Ag2O/TNBs were fabricated by depositing Ag2O nanoparticles on the surface of TiO2 nanobelts (TNBs). The disinfection activities of Ag2O/TNBs on two representative bacterial types: Gram-negative Escherichia coli ATCC15597 and Gram-positive Bacillus subtilis, were examined under both dark and visible light conditions. Ag2O/TNBs exhibited stronger bactericidal activities than Ag2O nanoparticles and TNBs under both dark and light conditions. For both cell types, disinfection effects of Ag2O/TNBs were greater under light conditions relative to those under dark conditions. The bactericidal mechanisms of Ag2O/TNBs under both dark and light conditions were explored. Ag+ ions released from Ag2O/TNBs did not contribute to the bactericidal activity of Ag2O/TNBs under dark conditions, whereas the released Ag+ ions showed bactericidal activity under visible light irradiation conditions. Active species (H2O2, O-center dot(2)-, and e(-)) generated by Ag2O/TNBs played important roles in the disinfection processes under both dark and visible light irradiation conditions. Without the presence of active species, the direct contact of Ag2O/TNBs with bacterial cells had no bactericidal effect. (C) 2013 Elsevier Ltd. All rights reserved.
This thesis develops models and associated Bayesian inference methods for flexible univariate and multivariate conditional density estimation. The models are flexible in the sense that they can capture widely differing shapes of the data. The estimation methods are specifically designed to achieve flexibility while still avoiding overfitting. The models are flexible both for a given covariate value, but also across covariate space. A key contribution of this thesis is that it provides general approaches of density estimation with highly efficient Markov chain Monte Carlo methods. The methods are illustrated on several challenging non-linear and non-normal datasets. In the first paper, a general model is proposed for flexibly estimating the density of a continuous response variable conditional on a possibly high-dimensional set of covariates. The model is a finite mixture of asymmetric student-t densities with covariate-dependent mixture weights. The four parameters of the components, the mean, degrees of freedom, scale and skewness, are all modeled as functions of the covariates. The second paper explores how well a smooth mixture of symmetric components can capture skewed data. Simulations and applications on real data show that including covariate-dependent skewness in the components can lead to substantially improved performance on skewed data, often using a much smaller number of components. We also introduce smooth mixtures of gamma and log-normal components to model positively-valued response variables. In the third paper we propose a multivariate Gaussian surface regression model that combines both additive splines and interactive splines, and a highly efficient MCMC algorithm that updates all the multi-dimensional knot locations jointly. We use shrinkage priors to avoid overfitting with different estimated shrinkage factors for the additive and surface part of the model, and also different shrinkage parameters for the different response variables. In the last paper we present a general Bayesian approach for directly modeling dependencies between variables as function of explanatory variables in a flexible copula context. In particular, the Joe-Clayton copula is extended to have covariate-dependent tail dependence and correlations. Posterior inference is carried out using a novel and efficient simulation method. The appendix of the thesis documents the computational implementation details.