Charged Dilatonic AdS Black Branes in Arbitrary Dimensions

We study electromagnetically charged dilatonic black brane solutions in arbitrary dimensions with flat transverse spaces, that are asymptotically AdS. This class of solutions includes spacetimes which possess a bulk region where the metric is approximately invariant under Lifshitz scalings. Given fixed asymptotic boundary conditions, we analyze how the behavior of the bulk up to the horizon varies with the charges and derive the extremality conditions for these spacetimes.Comment: References update

Exhaustive prediction of disease susceptibility to coding base changes in the human genome

<p>Abstract</p> <p>Background</p> <p>Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genomic variation and can cause phenotypic differences between individuals, including diseases. Bases are subject to various levels of selection pressure, reflected in their inter-species conservation.</p> <p>Results</p> <p>We propose a method that is not dependant on transcription information to score each coding base in the human genome reflecting the disease probability associated with its mutation. Twelve factors likely to be associated with disease alleles were chosen as the input for a support vector machine prediction algorithm. The analysis yielded 83% sensitivity and 84% specificity in segregating disease like alleles as found in the Human Gene Mutation Database from non-disease like alleles as found in the Database of Single Nucleotide Polymorphisms. This algorithm was subsequently applied to each base within all known human genes, exhaustively confirming that interspecies conservation is the strongest factor for disease association. For each gene, the length normalized average disease potential score was calculated. Out of the 30 genes with the highest scores, 21 are directly associated with a disease. In contrast, out of the 30 genes with the lowest scores, only one is associated with a disease as found in published literature. The results strongly suggest that the highest scoring genes are enriched for those that might contribute to disease, if mutated.</p> <p>Conclusion</p> <p>This method provides valuable information to researchers to identify sensitive positions in genes that have a high disease probability, enabling them to optimize experimental designs and interpret data emerging from genetic and epidemiological studies.</p

Child Care Time, Parents’ Well-Being, and Gender: Evidence from the American Time Use Survey

This study used data from the ‘Well Being Module’ of the 2010 American Time Use Survey (N = 1699) to analyze how parents experience child care time in terms of meaning and stress levels. Multivariate multilevel regressions showed clear differences by gender and the circumstances of child care activities. Mothers experienced child care time as more stressful than fathers, and fathers as slightly more meaningful. Interactive child care was experienced as more meaningful and less stressful than routine child care, whereas these differences were stronger among fathers than among mothers. Mothers experienced child care with a minor child as highly meaningful, and with an adolescent as particularly stressful. Fathers experienced child care with an infant as highly stressful, and with an offspring in middle childhood as disproportionally meaningful. The spouse’s presence was moderately associated with higher senses of meaning and lower levels of stress during child care, but these differences were modest, and only visible among fathers. Paid work hours increased mothers’ stress levels during child care activities, but reduced fathers’ stress levels. Meanwhile, nonemployed fathers reported child care time as less meaningful than non-employed mothers. Overall, this study has important scientific and practical implications for `understanding the gendered nature of parents’ child care time and well-being

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin

No Evidence for a Trade-Off between Reproductive Investment and Immunity in a Rodent

Life history theory assumes there are trade-offs between competing functions such as reproduction and immunity. Although well studied in birds, studies of the trade-offs between reproduction and immunity in small mammals are scarce. Here we examined whether reduced immunity is a consequence of reproductive effort in lactating Brandt's voles (Lasiopodomys brandtii). Specifically, we tested the effects of lactation on immune function (Experiment I). The results showed that food intake and resting metabolic rate (RMR) were higher in lactating voles (6≤ litter size ≤8) than that in non-reproductive voles. Contrary to our expectation, lactating voles also had higher levels of serum total Immunoglobulin G (IgG) and anti-keyhole limpet hemocyanin (KLH) IgG and no change in phytohemagglutinin (PHA) response and anti-KLH Immunoglobulin M (IgM) compared with non-reproductive voles, suggesting improved rather than reduced immune function. To further test the effect of differences in reproductive investment on immunity, we compared the responses between natural large (n≥8) and small litter size (n≤6) (Experiment II) and manipulated large (11–13) and small litter size (2–3) (Experiment III). During peak lactation, acquired immunity (PHA response, anti-KLH IgG and anti-KLH IgM) was not significantly different between voles raising large or small litters in both experiments, despite the measured difference in reproductive investment (greater litter size, litter mass, RMR and food intake in the voles raising larger litters). Total IgG was higher in voles with natural large litter size than those with natural small litter size, but decreased in the enlarged litter size group compared with control and reduced group. Our results showed that immune function is not suppressed to compensate the high energy demands during lactation in Brandt's voles and contrasting the situation in birds, is unlikely to be an important aspect mediating the trade-off between reproduction and survival

Time spent on work-related activities, social activities and time pressure as intermediary determinants of health disparities among elderly women and men in 5 European countries: a structural equation model

Background Psychosocial factors shape the health of older adults through complex inter-relating pathways. Besides socioeconomic factors, time use activities may explain gender inequality in self-reported health. This study investigated the role of work-related and social time use activities as determinants of health in old age. Specifically, we analysed whether the impact of stress in terms of time pressure on health mediated the relationship between work-related time use activities (i.e. housework and paid work) on self-reported health. Methods We applied structural equation models and a maximum-likelihood function to estimate the direct and indirect effects of psychosocial factors on health using pooled data from the Multinational Time Use Study on 11,168 men and 14,295 women aged 65+ from Italy, Spain, UK, France and the Netherlands. Results The fit indices for the conceptual model indicated an acceptable fit for both men and women. The results showed that socioeconomic status (SES), demographic factors, stress and work-related time use activities after retirement had a significant direct influence on self-reported health among the elderly, but the magnitude of the effects varied by gender. Social activities had a positive impact on self-reported health but had no significant impact on stress among older men and women. The indirect standardized effects of work-related activities on self-reported health was statistically significant for housework (β = − 0.006; P  0.05 among women), which implied that the paths from paid work and housework on self-reported health via stress (mediator) was very weak because their indirect effects were close to zero. Conclusions Our findings suggest that although stress in terms of time pressure has a direct negative effect on health, it does not indirectly influence the positive effects of work-related time use activities on self-reported health among elderly men and women. The results support the time availability hypothesis that the elderly may not have the same time pressure as younger adults after retirement

Predicting environmental chemical factors associated with disease-related gene expression data

<p>Abstract</p> <p>Background</p> <p>Many common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease.</p> <p>Methods</p> <p>We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.</p> <p>Results</p> <p>We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.</p> <p>Conclusions</p> <p>We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.</p

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download