Seleção de características para identificação de diferentes proporções de tipos de fibras musculares por meio da eletromiografia de superfície

Skeletal muscle consists of muscle fiber types that have different physiological and biochemical characteristics. Basically, the muscle fiber can be classified into type I and type II, presenting, among other features, contraction speed and sensitivity to fatigue different for each type of muscle fiber. These fibers coexist in the skeletal muscles and their relative proportions are modulated according to the muscle functionality and the stimulus that is submitted. To identify the different proportions of fiber types in the muscle composition, many studies use biopsy as standard procedure. As the surface electromyography (EMGs) allows to extract information about the recruitment of different motor units, this study is based on the assumption that it is possible to use the EMG to identify different proportions of fiber types in a muscle. The goal of this study was to identify the characteristics of the EMG signals which are able to distinguish, more precisely, different proportions of fiber types. Also was investigated the combination of characteristics using appropriate mathematical models. To achieve the proposed objective, simulated signals were developed with different proportions of motor units recruited and with different signal-to-noise ratios. Thirteen characteristics in function of time and the frequency were extracted from emulated signals. The results for each extracted feature of the signals were submitted to the clustering algorithm k-means to separate the different proportions of motor units recruited on the emulated signals. Mathematical techniques (confusion matrix and analysis of capability) were implemented to select the characteristics able to identify different proportions of muscle fiber types. As a result, the average frequency and median frequency were selected as able to distinguish, with more precision, the proportions of different muscle fiber types. Posteriorly, the features considered most able were analyzed in an associated way through principal component analysis. Were found two principal components of the signals emulated without noise (CP1 and CP2) and two principal components of the noisy signals (CP1 and CP2 ). The first principal components (CP1 and CP1 ) were identified as being able to distinguish different proportions of muscle fiber types. The selected characteristics (median frequency, mean frequency, CP1 and CP1 ) were used to analyze real EMGs signals, comparing sedentary people with physically active people who practice strength training (weight training). The results obtained with the different groups of volunteers show that the physically active people obtained higher values of mean frequency, median frequency and principal components compared with the sedentary people. Moreover, these values decreased with increasing power level for both groups, however, the decline was more accented for the group of physically active people. Based on these results, it is assumed that the volunteers of the physically active group have higher proportions of type II fibers than sedentary people. Finally, based on these results, we can conclude that the selected characteristics were able to distinguish different proportions of muscle fiber types, both for the emulated signals as to the real signals. These characteristics can be used in several studies, for example, to evaluate the progress of people with myopathy and neuromyopathy due to the physiotherapy, and also to analyze the development of athletes to improve their muscle capacity according to their sport. In both cases, the extraction of these characteristics from the surface electromyography signals provides a feedback to the physiotherapist and the coach physical, who can analyze the increase in the proportion of a given type of fiber, as desired in each case.

Desempenho agronômico, bromatológico e estabilidade fenotípica de sorgo silageiro em Uberlândia - MG

Sorghum (Sorghum bicolor (L.) Moench) is a good alternative to be used as silage, especially in places with water scarcity and high temperatures, due to their morphological and physiological characteristics. The appropriate management, as the ideal seeding time, interferes both productivity and the quality of silage. The work was conducted with the objective of evaluating the agronomic and bromatological performance of varieties and hybrids of silage sorghum and their phenotypic stability in two seasons, season and off-season, in the city of Uberlândia, Minas Gerais. The experiments were performed at Capim Branco Experimental Farm of Federal University of Uberlândia (UFU), located in the referred city. There were two sowing dates in the same experimental area, off-season (March to June 2014) and season (November 2014 to March 2015), and the varieties and hybrids were evaluated in both situations. The design was a randomized block with 25 treatments (hybrids and varieties of sorghum) and three replications. Agronomical and bromatological data were subjected to an analysis of variance; averages were grouped by Scott-Knott test at 5% of probability, through Genes computer program; and to estimate the stability, it was opted for Annicchiarico method. The flowering of cultivars, dry matter productivity, plant height, Acid Detergent Fiber (ADF), Neutral Detergent Fiber (NDF) and Crude Protein (CP) are affected by the environment and the variety. Regarding productivity and quality of the fiber, SF11 variety was superior, independent of the rated environment. In relation to the performance stability of dry matter, the varieties SF15, SF11, SF25, PROG 134 IPA, 1141572, 1141570 and 1141562 were highlighted. For the stability of the quality of fibers (FDA and FDN), the variety 1141562 stood out. The environment reduces the expression of characters “days of flowering”, “plant height” and “productivity of dry matter of hybrids”. From the 25 hybrids analyzed for productivity and stability of dry matter performance, seven were highlighted, regardless of the rated environment: Volumax commercial hybrid and experiments 12F39006, 12F39007, 12F37014, 12F39014, 12F38009 and 12F02006.