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Nuclear receptor coactivators (NCOAs) and corepressors (NCORs) bind to nuclear hormone receptors in a ligand-dependent manner and mediate the transcriptional activation or repression of the downstream target genes in response to hormones, metabolites, xenobiotics, and drugs. NCOAs and NCORs are widely expressed in the mammalian brain. Studies using genetic animal models start to reveal pivotal roles of NCOAs/NCORs in the brain in regulating hormonal signaling, sexual behaviors, consummatory behaviors, exploratory and locomotor behaviors, moods, learning, and memory. Genetic variants of NCOAs or NCORs have begun to emerge from human patients with obesity, hormonal disruption, intellectual disability, or autism spectrum disorders. Here we review recent studies that shed light on the function of NCOAs and NCORs in the central nervous system.Objective In an effort to improve the efficiency of computer algorithms applied to screening for COVID-19 testing, we used natural language processing (NLP) and artificial intelligence (AI)-based methods with unstructured patient data collected through telehealth visits. Methods After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients’ self-reported symptoms. Results Text analytics revealed that concepts such as “smell” and “taste” were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an AUC of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling. Discussion Informatics tools such as NLP and AI methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.Motivation Understanding an enzyme’s function is one of the most crucial problem domains in computational biology. Enzymes are a key component in all organisms and many industrial processes as they help in fighting diseases and speed up essential chemical reactions. They have wide applications and therefore, the discovery of new enzymatic proteins can accelerate biological research and commercial productivity. Biological experiments, to determine an enzyme’s function, are time-consuming and resource expensive. Results In this study, we propose a novel computational approach to predict an enzyme’s function up to the fourth level of the Enzyme Commission (EC) Number. Many studies have attempted to predict an enzyme’s function. Yet, no approach has properly tackled the fourth and final level of the EC number. The fourth level holds great significance as it gives us the most specific information of how an enzyme performs its function. Our method uses innovative deep learning approaches along with an efficient hierarchical classification scheme to predict an enzyme’s precise function. check details On a dataset of 11,353 enzymes and 402 classes, we achieved a hierarchical accuracy and Macro-F1 score of 91.2% and 81.9%, respectively, on the 4th level. Moreover, our method can be used to predict the function of enzyme isoforms with considerable success. This methodology is broadly applicable for genome-wide prediction that can subsequently lead to automated annotation of enzyme databases and the identification of better/cheaper enzymes for commercial activities. Availability The web-server can be freely accessed at http//hecnet.cbrlab.org/. Supplementary information Supplementary data are available at Bioinformatics online.Background and objectives Nursing homes pose unique challenges for implementation of research and quality improvement (QI). We previously demonstrated successful implementation of a nursing home-led intervention to improve relationships between frontline staff and residents in 6 U.S. Department of Veterans Affairs (VA) Community Living Centers (CLCs). This article discusses early adaptations made to the intervention and its implementation to enhance frontline staff participation. Research design and methods This is a formative evaluation of intervention implementation at the first 2 participating CLCs. Formative evidence-including site visitors’ field notes, implementation facilitation records, and semistructured frontline staff interviews-were collected throughout the study period. Data analysis was informed by the Capability, Opportunity, Motivation, and Behavior model of behavior change. Results Adaptations were made to 5 a priori intervention implementation strategies (a) training leaders, (b) training frontline staff, (c) adapting the intervention to meet local needs, (d) auditing and providing feedback, and (e) implementation facilitation. On the basis of a 6-month implementation period at the first CLC, we identified elements of the intervention and aspects of the implementation strategies that could be adapted to facilitate frontline staff participation at the second CLC. Discussion and implications Incremental implementation, paired with ongoing formative evaluation, proved critical to enhancing capability, opportunity, and motivation among frontline staff. In elucidating what was required to initiate and sustain the nursing home-led intervention, we provide a blueprint for responding to emergent challenges when performing research and QI in the nursing home setting.Microbial metabarcoding is the standard approach to assess communities’ diversity. However reports are often limited to simple OTU abundances for each phylum, giving rather one-dimensional views of microbial assemblages, overlooking other accessible aspects. The first is masked by databases incompleteness; OTU picking involves clustering at 97% (near-species) sequence identity, but different OTUs regularly end up under a same taxon name. When expressing diversity as number of obtained taxonomical names, a large portion of the real diversity lying within the data remains underestimated. Using the 16S sequencing results of an environmental transect across a gradient of 17 coastal habitats we first extracted the number of OTUs hidden under the same name. Further, we observed which was the deepest rank yielded by annotation, revealing for which microbial groups are we missing most knowledge. Data were then used to infer an evolutionary aspect what is, in each phylum the success of the present time individuals (abundances for each OTU) in relation to their prior evolutionary success in differentiation (number of OTUs).