251 resultados para Classificação de fibras


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INTRODUÇÃO: as oclusopatias estão entre os principais problemas de saúde bucal em todo o mundo, juntamente com a cárie dentária e a doença periodontal, e vários índices têm sido utilizados para registrá-las. OBJETIVOS: verificar a prevalência de oclusopatias utilizando a Classificação de Angle e o Índice de Estética Dentária (DAI), sua severidade e a necessidade de tratamento ortodôntico registradas pelo DAI, e comparar os resultados de ambos os índices, visando correlacionar o padrão dos dados coletados e a viabilidade de utilizá-los de forma conjunta. MÉTODOS: a amostra consistiu de 734 escolares com idade de 12 anos, de ambos os sexos, da rede pública do município de Lins/SP. Foram realizados exames nos pátios das escolas com utilização de sondas IPC a olho nu. RESULTADOS: pela Classificação de Angle, encontrou-se 33,24% das crianças com oclusão normal e 66,76% com má oclusão. Pelo DAI, observou-se que 65,26% das crianças apresentavam-se sem anormalidades ou com más oclusões leves. A má oclusão definida esteve presente em 12,81%, a má oclusão severa foi observada em 10,90% e a muito severa ou incapacitante em 11,03%. A maioria das crianças (70,57%) apresentou relação molar normal, e o overjet maxilar anterior foi a alteração mais frequentemente observada. No cruzamento dos índices houve semelhanças e divergências. CONCLUSÃO: o DAI não foi sensível a alguns problemas de oclusão detectados pela Classificação de Angle, e a recíproca foi verdadeira, demonstrando que ambos os índices possuem pontos distintos na detecção das oclusopatias, podendo ser utilizados de forma reciprocamente complementar.

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Pós-graduação em Engenharia Elétrica - FEIS

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Pós-graduação em Engenharia Mecânica - FEG

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Recent research seeking to elucidate the possible effects of different types of physical training on the morphological adaptations of skeletal muscle. Although it is relatively easy to study the effects of exercise training in humans, such research becomes limited due to the invasive nature of the biopsies and the risk inherent in the use of human subjects. Thus, the application of animal models of training has been considered an appropriate strategy for the study of muscular adaptations in response to exercise. Objective: This study used a rodent model to determine the possible effects of aerobic and strength training on the CSA of fibers of the plantaris muscle. Methods: 24 male Wistar rats (80 to 120 days, 250 to 400 g) were randomly divided into 3 groups: aerobic training (TA, n = 8), strength training (ST, n = 8) and control (CO, n = 8). The animals in groups TA and TF were subjected to 8 weeks of training, while the animals of group C remained without any stimulus from start to finish the training period. At the end of the experiment, the animals were sacrificed and right plantar muscles dissected and removed. For morphological and morphometric analysis of muscle fibers was performed staining was performed H.E. Results: There was no significant difference in initial body weight between experimental groups. After 8 weeks of training, the TA group showed a significant reduction in final body weight, compared to CO and TF groups. With respect to the CSA of fibers of the plantaris muscle, no significant difference between the groups CO and TA. On the other hand, the strength training promoted a significant increase in AST of the group TF in compared with the groups CO and TA. Conclusion: Strength training used in this study promoted an increase in CSA of fibers of the plantaris muscle. On the other hand, animals submitted to aerobic training showed no changes in the CSA of the fibers, however, there was reduction in PC animals. The data strongly suggest the use of animal model of strength training used in this study as an appropriate strategy for studying the hypertrophic response of skeletal muscle.

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Knowing the annual climatic conditions is of great importance for appropriate planning in agriculture. However, the systems of climatic classification are not widely used in agricultural studies because of the wide range of scales in which they are used. A series with data from 20 years of observations from 45 climatological stations in all over the state of Pernambuco was used. The probability density function of the incomplete gamma distribution was used to evaluate the occurrence of dry, regular and rainy years. The monthly climatic water balance was estimated using the Thornthwaite and Mather method (1955), and based on those findings, the climatic classifications were performed using the Thornthwaite (1948) and Thornthwaite and Mather (1955) for each site. The method of Kriging interpolation was used for the spatialization of the results. The study classifications were very sensitive to the local reliefs, to the amount of rainfall, and to the temperatures of the regions resulting in a wide number of climatic types. The climatic classification system of Thornthwaite and Mather (1955) allowed efficient classification of climates and a clearer summary of the information provided. In so doing, it demonstrated its capability to determine agro climatic zones.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.