Adriano Zanin Zambom

Oral supplementation with L-glutamine alters gut microbiota of obese and overweight adults: A pilot study.

OBJECTIVE The aim of this study was to determine whether oral supplementation with L-glutamine (GLN) modifies the gut microbiota composition in overweight and obese adults. METHODS Thirty-three overweight and obese adults, ages between 23 and 59 y and body mass index between 25.03 and 47.12 kg/m(2), were randomly assigned to receive either oral supplementation with 30 g of L-alanine (ALA group control) or 30 g of GLN (GLN group) daily for 14 d. We analyzed the gut microbiota composition with new-generation sequencing techniques and bioinformatics analysis. RESULTS After 14 d of supplementation, adults in the GLN group exhibited statistically significant differences in the Firmicutes and Actinobacteria phyla compared with those in the ALA group. The ratio of Firmicutes to Bacteroidetes, a good biomarker for obesity, decreased in the GLN group from 0.85 to 0.57, whereas it increased from 0.91 to 1.12 in the ALA group. At the genus level, Dialister, Dorea, Pseudobutyrivibrio, and Veillonella, belonging to the Firmicutes phylum, had statistically significant reduction. CONCLUSION Oral supplementation with GLN, for a short time, altered the composition of the gut microbiota in overweight and obese humans reducing the Firmicutes to Bacteroidetes ratio, which resembled weight loss programs already seen in the literature.

Consistent variable selection for functional regression models

The dual problem of testing the predictive significance of a particular covariate, and identification of the set of relevant covariates is common in applied research and methodological investigations. To study this problem in the context of functional linear regression models with predictor variables observed over a grid and a scalar response, we consider basis expansions of the functional covariates and apply the likelihood ratio test. Based on p -values from testing each predictor, we propose a new variable selection method, which is consistent in selecting the relevant predictors from set of available predictors that is allowed to grow with the sample size n . Numerical simulations suggest that the proposed variable selection procedure outperforms existing methods found in the literature. A real dataset from weather stations in Japan is analyzed.

Monte Carlo algorithm for trajectory optimization based on Markovian readings

This paper describes an efficient algorithm to find a smooth trajectory joining two points A and B with minimum length constrained to avoid fixed subsets. The basic assumption is that the locations of the obstacles are measured several times through a mechanism that corrects the sensors at each reading using the previous observation. The proposed algorithm is based on the penalized nonparametric method previously introduced that uses confidence ellipses as a fattening of the avoidance set. In this paper we obtain consistent estimates of the best trajectory using Monte Carlo construction of the confidence ellipse.

A nonparametric hypothesis test for heteroscedasticity

ABSTRACT In this paper, a hypothesis test for heteroscedasticity is proposed in a nonparametric regression model. The test statistic, which uses the residuals from a nonparametric fit of the mean function, is based on an adaptation of the well-known Levenes test. Using the recent theory for analysis of variance when the number of factor levels goes to infinity, the asymptotic distribution of the test statistic is established under the null hypothesis of homocedasticity and under local alternatives. Simulations suggest that the proposed test performs well in several situations, especially when the variance is a nonlinear function of the predictor.

Constrained optimization with stochastic feasibility regions applied to vehicle path planning

In real-time trajectory planning for unmanned vehicles, on-board sensors, radars and other instruments are used to collect information on possible obstacles to be avoided and pathways to be followed. Since, in practice, observations of the sensors have measurement errors, the stochasticity of the data has to be incorporated into the models. In this paper, we consider using a genetic algorithm for the constrained optimization problem of finding the trajectory with minimum length between two locations, avoiding the obstacles on the way. To incorporate the variability of the sensor readings, we propose a modified genetic algorithm, addressing the stochasticity of the feasible regions. In this way, the probability that a possible solution in the search space, say x, is feasible can be derived from the random observations of obstacles and pathways, creating a real-time data learning algorithm. By building a confidence region from the observed data such that its border intersects with the solution point x, the level of the confidence region defines the probability that x is feasible. We propose using a smooth penalty function based on the Gaussian distribution, facilitating the borders of the feasible regions to be reached by the algorithm.

Nonparametric significance testing and group variable selection

In the context of a heteroscedastic nonparametric regression model, we develop a test for the null hypothesis that a subset of the predictors has no influence on the regression function. The test uses residuals obtained from local polynomial fitting of the null model and is based on a test statistic inspired from high-dimensional analysis of variance. Using p -values from this test, and multiple testing ideas, a group variable selection method is proposed, which can consistently select even groups of variables with diminishing predictive significance. A backward elimination version of this procedure, called GBEAMS for Group Backward Elimination Anova-type Model Selection, is recommended for practical applications. Simulation studies, suggest that the proposed test procedure outperforms the generalized likelihood ratio test when the alternative is non-additive or there is heteroscedasticity. Additional simulation studies reveal that the proposed group variable selection procedure performs competitively against other variable selection methods, and outperforms them in selecting groups having nonlinear or dependent effects. The proposed group variable selection procedure is illustrated on a real data set.