Nonlinear control system for optical interferometry based on variable structure control and sliding modes.

This work presents a novel nonlinear control system designed for interferometry based on variable structure control and sliding modes. This approach can fully compensate the nonlinear behavior of the interferometer and lead to high accuracy control for large disturbances, featuring low cost, ease of implementation and high robustness, without a reset circuit (when compared with a linear control system). A deep stability analysis was accomplished and the global asymptotic stability of the system was proved.

Low-Level Laser Application in the Early Myocardial Infarction Stage Has No Beneficial Role in Heart Failure.

Low-level laser therapy (LLLT) has been targeted as a promising approach that can mitigate post-infarction cardiac remodeling. There is some interesting evidence showing that the beneficial role of the LLLT could persist long-term even after the end of the application, but it remains to be systematically evaluated. Therefore, the present study aimed to test the hypothesis that LLLT beneficial effects in the early post-infarction cardiac remodeling could remain in overt heart failure even with the disruption of irradiations.

Primary proximal urethral adenocarcinoma: Case report and brief review.

Primary urethral cancer in females is rare. It has a poor prognosis. The published data on this topic are limited, composed mostly of small case series.

Reranking candidate gene models with cross-species comparison for improved gene prediction.

Background: Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features.

Evigan: a hidden variable model for integrating gene evidence for eukaryotic gene prediction.

Motivation: The increasing diversity and variable quality of evidence relevant to gene annotation argues for a probabilistic framework that automatically integrates such evidence to yield candidate gene models.

Results: Evigan is an automated gene annotation program for eukaryotic genomes, employing probabilistic inference to integrate multiple sources of gene evidence. The probabilistic model is a dynamic Bayes network whose parameters are adjusted to maximize the probability of observed evidence.

Automated recognition of malignancy mentions in biomedical literature.

Background: The rapid proliferation of biomedical text makes it increasingly difficult for researchers to identify, synthesize, and utilize developed knowledge in their fields of interest. Automated information extraction procedures can assist in the acquisition and management of this knowledge. Previous efforts in biomedical text mining have focused primarily upon named entity recognition of well-defined molecular objects such as genes, but less work has been performed to identify disease-related objects and concepts.