University of Turin

About: University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 29607 authors who have published 77952 publications receiving 2480900 citations. The organization is also known as: Universita degli Studi di Torino & Università degli Studi di Torino.

On the Use of Human Mobility Proxies for Modeling Epidemics

Abstract: Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.

Insulin resistance in nonalcoholic fatty liver disease.

Abstract: Nonalcoholic fatty liver disease (NAFLD) refers to a spectrum of liver damage ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), advanced fibrosis and cirrhosis. NAFLD is considered the hepatic component of the metabolic syndrome and insulin resistance represents its pathophysiological hallmark. Insulin resistance in NAFLD is characterized by reduced whole-body, hepatic, and adipose tissue insulin sensitivity. The mechanism(s) underlying the accumulation of fat in the liver may include excess dietary fat, increased delivery of free fatty acids to the liver, inadequate fatty acid oxidation, and increased de novo lipogenesis. Liver fat is highly correlated with all the components of the metabolic syndrome, independent of obesity, and NAFLD may increase the risk of type 2 diabetes and atherosclerosis. Overproduction of glucose, very low-density lipoproteins, C-reactive protein and coagulation factors by the fatty liver could contribute to the excess risk of cardiovascular disease. The reason(s) why some patients will develop NASH are poorly understood. Circulating free fatty acids may be cytotoxic by inducing lipid peroxidation and hepatocyte apoptosis. Insulin resistance is often associated with chronic low-grade inflammation, and numerous mediators released from immune cells and adipocytes may contribute liver damage and liver disease progression. Understanding the molecular mediators of liver injury would promote the development of mechanism-based therapeutic interventions. This article briefly summarizes the recent advances in our understanding of the relationship between NAFLD/NASH, insulin resistance and the metabolic syndrome.

The Weak s-Process in Massive Stars and its Dependence on the Neutron Capture Cross Sections

Abstract: The slow neutron capture process in massive stars (weak s process) produces most of the s-process isotopes between iron and strontium. Neutrons are provided by the 22Ne(?,n)25Mg reaction, which is activated at the end of the convective He-burning core and in the subsequent convective C-burning shell. The s-process-rich material in the supernova ejecta carries the signature of these two phases. In the past years, new measurements of neutron capture cross sections of isotopes beyond iron significantly changed the predicted weak s-process distribution. The reason is that the variation of the Maxwellian-averaged cross sections (MACS) is propagated to heavier isotopes along the s path. In the light of these results, we present updated nucleosynthesis calculations for a 25 M ? star of Population I (solar metallicity) in convective He-burning core and convective C-burning shell conditions. In comparison with previous simulations based on the Bao et?al. compilation, the new measurement of neutron capture cross sections leads to an increase of s-process yields from nickel up to selenium. The variation of the cross section of one isotope along the s-process path is propagated to heavier isotopes, where the propagation efficiency is higher for low cross sections. New 74Ge, 75As, and 78Se MACS result in a higher production of germanium, arsenic, and selenium, thereby reducing the s-process yields of heavier elements by propagation. Results are reported for the He core and for the C shell. In shell C-burning, the s-process nucleosynthesis is more uncertain than in the He core, due to higher MACS uncertainties at higher temperatures. We also analyze the impact of using the new lower solar abundances for CNO isotopes on the s-process predictions, where CNO is the source of 22Ne, and we show that beyond Zn this is affecting the s-process yields more than nuclear or stellar model uncertainties considered in this paper. In particular, using the new updated initial composition, we obtain a high s-process production (overproduction higher than 16O, ~100) for Cu, Ga, Ge, and As. Using the older abundances by Anders & Grevesse, also Se, Br, Kr, and Rb are efficiently produced. Our results have important implications in explaining the origin of copper in the solar abundance distribution, pointing to a prevailing contribution from the weak s-process in agreement with spectroscopic observations and Galactic chemical evolution calculations. Because of the improvement due to the new MACS for nickel and copper isotopes, the nucleosynthesis of copper is less affected by nuclear uncertainties compared to heavier s-process elements. An experimental determination of the 63Ni MACS is required for a further improvement of the abundance prediction of copper. The available spectroscopic observations of germanium and gallium in stars are also discussed, where most of the cosmic abundances of these elements derives from the s-process in massive stars.