In recent times there has been great demand for natural products that have possible preventive action against diabetes and its secondary complications. Keeping this in mind, this study was undertaken to investigate the influence of the flavonoid, quercetin, on oxidative stress markers and the antioxidant defence system of hepatic and neuronal tissues from galactose-induced hyperglycaemic rats. Weanling male Wistar rats were treated with 30% galactose in AIN 93 diet (group B, n=8) to induce hyperglycaemia. Control rats received normal Stock AIN 93 diet (group A, n=8). The third set of rats received group B diet with quercetin at 400 mg/100 g diet (group C, n=8). Glucose levels and body weights were measured on a weekly basis for four weeks to monitor the hyperglycaemia induced by galactose feeding. Parameters involved in the pathogenesis of galactose-induced hyperglycaemia, which included organosomatic index, protein content, antioxidant enzymes superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), tryptophan fluorescence, content of protein carbonyls, prooxidant malonaldehyde (MDA) and glutathione (GSH) in hepatic and neuronal tissues were determined at the end of the fourth week. The study suggest that quercetin counters the pro-oxidant effects of galactose-induced hyperglycaemic stress, as there was a significant reversal of changes with respect to body weights, organosomatic index of hepatic and neuronal tissues, lipid peroxidation, protein carbonyl content, reduced glutathione and activities of antioxidant enzymes. In addition, treatment with quercetin appears to reduce the osmotic stress induced by hyperglycaemia, as assessed by polyol pathway enzyme aldose reductase. These results imply that inclusion of quercetin in the diet controls, to some extent, galactose-induced hyperglycaemia and its attendant complications.
Similarities are used with people known already as a means to enhance speaker verification accuracy. Motivated by this, experimental work has been conducted regarding the use of cosine distance (CD) similarity with respect to a set of reference speakers, CD features, with a back-end support vector machine (CDF-SVM) classifier for speaker verification. A state-of-the-art i-vector with CD scoring (i-CDS) is used as the baseline system for the experiments and for the computation of CD similarity. Experimental results on the telephone speech of the core short2-short3 conditions of NIST 2008 speaker recognition evaluation (SRE), for female, male and both-gender trials, show that the proposed CDF-SVM outperforms the baseline i-CDS system. The CDF-SVM achieved an absolute improvement of 1.16% in equal error rate (EER) and 0.38% in minimum DCF over the baseline i-CDS for female trials. Similar performance improvements were also obtained for the male and all-gender trials of the SRE. Finally, fusing the CDF-SVM with i-CDS gave the best overall performance, an absolute improvement of 4.19% in EER and 1.99% in minimum DCF, over the individual CDF-SVM system performance for the all-gender trials. Similar performance improvements were also achieved for male and female trials.Introduction: In recent years, i-vector [1]-based speaker verification systems have become very popular owing to their state-of-the-art performance and ability to compensate for channel variations. The i-vectors are generated by projecting the Gaussian mixture model (GMM) supervectors [1] to a low-dimensional total variability subspace, to be followed by linear discriminant analysis (LDA) and within class covariance normalisation (WCCN) [1]. Dehak et al.[1] report that the best channel compensation was obtained with LDA followed by WCCN, when compared with other techniques. Finally, the i-vectors could be effectively used for speaker verification using cosine distance (CD) as a measure to find the similarity between the test utterance and the reference speaker i-vector.Although, the i-vector with CD scoring (i-CDS) has become very popular owing to its performance, the i-vector as an input feature to a support vector machine (i-SVM) back-end resulted in a significant degradation in performance [2] compared with the i-CDS. Rao and Mak [3] proposed that by first resampling the training utterance into a number of sub-utterances using utterance partitioning with acoustic vector resampling (UP-AVR) [4] before the i-vector extraction, the performance of the i-SVM could be significantly enhanced. The utterance partition is performed to address the training sample scarcity and imbalance problems in SVM training.When we meet persons whose voice is similar to one or more people whom we know already, reference speakers (codebook), similarity with the members of the codebook is used to enhance our ability to recall/ verify the speaker. Motivated by this, we propose using CD features (CDFs), CD with respect to a set of reference background speakers, with an SVM (CDF-SV...
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