Robust coefficients of a higher order AR modelling in a speech enhancement system using parameterized Wiener filtering

We study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim-Oppenheim (1978), where the AR spectral estimation of the speech is carried out using a second-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors, providing a generalization of third- and fourth-order algorithms by means of adding two parameters in the general expression of Wiener filtering. These parameters allow a better control of their performance. Some results are presented considering AWGN but listening tests give similar performance when other noises (diesel engine) are considered.Peer ReviewedPostprint (published version

Crk and CrkL adaptor proteins: networks for physiological and pathological signaling

The Crk adaptor proteins (Crk and CrkL) constitute an integral part of a network of essential signal transduction pathways in humans and other organisms that act as major convergence points in tyrosine kinase signaling. Crk proteins integrate signals from a wide variety of sources, including growth factors, extracellular matrix molecules, bacterial pathogens, and apoptotic cells. Mounting evidence indicates that dysregulation of Crk proteins is associated with human diseases, including cancer and susceptibility to pathogen infections. Recent structural work has identified new and unusual insights into the regulation of Crk proteins, providing a rationale for how Crk can sense diverse signals and produce a myriad of biological responses

Robust coefficients of a higher order AR modelling in a speech enhancement system using parameterized Wiener filtering

We study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim-Oppenheim (1978), where the AR spectral estimation of the speech is carried out using a second-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors, providing a generalization of third- and fourth-order algorithms by means of adding two parameters in the general expression of Wiener filtering. These parameters allow a better control of their performance. Some results are presented considering AWGN but listening tests give similar performance when other noises (diesel engine) are considered.Peer Reviewe

Lipidomic profiling identifies signatures of metabolic risk

BACKGROUND:Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS:We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS:Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS:We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility

Lipidomic profiling identifies signatures of metabolic risk

BACKGROUND:Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS:We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS:Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS:We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility