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. The results showed that the nonlinear control is able to keep the interferometer in the quadrature point and suppress signal fading for arbitrary signals, sinusoidal signals, or zero input signal, even under strong external disturbances. The system showed itself suitable to characterize a multi-axis piezoelectric flextentional actuator, which displacements that are much smaller than half wavelength. The high robustness allows the system to be embedded and to operate in harsh environments as factories, bringing the interferometry outside the laboratory.

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. Consensus gene predictions are then derived by maximum likelihood decoding, yielding n-best models (with probabilities for each). Evigan is capable of accommodating a variety of evidence types, including (but not limited to) gene models computed by diverse gene finders, BLAST hits, EST matches, and splice site predictions; learned parameters encode the relative quality of evidence sources. Since separate training data are not required (apart from the training sets used by individual gene finders), Evigan is particularly attractive for newly sequenced genomes where little or no reliable manually curated annotation is available. The ability to produce a ranked list of alternative gene models may facilitate identification of alternatively spliced transcripts. Experimental application to ENCODE regions of the human genome, and the genomes of Plasmodium vivax and Arabidopsis thaliana show that Evigan achieves better performance than any of the individual data sources used as evidence. AVAILABILITY: The source code is available at http://www.seas.upenn.edu/~strctlrn/evigan/evigan.html.

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. RESULTS: We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. CONCLUSION: Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.

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. Female Wistar rats were subjected to the coronary occlusion to induce myocardial infarction or Sham operation. A single LLLT application was carried out after 60 s and 3 days post-coronary occlusion, respectively. Echocardiography was performed 3 days and at the end of the experiment (5 weeks) to evaluate cardiac function. After the last echocardiographic examination, LV hemodynamic evaluation was performed at baseline and on sudden afterload increases. Compared with the Sham group, infarcted rats showed increased systolic and diastolic internal diameter as well as a depressed shortening fraction of LV. The only benefit of the LLLT was a higher shortening fraction after 3 days of infarction. However, treated-LLLT rats show a lower shortening fraction in the 5th week of study when compared with Sham and non-irradiated rats. A worsening of cardiac function was confirmed in the hemodynamic analysis as evidenced by the higher LV end-diastolic pressure and lower +dP/dt and -dP/dt with five weeks of study. Cardiac functional reserve was also impaired by infarction as evidenced by an attenuated response of stroke work index and cardiac output to a sudden afterload stress, without LLLT repercussions. No significant differences were found in the myocardial expression of Akt1/VEGF pathway. Collectively, these findings illustrate that LLLT improves LV systolic function in the early post-infarction cardiac remodeling. However, this beneficial effect may be dependent on the maintenance of phototherapy. Long-term studies with LLLT application are needed to establish whether these effects ultimately translate into improved cardiac remodeling.

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. Furthermore, promise has been tempered by an inability to efficiently scale approaches in ways that minimize manual efforts and still perform with high accuracy. Here, we have applied a machine-learning approach previously successful for identifying molecular entities to a disease concept to determine if the underlying probabilistic model effectively generalizes to unrelated concepts with minimal manual intervention for model retraining. RESULTS: We developed a named entity recognizer (MTag), an entity tagger for recognizing clinical descriptions of malignancy presented in text. The application uses the machine-learning technique Conditional Random Fields with additional domain-specific features. MTag was tested with 1,010 training and 432 evaluation documents pertaining to cancer genomics. Overall, our experiments resulted in 0.85 precision, 0.83 recall, and 0.84 F-measure on the evaluation set. Compared with a baseline system using string matching of text with a neoplasm term list, MTag performed with a much higher recall rate (92.1% vs. 42.1% recall) and demonstrated the ability to learn new patterns. Application of MTag to all MEDLINE abstracts yielded the identification of 580,002 unique and 9,153,340 overall mentions of malignancy. Significantly, addition of an extensive lexicon of malignancy mentions as a feature set for extraction had minimal impact in performance. CONCLUSION: Together, these results suggest that the identification of disparate biomedical entity classes in free text may be achievable with high accuracy and only moderate additional effort for each new application domain.