The ‘Role’ of the Community/Public Service Interpreter

This paper discusses the problematic nature of the concept of role as defined by professional sign language interpreters . The authors argue for a more rational approach that takes into account the expected behaviours of the monolingual participants in the interpreted interaction

Identification of Enterobacter sakazakii from closely related species: The use of Artificial Neural Networks in the analysis of biochemical and 16S rDNA data

BACKGROUND: Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process. RESULTS: Artificial Neural Networks were applied to biochemical and 16S rDNA data derived from 282 strains of Enterobacteriaceae, including 189 E. sakazakii isolates, in order to identify key characteristics which could improve the identification of E. sakazakii. The models developed resulted in a predictive performance for blind (validation) data of 99.3 % correct discrimination between E. sakazakii and closely related species for both phenotypic and genotypic data. Three main regions of the partial rDNA sequence were found to be key in discriminating the species. Comparison between E. sakazakii and other strains also constitutively positive for expression of the enzyme α-glucosidase resulted in a predictive performance of 98.7 % for 16S rDNA sequence data and 100% for phenotypic data. CONCLUSION: The computationally based methods developed here show a remarkable ability in reducing data dimensionality and complexity, in order to eliminate noise from the system in order to facilitate the speed and reliability of a potential strain identification system. Furthermore, the approaches described are also able to provide valuable information regarding the population structure and distribution of individual species thus providing the foundations for novel assays and diagnostic tests for rapid identification of pathogens

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.</p

ValiDichro: a website for validating and quality control of protein circular dichroism spectra

Circular dichroism (CD) spectroscopy is widely used in structural biology as a technique for examining the structure, folding and conformational changes of proteins. A new server, ValiDichro, has been developed for checking the quality and validity of CD spectral data and metadata, both as an aid to data collection and processing and as a validation procedure for spectra to be included in publications. ValiDichro currently includes 25 tests for data completeness, consistency and quality. For each test that is done, not only is a validation report produced, but the user is also provided with suggestions for correcting or improving the data. The ValiDichro server is freely available at http://valispec.cryst.bbk.ac.uk/circularDichroism/ValiDichro/upload.html

Participation in environmental enhancement and conservation activities for health and well-being in adults: a review of quantitative and qualitative evidence

PUBHLT

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Proteomics Coupled with Metabolite and Cell Wall Profiling Reveal Metabolic Processes of a Developing Rice Stem Internode

Internodes of grass stems function in mechanical support, transport, and, in some species, are a major sink organ for carbon in the form of cell wall polymers. This study reports cell wall composition, proteomic, and metabolite analyses of the rice elongating internode. Cellulose, lignin, and xylose increase as a percentage of cell wall material along eight segments of the second rice internode (internode II) at booting stage, from the younger to the older internode segments, indicating active cell wall synthesis. Liquid-chromatography tandem mass spectrometry (LC-MS/MS) of trypsin-digested proteins from this internode at booting reveals 2,547 proteins with at least two unique peptides in two biological replicates. The dataset includes many glycosyltransferases, acyltransferases, glycosyl hydrolases, cell wall-localized proteins, and protein kinases that have or may have functions in cell wall biosynthesis or remodeling. Phospho-enrichment of internode II peptides identified 21 unique phosphopeptides belonging to 20 phosphoproteins including a leucine rich repeat-III family receptor like kinase. GO over-representation and KEGG pathway analyses highlight the abundances of proteins involved in biosynthetic processes, especially the synthesis of secondary metabolites such as phenylpropanoids and flavonoids. LC-MS/MS of hot methanol-extracted secondary metabolites from internode II at four stages (booting/elongation, early mature, mature, and post mature) indicates that internode secondary metabolites are distinct from those of roots and leaves, and differ across stem maturation. This work fills a void of in-depth proteomics and metabolomics data for grass stems, specifically for rice, and provides baseline knowledge for more detailed studies of cell wall synthesis and other biological processes characteristic of internode development, toward improving grass agronomic properties.This work was supported by the U.S. National Science Foundation (grant numbers EPS-0814361, 0923247, and CHE-1626372), the U.S. Department of Energy (DOE), Office of Science (DE-SC0006904), and the U.S. Department of Agriculture National Institute of Food and Agriculture, (2010-38502-21836). A portion of this research was performed in the Environmental Molecular Sciences Laboratory at the Pacific Northwest National Laboratory (PNNL). The Environmental Molecular Sciences Laboratory is a DOE Office of Biological and Environmental Research scientific user facility on the PNNL campus. PNNL is a multiprogram national laboratory operated by Battelle for the DOE under contract DE-AC05-76RL01830. Collaboration with EMSL was supported through Projects 49477 and 49510. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. Open access fees fees for this article provided whole or in part by OU Libraries Open Access Fund.Ye