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© 2020 Her Majesty the Queen in Right of Canada Lipids © 2020 AOCS.As understanding the nature of brain networks through dynamic functional connectivity (dFC) estimation is of paramount significant, the introduction and revision of blood-oxygen-level dependent (BOLD) signal simulation methods in brain regions and dFC estimation methods have gained significant ground in recent years. Based on the observation of BOLD signals with multivariate nonnormal distribution in functional magnetic resonance imaging (fMRI) images, we first propose a copula-based method for the production of these signals, in which nonnormal data are generated with a selected time-varying covariance matrix. Therefore, we can compare the performance of models in the cases where brain signals have a multivariate nonnormal distribution. Then, two kendallized exponentially weighted moving average (KEWMA) and kendallized dynamic conditional correlation (KDCC) multivariate volatility models are introduced which are based on two well-known and commonly used exponentially weighted moving average (EMWA) and dynamic conditional correlation (DCC) models. The results show that KDCC model can estimate conditional correlation significantly far better than the former ones (ie, DCC, standardized dynamic conditional correlation, EWMA, and standardized exponentially weighted moving average) on both types of data (ie, multivariate normal and nonnormal). In the next step, the bivariate normal distribution in Iranian resting state fMRI data is confirmed by using statistical tests, and it is shown that the dynamic nature of FC is not optimally detected using prevalent methods. Two alternative Portmanteau and rank-based tests are proposed for the examination of conditional heteroscedasticity in data. Finally, dFC in these data is estimated by employing the KDCC model. © 2020 John Wiley & Sons, Ltd.BACKGROUND There is a paucity of contemporary data assessing the implications of atrial fibrillation (AF) on major adverse cardiovascular events (MACE) in patients with or at high-risk for atherosclerotic disease managed in routine practice. HYPOTHESIS We sought to evaluate the 4-year incidence of MACE in patients with or at risk of atherosclerotic disease in the presence of AF. METHODS Using US MarketScan data, we identified AF patients ≥45 years old with billing codes indicating established coronary artery disease, cerebrovascular disease, or peripheral artery disease or the presence of ≥3 risk factors for atherosclerotic disease from January 1, 2013 to December 31, 2013 with a minimum of 4-years of available follow-up. We calculated the 4-year incidence of MACE (cardiovascular death or hospitalization with a primary billing code for myocardial infarction or ischemic stroke). Patients were further stratified by CHA2 DS2 -VASc score and oral anticoagulation (OAC) use at baseline. RESULTS We identified 625,951 patients with 4-years of follow-up, of which 77,752 (12.4%) had comorbid AF. The median (25%, 75% range) CHA2 DS2 -VASc score was 4 (3, 5) and 64% of patients received an OAC at baseline. The incidence of MACE increased as CHA2 DS2 -VASc scores increased (P-interaction less then .0001 for all). AF patients receiving an OAC were less likely to experience MACE (8.9% vs 11.6%, P less then  .0001) including ischemic stroke (5.4% vs 6.7%, P less then  .0001). CONCLUSION Comorbid AF carries a substantial risk of MACE in patients with or at risk of atherosclerotic disease. MACE risk increases with higher CHA2 DS2 -VASc scores and is more likely in patients without OAC. © 2020 The Authors. Clinical Cardiology published by Wiley Periodicals, Inc.AIMS To assess the impact of periodontal treatment on systemic inflammation in type 2 diabetes. MATERIALS AND METHODS Adults with type 2 diabetes (n=83) and without diabetes (controls, n=75) were recruited, and participants with periodontitis received periodontal treatment and 12 months' follow-up. Biomarkers for periodontal inflammation (gingival crevicular fluid interleukin-6, tumour necrosis factor-α, interleukin-1β, interferon-γ, matrix metalloproteinase-8, matrix metalloproteinase-9, adiponectin) and serum markers of inflammation and diabetes control (glycated haemoglobin, high sensitivity C-reactive protein, interleukin-6, tumour necrosis factor-α, interleukin-1β, interferon-γ, leptin, adiponectin) were measured. Structural equation modelling was used to evaluate periodontal treatment effects on oral and systemic inflammation. RESULTS Periodontal treatment resulted in significant improvements in clinical status and reductions in gingival crevicular fluid biomarkers from baseline to month 12. GSK805 cell line Structural equation modelling identified that, at baseline, individuals with diabetes and periodontitis had significantly higher systemic inflammation than non-diabetic controls with periodontitis (Δ=0.20, p=0.002), with no significant differences between groups for oral inflammation. There was a greater reduction in systemic inflammation following periodontal treatment in individuals with diabetes and periodontitis compared to those with periodontitis but not diabetes (Δ=-0.25, p=0.01). CONCLUSIONS Diabetes and periodontitis together appear to increase systemic inflammation, with evidence of reductions following periodontal treatment. This article is protected by copyright. All rights reserved.Multiple comparison adjustments have a long history, yet confusion remains about which procedures control type 1 error rate in a strong sense and how to show this. Part of the confusion stems from a powerful technique called the closed testing principle, whose statement is deceptively simple, but is sometimes misinterpreted. This primer presents a straightforward way to think about multiplicity adjustment. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.A HPLC-DAD/ESI-MS method has been developed and validated for the analysis of the most representative phenolic compounds in extra-virgin olive oil (EVOO) samples using a green extraction approach based on deep eutectic solvents (DESs) at room temperature. We examined ten DESs based on choline chloride and betaine in combination with different hydrogen bond donors comprising six alcohols, two organic acids, and one urea. Five phenolic compounds, belonging to the classes of secoiridoids and phenolic alcohols, were selected for the evaluation of extraction efficiency. A betaine-based DES with glycerol (molar ratio 12) was found to be the most effective for extracting phenolic compounds as compared to a conventional solvent. The optimization of the extraction method involved the study of the quantity of water to be added to the DES and evaluation of the sample-to-solvent ratio optimal condition. Thirty percent of water added to DES and sample to solvent ratio 11 (w/v) were selected as the best conditions. The chromatographic method was validated by studying LOD, LOQ, intraday and interday retention time precision, and linearity range.

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