Pairing of Competitive and Topologically Distinct Regulatory Modules Enhances Patterned Gene Expression

Biological networks are inherently modular, yet little is known about how modules are assembled to enable coordinated and complex functions. We used RNAi and time series, whole-genome microarray analyses to systematically perturb and characterize components of a Caenorhabditis elegans lineage-specific transcriptional regulatory network. These data are supported by selected reporter gene analyses and comprehensive yeast one-hybrid and promoter sequence analyses. Based on these results, we define and characterize two modules composed of muscle- and epidermal-specifying transcription factors that function together within a single cell lineage to robustly specify multiple cell types. The expression of these two modules, although positively regulated by a common factor, is reliably segregated among daughter cells. Our analyses indicate that these modules repress each other, and we propose that this cross-inhibition coupled with their relative time of induction function to enhance the initial asymmetry in their expression patterns, thus leading to the observed invariant gene expression patterns and cell lineage. The coupling of asynchronous and topologically distinct modules may be a general principle of module assembly that functions to potentiate genetic switches.Molecular and Cellular Biolog

Efficient Methods for Natural Language Processing: A Survey

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.Comment: Accepted at TACL, pre publication versio

Inferred Biomolecular Interaction Server—a web server to analyze and predict protein interacting partners and binding sites

IBIS is the NCBI Inferred Biomolecular Interaction Server. This server organizes, analyzes and predicts interaction partners and locations of binding sites in proteins. IBIS provides annotations for different types of binding partners (protein, chemical, nucleic acid and peptides), and facilitates the mapping of a comprehensive biomolecular interaction network for a given protein query. IBIS reports interactions observed in experimentally determined structural complexes of a given protein, and at the same time IBIS infers binding sites/interacting partners by inspecting protein complexes formed by homologous proteins. Similar binding sites are clustered together based on their sequence and structure conservation. To emphasize biologically relevant binding sites, several algorithms are used for verification in terms of evolutionary conservation, biological importance of binding partners, size and stability of interfaces, as well as evidence from the published literature. IBIS is updated regularly and is freely accessible via http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.html

The relationships among self-care, dispositional mindfulness, and psychological distress in medical students

Background: Past research suggests that medical students experience high levels of psychological distress. Objective: The aim of the current study was to investigate the relationships among engagement in self-care behaviours, dispositional mindfulness, and psychological distress. Methods: The sample consisted of 139 female and 68 male Australian medical students (N=207) aged 17–41 years (M=21.82, SD=3.62) across the 5 years of the Monash University medical course. Participants completed an online survey comprising a demographics questionnaire, the Five Facet Mindfulness Questionnaire, the Health-Promoting Lifestyle Profile II, and the Depression, Anxiety, and Stress Scales. Results: Results revealed significant and interpretable multivariate correlations between distress and both mindfulness and self-care. Furthermore, the dispositional mindfulness observation subscale was found to be a significant moderator of the relationship between several dimensions of self-care and psychological distress. Conclusions: The present study points to the potential of self-care and mindfulness to decrease medical student distress and enhance well-being

DFLAT: functional annotation for human development.

BACKGROUND: Recent increases in genomic studies of the developing human fetus and neonate have led to a need for widespread characterization of the functional roles of genes at different developmental stages. The Gene Ontology (GO), a valuable and widely-used resource for characterizing gene function, offers perhaps the most suitable functional annotation system for this purpose. However, due in part to the difficulty of studying molecular genetic effects in humans, even the current collection of comprehensive GO annotations for human genes and gene products often lacks adequate developmental context for scientists wishing to study gene function in the human fetus. DESCRIPTION: The Developmental FunctionaL Annotation at Tufts (DFLAT) project aims to improve the quality of analyses of fetal gene expression and regulation by curating human fetal gene functions using both manual and semi-automated GO procedures. Eligible annotations are then contributed to the GO database and included in GO releases of human data. DFLAT has produced a considerable body of functional annotation that we demonstrate provides valuable information about developmental genomics. A collection of gene sets (genes implicated in the same function or biological process), made by combining existing GO annotations with the 13,344 new DFLAT annotations, is available for use in novel analyses. Gene set analyses of expression in several data sets, including amniotic fluid RNA from fetuses with trisomies 21 and 18, umbilical cord blood, and blood from newborns with bronchopulmonary dysplasia, were conducted both with and without the DFLAT annotation. CONCLUSIONS: Functional analysis of expression data using the DFLAT annotation increases the number of implicated gene sets, reflecting the DFLAT\u27s improved representation of current knowledge. Blinded literature review supports the validity of newly significant findings obtained with the DFLAT annotations. Newly implicated significant gene sets also suggest specific hypotheses for future research. Overall, the DFLAT project contributes new functional annotation and gene sets likely to enhance our ability to interpret genomic studies of human fetal and neonatal development. BMC Bioinformatics 2014; 15:45