Paola Palmitesta

Morphological and Functional Adaptation of Left and Right Atria Induced by Training in Highly Trained Female Athletes

Background—Exercise is able to induce atrial remodeling in top-level athletes. However, evidence is mainly limited to men and based on cross-sectional studies. The aim of this prospective, longitudinal study was to investigate whether exercise is able to influence left and right atrial morphology and function also in female athletes. Methods and Results—Two-dimensional echocardiography was performed before season and after 16 weeks of intensive training in 24 top-level female athletes. Left and right atrial myocardial deformation was assessed by two-dimensional speckle-tracking echocardiography. Left atrial volume index (24.0±3.6 versus 26.7±6.9 mL/m2; P<0.001) and right atrial volume index (15.66±3.09 versus 20.47±4.82 mL/m2; P<0.001) significantly increased after training in female athletes. Left atrial global peak atrial longitudinal strain and peak atrial contraction strain significantly decreased after training in female athletes (43.9±9.5% versus 39.8±6.5%; P<0.05 and 15.5±4.0% versus 13.9±4.0%; P<0.05, respectively). Right atrial peak atrial longitudinal strain and peak atrial contraction strain showed a similar, although non-significant decrease (42.8±10.6% versus 39.3±8.3%; 15.6±5.6% versus 13.1±6.1%, respectively). Neither biventricular E/e′ ratio nor biatrial stiffness changed after training, suggesting that biatrial remodeling occurs in a model of volume rather than pressure overload. Conclusions—Exercise is able to induce biatrial morphological and functional changes in female athletes. Biatrial enlargement, with normal filling pressures and low atrial stiffness, is a typical feature of the heart of female athletes. These findings should be interpreted as physiological adaptations to exercise and should be considered in the differential diagnosis with cardiomyopathies.

Precompetitive assessment of heart rate variability in elite female athletes during play offs.

Heart rate variability (HRV) has been rarely applied in elite athletes prior to competition. The aim of this study was to examine the changes in HRV in elite female volleyball players before a stressful match during play offs and to evaluate the impact on sport‐specific performance.

Confidence Interval Estimation for Inequality Indices of the GiniFamily

In this paper we present some nonparametric bootstrap methods to constructdistribution-free confidence intervals for inequality indices belonging to theGini family. These methods have a coverage accuracy better than that obtainedwith the asymptotic distribution of their natural estimators, typically thestandard normal. The coverage performances of these methods are evaluated forthe index R by Gini with a Monte Carlo experiment on samples simulated fromthe Dagum income model (Type I), which is usually used to describe the incomedistribution.

Approximated Distributions of Sampling Inequality Indices

Often, in finite samples, the true level of the confidence intervals for natural estimators of inequality indices belonging to the Gini family differs greatly from their nominal level, which is based on the asymptotic confidence limits. This paper shows how the Gram-Charlier series can be used to obtain improved finite-sample confidence intervals. Our work focuses on the implementation in Mathematica 3.0 of computational procedures to compute the Gram-Charlier distribution for the following sampling inequality indices: R by Gini, P by Piesch and M by Mehran for the Dagum (Type I) distribution. The results of a Monte Carlo experiment confirm that, for the cases investigated, the Gram-Charlier distribution largely eliminates the problem of incorrect finite-sample level.

Digital Discrimination: An Ergonomic Approach to Emotional Education for the Prevention of Cyberbullying

The aim of this study is to shed some light on the complex relationship between cognitive, socio-affective and contextual (i.e. the technology and the way in which it is used) factors, which intervene in the context of prosocial and antisocial behavior, both in the real and virtual world. Results coming from a survey with a sample of 264 subjects show that those who perform victimization on the Internet are more likely to be more dependent on the use of the Internet and stay on social networks for more time in a day. In addition, while the aggressive behavior and the disengagement seem to be more correlated with a detachment towards the victim and therefore to a lower level of affective empathy, the helping behavior seems to be characterized by a greater cognitive capacity and a greater understanding of the other.

GSH Dependence Modeling with an Application to Risk Management

The generalized secant hyperbolic distribution (GSH) can be used to represent financial data with heavy tails as an alternative to the Student-t, because it guarantees the existence of all moments, also with a high kurtosis value. In order to obtain a multivariate extension of the GSH distribution, in this article we present two approaches to model the dependence, the copula approach and independent component analysis. Since the methodologies considered allow to simulate the GSH dependence, we show also the empirical results obtained in the estimation of risk of a financial portfolio by the Monte Carlo method.

Design and selection of products via genetic algorithms and neural networks

In the paper we address the design and selection of products in the framework of genetic algorithms and neural networks. We propose a procedure in alternative to the current methodologies and illustrate its advantages and disadvantages. The results of a real‐world application are reported. Although these results refer to a specific consumer product, we believe that it is worth to continue the analysis of this methodology. Copyright