Testing supervised classifiers based on non-negative matrix factorization to musical instrument classification

In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Two feature sets were employed, the first containing perceptual features and MPEG-7 descriptors and the second containing rhythm patterns developed for the SOMeJB project. The features were measured for 300 sound recordings consisting of 6 different musical instrument classes. Subsets of the feature set are selected using branch-and-bound search, obtaining the most suitable features for classification. A class of supervised classifiers is developed based on the non-negative matrix factorization (NMF). The standard NMF method is examined as well as its modifications: the local and the sparse NMF. The experiments compare the two feature sets alongside the various NMF algorithms. The results demonstrate an almost perfect classification for the first set using the standard NMF algorithm (classification error 1.0 %), outperforming the state-of-the-art techniques tested for the aforementioned experiment

Inhibition of glycogen synthase kinase-3 enhances NRF2 protein stability, nuclear localisation and target gene transcription in pancreatic beta cells

Accumulation of reactive oxygen species (i.e., oxidative stress) is a leading cause of beta cell dysfunction and apoptosis in diabetes. NRF2 (NF-E2 p45-related factor-2) regulates the adaptation to oxidative stress, and its activity is negatively regulated by the redox-sensitive CUL3 (cullin-3) ubiquitin ligase substrate adaptor KEAP1 (Kelch-like ECH-associated protein-1). Additionally, NRF2 is repressed by the insulin-regulated Glycogen Synthase Kinase-3 (GSK3). We have demonstrated that phosphorylation of NRF2 by GSK3 enhances β-TrCP (beta-transducin repeat-containing protein) binding and ubiquitylation by CUL1 (cullin-1), resulting in increased proteasomal degradation of NRF2. Thus, we hypothesise that inhibition of GSK3 activity or β-TrCP binding upregulates NRF2 and so protects beta cells against oxidative stress. We have found that treating the pancreatic beta cell line INS-1832/13 with the KEAP1 inhibitor TBE31 significantly enhanced NRF2 protein levels. The presence of the GSK3 inhibitor CT99021 or the β-TrCP-NRF2 protein-protein interaction inhibitor PHAR, along with TBE31, resulted in prolonged NRF2 stability and enhanced nuclear localisation (p

CXCL8 Chemokines in Teleost Fish: Two Lineages with Distinct Expression Profiles during Early Phases of Inflammation

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Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

A survey of tagging techniques for music, speech and environmental sound

Sound tagging has been studied for years. Among all sound types, music, speech, and environmental sound are three hottest research areas. This survey aims to provide an overview about the state-of-the-art development in these areas.We discuss about the meaning of tagging in different sound areas at the beginning of the journey. Some examples of sound tagging applications are introduced in order to illustrate the significance of this research. Typical tagging techniques include manual, automatic, and semi-automatic approaches.After reviewing work in music, speech and environmental sound tagging, we compare them and state the research progress to date. Research gaps are identified for each research area and the common features and discriminations between three areas are discovered as well. Published datasets, tools used by researchers, and evaluation measures frequently applied in the analysis are listed. In the end, we summarise the worldwide distribution of countries dedicated to sound tagging research for years

Biomarker Research in ADHD: the Impact of Nutrition (BRAIN) - study protocol of an open-label trial to investigate the mechanisms underlying the effects of a few-foods diet on ADHD symptoms in children

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