Neurophysiological aspects of musical auditory stimulation on cardiovascular system

Introduction: The literature has shown that musical stimulation can influence the cardiovascular system, however, the neurophysiological aspects of this influence are not yet fully elucidated. Objective: This study describes the influence of music on the neurophysiological mechanisms in the human body, specifically the variable blood pressure, as well as the neural mechanisms of music processing. Methods: Searches were conducted in Medline, PEDro, Lilacs and SciELO using the intersection of the keyword “music” with the keyword descriptors “blood pressure” and “neurophysiology”. Results: There were selected 11 articles, which indicated that music interferes in some aspects of physiological variables. Conclusion: Studies have indicated that music interferes on the control of blood pressure, heart and respiratory rate, through possible involvement of limbic brain areas which modulate hypothalamic-pituitary functions. Further studies are needed in order to identify the mechanisms by which this influence occurs.

The detection of Cryptosporidium serpentis in snake fecal samples by real-time PCR

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

Measurement of the muon charge asymmetry in inclusive pp -> W plus X production at root s=7 TeV and an improved determination of light parton distribution functions

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Schur-Szegö composition of entire functions

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

Application of effective theory and Grassmann variables to model compact binaries with spin

Pós-graduação em Física - IFT

More of the same: high functional redundancy in stream fish assemblages from tropical agroecosystems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Constraints on parton distribution functions and extraction of the strong coupling constant from the inclusive jet cross section in pp collisions at root s=7TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modeling amino acid requirements of poultry

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

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.

Máximos e mínimos com proposta de aplicação para funções quadráticas

Pós-graduação em Matemática em Rede Nacional - IBILCE

Instrumental activities of daily living performance in healthy and cognitively intact seniors from a Brazilian sample and its relation to age and other socio-demographic variables

Background: Studies on functional capacity in community-dwelling older people have shown associations between declines in instrumental activities of daily living (IADL) and several factors. Among these, age has been the most consistently related to functional capacity independent of other variables. We aimed at evaluating the performance of a sample of healthy and cognitively intact Brazilian older people on activities of daily living and to analyze its relation to social-demographic variables. Methods: We conducted a secondary analysis of data collected for previous epidemiological studies with community-dwelling subjects aged 60 years or more. We selected subjects who did not have dementia or depression, and with no history of neurological diseases, heart attack, HIV, hepatitis or arthritis (n = 1,111). Functional capacity was assessed using the Brazilian version of the Older American Resources and Services Questionnaire (BOMFAQ). ADL performance was analyzed according to age, gender, education, and marital status (Pearson's chi(2), logistic regression). Results: IADL difficulties were present in our sample, especially in subjects aged 80 years or more, with lower levels of education, or widowed. The logistic regression analysis results indicated that "higher age" and "lower education" (p <= 0.001) remained significantly associated with IADL difficulty. Conclusions: Functional decline was present in older subjects even in the absence of medical conditions and cognitive impairment. Clinicians and researchers could benefit from knowing what to expect from older people regarding IADL performance in the absence of medical conditions.

Mapping an uncertainty zone between interpolated types of a categorical variable

Categorical data cannot be interpolated directly because they are outcomes of discrete random variables. Thus, types of categorical variables are transformed into indicator functions that can be handled by interpolation methods. Interpolated indicator values are then backtransformed to the original types of categorical variables. However, aspects such as variability and uncertainty of interpolated values of categorical data have never been considered. In this paper we show that the interpolation variance can be used to map an uncertainty zone around boundaries between types of categorical variables. Moreover, it is shown that the interpolation variance is a component of the total variance of the categorical variables, as measured by the coefficient of unalikeability. (C) 2011 Elsevier Ltd. All rights reserved.

Local power properties of some asymptotic tests in symmetric linear regression models

In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the nonnull distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the class of symmetric linear regression models. This is a wide class of models which encompasses the t model and several other symmetric distributions with longer-than normal tails. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters. Furthermore, in order to compare the finite-sample performance of these tests in this class of models, Monte Carlo simulations are presented. An empirical application to a real data set is considered for illustrative purposes. (C) 2011 Elsevier B.V. All rights reserved.

The new class of Kummer beta generalized distributions

Ng and Kotz (1995) introduced a distribution that provides greater flexibility to extremes. We define and study a new class of distributions called the Kummer beta generalized family to extend the normal, Weibull, gamma and Gumbel distributions, among several other well-known distributions. Some special models are discussed. The ordinary moments of any distribution in the new family can be expressed as linear functions of probability weighted moments of the baseline distribution. We examine the asymptotic distributions of the extreme values. We derive the density function of the order statistics, mean absolute deviations and entropies. We use maximum likelihood estimation to fit the distributions in the new class and illustrate its potentiality with an application to a real data set.

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.