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The influence of short term (7-day) exposure of male rats to the brominated flame retardant hexabromocyclododecane (HBCD) was studied by investigation of the liver proteome, both in euthyroid and hypothyroid rats and by comparing results with general data on animal physiology and thyroid hormone, leptin, insulin and gonadotropin concentrations determined in parallel. Proteome analysis of liver tissue by two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) revealed that only small protein pattern changes were induced by exposure in males, on just a few proteins with different functions and not involved in pathways in common. This is in contrast to previous findings in similarly exposed eu- and hypothyroid female rats, where general metabolic pathways had been shown to be affected. The largest gender-dependent effects concerned basal concentrations of liver proteins already in control and hypothyroid animals, involving mainly the pathways which were also differently affected by HBCD exposure. Among them were differences in lipid metabolism, which – upon exposure to HBCD – may also be the reason for the considerably higher ratio of γ-HBCD accumulated in white adipose tissue of exposed female rats compared to males. The results further elucidate the already suggested different sensitivity of genders towards HBCD exposure on the protein level, and confirm the need for undertaking toxicological animal experiments in both genders. [ABSTRACT FROM AUTHOR]

As probabilistic analyses spread in industrial practice, inverse probabilistic modelling of the sources of uncertainty enjoys a growing interest as it is often the only way to estimate the input probabilistic model of unobservable quantities. This article addresses the identification of intrinsic physical variability of the systems. After showing its theoretical differences with the more classical data assimilation or parameter identification algorithms, this article introduces a new non-parametric algorithm that does not require linear nor Gaussian assumptions. This technique is based on the simulation of the likelihood of the observed samples, coupled with a kernel method to limit the number of physical model runs and facilitate the subsequent maximization. It is implemented inside an industrial application in order to identify the key parameter that controls the vibration amplification of steam turbines. Hence, experimental resonance frequencies observations are used to adjust the probabilistic model of the unobservable manufacturing imperfections between theoretically identical units. [ABSTRACT FROM AUTHOR]