Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

The learning about imaging diagnosis technology

Objective To investigate the learning about management of the technology (efficient use, acquisition and maintenance of imaging diagnosis equipment) in the radiology residency program of Escola Paulista de Medicina - Universidade Federal de São Paulo, with a view to improving the education of radiologists. Materials and Methods Exploratory research where residents, faculty staff and tutors of the program were quantitative and qualitatively approached with Likert scale questionnaires (46), and deepening with recorded interviews (18) and categorization based upon meaning units (thematic analysis). Results Among the participants, 66% agreed that they had the opportunity of learning about the use of radiological equipment; for 61% the program should include knowledge on the importance of acquiring equipment; and 72% emphasized the lack of learning about equipment management and maintenance. Conclusion As the major moment in the education of specialists, the medical residency program provides residents with a favorable environment to the learning of the skills required to the future of their professional practice, but with limited emphasis on the management of the technology: efficient use, acquisition and mainly maintenance of equipment, still poorly explored. Both the investigated program and the medical residency in radiology should incorporate, whenever possible, the commitment with the training in supplementary skills related to equipment management, developing the competence of the future radiologists.

Active teaching-learning methodologies: medical students' views of problem-based learning

The prevailing undergraduate medical training process still favors disconnection and professional distancing from social needs. The Brazilian Ministries of Education and Health, through the National Curriculum Guidelines, the Incentives Program for Changes in the Medical Curriculum (PROMED), and the National Program for Reorientation of Professional Training in Health (PRO-SAÚDE), promoted the stimulus for an effective connection between medical institutions and the Unified National Health System (SUS). In accordance to the new paradigm for medical training, the Centro Universitário Serra dos Órgãos (UNIFESO) established a teaching plan in 2005 using active methodologies, specifically problem-based learning (PBL). Research was conducted through semi-structured interviews with third-year undergraduate students at the UNIFESO Medical School. The results were categorized as proposed by Bardin's thematic analysis, with the purpose of verifying the students' impressions of the new curriculum. Active methodologies proved to be well-accepted by students, who defined them as exciting and inclusive of theory and practice in medical education.

Integrating professional education, research and extensionin irrigated agriculture technology centers

With the objective to stimulate the use of irrigation and the electric energy fee reduction during night time program granted by the 2004 Federal law, the Government of the state of Paraná, Brazil launched the Night Irrigation Program - NPI. Beyond this discount, the farmer that adheres to NPI will get additional benefits, as completion of the electric grid without cost, subsidized financing of equipment, technical assistance, support with environmental farm compliance, and the possibility of replacing the entire pump energy matrix. As part of the NPI strategy of action, installation of learning centers for irrigation technology was planned in agricultural schools, thus contributing both to improve technical professional training in agriculture, and for the dissemination of knowledge in irrigated agriculture, in order to increase agricultural productivity.

Learning about brain physiology and complexity from the study of the epilepsies

The brain is a complex system, which produces emergent properties such as those associated with activity-dependent plasticity in processes of learning and memory. Therefore, understanding the integrated structures and functions of the brain is well beyond the scope of either superficial or extremely reductionistic approaches. Although a combination of zoom-in and zoom-out strategies is desirable when the brain is studied, constructing the appropriate interfaces to connect all levels of analysis is one of the most difficult challenges of contemporary neuroscience. Is it possible to build appropriate models of brain function and dysfunctions with computational tools? Among the best-known brain dysfunctions, epilepsies are neurological syndromes that reach a variety of networks, from widespread anatomical brain circuits to local molecular environments. One logical question would be: are those complex brain networks always producing maladaptive emergent properties compatible with epileptogenic substrates? The present review will deal with this question and will try to answer it by illustrating several points from the literature and from our laboratory data, with examples at the behavioral, electrophysiological, cellular and molecular levels. We conclude that, because the brain is a complex system compatible with the production of emergent properties, including plasticity, its functions should be approached using an integrated view. Concepts such as brain networks, graphics theory, neuroinformatics, and e-neuroscience are discussed as new transdisciplinary approaches dealing with the continuous growth of information about brain physiology and its dysfunctions. The epilepsies are discussed as neurobiological models of complex systems displaying maladaptive plasticity.