Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.

Agglomerative joint clustering of metabolic data with spike at zero: A Bayesian perspective.

In many biological applications, for example high-dimensional metabolic data, the measurements consist of several continuous measurements of subjects or tissues over multiple attributes or metabolites. Measurement values are put in a matrix with subjects in rows and attributes in columns. The analysis of such data requires grouping subjects and attributes to provide a primitive guide toward data modeling.

Impact of Occupational Exposure to Chemicals in Life Cycle Assessment: A Novel Characterization Model Based on Measured Concentrations and Labor Hours.

According to Lim et al., based on World Health Organization (WHO) data, hazardous chemicals in the workplace are responsible for over 370,000 premature deaths annually. Despite these high figures, life cycle impact assessment (LCIA) does not yet include a fully operational method to consider occupational impacts in its scope over the entire supply chain.

Testing multiple variance components in linear mixed-effects models.

Testing zero variance components is one of the most challenging problems in the context of linear mixed-effects (LME) models. The usual asymptotic chi-square distribution of the likelihood ratio and score statistics under this null hypothesis is incorrect because the null is on the boundary of the parameter space. During the last two decades many tests have been proposed to overcome this difficulty, but these tests cannot be easily applied for testing multiple variance components, especially for testing a subset of them.

Rapid classification of phenotypic mutants of Arabidopsis via metabolite fingerprinting.

We evaluated the application of gas chromatography-mass spectrometry metabolic fingerprinting to classify forward genetic mutants with similar phenotypes. Mutations affecting distinct metabolic or signaling pathways can result in common phenotypic traits that are used to identify mutants in genetic screens. Measurement of a broad range of metabolites provides information about the underlying processes affected in such mutants.