68hxldcbvb3a7hN0Ew8tG6G4fx6MMa2uVE745hfvSjH23oXju0rE6lLQisWmclp2BRRYsc/WDAZL4rg8eWsMgS4pInMTgwpK1QkTzbz47PiL4gRkYaZnY+XjSGgkT0BJvoNC9ZdJGBZDeAACIgGUCEIgNQdJszIzIkuih65SywyPD1rne9fhRAf8azGhMjmqAajp3gJcQIWvfKzJej4ogYQQdb0TuRI2siMVLVhQfhSaHsNxcBUSZ/W4lgGJdHRCNEKJ8s42CK7Dq2koo6y/Qh5fPuvhq3ZBxoVWSM0Nu4caOsnghSxgmjWiQWtqJmjGb49Uqjf6wLZTXMtk6MKf1lvvViSDIKCVfrkmV5FdW0y2A3AS3sDH4MWpFQRlT2a3wMYkpTnyLQnq2fOmiFkkcnoqf4lZHHscB3+J3xhhe112ac9IVn9eH7MgaRHSnP2YYscM8BzPbeja70zeBmKNkZoszgJ1rn2CMjINvM8hVtoR/rZx2MlQGSRiJDVl4b/MN4RY+c3cyGCxoig60b51FbRlTW0a9dZzKZQQBv7x/2Ywzr5oIJ+re28LW7KBLKkbEDgy7JEv1n37N+xWMpyxisHDV0J9CTY2dYmZudpAMOOKDd9KY37TtMaDXrTLviDw4znTKWa+pbm3w3fYTGGUQMJeUwZpgP8TReRp18OwHqLWeiw/HQ8B3GTO5w7NwPy9zDhQ7ee++9W847Iho6ZBSKTquHxuw6oC3HHPUpPxO+tZuIb+9whzs08oMdIZBB5ma9pbjii9wJ4tSTF/Iy6DJ8B9kzfM57esH6eDYX/GScnD1505K1JsfHZfieIZ35xmE3i3PLgB6PAR/CkbGvTcQk2IjGBLfkzVIiw/GTNYabsZkzGUNH4Ad5w0SGoR06JvO18cz2I7syHz5SPucVvQrMuGaeegJAnCCBCvneRX/iSac92Dx26e14kQfqVNqCwKp3ViKiOfPJcOZYUKamh5E5IhQCAeZZvsQAYWRgMlFVhjNFQXggKsYRZ8M2PcIW7RXFIGAYcIjwmte8ZuOEiO7YRpbHcGLEcIRcCVTKgMdsa1Uf3odJnIUnHIxnNSbzlYZjJ8wYc5guYhJtEAWy4yJaTkkSao7rqDdsS+kyfh/84Ac3ipOSGZbv6j2hLvrD4NnVvlaqPRpiiHNCCMmhEQx7zsotbnGL/mG9aAzFTvhR4NPGmEouy9A6oxH9E8byCUmOt4isCBrjSv4wWR+Cl0HFAB3yVtZD94Qxnsi8vFpb72EI4UHGSpYxaiOi7/YYnyiUqBMDRB15+Mq4vBe/41sKSX8MN98qOWrD6NVmnDhjlDLHiEITSXMcDF06zqS+SFhENLzrffK823y81z0Dxw88GD9F5vijAAiFQ8ZoM8uJQ2D+xp/KPCIa5Yz2rPN4/NbN3NJgyXLPcLFO5CTZNjaQPMsnX9FzRPRdsOzDVT/oHP0Yk10VP95hrNoJDpHH6Eb91ZRgLWAioAVf88nx403HehwZufGNb9y/U+N8k5toLesNr/BGx2Qp/Zd0OqzjHr+Ru45Y2dnCO5IyOsm6oHN6SZ73oXP3Wc/9cqWIaORZG/zBh8bGcRtkb77lSKEhtMuAVhAR3XFBY2hUHrnPYOXMkS3mJj8T3JUJ8oi2O8qsDsdlIRqDOzo3huwnr9rCTp3MyysnCB0rIzfYAN4jX5ust5grHnvjG9/Yv9kl9/DlQu1gGzHRx+pYUzLTyQg7M/IkstP48FzElvrKJCcT6JZ0DuXBD05kqudZStYILZBnMDA240VXaGeMmbUje6wrfaK+pC0HQ1/K5e1oEuCiLzbM7TZHTLBFu5xMulvwT9DOsU961zt39B1rvf6qd1YQGC+WESpKwXmwaCJ8DIgb3OAGHuclzIUhKQPKUSFhgekJNJEFhozokgiYj60cVRK95pGLOiBmRGW7nFKlLNTXrzHpE4E7ooY5GJIcHOPUFwPMO9VbC4lycGzgoIMOao4U5JwwJIwcFWJYMoR9rzJWrAS4dYGZ40EcTwZL9rOrV4LYGgwjSbva53K3hwmhhUYdU3DePQ0K7+ZI26VCr5Qy5YVGlS0mMdIJZjySCh9dUliOZBDs1s8YIiYClqITjYcjWlcHz6gjea8PUxnsdnw4n8Y1FPKUHkcCDWhv3bXLZN55v3HjxmZudjONF224MrDwFYOPoDcWThdjw5EHioEDYrzZV15FsihXTvVVr3rV/itJjmUIKNiW1ycepUgYd+ga/s5mkxGOBMBLUMJVZJacoGjs3KkPo5QD+d5Zu8LfmHKN3Uvwl6aNX56yocGSbVwl5eqhW8+ZGM15n+8e96OdpB7a4BBzTtAbx9g9rMd9qz/ryfzROrydi0driYN8O6JoDp9zWhhTzrDj0Wlzo9/sKKJhOEp4UBrWh6d3k63khw+O8Yc6drbsONCX3sv5wa8CTwxTjpB6K5nwn8CVb/WGxvRwDHCjP2FJ/mSZuUqe1TEP0Wy7yhHRv/2D9RAj/KzcMTGyz9HIheSodwpOkm3sAnqKPEPz3kkPwpZh63mYOIQCp2wADinHmwx3fJJMGtbd3r33RUwCC04rGI+xba+dco4Secw5TuzIUFj5tozRDNeI6EfntZHQI+zIPM+Z4G08+TwrV7I/Iho6jpjorxzrQuPFJ8o4OMN5yBs+7+i9XUB24/C4HpzZoOxIwUF2LDuJDsl12dH3rOX623FWVsfUCR5HwRgwjm6JYDm2xYjgdCDQ4Uw4GiIpCMKRAkYKRlcvIrpA0yfBgjEZReoiYEKI0a0/dTCvMszNaZEXMWEM98rlE0qYRl3vJ0i9Tz+rPWFk29uMRetgvjknSlA00bY6RxIGDFgOy3D+lKIPO+14YVZ1Cf7sZ1euopiMR0b5cGy70udKtEVvjtFRZqJgInAijvluz3YEGTaipnZJHJth3Gedha4iS34Fylqkc0m4+2ifwtYXpYw3PIvEic5SzL4Dk+d4hTqOToj0OdbliItdSbwoYsTot+Ngbe0Q2ZFBKxHR1MEH6CfHaQwUBn6JiIaeGHV4jhFlLdGFfvEcvqV4CXu8zohFh45GGCdZkH3n1djxse+Z7Chpwznj8Di/LwpmbGQJukU7DBDvNl7OIcPQzhHZQAHhaWMWCTYuGAzpO989S1fjJd8EBYZjhQ1HwZzG4yW/zB3uwzJGsGcGsXJyTz/yMjFC9UsGUNTyrbdrJoZ5tpdnfPpC1z5IJlONIWIiY9VZLYlj4HgxAxcNuaKhHL+5wQc90l2MYfxk1znr5BXNc461EUDDh/gRjZKtQzmhT46Q+vjbWjHI9QVLu7NkCINZP3bURKUZ78OjfOovdyILjI0B57sJ85v2TnQmoUPyQh00jJbNV5ldKUYieeHKJqCPfGfHwEeP2gmgZVCC/iEf7FCTA8qHiWwVsCDPyGRy1I4sbMliAQ110OywnXnBFE8Jahof51s/AkQCrcP627vHP94viEPm0Jcc1+21E0gyX8EZ71cfPdiNJsvIcjjBB65kPvqCK1kpb8izniXz0dcsJcdZ0YLxog1jM1Zyiewbr1FE9B9YIZ/MV31JW3yFFsl2eTuS6Ay6yFqTZ9k2Ippn8s6vinFijde4IlaffMt5Ldd1r+XqeKX75bWKqDKmRDYJGos/bRwiBL4fweh2OzC76Mq0usM8uyeOMmFkxohICcEm+suwHNYd3yP4zBveZ95qvRIEonGcAEfcMNtwLj5Kk2cHgAFJQRMGlIe2WTdiwpyiC9YEA2PyLN+Vq6iRdWKM7ko/S9J2BzuJiEYxUdxwJDR1wXhnzDjmyNkgCO1mUMSU0bZojNJ05MHxGuuiP4mhyGBx9Izy4gCJcDL4HamkjOFIGVPKykVp7VoxqDgj7o1HtJczIFLs2y7fhKERRzI4Gs6oM9iscc7JGDg96IORFDGJiqEHDpuf8qTsKRvRdXgwtuQZl/aS9sagb8pJ3jBRrOp4V+ZHRDMeuGUbxiUjhCEnAmZu8Npw9Fa+YIZ3kCfZD0Mz8zNvVq/wc8yOsWIexsn4s97kKYdN3jCRc87Ao7/MhxcD19wZYgIP6jHksg5e98yhhKv3KvMuV8m7GeFkAP6Xl0nfjKl8Xo3XiOjHnWDEaWEQwaUt8AefwDFiIhuH1eCG5u004lW8hyfl4W0G57B+RPQouW9/HJse8hys8RNZTaYwyvAqBxGNDPtZznu8J+rPuSDPtvVufM+hQbvkgXFpzxhft25d/9VPcoo9gH/JKkEPH4MzyP3iGDkIR8E0H0Sja7zt17rgCA/9DhMa9MtSdvg4c444MzTt2jPqGciCJQzOYbuIaOger0jKIqLhFX1uiw7UHaeICU14H1mLTpKHx3XzGVaOtvvAnqzLfHLffNk1dmjQku8m9Mdx5AihF/YPrPWTbd3jW/PPvFm5sulgTqZwFo2LrOakCm7BTN4woR3t0EXmmzt5h2/hnfmLvdoBha9dk2ltIiY6fu+9955WXHlHI7CqnRVC1TwIKfcMCgqW0KbwEKryiOi/EBURHpsPagkiQgfTJtGOBUyvPPiH8PRTudowzhAgQUU4eb+qEZN3REyumRex9XPEljz1VmMSiSb0HSVgUBAKHMU0BEWMCOOMSHBYRA2VW7fxnCOi72xRRIzQLCfMF6ovX8q6w6vxMJB9gzDsb1gnIjp9qDvMn6V79CWyJYpvXASwCB/D2LMIjW+oOAEMx8SDYpTUkdAsJxsNU9AR0X/9hmLHBz4891vweIhDTxlTqHZgRLatizI/L+xq90GEjyDHU+4d3WCUeh+eIeAdh3Q+mwHEwTJeBgnl50iYupIoo/bWy7OEL/E14847OS8MrojoP13OgaAQcv0iov/HjpQyxaMPijdxcORD/yKv2UYdigzGkmeJIUeBUdp2V/yfBuaqzG6MdYFd4q0/czaHiFBtppOADn5lIBoow4XDymjDt/LgBj/31sF3aGSofHmUPwz233//biSiUcaLYyXwUAdNMhytvTVBDxxKmCZ2dhnQg/5TXmi7vbQayyOiOR6bfGIOiYN7CcaOMO2zzz4ee7IO6tFvvnnAo3g0zqNjAAAQAElEQVRCckpAYMP3WoJqvcHoH/SKz/HGsAiPGQsjjaPDmVrp4A45bScJHeFB86RnOSA5Voax+4jo34yiPfQqD62RiYIkMEPbdnHJNPgIYqJrcs+uMoccvXtH2gpwEGChp7JffWfSL9kBx4jo/9cbmUj2kg127+GY9fMaEU2/9J73Zb4xm6uxZJ6r/l1h4LqthFfQkn6yHjpJ/pRHdtp5U5fRTP4qx5PyOHJDWhKEggU68h0e3c7OIdt8gG5cxk52kO8w955ZSuSX9babRrYbG6cDjbP9OOTyYGUu7u02mQu7xrPEvhFooXfgIE+KiG43wEKSN07yHRemb5PGxnXqeXEIrGpnBQMSZBSc6WJ4DgtGpAxFX+QrVxeheiag3NviZChhYv34ANsWOiaWxsIKkYs6aIuA0+O2/aq+vhnV3oUBPGOCjD4gXEm5tsrUWY3J2Ck1RizGphQIOFF0iiCNH44JYUGomSd8IqKJ8BF8MCYkYa9PglCEykePDEVtGDqOLzCi4Scvk/4IIphm3vAKe0rQr20kPQzL3Vs77/Yez7s7wcpcRQHNjxHneIYoPyPF+CgIkT07VGhJnrkS0Awcyo5iFynzS3R4QLlnuyWOd9lZFHF0NhzmhLdvAxibnB4p38dA5xhZE1HxrOPKQPI+9Qlk6+rZmDJ5tqOxbi7qmevAKDUH34PgSYrcPYfHzkq2dUUfzliL7vlhAf3IdxUV5qzgZ3l2l+ymoU3RKvTBkWLMuWdMUMJ2R71PG8rIjpRdUga5PAld2VFyxE001hFFc1HGQDjggAP6f/zlGxh5zrrDI50pebOc8Kd1F9E2TvIQPflWJCL6f5Bn/rBDT2Qrowsu+F8bOOJLeOuLQncEkFzgEKrjyCJDgHyGnzXwK4zKOajqWCM0wcCICFlrIjHAyThykF4wX7t0eBFWJml32jcMMCOHHJ1UB454Th16B8ai43gMH+K5THgU/njWLgGZhr4dQdGWnMP3+ND3WvocJoarHUyyUmCCsTosX657tGP+jiahJUdFBQHJJvqWrvVuMsoOLVng2RzwIKcYPapn14TDwCGWEhtXAQR4S+7RMl4X7IATfPTLAXJ1BNV1mOArDfMiov+ntfg+12pYnvdkBblmbckhsp29wYHnAGU9V+VwybHIk+gGO0GO+Rkv/WlNfauXwTj8Cz/6GM/qg/60ttr5Dgqudqr9Wqn5oB0YHZ2a8URMdoOMWR38axcHXeqTThH4gpM6xjdLyfoKorIpyDV44S+6U76x0jsCbnQhvGHoux10hBbxkG8kBVZgHLFFLqlvDQUA1dPfONFpAjJ+UTZiS9txvXrePgKr0lkh8DE55mNkYD4EGRGNMkRUInuIiZFBWIseUK4EIWEuuuDYjA/CES8jiMJG0BSryLWz6mnogRIhM9gYkgxH59kZ1QwYR2fsIjDaKRJ9MJIZhhHRCENE6+y/hMApMEJJ36stMQxtl2NqGDHkMLy1gCFMzcl5aJEs5eZre5lAJLgJQE4iwUngWTf17F6JiinXB8Fo7ShpayqPcpIHd/eELgNV2TBxMBn7ol+MpGGZewrO+AkVStD6paOlfHckY0KbjEL4MWrQJ3wSE06JSCGDgiFJ4DoOog5jz7jxiWMi6I3Sgi1Fh66tg2dHJJQTxtPw4VgwfFz1OS0xfoZRvWl1puVRJhwlCoPiFBEW8XKcMN/HsMNv5odG1KdYIyaCH6+JeDF0GDIihJxlCkd+RDRzRzuOM1Au5qkvv8rkjDrcONkMNEa6cvwJFzIGjTMSfT+U4zIf7+ZAUdQMKwaBoxQikZw+dWY9MfbIQHQCAx9+O6/PaTB2eDES8Zd7eaLOfrwAjTo2xJmxLhmtj4hGJsIOjcGI4w1/u1r6iJhExjmasCNPGemMrJ2hJX3OYiKvOAH+vxiYOKpMRnLI0XrEhI4ZfmicPkH/nDiRenIrYlIHTdIn6FG/4/miR3IXXylTh2HpP6fTLx6hgzhAAnvqWFOGHB4jhzmvuZuqfCUSZw4NCDjQ1WiGLsGbnKwcq50mNCQ4aFz0sR8FoHfpIjyKnvA2LNQZpojovz6nXcQEU7tM5I6gh11fOzCOKtspscMzbL+r9+Qovsn5kjtkn+O4HCj90514xW4Qh54zIFkn5XZl4MLY3rhxY7Nm+M68IyZzQkuCW3ia7KMj7YR7L9pLfB3thS1dou9homfw4bBM4Mf3Oa7+g0PY0xvoi301bD8L9xHRBMPwkznDnu4jjzhjxggrjjKMYB8R/f/7USfnaD20h5U2krpsPPylD+slGKFsmOw4K+cQD/PrfscRWJXOCkbijDzoQQ/qTgBitD1r+ggK8xBCjA7nfilAwp9AtpXJ2KE8RKUZfBQHReEDXhF/BqIySphHzEAh+BnDDGkEyvhBrJQ4A5FjQriIFmIIjMxgoRgIG8TOMBD1JjB46xwrfRv3akuUKMUiUmGbk/MowdncU8hRAn7MgEC2FcoYpAjkm3P2AxOCUHSRkhgKP+vF0bGO1lQ7uHGCCG7ChsFl7ZUNE+XNoIL9MD/vRd+sdzo+1pvxneW74woTRiPHgiL1c9mMDUpiOB5GMgOT02wnUV1OHmzUgyHlzojHD45hWSsfz1sn6yVRbAxQbcYJvXPwKcRxmWdGAaXJyRuPT/n2EuOVUkAT1okxl7SjrTkYA8ONEWIe8ofJex1bYTxv2LChoTfOirGpZz0ZP/g523Py0CKDmhHJWMaPaeihMzQKd7hSNhETY0CfmQQvOFr4XKSX0U6GaJ91ZvlKlgrcMBbhgHcZrPAxbvjjLcrcvTxzcwSOgiY70ef4yBDs4Su668qYtAunfSbvsFbaoyGG1zQezvqr8RoRDV2QlXaOYMkQgnHEFnqil/Amnudwc3jHWAiyOQVA31m3MR7wJcfs6ilTh4NCT+Exxjg5rJ41VCci+q8lkR/qMILxi7KVSmQA+S4ABgMyKZMAQBry+J+sIveNLSL6f/DLCLWbQs75ti/pVJ1hQpNkDB09nCNczR02nHT6HN+jz2H7Xb2PiP5dHD0GZw4GuTZcZ2smmMfxgoGACtls7N7P7qEb4GVt8Y4gynBHZ93c7jU64tjK9y7BUsHYodxnpxiDfscJJoI1+h6WGSv86Ex2FszsTg3rzNI9ee6X19CI4B9c2SFJ/3bDrAfHDfbGbt3pE4E8R5XJRLgry6Q9vubYsgcFuujjLM8rp8g70z7N/LruOAKr0lmJmPxqg0gF5mGQpoGGiBg7ERNFgHEZFOphQMYpxsdgfiHF1ibhpgyx6YfT4V4b9fUpek/Q2XGx/Y7gGUaSqDdj2HtzTIiToDA2/RiDckLSu+QpSwbZ8aXbvS3MjRMhia5kgifMh6NjIFJIDD7lcMhywoRxbiucUIabvCx3tV4ULAw9SxHRvCexnNZOPeO0Ngxaz+Okb0eXcr2tvbxxvZV8Nn9jJgzRKFpZaPzoEzY+GIQP+s2xRkTjMMMIDaM3+Eu5Xq7Ddtk2r96Lpo0p84bXiOjntuEfMeG5Yfn27o1LBM/aO5phvYZtjN38jHFb62Kc+NIRFrTmOfvxDvPUj/vMp5QoE+92Hc7ROKyBvii0Ybts7xoxoUM07KyzuWxrnNrMWkIzaEIgBT97zjFGRLP+DKghBuaoLuzQJyyzTV7lwY+jiz4yf3glC+BMhpLJw7K1co+W0AaHBF7whN9wfugVDgxxvIzmhuXutVFGRkVszWvWDZ/oS32JbOOI6Bd/6DdiS1t94jG8Z430od1KJuMlf9ETPs2EJoc0QXcyGtFMjo/+hCdc0Zq+smx8jYj+jRuZOaRl9egSGOBjYxnKAuVLleBtjYzXu7w3Yst6eA96MSd8ZW3MO2JSx7jQCd2AX411PGdzs9aSeztJsJUSW1fPbB/vHCfj1PcQa3Uiov/gC3pGi8Ymf5aTOVpbugGu5pbjRT+wIP8jJhgrgzPdaZ7awFF+pogJDtbHOuEdbbI8r7Cnd8jCzKvrziGwKp2VnZvqrrVCrJhblEoUlbfseI7ItqNGollFkAtjHBGNYGhT/kRE/5UcGLdl+BOxRQgtQ/fL1iU8pO29ICIWxLatkj8LzRPNLFQ2bWrqRyx+vSOiYxcxv01E9F9Pipif3xb4Y4zevUDxqsjemfFrE7EwRhHRebtt409E9A9Vt1FlNRdtHjsaiYjNz9Nu1JmWvyt5EbHNNbCGy/HetoJ/lmr8sIjY9hotxbS8Z2fHHBF9PXe2fVuCP94dsfw4LcFQN3cB880Pi7wxz0VWnVotIvYI2TZ18kucWc7KIgEVcXJ0zLafKKOP2HjsjlDYSuVdR8Qie6tqhUAhUAgUAoVAIVAIFAI7hkDV3hMRKGdlB1bdlifnxJljZ7CdU/QBV56p3YGuqmohUAgUAoVAIVAIFAKFQCFQCGwHgXJWtgPQuDgimnOcziI6W7qtbcJx23ouBAqBQqAQKAQKgUKgECgECoHFI1DOyuKxqpqFQCGwexGotxcChUAhUAgUAoXAHoZAOSt72ILXdAuBQqAQKAQKgQkC9W8hUAgUArOPQDkrs79GNcJCoBAoBAqBQqAQKAQKgVlHoMa3LAiUs7IssFanhUAhUAgUAoVAIVAIFAKFQCGwqwiUs7KrCK7e9jXyQqAQKAQKgUKgECgECoFCYKYRKGdlppenBlcIFAKrB4EaaSFQCBQChUAhUAgsNQLlrCw1otVfIVAIFAKFQCFQCOw6AtVDIVAIFAJzCJSzMgdC/S0ECoFCoBAoBAqBQqAQKATWMgKrdW7lrKzWlatxFwKFQCFQCBQChUAhUAgUAmscgXJW1vgCr97p1cgLgUKgECgECoFCoBAoBPZ0BMpZ2dMpoOZfCBQCewYCNctCoBAoBAqBQmAVIlDOyipctBpyIVAIFAKFQCFQCOxeBOrthUAhsDIIlLOyMjjXWwqBQqAQKAQKgUKgECgECoFCYDoCC+aWs7IgNFVQCBQChUAhUAgUAoVAIVAIFAK7E4FyVnYn+vXu1YtAjbwQKAQKgUKgECgECoFCYNkRKGdl2SGuFxQChUAhUAhsD4EqLwQKgUKgECgEpiFQzso0VCqvECgECoFCoBAoBAqB1YtAjbwQWDMIlLOyZpayJlIIFAKFQCFQCBQChUAhUAisLQRmw1lZW5jWbAqBQqAQKAQKgUKgECgECoFCYAkQKGdlCUCsLgqBWUOgxlMIFAKFQCFQCBQChcBaQKCclbWwijWHQqAQKAQKgeVEoPouBAqBQqAQ2E0IlLOym4Cv1xYChUAhUAgUAoVAIbBnIlCzLgQWj0A5K4vHqmquMAL/+9//VviN9bpCoBCYRQRKFsziqtSYCoFCYCkQ+O9//7sU3azpPta8s/K9732v/eUvf9lqEf/5z3+2r371q+1Tn/pU+8xnPtM++clPtiOOOKJ961vfan/+858313f/xS9+sddR72Mf+1gbJu1+/vOft//85z/tG9/4xub+PvGJTzTt5P3xIdpcgAAAEABJREFUj3/c3N9avPn3v//dvvnNb7af/vSnW03vr3/9a8cBdsP05S9/uWM2bvCvf/1rM47f//73x8VbPX/3u9/dvDaf/vSn269+9at5dayzNch3f+ELX2h/+MMf5tWxfh/4wAfaBz/4wbY71+pPf/rT5rkY79e//vV54/z73//e6fQjH/lIM9d//OMf88qnPTDyfvGLX7TPfvazvW/XH/3oRy2F41FHHbW5zDsz4QNts0/1PvzhD7f3v//9U9c56+3q1Xp5d44DD3l39mvOn//859s73/nO9rnPfa6hvSzLq/r4T52vfOUrTZ9Ztr3rD37wg/bDH/6wDeeebcb4T+tXHRi/5z3vad/5zney6Uxf0QJag5exm8NiBqyNeVoP6zKtDX5Th3yYVk42k6HqoMtpdawF3kT308pXUx4ep3fIxW2Nmxy0Hr/85S+3VW1emXWDN9753e9+t7mMDoPx2972tkbujtfKWORrlwkd4K+Vlof0KJm+EC1sntTg5ve//33LeRv/t7/97Qa/rGIO5OVHP/rRLjePmpN5WTa8HnnkkV1G6kMiN4flS3WPnsmGhXji17/+dfvQhz7U3vWudzU6UP1p7yanjDMTPTat3jDPWpOJrsN8997zs5/9rL+bPvzxj38se16yPvB93/ve1zZt2tSMdV6FGXxAC2j53e9+d7f5pumMacMm18glcm6hNmgLnmOe0t94fawTvmInkbnqSMb3xje+sY/Nc6XpCKxpZwURHHLIId2oGU8fY1KUL3jBC9rd73739pKXvKS96U1vag9+8IPbve99794GQWFODs+jHvWodq973at97Wtf64Y5hcNpedKTntTe/OY3d+Pvb3/7W3v961/f7nKXu7TnPve5jXJ45CMf2ftn6OlrPI7V/kypPOMZz2h3utOdGuYez+fwww9vBx54YLvVrW7VbnnLW/Z0m9vcph166KFtzOAU+eMf//j21re+tcFy7733Hnc379n6ea++N27c2G5961u3scCmFG5/+9v396vzwAc+sA0VeY6fkUooPeYxj9nK4Zn30mV8ePGLX9yMMdM73vGOzW8z5kPmaJlDzUjmZD/iEY9o05TO5kZzNzC1PtknvIZG3+te97r+zlwbWN72trdtr3zlKzc7kzB94hOf2B35n/zkJ+1Zz3pWY1DMdb+zfxdsZx3wj3EYs/UaKsRXvepV7dGPfnQ7+OCD2+1ud7v2vOc9bx4d/eY3v2nJq/e4xz3aHe5wh/ayl71suzh579Oe9rR25zvfuVHE5MNwkAychz/84T2gkfh7RoPDeocddlh3pH7729/2caD/Yfms3ZNJHFDjxo/vfe9722te85rOf9saK0MKTcAFv1K2w/rwQ8/oCBbPfvazezBgXOf5z39+IxsZ5Q95yEO2kiEMRka48b32ta/tay0gsZDxMOx/lu5hu2nTpna/+92vPfaxj92ukUd3oF8yaTHz4Gzc//7378Y27P/v//6vNxOYsQ7Wivy44x3v2N7whjd0HHuFuX849ne96127jEw5QGaTFdZlrsqK/GUo05t4cCijtvVydIBeyX7yQiLT6H7tyIMXvehF7Utf+lIPbLiiOTSpPBM5OpQ7+jGeLF+Kq3VJ+WmO1njcL0OWbnzKU57S7nOf+zQ0YH3YIsO6eILdknLSlS4b1hneezdj+WEPe1hDJ3hoWO6eE+LdnBROEnuHk6dMMgZO1Cte8YqG7+kiOoh9pHwWE7579atf3d7ylrf0IDRaId/RzULjJRPxzMtf/vLGESZ7yKhhffSFnx/wgAe0xz3ucVsFQJXjdXRkbZKv0Bjath7GgL/YOtZbf/QJmoX18H1139qadlY+/vGPN4KLR414hgt+rGMdq13oQhdq5z73uXuk+KpXvWo3uAltUSiCGtOe8IQnbJe61KW6cCcMbnzjG7cb3ehG7aY3vWk3lu55z3v2yC3iOt/5ztcucpGLdEPuyle+cmMkEzYMO0YTB2c4hrVyv9dee3UHjsAfzgkmnLeLXvSi3QGkLAnLC1zgAu1kJztZO8YxjrG5OiF+3etet1EQt7jFLdplLnOZdprTnGZz+bQbhtD69ev7OlBWhPc5znGOeVVFJ60Jw1Ydhnj2S2BwJgkLa0qwELwvfOEL+5rO62iZH344F80XPTY+zpzrAQccsPmtL33pS3vkBd1d/vKXbze5yU3a29/+9m5Ytm38OXIuWsjR2TjnzGW/l7zkJZs1s17wufrVr94eMCd0rY/rGc5whnaKU5yi12GYwxkuMLr+9a/f85/61KfOi15uYwg7VMRwxpO5XtbsdKc7Xe9D9JSSNc7nPOc57XjHO153euX3CnP/mA8FxelkIEdEdxoo6rniBf+ihVOf+tQLRl4pLwZh4u8KfwZ0dnr44Yc3htA1rnGNBqerXOUqjbNF8WedWbsam7Uk46597Ws3YzYnEcCFxgqHg+ecRRhc73rXa1e60pWaPobyTWSWsUVe3uAGN+gy9GY3u1njFGa/6IrhDCu0dcELXrA96EEP2hwsQJ9kAh491alO1c52trN1Qx8dkxfZz2q4oi/BF84Do5ChstC47QTDk0zAfwvVy3xBs9vf/vZt33337bqJvD3RiU7UvNPOJMeFA8+xd48vyFntGUfo9uxnP3u7293u1g6ZC4gwQMlffJSyUt3lTozE4x73uH1HEk6LeR8dTTahMXRBz5JnsDZ/MlWA8WpXu1q73OUu1/bff//uEJMzw/7x8klOcpKus8kcevuc5zznsMqS3JO7xz72sfsO13iOaIJjwo6w/hxMY6ePxnUFC45//OPPG+95znOeBccIC+9mB9kJGNMV7AVpT3ziE7cb3vCGDa+e8YxnbHhPsErHxvDMZz6znf/85290E/5nzBsfXlVn1hJn67DDDmvkyHWuc51un5nDtuQH2hCEpoPJN3KJXkwcco7WESYCovDLfFc2JLrkqAiA4ytO4nGOc5yGN62FcnaiYNr6OTvm5Cc/eWNPkoGCjPqptAWBvbbcrq07kQdRWMLW9i+lPJxhRHRjOY3mk570pI1CvMQlLtGZleIVMYyIRoBybo55zGP2+xOc4ASNE6MNw5uyRnyMb/2piyD32Wef7hBhfBFCEYm2xv7AF2awGE9NRPya17xmI3QxLYVCWcBIG3hqAxsM6pkzo09YKlsocRxFgQkACoqTeeDcDg4Bkm1ss0sEAOdTHULW+qjDmdXHZS972b6e1tX4GLxjelF/uRIFYlePwUbhGqcIDOPMOykxBjmaNna0RqnAa1vjJEDNBX2KqOqXIqaE9Etgew/Mb37zmzfrwxE65SlP2enWexgDxrbffvt1IQuji13sYj1KzmjVz1IlxhlDbbheFAzDwzvWrVvXlfN5z3veduELX7gLdpikouQcM8YoFuM1b1HsiGiigMr1My1x0K51rWs1cx+Xw9GuIacW5nCBv7VI/I3Be62Z8aFDRpMIpAiqNRz3OwvPdu/QnzHD7sxnPnMPJJBV0/CCN3rAnxs2bGjwYFzh/4c+9KHdgaXA7b7tu+++jXyEE7qCAcMGnpxOOykCCd5pjTktFLjIp3fLQ7PqOhrIAECPopSc6baK/qAHRo/5bmvYAgscPQEF9SLCZcFET3FEGD30DLqM2NLmrGc9aw+snetc5+o8TQ5bQ2uhUzRtbfTBOCMD8FxEdIOV7lNvJZJgwaUvfemGFyO2zGGhd6MRx3s4FSnfyTnGNB5VzpB0VAf+8hj4rugo+2WEctjslAowojm6hGGZdZbiGhGNTQBv9sG4T+NCH4IG1u2KV7xiE0R1jA/PZH38wKAmt3O85LrgTdYZX/UNF7puWj07JvQhh8681aET7Oxt2rSpnxwhx+h0/I6n6fErXOEK/QTJMAgxfvfuekbbgtWnPe1p25nOdKZu73Ho0L/8aePifAmioKlzzwWyzVMgh1P/9Kc/veOgHflHtuFp2MrLhO44G3Z0OL0Ci/jK2rIxBeMioqF3QTk07L1kMRkswAP/7K+uEwTWrLMiMoiIDp6LAGIkHvaQ4SfT3/pfxLl+/fqGaAgFNUQlXKWI+UKUsXKOuWg+4lWOUNXPd8nHLAQAo1ydtZTgxGCB9XheDBjClmDOOoSdJJIXEd24IQRFAK0VnBh+MBz3l88UrYis7VRtRCb06R1ZR1TQsR6Gj+gQA4tRNqzj+J5xp/Gu7VnOcpZGkVFy2xqDukuVvMv4RD4dg3PEa/huY2RMomnHFSlfyhWtMYgXGocImj4lR8FEdNF3xISGGQWMegocnXqPnRj9KXNlJMKDsFcuL51J6+Z5KZK1EfES4XTMy/paw+F6GRNjLN8How1zBjNs5BmfCColEBEtItrpT3/6ZmfG/CJCtakJHcNGPf0OK+mXguawiNIl/ujQ+9QVAUXD6IdhJE9Ag3Kyq8NpkTdLyTw56xw0PGpsaAFe6IuRJG+YGNNwgGuuDQcWfTCy0Qp65nRS5PDUnizgyAkcOeZAHgs4kAOwVwdWlDQ6JwPQNxrQh7VRz1rA3di1WS0pIrpjZw4LjdmcHDexBpzxheplPh2DHuHEIGJQMcSyPCJ6AC55Rv9w5MSnsycAwDHyrAy+1pDTyHnMvlbiChs05bqY9+EpO5kcYEdxGNvGL2kfEd3xwZfkid0ExjdZwwBVB4Z0hG8TONv6E8RBc8qXOhkb+RCxtSxShq/2Hhx/5hBwRMkSYxmOl3NlvNZrMeNVB77eo69hcjxO38N340fOKtkGO3wZMaFjbfVDVjDM6Ql5s5SMi43A/sp5kUMwFcRDB+PxwtJ8OfcRkzXCG/rgTKA5bSImOMAzYlJPvhQRjV7lcHoveoYVGmNbwlU9eMMVPejH+kRMbCK8qk6lLQisSWeFghMpENXn+VOkCI0C3DL1LXcR0Q0bOeqIYkdEI8TlZUJcHA7JWUPHyyhoxBgRWa33RfBHRD8+xmik+EUtNldaupuZ7QnzYdbhAGFru52Qky+iwOiloCkVCkN0zHrBW51xko/pRVgpFg6L87XO+2ZdBg0DU7SckHV+VEphQ8HLJyRSEWjL8FLGwXWVt9zJGBi9FJWItsieyI95ejdBZ8udAGTI201iTNqqFq1RZ1qCgS19tMhZEYUjpLNuCsl8dnWkRKTblrRnzrir9XGVGJTaigh5XooEa8LdESo0Yq0c68uAgXfgM1dKxrsdjROZyrEqQ1sRW3hRdA0NooWILfnqjtNCCiJiEmUmSxwvMzZGpaMaiT8DHd7DsUREMx6yAv+P37e7nxm2jAwBArxqPDA2ZtFm45Y3TAwAfKZO5qNP8zZHu2OOGKnHCRrW4eT6VkBgwXuVpSHtXj/61T85jP4di+HQaOsq4slZ8i5tVltaiMbMwy4wGSiijmblbSvBmRFu/fC1HcmDDjqo/2AGftIWpq6MIruLnEUyMWWedcfLEVt4g1xknA2DOPqYtYQ+BCvod3LTDrvADLo2VnMnM+lxQSBHMulru9ei4q3nrBcAABAASURBVOrAifHIMeOIk6/kDqdG+XKkbdGAMXsnuUUH0o+OZpLh8rXFV+Q6XUYeOb5nh0359pL20+rge3jSe1mOLjgrcCFz0Yp3CjZkHbqA/mITZd6sXOkOcoxOT1zNSWAGr02TIeZGPpFDOQ9tODnmqM/MXwjLiMlpnKznyiYViLOzlWvp/fQYx9k7Ln7xizfyEJ/CW7tKWxBYk86Kb0REPx1X4UWLPtvKlL9l6lvuEA3jj9HMWBZxt303di4wre1yZ3oZjIQjwt7SU+uOCsL0PsRJ+DnvzrB2/rHt4X9EsRiOafxSuI4TiUhTKrbfCU4GO6U5DS7MTuFaCx/BcVTg7VgJ5aMNIetYgyNoPqrjuPrQ0m6LOoQBw4kApuy1kTx7P2NrIWGk3lImzoE5ODrj6CLBiA7thOR7GA+M8/Vzu34iiaLS8DLerDO+7rvvvg2t2o2xhc2g1m8aiuP6cOGsiOpSQMoZrBQSYepZIvgjolFcnseJUBcsEKWiAIc4MprwkXcN21lTa5/r5UgcLDhk1irrMkR8B2Vtndt+8pOf3H8xJ8vHVzTEwHUEYly28PPWJfBHa+vn8EdH5kbxJE6UnHlau2FruDG8h3MYlu/Oe8e1GEV4JekoIpq1sD5k6Hh8ZJsIfhq7WZ48RJYqt85kb5a74nkOnTVkhMljOLhm0o9y9YyD/LTWeIGcIBcctxvjnO1X6xWN4NUDDjig7wQuZh54FS+JAtNxsLGjxZnmNGYf1swOjOMleIr8GOutrIseGEu+WcHnmT+LVzTguyhyk9MmAk4e0OV40ZjxrZ11Bj7Zzxm0E4vOlKN7x7JgT77SKYKLdi3QsDrjhMYFK9RDp/ku9cg7a7Ir/E7mCsAJLjmSRH+hD/0bLwfMNxDGiz+sOd1nndXZmeT4nfn6EB3f4388SsbDik4UrEFXdiu8y1zJPWMby4OdGcNSt6HfzWPorKBpMpl+H6+RdZRP/thxHI4HBuarbJi/2HsBFn2zSbONI4F+0MDpEztb1t33LdZ9McGK7GdPue611iaKoERmGVgMYdvZonIIlMBGvOM5MzgoTYLAtxQExH3ve9/+fcqwLq8XMTkrKqLBaEFwwzruMba6+mOYq0cIjhlA3T0pMfCsB0UYMYnkESiEv61uZzkZ2JxAAkbkmgAZYxQR/fwpAWKHxi+riJYRtPpXPyL6sQt0ICILf1F7x61EW6yRsqzrKhFgEgMuYjJG+cuZUoCiQfRHcZq/yFq+l6Dzy3UUlflyBuxAURZZZ3xFf4Qeo5ERxNhmpDj7Pw1XR3PwjzXIvuCQ93lVBx9NK+NE+UUfjgSFi198+yWSZRdMJAl/jt8fsWW97LBo68w059Z65bsZsL4rwlO+t2GwMcAoz6yTV/TmSJ1z+ruqTOEvagt/5+N9uIvm4Ol9aNE1Yj7NUG5kj7VQPksJ/cMT/eW4rIsxu3IuMj+vZOW4jTIOiiuaUI6/hv0qYwTJh4d68iLm46WfbB8R/YcejMOOgYi3MvQ8i3iaz84kDgKeEXygT/AJmagvdMdoxG+eh0nwTQQYTTqK7AdjBNnwmh2ErGvNfIfiDL0dWnLFkZQsH17pTEaTI3zD/F2+X4YO0BdaolfJTca7HT4yIeUBmnMCws4JJ5ez4vtIssGQIqLrEnIS9mSFwJE+8kisepnoK8FH8s2PfPgo3XsZm9aNfLMbSIdkmx29WlO0zkmxpuTOcKcH7RsvB8yP0RgvmY4edvRdWZ/Mf8ITntDQDnzQowAX/UIew9kuFtnHMaRPHUGjU/ThiKfrLCU6FFbDMeEj8o28HpepB1f8ErG1XIqIblOotyPJO/1aoh1Ta5ttvZ+DjX7h6UcAyEW2I9rOenWdILDmnBXCiTHEoMLAogAEjCM2DNXhNucEgtYjiQxlERYRe9txDMeI+QSLuAg0ybl49Z3/zX5cKXkKmfKgRBjIohaiQBHz+1N/T0qMVlv2otM5b3hhTEwaEX1nyi6LOgSndcy6C10JJVHDiNjqp4uzDQPHdjljiPK3lo456D8Vm7oMB8LF0QDjkreSCe04WiSl42U8dhMYGBwVxxk2btzYfzKbwlC+vTESwGiV8rOVDfdhG8+Uo18SQqtZJjIJn6HyzWfrlPXyitc4o8Yo+md30o6O74o4W3Y67CSZZ7aZdiXAOSMilyKVWceaEPiMKU6dK5wooKzjymkSnPBho4BBRMjeqQRfx/IYeZQ1ZW4XyA6sOaEh9BIR8/6fHpgyNskSSnCnXr6MjTgBsEya9ypzZSgbszJ5w2QelKk6mW+edmnwFGPR2qknKjusgzY4SBxHMlTZuB946Z9xpFyKiEbWMuY9r7XkqCVjhgPM+JUYwujK8U2/zjambxgohznDKyK67LSLSDcN9RyesS54X98+MmZc62OYrCNeIovR87Bs1u8jotEZjtOS7+QVWhbUYQdwVgQg7T45Coefp80JliLdaJ/jNq6Dpul2OzqClk5LoFn9kQV2deCP/sdtF/tMVguwonmOiHXhCE1rn+Mlsx2fnFZnMXneKaIv0u+99ACaI//pIvKavLBrR66Tu3bg6QK7BbBfzHtWsg5nzjrY+YGhd9s9YgPAF3byMkVE/6Ed8xzKJbTkmZ1BNmX9xV7pW06so3sRsVUz2Asm4N2tCreRsacVrTlnhYGLQBk6DFjJL2UwLihUBtm0RSb0taMkCfdpdRA8AZhlGFjUHjGK/ipXFhH95131pU/12h7wJ2JrRsxpE2qcRc6b6GjmM1oYRvBLbAkRAoNRs9BaZPu8EtaSnZbMG18JXpEMiluZ3RwC2bs9S6KKxuNj7oiF56PuciU0Yy6UondQho4UOr7AiCCERd4oFR9Im4N620tonJDWPmL+3BiSjCXH5YaYo290jMaTvhkDFMD++++/1Ss5IqKcxg9v0SRK0C6J+owG2G7VcEqGuVovfY2LI6Ixihmw6MXcso5jaHbZBAs4TlnGuMs6O3KlqPTnKJkxwQ/+jkXAjLHPIUKvjMTEiZITmYThzii5HRnjztSNiP6TwtbeeuoDLeEBR4umjVmeSGtGprXB23iIwSIYwYkVHCCLlUsMBEdI0Af8GEPojFOpXBJoIqPhhf/lZaLQcx0zb61cYSWQgvfIJEmkOyL6r6lZi2lzR3PWDT8mFniVUcXYzrzhlXy0PmTcMN89+WvnQZQcb8nbXSlivnxazDjQE0eBjKNj8DtnBc2SI+bMDiA35S/UJz7G5+TXuI58MgcN012+N2BfSJwczgs5bSzjtuPniG3Pkd1grfAUB3LcPp/Ny1imjTfrLOYqkCBQiw7pIMFFBjb60z4iGt0JP3ocT9vN8eMOC9GbdrsrWUeOOXlGFhsHXYpfyB9zlDdMaId+Isczn0ziuOI388/8xV45KvgJXSzUBr1a74XKK7+trf9nhZEg2onhCC1MnAkDEtS2winWXPyI6P/5HUGdeeMrwRMR3QFBVN6TiXJnsDCQIqJHtwhJqe0hfxIfUYutpzzJcb5XhJyBN8mZ/EvAMihF+rI9TBkuvvPRN6zhSzhqBVtCx1WZtXNciCIiaNQRkSdklGUd326okwLHtitDl4LWRj2/3OTjSwpb3kokBoc5e79EAZgbR9v70RwcOAyeIyZKA50TyAw5+fAhVPXhWRRQP54lAhjGjKGIUGVzcuyBUmeQb86cu7H7REjDCJZzWf0bEcrRronnYTKeoWI1NjxI4XFiKANzGbZxb72M1zuM1dVuDOWPl9WRl/PJZ84Ag8E6ykM3ftZTNBA+8hjTvkdDQ57hRIF5j+dMxhUxkQfDMvhTJGP8GS3mq5xs8Z2NKC7a1Kf6aJ7DNk0xqrO7k0i0qC08jIUid/wF7mkgwzSPu1lLxgpHBN1qowxtidpzMmDho2AONrpWh1MiMWxggR58Z4Hu0KQ6vqXyzg0bNvSjOfLWWkJjUsQW/mP44nXHCjPBTz0Y+TYKjaFJ65S4k6WMcN8s4B9YcazRKl7zPOYZ9CiPI698mPThexWOEr4dlq3UfUR0PZvGZRv8wfvkm3HKRlt4GS4SPrdLhd/gFRGNXBBM0FYbsl8QCOaeYaEPferDMx0AW7JPnWEi29B45sGakc8JJ29ct4eddY2Y/OpT9pNX4zQGz8aDH43DmOUps8bwUe7ZUVfrOdRZ5KIghDbDhL+0G+YN782H3sTL5ConGobDOsaPDgWEBZ+cRtHvsM4s3Bu3AJlf9yLDjIkOwAP4LWLCg3CCs3K6xkkYu2+wlUduaeNUw3BtI6L/aqx6C2GKrvxKHZoUSGj1Z6cRWDM7KxhYJIBRQngQXIkKYnJPUGEyR2cIPYTryJi6lC+lOyY6ApEyQOyMKQoYIUs+6nbO08d2hB/jRH8EiTb6z3d7/1pMFKeIKaZMDMdz9iySxbgVYRjiwNjjQIjUOltM2XA8bIkShOoqIygcv7E+HBtnr23Fi3hzQghY33AQpNroy7EnR4XU8RGmNfJ/EWQdxrlzzAxMyXp6v6MCKxUpQrc+KhfB8q2U4yB2oCgAhoi5MO7M3xENZ6XhwXlwjtkxAQIULrboOXiEK+HLMIQTB913Hb7zgBFHQ7+ZGIvmzkCl5DPf1a6G9fFxsyNcEhp3Nt7aqbMUybwYCI4Y4GPrRbibDwXqHT6QtUPjyIX1UoezK8pnTc3Dd076cOSDUyYxrrUxF7zpnLX/y4AM0K/EYPGLOpQSfMkJ+ZJxUNpow68uKWfUwUQ+zCKi+cjVmjnTbW38Hy8irpSffpYsLWFHjnBwumDOSebocSTQn9cw9PykNPrE6xHRYKqND5fxvLbm6Py8NtbCd2d2WvK8vSN5+FVbdaypn5slewWYYO8Iih0rxrI6aynhT/oBzeF5MpNuWWiO6tNjrllHkM26+OUr8hb/4XnGte8kyAdyltwTxSV3/QeQdjbJQBFeskVUWUAm+80rmcJYJnszbyWv8OAok+WOjsLLHHIM5uBYJ+NPHvpzfIY+V+bHOBjY6CciuiEpIKU/x7M45eaIx8m0NvcHzftOBd3R7WQDHNEyGp2rsqR/zdHcdOo95ghzz/iLk+DHe9gUbAw75wcccED/PznUMV4yZThe/ZlDOgzm60cpzBH/aicxytVVLjCADuVL5CIHh27F73AkXwUnlEt4lVyEP95FV/QPp0D5rCV4CMaw+6wtWUW+4SE8YLxowXeVflXSOmhj7e124h1yCW3Bkx7RRsKXsEBT7EI61/opGybljucJyA7z637HEVgzzgoGZHCITGE8QiHhIPAIBcYvhWkrl+CnMEQsCAhb55wLRJjtXDE7giYUtcXQjBUJITJqCVBXz4wV5/VFkCkm79bPWk0ZRYa7CNC0ORMCMGd4RkyiGYkHw0YU0K9UEaY+8mb8Mc7TsbE2oq0MpIhoDFQRR9gS+IQRo9waZL++QSKUOJnWigFGeIuMZR1XBjq6IMjQA0UhQhYxf5zqLkfiiJgbA4ECgadoEGUBG++MiCaHEGcCAAAQAElEQVS6yhBHfwxlwpGhzEjMOtowBuEl+kfookMYRUQjkFORa5OJkY8P0Hjm5dUYRO2sB8FtjDCDv7Kst6tXOzreb/7WAR7Gay7ZNwOMoyVSJhjgOBLnK+kEz1MyaJGSEs2SjHXjxo2N02HM5srBgVH2jU68lxHgPDacUxZETPDnLKFR+GfdoRKCtR8G0CcDknLT35jmlM9K4sAZI8eCwQsTdIVfjZHytrPmiAunWB6Mndd3PIXTSOaRoYxr5ZJvBJ3hlwcLPM4oGBo2jnvhe2vu13IEEqwT/tfHWkpkFdnoWA9jk66SFpojGhWcUT/roCMnBJSh44hoZBw+4UhzRqwJOYdvIqKJtjM4GaFknLXkeFqX7DevdB3eRhOZt5JX75cEWfA6fQq3HIO5oUPRb3l4nNNFvuNLPE3+JZ1GRMPLftIZfwuyMBw5MGmsol3yAc3BB27woWO8Y6mTNWfUMvTJVePheHoPXjNv9ou1RC90gSCOMnVyvGSZ8eJX48VvyiVzIfekbCdfMBbvCjSR+Wwm+ZJxke2cRe8jE9RVlkl9cpdsRGPmgLayfBavbBIOPX4hh8Z6hf4lm8w5YqLzyUJBaDhaBzQkgOk550g3sBfxFxzQ39D5G9ajB4wj8+q6cwisGWeFcmV0UrQIT9QpIcGwSaQYldHMOEKEIhIYU1t1EHW2c8WM+hMpPOiggxqjmECXOCkIldAhPAga5eoRuBSDd+tnrSaOAoHKmBX15iSM5+yZgmAoT8OBEOAgMFYoIEoHc0dMhMe6deu6oc2Y1d47CUsRdMaq99s1iZjUV4fAYUAxqhivjP0hTagjyaMArb9jPGjCeJWtRPIuTgajjTOMbpyzhcnw/RQwBUsxbdiwoTkm4oNZ+VnPHAhVO0by0SfjGc2jVwptWrSQEWTtHN/JvoZXAh1+xibKx0CfZuwM2+zovXfneoli2YVjOAz7MW9rLppnGx9fmmvWUd9aM07UGSZtI6JHW+GtzNq3o/+gHxFpsoCTx7mN2EJP8Bzj7wNT+Ud30S/o39isBcOS/OgFM/pPxOTDZHRlt8n64r2Ug+bHACRX3ZtGRPT/bJMcRVcM5rFxFxGNUY2n1WEQDvHWj3c4roeu1EHTS01X3jMLCd/RD/jM8RmYDml3PEbHidBiHmtVjpbwCOMKdvIYq4xS+JGDEoM2IvqRZLqJDKCT1MG75GlEaD4v2WVDA9n3vMIVeDA/hp0fsUBb8CIf89X0Kb6FjTw6g9y0i4rO6HR6WFkmNEtOmjuZAisyPut5J50NI++le9DycmFgzY2BviSrzDFlvat5GAu7gr7jjJG/OR/jxS/qGC8ZQ3YOx6s+GaoP88+2gjt0An1gnvvss08WNXxH7+BB9LVuTuduLjz6Rh7s0JhxDQMPR1eZuQtcyGRrjLbZF8kfBmsO9D57Ql15ruQ/PcBu0WYYCM066FG/+JnuGDt36lkbsnUs+5St8bTk01szzgoCI4AwKqaPmC+MCb0sV8czRnYvKZM3Rli/+lNnoaRdRPSftdOPeq76H/e31p7H+MBiPMeI6MKQwh6X5bMyRjNhDDv9ZlnE5OyxdZAXET1KTnhS1vIj5q+39gSROgTxttZCGUNXfe28YyUTzMzD3NEOLKa939iM0VjV8zysBwcYRkywMC9CUoJpxCR/2Ma9frzf1fO0ZIxwVM/9tDq7kufd5rat9fJec1HHPM1v+M6IaHCZlrTNutppH7EFD++Hn7awGtbPduoY40L4q6eOvuGkP3mzniIm/IT+zD1iCy7Gbh7m7T5TxKSN9YBZxPw26kVE/2YAXvqQN05oPenKuozL19IzmoIvvOCBVhaanzL1XLNOxATPMU76hbH102/ElrVQ1xrhGWuobvY3vqJb7xznr9SzuRq/MUjGPnw3WjFG9eR7xmfmbW6Zr2yY1NOfeq6eszxiQsfwgaH3R2zBL+st1dUYvcM4JOsRseV98ozFWNHKcKzGEBH910vVWWi8EbFZDmqTybv06R2uw75h7Z3K1Ms2w6txoyV1zGNYNsv3xoo+zM8cIrbgHTEdq2xjvuM2OVc4wREe6miTZXlVB81GbHlnltV1xxDYa8eqV+1CYI0hUNMpBAqBQqAQKAQKgUKgEJhZBMpZmdmlqYEVAoVAIbD6EKgRFwKFQCFQCBQCS4lAOStLiWb1VQgUAoVAIVAIFAKFwNIhUD0VAns8AuWs7PEkUAAUAoVAIVAIFAKFQCFQCBQCs4nA0jorsznHGlUhUAgUAoVAIVAIFAKFQCFQCKxCBMpZWYWLVkPecxComRYChUAhUAgUAoVAIbAnI1DOyp68+jX3QqAQKAT2LARqtoVAIVAIFAKrDIFyVlbZgtVwC4FCoBAoBAqBQqAQmA0EahSFwPIjUM7K8mNcbygECoFCoBAoBAqBQqAQKAQKgZ1AYI9yVnYCn2pSCBQChUAhUAgUAoVAIVAIFAK7CYFyVnYT8PXaQmANIFBTKAQKgUKgECgECoFCYFkRKGdlWeGtzguBQqAQKAQKgcUiUPUKgUKgECgExgiUszJGpJ4LgUKgECgECoFCoBAoBFY/AjWDNYFAOStrYhlrEoVAIVAIFAKFQCFQCBQChcDaQ6CcldlZ0xpJIVAIFAKFQCFQCBQChUAhUAgMEChnZQBG3RYChcBaQqDmUggUAoVAIVAIFAKrHYFyVlb7Ctb4C4FCoBAoBAqBlUCg3lEIFAKFwG5AoJyV3QB6vbIQKAQKgUKgECgECoFCYM9GoGa/OATKWVkcTlWrECgECoFCoBAoBAqBQqAQKARWGIFyVlYY8NX7uhp5IVAIFAKFQCFQCBQChUAhsLIIlLOysnjX2wqBQqAQmCBQ/xYChUAhUAgUAoXAdhEoZ2W7EFWFQqAQKAQKgUKgEJh1BGp8hUAhsDYRKGdlba5rzaoQKAQKgUKgECgECoFCoBDYWQRmpl05KzOzFDWQQqAQKAQKgUKgECgECoFCoBAYIlDOyhCNul+9CNTIC4FCoBAoBAqBQqAQKATWHALlrKy5Ja0JFQKFQCGw6whUD4VAIVAIFAKFwCwgUM7KLKxCjaEQmILA//73vyZNKdqlLH1Ku9TJKmv83//+d1mwXGUwrNrholdp1U5gxgYOS2nGhrXWh7Os89sZGbczbZZ1Entw5//+97/34Nlvf+pr1ln51Kc+1X7yk59shcC//vWv9tGPfrQ9+9nP7unQQw9tz33uc9sb3/jG9r3vfa8lwfzhD39or3zlK9szn/nMXu8Zz3hGe/rTn97T0572tPbUpz61vetd72r/+c9/2uc///nehz7VUfayl72sveY1r2lf+tKX2j//+c9GKGw1mDWQ8cc//rG94hWvaPe73/0aLH/xi19sZRRSinB/97vf3d7+9rdPnTV84PSWt7ylffazn51aZ1rm17/+9fasZz1rc3rf+963GWvvPfLII9uTnvSk9rCHPay94x3v6Os17scafvvb327W+B//+Me4eEWf4fTlL3+5Pf/5z29oCB3mAGB01FFHtTe96U3tu9/9bmZv9woH/X7wgx/sOKHbv/zlL5vboXlrkzg+73nPa/DQbnOlo29+97vfdVr/yle+cnTO0l0+8pGP9PHlOFyf85znNPnG703e/5KXvKTTm/X69a9/vRW9qSeZ19e+9rWOJV78+9//LntqMlfveP/739/lA6ynVfzqV786b4ybNm3a/H59fPOb3+x0hOZe+MIXtiHO0/qbhTx89+Y3v7lj+uIXv7j9/ve/3+6w8AyZ+aAHPai9/OUvn9oG/ocddlh7yEMesiDvkRfWEX9+7GMf28y7OQCYWotDDjmkPf7xj+/Z8vrNKvoHXu95z3s6Fo9+9KMbuhzPA14f/vCH28EHH9zU+da3vrUVHuMpk1f4Gn7a0DfwGtfzbAx03Ite9KL20pe+tJHd8o3DmuM360nueFa2kumoo47q+vUBD3hApyn8uL33//SnP+06mv45bI7W/vSnP23VBEb4P+fmPcNK8MK3j3nMY7qesE5/+9vfhlWW5f5HP/pRe+ITn9h++ctfzusfP77uda9rxmtO21oLa2i8bA9Xc5nX2ejBWpNJ9DDb5kMf+lC3T1SDtz7uc5/7dF5jP6mvLBPs8PtBBx3U5eBvfvObzfIv68zaFSZkCznEPvvxj3+83TFr85nPfKY9/OEP7zT529/+dqtpqQP/1772tZ2ft6owl6HOr371q64T0Opc1ry/8JWs98aNG7u+8DyvUj10BKY7K71o9f7DoCF4Mfp44f/v//6vnfWsZ22YjALErJiUA3Pd6163YVRC5DjHOU675CUv2Y444ojOuH/+85/bGc5whp5Od7rTNYRH6EdEW79+ffOeRz7ykd3A3GuvvZrkHU9+8pPbDW5wg/aqV72q6WP1orr1yBnSD33oQxvjkXN46KGHtpvc5Cbtc5/7XMdDC7gcOecwPPaxj223uc1t2sc//nHZ8xIlykC/173u1QhBinxehQUeCF1ryKDNRDBERG/x6U9/ugv8c5/73O3KV75yQw+PetSjukDoFeb++etf/9oYare+9a0bJ9RY5rJ3y180BSfK4DznOU/bf//92wlOcILNY/nCF77Q50Mxo9HNBdu4MR80fP/7378R2Je5zGXaJS5xiXbc4x63t7I+DBwGY2IID9hGTHDsFef+UZdQpWCnKbK5Kjv9l9OB9/DtC17wgoa3pMc97nHdeY2Ihq8f+MAHduf4k5/8ZHdCb3nLWzaOk7ENXw4ftKmvc53rXO0KV7hCO/axjz2sMu+ec8ZYue1tb9vpd17h0Q9oxfokTq4MiYgJTmQIg/p85ztfI0s0u+c979l+9rOfuZ3JRCZZe4r5wAMPbBw6c/j5z3++4HgZhHDg2JmntbNOaCYbUe53vOMd2w9/+MN2rWtdq5GzeG9oBHqHNdp7773b1a52ta7QyRLGmn7Q7lvf+tYmIME5xx/uBSSMQZ3VkNAN2QgzchJ/3/CGN+yBG8aMOdBB1uFtb3tbu/rVr951FHnJqISDOuOkLZnGWbn2ta/dznve8zbyUOBh3IZBLIgmnf70p+/yMGXLd77zna73TnziE3ddxXDHZwzT8TuX69k78bL5H3744c37b3GLW2yTd+iV+973vg2N4D20dNe73rXTXI7THNCzIMPNbnazhi7pZLpZHTi9973v7bLkUpe6VLviFa/Yg4x0AedRneVIxnGPe9yjCc4xePMddKrxktk3velNm/EPx5v1yDtB0gc/+MHtG9/4Rttvv/26XGffZJ3xFb184hOfaJxBTiv9cuELX7gd61jHahw6RrdAmLHhQ3bLUBcbJ0eW7KVn6QHyDV+O3zUrz2SJoLPA0TWvec12spOdrJFVaH6hMeJF9pp1uPzlL99OdKITNWv1gx/8YF4TNHvIXBAFBuTcvMK5B7QFK205PWOZ5T2bNm1q7BRBG8EJelVQ1brPdVF/BwjsNbhfE7eYGIOJzmMsCY9ITAAAEABJREFUTDWcGCfilKc8ZbvQhS7UGfTiF794Y6gSjgxakUVRbYTECWF4YHL1MbdEmTAYtVV2kpOcpGF6DH/2s5+9MXiud73rtY1znjIBeqYznak94hGP6BFeBDwcz2q+Z6yc+cxn7jtPsBbdY0wQDhR0zo2A2LBhQ5M3TQFERKNAYUtYwzTbbutKkTPqGbgUvau1iYge6RWZOsc5ztENVWvFESKEKHd0om+C2tqd9rSn7fQgbznS9vok7DhrFPDtbne7dpGLXKSh06Hy4SRf6UpX6rt/i8WIkhZxPcUpTtEoco4besUHxsR4VOfmN795j5SJtjHa0bHyYaLoRH4jYrMzOizf2XtrwWGiTKwjxcJRwYfmjAePcYxjtHe+853N+DkJDD70RqlTsozsfD8MKXFG9N3vfvd2sYtdrGOZc856wyt8KGdjmUaj6tqRusAFLtBxMk70Rpkpsx54HLaCHGc84xnbjW50o25IUFjqzGJiPMMV9ngFfVHC5jptvOb5gQ98oBuI9773vRs88JwgxKtf/ereBH7WD62QrWQn41xUE++pxIigwBmNBxxwQKd3zpJdbs6nOvrB49rh0+Md73iN0c8YJ0vUWQ2JPoqILicZgXbd0CL5xDA0BwEeRinn74IXvGB38BjOjGb0rM44MTjtGKDBfffdtzsg5AYHnSOY9d0zqvAvB/Jyl7tcO/WpT90Davjfuh3zmMds17nOddr5z3/+RhYwhO2wWu/sZ7muaMGa4hd0wwFLx0VwZNp7jUtQ5SpXuUozTvLgTne6U+PI2h1AO3hZgMbO7PWvf/0maEHHC/qoo19OnF0mMoKzgnfxgv7QtDpLnTj19BBdx84wTu9wNd5NcwYsWUTW4UfjHfMjh+7Od75zO8tZztLXi7zmbOpnoYTXOczqcgTp7nXr1rWI6E6cABaeRJ/sITIZr2d/5Ok5z3nOpg66REucRPSddWbtSpax6awpPQ9PThcdgoamjZc+hJMAAJo4YE4+cR7Id/ySbehn9EI3WbvMz2tE9KAD/YXGMz+vaBRPkm/kAF2H/tlPxpj16jpBYM05K4wWBgwjlpAm0CZT3fJvRPQoa0Q0hEYJIryNGzc2wkOUURQ3IpqoX0T06IPIbCaCQaSG0omIzf0R+pjezszxj3/8Lkww9WlOc5r2hCc8oR8Z2zKS1XuHOU91qlM1mJ3tbGdr69evb/DgFIhqpzEREQ0OHDZYR8RWk4YhPPUTsXX5Vg3mMkS0KamTnvSkTbvLXvay7dKXvnTTT5v7Q+BQuJQvQ8e7KXLRRIoqxyefo7LPPvt0oT3XdMX/im4T/sbMyLbzh47GA2FQU07oa1w27Rkti2ajWcoJVkPnRxtrxYDEA4wkOy8Mn/E7OFOEPicchtZf+6VK5ixCtWHOqTUOYzB+72IQex9hfuCBB3YFgN4YVeqJeKUSgSVnx24GZ4bTMA3L8bjXrVvXGOsnPOEJpzpilIk14oxQ2Elvom76Mj7vFB0TQYuIfoQHncJfnVlMongwhmdENDzNkH3961/fneLxmM1NJFoAAi2iJ23x0FOe8pR+tCjlrh08+ergQ/THSLNWdrLcc2T0pQ7DgJK2C2Pt8a3IuT4Y5hQ7OXKHO9xBhHQ8tJl8Rhdoxo4zYxkNM7A5eKK76MPAGaNolyEJC3rnGte4RkNPAm/6UW+YGPjwsuOfbcg4zh4jTV36UCAJdgwwY4CrMomzyIiFK51FFltXMtXRIkaaesuZGG1kN4fWXLyb44G3zGWaUSmPXODgGLsx07Po9/vf/36nXf1yxNEXGoqIRg97dnRZHxxB74A7XSDpVxnDcRruu4KF/tA3nO0mDvtC89YUvaCTiGjGTe5znsxHfbwjCMoIJg9PfvKTd8dT2UIJTwqGmb8dG7wYsUXXkn/Go1wZox4W+s4+2Ujknqt8wUW6nczMOrN0FRjmcKJxegKPmBvc3vCGN/TTMePxwph9wP7DpzBAhxx8a4Mfs02uEwwyb3i1xnASiNXPsMw9HqeX0T6bM3eonDBAx+pU2oLAXltu18Yd4c+wsuCYSpRG3mJmh+giojsmaeAQLhETpnYvUQAMYcSKIMd9q5N5ERPvmmcvkkJIZtlqvkZE45gQVBETfBhlnAWCj+LL+UVMyoe4ZFleIyZ18nl7VxG0L37xi/24AMUPV4Im24mYEf7plMg3PkqAgW4t5EkR0Q3UbY1PveVKtoE51Xbr4EnZMlzG44nYMYxEJRkporWw0S9Mhv06dmFnwpETwlnEblhuzoxL0UeGgSAARS5/qVJENEYK/qNQ8FRE9HPrDAcKJiIaR4phHTHBwXqiNTTn3ngciTAfCllfjg8xsMdzUneYIiZ9RkyuwzL3cMLzdhMYkb7XGPbpXQwskVFGO+fmAQ94QKPkKHh9zFoyfrsfMEx+pUApSrxlDuMxy+NU7zNw7mHPqGIgozEKnePGgIyY4Kl/jqM6jjzYbRJhZhxFTOpYf4aBSC36F40UzWRwcHbsNDA+RSIZE+OxzeJzRDTzHuJFt+Bzc6VD8Ca8GFd0S84DT6ArmKqT+XnlwOFnOGUeQ1z/cJaH/u2+cJYYvdqQfdZeOfkIS2vhWdKeA0qGMublLWdCG6Le3us9EdGP3nCq0Ad5IH+Y4EIeoNfMF2CBKQwYh046kH/oW1/qqWMt0LdTF2QDPNRVLmkvcWKGuCjb1WQ9yBE6K8eUfRoDviDPssx4kx85HNaak28dfYOBHxm58rOf8dVa2wV26mHjxo09oOC4EdrJut6z995790f1OdJ2FNgtPXPuH/yJ1+dueyAGXaIrjoC8WUswsSuF18zP+NASWWVd7WbKGybOP9pAM1KWcfLRit2mzIuI7QY4IyKrb3XF6wJrkiADJwq/WVcycqsGe3jGXmtt/pvmtlBFBkVnRWownTwMOG2uDDFCmRBxBhOBihgwkLK+OoheREQUGnGJhE1TINlmeMUgorEEkG8zEOmwfLXeUxjDsWN0OMIvhcOwfCnvOUqUsAiTNcPgjlEQ+N7DUaX8GI+Zx8iWCLHFrp2+ljOhBVvpsLPNjrY4LXYNKLWdfTeatRMSEf1jecdPbn/72zfKijGY/EAZOQbi6BlhLGrtGUbeTaE5CkB5M7wZVPK3lbQRkU3ch3W9dzHYMyTwm634iInAH9MbI4NQH9Ib/jQPAQsRZUdtHClhtHj3cCw7cm/uHBTf/jCQOC3O/w/nyOFz7EmAhKIXVfMdG2W5I+9aqbpwYuyYD+POeyMmO6GUOXqUN0zoFe7jOeFB+FoPa6/eUIbqAw2hS+3Vi5gYpcoy6UdwSXvrzUkReNKOAwNP32aQqdlm1q/GGjGhYWPlIDCI7ARwVsyTzjLnIc/Dk7xCY67aDpNjPKLGHJMsx5/aeYerQAjD1JqQBwcffHDjyNsd0xc5zTjGHzCWp53+XDNP/nKliNjK6BN08D7Ht1zHKSIa3NrgD6M9ItqGud1Z9GzOHDG7xp5VdTVn8hadoWN62e6S8mGyHtNkFWz0jR6H9d3DTBtXz8MES0enL3rRi/YdE/0My/Gj9TTenFuO17vMhcNircgWctmxyVvd6laNPCIzh/3lPUfYDir+Y6A7AuUUhCOyMMt6rt7hGyC70gx09CQ/kzGjJ7LVcWGGv7wsn6WrdSBnyBQ45tjIOzzJMci8vJJ71hZWwzboBBb6y7q7ejUGgQw6ikOKj33fQtcM372r71kr7Vels7IQ+BiaMBCBI4AYD6I2HAvCaVo7R0icaRXZJiyc7RUtYOhmfcxIwRIkFDjmpwyyfDFXAjIi+ncbaQgupt1qqsNIE2WxhYwRl3Psdki85y53uUv/NRhnkR3TSWVvB4ARKyJuV4CAp6BFhSlnCmE5x7fYvglH2/qElp0NBsxBBx3UfyHGFc0ttq9hPe04xo5UOP9MMTG0KWUKjqJW3/EHH0A7fsZRMg6OjV9MUc7ol6561av2o47WNSL6fUSoMi+Zj+8MHCHx/QveEwxI5Y2PGCIE/7yGowcOHIeTYToq2vzobL6IFOEeMRmL7XRzgKWdDkrcXClW8mFz4x28Wb9+ff8mAI7mZbdEcGMYacPjxsLAtqbGIgK6g69aseppBJOR1nX4YutF7g3z3MtjBCzEP1nuKsqtTaaILTuYKQOn9ePd2jDYfGcFU8aZyK5oOocbD6uzGpOdIsaPbw7gHhGNc2snSgTccRPyyg4hg9mOyDTjxbEdRg5+Jdu0YcByQO1IWAO7AwwvPzBBHzpWpy/BHYaXMnJUPcEefQgSkBOMOrsQK40xPSxgYqwM5sW8H00ywgVfUmagb3IGjcF52A8aU+YI2X777dd/tMNOtJ0N8otBDkM8PWyHbtkLjiCTAz5It8OgL/XYGXYwvNvzMMEVTTv2Yw3YKMYlTz1968c7M09+JmPmkJDH5I/1J9/Id+Og/+CQ9fPKxqEPvNd3SdrgIfRFTnOi1NU/WUlm6UcwxlG4LFcHTaEbczZHH6E75aC+8llLMLX+0/CcNmb4y8ef4zbwUb5Uc7T+N77xjZuAHFnJ8bO7Qsfg+aV6z1rpZ6+1MhHzEFW1A4J5MK4jAxQcAWSHRZ1xstUu0uGoDINGlGIcEaTMMTqFoh7vl4AfE/O47+EzIw2j65swGpathXtKjhFKwayEIZFC3trYtbIbYYuakQtP0RRntAllNMGJoYAIe0eLCCP1dneyC4E20CFjBXbO+qMvUTDfX+3MGBnmHGrRVwpZBNeHuIx4/CBCp184HuMYx+jfFTH8OTTGI+JGOHNgOOccH0rcsSEKmcGVWOsnEz6DM56i7Cl2UTofRuNH/VGI3pttxlfKwnt8H7MQr3g3pUqwD9fS+jLgzBsN2GHlaIkwT4ukjd+90LPx4nfj4Qj7ABU/6zfboDNnofGAb4V8a8AJdCwq68zSlYLEM2jQWufYGMjmO8Q1ywRx8Jw1yjxX9ObqmJ5yxhhDQV4mdKU9Izj79u4sd9WPcu0jojE0KHVBCTuDuQbGp/5qS3iJI+BbA854jn/fffftP8DiuykOB4MUz1kjH1qbd9bNK6ecw+xK9+EHhikZoo01TcdFUAI/MOQdY1QPD1sru7icF3TqFIJ+vJ8cwsP5vpW44im8j4dhhA4W815BKTRhFxX9aIPG3KMxWMjTP7pMOuQE0x0MfoFOMtf8ybhpR7XIRc4IoxKW7tkNdvz8spdvSxjyxuJ9mUTNOYPW0VFbstT6koXWm8xkq+R4jVNbV+PVzlrhTQEhMogjyai1+7R+LpjC2dKfdsOE72BgzNZTG0d6OYLkqGCS+hHR9IPPODFkqF0WNKtcsh6CMRwexzHN0zcedI3yWUoR0X9NE365/sYHC7iiD8/DRNZYA46YOlnGYTNX8j/zdvUaEc2aokWOpx/CQQNknvXe1f7XWvs146wgLoLCR2TOvxIkmMpHuwwzRhMiHS8g4mAoYkyMjFjHdSKiISiKXaJkbDUjagQ9ZIRxW8+8cWOwq64AABAASURBVIYMpmGEYnj5ayUR7gQuAQbLlZ5XRDTK2Jozdrw/IhojnRJmOHJCKQyC2o6L9VRvdydCicBCQxGT3QH0QZEYmy1p1x1NaJNw1a+r9t4FI8oOv8gbJvXgyPhhjEqigY6PiALb9fENgV0XwQA/GDFs7x5/iG7a3RQ1YrgzIAh7BpW+OUURk7lqM06+KWIsiTiNy8wnjSqOgGjUsA5B7zli0j9+xtvmnrShfFcSnBiDsMw+8TbZ4ziPqCdDi8LH945gkAG78s7laAsbH39amxyfNWfwMBZF3cfvhS8eYsxlGRmoDZqzHsrJVYZy1tE/o4jBLGCDDpQN+1HHMyNsbBSQzfK1WR1p61HSP4xqdOPXpyImNKom+rRrJMDiWyf84wgrJ4bDrc44aUMPMZYZ3KLmeENe4mu9rCm+0V4bZfDN9aHPHP8UUHDVhvFJd1pT7VYqkdF2JTlQxrG995qXgIGjrY7ToK1sgwbRInmHtuSTe2Qq+jZvvEzmkSWMRUfH8TSHz1EpbYaJbLY2dAgDU7Dkbne7W/9hFzYGveKHIvDWsJ1AnoCZY3jkqGT3iNPoKDMHlh5A47DHU9qTK3jLWI3Z+70jIjYfnTNngS7zMj/thkm/1h1WEdGLrD/awvscoJ459496Ag5sJ79qCjt15oo2//V+TrRfwoKD96KxzRVm5AZW5AaZMhwf5w09kOHjoZo7PB2HyzVQhw1p3mwKz0udyEV9R0zWZ6n7Xwv9rRlnRYSAYGIgEfoMhtwN8X2DSAamykWLmBDFkCCzbHglzIbPec8os7XKSM8814jYLEQ8658wpaQIAEfM5K+VxHAVCROxx2zmZRsf1oSj50wR87HJ/LxGTMojIrM2XwkXyj4z4Jr3rgSSqy1910zWya4Co1HUm0EpKpXleY2Ivm4RkVkrcqVQRVPhSDHlS2FHWTvGlXmuEZPxKfc8TDDJPig3Rg5H0npkvYjJL+JQxvK0cc1EUTO4GZ2EvWidaCs6l+yMMP79Vr2f7812efWrXkOHleAXoaXQOY6iupRn1h9fzUvEkWKmuMflopboTT8UkXLKR3KP9x3VTHqQp0+ywZw8m/OQluRJEdvGVp1M3kexkzHyPDsSZkxkBuzswjp+w0hHv+rNWoKjyKnxG5v1Fz02L/Qnz9hT2XNgBIMYxXBUjr7sWuE9xzPtdKJbxnbWYRhbO7SgD+tkfa219dEPg4Chpn/GlLy1kuDrw2iyyJFVhg9MHalxzXkyluDu+A1e5oTIy3J0m5jKQ2vKGY+Ou8LPd1OixsrsIttt9H71M+FB+iifjQevktt2COgpO7FZvhJXu7ICGpwA/I8ujAd95fthIt+zK/rx/Q/HgsEnj6EJB3Oil8hWRrc2rjDncJC98iR42GWxU2LnmazyrGyYyEbyLyK6voAzHH3zpw0na1o777PTaveGHJUccST7ff8hiElXoQ3BGuP0XvyIL7S3zniGnuXQKc+EJjhdKdfRSdKV8ZJ/dunVyzau+rPu7seJvLQO+HVc5pn80954VsSp9dIdSBxGth/Zk/oALnSiMVs33aEZdOUe75FNMJfkSbCDIX7yPEwRE1oY5g3vIyblETHMrvsdRGBNOCuEk49fCSYMj4kYC66YV6RDVIPDksxKCFLCDIltYUbIi1aIbhAchAjCJ9Rs/WobEf0XNvRNsOpbPULR0RfbvwSYLXvMrc1aSD6mhwFs4UEZOz8tQieSNJwjwQkfuBAOw7K8JzDUE+kZ1pHnHDfj2BrA38f1lCqMGUF+apVAF+3J/lwJJ8cdjItyEOVHG8oyeZc1puCMIfNX4krIU84R0UTgvdP8fO9A8VFm8jIZJwzhYNyZ7woPZ6nRH+VrVwNvcORhb36cNu+jhNCq71ccS2Dgi2oyFkRmORwRk2MBxsAAlQh5RhCjXB3vHSbYKh/meaZoGWr4c1g2vhfFY/QzWBkbw3IKWvQTranj9+jt+tjloYDUdXTDGKw5fGAl6mqXBu/BgTGClvC2NpmsvfpwIRsyH55+bc76kBfo3nlv3/o47qYeo4VjIjChvjz9UX4imMYkb9YSIwlODD5jYzCiP8ZqRPSfcuccoivzsX4wRkucWG0pcnQpQmyeDGfHLx2pUQZzR3PJQDuc6IHiR5/oXHQZ3tbTURVOT8TaUexoxi/E0VGw9X2BUwDy6CRzh72E9sg2fIqu8UHEBAv06jsJmMFUfUk+7NCnHYJsA2e8zOiEv7pkK96BcRpr8iW7mb4Ds9vmqPOY/9RZjmT+jhF7J0fZ3O06cNjM17i813c1ntGLZ8a/byYY9/gdj/q/mXz3A3P6n00AUzIArdJLaDXpWz+Z8K4+8DV5ETHBPctd9Sm5HybONfmG34f5eU/+ceDJ0Ez4gfwnU/GMfvEjGWiuOV58RxdERP//cYwNT5KH+qf/BBzshBmHNea40nn0H1ltpxdG+FEbx844enaSOCv6oEPJNm08oyc4Gac2vvdkz3gXnWyM7skD+kadWUro3w6mo22cRGPjuMLN94zWRB56w4toTxt2hLXCU2gTveE5R1FhqU0m5bCgNzJvfLV++HVbdcZt6nlrBFa9s0JpUgIEMKLDZMNp8o4ZYiLNBKC6tlwZcAw2ypmBRokM2zEGGciIlMNjZ8QHZxLCFlnG2I60iDJSBoyhX/7yl005g52CF0Gxy8NhYUhHbC0Ah+9dLfcUJMVh6xuejrxQroxAioExEjH5oJZDQVjAm5CkmAjknCuhDEuCEIaEooTB1SEQHEeiSLUjHKyfY2c+QieURdUYQoS/NupRZgQOY5xQdpQpBZQ6EsXFeBV9FEFi8FN0ylYiRUTzf034BsKPPMDRDxXAjzEnOpTjIGR9SCsiCHPORZbBEL2aL2M5IvoH4QwAtGtHxAeoom8UH5zg6p0w9H7KimNCWDNKs+/hlQHDmFku5SQKRmkwpCiOfDf+xHtox9ozSJLe8DCFq67jc+asDnoRtWRAiDYbO5oiJzg5eFwbCV2SD+bFOGJow1QZZUNG6JdBwNBEpwzyxMnVER407r3oyDvsUDgqMZyLPmclMZwc9yC/0B7jmYFCVhkjXrNDwvhxHxHNjp0dSsakNdDWzlk6bhHRGDCOMzEe1YG5e+/Tb0Q0H3yL3nOYyUu4+4GTsUGg/mpN5IsfmWD4mR8M4CGhTUey0A4dxfkjywQYfDuGbhiwOXf0iqYEh9CxeuiSvLDLz5EXiEDn2YYhrC+GKZzVNSbHl8gAa4p/6EV9M9Ks7bRARPa51Fe40JXkG30JG/SAxzkieMo7ORro0C4enUKnoisy0fcT2pFxAovr1q3rOx8i4XifnEPfvq8QsOLQ6ZP9AE/96ocjYJdEe+XLmQQ40HquV0Q0gQ3jJT/M33j9XxzsDGNBK37N7YADDuj/b5s149yqg+ciollfspw8s74CCJwVR8/gjG/RGT43X7IJDTguzYj3jaHAI7vFe5IG4cQZZrQ7ekjfeCcsI2bTrqGr2AmcNPqD7BZU8o0SPCW8ARe2ouf169c39h39isZgrL51gZU6EjokGzmIbAzvoFOVSe45dGxTNEy2snPwrvJKO4bAqndWGHOEOiJkrDI4hxCIdjAyCCKCjKdNQcjDbIQiBUpwD9sRCs4WM0AoG0zqiJkP02w52/LF9OvWrev/OzZhwNimDAhD9eRpx0h29IjQGL5jNd9jUAoPNo5XEZgSIQlTDG9+EZOfJxXhwqwEhvO1KaCzjugNgWBN/FQpIR4xEYDWmJHoSJL1hDmjhhCBLaPHdxLK9CcxNL2DkHcOG40wWpUNkzVhwPtfe81FJHLs0AzrL8c9WhPN4+RyXBhwdoFERIfv48BQMHCkeAjALI+I/j8LM5RhJx9ulBhhzfh35Ec7jrty0TxOjDXxUSUcRVy3NX+8g+5tr0dM1kdfS5WsIecJ723pszX8aR54bEhv7q1dYgVL8xTlF6XnLJibceuP4sXHaDVxkI+mzB22vstBoxGT+ZEpIrjmTZlTWoxCxoa2mRyBIxeUMfaMg2FhTlln1q7wYpAwUBlKaMAHu8krcGe8kWscOePXxo8fMG4c69IG/cJWuUQ+MGr0a2fb2jleMVT28MPXaBTf2aVFp8M6+lrNiXzxAw9klx0VBiTaszMniMAgND/yCh7kFHnFIdRWWSb0qq3AS2ItkIbeyMFpbchAdM34sr74Fg1z6vXrvfBGu9bZWuCliAntq7PcieP/8Ic/vHF64WKOEt5mFGcgAjb0PKcYv6I/Mhu26kucLjyo3LjRLHomUzguZAF6l69cQuMMbrjQI9pGLP/88ZCAH7yNQzIu48U3dIE1GY+XPKIj8Y7vLtgabBLz0Af5LWjI3sk8tKW+xBYRsNK3fG3wKIfNsUPHVzdu3NhcBcaUSxwbzja+xu/4nq7Kd6gzawkPkTtkOtrHO+Q3vsmxwhfdwEUefiDX2H5+iRBWaIO+VJ4JXVorba0XvattlruXl3YSu9DaRSw/beUY1tJ11TsriNE5aecyRZGS+XKRMC7DWRkDhKdN6DNe5GnnHCrDLtu4EvKEiHL1tCXQM4lAMRYjohFuw/7UUd+7GJj60udaSuYEFwlGw0T5pDI1ZwIYFupK8LduyjIRBImhKwMvYsLUEdGsMQNWv4QAXAkSgsj6jtfPOwlyjihHSLt81/AqX1/eaWyMTIbWsM5K3BuHOXKuGBLodvzeIZ0Zr+dhHfQKI2uT+e7Ro345ZWmEKo+Y/GeMGzZsaAwAfABbZQslOBPAy4WR8Vmzcf/ei6eGdJb36Al+OWa0hSbMGQ2grYgttIQeKCb1sg2lbO3RAGzVyTKYoGk4cX7Ug2uWD6/WxPpxlPQzy4o8x20usBTIMX9YZ1lE9P/125wiJhgqg535wVgbfcgfJnWS9tDMsCzvrbe1YGhT5Jm/Vq7oEu3Ad5zgllibO5qhV/B+xBasEwt4ome0GTHRO9rgC/Sb9cZX9EtX4XH1GWox114964ZXrAH54Vn+Sia8DgtpjBFccix0u/GbK75CW9PaDHWHtjDWL/pGs57lS/QEuYi+6YGIrXFXbzkSuYQvxph7Ni/rNR5vjsO40cI0/rPe6Mi8IrbMR5BBHsfMmqOn7A+mnFm76srRgn6y3JVsMyY7E+qSuxFb+ldnFlNENDxo/ekW2A3HiR/In7333nuY3ZJnYAm7eYVzD/gUjtZKSr6cK+p/I6KhRWuo3BWGEbOPWZ/AjP2z6p2VGcOzhrPCCERMZ/yIyS+4jQXuCg9vh19nvNION9xOg+Xoczuv3OniiOlruqMdMhSXet4R2x9bRGz1H9a1VfAHXjs6zO3hGxH9OM62+o3Yfp1ttV8LZXAcGo+LmVO2iYjFVG/qS+PK1n1a/rjean82z/EcIqLBPWJxGI7bL+fztPGO32fdIhY/9ojosiliepuIWBS/tlX4B1Y7OuyI7eORfdZ1eREoZ2V58a3eC4FCoBAoBAqBQqAQKAQKgUJgJxG+IvxDAAAQAElEQVQoZ2UngVu9zWrkhUAhUAgUAoVAIVAIFAKFwOpAoJyV1bFONcpCoBCYVQRqXIVAIVAIFAKFQCGwbAiUs7Js0FbHhUAhUAgUAoVAIbCjCFT9QqAQKASGCJSzMkSj7guBQqAQKAQKgUKgECgECoG1g8Cqn0k5K6t+CWsChUAhUAgUAoVAIVAIFAKFwNpEoJyVtbmuq3dWNfJCoBAoBAqBQqAQKAQKgULgaATKWTkaiLoUAoVAIbAWEag5FQKFQCFQCBQCqxmBclZW8+rV2AuBQqAQKAQKgUJgJRGodxUChcAKI1DOygoDXq8rBAqBQqAQKAQKgUKgECgECgEIbD+Vs7J9jKpGIVAIFAKFQCFQCBQChUAhUAjsBgTKWdkNoNcrVy8CNfJCoBAoBAqBQqAQKAQKgZVDoJyVlcO63lQIFAKFQCEwH4F6KgQKgUKgECgEtolAOSvbhKcKC4FCoBAoBAqBQqAQWC0I1DgLgbWHQDkra29Na0aFQCFQCBQChUAhUAgUAoXAmkBgtzorawLBmkQhUAgUAoVAIVAIFAKFQCFQCCwLAuWsLAus1WkhsFsQqJcWAoVAIVAIFAKFQCGwphAoZ2VNLWdNphAoBAqBQmDpEKieCoFCoBAoBHY3AuWs7O4VqPcXAoVAIVAIFAKFQCGwJyBQcywEdgKBclZ2ArRqUggUAoVAIVAIFAKFQCFQCBQCy49AOSsLY1wlhUAhUAgUAoVAIVAIFAKFQCGwGxEoZ2U3gl+vLgT2LARqtoVAIVAIFAKFQCFQCOwYAuWs7BheVbsQKAQKgUKgEJgNBGoUhUAhUAjsAQiUs7IHLHJNsRAoBAqBQqAQKAQKgUJg2whU6WwiUM7KbK5LjaoQKAQKgUKgECgECoFCoBDY4xEoZ2UHSOB///tfk3agyTJWXVtdLxbXxdbbEXSWo88def8s1F1NGBirtBS47Wg/6ktL8e5Z62Nn5rUzbXZm3t4j7Uzb1dRmsXNcbL3VNPelHutSYLQUfSz1vPRnXJL7SotDYGfw2pk2ixvN/Fr//e9/y7acD8lWT2veWfn73//evvOd77Qvf/nL7Rvf+Eb797//PQ8ERPKLX/yiff3rX29f+cpX2o9+9KP2j3/8Y3MdxPrb3/62fe1rX2uf+tSn2mc/+9n27W9/u/3+979vRx55ZPvXv/7VfvOb32xu/9WvfrX38c9//nNzH3vSDTzg/YlPfKJ98Ytf7Nhsa/6wsy7w/etf/7pgVXjq90tf+lJfy5///OfbZG7ln/nMZ5r1ndapcX7/+9/vNPGDH/ygr+O0ers7D72iyZ/97GcLzheNo0l0uqPj/fGPf9xpW/v//Oc/vTma/8lPftI++clPNhh69xhHdb/73e/2Ol/4whfar3/96952Of9BH9/61rf6eH/5y1/Oe5Xx4WM0YmzzCqc8qG/8n//85zudduym1JMFD32jU+mPf/yj7J5+97vfNX18+tOf7nIBXfWCVfSPMX/ve9/r8s384Ly94cMP1uShNkOZmW3VUaYOrDN/eP3LX/7S+VkddD4sy3v4f/SjH20wzry1dkXPsPrmN7/ZhvQ1niecYfm5z32u40YPDeukLKDvhol+I0OHdd1bI/2RhZ5nLVn7P/3pT53np41/2njRFCzRi3mh72n15MGL3h/zv/fiicSQnFR/OVOuBX3kftq7zM16sTN++tOfTqvS84wff9LD5PMf/vCHnj/8xzvYMOTXEUccsZUMVz7EILGgq//85z8Pu5rp+7/97W8NX6EH2C2GjtAMniGXYEAPDycJX7SlT/j96le/GhZvvkdf1iGxo7/G8lXf73znO5uyzQ3rZisE9ghnBaE86UlPane4wx3ae97znnkgYEiE9qIXvag97nGP644GQs1KDLanPOUp7R3veEdD6Aj08MMPb09/+tPbgx/84EZ4MLjf+MY39v4f+chHNsJmMQyR71hL17e+9a3t/ve/f7v3ve/dbnnLW7ZDDjmk4zZtjnB89rOf3R1ACokAmFaPgn7b297W3vSmNzVGNCHyvOc9ryuwafUpcGtmfbQd17Fer3zlK9trX/vaRhDN6lqhS3M+6KCDGmGGVsdzoWxe8pKXtDvf+c7d2R6XL/SMbl/xile0l770pd3ITpysAWVkXWB417vetdM5Zyb74gy8613v6ut8v/vdr932trdtD3zgAzuWWWepr95vPO973/saw84YjNV7KOIPfOAD7b73vW97whOe0CgI+QslyuG9731vw/P61ZZ84JSN28BF+Ytf/OLGUR4qGuuD39H7He94x3b729++ve51r5sX7Bj3N2vPZJ35oQNy6y1veUt7wxve0OXaQmOFPTlKBlKwr3/96xv5N6yv35e97GUNn6Gn5z//+c3aDevgO7T77ne/uxvej3/847ssyDrWV/9HHXVUg/+rXvWqPi48a12y3mq+mseHPvSh9sIXvrA7zYxA8542J/z/9re/vdMYA4hMOPTQQ9vQaEWj9NytbnWrtnHjxp5ufetbt3vd615tSLvZP3n6kIc8pPeZeUt53ZW+zPeIOSP6yU9+ctetdMT2+lMHnbz5zW/u8ghdH3744VsFKfUDZ8GYe9zjHs175Eneiya1PXIuIElGkIcMf+XLlQTY7nOf+3Q9h3/G7zEWeg8fCQ4Z57iOZ/kf/vCHG3n52Mc+tusG17ET/MEPfrDhP47d+9///vawhz1snsFMvtErQ1o68MADG320UGDB+2cpoXnyjFxmv6EN8m4avjluPEluvfzlL+9ODoxgleXohvyzVnTObW5zm657yLms46oeR/Eud7lLt4XYQ2Qc+0O596At+ku+NSPrrDMZq06lLQiseWflhCc8YbvKVa7SrnCFK3SF+NCHPrRHixOCYxzjGO2c5zxnO8MZztBOd7rTtYte9KLteMc7Xi/etGlTN7r33nvvdtOb3rTd4AY3aAcccEC71rWu1U584hP3qDKCO8tZztLOd77z9d2VE53oRO1iF7vY5j56R3vIPxgPE9/4xjduj3jEIzruDBaKmBJOGDAxAfKABzyg43aNa1yjXeQiF2nHPe5xs8q8KyFAcVztaldrV77yldvVr371vmNCAI+ZmiFq3Sgrwta7hp0RDAxTERHvvdKVrtTOetaztmMe85jDajNzf6xjHavv/owjfznA//u//+vGsUjb9oz0bENpPfGJT2yMzKte9aod03Of+9xNX8oo7ktf+tLdSdk4Z/Aknok1Q4lgtc4HH3xwsy6cl4c//OEt6+S7luIq8skhwIfXuc512mUuc5l2qlOdqkXE5u7xuZ3PH/7wh2285psrHX3D2X3GM57RrP91r3vdZh4ibxyP8fjR6XOf+9x2/vOfv13zmtds++67b/Mu9RgO69at604bxWWN0KRo3NGvmvkLw/85z3lOu8AFLtBgsd9++3XHgxG30OAFfyhX+F3/+tdvGzZsaLAQ7c82DALGjzo3vOEN24UvfOFuNHE+sg6DQHDj2te+dl+Ds5/97M0apNPImRH0gCm5evrTn749+tGPbmiOsZb9rNYrOhWEYQjTH+jLOqCvaXMSKGM4XeISl2j44GY3u1nXaQwxeoiR+pGPfKSd7Wxna7e73e0ampTQLNl6ghOcYF63aPg1r3lNY4jh+3mFM/JA/jHu0By8tjUs5YKLMKUn0PO5znWuBt9pOyMMWU60voeyk3xD3/rYf//9u3w75SlP2fmc/tjWGHa2zFo87WlPax/72Mcah8tchn2RT9aUHkUn5PNpT3vaYZXN94xd9W9+85s3jujFL37xHpihm7MSPASYyDU43eIWt2j0pTF4v3pk+slOdrKWzi5aIuvpyjEtqT9rCYb0/Ktf/equ4653vet1e4OsgtFC48UPz3rWs9qNbnSjRr6d97zn7fZMtkEfgjrKyDj2oGA22zKx0zeeJEfhnw4frE9xilMo7kEGgT48bS2Pc5zjdHvzmc985tTAQm/U2h57WfPOyl577dWOf/zjd4OUsqMsKbwfzhk1ueqYjzI89alP3QjHiGiYmaLU/k53ulN3ZBhLEsFFcBAYPGEOTzLvyU9+8qZORGT3e8yVkSuqx6njBHBYYIRh07ggQD7+8Y93owMDY/STnOQkDYYR0zEjJBhVsLU+sOYs2n4dR0hECkUz1q9fvxXu3v2CF7ygOVJCAJ/nPOdpxz72sbuRvlXlGcgwX84zJ3qh4RBy6qDfheoM8+HFQOHMHTK363WhC12oO9YcFfXQ7hWveMUu3Pfdd9+WAp4hDj/JzhXnnbAWBBBdutzlLtf0KTqon6VKjl/hV+styme+aABf5jvMnUJYv359d2Izf9qVMyuSzTiDm/nC1zw4xEODm+HzqEc9qjvdDBdBjKRTBpQxmTtah8dTn/rUroDQJcNx2vtnLY9BwgCy1rBg6DKWRSCt9Xi8FLB1RkfohIJlYLtyIswbthSuvmDMUIYvQ++www7rziT87HwyBDgpsCU3BCasj3eTy3asvEtUVGTbjiAjjBE1HttqeyYv0RdsBNTItaSvaXNhdDOqrRW88cIlL3nJvkNsd8XaCDowtOksNIl/OfaMzORxfcOXYUyuCtbJm7WExzkbaIic3t740AnDlB5HezDiBJIhdkqG7TkHaJSMhc+wjL5Bb/SS96JfdQSE0Paw7lLc4xn8Zr7Wz9oM+yVT7WYYy93vfvdui4xl4LD+unXr2k1ucpN2qUtdqgdO8Ys5cEaynl0DNEM/K9tnn316GzvO5JdAAXoU1LEzoA9BHYE9gYeTnvSk2dXMXvGKYJM1Psc5ztGSHqyhEwvTBk63CaIIXuMz2MCI/YL/rA3bkf2Av8hAQVf6QaCA3Zj9qsc+tKtp/e52t7v1QCs5qw470m68HW1rY8eMXBPc8V51Km1BYM07KzlVzIfJGGiYkeeMaLMcEeY9QcZztj2KcBBQluUV4TOekqiyfV6z3p50ZbRd8IIXnES85yYOG4YMJSnNZfVvfUSzznSmM/UdKkJAhEvZQonyIchFLggDykc0TNSfAM92jC7KinDhmI7XgiEqimx3TDmDifGa7WftGhEdS3NfaGwR0ZRHRFvMHxE3x58oHrgSkoycbAtPQpTRJA8+6J8wtoYR0XcZROQiJu/kLDAOtJG0W6pEqQgwcGzxK+FPuU/r3/im5Q/zGDRoyLyG+Qw2ZSJx8hkIjoeYF0OSEnP2WZnEUbGtj8Y9R0TfPWDowyBigo2yWU34w/EPhopxG6fADueN4oW3vGHitPoWjTJPvLU585nP3Oy2MfREHhl7eB9tag8vhg4DGc3ZBYOxvIgJViKODCQ7U5wS4yMb0CdM9WXdrJM+V3NCx/e85z0bhwNvoS9zXWhOdBL5FRFdJqgXEY2DDkcJ717+8pdvjNWIUKXZpWKUkss94+h/rJFjoGhY/aOzZ+4SEV2+tUX8Qa8CYac5zWl6wFATDgf6ZoSjJ3nkB8OQ/LdDha7kZ9KezHPE1W4WPNXayQAAEABJREFUHaW+nSx9Zb2luBoTfuIIOXLLCZGXfTO4BVGcWnjMYx7T2DGcfnPIOuMrXpMyH+0ICtiRkYeHBAWSp+RJeJGMM2eY2BWFn3vl+BANCvKlwS1/VhN7QGASj+V4zYfzIkgzTY7QDXQkfZDzQgvsBW3wqcCYIF+W0wH4C57WTz7M2RrWTlDGzotdl4gJX6pjDfG8ceBdedbGGg9pQH6l1vYYZ8ViIyYesa04Rm1G+ZQNE6ISVUU0jnQNy4b3nB8G3zBvT74nINOASRwoSoYthpdHSDoiwjAR7XIky3cGHMOFGJRwpFQxvN0uxrZjY6ISEaHbfvxIdMoYHBOKmOT3wrl/CASRNGs799isPyFiC5bSkrcnJEcBrImotW92HLvhPFLIOf+I6Ge8CW5OOwVNGEdMMCWctc/6sGUsUW65zlm2K1cGKx5lSDN+bd/bdmdgU+I72zcaNV5KP/tIg41xhw4pJpFvSgq92t1hLDAatKGUjMt9Jn0ythnpEROssmwprkvdBww5ggIvZKP+YYOHBASGNKFMwj/oRx3PUkQ0kVaK11l25erhceUSg4cRSNnjNwZBRPTjtMoldayDNdAXhc8BEtjgHJEDrnZYGKbarNaEpsyD/vBNhaNudlkcUZw2p4joO8CcHPi1uT8R0Rhf+E8e/NIomyvuf+2cWF8GW8+Y+4ezSP6JvsOT0TSXver/imSjWcETWJgQPOzaoTc4yaNrODV2DJThd/mZGO0CkQJiAhYCm2hPsGwo97L+rlytxaZNm5oAmrUcr4WTAmQ23iEDRfcFXB1VWui9ERPZw36hbwV8HLUUhNAmIjotka94tR39Bw/DzY+GwATdHF3UL74VVJ/jo17PnOF/YEuOCabkeDkF6IPc48yMh4+PtLMWWaYNbPSljIxPeZl18BlbET/LQ2vwvuxlL9s4eHZV6BDtlUv0D2dKO3RoJ4xeEayxdupU2oLAHuWsYEBEiHBsD1MQ4w9DQYOIMSaCHCplZcOknHIf5tX9FgREaETwnP1MwedIGIFsu5sBjEEd+/C9AwNnS+std4SNYw2OHb3lLW/pjoZILsMma4kE2071LgrKWmeZKwHsPYRJvlcf3mtLXNRIvbWcYML4c6TBtrgjJCJIIkA+YBbVyfkzGH2rwZl0Jl5k0rpl+fBK8BPktsPxxLAs7xmxnElGAiWZ+a6MZu2Nz3MmRoW+HROiIO3cMVJ9v+KoX9bbkSvFoy/rbc7amjcnxPvRmjI7ABScKBrnw3cC6qOXocLRPhO8jFH9zJvlKyw5BRTlcN0oTI6C8vH4KVHrN3bU8Jy6ZKdy9caOKycX1tab0a3+tH5Ed9VjGIpuH3jggY0hyiBwzNTRsG3JZf3OckJnIvWujjihF7vBdjoYogzC8fjRIoOTjCPrrA9+5Ny4h+24jXJ8Qs4lXowovHzGM56xMaQioh/LG7ddjc90tnEzJuHlnn5G22gZHvSA+dvNOsMZzqBKT9ai38z9Q0YIjnFYOAX0AwztOM8VT/0rsKGe7+tgPKzEKLUjOcxzb904Kvrdd999ZW2VyE2yCZ34NlYgTp5dOT+msFWDQQZ+cTTVd6MCPWSvYpg4eohX80infM4Z2UceJH7yM9EdeHr9+vWZNdNXaw1jMj3n40q+sTXG64QGYEI+cWiGkyPf0I+yYb57No519w1Q8iEaYrP4cQj61Xe2goJ2WshG7QRzHJV3RJNTaUyCsWnDqFNpCwJ7lLNi2hHRMP3BBx/cv09BYHZREKpyCTNLiHOYr6zS4hBgbPh1NTtZnIOISbSHAyNK5BsHShoTO6suWi6iMK136+B4jquP9QkgwtpxPvVFn6yhKBihQuBaN4IpYrJLQJExkIxlw4YN/YN+fTgT7Rd2GOf6WssJfgQr459xZCtbdJUiJEQJy5w/Bcoo9MEl5cWxH5ZnPYalKLEjBhzQzB9eHQ3yq0XWyy9I+aiQAqVMtJdP2FuzYTvKFh9yFOykca6c2aZQrBk6GNZfzD2aQHPmjT5dGYh2cLyf0UAhMY4ZeCKeaIYC8asvjAtHmIbv0o4hwLHisKVjPqwzi/cUK8UdMeHNHCNlilYo+czLK8NPG+uSea7W0RX/Zfm4jnWDPydEPfXHdfST7SOiMaoFmHzci78ZEXa79KH9akwcRAanYAtaJAcFYtC2XRb0habGc+O04TMRWjzk6mNydCoIM67PGHPEyM524sXwZTg7a69csgbKYW/dx/2slmcGIvqScsz0kDniSfMU5WYkwgSfo3X10aNn7WAvUCKwxtjkUDuixXFRps4w0T0MUnJMMIOTYzfEOsPUmk5zQOlCSYBDf95vjNbCmK0FveXeD6EIsHIyOOxkp+DItPHoS7JrRh6xccgz15ThdK6dJacUfI9i18gpA7xnR8A49JHJezg2gouwzPxZvpJv5JX1zXHCFM7WW1nm51U+/Mfzt5YR0W3GrOuKfuxccW7tkMiTIqL/YBDnjv6gPx0nFqSwe6MOrMk3x9IEBX1fpj5eHo5Z3dWTlm+key1f17Pbc0Q0BORYEGJjtDCoIiZKW2SGQY2wHYXZ1kww8bbK98QykVXReFvnPkIbMz4mJRBg48phWLdu3YI/cWwLn9LQl28tMLZ2Pm5mADlOwXilUAgNv2zCqLSmnCUCwrskAizHYxyUtqMDol/K13KKiH7eHXaJAVpnoMMAljl/ip/SoiRFiAhYijPL88pIJ1hFfV0zP6/4w/EoZ8Mdu7OOjH4RYr9UI1HmIpfj9p4lY4mI3qVdIc4WY0/fPXMH/2H8MirsqjqHTongdTsuHCLdRUQ/9w6rfGZYUnTmI08yBg6gXTu/sMbJk78aEmeEocugSscPDgwaToGy8Twoc7yqTpbBAP+gKYagcvUc+co6+kVD+E/f1lHZsB91PHuvNVcuRUSzLmS257WSYDScJx5iXAucwGI8T7g5MitC61tKDg4jSuCHvhrXFwCg38hhZfr062t2ODlGZKVTBgx4vwzIGcKX6q7GxDjHr/QPmjQHhr7gFXzM38fTAl6cPfNnpNMTAhb0ifoMe0Y+fUN/0DeMSMEtcke/meArCMaRdkRVXZFy8sCuoDxthsfwsq1xkEGpt4zJbokjehwefZN/+GpIJxwt4xGNRy/Z3/iK1wRm/RgQQ1j95EllArbwgAN6UibgxCka9yUQAxe0Ni6b1WfHl/HYkB5gimc4BGOnKyIafUgukkM5LzRBN8rHg5lPZrIzYMn5i5joqCwfXjm8jq3TH3b5hmWcJjrIWg3z634+AnukswICAsDPEjKgfNxGgKeAQ3yMWMQscoVYtRknQsjxlXH+nvxMMeQxBU4DYQEPQgK+zgMT7kNhAG/CWIRB3WGiYGyxa0OQpoNBsItk2VXh7BC4tsjtGLgyGu0IOMZDUXl27pTAJeDzHfIIoRxn5q/FK8Xn2BdhyUA1R3kENBymYUD5cw4oWzhpk8k6i0D6RbdcO+tszbKOq/WxM8JJ0I+dLYqfocTgEtljaKg7TN4bEf1HGdCOMuMxDn1FLKwc1N1WYlA7BiOqiYc5awwWhjK+F/ESvaTAsx/vRKsUT+ZxXDhb5AX6lE9eUGTuZzlFRMMvnIikB1dOKccNFuPxy7MuZF+W4XkGNmULN44nfkMbWYeBoA3+59D43g8v48esQyZwevSPdzPflbxGq+5Xe8JnfrhhTF9oEm1JC80VdvgHvdlthhdDSZsxLr5JS8NWWUQ0u5T4jYy09nZVrSm+FDUnC9RdjQnNwRU/40FzYKjC2VzRkJ0pgRPPkl0tPE1GoUm4c94EKffbb7/+a5HkhF0JuyBDmta/PukYdE0+oH9BDc4OHcUJkGCs/jDpn440Dgn+jGE7h6L0+mZwk0dDOWS8ElpQZ9jntHvt9Y2n3Gcd7/JeR+IEXDi39Ch9mXXy6ntRWE0ryzqzdsUTHPUhPZAxsMQH+HA8Zk4M/K11luEx8s38U88ps/NGftF/ub7k/lj/qSvRsfCDu+dh4rBE7Lw+G/a1Vu/3GGdlGlMzfETqHQVhdOQiE1giygwZW5/OuI4JkOL1Ea4+tMv+EZ3nPTX5YAyjw46wYLzaJne0AYYULSNFBC8NUMqEEHAsCW6iGBxIbSOiKwzKJ2LCzBwbUVb9U64UjWi9Y0uZGOWEg3wCy70oOqFs7bxHSmcnI73yZilFRP81HEKwLfAHvUZMjruNq8DWzhT8lFHUsE96169vMCgteKozTNaI8cq4gWGWMdCtq/4YOtbWe2yJ6zPrRUQjnIeKAY94l+9mGEt2cJJ/sp2rckqbEmV0yGP0MqgZEOYtL5Nn7zbmzHMVzRKxdPWcyTvRgiNpDDg8HxF9q1+U27sopKxPMYloJp0y0kWkGUgUmffCm1yAabab5SvDCi1YY+OkyGEiEmud5ClzfMi9dfOLU54pcXmMgR/84AeNA8qRRA8ihXY78bI6jvThNdFqBhP+pOR9G5TrYo3wNiMu5aq2ay2hO046nrQrnPNjEMHXTmfEhJ/RfmKY9bQnZ50IsEOCd9F+lrsK7jiKJCCnvryI6P9HUcpIV8ayQAGaFvklh9WdpWRueAtvD8clj1OBL8kfBrxdDZglbaJd8gKdo2d8bd6Z0Dme9uMg7ABY6Qfm+N/70CKMybA0SuVL6stTx3PERH5wXOxQwBWmERPdpU4mDqdd6xwLXYV3BHb8+qE+Of54xU5NtsOjAk54CTZwMedtyRzfNtF/xpr9uGqvrR0htOTYqzkpy8TAd9QXPjDM/Fm/kjNkurnDy3jJOvcc/Ijo32vZWZOUc1bYKPgLrvLILphbJ7aLPCcK6ECOnjVDf2Qip45egBk7SN1MdjU5y+gh8+q6eAT2WnzV1VmTQCN0bIEymBElIzhnQzkQGIzr/2fvPMD1KKoGfCb03nsLvQtKVXoXEKQJEaQLyIPSCYIIQUCkBhABEVCKIFU6IkUUUJpUqYIk9N57/fPO/SfZ+3Fvbm5yy1fePJm73+7Ozs68O3tmzpwzs9URd4QN/vV0lvbcc8/8sTJGXEjnmmuuCdycSgcOwcboP/eiA4cAraZV7tXMW15WOqoIRCaNYS5GiUBRgC8vMIIRwcuSw3QQEQJ0jBDEHGPUAYYohzTmdGRSSnm9eK7D7xN/awQODTF+7CgZpIsQoaNDQMjzjOngcg7hi5Bl8hodYCYb0sFC4GCdoZGmHtTb86EzQweGBhdBSNnhXM0nHT3qNHWcjjWCGIYlDqvHwB8ljWM05nSGmKNCx5oJuCgZuIDAkHg03IwMMnLLM6Xe83xgiwDn+eC6gEsXDS4WBZ41v8kHrLlXVyGllFelSemrDXmM+Ed+yAd54nnTAeHdY3UoGpSU2q7jXaNOwAErAcoYx0Ykkf/T8ND4o1hwAIZwgxd+/zCpNkQppfydGTrfzNmhsWMlHty8cPfAgsAzYaADdzI6GYxgw4DRWTrp5J171XtghJE84w5Cg4wlmQ4SnTryzvNkvsk1V/4AABAASURBVBGdOhpg3ic6AHSocHPh/WUEHzctGKeU8jeTcGPh3YQf7zkut8RHiSFd+PA9EDrV1DHqIJYtZC2KH3GaOcALNxzmDNB2UHcpP/WQ94myM0eCUXnqPu807x7PgAE03mtcwmBOJ5r41YBMRXGkw1w9zrsJe95lAvs8U94J9lNqe6eq1/Tnb+oHdQN5TvuN4lHywzFkOu8gshE5T7tBmeg00nnnnUcpLnWKtoFylkDZkQXwhQtpY4WgnmJNQcHG6sp8DlgjO4kzJoG0CSl1zJR8cs+SF/KGzOdZ8Jt70HnGAk1dQK5QJrxA6CQjp4kDH9zVDjzwwNz5pszIx+OPPz7oj7CaGe8xspR7cg33oE3GOs67SRsIS+oM56uBNgLW9Heqx+v9N2WFEWWiv4bMZoEe5BfvH/mnvcBVDxlOmwF3njPX8H7RlvBdMlzpaN/ghls5vGkPqCfIfQL3oh3ieSLT6FPSR6GtxYOHwT5WoiVf3NvQPQJNr6xQuRA4aNmMnjJiT6NcxYRAoMLSaFePozHzotOoICTxL0Vg8OJT6ehA04DQ0NDZRRgwkoV/J3GqaTX7b150GNGJJaC0EGDOPkI+pZStJLzodH556ZlYz0vNSAcvcUopf8CTRocRP7hh3kdRoZNII0Tnk5F1zOw0BsSphpRS/iI2zwPhU86hgNLxIh1GOeiEsnQvo18pddyglGv7Y0vnHMWXES3qJi4IcK7mBQFMHS+dQzrV1MkSB8sFnXEEJccY1aGuI1xp1GDAREvipdS2lCyjiCgyjKbRkMO5dKBIG+aM6jESx5bnTCAP3CulkSy55TgFBgt4RpSTTjGuMjSqjIaWhBksoBwoXDQ0KHjV94/Va+iw4NpEp4TzjIpxDXWTa2icSnpsaXDwZ6cRo+PHSjh83ItnQfnpRJE3GiNcJyg/AVbsk0/SqfdAPvHVxzqEwsI+SgXvHHnn/cHNhU5z6RQzOssyznSEafyxjPGulfNch6JCI49VjSXK6SDQ+aNzxnkCLjMoLHQ0qWuwZKQfOcD5Zg6UEcWX+oc8QxZS16iLnKPsKOW0M7gYpZSCjhM+8nQceSewFlBPiVsbqOe8n9VnUhuHfZ4PzGm32K+nQBmQf7jlUHeQh3QGOU4+UU7oLNIxxxrCMeoUygsdceQbnVI64rhAcb42UHep/8jDcg4m1Esms1MvURSo68jNzniXa8dly7vB5HnKVOoAsoZ8oFDQr6Ddooy8b8Tnfsh06gKd6ZRSMCBHe4ESw4Af/Rvi894Sn0CfCJmGLKQOItc6qiuFNfWyMOb6RgnUHQZPqSvUB/ohDN6V+sCWgVXaB+JQLvp9LF5AeelvoKTBiGdC+0t7Cw8Gs6rtH7Kf58Azo3/CoiwMmDIwyrOhfaIfyj0M3ScwoPuXNNYVVEBebjrDVDhe/GpHh9KklAJhRKPMfjXMPPPMQUONsKQxRZigQdMBolJSgWnoGWWl8WFkjM5ebeenmmYz/qZTg0sQHUkCrEqgMawypzFAONC547ngVoKyV7jQcMAbxhxLKeWPpxGfe9AhpoHCBYzztSGlFAh87ku+qudRgEgHQYKAZkScZ1iNUy+/Ua4YQUMJoCND57i2PFijYMnoGwIUS1a1PHBCGaFBo1wppagygD8udSVdlBrSQjnkOnjTWSppEg9BTF2n816eMVtGxUsnl3v1ROB+lBsFgLzipkBjktIohYi6w2go+YUVZa6+f3T66ICjrKSUAhZ0YkiPsnfWgFC/6CxSj3i/kR0lLtYHOHXEgLSROz1R/t5OI6WUv4hdGl0GCejApdTGF/50ZCknv0t+qHc0vjwX6ggdonKubBkUKHGQmdVnUuLQqWLggnSo64VvOd/MWzpKWLAoO/WrtvzUW+RgGSiA+SqrrBK878zrGV0d45nxXLrixzPhnaLN6ipuX59PKQXlpG4ib6ibdD5TaqubyCRkOezoWJI/mMALWcg5Oo/IUc51FJBXvN/0EarnkYO0D1gUSYNnQ7tVjdPTv3m/KCPWTspW0kcp4fnQ96AvwvMn3+U88gyFqzxv2k8UNgYUUNRoL+nHlPhsuR6ZSVq0gZ0pYSmlQP5yX65rtEAfDeUNGcPAKW5Y1b4I8ob6BaeU2upVSino06F88G7y/IuixzPCTZW2DplIu1cC7QFtE4zou2Dp4hnQJqHw8K5xzjB2BJpeWQELFQxhRuNA5USgcbwaOIZQqB4rvzmOoEKAUWlJg5egep70CdyDEYrq+RKvmbcptS3VB4PagCBMqU0QFAaFKR1NfpfjbBHUcOSZsF8C3InPS88zLcc72pImz6H2XEpt+Zx66qmDdOr5OZE3ylx4Up6U2nOEVTnPlnJXy8w+x0mrHE8pBekWBqRRPQd76jrnO7qW58nxjkJK7fNX0h2XLfUAxaG8eym1vwdlg03JD785Vu7J9Txrthwj/6RHOavxOFcbiMu7T/wqJ35zfblndcu52nTqeT+ltvrQGV8YUNbaMlBm6gh1KaX2z6TE5TreWephOVbdwh+2xGk0btVyjO1vuFC/KH+tTKO+Um8LFzgTjy3cRndPuPMejC4O51JK2dpde2/O1UOAD/WshMKi5I0yElIaVf9gQ/mpm9TdErejLXG5Hta15zlO3SSQj9rzvbHPPTt6FuSPfFBXiJPSqPKmlILylrKWuHTKic/xlEbFJ98cIz24Ep9jnQXuR53r7Hy9H+cZw4f6QFmq+U0pxSSTTJLbw9rjXFP4lXMptb0vcKsNMEqpjXNKKfcvkKlwrq23JT23Y05gwJhHNaYEJCABCUhAAhKQgAQkIIG+I6Cy0nesx/VOXi8BCUhAAhKQgAQkIIGWIqCy0lKP28JKQAKjCPhLAhKQgAQkIIF6J6CyUu9PyPxJQAISkIAEGoGAeZSABCTQCwRUVnoBqklKQAISkIAEJCABCUhgXAh4bRsBlZU2Dv6VgAQkIAEJSEACEpCABOqMgMpKnT2Qxs2OOZeABCQgAQlIQAISkEDPElBZ6VmepiYBCUigZwiYigQkIAEJSEACobJiJZCABCQgAQlIoOkJWEAJSKAxCaisNOZzM9cSkIAEJCABCUhAAhLoLwJ9dl+VlT5D7Y0kIAEJSEACEpCABCQgge4QUFnpDi3jNi4Bcy4BCUhAAhKQgAQk0HAEVFYa7pGZYQlIQAL9T8AcSEACEpCABPqCgMpKX1D2HhKQgAQkIAEJSKBzAp6RgAQ6IaCy0gkYD0tAAhKQgAQkIAEJSEAC/Utg7JSV/s2zd5eABCQgAQlIQAISkIAEWoCAykoLPGSLWP8EzKEEJCABCUhAAhKQwFcJqKx8lYlHJCABCUigsQmYewlIQAISaBICKitN8iAthgQkIAEJSEACEugdAqYqgf4joLLSf+y9swQkIAEJSEACEpCABCQwGgJNqayMpryekoAEJCABCUhAAhKQgAQahIDKSoM8KLMpgX4k4K0lIAEJSEACEpBAvxBQWekX7N5UAhKQgARal4All4AEJCCBMSWgsjKmpIwnAQlIQAISkIAEJFB/BMxRUxNQWWnqx2vhJCABCUhAAhKQgAQk0LgEVFb6/tl5RwlIQAISkIAEJCABCUhgDAiorIyA9Mknn8Sbb74ZH3300Yi9tv9ffPFF/vHll1/G559/nn/7RwISqEcC5qknCCADuyvr3nvvvfj44487vT3pvfvuu/HZZ591Gqd64sMPPwxkbvWYvyUgAQlIoLUJtLSy8vbbb8fpp58eQ4YMiRNOOCGOOuqoOOuss+Kmm26Ku+++Oysp77zzTlx00UVx0EEHxeDBg2P33XePQw89NI488sg4/vjj44477min5LR2dbL0EpBAoxF47bXX4pRTTomhQ4fGscceG0899VSXRUCpOOmkk+K4446LI444IsvM2ovef//9LFeRk0NGyNiHHnqoNkq7/auuuirLYBSgdifc6XsC3lECEpBAHRFoSWUFq8m9994bG220UTzwwAN5u+OOO8Y222yTR/X22WefuOWWW4J4k08+eSy11FJZeTnxxBNjjjnmiGWXXTYWX3zxoFHdaaedYuuttw4aYkcE66hmmxUJSKBLAgzYoEy89NJL8cMf/jBmnHHGrDA899xznV6LonLIIYfE008/Hdtuu20sv/zyeQDn+uuvjyIDsaYMGjQoy9Dtttsu5p133thkk03irrvu6jDdxx57LHbZZZe4/fbb49NPP+0wjgclIAEJNCoB8z1uBFpOWaExffjhh7OVZK655sqN7NJLLx1zzjlnzD333LH55pvH9ttvn90WcGEYb7zxsoLC+QkmmCA3uGuttVass846seeee2bryrPPPht77bVXPPnkk+P2NLxaAhKQQB8SQDkgbLjhhlkGrr766vHyyy/Hn//85w5zgfxkIOfWW2+NfffdNwYOHBhrrrlmLLPMMrH33nvHiy++mBWUs88+Ox599NFAUUHOorjMP//82RLzxhtvtEub+2G9nm666SKl1O6cOxKQgAQkIIEBrYaAeSmXXHJJdnXApWv66aePAQNGYZhiiiligw02iIUXXngkmpRSoKiklALlZfzxx8/7U045Zayyyiqx8847xyOPPBInn3zyyJHFaNh/ZlwCEmgVAldffXVMM800Mdtss+UiIw8ZmLn88suDuXz5YOUPVg9cX1FaZp555nxmwgknzHIQ6whKDJaXa6+9Nis/0047bY4zySSTZAs2Fm3i5YMj/nzwwQdx3XXXxYILLhiLLrpodr0dcdj/EpCABCQggZEERvXSRx5q7h+4PdA4Mso3zzzzdFhYGuvVVlstaIRrI9BIV48RZ6WVVsojjBdffHE8//zz1dP+loAEWp1AnZYfN9fbbrstpppqqph44olzLieaaKKYaaaZ4sEHH4zXX389H6v+wdr81ltvBXP5uL6cw3rCPlZrJtxjPcFNlmMlzkILLRRciyW6HMP68sorrwTytla2ljhuJSABCUigtQm0nLJCI8oEUkYSsZZ09PixnDDaWLW4dBSvHJthhhmyrzeN7rBhw8phtxKQgATqlgAKBwoJVg9kHhlF5mFdxjry6quvcqhdIB7y7oUXXsjz9DhZVTKwxpAeCg/WZua1lDgppewiVlYGQ6m57LLLsqJCmsQzSGBMCBhHAhJoLQItp6ww8ZPGctJJJ+0x/2gaeEYkabRxa2itKmRpJSCBRiSASxcyC6sKMowysI/1hC37tYEBHubrfe1rX8tzVJi/cs899+QVE4mLhYX0WLCEOSiHH354YL0hDistTjbZZIFigjJ06qmnxmKLLRZLLrlklsXkgYCrLWkZJCABCUigTwjU/U1aTlnBYoKigjtY1UVhXJ4U82BQgmjIix/3uKTntRKQgAR6mwDuX1hBitJS7oc8w4Iy9dRTl0PttigXv/jFL2KJJZaIm2++OZ544om8qiJprbDCClnxQKE54YQTAoXlH//4RzzzzDNx55135rkxzE+58cYb47zzzgsj2znZAAAQAElEQVSs0WeeeWacdtppeR4hbrQXXHCB7rTtiLsjAQlIoLUJtJyyQgNMY8kkT1wWOnv8WF86G12svWbYsGExfPjwvCLOAgssUHva/Z4kYFoSkECPEGC+HUsKYw1G3pEoMhHXMNxkmWzPsdrAoAzz9A477LDYY489snUEJYOl38vCJFiaWahkyJAhseuuuwZpsVripptuGrPPPntWUkiHb6sw149FT1BoWEL5iiuuyCuS1d7XfQlIQAISaE0CLaesYFlhmU4aTiba4/JQffQoKPhq47pAw109l1Kq7ubfWGguvfTS3LgecMABIyeq5pP+kYAEJFDHBNZff/1ASWDiO9l8//338/LDK6+8cmCB5hgyEVlYtUTjqsVqiMRh5TC+R3XEEUfkVRK5hoB1BusNv/lGFSsn8l0qrt1iiy0CBYUljs8555xgy/daWBGMuGy5ziABCUhAAhJoOWWF0byNN944UFgY9Tv33HPz91EYTXzttdfytwHww6YhJi4NNKva0Jgz+sjIH6vZYEm57777YujQodkVYv/9949VV13VGiUBCUigYQjwjRSsIHfffXfg/sXHbZlPstVWW40sA5PkjznmmLxCWDnIIA8uXCgcnD/yyCOzy1c5zxZ5ybnTTz89z1M544wz8spjnEO5wXpTDSgxKDjMaSFPxDNIoAEJmGUJSKCHCbScsgI/JoEefPDBsfXWW8c111wTJ510UtCQ0vDyHQCWNF5kkUXy91douDlG48nIH/GvvPLK4DsCWGZoXBlR5FsrjDKSvkECEpBAIxBgCfdddtklzzlhZS4mwm+77bbBh3JL/vloI5ZmFA+sLFie77rrriwDscTwcVxkY4lPHKw1fGzypptuykshY3VG7pY4HW1xIVt88cXzt6w6Ou8xCUhAAhJoRQIRLamssOIMvtr77bdfoGgwiohVZMUVVwwmhjKBlBVtqBL4deOScOCBB+ZJoCg4a6+9do6Hj/bee+8da6yxxsgRQ64xSEACEmgEAsi3ddddN3bYYYf8UcbNNtss1ltvvWCyfMk/CgSWldVXXz1PnucaliZmMj2yk5XBGLQp8dkiP7GaYLnZfPPNoytFhWt22223GDx4cGB1Yd8gAQlIQAISgEBLKisUPKWUG2QmxC+33HJBYBlNXBBwRyAOAVcwGl1GIInL5Hx+Y31hoijWlGp8rjG0LgFLLoFGI4DygTxjda+555472K+WAeUB2Vjmn7AlPnKQb7Iw+FONn1LK352ab775YuDAgXnuS0qpGqXD37PMMkuefF+bXoeRPSgBCUhAAi1DoGWVlZZ5whZUAhKQQOMSMOcSkIAEJNDiBFRWWrwCWHwJSEACEpCABFqFgOWUQOMRUFlpvGdmjiUgAQlIQAISkIAEJNASBOpaWWmJJ2AhJSABCUhAAhKQgAQkIIEOCaisdIjFgxJoSgIWSgISkIAEJCABCTQUAZWVhnpcZlYCEpCABOqHgDmRgAQkIIHeJqCy0tuETV8CEpCABCQgAQlIoGsCxpBABwRUVjqA4iEJSEACEpCABCQgAQlIoP8JqKyM/TPwSglIQAISkIAEJCABCUigFwmorPQiXJOWgAS6Q8C4EpCABCQgAQlIoD0BlZX2PNyTgAQkIAEJNAcBSyEBCUigCQiorDTBQ7QIEpCABCQgAQlIQAK9S8DU+4eAykr/cPeuEpCABCQgAQlIQAISkEAXBFRWugDUuKfNuQQkIAEJSEACEpCABBqbgMpKYz8/cy8BCfQVAe8jAQlIQAISkECfE1BZ6XPk3lACEpCABCQgAQlIQAISGBMCKitjQsk4EpCABCQgAQlIQAISqF8CTZszlZWmfbQWTAISkIAEJCABCUhAAo1NQGWlsZ9f4+benEtAAhKQgAQkIAEJSKALAiorXQDytAQkIIFGIGAeJSABCUhAAs1IQGWlGZ+qZZKABCQgAQlIYFwIeK0EJFAnBFRW6uRBmA0JSEACEpCABCQgAQk0J4GxL5XKytiz80oJSEACEpCABCQgAQlIoBcJqKz0IlyTblwC5lwCEpCABCQgAQlIoP8JqKz0/zMwBxKQgASanYDlk4AEJCABCYwVAZWVscLmRRKQgAQkIAEJSKC/CHhfCbQOAZWV1nnWllQCEpCABCQgAQlIQAINRaBPlJVaIh9//HG8/fbb8eSTT8b//ve/ePfdd4Nj77//fnzxxRe10dvtf/nll/HBBx/EfffdF/fff3+7c53tkCZxuaY2Dve8++6748ILL8zp3XzzzTF8+PDaaD22/9Zbb8Wtt96ay99jiZqQBCQggbEkgHx877334vHHH4933nknPvvssy5TQg4jt5977rl46aWXsvyuvYg4yLtnn302Xnvttfj0009ro+R7EQeZy5Z24CuRPCABCUhAAi1NoM+VlUceeSROOeWUOPbYY+Oaa66Ja6+9Ns4444w4/fTT44ADDsiKS4zm3yeffBKXXXZZ7LjjjnH11VePJuaoUyg3Z511Vpx44onx+eefjzxBY3vyySfHvffeG/fcc08MGjQodt5557j++uu7VJpGJtLFD/JLo12iPfTQQ3HYYYfFE088UQ65lUB3CRhfAj1CAMXkzjvvjKOPPjpuuummOOmkk+K6666Ljz76qNP0UW6QmUcccURcfvnlceqpp+brXn/99ZHXEOdvf/tbDB06NK644or41a9+Fb/73e/aDdKgvFx66aVx5plnxj//+c8499xz47TTTouXX355ZDr+kIAEJCABCfSpsoIFY/DgwcFI3BZbbBHbb799bLPNNrHBBhvkRoxGsquRtQkmmCAWWWSRmHDCCTscqevokU4yySSx9dZbx49+9KMYMGDAyChXXXVVVng23HDD2GuvvWKrrbbKcdZYY41IKY2MNy4/sKLQIShpkHcYzDvvvOWQWwlIQAL9QuCZZ57Jisb888+fZeSKK64Y5513XrZcd5ah4SMsz0cddVQss8wyWX5vueWWcfvttwfHiqx74IEHAjm38sor5ziDRgwEMUh1/vnnjxww+ve//50VlLXWWiuQwaSDBRwFBkWms/v3znFTlYAEJCCBeiUwqufeyzl84YUXYv/998+jZj/+8Y9j0UUXjSmnnDIHOu677rprfOMb38hKy+iygrIxyyyz5OtGF696brzxxoullloqll122XZKCJYZzs0444wx66yzxj777BM/+clPYp555mkXr5pWd37fcMMNcfHFFwf3KNdNO+20sdpqq8U000xTDrmVgAQk0C8EsCJjyVh++eVjiimmyHIZeXXBBRd0mB8sJsi1559/Pr7zne9kObzAAgsEAzxYRVA2UFiwljOgtNJKK+U4yHaUknPOOSeefvrpnPYtt9yS3cNmmmmmmGyyyWK66aaLqaaaKrC+Y5HOkfwjAQk0JgFzLYEeJDCgB9PqNCncoHD5uuuuu2KTTTaJOeaYo50ykFLKDdUuu+wSWE6Iz1ySv//973HRRRfFX//613jjjTeC453dhEaUeTDMObnkkkviv//9b7a8cM2HH34YTz31VJ4rwvWkzXlGCGkUH3vssaxE4SJGQ/noo48SLQeO4XONuxojfvho0xhzknPsX3nllXnOC3NwOMZ5GmKUMubkkD75Z7QQVwksTK+++ipJ5IA16T//+U9Og7xzP9LhJGkRFwsN+Ubp437kscQhnkECEpBAdwkgl1EWGEThWpSFueaaK7uEMY+FY9VQ5CWybPzxx8+nUkrx9a9/Pbvw/utf/wquw7KCAoLiQyQGmVZZZZVAXiJ7OTbffPPFsGHDspsYbmfIN+TacsstFxNPPDFRDBKQgAQkIIHoE2WFxgs3ARok3AI6484oHIoMo3b77rtvVi4mn3zyYJSPeR7MMenoWhQVGkl8qJnIiWsDc09QdGhUUXZwOcNnmutRah588ME8mfSdd97JLg/4beOmsPnmm8dtt91GtBwYRWS+C/Ne8NNG2eIY98Qys9NOO+UJ/y+++GJsttlmgbKE8kHDyzVMGr3//vvzCCKN9M9//vPYe++9gzxyA+Iw2ogCQuNOHJQc7oEygpLFNVh8cFtj3g3lwK2NPJOGQQISkEB3CSCnUA4mnXTSKIoHygVWX5SKV155pcMkUTywxjAZv0TgGn4z0MKWOAwGIQfZJ8w888xZVjJgw/4666wTa6+9dp5L+Otf/zqOP/742GijjeK73/1uO2s0cQ0SkIAEJNC6BPpEWUHJoDNPQzj99NN3ShurCnGwRtDBp+FaffXVAwUHJQDrREcX07D+5je/CVwZmP+y6aabxsCBAwPLDI3w4osvnkfqsLBwPSOJ6623XuD+Rdh4442zaxauDFgxGD0kHvlgYijprr/++oHlZ6KJJsqrhqFIoAwxH4Z7fv/73w8aZib/MypIWqQ922yz5cYX17I555wz+3lTDq7nHlibUHpotHGT2H777YNGnQUI6Cxw/cILLxzkC2UPJeyggw7Klikmt5KGQQL1RcDcNAIB5DKDJSgryN2SZ9y3kE9VZaR6DnmKLPr973+f558wIMQgC3EYcGGACZmJ1YR5iAzsEJ8BIuTxxP9vNcHtbL/99ssuuL/85S/jxhtvjDXXXDO7jZGWQQISkIAEJACBPlFWUkp5pAyXrBiDf8xnYaQNX2isGUzERNHAJar2ctLEhQprDHNSUB5mn332wBqBdYYRPjr/uDeUa2mYaTA5R+A3DSeTTGlMU2qbXI8lA8UCFwfSxerDqmVMxCceFhKUGSxHNLTkEYUlpRQoNSmlPKGf9IlPI878nKj8Y8lkzs0999z5GhQpGmxGPLGccA35Z4vShtJDPvHvLqOYleT8KQEJSGCMCJTBIWRgSm0yjwuRs8hV5Bb71UDcb3/72/G9730vDj/88Nhtt93ihBNOyJPyiccCIsgzFjNBjjGwgpxkxbA//vGPWRFhAIa4BNxuV1hhhfjBD34QWMcPOeSQ7BrGOUOFgD8lIAEJtDCBAX1RdjraWFQYrcNCMLp70kiiWDC6d/DBBweKCD7U7HOu9lqOsboYCgNKCOfZlk495zk2ulDipDSqwSY+ywzTOJMe+ymlwMqB0sI+v1GkaIhRdsh3SYvzJXR0rJzj2y8ppWwp4VhKKVBYUI6Y68IxAvmgc5FSW9yUUo8tr0z6BglIoLUIlAVOGGTB+kHpkVUMuGBtmXrqqTn0lYB8wuUWyzIKCYMoWGhQQlZbbbUcH/l79tln55XGsDITZ9iwYXklRwajiMS8FgZ/WAkMywquvn/5y1/yCmFYYohjkIAEJNAbBEyzsQj0mbLC6BkdcKwVjNx1hIn5Jlgy+OYJI3Jcg3sVEzFTSl+5JKW2Y6TLtUxwr0biWyY0ouVYSm3xy35XWyadkm5ZvYb4uDzgdsa8F0YBcftiVHDVVVcNGngUMuKNaeAeb775Zp4/U65hZBIFiVXPyjG2dCTYGiQgAQmMK4GUUnzrW9/KKzAW5YAt81FQKLDednYPBmZQQlA0NQMqpwAACgBJREFUmJeHlRdLS/WaGWaYIcq8FAZ1GFDC4sLADun+4Q9/yBZ3LOgcY14ecwKxppMmcQwSkIAEJCCBPlFWUkrBnA/W8Ge1q1tuueUrVgHmtDCpnMbyjjvuyKvGsI4/ighzNxjtY/QPP2qsDAQ672wZxaOxY54HigUKBVYR5oLQ6ScOgeurjzyllH2uyzGUBH6TLlsUEO7HBHhW4ULJotHFXYH8/ulPfwrmxuDaReOKDziTVpnzQh5SSvnLztwX5YZtuQfpE2jMmRuDYlXui8KGEvPNb34zImKk1YUyxIh/bEsYset/CUhAAmNFALmMbEPGkgADJyz+wSR3LLkcQybjllod+OF4SilQUpCPLGPMt1RS+uqAEO6sWGGwlKPgpNQWh/SRk8hF0mOwhzYC9zPkNscMEpCABCQggT5RVsCMiwCrvTBixwgaZn9coJiYiZLCssALLbRQdoFiZRmsF1gujjzyyOzLTCNalBEaSCwejMDRsSdNfKj5YjKredFo0jAyhwX3MRpjVqB5+OGHg7ktKBO4WBFQbvCbRtFgAihbjqOksNQmK3zRGGPh4UOWxx13XGDpYQQRNwksK9yLODTqKDLMQ+E3/ttMoMdKhPKEwoVbG3nHCkRDzUgi8SgbbhF0CnCFYF4MihAdBNwnWFmHNEiXMqAcoeAwClqUHDgbJCCBLgh4eiQBlIPFFlssr7iIwsKqhFh0kUslEjKNxUVQOMoxBmWQbUcffXRetnivvfbKi5iU82xxL2NpZNzBWDgEqwrymHMEVlJk8AYZymAOch0Zt9FGG/kdKgAZJCABCUggE+gzZSWllP2Vf/vb3+ale1mS95hjjsmTM+mIY2FYYokl8hKajPb99Kc/zdYXXAT4WCOT5fGPpgPPvBdW4MLyQueeyexM4kQZYnI9vthM/MQygVsWI4WM6KHQoCzQ0Uch2WabbYLw+OOP5++yoEBwLywlWE4Y6UNZQtkgTRSuAw88MMgnLg74W7NaGSOEjEQeddRRse2228bSSy+dP3KGsoWLGKuALbjggvlbLihKNPxscYsgTfLNtwWYqIq1hnKSDg07ShZWoz322CNQuuhQoKwwyRVOlIcy5qfpHwlIQALdIMBcQmQagy+sqJhSCvaRWSUZ5qgwoMLCHgyMMMDEx25RYrbbbrvgg76sfFjiYylBkTn//POD+YTILgaRkNMlDlvSGzp0aHaBZfI9H6hEjhJQYohjaAwC5lICEpBAbxLoM2WlFIJROxQELBR00ocMGRKDBg0KFARcm4hHw0kDiDLDKBuWE0buWGIYpWHVVVcNlAqOLbXUUlnBoUPPt0fOPPPMoAFkoicNHoFlNGkwUYD4lgvpoxyxP3jw4GAZY+7BvWioGfHDtSyllJc35rsnjA7ik73kkktmP+uUUqBgYBEhH1xP/n72s5/lSfi4MbDC16GHHho77LBD0ClgoQAa9wMOOCBovPH7pswoZPvvv3+g7JDPddddNygPLOBFuUgXy86ss84ajIZyT/KOzzn3Iq5BAhKQQHcIpJTyR3qRebvvvnsebBk4cOBI11PSQj4hm7BUp5QCRYbBIqwlyECWKyZeCcg05CFW6S233DKw3DCgU85XtyhByEcs18hE5BluYNU4/paABCTQYgQsbg2BPldWuD8jbMzJYEleLBSsFkYDxzlCSimYq4KbFXE5h5UDS0NKbee4hg49DVtKbT7QxCVdrisd+JRSdk8gPoF0OMeWfQKNLdfyu4TSuKbUdj3poiil1HYv8km+UDjIB7/JH9fzm/Mppax0cK+UUpAmcYnDPavxOMYIJvchL1xPQNniXAnsw6bs8zulUXniGoMEJCCB7hBAnjCIg1xKqb08QeYg55BfpIl8Yh95WGQYx6uBdIjDtrM4xOcc98b1l/S4F8cNEpCABCQggUKgX5SVcnO3TUDAIkhAAhKQgAQkIAEJSKCXCKis9BJYk5WABCQwNgS8RgISkIAEJCCBUQRUVkax8JcEJCABCUhAAs1FwNJIQAINTkBlpcEfoNmXgAQkIAEJSEACEpBA3xDo+7uorPQ9c+8oAQlIQAISkIAEJCABCYwBAZWVMYBklMYlYM4lIAEJSEACEpCABBqXgMpK4z47cy4BCUigrwl4PwlIQAISkECfElBZ6VPc3kwCEpCABCQgAQkUAm4lIIGuCKisdEXI8xKQgAQkIAEJSEACEpBAvxDolrLSLzn0phKQgAQkIAEJSEACEpBASxLotrLyxRdfxOuvvx4vvfSSQQbWgXGrA/KTn3XAOmAdsA5YB6wDY1UHXnnllfjss8+aXoHptrLy7rvvxtChQ2Pfffc1yMA6YB2wDlgH6qgO2C7ZNlsHrAOtUwcGDx4czz//vMpKLYGPP/44brjhhrjwwgsNMrAOWAesA9YB64B1wDrQrHXActV13b700kvj7bffru2qN91+ty0r4403Xsw444wxyyyzxKyzzmqQgXXAOmAdsA5YB6wD1gHrgHWgD+sA/fDZZ589JphggqZTTmoL1G1lZbrppourrroqnnnmmRg+fHg9BfPi87AOWAesA9YB64B1wDpgHWj6OkA//NFHH42FF164tm/fdPvdVlaajoAFkoAEOiHgYQlIQAISkIAEJNC/BFRW+pe/d5eABCQggVYhYDklIAEJSKDbBFRWuo3MCyQgAQlIQAISkIAE+puA928NAiorrfGcLaUEJCABCUhAAhKQgAQajoDKSp89Mm8kAQlIQAISkIAEJCABCXSHgMpKd2gZVwISqB8C5kQCEpCABCQggaYnoLLS9I/YAkpAAhKQgAS6JmAMCUhAAvVIQGWlHp+KeZKABCQgAQlIQAISaGQC5r2HCKis9BBIk5GABCQgAQlIQAISkIAEepaAykrP8mzc1My5BCQgAQlIQAISkIAE6oyAykqdPRCzIwEJNAcBSyEBCUhAAhKQwLgTUFkZd4amIAEJSEACEpBA7xIwdQlIoEUJqKy06IO32BKQgAQkIAEJSEACrUqgccqtstI4z8qcSkACEpCABCQgAQlIoKUIqKy01ONu3MKacwlIQAISkIAEJCCB1iOgstJ6z9wSS0ACEpCABCQgAQlIoCEIqKw0xGMykxKQgAQkIAEJ1C8BcyYBCfQWAZWV3iJruhKQgAQkIAEJSEACEpBA9wlUrlBZqcDwpwQkIAEJSEACEpCABCRQPwRUVurnWZiTxiVgziUgAQlIQAISkIAEeoGAykovQDVJCUhAAhIYFwJeKwEJSEACEmgjoLLSxsG/EpCABCQgAQlIoDkJWCoJNDABlZUGfnhmXQISkIAEJCABCUhAAs1MoB6VlWbmbdkkIAEJSEACEpCABCQggTEkoLIyhqCMJoHGJWDOJSABCUhAAhKQQGMSUFlpzOdmriUgAQlIoL8IeF8JSEACEugzAv8HAAD///y3yvkAAAAGSURBVAMAKo9jHMlVHKIAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nomograms, DECT, Thyroid, Lymph node metastasis, Papillary thyroid carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-8628721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8628721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTo evaluate the predictive value of dual-energy computed tomography (DECT) iodine quantification combined with lymph node morphological characteristics for identifying metastatic lymph nodes in patients with papillary thyroid carcinoma (PTC).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThis retrospective study included 123 histologically confirmed PTC patients who underwent DECT between 2021 and 2023 as the derivation cohort. Among them, 78 patients were randomly selected for internal validation. An additional 47 patients scanned between 2023 and 2024 composed the external validation cohort. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of lymph node metastasis(LNM). A predictive model was then developed and validated using both internal and external datasets.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eMultivariate analysis revealed that the arterial phase iodine concentration (\u0026ge;\u0026thinsp;2.6 mg/mL), marked arterial enhancement, heterogeneous enhancement pattern, irregular shape, indistinct margins, and incomplete capsule of the primary thyroid nodule were independent predictors of LNM. A nomogram incorporating DECT-derived iodine metrics and CT-based morphological features was developed. In the internal validation cohort, the model achieved an area under the curve (AUC) of 0.992 (95% CI: 0.956\u0026ndash;0.984), with a cutoff value of 0.2, sensitivity of 98%, and specificity of 95%. In the external validation cohort, the AUC was 0.950 (95% CI: 0.893\u0026ndash;0.884), with a cutoff value of 0.486, sensitivity of 89%, and specificity of 88%.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eA predictive model combining DECT iodine concentration with CT-based morphological features provides high diagnostic accuracy for preoperative identification of metastatic lymph nodes in patients with PTC.\u003c/p\u003e","manuscriptTitle":"Dual-energy CT iodine mapping and lymph node characteristic parameters for distinguishing metastatic lymph nodes in papillary thyroid carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 16:12:08","doi":"10.21203/rs.3.rs-8628721/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-08T06:02:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310383724922848423551293612559640328045","date":"2026-03-07T03:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T02:15:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T02:11:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-22T08:20:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T14:28:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-01-21T14:15:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f593d06-e754-4262-acac-2629bd1c6462","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T16:12:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 16:12:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8628721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8628721","identity":"rs-8628721","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}