Batchelorjohnson0945

ever high schools have higher rates.

We collected published records of COVID-19 school outbreaks globally to investigate the considerable heterogeneity in secondary attack rates (SAR) reported from school outbreaks and compiled information regarding potential risk factors.Higher community death and case rates are associated with higher SARs in children in school settings.Mask-wearing and social distancing are associated with lower SARs.When compared to pre-schools and early childhood education centers, primary schools have lower rates of transmission of SARS-CoV-2 however high schools have higher rates.Early detection of infectious disease is crucial for reducing transmission and facilitating early intervention. We built a real-time smartwatch-based alerting system for the detection of aberrant physiological and activity signals (e.g. resting heart rate, steps) associated with early infection onset at the individual level. Upon applying this system to a cohort of 3,246 participants, we found that alerts were generated for pre-symptomatic and asymptomatic COVID-19 infections in 78% of cases, and pre-symptomatic signals were observed a median of three days prior to symptom onset. Furthermore, by examining over 100,000 survey annotations, we found that other respiratory infections as well as events not associated with COVID-19 (e.g. stress, alcohol consumption, travel) could trigger alerts, albeit at a lower mean period (1.9 days) than those observed in the COVID-19 cases (4.3 days). Thus this system has potential both for advanced warning of COVID-19 as well as a general system for measuring health via detection of physiological shifts from personal baselines. The system is open-source and scalable to millions of users, offering a personal health monitoring system that can operate in real time on a global scale.The ongoing COVID-19 pandemic has highlighted the dearth of approved drugs to treat viral infections, with only ∼90 FDA approved drugs against human viral pathogens. To identify drugs that can block SARS-CoV-2 replication, extensive drug screening to repurpose approved drugs is underway. Here, we screened ∼18,000 drugs for antiviral activity using live virus infection in human respiratory cells. Dose-response studies validate 122 drugs with antiviral activity and selectivity against SARS-CoV-2. Amongst these drug candidates are 16 nucleoside analogs, the largest category of clinically used antivirals. This included the antiviral Remdesivir approved for use in COVID-19, and the nucleoside Molnupirivir, which is undergoing clinical trials. RNA viruses rely on a high supply of nucleoside triphosphates from the host to efficiently replicate, and we identified a panel of host nucleoside biosynthesis inhibitors as antiviral, and we found that combining pyrimidine biosynthesis inhibitors with antiviral nucleoside analogs synergistically inhibits SARS-CoV-2 infection in vitro and in vivo suggesting a clinical path forward.Protein complexes can be computationally identified from protein-interaction networks with community detection methods, suggesting new multi-protein assemblies. Most community detection algorithms tend to be un- or semi-supervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and by using parallel algorithms, respectively. PLB-1001 cell line Here, we present Super.Complex, a distributed supervised machine learning pipeline for community detection in networks. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities with epsilon-greedy and pseudo-metropolis criteria, and an embarrassingly us to better understand the association of protein and disease. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. In this work, we present Super.Complex, a generalizable and scalable supervised machine learning-based community detection algorithm that outperforms existing methods by accurately learning and using patterns from known communities. We propose 3 novel evaluation measures to compare learned and known communities, an outstanding issue. We use Super.Complex to identify 1028 human protein complexes, including 234 complexes linked to SARS-CoV-2, the virus causing COVID-19, and 103 complexes containing 111 uncharacterized proteins.

Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed integrative multi-omics analysis from genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.

First, we cluster gene co-expression networks and identify gene modules for various AD phenotypes given population gene expression data. Next, we predict the transcription factors (TFs) that significantly regulate the genes in each module and the AD risk variants (e.g., SNPs) interrupting the TF binding sites on the regulatory elements. Finally, we construct a full gene regulatory network linking SNPs, interrupted TFs, and regulatory elements to targe and AD phenotypes, including disease progression and Covid response. Our analysis is open-source available at https//github.com/daifengwanglab/ADSNPheno .With global vaccination efforts against SARS-CoV-2 underway, there is a need for rapid quantification methods for neutralizing antibodies elicited by vaccination and characterization of their strain dependence. Here, we describe a designed protein biosensor that enables sensitive and rapid detection of neutralizing antibodies against wild type and variant SARS-CoV-2 in serum samples. More generally, our thermodynamic coupling approach can better distinguish sample to sample differences in analyte binding affinity and abundance than traditional competition based assays.