Efeito do exercício físico na estabilidade de membrana de eritrócitos

CHAPTER II - This study evaluated the effects of two different types of acute aerobic exercise on the osmotic stability of human erythrocyte membrane and on different hematological and biochemical variables that are associated with this membrane property. The study population consisted of 20 healthy and active men. Participants performed single sessions of two types of exercise. The first session consisted of 60 min of moderate-intensity continuous exercise (MICE). The second session, executed a week later, consisted of high-intensity interval exercise (HIIE) until exhaustion. The osmotic stability of the erythrocyte membrane was represented by the inverse of the salt concentration (1/H50) at the midpoint of the sigmoidal curve of dependence between the absorbance of hemoglobin and the NaCl concentration. The values of 1/H50 changed from 2.29 ± 0.1 to 2.33 ± 0.09 after MICE and from 2.30 ± 0.08 to 2.23 ± 0.12 after HIIE. In MICE has occurred an increase in the mean corpuscular volume, probably due to in vivo lysis of older erythrocytes, with preservation of cells that were larger and more resistant to in vitro lysis. The study showed that a single bout of acute exercise affected the erythrocyte osmotic stability, which increased after MICE and decreased after HIIE.

Padrão de especialização comercial e crescimento econômico : uma análise sobre o Brasil no contexto da fragmentação da produção e das cadeias globais de valor

Globalization and technological changes that has happened since the 80s have brought remarkable changes in the industrial and commercial paradigm, which are expressed mainly in the international fragmentation of production and in the formation of Global Value Chains (GVC). This thesis sought to understand such phenomena and discuss new relevant variables in this context for a more accurate analysis of the current trade patterns not addressed by the seminal economic theories that relate trade and economic growth. It sought to evaluate how the trade specialization pattern of Brazil evolved compared to other economies (China, India, Russia, United States, Japan and selected Latin American economies) in the light of these phenomena from 1995 to 2011. Therefore, we have used the methodology of gross exports decomposition in value added measures, developed by Koopman et al. (2014), and indicators estimated from data of two global matrices I-O: a WIOT (2013) and the TiVA (2015). It was also tested two hypotheses regarding the role of these phenomena as determinants of economic growth in recent years: 1º) fragmentation and participation in GVC ensure higher growth rates for countries; 2º) the place (stage) in which the country finds itself in GVC associated with sectoral technological aspects is also important for economic growth. For this, we used dynamic panel models (Difference GMM and System GMM) for a sample of 40 countries from 2003 to 2011. The studies carried out on Brazil show that the country is no longer on the margins of these phenomena, because it shows increasing rates of participation in GVC, including in sectors considered most strategic for fragmentation. However, there is not a standard convergence of trade specialization of the country to those presented by developed countries or movements earned by China and Mexico in terms of their position and profile of participating in GVC. Another important result obtained by the thesis is the identification of these phenomena are in fact new variables relevant for economic growth, because it shows empirical evidences to support the hypothesis 1 and, partially, the hypothesis 2. A joint analysis of the estimated econometric results with the results of the descriptive analysis of the Brazilian economy, it leads us to conclude that the trade specialization pattern of the country in the context of the new trade setups is presented unfavorably to its growth strategy.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.