Gender, ethnicity, health behaviour & self-rated health in Singapore

10.1186/1471-2458-7-184BMC Public Health718

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship

Evidence of Key Tinnitus-Related Brain Regions Documented by a Unique Combination of Manganese-Enhanced MRI and Acoustic Startle Reflex Testing

Animal models continue to improve our understanding of tinnitus pathogenesis and aid in development of new treatments. However, there are no diagnostic biomarkers for tinnitus-related pathophysiology for use in awake, freely moving animals. To address this disparity, two complementary methods were combined to examine reliable tinnitus models (rats repeatedly administered salicylate or exposed to a single noise event): inhibition of acoustic startle and manganese-enhanced MRI. Salicylate-induced tinnitus resulted in wide spread supernormal manganese uptake compared to noise-induced tinnitus. Neither model demonstrated significant differences in the auditory cortex. Only in the dorsal cortex of the inferior colliculus (DCIC) did both models exhibit supernormal uptake. Therefore, abnormal membrane depolarization in the DCIC appears to be important in tinnitus-mediated activity. Our results provide the foundation for future studies correlating the severity and longevity of tinnitus with hearing loss and neuronal activity in specific brain regions and tools for evaluating treatment efficacy across paradigms

The Complete Chloroplast Genome Sequence of Date Palm (Phoenix dactylifera L.)

BACKGROUND: Date palm (Phoenix dactylifera L.), a member of Arecaceae family, is one of the three major economically important woody palms--the two other palms being oil palm and coconut tree--and its fruit is a staple food among Middle East and North African nations, as well as many other tropical and subtropical regions. Here we report a complete sequence of the data palm chloroplast (cp) genome based on pyrosequencing. METHODOLOGY/PRINCIPAL FINDINGS: After extracting 369,022 cp sequencing reads from our whole-genome-shotgun data, we put together an assembly and validated it with intensive PCR-based verification, coupled with PCR product sequencing. The date palm cp genome is 158,462 bp in length and has a typical quadripartite structure of the large (LSC, 86,198 bp) and small single-copy (SSC, 17,712 bp) regions separated by a pair of inverted repeats (IRs, 27,276 bp). Similar to what has been found among most angiosperms, the date palm cp genome harbors 112 unique genes and 19 duplicated fragments in the IR regions. The junctions between LSC/IRs and SSC/IRs show different features of sequence expansion in evolution. We identified 78 SNPs as major intravarietal polymorphisms within the population of a specific cp genome, most of which were located in genes with vital functions. Based on RNA-sequencing data, we also found 18 polycistronic transcription units and three highly expression-biased genes--atpF, trnA-UGC, and rrn23. CONCLUSIONS: Unlike most monocots, date palm has a typical cp genome similar to that of tobacco--with little rearrangement and gene loss or gain. High-throughput sequencing technology facilitates the identification of intravarietal variations in cp genomes among different cultivars. Moreover, transcriptomic analysis of cp genes provides clues for uncovering regulatory mechanisms of transcription and translation in chloroplasts

A Complete Sequence and Transcriptomic Analyses of Date Palm (Phoenix dactylifera L.) Mitochondrial Genome

Based on next-generation sequencing data, we assembled the mitochondrial (mt) genome of date palm (Phoenix dactylifera L.) into a circular molecule of 715,001 bp in length. The mt genome of P. dactylifera encodes 38 proteins, 30 tRNAs, and 3 ribosomal RNAs, which constitute a gene content of 6.5% (46,770 bp) over the full length. The rest, 93.5% of the genome sequence, is comprised of cp (chloroplast)-derived (10.3% with respect to the whole genome length) and non-coding sequences. In the non-coding regions, there are 0.33% tandem and 2.3% long repeats. Our transcriptomic data from eight tissues (root, seed, bud, fruit, green leaf, yellow leaf, female flower, and male flower) showed higher gene expression levels in male flower, root, bud, and female flower, as compared to four other tissues. We identified 120 potential SNPs among three date palm cultivars (Khalas, Fahal, and Sukry), and successfully found seven SNPs in the coding sequences. A phylogenetic analysis, based on 22 conserved genes of 15 representative plant mitochondria, showed that P. dactylifera positions at the root of all sequenced monocot mt genomes. In addition, consistent with previous discoveries, there are three co-transcribed gene clusters–18S-5S rRNA, rps3-rpl16 and nad3-rps12–in P. dactylifera, which are highly conserved among all known mitochondrial genomes of angiosperms

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors