Browneshaffer4338

The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. selleckchem To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http//camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.Nowadays, advances in high-throughput sequencing benefit the increasing application of genomic prediction (GP) in breeding programs. In this research, we designed a Cosine kernel-based KRR named KCRR to perform GP. This paper assessed the prediction accuracies of 12 traits with various heritability and genetic architectures from four populations using the genomic best linear unbiased prediction (GBLUP), BayesB, support vector regression (SVR), and KCRR. On the whole, KCRR performed stably for all traits of multiple species, indicating that the hypothesis of KCRR had the potential to be adapted to a wide range of genetic architectures. Moreover, we defined a modified genomic similarity matrix named Cosine similarity matrix (CS matrix). The results indicated that the accuracies between GBLUP_kinship and GBLUP_CS almost unanimously for all traits, but the computing efficiency has increased by an average of 20 times. Our research will be a significant promising strategy in future GP.

Sars-CoV-2 outbreaks resulted in a high case fatality rate in nursing homes (NH) worldwide. It is unknown to which extent presymptomatic residents and staff contribute to the spread of the virus.

To assess the contribution of asymptomatic and presymptomatic residents and staff in SARS-CoV-2 transmission during a large outbreak in a Dutch NH.

Observational study in a 185-bed NH with two consecutive testing strategies testing of symptomatic cases only, followed by weekly facility-wide testing of staff and residents regardless of symptoms. Nasopharyngeal and oropharyngeal testing with RT-PCR for SARs-CoV-2, including sequencing of positive samples, was conducted with a standardized symptom assessment.

185 residents and 244 staff participated. Sequencing identified one cluster. In the symptom-based test strategy period 3/39 residents were presymptomatic versus 38/74 residents in the period of weekly facility-wide testing (p-value<0.001). In total, 51/59 (91.1%) of SARS-CoV-2 positive staff was symptoma identification of SARS-Cov-2 cases, resulting in fast mitigation of the outbreak.

In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data.

In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA-disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction pirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.

Due to the inherent stability and close relationship with the progression of diseases, circRNAs are serving as important biomarkers and drug targets. Efficient predictors for identifying circRNA-disease associations are highly required. The existing predictors consider circRNA-disease association prediction as a classification task or a recommendation problem, failing to capture the ranking information among the associations and detect the diseases associated with new circRNAs. However, more and more circRNAs are discovered. Identification of the diseases associated with these new circRNAs remains a challenging task.

In this study, we proposed a new predictor called iCricDA-LTR for circRNA-disease association prediction. Different from any existing predictor, iCricDA-LTR employed a ranking framework to model the global ranking associations among the query circRNAs and the diseases. The Learning to Rank (LTR) algorithm was employed to rank the associations based on various predictors and features in a supervised manner.