Insulin concentrations in cerebellum and body balance in diabetic male rats: Aerobic training effects

Brain insulin has had widespread metabolic, neurotrophic, and neuromodulatory functions and has been involved in the central regulation of food intake and body weight, learning and memory, neuronal development, and neuronal apoptosis. Purpose: The present study investigated the role of swimming training on cerebral metabolism on insulin concentrations in cerebellum and the body balance performance of diabetic rats. Methods: Forty Male Wistar rats were divided in four groups: sedentary control (SC), trained control (TC), sedentary diabetic (SD), and trained diabetic (TD). Diabetes was induced by alloxan (32 mg kg b.w.), single dose injection. The mean blood glucose of diabetic groups was 367 ± 40 mg/dl. Training program consisted in swimming 5 days/week, 1 h/day, 8 weeks, supporting a workload corresponding to 90% of maximal lactate steady state (MLSS). For the body balance testing rats were trained to traverse for 5 min daily for 5-7 days. All dependent variables were analyzed by one-way analysis of variance (ANOVA) and a significance level of p < 0.05 was used for all comparisons. Results: The body balance testing scores were different between groups. Insulin concentrations in cerebellum were not different between groups. Conclusion: It was concluded that in diabetic rats, aerobic training does not induce alterations on cerebellum insulin but induces important metabolic, hormonal and behavioral alterations which are associated with an improvement in glucose homeostasis, serum insulin concentrations and body balance. © 2013 Elsevier Inc.

Mensuração de estratégias de estudo e aprendizagem de alunos universitários: learning and study strategies inventory (lassi) adaptação e validação para o Brasil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.