Balance and ankle muscle strength predict spatiotemporal gait parameters in individuals with diabetic peripheral neuropathy

The aims of this study were to evaluate aspects of balance, ankle muscle strength and spatiotemporal gait parameters in individuals with diabetic peripheral neuropathy (DPN) and verify whether deficits in spatiotemporal gait parameters were associated with ankle muscle strength and balance performance. Thirty individuals with DPN and 30 control individuals have participated. Spatiotemporal gait parameters were evaluated by measuring the time to walk a set distance during self-selected and maximal walking speeds. Functional mobility and balance performance were assessed using the Functional Reach and the Time Up and Go tests. Ankle isometric muscle strength was assessed with a handheld digital dynamometer. Analyses of variance were employed to verify possible differences between groups and conditions. Multiple linear regression analysis was employed to uncover possible predictors of gait deficits. Gait spatiotemporal, functional mobility, balance performance and ankle muscle strength were affected in individuals with DPN. The Time Up and Go test performance and ankle muscle isometric strength were associated to spatiotemporal gait changes, especially during maximal walking speed condition. Functional mobility and balance performance are damaged in DPN and balance performance and ankle muscle strength can be used to predict spatiotemporal gait parameters in individuals with DPN.

Produtividade de biomassa de cana-de-açúcar em função dos índices de vegetação utilizando técnicas de sensoriamento remoto

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

Hipomineralização molar incisivo: qualidade de vida relacionada à saúde bucal e percepção estética em escolares de 8 a 12 anos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Equação de regressão linear múltipla para estimativa do erro experimental

Este trabalho teve por objetivo estimar equações de regressão linear múltipla tendo, como variáveis explicativas, as demais características avaliadas em experimento de milho e, como variáveis principais, a diferença mínima significativa em percentagem da média (DMS%) e quadrado médio do erro (QMe), para peso de grãos. Com 610 experimentos conduzidos na Rede de Ensaios Nacionais de Competição de Cultivares de Milho, realizados entre 1986 e 1996 (522 experimentos) e em 1997 (88 experimentos), estimaram-se duas equações de regressão, com os 522 experimentos, validando estas pela análise de regressão simples entre os valores reais e os estimados pelas equações, com os 88 restantes, observando que, para a DMS% a equação não estimava o mesmo valor que a fórmula original e, para o QMe, a equação poderia ser utilizada na estimação. Com o teste de Lilliefors, verificou-se que os valores do QMe aderiam à distribuição normal padrão e foi construída uma tabela de classificação dos valores do QMe, baseada nos valores observados na análise da variância dos experimentos e nos estimados pela equação de regressão.

Response surface models to predict broiler performance and applications for economic analysis

A study was carried out to elaborate response surface models using broiler performance data recovered from literature in order to predict performance and elaborate economic analyses. Nineteen studies published between 1995 and 2005 were retrieved using the systematic literature review method. Weight gain and feed conversion data were collected from eight studies that fulfilled the pre-established inclusion criteria, and a response surface model was adjusted using crude protein, environmental temperature, and age as independent variables. The models produced for weight gain (r² = 0.93) and feed conversion (r² = 0.85) were accurate, precise, and not biased. Protein levels, environmental temperature and age showed linear and quadratic effects on weight gain and feed conversion. There was no interaction between protein level and environmental temperature. Age and crude protein showed interaction for weight gain and feed conversion, whereas interaction between age and temperature was detected only for weight gain. It was possible to perform economic analyses to determine maximum profit as a function of the variables that were included in the model. It was concluded that the response surface models are effective to predict the performance of broiler chickens and allow the elaboration of economic analyses to optimize profit.

Models for the analysis of growth curves for rearing tinamous (Rhynchotus rufescens) in captivity

Growth curves models provide a visual assessment of growth as a function of time, and prediction body weight at a specific age. This study aimed at estimating tinamous growth curve using different models, and at verifying their goodness of fit. A total number 11,639 weight records from 411 birds, being 6,671 from females and 3,095 from males, was analyzed. The highest estimates of a parameter were obtained using Brody (BD), von Bertalanffy (VB), Gompertz (GP,) and Logistic function (LG). Adult females were 5.7% heavier than males. The highest estimates of b parameter were obtained in the LG, GP, BID, and VB models. The estimated k parameter values in decreasing order were obtained in LG, GP, VB, and BID models. The correlation between the parameters a and k showed heavier birds are less precocious than the lighter. The estimates of intercept, linear regression coefficient, quadratic regression coefficient, and differences between quadratic coefficient of functions and estimated ties of quadratic-quadratic-quadratic segmented polynomials (QQQSP) were: 31.1732 +/- 2.41339; 3.07898 +/- 0.13287; 0.02689 +/- 0.00152; -0.05566 +/- 0.00193; 0.02349 +/- 0.00107, and 57 and 145 days, respectively. The estimated predicted mean error values (PME) of VB, GP, BID, LG, and QQQSP models were, respectively, 0.8353; 0.01715; -0.6939; -2.2453; and -0.7544%. The coefficient of determination (RI) and least square error values (MS) showed similar results. In conclusion, the VB and the QQQSP models adequately described tinamous growth. The best model to describe tinamous growth was the Gompertz model, because it presented the highest R-2 values, easiness of convergence, lower PME, and the easiness of parameter biological interpretation.

Analysis of bone microarchitecture related to anthropometry in climateric women

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

Assessing relationships between genetic evaluations using robust regression with an application to holsteins in Uruguay

Globalization of dairy cattle breeding has created a need for international sire proofs. Some early methods for converting proofs from one population to another are based on simple linear regression. An alternative robust regression method based on the t-distribution is presented, and maximum likelihood and Bayesian techniques for analysis are described, including the situation in which some proofs are missing. Procedures were used to investigate the relationship between Holstein sire proofs obtained by two Uruguayan genetic evaluation programs. The results suggest that conversion equations developed from data including only sires having proofs in both populations can lead to distorted results, relative to estimates obtained using techniques for incomplete data. There was evidence of non-normality of regression residuals, which constitutes an additional source of bias. A robust estimator may not solve all problems, but can provide simple conversion equations that are less sensitive to outlying proofs and to departures from assumptions.

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data. © FUNPEC-RP.

Linear and undulating periodized strength plus aerobic training promote similar benefits and lead to improvement of insulin resistance on obese adolescents

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

Analysis of metabolic pools in broilers chicks

This paper shows the possibility of obtaining new parameters for the mathematical modelling of data on stable isotopes in biological systems and its application in obtaining data on metabolic pools of blood plasma, blood serum, liver and muscle of broilers. This theory states that the modelling of turnover used for studies of isotopic incorporation when the metabolism has a single metabolic pool is feasible by the technique of setting an exponential. However, when the metabolism has more than one metabolic pool, it is necessary to apply the linearization technique, linear regression adjustment and evaluation of the assumptions of regression to obtain the kinetic parameters such as half-life (T1/2) and isotope exchange rate (k). The application of this technique on carbon-13 data from 100 one-day-old chicks, with the change of diet composed of grains of the photosynthetic cycle of plants from C4 to C3, in broilers has enabled the discovery that the liver, blood plasma and blood serum have a single metabolic pool; however, the pectoral muscle has two metabolic pools. For the liver, blood plasma and blood serum, the half-life values were found by the exponential fit being T1/2 = 1.4 days with the rate of exchange of k = 0.502, T1/2 = 2.4 days with k = 0.293 and T1/2 = 2.0 days with k = 0.348, respectively. For the pectoral muscle, after linearization, the half-life values were found for T1/2(1) = 1.7 and T1/2(2) = 3 days, with exchange rates of k1 = 0.405 and k2 = 0.235, representing approximately 66 and 34 %, respectively.

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model