Depression during menopausal transition: a review of treatment strategies and pathophysiological correlates

It has long been recognized that women are at a higher risk than men to develop depression and that such risk is particularly associated with reproductive cycle events. Recent long-term, prospective studies have demonstrated that the transition to menopause is associated with higher risk for new onset and recurrent depression. A number of biological and environmental factors are independent predictors for depression in this population, including the presence of hot flushes, sleep disturbance, history of severe premenstrual syndrome or postpartum blues, ethnicity, history of stressful life events, past history of depression, body mass index, socioeconomic status and the use of hormones and antidepressants. Accumulated evidence suggests that ovarian hormones modulate serotonin and noradrenaline neurotransmission, a process that may be associated with underlying pathophysiological processes involved in the emergence of depressive symptoms during periods of hormonal fluctuation in biologically predisposed subpopulations. Transdermal estradiol and serotonergic and noradrenergic antidepressants are efficacious in the treatment of depression and vasomotor symptoms in symptomatic, midlife women. The identification of individuals whom might be at a higher risk for depression during menopausal transition could guide preventive strategies for this population.     

A new quantitative method for assessing sleepiness: The alpha attenuation test

Abstract

Sleepiness interferes with the ability of night-workers and shiftworkers to safely and effectively perform their jobs, and has been increasingly recognized as a major cause of industrial and transportation accidents. Current methods for the assessment of physiological sleepiness levels, such as the Multiple Sleep Latency Test (MSLT) have several limitations and are particularly impractical in field or occupational studies. In this study, the effectiveness of the Alpha Attenuation Test (AAT) for the quantification of sleepiness has been evaluated and compared to the MSLT during a 40-h sleep deprivation protocol. The AAT, a 12-min eye closed/open protocol, is a quantitative method based on the fact that EEG alpha (8-12 Hz) activity tends to increase with eyes open as individuals get sleepier, while it decreases with eyes closed. In this validation study, the AAT proved to be a sensitive measure of sleepiness induced by sleep deprivation, and it also correlated well with the MSLT. Relative to the MSLT, the AAT has the advantages of being objective, minimally intrusive, and more practical especially in industrial or field studies.     

An Integrated Approach to Select Key Quality Indicators in Transit Services

Social Indicators Research - Recent interests in transit services have captured attention of experts on the monitoring of public transport quality. Previous research focused on relevant models and...     

A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms

Summary.  Genetic polymorphisms in deoxyribonucleic acid coding regions may have a phenotypic effect on the carrier, e.g. by influencing susceptibility to disease. Detection of deleterious mutations via association studies is hampered by the large number of candidate sites; therefore methods are needed to narrow down the search to the most promising sites. For this, a possible approach is to use structural and sequence-based information of the encoded protein to predict whether a mutation at a particular site is likely to disrupt the functionality of the protein itself. We propose a hierarchical Bayesian multivariate adaptive regression spline (BMARS) model for supervised learning in this context and assess its predictive performance by using data from mutagenesis experiments on lac repressor and lysozyme proteins. In these experiments, about 12 amino-acid substitutions were performed at each native amino-acid position and the effect on protein functionality was assessed. The training data thus consist of repeated observations at each position, which the hierarchical framework is needed to account for. The model is trained on the lac repressor data and tested on the lysozyme mutations and vice versa. In particular, we show that the hierarchical BMARS model, by allowing for the clustered nature of the data, yields lower out-of-sample misclassification rates compared with both a BMARS and a frequen-tist MARS model, a support vector machine classifier and an optimally pruned classification tree.     

Genetic algorithms for the analysis of Bayesian hierarchical partition models

Hierarchical partition models (see Malec and Sedransk, 1992, Consonni and Veronese, 1995) aim at finding an optimal grouping (partition) of a set of experiments regarding a target variable. In this class of models the partition is regarded as an unknown parameter, and one of the main goals is computing the posterior distribution over the class of the possible partitions. This problem has been addressed in Sampietro and Veronese (1998), where a Metropolis-Hastings algorithm is applied. In this paper the performance of an alternative procedure, based on the logic of genetic algorithms, is evaluated. The results of the two approaches are compared, even if a conjoint use of them is to be advised.     

Testing for Independence of Wage Drift and Minimum Contractual Wage: An Empirical Investigation Using Italian Aggregate Time Series

A theoretical two-stage bargaining model for wage drift and minimum contractual wage is discussed and its implication in terms of Granger-causality between the two variables is tested by using Italian aggregate time series. Empirical evidence suggests rejecting the independence of the two series. This result has, in turn, implications for policy analysis. Our findings seem to indicate the appropriateness of a policy oriented towards decentralized wage setting.     

Union Activism,Workers’ Satisfaction and Organizational Change

Abstract. This paper investigates the reaction of active union members towards workplace organizational changes. We use micro‐data from a large firm and estimate an empirical model which deals explicitly with the potential endogeneity of the union activist status. The data indicate that workers who become union activists are more likely to complain than members. Moreover, after proper allowance is made for the endogeneity of union activism, we find this effect to be reinforced. This result suggests that activists are not inherently against organizational changes, as compared to non‐activists, though they report higher dissatisfaction in anticipation of the greater loss in union rents that is likely to be associated with the change.