Explaining standing order reforms in the Camera dei Deputati during the liberal age

How might the continuous changes in the standing orders of the Camera dei Deputati between 1861 and 1922 be explained? To answer this question, the text investigates the political events associated with standing order reforms. Two results are emphasized. On the one hand, and contrary to common views, the study shows that the reforms were not casual or episodic, but resulted from different sets of political pressure, internal or external to the parliamentary ambit. This fact, on the other hand, draws attention to the need to go deeper into the question of the institutional evolution of the liberal parliament, chiefly with regard to relations among institutional actors.

Latent residual analysis in binary regression with skewed link

Model diagnostics is an integral part of model determination and an important part of the model diagnostics is residual analysis. We adapt and implement residuals considered in the literature for the probit, logistic and skew-probit links under binary regression. New latent residuals for the skew-probit link are proposed here. We have detected the presence of outliers using the residuals proposed here for different models in a simulated dataset and a real medical dataset.

Probabilistic patterns of interaction: the effects of link-strength variability on food web structure

Patterns of species interactions affect the dynamics of food webs. An important component of species interactions that is rarely considered with respect to food webs is the strengths of interactions, which may affect both structure and dynamics. In natural systems, these strengths are variable, and can be quantified as probability distributions. We examined how variation in strengths of interactions can be described hierarchically, and how this variation impacts the structure of species interactions in predator-prey networks, both of which are important components of ecological food webs. The stable isotope ratios of predator and prey species may be particularly useful for quantifying this variability, and we show how these data can be used to build probabilistic predator-prey networks. Moreover, the distribution of variation in strengths among interactions can be estimated from a limited number of observations. This distribution informs network structure, especially the key role of dietary specialization, which may be useful for predicting structural properties in systems that are difficult to observe. Finally, using three mammalian predator-prey networks ( two African and one Canadian) quantified from stable isotope data, we show that exclusion of link-strength variability results in biased estimates of nestedness and modularity within food webs, whereas the inclusion of body size constraints only marginally increases the predictive accuracy of the isotope-based network. We find that modularity is the consequence of strong link-strengths in both African systems, while nestedness is not significantly present in any of the three predator-prey networks.

Leptin as a link between the immune system and kidney-related diseases: leading actor or just a coadjuvant?

Food intake and nutritional status modify the physiological responses of the immune system to illness and infection and regulate the development of chronic inflammatory processes, such as kidney disease. Adipose tissue secretes immune-related proteins called adipokines that have pleiotropic effects on both the immune and neuroendocrine systems, linking metabolism and immune physiology. Leptin, an adipose tissue-derived adipokine, displays a variety of immune and physiological functions, and participates in several immune responses. Here, we review the current literature on the role of leptin in kidney diseases, linking adipose tissue and the immune system with kidney-related disorders. The modulation of this adipose hormone may have a major impact on the treatment of several immune- and metabolic-related kidney diseases.

Link prediction using a probabilistic description logic

Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic crALC). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature.