Processo de formação de tenistas talentosos

O objetivo do presente estudo foi obter informações a respeito do processo de formação de tenistas talentosos e os consequentes fatores que contribuíram para o alcance do profissionalismo. Para tanto, se utilizou uma amostra de cinco tenistas brasileiros profissionais, caracterizados por terem pontos na ATP (Associação dos Tenistas Profissionais). A pesquisa foi constituída através de um delineamento qualitativo, que utilizou como instrumento uma entrevista composta por três perguntas abertas, elaboradas para explorar o contexto de formação do talento. Para a análise dos resultados foi utilizado o “Discurso do Sujeito Coletivo”. De acordo com os resultados, pode-se observar a importância que os professores tiveram no desenvolvimento destes tenistas talentosos, tanto na iniciação como no treinamento. No que se diz respeito ao planejamento dos treinos, o presente estudo demonstra a falta de profissionalismo nas fases iniciais do processo de formação, e a posterior preocupação apenas quando este atleta demonstra possuir algum potencial para seguir carreira profissional.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.

Midwifery Training in Brazil: Context, Challenges and Perspectives

This article describes the creation process of the first direct-entry Midwifery Course in Brazil, at the School of Arts, Sciences and Humanities of the University of Sao Paulo, Brazil. It presents the course's political-educational project and the theoretical references that support it; the movements or resistances that are opposed to the training and registration of midwives; the challenges that need to be overcome. Finally, the paper presents the foreseen perspectives of contribution of these professionals towards the improvement of healthcare quality, the decrease in maternal and perinatal morbidity and mortality rates, as well as of unnecessary cesarean sections in the country.

Social Mobilization in Health and Sanitation in an Action Research Process in an Indigenous Community in Northwestern Amazon

The living conditions of the inhabitants of Iauarete, an indigenous area in the municipality of Sao Gabriel da Cachoeira, State of Amazonas (Northern Brazil), have been negatively affected by population density, poor sanitation and maintenance of sanitation practices that are incompatible with that reality. To improve the population's quality of life, sanitation systems that are adequate to the local socio-cultural characteristics should be implemented, as well as educational processes with emphasis on social mobilization and community empowerment. The aim of this paper is to report and discuss a training course on health and sanitation using action research, directed to the mobilization of the Iauarete indigenous people, with the objective of assisting other studies of this nature. In the meetings, issues related to environmental health were discussed, a Community Newspaper was constructed, the course participants made interviews and drew up claims documents. This experience has enhanced the participants' understanding of local problems and of the importance of social mobilization for the dialogue with governmental institutions that are responsible for providing sanitation services and for seeking better living conditions. The researchers and teachers of the training course benefitted from the construction of collective knowledge resulting from interaction with subjects of the investigated situation and from the recognition and redefinition of their representations, fulfilling the fundamental premise of action research.

Effects of high-intensity intermittent training on carnitine palmitoyl transferase activity in the gastrocnemius muscle of rats

We examined the capacity of high-intensity intermittent training (HI-IT) to facilitate the delivery of lipids to enzymes responsible for oxidation, a task performed by the carnitine palmitoyl transferase (CPT) system in the rat gastrocnemius muscle. Male adult Wistar rats (160-250 g) were randomly distributed into 3 groups: sedentary (Sed, N = 5), HI-IT (N = 10), and moderate-intensity continuous training (MI-CT, N = 10). The trained groups were exercised for 8 weeks with a 10% (HI-IT) and a 5% (MI-CT) overload. The HI-IT group presented 11.8% decreased weight gain compared to the Sed group. The maximal activities of CPT-I, CPT-II, and citrate synthase were all increased in the HI-IT group compared to the Sed group (P < 0.01), as also was gene expression, measured by RT-PCR, of fatty acid binding protein (FABP; P < 0.01) and lipoprotein lipase (LPL; P < 0.05). Lactate dehydrogenase also presented a higher maximal activity (nmol·min-1·mg protein-1) in HI-IT (around 83%). We suggest that 8 weeks of HI-IT enhance mitochondrial lipid transport capacity thus facilitating the oxidation process in the gastrocnemius muscle. This adaptation may also be associated with the decrease in weight gain observed in the animals and was concomitant to a higher gene expression of both FABP and LPL in HI-IT, suggesting that intermittent exercise is a "time-efficient" strategy inducing metabolic adaptation.