Compositional transformations can reasonably introduce phenotype-associated values into sparse features.

It was recently argued that an analysis of tumor-associated microbiome data is invalid because features that were originally very sparse (genera with mostly zero read counts) became associated with the phenotype following batch correction . Here, we examine whether such an observation should necessarily indicate issues with processing or machine learning pipelines. We show counterexamples using the centered log ratio (CLR) transformation, which is often used for analysis of compositional microbiome data .

Early prediction of preeclampsia using the first trimester vaginal microbiome.

Preeclampsia is a severe obstetrical syndrome which contributes to 10-15% of all maternal deaths. Although the mechanisms underlying systemic damage in preeclampsia-such as impaired placentation, endothelial dysfunction, and immune dysregulation-are well studied, the initial triggers of the condition remain largely unknown. Furthermore, although the pathogenesis of preeclampsia begins early in pregnancy, there are no early diagnostics for this life-threatening syndrome, which is typically diagnosed much later, after systemic damage has already manifested.

Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.

As investigations of low-biomass microbial communities have become more common, so too has the recognition of major challenges affecting these analyses. These challenges have been shown to compromise biological conclusions and have contributed to several controversies. Here, we review some of the most common and influential challenges in low-biomass microbiome research.

Distributional bias compromises leave-one-out cross-validation.

Cross-validation is a common method for estimating the predictive performance of machine learning models. In a data-scarce regime, where one typically wishes to maximize the number of instances used for training the model, an approach called 'leave-one-out cross-validation' is often used. In this design, a separate model is built for predicting each data instance after training on all other instances.

Multimodal stimulation screens reveal unique and shared genes limiting T cell fitness.

Genes limiting T cell antitumor activity may serve as therapeutic targets. It has not been systematically studied whether there are regulators that uniquely or broadly contribute to T cell fitness. We perform genome-scale CRISPR-Cas9 knockout screens in primary CD8 T cells to uncover genes negatively impacting fitness upon three modes of stimulation: (1) intense, triggering activation-induced cell death (AICD); (2) acute, triggering expansion; (3) chronic, causing dysfunction.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models.

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models.

The Salivary Microbiome and Predicted Metabolite Production Are Associated with Barrett's Esophagus and High-Grade Dysplasia or Adenocarcinoma.

Background: Esophageal adenocarcinoma (EAC) is rising in incidence, and established risk factors do not explain this trend. Esophageal microbiome alterations have been associated with Barrett's esophagus (BE) and dysplasia and EAC. The oral microbiome is tightly linked to the esophageal microbiome; this study aimed to identify salivary microbiome-related factors associated with BE, dysplasia, and EAC.

Patterns of colorectal cancer screening and adherence rates among an average-risk population enrolled in a national health insurance provider during 2009-2018 in the United States.

While colorectal cancer (CRC) is the second leading cause of cancer-related mortality in the United States (US), outcomes can be improved through timely screening. Despite the benefits and widespread availability of screening tests, adherence to recommended screening strategies is low. The study aimed to provide recent evidence regarding screening rates and adherence to screening recommendations among adults at average risk for CRC in a commercially insured and Medicare Advantage population.

Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data.

Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination.

Patterns of initial colorectal cancer screenings after turning 50 years old and follow-up rates of colonoscopy after positive stool-based testing among the average-risk population.

Objectives: Effective colorectal cancer (CRC) screening requires proper adherence beginning at the recommended screening age. For those with positive results on stool-based tests (SBTs), a follow-up colonoscopy is warranted. The objectives of this study were to 1) examine initial screening rates after turning 50 years old; and 2) assess rates of follow-up colonoscopy after a positive SBT.

Do medical student's surgical examination scores correlate with performance markers?

Background: Some medical school training consists of oral examinations.

Methods: We conducted a 9-year review of third-year medical student examinations including oral examinations, National Board of Medical Examiners Surgery Subject Examination (SSE, ie, shelf), and United States Medical Licensing Examinations Step 1 and Step 2.

Results: Step 1 showed a moderate to strong association with Period 1 orals (Somers' D = .

Insular biogeography of the montane butterfly faunas in the Great Basin: comparison with birds and mammals.

Butterfly species lists were assembled for 18 Great Basin mountain ranges for which distributional data on mammals and birds have been analysed previously by other workers. The ranges represent remnant islands of the boreal habitat that once was continuous across the Great Basin but is now restricted to higher elevations as a result of climatic change at the close of the Pleistocene. The effects of biogeographic factors (area, distance, elevation) and habitat diversity on butterfly species number were examined.