Characterization of the alpha-haemolysin determinant from the human enteropathogenic Escherichia coli O26 plasmid pEO5

The 157-kb conjugative plasmid pEO5 encoding alpha-haemolysin in strains of human enteropathogenic Escherichia coli (EPEC) O26 was investigated for its relationship with EHEC-haemolysin-encoding plasmids of enterohaemorrhagic E. coli (EHEC) O26 and O157 strains. Plasmid pEO5 was found to be compatible with EHEC-virulence plasmids and did not hybridize in Southern blots with plasmid pO157 from the EHEC O157:H7 strain EDL933, indicating that both plasmids were unrelated. A 9227-bp stretch of pEO5 DNA encompassing the entire alpha-hlyCABD operon was sequenced and compared for similarity to plasmid and chromosomally inherited alpha-hly determinants. The alpha-hly determinant of pEO5 (7252 bp) and its upstream region was most similar to corresponding sequences of the murine E. coli alpha-hly plasmid pHly152, in particular, the structural alpha-hlyCABD genes (99.2% identity) and the regulatory hlyR regions (98.8% identity). pEO5 and alpha-hly plasmids of EPEC O26 strains from humans and cattle were very similar for the regions encompassing the structural alpha-hlyCABD genes. The major difference found between the hly regions of pHly152 and pEO5 is caused by the insertion of an IS2 element upstream of the hlyC gene in pHly152. The presence of transposon-like structures at both ends of the alpha-hly sequence indicates that this pEO5 virulence factor was probably acquired by horizontal gene transfer.

Iterative strong coupling of dimensionally heterogeneous models

In this article we address decomposition strategies especially tailored to perform strong coupling of dimensionally heterogeneous models, under the hypothesis that one wants to solve each submodel separately and implement the interaction between subdomains by boundary conditions alone. The novel methodology takes full advantage of the small number of interface unknowns in this kind of problems. Existing algorithms can be viewed as variants of the `natural` staggered algorithm in which each domain transfers function values to the other, and receives fluxes (or forces), and vice versa. This natural algorithm is known as Dirichlet-to-Neumann in the Domain Decomposition literature. Essentially, we propose a framework in which this algorithm is equivalent to applying Gauss-Seidel iterations to a suitably defined (linear or nonlinear) system of equations. It is then immediate to switch to other iterative solvers such as GMRES or other Krylov-based method. which we assess through numerical experiments showing the significant gain that can be achieved. indeed. the benefit is that an extremely flexible, automatic coupling strategy can be developed, which in addition leads to iterative procedures that are parameter-free and rapidly converging. Further, in linear problems they have the finite termination property. Copyright (C) 2009 John Wiley & Sons, Ltd.

Transformed symmetric models

For the first time, we introduce a class of transformed symmetric models to extend the Box and Cox models to more general symmetric models. The new class of models includes all symmetric continuous distributions with a possible non-linear structure for the mean and enables the fitting of a wide range of models to several data types. The proposed methods offer more flexible alternatives to Box-Cox or other existing procedures. We derive a very simple iterative process for fitting these models by maximum likelihood, whereas a direct unconditional maximization would be more difficult. We give simple formulae to estimate the parameter that indexes the transformation of the response variable and the moments of the original dependent variable which generalize previous published results. We discuss inference on the model parameters. The usefulness of the new class of models is illustrated in one application to a real dataset.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Molecular analysis of maltotriose active transport and fermentation by Saccharomyces cerevisiae reveals a determinant role for the AGT1 permease

Incomplete and/or sluggish maltotriose fermentation causes both quality and economic problems in the ale-brewing industry. Although it has been proposed previously that the sugar uptake must be responsible for these undesirable phenotypes, there have been conflicting reports on whether all the known alpha-glucoside transporters in Saccharomyces cerevisiae (MALx1, AGT1, and MPH2 and MPH3 transporters) allow efficient maltotriose utilization by yeast cells. We characterized the kinetics of yeast cell growth, sugar consumption, and ethanol production during maltose or maltotriose utilization by several S. cerevisiae yeast strains (both MAL constitutive and AM inducible) and by their isogenic counterparts with specific deletions of the AGT1 gene. Our results clearly showed that yeast strains carrying functional permeases encoded by the MAL21, MAL31, and/or MAL41 gene in their plasma membranes were unable to utilize maltotriose. While both high-and low-affinity transport activities were responsible for maltose uptake from the medium, in the case of maltotriose, the only low-affinity (K-m, 36 +/- 2 mM) transport activity was mediated by the AGT1 permease. In conclusion, the AGT1 transporter is required for efficient maltotriose fermentation by S. cerevisiae yeasts, highlighting the importance of this permease for breeding and/or selection programs aimed at improving sluggish maltotriose fermentations.