BackgroundThe emergence, resurgence and spread of human food-borne pathogenic Vibrios are one of the major contributors to disease burden and mortality particularly in developing countries with disputable sanitary conditions. Previous research on pathogenic Vibrio cholerae and Vibrio parahaemolitycus derived from clinical samples has proposed links between acquisition of virulence and multiple drug resistance traits and intercellular transmissibility of mobile genetic elements in the environment. To date, very few information is available on environmental Vibrio isolates. In this study, we characterized eleven Vibrio strains bearing the SXT/R391-like integrative and conjugative elements (ICEs) derived from aquatic products and environment in the Yangtze River Estuary, China.ResultsThe eleven Vibrio strains were isolated in 2010 to 2011, and taxonomically identified, which included six Vibrio cholerae, three Vibrio parahaemolyticus, one Vibrio alginolyticus and one Vibrio natriegens. Most of the strains displayed strong resistance phenotypes to ampicillin, mercury and chromium. The majority of their ICEs, which belong to S and R exclusion system groups, contain ICEs-chromosome junction sequences and highly conserved core-genes required for ICE transfer. However, comparative sequence analysis uncovered interesting diversity in their mosaic accessory gene structures, which carry many novel genes that have not been described in any known ICEs to date. In addition, antibiotic resistance was transmitted by ICEVchChn6 and ICEVpaChn1 from V. cholerae, V. parahaemolyticus to E. coli MG1655 via conjugation, respectively. Our data also revealed that the ICEs characterized in this study are phylogenetically distant from most of the SXT/R391 ICEs reported previously, which may represent a novel cluster likely shaped by the ecological environment in the Yangtze River Estuary, China.ConclusionsThis study constitutes the first investigation of ICEs-positive Vibrio spp. in the Yangze River Estuary, China. The newly identified ICEs were characterized with mosaic accessory gene structures and many novel genes. The results demonstrated self-transmissibility of antibiotic resistance mediated by two of the ICEs from V. cholerae, V. parahaemolyticus to E. coli via conjugation, respectively. Our results also revealed that the ICEs examined in this study may represent a novel cluster in the SXT/R391 family.

The kinetics of the hydride exchange reaction between NADH model compound 10-methyl-9,10-dihydroacridine (MAH) and 1-benzyl-3-cyanoquinolinium (BQCN+) ion in acetonitrile were studied at temperatures ranging from 291 to 325 K. The extent of reaction-time profiles during the first half-lives are compared with theoretical data for the simple single-step mechanism and a 2-step mechanism involving initial donor/acceptor complex formation followed by unimolecular hydride transfer. The profiles for the reactions of MAH deviate significantly from those expected for the simple single-step mechanism with the deviation increasing with increasing temperature. The deviation from simple mechanism behavior is much less pronounced for the reactions of 10-methyl-9,10-dihydroacridine-10,10-d2 (MAD) which gives rise to extent of reaction dependent apparent kinetic isotope effects (KIEapp). Excellent fits of the experimental extent of reaction-time profiles with theoretical data for the 2-step mechanism, in the pre-steady-state time period, were observed in all cases. Resolution of the kinetics of the hydride exchange reaction into the microscopic rate constants over the entire temperature range resulted in real kinetic isotope effects for the hydride transfer step ranging from 40 (291 K) to 8.2 (325 K). That the reaction involves significant hydride tunnelling was verified by the magnitudes of the Arrhenius parameters; Ea D - EaH = 8.7 kcal mol-1 and AD/AH = 8 x 10(4). An electron donor acceptor complex (lambda max = 526 nm) was observed to be a reaction intermediate. Theoretical extent of reaction-time profile data are discussed for the case where a reaction intermediate is formed in a non-productive side equilibrium as compared to the case where it is a real intermediate on the reaction coordinate between reactants and products. The common assumption that the two cases are kinetically indistinguishable is shown to be incorrect.

NCT02234635, post-results.

Apparent deuterium kinetic isotope effects (KIE app ) of four different methylarene radical cationpyridine base reactions in dichloromethane (0.2 M tetrabutylammonium hexafluorophosphate) were observed to increase toward a constant value with increasing extent of reaction. The reactions were studied by derivative cyclic voltammetry (DCV), and rate constants were assigned by comparing the experimental with the theoretical DCV data. The kinetic results rule out a simple second-order proton-transfer reaction and implicate a mechanism in which a complex is first formed that then undergoes proton transfer, followed by separation of the products. That KIE app are extent of reaction-dependent is observed before steady-state is reached. The concurrent analysis of kinetic data for the reactions of both ArCH 3 •+ and ArCD 3 •+ with bases under non-steady-state conditions facilitates the resolution of the apparent rate constant [k app ) k f k p /(k b + k p )] into the microscopic rate constants (k f , k b , and k p ) for the individual steps. The KIE app observed during proton-transfer reactions need not be the real kinetic isotope effects (KIE real ). Having access to the microscopic rate constants for the steps in which the proton is transferred allows KIE real to be evaluated and compared with the corresponding KIE app . The present study shows that the KIE real are much greater than the KIE app derived in the usual way from the rate of the overall reaction.

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We study the adaptive estimation of copula correlation matrix Σ for the semi-parametric elliptical copula model. In this context, the correlations are connected to Kendall's tau through a sine function transformation. Hence, a natural estimate for Σ is the plug-in estimator Σ with Kendall's tau statistic. We first obtain a sharp bound on the operator norm of Σ − Σ. Then we study a factor model of Σ, for which we propose a refined estimator Σ by fitting a low-rank matrix plus a diagonal matrix to Σ using least squares with a nuclear norm penalty on the lowrank matrix. The bound on the operator norm of Σ − Σ serves to scale the penalty term, and we obtain finite sample oracle inequalities for Σ. We also consider an elementary factor copula model of Σ, for which we propose closed-form estimators. All of our estimation procedures are entirely data-driven.

Multiple linear regression analysis is widely used in many scientific fields, including public health, to evaluate how an outcome or response variable is related to a set of predictors. As a result, researchers often need to assess "relative importance" of a predictor by comparing the contributions made by other individual predictors in a particular regression model. Hence, development of valid statistical methods to estimate the relative importance of a set of predictors is of great interest. In this research, the authors considered the relative importance of a predictor when defined by that portion of the squared multiple correlation explained by the contribution of each predictor in the final model of interest. Here, a number of suggested relative importance indices motivated by this definition are reviewed, including the squared zero-order correlation, squared semipartial correlation, Product Measure (i.e., Pratt's Index), General Dominance Index, and Johnson's Relative Weight. The authors compared these indices using data sets from an occupational health study in which human inhalation exposure to styrene was measured and from a laboratory animal study on risk factors for atherosclerosis, and statistical properties using bootstrap methods were examined. The analysis suggests that the General Dominance Index and Johnson's Relative Weight are preferred methods for quantifying the relative importance of predictors in a multiple linear regression model. Johnson's Relative Weight involves significantly less computational burden than the General Dominance Index when the number of predictors in the final model is large.