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.     

Policies to Attract R&D-related FDI in Small Emerging Countries: Aligning Incentives With Local Linkages and Absorptive Capacities in Chile

Over the last decade we have witnessed an unprecedented growth in the number of cross-border R&D investments towards large emerging countries such as China and India. However, small middle-income countries have played a marginal role as recipients of R&D-related FDI despite increasing policy efforts. In particular, several Latin American countries have recently launched new policy programs with the aim of attracting this kind of investments, but it remains uncertain whether public incentives can be useful to compensate for other locational disadvantages. The case of Chile provides an interesting empirical setting to explore these issues because during the last decade its government has been actively promoting R&D-related FDI through a new policy mix. This article suggests that for national innovation systems to benefit from the attraction of internationally-mobile R&D it is critical for public policies to ensure that appropriate linkages are established with local actors that hold absorptive capacities. Equally important for a small emerging economy like Chile is to prioritize R&D-related FDI in strategic technology niches where the country can realistically attain critical mass.     

Farming without a recipe: Wisconsin graziers and new directions for agricultural science

From 2005 through 2008, we undertook a participatory research project involving graziers from 8 farms in Southern Wisconsin, all of whom practice management intensive grazing. We used semi-structured interviews and participant observation during research and field days to investigate graziers’ engagement with university research. Grazing farms demonstrate rich variability and individuality as a result of their position within a number of biophysical and social contexts. Graziers emphasized the importance of finding ways to work with the variables of their specific context, rather than trying to control that variability. This effort entails the development and use of local knowledge, as graziers respond to the idiosyncrasies of their farms. It also leads graziers to reject mainstream agricultural research that has produced formulas for agricultural uniformity. The picture of grazing that emerged from our interviews leads us to propose the search for agroecological principles and agroecological tools, embedded in context, as an alternative to conventional research. We argue that this proposal speaks to the greater conundrum of how to relate the general knowledge of science to the place-specific, experience-based knowledge of grass-based farmers.     

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...     

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.     

Impact measurement and dimension reduction of auxiliary variables in calibration estimator using the Shapley decomposition

Statistical Methods & Applications - In multipurpose surveys several interest variables and a very large number of auxiliary variables are collected. Auxiliary variables are usually considered...     

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.     

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.