Capsule Locus Polymorphism among Distinct Lineages of Enterococcus faecalis Isolated from Canals of Root-filled Teeth with Periapical Lesions

Introduction: Although Enterococcus faecalis is a member of the normal microbiota, it is also a major cause of nosocomial infections. Some strains of E. faecalis produce capsule, which contributes to pathogenesis through evasion of host defenses, and its production is dependent on the capsule (cps) operon polymorphism. This study investigated cps locus polymorphism in distinct lineages of E. faecalis isolated from canals of root-filled teeth with periapical lesions. Methods: Twenty-two E. faecalis isolates were evaluated regarding the cps operon polymorphism and genetic diversity. The 3 known CPS types were determined by polymerase chain reaction. This information was correlated with multilocus sequence typing data, which were used to define genetic lineages. Results: cpsA and cpsB were the only detected genes within the cps operon in 62.5% of E. faecalis strains (14/22), indicative of genotype CPS 1, which lacks capsule expression. The essential genes in the cps operon for capsule production were detected in the remaining strains, whereas 3 belonged to genotype CPS 5 and 5 strains to genotype CPS 2. A total of 14 sequence types (STs) were resolved in 22 E. faecalis isolates. Comparison with the E. faecalis international multilocus sequence typing database revealed that 9 STs were previously found, and that the 5 STs were novel. Conclusions: Certain E. faecalis genotypes from canals of root-filled teeth with periapical lesions belong to lineages associated with capsule expression and production of multiple virulence factors, which might account for their increased pathogenic potential. (J Endod 2012;38:58-61)

Charge dynamics in half-filled Hubbard chains with finite on-site interaction

We study the charge dynamic structure factor of the one-dimensional Hubbard model with finite on-site repulsion U at half-filling. Numerical results from the time-dependent density matrix renormalization group are analyzed by comparison with the exact spectrum of the model. The evolution of the line shape as a function of U is explained in terms of a relative transfer of spectral weight between the two-holon continuum that dominates in the limit U -> infinity and a subset of the two-holon-two-spinon continuum that reconstructs the electron-hole continuum in the limit U -> 0. Power-law singularities along boundary lines of the spectrum are described by effective impurity models that are explicitly invariant under spin and eta-spin SU(2) rotations. The Mott-Hubbard metal-insulator transition is reflected in a discontinuous change of the exponents of edge singularities at U = 0. The sharp feature observed in the spectrum for momenta near the zone boundary is attributed to a van Hove singularity that persists as a consequence of integrability.

Electromagnetic torque ripple and copper losses reduction in permanent magnet synchronous machines

This paper presents a method for electromagnetic torque ripple and copper losses reduction in (non-sinusoidal or trapezoidal) surface-mount permanent magnet synchronous machines (SM-PMSM). The method is based on an extension of classical dq transformation that makes it possible to write a vectorial model for this kind of machine (with a non-sinusoidal back-EMF waveform). This model is obtained by the application of that transformation in the classical machine per-phase model. That transformation can be applied to machines that have any type of back-EMF waveform, and not only trapezoidal or square-wave back-EMF waveforms. Implementation results are shown for an electrical converter, using the proposed vectorial model, feeding a non-sinusoidal synchronous machine (brushless DC motor). They show that the use of this vectorial mode is a way to achieve improvements in the performance of this kind of machine, considering the electromagnetic torque ripple and copper losses, if compared to a drive system that employs a classical six-step mode as a converter. Copyright (C) 2011 John Wiley & Sons, Ltd.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.