Virulence Factors of Enteric Pathogenic Escherichia coli: A Review

Escherichia coli are remarkably versatile microorganisms and important members of the normal intestinal microbiota of humans and animals. This harmless commensal organism can acquire a mixture of comprehensive mobile genetic elements that contain genes encoding virulence factors, becoming an emerging human pathogen capable of causing a broad spectrum of intestinal and extraintestinal diseases. Nine definite enteric E. coli pathotypes have been well characterized, causing diseases ranging from various gastrointestinal disorders to urinary tract infections. These pathotypes employ many virulence factors and effectors subverting the functions of host cells to mediate their virulence and pathogenesis. This review summarizes new developments in our understanding of diverse virulence factors associated with encoding genes used by different pathotypes of enteric pathogenic E. coli to cause intestinal and extraintestinal diseases in humans.     

家用经济型打印机选购指南

伴随着生活水平的日益提高，人们对家用小型打印机的需求主要表现在文字、多媒体以及图像的打印输出等方面，这就要求打印机拥有较好的色彩表现能力、快速打印能力和很好的稳定性。从目前的打印机市场来看，价格偏高的激光打印机普通家庭用户接受起来有一些困难，老式的针式打印机已经无法满足用户对一些打印效果的要求，所以只有打印成本适中且打印效果出色的彩色喷墨打印机才能满足大家的要求。但是，喷墨打印机产品种类繁多，对于普通家庭用户而言，想要在市场上挑选到一款称心如意的产品确实是一个棘手的问题。[编者按]     

Calculating adjusted R(2) measures for Poisson regression models

In regression models not only the parameter estimates and significances of explanatory variables are of interest, but also the degree to which variation in the dependent variable can be explained by covariates. In recent publications, an R(2) measure based on deviance was recommended for Poisson regression models, one of the most frequently used modelling tools in epidemiological studies. However, when sample size is small relative to the number of covariates in the model, simple R(2) measures may be seriously inflated and may need to be adjusted according to the number of covariates in the model. We present a SAS-macro that calculates adjustments for the R(2) measures in Poisson regression models based on log-likelihood and on sums of squares. The proposed measures are applied to real data sets and their performance is discussed.     

Passivity based control of a distributed PEM fuel cell model

This work considers the controlled load change of proton exchange membrane (PEM) fuel cells. Due to the intrinsic nonlinearities of fuel cells, load changes are quite challenging. In the case of a low temperature PEM fuel cell, there is the possibility of undesired liquid water formation. Most available control concepts are heuristic linear controller structures based on a perfectly mixed fuel cell model. In this work a nonlinear controller for one-dimensional spatially distributed model of a PEM fuel cell is presented. The fuel cell model is derived from first principles. The concept of passivity is used to design the controller. A suitable control Lyapunov function is chosen and passivity of the fuel cell is shown. A state-feedback law is derived that can guarantee stability of the closed-loop system over a wide range of operation conditions. In order to make the feedback law applicable to fuel cells with limited measurement information an observer is designed. In a final step the state-feedback law and the observer are combined to an output-feedback controller.