Using Boundary Element Method to Calculate the Bending Center of General Section

Using Siant — Venant bending theory, transverse bending of a general section cylinder, it comes down to solving two boundary integral equations of the same type, the bending function and additional torsion function of the cylinder are obtained. On this basis, using boundary element method to determine the bending center of general section. Finally, to illustrate the application of the method, a numerical example is given

Estimation of the Incubation Period and the Serial Interval of COVID-19 in Chongqing, China

In December 2019, the initial case of COVID-19's disease appeared in Wuhan, Hubei Province, China. But it soon caused an outbreak in Chongqing as well. Reasonable estimates for the incubation period distribution and the serial interval distribution would provide timely information to guide intervention policy for the government of Chongqing. We collect individual information from Chongqing Center for Disease Control and Prevention(CQCDC). We use doubly interval-censored data to estimate the incubation period and the serial interval for confirmed cases exposed to COVID-19 during incubation period,the mean incubation period is estimated to be 7.5(6.6-8.6,95% CI) days and the mean serial interval is estimated to be 6.1 (5.0-7.5,95% CI) days. The analysis result show that COVID-19 could spread in the incubation period,which may complicate quarantine work. The implementation of control measures is indispensable in reducing the spread of asymptomatic incubation period in high-risk population

Negative binomial mixed models for analyzing microbiome count data

Background: Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. Results: In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. Conclusions: We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data

Longitudinal biomarkers in amyotrophic lateral sclerosis

OBJECTIVE: To investigate neurodegenerative and inflammatory biomarkers in people with amyotrophic lateral sclerosis (PALS), evaluate their predictive value for ALS progression rates, and assess their utility as pharmacodynamic biomarkers for monitoring treatment effects. METHODS: De-identified, longitudinal plasma, and cerebrospinal fluid (CSF) samples from PALS (n = 108; 85 with samples from \u3e /=2 visits) and controls without neurological disease (n = 41) were obtained from the Northeast ALS Consortium (NEALS) Biofluid Repository. Seventeen of 108 PALS had familial ALS, of whom 10 had C9orf72 mutations. Additional healthy control CSF samples (n = 35) were obtained from multiple sources. We stratified PALS into fast- and slow-progression subgroups using the ALS Functional Rating Scale-Revised change rate. We compared cytokines/chemokines and neurofilament (NF) levels between PALS and controls, among progression subgroups, and in those with C9orf72 mutations. RESULTS: We found significant elevations of cytokines, including MCP-1, IL-18, and neurofilaments (NFs), indicators of neurodegeneration, in PALS versus controls. Among PALS, these cytokines and NFs were significantly higher in fast-progression and C9orf72 mutation subgroups versus slow progressors. Analyte levels were generally stable over time, a key feature for monitoring treatment effects. We demonstrated that CSF/plasma neurofilament light chain (NFL) levels may predict disease progression, and stratification by NFL levels can enrich for more homogeneous patient groups. INTERPRETATION: Longitudinal stability of cytokines and NFs in PALS support their use for monitoring responses to immunomodulatory and neuroprotective treatments. NFs also have prognostic value for fast-progression patients and may be used to select similar patient subsets in clinical trials

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO_2 Reduction

Bimetallic catalysts are promising for the most difficult thermal and electrochemical reactions, but modeling the many diverse active sites on polycrystalline samples is an open challenge. We present a general framework for addressing this complexity in a systematic and predictive fashion. Active sites for every stable low-index facet of a bimetallic crystal are enumerated and cataloged, yielding hundreds of possible active sites. The activity of these sites is explored in parallel using a neural-network-based surrogate model to share information between the many density functional theory (DFT) relaxations, resulting in activity estimates with an order of magnitude fewer explicit DFT calculations. Sites with interesting activity were found and provide targets for follow-up calculations. This process was applied to the electrochemical reduction of CO_2 on nickel gallium bimetallics and indicated that most facets had similar activity to Ni surfaces, but a few exposed Ni sites with a very favorable on-top CO configuration. This motif emerged naturally from the predictive modeling and represents a class of intermetallic CO_2 reduction catalysts. These sites rationalize recent experimental reports of nickel gallium activity and why previous materials screens missed this exciting material. Most importantly these methods suggest that bimetallic catalysts will be discovered by studying facet reactivity and diversity of active sites more systematically