Braunhancock8492

Motivation Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. Availability and implementation The R package starnet is available on GitHub https//github.com/rauschenberger/starnet. Supplementary information Supplementary data are available at Bioinformatics online.Chromosomal inversions are among the primary drivers of genome structure evolution in a wide range of natural populations. While there is an impressive array of theory and empirical analyses that have identified conditions under which inversions can be positively selected, comparatively little data is available on the fitness impacts of these genome structural rearrangements themselves. Because inversion breakpoints can disrupt functional elements and alter chromatin domains, the precise positioning of an inversion's breakpoints can strongly affect its fitness. Here, we compared the fine-scale distribution of low frequency inversion breakpoints with those of high frequency inversions and inversions that have gone to fixation between Drosophila species. We identified a number of differences among frequency classes that may influence inversion fitness. In particular, breakpoints that are proximal to insulator elements, generate large tandem duplications, and minimize impacts on gene coding spans are more prevalent in high frequency and fixed inversions than in rare inversions. The data suggest that natural selection acts to preserve both genes and larger cis-regulatory networks in the occurrence and spread of rearrangements. These factors may act to limit the availability of high fitness arrangements when suppressed recombination is favorable.Motivation Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g., X-ray crystallography, nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the secondary structure of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of secondary structure contains more useful information and is much more challenging than the Q3 prediction. Results We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception (Deep3I) network in order to effectively capture both the short-range and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step towards the accurate and reliable prediction of secondary structures of proteins. Availability SAINT is freely available as an open source project at https//github.com/SAINTProtein/SAINT. Supplementary information Supplementary data are available at Bioinformatics online.Objectives To summarize studies on prescribing medicine to general outpatients through the WHO/International Network for Rational Use of Drugs (INRUD) prescribing indicators with a focus on antibiotic prescription. Methods A systematic review and random-effects meta-analysis of studies on the WHO prescribing indicators with a focus on the percentage of encounters with antibiotics prescribed (PEAP) was performed. The databases PubMed, Web of Science, EMBASE and Global Index Medicus were searched. Results Twenty-six studies with a total of over 34 000 prescription encounters were included in the systematic review, showing a mean of two medicines per encounter. In each meta-analysis, a range of 19 to 25 studies was included. The percentages of medicines prescribed with an international non-proprietary name (INN) and from the essential medicines list (EML) were 91% and 96% of the total number of medicines, respectively, while 19% of encounters contained injections. Studies with over 25 000 prescription encounters reported an average PEAP of 58% and PEAP showed an increasing trend over the years included in this review. Multivariable meta-regression showed that PEAP increased with the average number of medicines per encounter (estimate = 0.83, P value = 0.0005). The number of medicines, study design and year of prescription explained over 40% of the variation in PEAP across studies. Conclusions Patterns of medicine use within and close to the WHO reference values were reported for the number of medicines, INN prescribing, prescription of injections and compliance with the EML, on average. Prescription of antibiotics requires attention as amounts much higher than the reference values were prescribed, which were even higher with polypharmacy and increasing over the years included in this review.Summary Biological pathways are fundamental for learning about healthy and disease states. Many existing formats support automatic software analysis of biological pathways, for examples BioPAX (Biological Pathway Exchange). Although some algorithms are available as web application or standalone tools, no general graphical application for the parsing of BioPAX pathway data exists. Also, very few tools can perform Pathway Enrichment Analysis (PEA) using pathway encoded in the BioPAX format. To fill this gap we introduce BiP, an automatic and graphical software tool aimed at performing the parsing and accessing of BioPAX pathway data, along with pathway enrichment analysis by using information coming from pathways encoded in BioPAX. Availability BiP is freely available for academic and non-profit organizations at https//gitlab.com/giuseppeagapito/bip under the LGPL 2.1, the GNU Lesser General Public License. Supplementary information Supplementary data are available at Bioinformatics online.Background The ongoing outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a challenge for worldwide public health. A reliable laboratory assay is essential both to confirm suspected patients and to exclude patients infected with other respiratory viruses, thereby facilitating the control of global outbreak scenarios. Content In this review, we focus on the genomic, transmission and clinical characteristics of SARS-CoV-2, and comprehensively summarize the principles and related details of assays for SARS-CoV-2. We also explore the quality assurance measures for these assays. Summary SARS-CoV-2 has some unique gene sequences and specific transmission and clinical features that can inform the conduct of molecular and serological assays in many aspects, including the design of primers, the selection of specimens and testing strategies at different disease stages. Appropriate quality assurance measures for molecular and serological assays are needed to maintain testing proficiency. Because serological assays have the potential to identify later stages of the infection and to confirm highly suspected cases with negative molecular assay results, a combination of these two assays is needed to achieve a reliable capacity to detect SARS-CoV-2.Anurans (frogs and toads) have a unique pelvic and hindlimb skeleton among tetrapods. Although their distinct body plan is primarily associated with saltation, anuran species vary in their primary locomotor mode (e.g., walkers, hoppers, jumpers, and swimmers) and are found in a wide array of microhabitats (e.g., burrowing, terrestrial, arboreal, and aquatic) with varying functional demands. Given their largely conserved body plan, morphological adaptation to these diverse niches likely results from more fine-scale morphological change. Our study determines how shape differences in Anura's unique pelvic and hindlimb skeletal structures vary with microhabitat, locomotor mode, and jumping ability. SAHA price Using microCT scans of preserved specimens from museum collections, we added 3D landmarks to the pelvic and hindlimb skeleton of 230 anuran species. In addition, we compiled microhabitat and locomotor data from the literature for these species that span 52 of the 55 families of frogs and ~210 million years of anuran evolution. Using this robust dataset, we examine the relationship between pelvic and hindlimb morphology and phylogenetic history, allometry, microhabitat, and locomotor mode. We find pelvic and hindlimb changes associated with shifts in microhabitat ("ecomorphs") and locomotor mode ("locomorphs") and directly relate those morphological changes to the jumping ability of individual species. We also reveal how individual bones vary in evolutionary rate and their association with phylogeny, body size, microhabitat, and locomotor mode. Our findings uncover previously undocumented morphological variation related to anuran ecological and locomotor diversification and link that variation to differences in jumping ability among species.Blockade antibodies of the immunoinhibitory receptor PD-1 can stimulate the anti-tumor activity of T cells, but clinical benefit is limited to a fraction of patients. Evidence suggests that BTLA, a receptor structurally related to PD-1, may contribute to resistance to PD-1 targeted therapy, but how BTLA and PD-1 differ in their mechanisms is debated. Here, we compared the abilities of BTLA and PD-1 to recruit effector molecules and to regulate T cell signaling. While PD-1 selectively recruited SHP2 over the stronger phosphatase SHP1, BTLA preferentially recruited SHP1 to more efficiently suppress T cell signaling. Contrary to the dominant view that PD-1 and BTLA signal exclusively through SHP1/2, we found that in SHP1/2 double-deficient primary T cells, PD-1 and BTLA still potently inhibited cell proliferation and cytokine production, albeit more transiently than in wild type T cells. Thus, PD-1 and BTLA can suppress T cell signaling through a mechanism independent of both SHP1 and SHP2.