Physical exercise,nutrition and hormones: three pillars to fight sarcopenia

Background: Sarcopenia is a pathophysiological condition diffused in elderly people; it represents a social issue due to the longer life expectancy and the growing aging population. It affects negatively quality of life and it represents a risk factor for other pathologies, such as diabetes, cardiovascular disease, and obesity. No silver bullet exists to hinder sarcopenia, but it may be counteracted by physical exercise, nutrition, and a proper endocrine milieu. Indeed, we aim to analyze the scientific literature to give to clinician effective advices to counteract sarcopenia.

Main text: Physical exercise, proper nutrition, optimized hormonal homeostasis represent the three pillars to fight sarcopenia. Physical exercise represents the most effective remedy to face sarcopenia, in particular if it is combined with a proper diet and with an adequate endocrine milieu. Consistency in training, adequate daily protein intake and eugonadism seems to be the keys to fight sarcopenia. The combination of these three pillars might act synergistically.

Conclusions: Optimization of these factors may increase their efficiency; however, scientific data may be sometimes confusing so far. Therefore, we aim to give practical advices to clinician to identify and to highlight the most important aspects in each of these three factors that should be addressed.     

Model-based clustering of multivariate skew data with circular components and missing values

Motivated by classification issues that arise in marine studies, we propose a latent-class mixture model for the unsupervised classification of incomplete quadrivariate data with two linear and two circular components. The model integrates bivariate circular densities and bivariate skew normal densities to capture the association between toroidal clusters of bivariate circular observations and planar clusters of bivariate linear observations. Maximum-likelihood estimation of the model is facilitated by an expectation maximization (EM) algorithm that treats unknown class membership and missing values as different sources of incomplete information. The model is exploited on hourly observations of wind speed and direction and wave height and direction to identify a number of sea regimes, which represent specific distributional shapes that the data take under environmental latent conditions.     

Approximate Bayesian inference in semi-mechanistic models

Inference of interaction networks represented by systems of differential equations is a challenging problem in many scientific disciplines. In the present article, we follow a semi-mechanistic modelling approach based on gradient matching. We investigate the extent to which key factors, including the kinetic model, statistical formulation and numerical methods, impact upon performance at network reconstruction. We emphasize general lessons for computational statisticians when faced with the challenge of model selection, and we assess the accuracy of various alternative paradigms, including recent widely applicable information criteria and different numerical procedures for approximating Bayes factors. We conduct the comparative evaluation with a novel inferential pipeline that systematically disambiguates confounding factors via an ANOVA scheme.     

SPECULATIVE PROFITS,INNOVATION, AND GROWTH

When technological change affects the prices of tradeable assets, innovators can obtain speculative profits by exploiting their inside information as to the occurrence of innovations. We propose a tractable model of endogenous growth that formalizes this argument, originally due to Hirshleifer (1971). We then use the model to assess two claims advanced by Hirshleifer, namely, that speculative profits can generate excessive investment in R&D when they add to monopoly rents guaranteed by patent protection, or else even in a perfectly competitive economy. The analysis confirms the first claim, but casts doubts on the second one. (JEL O30, O40)     

Impacts of urban sprawl on species richness of plants,butterflies, gastropods and birds: not only built-up area matters

Urban growth is a major factor of global environmental change and has important impacts on biodiversity, such as changes in species composition and biotic homogenization. Most previous studies have focused on effects of urban area as a general measure of urbanization, and on few or single taxa. Here, we analyzed the impacts of the different components of urban sprawl (i.e., scattered and widespread urban growth) on species richness of a variety of taxonomic groups covering mosses, vascular plants, gastropods, butterflies, and birds at the habitat and landscape scales. Besides urban area, we considered the average age, imperviousness, and dispersion degree of urban area, along with human population density, to disentangle the effects of the different components of urban sprawl on biodiversity. The study was carried out in the Swiss Plateau that has undergone substantial urban sprawl in recent decades.Vascular plants and birds showed the strongest responses to urban sprawl, especially at the landscape scale, with non-native and ruderal plants proliferating and common generalist birds increasing at the expense of specialist birds as urban sprawl grew. Overall, urban area had the greatest contribution on such impacts, but additional effects of urban dispersion (i.e., increase of non-native plants) and human population density (i.e., increases of ruderal plants and common generalist birds) were found. Our findings support the hypothesis that negative impacts of urban sprawl on biodiversity can be reduced by compacting urban growth while still avoiding the formation of very densely populated areas.     

Keep Your Friends Close? Supply Chain Design and Disruption Risk

In this article, we evaluate the relationship between supply chain design decisions and supply chain disruption risk. We explore two supply chain design strategies: (i) the dispersion of supply chain partners to reduce supply chain disruption risk versus (ii) the co‐location of supply chain partners to reduce supply chain disruption risk. In addition, we assess supply chain disruption risk from three perspectives: the inbound material flow from the supplier (supply side), the internal production processes (internal), and the outbound material flow to the customer (customer side) as a disruption can occur at any of these locations. We measure disruption risk in terms of stoppages in flows, reductions in flow, close calls (disruptions that were prevented at the last minute), disruption duration (time until normal operation flow was restored), and the spread of disruptions all the way through the supply chain. We use seemingly unrelated regression (SUR) to analyze our data, finding that lead times, especially supply side lead times, are significantly associated with higher levels of supply chain disruption risk. We find co‐location with suppliers appears to have beneficial effects to the reduction of disruption duration, and, overall supply side factors have a higher impact when it comes to supply chain disruption risk than comparable customer side factors.