In this research, polypyrrole nanocone arrays doped with β-Naphthalene sulphonic acid （PPy-NSA） were built. This film was expected to control protein adsorption and bacterial adhesion by potential-induced reversibl...In this research, polypyrrole nanocone arrays doped with β-Naphthalene sulphonic acid （PPy-NSA） were built. This film was expected to control protein adsorption and bacterial adhesion by potential-induced reversibly redox. The scanning Kelvin probe microscopy （SKPM） and surface contact angles （SCA） tests suggested that the surface potential and wettability of PPy-NSA nanocone arrays could be controlled by simply controlling its redox property via applying potential. The controllable surface potential and wettability in return controlled the adsorption of protein and adhesion of bacteria. The proposed material might find application in the preparation of smart biomaterial surfaces that can regulate proteins and bacterial adhesion by a simple potential switching.展开更多
The Indian buffet process(IBP)and phylogenetic Indian buffet process(pIBP)can be used as prior models to infer latent features in a data set.The theoretical properties of these models are under-explored,however,especi...The Indian buffet process(IBP)and phylogenetic Indian buffet process(pIBP)can be used as prior models to infer latent features in a data set.The theoretical properties of these models are under-explored,however,especially in high dimensional settings.In this paper,we show that under mild sparsity condition,the posterior distribution of the latent feature matrix,generated via IBP or pIBP priors,converges to the true latent feature matrix asymptotically.We derive the posterior convergence rate,referred to as the contraction rate.We show that the convergence results remain valid even when the dimensionality of the latent feature matrix increases with the sample size,therefore making the posterior inference valid in high dimensional settings.We demonstrate the theoretical results using computer simulation,in which the parallel-tempering Markov chain Monte Carlo method is applied to overcome computational hurdles.The practical utility of the derived properties is demonstrated by inferring the latent features in a reverse phase protein arrays(RPPA)dataset under the IBP prior model.展开更多
基金the financial support of the National Basic Research Program of China (Grant No. 2012CB619100)the National High Technology Research and Development Program of China (863 Program, Grant No. 2015AA033502)+2 种基金the National Natural Science Foundation of China (Grant Nos. 51372087, 51072055 and 51232002)the Science and Technology Planning Project of Guangdong Province, China (Grant No. 2014A010105048)the State Key Laboratory for Mechanical Behavior of Materials, China (Grant No. 20141607)
文摘In this research, polypyrrole nanocone arrays doped with β-Naphthalene sulphonic acid （PPy-NSA） were built. This film was expected to control protein adsorption and bacterial adhesion by potential-induced reversibly redox. The scanning Kelvin probe microscopy （SKPM） and surface contact angles （SCA） tests suggested that the surface potential and wettability of PPy-NSA nanocone arrays could be controlled by simply controlling its redox property via applying potential. The controllable surface potential and wettability in return controlled the adsorption of protein and adhesion of bacteria. The proposed material might find application in the preparation of smart biomaterial surfaces that can regulate proteins and bacterial adhesion by a simple potential switching.
文摘The Indian buffet process(IBP)and phylogenetic Indian buffet process(pIBP)can be used as prior models to infer latent features in a data set.The theoretical properties of these models are under-explored,however,especially in high dimensional settings.In this paper,we show that under mild sparsity condition,the posterior distribution of the latent feature matrix,generated via IBP or pIBP priors,converges to the true latent feature matrix asymptotically.We derive the posterior convergence rate,referred to as the contraction rate.We show that the convergence results remain valid even when the dimensionality of the latent feature matrix increases with the sample size,therefore making the posterior inference valid in high dimensional settings.We demonstrate the theoretical results using computer simulation,in which the parallel-tempering Markov chain Monte Carlo method is applied to overcome computational hurdles.The practical utility of the derived properties is demonstrated by inferring the latent features in a reverse phase protein arrays(RPPA)dataset under the IBP prior model.
