Endocrine-disrupting chemicals (EDCs) that interfere with the steroid axis can affect amelogenesis, leading to enamel hypomineralization similar to that of molar incisor hypomineralization, a recently described enamel disease. We investigated the sex steroid receptors that may mediate the effects of EDCs during rat amelogenesis. The expression of androgen receptor (AR), estrogen receptor (ER)-α, and progesterone receptor was dependent on the stage of ameloblast differentiation, whereas ERβ remained undetectable. AR was the only receptor selectively expressed in ameloblasts involved in final enamel mineralization. AR nuclear translocation and induction of androgen-responsive element-containing promoter activity upon T treatment, demonstrated ameloblast responsiveness to androgens. T regulated the expression of genes involved in enamel mineralization such as KLK4, amelotin, SLC26A4, and SLC5A8 but not the expression of genes encoding matrix proteins, which determine enamel thickness. Vinclozolin and to a lesser extent bisphenol A, two antiandrogenic EDCs that cause enamel defects, counteracted the actions of T. In conclusion, we show, for the first time, the following: 1) ameloblasts express AR; 2) the androgen signaling pathway is involved in the enamel mineralization process; and 3) EDCs with antiandrogenic effects inhibit AR activity and preferentially affect amelogenesis in male rats. Their action, through the AR pathway, may specifically and irreversibly affect enamel, potentially leading to the use of dental defects as a biomarker of exposure to environmental pollutants. These results are consistent with the steroid hormones affecting ameloblasts, raising the issue of the hormonal influence on amelogenesis and possible sexual dimorphism in enamel quality.

h i g h l i g h t s• This paper introduces a regime switching model for Value-at-Risk estimation.• Hidden Markov models and extreme value theory are combined into a hybrid model.• The regime switching model is applied to real data NYSE Euronext stocks.• Classifying data in two states permits a fast detection of regime switching. This paper constructs a regime switching model for the univariate Value-at-Risk estimation. Extreme value theory (EVT) and hidden Markov models (HMM) are combined to estimate a hybrid model that takes volatility clustering into account. In the first stage, HMM is used to classify data in crisis and steady periods, while in the second stage, EVT is applied to the previously classified data to rub out the delay between regime switching and their detection. This new model is applied to prices of numerous stocks exchanged on NYSE Euronext Paris over the period 2001-2011. We focus on daily returns for which calibration has to be done on a small dataset. The relative performance of the regime switching model is benchmarked against other well-known modeling techniques, such as stable, power laws and GARCH models. The empirical results show that the regime switching model increases predictive performance of financial forecasting according to the number of violations and tail-loss tests. This suggests that the regime switching model is a robust forecasting variant of power laws model while remaining practical to implement the VaR measurement.

This paper introduces a novel approach for secure navigation of wheelchairs. The approach is based on a combination of robotic road train based navigation and Cloud Computing technologies. The navigation strategy is inspired from elephants. Marching trunk to tail, each wheelchair in a platoon takes cues from the wheelchair just in front of it. The wheelchair also communicates directly with the leader in order to anticipate any turns or braking action. The cloud computing technologies are used to perform shared computations and remote teleoperation by a caregiver. Caregivers are persons connected through cloud interfaces. Their role is to watch in real time the navigation progress and to control the system navigation when problems occur. Some experimental results are given in the paper to demonstrate the feasibility and performance of the developed system.

This paper provides a thorough survey of the European option pricing, with new trends in the risk measurement, under exponential Lévy models. We develop all steps of pricing from equivalent martingale measures construction to numerical valuation of the option price under these measures. We then construct an algorithm, based on Rockafellar and Uryasev representation and fast Fourier transform, to compute Risk indicators, like the VaR and the CVaR of derivatives. The results are illustrated with an example of each exponential Lévy class. The main contribution of this paper is to build a comprehensive study from the theoretical point of view to practical numerical illustration and to give a complete characterization of the studied equivalent martingale measures by discussing their similarity and their applicability in practice. Furthermore, this work proposes applications to the Fourier inversion technique in risk measurement.

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In this paper, we study the hedging problem based on the CVaR in incomplete markets. As the superhedging is quite expensive in terms of initial capital, we construct a self-financing strategy that minimizes the CVaR of hedging risk under a budget constraint on the initial capital. In incomplete markets, no explicit solution can be provided. To approximate the problem, we apply the Neyman-Pearson lemma approach with a specific equivalent martingale measure. Afterwards, we explicit the solution for call options hedging under the exponential-Lévy class of price models. This approach leads to an efficient and easy to implement method using the fast Fourier transform. We illustrate numerical results for the Merton model.