Obesity contagion among classmates: Children's relation with each other regarding weight status, physical activity, and dietary intake.

A series of influential articles suggests that obesity may spread between couples, siblings, and close friends via an obesity contagion phenomenon. Classmates, as important structural equivalents in one's social network, may experience obesity contagion. However, this has rarely been examined. Anthropometric measurements, questionnaire surveys, and geographic information were collected from 3670 children from 26 schools in Northeast China. We found that classmates were positively related in terms of body mass index (BMI), body fat, physical activity, and intake of vegetables, fruits, fast food, snacks, and sugar-sweetened beverages. One standard deviation (SD) increase in classmates' mean BMI and percentage body fat was associated with 0.19 SD higher individual BMI (95% confidence interval [CI]: 0.00, 0.39) and 0.31 SD higher percentage body fat (95% CI: 0.13, 0.48). Coefficients ranged from 0.48 to 0.76 in models for physical activity, and the dietary intake of vegetables, fruit, fast food, snacks, and sugar-sweetened beverages. Children's BMI and body fat were more strongly associated with the maximum and minimum body fat levels of their same-sex classmates than with those of their general classmates. Their dietary intake and physical activity were more strongly associated with the mean/median levels of their general classmates than with those of their same-sex classmates. This study suggests that children's BMI, body fat, physical activity, and dietary intake may be related to those of their classmates. Modeling healthy behaviors in the classroom may help children develop habits that support achieving and maintaining a healthy weight. Future interventions should consider the inclusion of classmates as a social network strategy for obesity prevention.

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review.

Research on obesity and related diseases has received attention from government policymakers; interventions targeting nutrient intake, dietary patterns, and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning (ML) methods have been widely applied in obesity-related studies to detect obesity disease biomarkers or discover intervention strategies to optimize weight loss results. In addition, an open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies by other researchers. Here, we proposed a mini-review of several open-source ML algorithms, platforms, or related databases that are of particular interest or can be applied in the field of obesity research. We focus our topic on nutrition, environment and social factor, genetics or genomics, and microbiome-adopting ML algorithms.

A probabilistic model for co-occurrence analysis in bibliometrics.

The co-occurrence analysis of Medical Subject Heading (MeSH) terms extracted from the PubMed database is popularly used in bibliometrics. Practically for making the result interpretable, it is necessary to apply a certain filter procedure of co-occurrence matrix for removing the low-frequency items due to their low representativeness. Unfortunately, there is rare research referring to determine a critical threshold to remove the noise of co-occurrence matrix. Here, we proposed a probabilistic model for co-occurrence analysis that can provide statistical inferences about whether the paired items co-occur randomly. With help of this model, the dimensionality of co-occurrence matrix could be reduced according to the selected threshold. The conceptual model framework, simulation and practical applications are illustrated in the manuscript. Further details (including all reproducible codes) can be downloaded from the project website: https://github.com/xizhou/co-occurrence-analysis.git.

Common Features of Regulatory T Cell Specialization During Th1 Responses.

CD4+Foxp3+ Treg cells are essential for maintaining self-tolerance and preventing excessive immune responses. In the context of Th1 immune responses, co-expression of the Th1 transcription factor T-bet with Foxp3 is essential for Treg cells to control Th1 responses. T-bet-dependent expression of CXCR3 directs Treg cells to the site of inflammation. However, the suppressive mediators enabling effective control of Th1 responses at this site are unknown. In this study, we determined the signature of CXCR3+ Treg cells arising in Th1 settings and defined universal features of Treg cells in this context using multiple Th1-dominated infection models. Our analysis defined a set of Th1-specific co-inhibitory receptors and cytotoxic molecules that are specifically expressed in Treg cells during Th1 immune responses in mice and humans. Among these, we identified the novel co-inhibitory receptor CD85k as a functional predictor for Treg-mediated suppression specifically of Th1 responses, which could be explored therapeutically for selective immune suppression in autoimmunity.

Do count-based differential expression methods perform poorly when genes are expressed in only one condition?

A response to 'Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data' by Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND and Betel D in Genome Biology, 2013, 14:R95.

Statistical methods for detecting differentially methylated loci and regions.

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

Robustly detecting differential expression in RNA sequencing data using observation weights.

A popular approach for comparing gene expression levels between (replicated) conditions of RNA sequencing data relies on counting reads that map to features of interest. Within such count-based methods, many flexible and advanced statistical approaches now exist and offer the ability to adjust for covariates (e.g. batch effects). Often, these methods include some sort of 'sharing of information' across features to improve inferences in small samples. It is important to achieve an appropriate tradeoff between statistical power and protection against outliers. Here, we study the robustness of existing approaches for count-based differential expression analysis and propose a new strategy based on observation weights that can be used within existing frameworks. The results suggest that outliers can have a global effect on differential analyses. We demonstrate the effectiveness of our new approach with real data and simulated data that reflects properties of real datasets (e.g. dispersion-mean trend) and develop an extensible framework for comprehensive testing of current and future methods. In addition, we explore the origin of such outliers, in some cases highlighting additional biological or technical factors within the experiment. Further details can be downloaded from the project website: http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/.