Analysis of Multidrug Resistance in Staphylococcus aureus with a Machine Learning-Generated Antibiogram.

Multidrug resistance (MDR) surveillance consists of reporting MDR prevalence and MDR phenotypes. Detailed knowledge of the specific associations underlying MDR patterns can allow antimicrobial stewardship programs to accurately identify clinically relevant resistance patterns. We applied machine learning and graphical networks to quantify and visualize associations between resistance traits in a set of 1,091 isolates collected from one New York hospital between 2008 and 2018.

Corrigendum: Shared Multidrug Resistance Patterns in Chicken-Associated Identified by Association Rule Mining.

[This corrects the article DOI: 10.3389/fmicb.2019.

A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models.

A new empirical Bayes approach to variable selection in the context of generalized linear models is developed. The proposed algorithm scales to situations in which the number of putative explanatory variables is very large, possibly much larger than the number of responses. The coefficients in the linear predictor are modeled as a three-component mixture allowing the explanatory variables to have a random positive effect on the response, a random negative effect, or no effect.

Shared Multidrug Resistance Patterns in Chicken-Associated Identified by Association Rule Mining.

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S.

Human Salivary Amylase Gene Copy Number Impacts Oral and Gut Microbiomes.

Host genetic variation influences microbiome composition. While studies have focused on associations between the gut microbiome and specific alleles, gene copy number (CN) also varies. We relate microbiome diversity to CN variation of the AMY1 locus, which encodes salivary amylase, facilitating starch digestion.

Estimation of multidrug resistance variability in the National Antimicrobial Monitoring System.

Multidrug resistance is a serious problem raising the specter of infections for which there is no treatment. One of the most important tools in combating multidrug resistance is large scale monitoring programs, because they track resistance over large geographic areas and time scales. This large scope, however, can also introduce variability into the data.

Inferring the interaction structure of resistance to antimicrobials.

The growth of antimicrobial resistance presents a significant threat to human and animal health. Of particular concern is multi-drug resistance, as this increases the chances an infection will be untreatable by any antibiotic. In order to understand multi-drug resistance, it is essential to understand the association between drug resistances.

RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards.

With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e.

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported.

Monitoring Antimicrobial Resistance in the Food Supply Chain and Its Implications for FDA Policy Initiatives.

In response to concerning increases in antimicrobial resistance (AMR), the Food and Drug Administration (FDA) has decided to increase veterinary oversight requirements for antimicrobials and restrict their use in growth promotion. Given the high stakes of this policy for the food supply, economy, and human and veterinary health, it is important to rigorously assess the effects of this policy. We have undertaken a detailed analysis of data provided by the National Antimicrobial Resistance Monitoring System (NARMS).

SnIPRE: selection inference using a Poisson random effects model.

We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection.

A mixture-model approach for parallel testing for unequal variances.

Testing for unequal variances is usually performed in order to check the validity of the assumptions that underlie standard tests for differences between means (the t-test and anova). However, existing methods for testing for unequal variances (Levene's test and Bartlett's test) are notoriously non-robust to normality assumptions, especially for small sample sizes. Moreover, although these methods were designed to deal with one hypothesis at a time, modern applications (such as to microarrays and fMRI experiments) often involve parallel testing over a large number of levels (genes or voxels).

A bayesian mixture model for comparative spectral count data in shotgun proteomics.

Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose.

Open access publishing, article downloads, and citations: randomised controlled trial.

Objective: To measure the effect of free access to the scientific literature on article downloads and citations.

Design: Randomised controlled trial.

Setting: 11 journals published by the American Physiological Society.

Model-based Bayesian clustering (MBBC).

Motivation: The program MBBC 2.0 clusters time-course microarray data using a Bayesian product partition model.

Results: The Bayesian product partition model in Booth et al.

Simple sequence repeats in Neurospora crassa: distribution, polymorphism and evolutionary inference.

Background: Simple sequence repeats (SSRs) have been successfully used for various genetic and evolutionary studies in eukaryotic systems. The eukaryotic model organism Neurospora crassa is an excellent system to study evolution and biological function of SSRs.

Results: We identified and characterized 2749 SSRs of 963 SSR types in the genome of N.