ExonSkipAD provides twelve categories of annotations gene summary, gene structures and expression levels, exon skipping events with PSIs, ORF annotation, exon skipping events in the canonical protein sequence, 3'-UTR located exon skipping events lost miRNA-binding sites, SNversus in the skipped exons with a depth of coverage, AD stage-associated exon skipping events, splicing quantitative trait loci (sQTLs) in the skipped exons, correlation with RNA-binding proteins, and related drugs & diseases. ExonSkipAD will be a unique resource of transcriptomic diversity research for understanding the mechanisms of neurodegenerative disease development and identifying potential therapeutic targets in AD. Significance AS the first comprehensive resource of the functional genomics of the alternative splicing events in AD, ExonSkipAD will be useful for many researchers in the fields of pathology, AD genomics and precision medicine, and pharmaceutical and therapeutic researches.As the current worldwide outbreaks of the SARS-CoV-2, it is urgently needed to develop effective therapeutic agents for inhibiting the pathogens or treating the related diseases. Antimicrobial peptides (AMP) with functional activity against coronavirus could be a considerable solution, yet there is no research for identifying anti-coronavirus (anti-CoV) peptides with the computational approach. In this study, we first investigated the physiochemical and compositional properties of the collected anti-CoV peptides by comparing against three other negative sets antivirus peptides without anti-CoV function (antivirus), regular AMP without antivirus functions (non-AVP) and peptides without antimicrobial functions (non-AMP). Then, we established classifiers for identifying anti-CoV peptides between different negative sets based on random forest. Imbalanced learning strategies were adopted due to the severe class-imbalance within the datasets. The geometric mean of the sensitivity and specificity (GMean) under the identification from antivirus, non-AVP and non-AMP reaches 83.07%, 85.51% and 98.82%, respectively. Then, to pursue identifying anti-CoV peptides from broad-spectrum peptides, we designed a double-stages classifier based on the collected datasets. In the first stage, the classifier characterizes AMPs from regular peptides. It achieves an area under the receiver operating curve (AUCROC) value of 97.31%. The second stage is to identify the anti-CoV peptides between the combined negatives of other AMPs. Here, the GMean of evaluation on the independent test set is 79.42%. The proposed approach is considered as an applicable scheme for assisting the development of novel anti-CoV peptides. The datasets and source codes used in this study are available at https//github.com/poncey/PreAntiCoV.Shelterin, a protective complex at telomeres, plays essential roles in cancer. In addition to maintain telomere integrity, shelterin functions in various survival pathways. However, the detailed mechanisms of shelterin regulation in cancer remain elusive. Here, we perform a comprehensive analysis of shelterin in 9125 tumor samples across 33 cancer types using multi-omic data from The Cancer Genome Atlas, and validate some findings in Chinese Glioma Genome Atlas and cancer cell lines from Cancer Cell Line Encyclopedia. In the genomic landscape, we identify the amplification of TRF1 and POT1, co-amplification/deletion of TRF2-RAP1-TPP1 as the dominant alteration events. Clustering analysis based on shelterin expression reveals three cancer clusters with different degree of genome instability. To measure overall shelterin activity in cancer, we derive a shelterin score based on shelterin expression. Pathway analysis shows shelterin is positively correlated with E2F targets, while is negatively correlated with p53 pathway. Importantly, shelterin links to tumor immunity and predicts response to PD-1 blockade immune therapy. In-depth miRNA analysis reveals a miRNA-shelterin interaction network, with p53 regulated miRNAs targeting multiple shelterin components. We also identify a significant amount of lncRNAs regulating shelterin expression. In addition, we find shelterin expression could be used to predict patient survival in 24 cancer types. Finally, by mining the connective map database, we discover a number of potential drugs that might target shelterin. In summary, this study provides broad molecular signatures for further functional and therapeutic studies of shelterin, and also represents a systemic approach to characterize key protein complex in cancer.
  COVID-19, caused by the novel SARS-CoV-2, is considered the most threatening respiratory infection in the world, with over 40 million people infected and over 0.934 million related deaths reported worldwide. It is speculated that epidemiological and clinical features of COVID-19 may differ across countries or continents. Genomic comparison of 48,635 SARS-CoV-2 genomes has shown that the average number of mutations per sample was 7.23, and most SARS-CoV-2 strains belong to one of 3 clades characterized by geographic and genomic specificity Europe, Asia, and North America.
 
  The aim of this study was to compare the genomes of SARS-CoV-2 strains isolated from Italy, Sweden, and Congo, that is, 3 different countries in the same meridian (longitude) but with different climate conditions, and from Brazil (as an outgroup country), to analyze similarities or differences in patterns of possible evolutionary pressure signatures in their genomes.
 
  We obtained data from the Global Initiative on Sharing All Influenza Dequently, the protein product changes T (threonine) to G (glycine) in position 50 of the protein. This position is located close to the predicted transmembrane region. Mutation analysis revealed that the change from G (glycine) to D (aspartic acid) may confer a new function to the protein-binding activity, which in turn may be responsible for attaching the virus to human eukaryotic cells.  https://www.selleckchem.com/products/triptolide.html  These findings can help design in vitro experiments and possibly facilitate a vaccine design and successful antiviral strategies.The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy. This anxiety is further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stake-holders will feel obliged to cut corners on safety precautions, or ignore societal consequences just to "win". Starting from a baseline model that describes a broad class of technology races where winners draw a significant benefit compared to others (such as AI advances, patent race, pharmaceutical technologies), we investigate here how positive (rewards) and negative (punishments) incentives may beneficially influence the outcomes. We uncover conditions in which punishment is either capable of reducing the development speed of unsafe participants or has the capacity to reduce innovation through over-regulation. Alternatively, we show that, in several scenarios, rewarding those that follow safety measures may increase the development speed while ensuring safe choices.