Effects of the essential oil from Citrus aurantium L. in experimental anxiety models in mice

Citrus aurantium L. is popularly used to treat anxiety, among other indications suggesting central nervous system action. Previous studies showed anxiolytic effect in the essential oil from peel in mice evaluated on the elevated plus maze [Carvalho-Freitas, M.I.R., Costa, M., 2002. Anxiolytic and sedative effects of extracts and essential oil from Citrus aurantium L. Biological and Pharmaceutical Bulletin 25, 1629-1633.]. In order to better characterize the activity of the essential oil, it was evaluated in two other experimental models: the light-dark box and the marble-burying test, respectively related to generalized anxiety disorder and to obsessive compulsive disorder. Mice were treated acutely by oral route 30 min (single dose) or once a day for 15 days (repeated doses) before experimental procedures. In light-dark box test, single treatment with essential oil augmented the time spent by mice in the light chamber and the number of transitions between the two compartments. There were no observed alterations in the parameters evaluated in light-dark box after repeated treatment. Otherwise, single and repeated treatments with essential oil were able to suppress marble-burying behavior. At effective doses in the behavioral tests, mice showed no impairment on rotarod procedure after both single and repeated treatments with essential oil, denoting absence of motor deficit. Results observed in marble-burying test, related to obsessive compulsive disorder, appear more consistent than those observed in light-dark box. (c) 2005 Elsevier B.V. All rights reserved.

Fractional polynomial response surface models

Second-order polynomial models have been used extensively to approximate the relationship between a response variable and several continuous factors. However, sometimes polynomial models do not adequately describe the important features of the response surface. This article describes the use of fractional polynomial models. It is shown how the models can be fitted, an appropriate model selected, and inference conducted. Polynomial and fractional polynomial models are fitted to two published datasets, illustrating that sometimes the fractional polynomial can give as good a fit to the data and much more plausible behavior between the design points than the polynomial model. © 2005 American Statistical Association and the International Biometric Society.

Previsão da velocidade dos ventos por redes neurais artificiais e arima de box & jenkins

Pós-graduação em Engenharia Elétrica - FEIS

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.