A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spread of SARS-CoV-2; yet, rational vaccine design efforts are currently hampered by the lack of knowledge regarding viral epitopes targeted during an immune response, and the need for more in-depth knowledge on betacoronavirus immunology. To that end, we developed a computational workflow using a series of open-source algorithms and webtools to analyze the proteome of SARS-CoV-2 and identify putative T cell and B cell epitopes. Utilizing a set of stringent selection criteria to filter peptide epitopes, we identified 41 T cell epitopes (5 HLA class I, 36 HLA class II) and 6 B cell epitopes that could serve as promising targets for peptide-based vaccine development against this emerging global pathogen. To our knowledge, this is the first study to comprehensively analyze all 10 (structural, non-structural and accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for vaccine development.Minor allele frequency (MAF) of rs3782886 (BRAP) and rs671 (ALDH2) are reported to be inversely associated with blood pressure.  https://www.selleckchem.com/products/ve-822.html  Another study revealed that hematopoietic activity which is evaluated by reticulocytes could influenced on hypertension status partly by indicating activity of endothelial maintenance. Therefore, to evaluate the association between genetic factor and hypertension, influence of hematopoietic activity should be considered. A multi-faced analysis was performed in a simple general elderly population model (1,313 older Japanese aged 60-98 years). Participants were stratified by median values of reticulocytes (5.21 × 104 cells/μL for men and 4.65 × 104 cells/μL for women). Independent of known cardiovascular risk factors, MAF of rs3782886 and rs671 are significantly inversely associated with hypertension for participants with high hematopoietic activity (high reticulocytes level) (fully adjusted odds ratio (ORs) were 0.72 (0.55, 0.96) for rs3782886 and 0.72 (0.54, 0.96) for rs671) but not for low reticulocytes count (the corresponding values were 1.05 (0.79, 1.39) and 1.08 (0.81, 1.45), respectively). Hematopoietic activity evaluated by reticulocytes levels could influence on the association between single nucleotide polymorphism (rs3782886 and rs671) and hypertension. Those results were efficient tool to clarify the part of the mechanism that underlying the association between genetic factor and hypertension.Infections cause varying degrees of haemostatic dysfunction which can be detected by clot waveform analysis (CWA), a global haemostatic marker. CWA has been shown to predict poor outcomes in severe infections with disseminated intravascular coagulopathy. The effect of less severe bacterial and viral infections on CWA has not been established. We hypothesized that different infections influence CWA distinctively. Patients admitted with bacterial infections, dengue and upper respiratory tract viral infections were recruited if they had an activated partial thromboplastin time (aPTT) measured on admission. APTT-based CWA was performed on Sysmex CS2100i automated analyser using Dade Actin FSL reagent. CWA parameters [(maximum velocity (min1), maximum acceleration (min2) and maximum deceleration (max2)] were compared against control patients. Infected patients (n = 101) had longer aPTT than controls (n = 112) (34.37 ± 7.72 s vs 27.80 ± 1.59 s, p  0.05). CWA parameters demonstrated positive correlation with C-reactive protein levels (min1 r = 0.54, min2 r = 0.44, max2 r = 0.34; all p  less then  0.01). Different infections affect CWA distinctively. CWA could provide information on the haemostatic milieu triggered by infection and further studies are needed to better define its application in this area.The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.Oncogenic gene fusions are estimated to account for up-to 20% of cancer morbidity. Recently sequence-level studies have established oncofusions throughout all tissue types. However, the functional implications of the identified oncofusions have often not been investigated. In this study, identified oncofusions from a fusion detection approach (DEEPEST) were analyzed in detail. Of the 28,863 oncofusions, we found almost 30% are expected to produce functional proteins with features from both parent genes. Kinases and transcription factors were the main gene families of the protein producing fusions. Considering their role as initiators, actors, and termination points of cellular signaling pathways, we focused our in-depth analyses on them. Domain architecture of the fusions and their wild-type interactors suggests that abnormal molecular context of protein domains caused by fusion events may unlock the oncogenic potential of the wild type counterparts of the fusion proteins. To understand overall oncofusion effects, we performed differential expression analysis using TCGA cancer project samples.