A high frequency boundary element method for scattering by a class of nonconvex obstacles

This is a post-peer-review, pre-copyedit version of an article published in Numerische Mathematik. The final authenticated version is available online at: https://doi.org/10.1007/s00211-014-0648-7EPSR

Dietary soy and meat proteins induce distinct physiological and gene expression changes in rats

This study reports on a comprehensive comparison of the effects of soy and meat proteins given at the recommended level on physiological markers of metabolic syndrome and the hepatic transcriptome. Male rats were fed semi-synthetic diets for 1 wk that differed only regarding protein source, with casein serving as reference. Body weight gain and adipose tissue mass were significantly reduced by soy but not meat proteins. The insulin resistance index was improved by soy, and to a lesser extent by meat proteins. Liver triacylglycerol contents were reduced by both protein sources, which coincided with increased plasma triacylglycerol concentrations. Both soy and meat proteins changed plasma amino acid patterns. The expression of 1571 and 1369 genes were altered by soy and meat proteins respectively. Functional classification revealed that lipid, energy and amino acid metabolic pathways, as well as insulin signaling pathways were regulated differently by soy and meat proteins. Several transcriptional regulators, including NFE2L2, ATF4, Srebf1 and Rictor were identified as potential key upstream regulators. These results suggest that soy and meat proteins induce distinct physiological and gene expression responses in rats and provide novel evidence and suggestions for the health effects of different protein sources in human diets

Two new ArrayTrack libraries for personalized biomedical research

<p>Abstract</p> <p>Background</p> <p>Recent advances in high-throughput genotyping technology are paving the way for research in personalized medicine and nutrition. However, most of the genetic markers identified from association studies account for a small contribution to the total risk/benefit of the studied phenotypic trait. Testing whether the candidate genes identified by association studies are causal is critically important to the development of personalized medicine and nutrition. An efficient data mining strategy and a set of sophisticated tools are necessary to help better understand and utilize the findings from genetic association studies. </p> <p>Description</p> <p>SNP (single nucleotide polymorphism) and QTL (quantitative trait locus) libraries were constructed and incorporated into ArrayTrack, with user-friendly interfaces and powerful search features. Data from several public repositories were collected in the SNP and QTL libraries and connected to other domain libraries (genes, proteins, metabolites, and pathways) in ArrayTrack. Linking the data sets within ArrayTrack allows searching of SNP and QTL data as well as their relationships to other biological molecules. The SNP library includes approximately 15 million human SNPs and their annotations, while the QTL library contains publically available QTLs identified in mouse, rat, and human. The QTL library was developed for finding the overlap between the map position of a candidate or metabolic gene and QTLs from these species. Two use cases were included to demonstrate the utility of these tools. The SNP and QTL libraries are freely available to the public through ArrayTrack at <url>http://www.fda.gov/ArrayTrack</url>. </p> <p>Conclusions</p> <p>These libraries developed in ArrayTrack contain comprehensive information on SNPs and QTLs and are further cross-linked to other libraries. Connecting domain specific knowledge is a cornerstone of systems biology strategies and allows for a better understanding of the genetic and biological context of the findings from genetic association studies. </p

Advanced Genomic Data Mining

As data banks increase their size, one of the current challenges in bioinformatics is to be able to query them in a sensible way. Information is contained in differen

Physical Confirmation and Mapping of Overlapping Rat Mammary Carcinoma Susceptibility QTLs, Mcs2 and Mcs6

Only a portion of the estimated heritability of breast cancer susceptibility has been explained by individual loci. Comparative genetic approaches that first use an experimental organism to map susceptibility QTLs are unbiased methods to identify human orthologs to target in human population-based genetic association studies. Here, overlapping rat mammary carcinoma susceptibility (Mcs) predicted QTLs, Mcs6 and Mcs2, were physically confirmed and mapped to identify the human orthologous region. To physically confirm Mcs6 and Mcs2, congenic lines were established using the Wistar-Furth (WF) rat strain, which is susceptible to developing mammary carcinomas, as the recipient (genetic background) and either Wistar-Kyoto (WKy, Mcs6) or Copenhagen (COP, Mcs2), which are resistant, as donor strains. By comparing Mcs phenotypes of WF.WKy congenic lines with distinct segments of WKy chromosome 7 we physically confirmed and mapped Mcs6 to ∼33 Mb between markers D7Rat171 and gUwm64-3. The predicted Mcs2 QTL was also physically confirmed using segments of COP chromosome 7 introgressed into a susceptible WF background. The Mcs6 and Mcs2 overlapping genomic regions contain multiple annotated genes, but none have a clear or well established link to breast cancer susceptibility. Igf1 and Socs2 are two of multiple potential candidate genes in Mcs6. The human genomic region orthologous to rat Mcs6 is on chromosome 12 from base positions 71,270,266 to 105,502,699. This region has not shown a genome-wide significant association to breast cancer risk in pun studies of breast cancer susceptibility

BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources

BioGPS is a community based customisable gene annotation portal bringing together gene annotation resources on to a single platform

Expression of miRNAs and Their Cooperative Regulation of the Pathophysiology in Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of injury-related death and disability worldwide. Effective treatment for TBI is limited and many TBI patients suffer from neuropsychiatric sequelae. The molecular and cellular mechanisms underlying the neuronal damage and impairment of mental abilities following TBI are largely unknown. Here we used the next generation sequencing platform to delineate miRNA transcriptome changes in the hippocampus at 24 hours and 7 days following TBI in the rat controlled cortical impact injury (CCI) model, and developed a bioinformatic analysis to identify cellular activities that are regulated by miRNAs differentially expressed in the CCI brains. The results of our study indicate that distinct sets of miRNAs are regulated at different post-traumatic times, and suggest that multiple miRNA species cooperatively regulate cellular pathways for the pathological changes and management of brain injury. The distinctive miRNAs expression profiles at different post-CCI times may be used as molecular signatures to assess TBI progression. In addition to known pathophysiological changes, our study identifies many other cellular pathways that are subjected to modification by differentially expressed miRNAs in TBI brains. These pathways can potentially be targeted for development of novel TBI treatment