939 resultados para Non-gaussian Random Functions


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The purpose of this study was to analyze the effect of different exercise programs on the psychological and cognitive functions in patients with Parkinson's disease (PD). Forty-five patients with PD participated in the study. The participants were randomized in three intervention programs: Group-1 (n=15, cognitive-activities), Group-2 (n=15, multimodal exercise) and Group-3 (n=15, exercises for posture and gait). The clinical, psychological and cognitive functions were assessed before and after 4 months of intervention. Univariate analysis did not reveal significant interactions between groups and time (p>0.05). However, univariate analysis for time revealed differences in stress level and memory. Participants showed less physical stress (p<0.01) and overall stress (p < 0.04) and higher performance in episodic declarative memory (p < 0.001) after exercise. These findings suggest that group work with motor or non-motor activities can improve cognitive and psychological functions of patients with PD.

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The objective of this research was to estimate (co) variance functions and genetic parameters for body weight in Colombian buffalo populations using random regression models with Legendre polynomials. Data consisted of 34,738 weight records from birth to 900 days of age from 7815 buffaloes. Fixed effects in the model were contemporary group and parity order of the mother. Random effects were direct and maternal additive genetic, as well as animal and maternal permanent environmental effects. A cubic orthogonal Legendre polynomial was used to model the mean curve of the population. Eleven models with first to sixth order polynomials were used to describe additive genetic direct and maternal effects, and animal and maternal permanent environmental effects. The residual was modeled considering five variance classes. The best model included fourth and sixth order polynomials for direct additive genetic and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects. The direct heritability increased from birth until 120 days of age (0.32 +/- 0.05), decreasing thereafter until one year of age (0.18 +/- 0.04) and increased again, reaching 0.39 +/- 0.09, at the end of the evaluated period. The highest maternal heritability estimates (0.11 +/- 0.05), were obtained for weights around weaning age (weaning age range is between 8 and 9.5 months). Maternal genetic and maternal permanent environmental variances increased from birth until about one year of age, decreasing at later ages. Direct genetic correlations ranged from moderate (0.60 +/- 0.060) to high (0.99 +/- 0.001), maternal genetic correlations showed a similar range (0.41 +/- 0.401 and 0.99 +/- 0.003), and all of them decreased as time between weighings increased. Direct genetic correlations suggested that selecting buffalos for heavier weights at any age would increase weights from birth through 900 days of age. However, higher heritabilities for direct genetic weights effects after 600 days of age suggested that selection for these effects would be more effective if done during this age period. A greater response to selection for maternal ability would be expected if selection used maternal genetic predictions for weights near weaning. (C) 2013 Elsevier B.V. All rights reserved.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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

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

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Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.

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Classical procedures for model updating in non-linear mechanical systems based on vibration data can fail because the common linear metrics are not sensitive for non-linear behavior caused by gaps, backlash, bolts, joints, materials, etc. Several strategies were proposed in the literature in order to allow a correct representative model of non-linear structures. The present paper evaluates the performance of two approaches based on different objective functions. The first one is a time domain methodology based on the proper orthogonal decomposition constructed from the output time histories. The second approach uses objective functions with multiples convolutions described by the first and second order discrete-time Volterra kernels. In order to discuss the results, a benchmark of a clamped-clamped beam with an pre-applied static load is simulated and updated using proper orthogonal decomposition and Volterra Series. The comparisons and discussions of the results show the practical applicability and drawbacks of both approaches.

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Small non coding RNAs emerged as important characters in several biology aspects. Among then, the most studied are microRNAs (miRNAs) and short interfering RNAs (siRNAs), that regulate their target gene post-transcriptionally in plants, animals and RNAi pathway intermediates, respectively. Both of classes have similar biogenesis being processed by Dicer enzymes and subsequent association with Argonaute enzymes. In plants, miRNAs and siRNAs have important functions in development, genome integrity and biotic and abiotic stress responses. The advances in high-throughtput sequencing and in silico analisys provide the uncover of new small non coding RNAs classes, many of them with unknown functions and biogenesis. tRNA derived small RNAs (tRFs) are a small non coding RNA class, that have as precursor a tRNA molecule. These were uncovers in the last decade in many organisms and, recently, in plants. Recent works detected tRFs from different sizes, with different source portions of the mature tRNA molecule (5’ end; 3’ end, anti-codon loop) and some from the tRNA precursor (pre-tRNA), suggesting that may be a novel class of small RNA and not random degradation products. Works in humans showed that some tRFs are processed by the Dicer enzymes, have association with the Argonaute enzymes and cell differentiation, tumor appearance and gene silencing related functions. Works in Arabidopsis and pumpkin (Cucurbita maxima) showed, respectively, that the tRFs have nutritional stress response possible functions and long distance signaling function between source and drain tissues, and may affect the translation. The tRFs biogenesis in plants are, until now an unknown, absence information about it in the literature and its possible biological functions are few studied yet, making then interesting target for studies among the small non coding RNAs in plants

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The aims of this study were (1) to synthesize and characterize random and aligned nanocomposite fibers of multi-walled carbon nanotubes (MWCNT)/nylon-6 and (2) to determine their reinforcing effects on the flexural strength of a dental resin composite.Nylon-6 was dissolved in hexafluoropropanol (10 wt%), followed by the addition of MWCNT (hereafter referred to as nanotubes) at two distinct concentrations (i.e., 0.5 or 1.5 wt%). Neat nylon-6 fibers (without nanotubes) were also prepared. The solutions were electrospun using parameters under low- (120 rpm) or high-speed (6000 rpm) mandrel rotation to collect random and aligned fibers, respectively. The processed fiber mats were characterized by scanning (SEM) and transmission (TEM) electron microscopies, as well as by uni-axial tensile testing. To determine the reinforcing effects on the flexural strength of a dental resin composite, bar-shaped (20 x 2 x 2 mm(3)) resin composite specimens were prepared by first placing one increment of the composite, followed by one strip of the mat, and one last increment of composite. Non-reinforced composite specimens were used as the control. The specimens were then evaluated using flexural strength testing. SEM was done on the fractured surfaces. The data were analyzed using ANOVA and the Tukey's test (alpha=5%).Nanotubes were successfully incorporated into the nylon-6 fibers. Aligned and random fibers were obtained using high- and low-speed electrospinning, respectively, where the former were significantly (p<0.001) stronger than the latter, regardless of the nanotubes'presence. Indeed, the dental resin composite tested was significantly reinforced when combined with nylon-6 fibrous mats composed of aligned fibers (with or without nanotubes) or random fibers incorporated with nanotubes at 0.5 wt%. (C) 2015 Elsevier Ltd. All rights reserved.

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

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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)