Effects of isoflavone on the learning and memory of women in menopause: a double-blind placebo-controlled study

Hormone decline is common to all women during aging and, associated with other factors, leads to cognitive impairment. Its replacement enhances cognitive performance, but not all women present a clinical and family or personal history that justifies its use, mainly women with a history of cancer. The aim of this study was to determine whether a daily oral dose of 80 mg of isoflavone extract for 4 months can produce benefits in women with low hormone levels, contributing to improvement in cognitive aspects. The sample comprised 50- to 65-year-old women whose menstruation had ceased at least 1 year before and who had not undergone hormone replacement. The volunteers were allocated to two groups of 19 individuals each, i.e., isoflavone and placebo. There was a weak correlation between menopause duration and low performance in the capacity to manipulate information (central executive). We observed an increase in the capacity to integrate information in the group treated with isoflavone, but no improvement in the capacity to form new memories. We did not observe differences between groups in terms of signs and symptoms suggestive of depression according to the Geriatric Depression Scale. Our results point to a possible beneficial effect of isoflavone on some abilities of the central executive. These effects could also contribute to minimizing the impact of memory impairment. Further research based on controlled clinical trials is necessary to reach consistent conclusions.

Power-law distribution in a learning process: competition, learning and natural selection

In the present work, we propose a model for the statistical distribution of people versus number of steps acquired by them in a learning process, based on competition, learning and natural selection. We consider that learning ability is normally distributed. We found that the number of people versus step acquired by them in a learning process is given through a power law. As competition, learning and selection is also at the core of all economical and social systems, we consider that power-law scaling is a quantitative description of this process in social systems. This gives an alternative thinking in holistic properties of complex systems. (C) 2004 Elsevier B.V. All rights reserved.

An easy-learning and easy-teaching tool for indoor thermal analysis - ArcTech

ArcTech is a software being developed, applied and improved with the aim of becoming an efficient sensitization tool to support the teaching-learning process of Architecture courses. The application deals initially with the thermal comfort of buildings. The output generated by the software shows if a student is able to produce a pleasant environment, in terms of thermal sensation along a 24-hours period. Although one can find the very same characteristics in fully-developed commercial software, the reason to create ArcTech is related to the flexibility of the system to be adapted by the instructor and also to the need of simple tools for the evaluation of specific topics along the courses. The first part of ArcTech is dedicated to data management and that was developed using the visual programming language Delphi 7 and Firebird as the database management system. The second part contains the parameters that can be changed by the system administrator and those related to project visualization. The interface of the system, in which the student will learn how to implement and to evaluate the project alternatives, was built using Macromedia Flash. The software was applied to undergraduate students revealing its easy-learning and easy-teaching interface.

Computer tools to aid the learning and design steps for photovoltaic systems

This paper presents novel simulation tools to assist the lecturers about learning processes on renewable energy sources, considering photovoltaic (PV) systems. The PV behavior, functionality and its interaction with power electronic converters are investigated in the simulation tools. The main PV output characteristics, I (current) versus V (voltage) and P (power) versus V (voltage), were implemented in the tools, in order to aid the users for the design steps. In order to verify the effectiveness of the developed tools the simulation results were compared with Matlab. Finally, a prototype was implemented with the purpose to compare the experimental results with the results from the proposed tools, validating its operational feasibility. © 2011 IEEE.

Gestational protein restriction induces CA3 dendritic atrophy in dorsal hippocampal neurons but does not alter learning and memory performance in adult offspring

Studies have demonstrated that nutrient deficiency during pregnancy or in early postnatal life results in structural abnormalities in the offspring hippocampus and in cognitive impairment. In an attempt to analyze whether gestational protein restriction might induce learning and memory impairments associated with structural changes in the hippocampus, we carried out a detailed morphometric analysis of the hippocampus of male adult rats together with the behavioral characterization of these animals in the Morris water maze (MWM). Our results demonstrate that gestational protein restriction leads to a decrease in total basal dendritic length and in the number of intersections of CA3 pyramidal neurons whereas the cytoarchitecture of CA1 and dentate gyrus remained unchanged. Despite presenting significant structural rearrangements, we did not observe impairments in the MWM test. Considering the clear dissociation between the behavioral profile and the hippocampus neuronal changes, the functional significance of dendritic remodeling in fetal processing remains undisclosed. © 2012 ISDN.

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.