Silvacrane7314

For any given bacteriophage genome or phage-derived sequences in metagenomic data sets, we are unable to assign a function to 50-90% of genes, or more. Structural protein-encoding genes constitute a large fraction of the average phage genome and are among the most divergent and difficult-to-identify genes using homology-based methods. To understand the functions encoded by phages, their contributions to their environments, and to help gauge their utility as potential phage therapy agents, we have developed a new approach to classify phage ORFs into ten major classes of structural proteins or into an "other" category. The resulting tool is named PhANNs (Phage Artificial Neural Networks). We built a database of 538,213 manually curated phage protein sequences that we split into eleven subsets (10 for cross-validation, one for testing) using a novel clustering method that ensures there are no homologous proteins between sets yet maintains the maximum sequence diversity for training. An Artificial Neural Network ensemble trained on features extracted from those sets reached a test F1-score of 0.875 and test accuracy of 86.2%. PhANNs can rapidly classify proteins into one of the ten structural classes or, if not predicted to fall in one of the ten classes, as "other," providing a new approach for functional annotation of phage proteins. PhANNs is open source and can be run from our web server or installed locally.Mutations in the gene rhodopsin are one of the major causes of autosomal dominant retinitis pigmentosa (adRP). Mutant forms of Rhodopsin frequently accumulate in the endoplasmic reticulum (ER), cause ER stress, and trigger photoreceptor cell degeneration. Here, we performed a genome-wide screen to identify suppressors of retinal degeneration in a Drosophila model of adRP, carrying a point mutation in the major rhodopsin, Rh1 (Rh1G69D). We identified two novel E3 ubiquitin ligases SORDD1 and SORDD2 that effectively suppressed Rh1G69D-induced photoreceptor dysfunction and retinal degeneration. SORDD1/2 promoted the ubiquitination and degradation of Rh1G69D through VCP (valosin containing protein) and independent of processes reliant on the HRD1 (HMG-CoA reductase degradation protein 1)/HRD3 complex. We further demonstrate that SORDD1/2 and HRD1 function in parallel and in a redundant fashion to maintain rhodopsin homeostasis and integrity of photoreceptor cells. These findings identify a new ER-associated protein degradation (ERAD) pathway and suggest that facilitating SORDD1/2 function may be a therapeutic strategy to treat adRP.Global efforts to control morbidity associated with soil-transmitted helminth infections (STH) have focused largely on the targeted treatment of high-risk groups, including children and pregnant women. However, Tolinapant is not clear when such programs can be discontinued and there are concerns about the sustainability of current STH control programs. The DeWorm3 project is a large multi-country community cluster randomized trial in Benin, India and Malawi designed to determine the feasibility of interrupting the transmission of STH using community-wide delivery of mass drug administration (MDA) with anthelmintics over multiple rounds. Here, we present baseline data and estimate key epidemiological parameters important in determining the likelihood of transmission interruption in the DeWorm3 trial. A baseline census was conducted in October-December 2017 in India, November-December 2017 in Malawi and in January-February 2018 in Benin. The baseline census enumerated all members of each household and collected demogratively stable across age groups in Benin. These data demonstrate the significant variability between the sites in terms of demography, socio-economic status and environmental characteristics. In addition, the baseline prevalence and intensity data from DeWorm3 suggest that each site has unique epidemiologic characteristics that will be critical in determining correlates of achieving STH transmission interruption in the DeWorm3 trial. Trial registration The trial was registered at ClinicalTrials.gov (NCT03014167).

Public health evaluation methods have been criticized for being overly reductionist and failing to generate suitable evidence for public health decision-making. A "complex systems approach" has been advocated to account for real world complexity. Qualitative methods may be well suited to understanding change in complex social environments, but guidance on applying a complex systems approach to inform qualitative research remains limited and underdeveloped. #link# This systematic review aims to analyze published examples of process evaluations that utilize qualitative methods that involve a complex systems perspective and proposes a framework for qualitative complex system process evaluations.

We conducted a systematic search to identify complex system process evaluations that involve qualitative methods by searching electronic databases from January 1, 2014-September 30, 2019 (Scopus, MEDLINE, Web of Science), citation searching, and expert consultations. Process evaluations were included if they self-identifiedadaptive evaluation approach.

This study found no consensus on what bringing a complex systems perspective to public health process evaluations with qualitative methods looks like in practice and that many studies of this nature describe static systems at a single time point. We suggest future studies use a 2-phase framework for qualitative process evaluations that seek to assess changes over time from a complex systems perspective. The first phase involves producing a description of the system and identifying hypotheses about how the system may change in response to the intervention. The second phase involves following the pathway of emergent findings in an adaptive evaluation approach.Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.