Pasquale Valentini

Left ventricle relative wall thickness and plasma leptin levels: baseline relationships and effects of 4 months of walking training in healthy overweight postmenopausal women.

Objective:Whether leptin has positive or negative influences on cardiac structure and function in healthy sedentary overweight postmenopausal women is unknown. We investigated the role of leptin in cardiac health and whether aerobic fitness enhancement influences any relationships revealed between leptin and echocardiographic measurements. Methods:Thirty-nine sedentary postmenopausal women were enrolled after an initial screening. The women underwent blood sampling for hormone levels, anthropometric and echocardiographic measurements, dietary habits investigation, and fitness testing, both before and after 4 months of walking training. Results:After the intervention, the women who had an adherence to training of 75% or higher showed significantly reduced percentage fat mass (P = 0.006) and plasma leptin levels (P < 0.001), whereas their maximum oxygen consumption increased significantly (P < 0.001). The women showed a significant reduction in left ventricle relative wall thickness (P = 0.039) and significant increases in both left ventricular end-diastolic diameter (P = 0.040) and left ventricular mass index (P = 0.043). At baseline, a negative correlation was seen between plasma leptin levels and left ventricle relative wall thickness (r = −0.553; P = 0.009). Further negative correlations were seen for the changes in left ventricle relative wall thickness with leptin levels (r = −0.456; P = 0.038) and with tricipital skinfold (r = −0.436; P = 0.05). Conclusions:In healthy overweight sedentary postmenopausal women with low fitness level, high plasma leptin levels seem to have a protective role against left ventricle relative wall thickness hypertrophy and to participate in its remodeling after 4 months of aerobic training.

Hierarchical generalised latent spatial quantile regression models with applications to indoor radon concentration

Radon-222 is a noble gas arising naturally from decay of uranium-238 present in the earth’s crust. In confined spaces, high concentrations of radon can become a serious health concern. Hence, experts widely agree that prolonged exposure to this gas can significantly increase the risk of lung cancer. A range of variables, such as geological factors, soil properties, building characteristics, the living habits of dwellers and meteorological parameters, might have a significant impact on indoor radon concentration and its variability. In this paper, the effect of various factors that are believed to influence the indoor radon concentrations is studied at the municipal level of L’Aquila district (Abruzzo region, Italy). The statistical analysis is carried out through a hierarchical Bayesian spatial quantile regression model in which the matrix of explanatory variables is partially defined through a set of spatial common latent factors. The proposed model, here referred to as the Generalized latent-spatial-quantile regression model, is thus appropriate when some covariates are indicators of latent factors that can be used as predictors in the quantile regression and the variables are supposed to be spatially correlated. It is shown that the model has an intuitive appeal and that it is preferable when the interest is in studying the effects of covariates on one or both the tails of the response distribution, as in the case of indoor radon concentrations. Full probabilistic inference is performed by applying Markov chain Monte Carlo techniques.

Heterogeneity Measures in Customer Satisfaction Analysis

In this paper we deal with the problem of identifying a valid way to characterize heterogeneity in the analysis of customer satisfaction observing the phenomenon through a new perspective. In the literature, the variability of a Customer Satisfaction index is measured by the standard deviation or the coefficient of variation. In this way, heterogeneity among customers may be masked. To overcome this drawback, we provide a new approach to the construction of a multi-dimensional measure of heterogeneity of the Customer Satisfaction index not depending on the choice of a particular heterogeneity index. The approach is based on heterogeneity profiles which lead to a more detailed description of heterogeneity than alternative measures. Moreover, a latent class model is used for classifying individuals into distinct groups based on responses to a set of items. Once groups are formed, Customer Satisfaction researchers can make conclusions about the level of satisfaction and the characteristics of groups in terms of heterogeneity.

A Functional Spatio-Temporal Model for Geometric Shape Analysis

In this chapter we consider a functional spatio-temporal model for shape objects represented by landmark data. The model describes a time-varying deformation of the ambient space in which the objects of interest lie. The use of basis functions, defined by principal warps in space and time, facilitates both the model specification and the fitting of the data in Procrustes tangent coordinates. The fitted model can be interpreted either just in terms of the finite set of landmarks at the given set of time points, or in terms of a deformation of the space which varies continuously in time. The method is illustrated on a facial expression dataset.

Quantile regression and Bayesian cluster detection to identify radon prone areas

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys.