Correlations between Income inequality and antimicrobial resistance.

Objectives: The aim of this study is to investigate if correlations exist between income inequality and antimicrobial resistance. This study's hypothesis is that income inequality at the national level is positively correlated with antimicrobial resistance within developed countries. Data collection and analysis: income inequality data were obtained from the Standardized World Income Inequality Database. Antimicrobial resistance data were obtained from the European antimicrobial Resistance Surveillance Network and outpatient antimicrobial consumption data, measured by Defined daily Doses per 1000 inhabitants per day, from the European Surveillance of antimicrobial Consumption group. Spearman's correlation coefficient (r) defined strengths of correlations of: > 0.8 as strong, > 0.5 as moderate and > 0.2 as weak. Confidence intervals and p values were defined for all r values. Correlations were calculated for the time period 2003-10, for 15 European countries. Results: income inequality and antimicrobial resistance correlations which were moderate or strong, with 95% confidence intervals > 0, included the following. Enterococcus faecalis resistance to aminopenicillins, vancomycin and high level gentamicin was moderately associated with income inequality (r= ≥0.54 for all three antimicrobials). Escherichia coli resistance to aminoglycosides, aminopenicillins, third generation cephalosporins and fluoroquinolones was moderately-strongly associated with income inequality (r= ≥0.7 for all four antimicrobials). Klebsiella pneumoniae resistance to third generation cephalosporins, aminoglycosides and fluoroquinolones was moderately associated with income inequality (r= ≥0.5 for all three antimicrobials). Staphylococcus aureus methicillin resistance and income inequality were strongly associated (r=0.87). Conclusion: as income inequality increases in European countries so do the rates of antimicrobial resistance for bacteria including E. faecalis, E. coli, K. pneumoniae and S. aureus. Further studies are needed to confirm these findings outside Europe and investigate the processes that could causally link income inequality and antimicrobial resistance

Molecular Dynamics Simulation of Phosphorylated KID Post-Translational Modification

BACKGROUND:Kinase-inducible domain (KID) as transcriptional activator can stimulate target gene expression in signal transduction by associating with KID interacting domain (KIX). NMR spectra suggest that apo-KID is an unstructured protein. After post-translational modification by phosphorylation, KID undergoes a transition from disordered to well folded protein upon binding to KIX. However, the mechanism of folding coupled to binding is poorly understood. METHODOLOGY:To get an insight into the mechanism, we have performed ten trajectories of explicit-solvent molecular dynamics (MD) for both bound and apo phosphorylated KID (pKID). Ten MD simulations are sufficient to capture the average properties in the protein folding and unfolding. CONCLUSIONS:Room-temperature MD simulations suggest that pKID becomes more rigid and stable upon the KIX-binding. Kinetic analysis of high-temperature MD simulations shows that bound pKID and apo-pKID unfold via a three-state and a two-state process, respectively. Both kinetics and free energy landscape analyses indicate that bound pKID folds in the order of KIX access, initiation of pKID tertiary folding, folding of helix alpha(B), folding of helix alpha(A), completion of pKID tertiary folding, and finalization of pKID-KIX binding. Our data show that the folding pathways of apo-pKID are different from the bound state: the foldings of helices alpha(A) and alpha(B) are swapped. Here we also show that Asn139, Asp140 and Leu141 with large Phi-values are key residues in the folding of bound pKID. Our results are in good agreement with NMR experimental observations and provide significant insight into the general mechanisms of binding induced protein folding and other conformational adjustment in post-translational modification

The gene normalization task in BioCreative III

BACKGROUND: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). RESULTS: We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. CONCLUSIONS: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance