ially relevant for outcome of comprehensive stroke center patients, whereas door-to-groin time was much shorter in primary stroke center patients.Clinical Trial Registration https//clinicaltrials.gov/ct2/show/NCT03356392. Unique identifier NCT03356392. Post-stroke cognitive impairment can occur after damage to various brain regions, and cognitive deficits depend on infarct location. The Mini-Mental State Examination (MMSE) is still widely used to assess post-stroke cognition, but it has been criticized for capturing only certain cognitive deficits. Along these lines, it might be hypothesized that cognitive deficits as measured with the MMSE primarily involve certain infarct locations. This comprehensive lesion-symptom mapping study aimed to determine which acute infarct locations are associated with post-stroke cognitive impairment on the MMSE. We examined associations between impairment on the MMSE (<5th percentile; normative data) and infarct location in 1198 patients (age 67 ± 12years, 43% female) with acute ischemic stroke using voxel-based lesion-symptom mapping. As a frame of reference, infarct patterns associated with impairments in individual cognitive domains were determined, based on a more detailed neuropsychological assessment. Impairlocations.Suicide prevention begins with understanding depression and mental health protection.Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. This is a tedious and time consuming process and also involves a lot of monetary resources. The faster approval of a drug helps the patients who are in need of the drug. The in silico prediction of Adverse Drug Reactions can help speed up the aforementioned process. The challenges involved are lack of negative data present and predicting ADR from just the chemical structure. check details Although many models are already available to predict ADR, most of the models use biological activities identifiers, chemical and physical properties in addition to chemical structures of the drugs. But for most of the new drugs to be tested, only chemical structures will be available. The performance of the existing models predicting ADR only using chemical structures is not efficient. Therefore, an efficient prediction of ADRs from just the chemical structure has been proposed in this paper. The proposed method involves a separate model for each ADR, making it a binary classification problem. This paper presents a novel CNN model called Drug Convolutional Neural Network (DCNN) to predict ADRs using chemical structures of the drugs. The performance is measured using the metrics such as Accuracy, Recall, Precision, Specificity, F1 score, AUROC and MCC. The results obtained by the proposed DCNN model outperform the competing models on the SIDER4.1 database in terms of all the metrics. A case study has been performed on a COVID-19 recommended drugs, where the proposed model predicted the ADRs that are well aligned with the observations made by medical professionals using conventional methods.Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance-covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.FASTA data sets of short reads are usually generated in tens or hundreds for a biomedical study. However, current compression of these data sets is carried out one-by-one without consideration of the inter-similarity between the data sets which can be otherwise exploited to enhance compression performance of de novo compression. We show that clustering these data sets into similar sub-groups for a group-by-group compression can greatly improve the compression performance. Our novel idea is to detect the lexicographically smallest k-mer (k-minimizer) for every read in each data set, and uses these k-mers as features and their frequencies in every data set as feature values to transform these huge data sets each into a characteristic feature vector. Unsupervised clustering algorithms are then applied to these vectors to find similar data sets and merge them. As the amount of common k-mers of similar feature values between two data sets implies an excessive proportion of overlapping reads shared between the two data sets, merging similar data sets creates immense sequence redundancy to boost the compression performance. Experiments confirm that our clustering approach can gain up to 12% improvement over several state-of-the-art algorithms in compressing reads databases consisting of 17-100 data sets (48.57-197.97[Formula see text]GB).Background The COVID-19 pandemic shows variable dynamics in WHO Regions, with lowest disease burden in the Western-Pacific Region. While China has been able to rapidly eliminate transmission of SARS-CoV-2, Germany - as well as most of Europe and the Americas - is struggling with high numbers of cases and deaths. Objective We analyse COVID-19 epidemiology and control strategies in China and in Germany, two countries which have chosen profoundly different approaches to deal with the epidemic. Methods In this narrative review, we searched the literature from 1 December 2019, to 4 December 2020. Results China and several neighbours (e.g. Australia, Japan, South Korea, New Zealand, Thailand) have achieved COVID-19 elimination or sustained low case numbers. This can be attributed to (1) experience with previous coronavirus outbreaks; (2) classification of SARS-CoV-2 in the highest risk category and consequent early employment of aggressive control measures; (3) mandatory isolation of cases and contacts in institutions; (4) broad employment of modern contact tracking technology; (5) travel restrictions to prevent SARS-CoV-2 re-importation; (6) cohesive communities with varying levels of social control.