Effect of neonatal or adult heat acclimation on plasma fT3 level, testicular thyroid receptors expression in male rats and testicular steroidogenesis in vitro in response to triiodothyronine treatment

This study aimed to evaluate the effect of heat acclimation of neonatal and adult rats on their testes response to in vitro treatment with triiodothyronine (T3). Four groups of rats were housed from birth as: 1) control (CR) at 20°C for 90 days, 2) neonatal heat-acclimated (NHA) at 34°C for 90 days, 3) adult heat-acclimated (AHA) at 20°C for 45 days followed by 45 days at 34°C and 4) de-acclimated (DA) at 34°C for 45 days followed by 45 days at 20°C. Blood plasma and both testes were harvested from 90-day old rats. Testicular slices were then submitted to in vitro treatment with T3 (100 ng/ml) for 8 h. Plasma fT3 level was lower in AHA, NHA and DA groups than in CR group. Basal thyroid hormone receptor α1 (Thra1) expression was higher in testes of NHA and DA and β1 receptor (Thrb1) in DA rats vs. other groups. In the in vitro experiment, T3: 1) decreased Thra1 expression in all groups and Thrb1 in DA group, 2) increased Star expression in CR, NHA and DA groups, and Hsd17b3 expression in NHA group, 3) decreased the expression of Cyp11a1 in NHA and DA groups, and Cyp19a1 in all the groups, 4) did not affect the activity of steroidogenic enzymes and steroid secretion (A4, T, E2) in all the groups. These results indicate, that heat acclimation of rats, depending on their age, mainly affects the testicular expression of steroidogenic enzymes in response to short-lasting treatment with T3.</p

Open TURNS: An industrial software for uncertainty quantification in simulation

The needs to assess robust performances for complex systems and to answer tighter regulatory processes (security, safety, environmental control, and health impacts, etc.) have led to the emergence of a new industrial simulation challenge: to take uncertainties into account when dealing with complex numerical simulation frameworks. Therefore, a generic methodology has emerged from the joint effort of several industrial companies and academic institutions. EDF R&D, Airbus Group and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial challenges attached to uncertainties, which are transparency, genericity, modularity and multi-accessibility. This paper focuses on OpenTURNS and presents its main features: openTURNS is an open source software under the LGPL license, that presents itself as a C++ library and a Python TUI, and which works under Linux and Windows environment. All the methodological tools are described in the different sections of this paper: uncertainty quantification, uncertainty propagation, sensitivity analysis and metamodeling. A section also explains the generic wrappers way to link openTURNS to any external code. The paper illustrates as much as possible the methodological tools on an educational example that simulates the height of a river and compares it to the height of a dyke that protects industrial facilities. At last, it gives an overview of the main developments planned for the next few years

Vine copula-based asymmetry and tail dependence modeling

© Springer International Publishing AG, part of Springer Nature 2018. Financial variables such as asset returns in the massive market contain various hierarchical and horizontal relationships that form complicated dependence structures. Modeling these structures is challenging due to the stylized facts of market data. Many research works in recent decades showed that copula is an effective method to describe relations among variables. Vine structures were introduced to represent the decomposition of multivariate copula functions. However, the model construction of vine structures is still a tough problem owing to the geometrical data, conditional independent assumptions and the stylized facts. In this paper, we introduce a new bottom-to-up method to construct regular vine structures and applies the model to 12 currencies over 16 years as a case study to analyze the asymmetric and fat tail features. The out-of-sample performance of our model is evaluated by Value at Risk, a widely used industrial benchmark. The experimental results show that our model and its intrinsic design significantly outperform industry baselines, and provide financially interpretable knowledge and profound insights into the dependence structures of multi-variables with complex dependencies and characteristics

Factor copula models for item response data

Factor or conditional independence models based on copulas are proposed for multivariate discrete data such as item responses. The factor copula models have interpretations of latent maxima/minima (in comparison with latent means) and can lead to more probability in the joint upper or lower tail compared with factor models based on the discretized multivariate normal distribution (or multidimensional normal ogive model). Details on maximum likelihood estimation of parameters for the factor copula model are given, as well as analysis of the behavior of the log-likelihood. Our general methodology is illustrated with several item response data sets, and it is shown that there is a substantial improvement on existing models both conceptually and in fit to data

Prioritizing Emerging Zoonoses in The Netherlands

Background: To support the development of early warning and surveillance systems of emerging zoonoses, we present a general method to prioritize pathogens using a quantitative, stochastic multi-criteria model, parameterized for the Netherlands. Methodology/Principal Findings: A risk score was based on seven criteria, reflecting assessments of the epidemiology and impact of these pathogens on society. Criteria were weighed, based on the preferences of a panel of judges with a background in infectious disease control. Conclusions/Significance: Pathogens with the highest risk for the Netherlands included pathogens in the livestock reservoir with a high actual human disease burden (e.g. Campylobacter spp., Toxoplasma gondii, Coxiella burnetii) or a low current but higher historic burden (e.g. Mycobacterium bovis), rare zoonotic pathogens in domestic animals with severe disease manifestations in humans (e.g. BSE prion, Capnocytophaga canimorsus) as well as arthropod-borne and wildlife associated pathogens which may pose a severe risk in future (e.g. Japanese encephalitis virus and West-Nile virus). These agents are key targets for development of early warning and surveillance.Infrastructures, Systems and ServicesTechnology, Policy and Managemen