Sociodemographic, behavioral and genetic determinants of allostatic load in a Swiss population-based study.

Allostatic load (AL) is a marker of physiological dysregulation which reflects exposure to chronic stress. High AL has been related to poorer health outcomes including mortality. We examine here the association of socioeconomic and lifestyle factors with AL. Additionally, we investigate the extent to which AL is genetically determined. We included 803 participants (52% women, mean age 48±16years) from a population and family-based Swiss study. We computed an AL index aggregating 14 markers from cardiovascular, metabolic, lipidic, oxidative, hypothalamus-pituitary-adrenal and inflammatory homeostatic axes. Education and occupational position were used as indicators of socioeconomic status. Marital status, stress, alcohol intake, smoking, dietary patterns and physical activity were considered as lifestyle factors. Heritability of AL was estimated by maximum likelihood. Women with a low occupational position had higher AL (low vs. high OR=3.99, 95%CI [1.22;13.05]), while the opposite was observed for men (middle vs. high OR=0.48, 95%CI [0.23;0.99]). Education tended to be inversely associated with AL in both sexes(low vs. high OR=3.54, 95%CI [1.69;7.4]/OR=1.59, 95%CI [0.88;2.90] in women/men). Heavy drinking men as well as women abstaining from alcohol had higher AL than moderate drinkers. Physical activity was protective against AL while high salt intake was related to increased AL risk. The heritability of AL was estimated to be 29.5% ±7.9%. Our results suggest that generalized physiological dysregulation, as measured by AL, is determined by both environmental and genetic factors. The genetic contribution to AL remains modest when compared to the environmental component, which explains approximately 70% of the phenotypic variance.

Monitoring of the training load and recovery parameters in competitive swimmers

Nombreux sont les groupes de recherche qui se sont intéressés, ces dernières années, à la manière de monitorer l'entraînement des sportifs de haut niveau afin d'optimaliser le rendement de ce dernier tout en préservant la santé des athlètes. Un des problèmes cardinaux d'un entraînement sportif mal conduit est le syndrome du surentraînement. La définition du syndrome susmentionné proposée par Kreider et al. est celle qui est actuellement acceptée par le « European College of Sport Science » ainsi que par le « American College of Sports Medicine», à savoir : « An accumulation of training and/or non-training stress resulting in long-term decrement in performance capacity with or without related physiological and psychological signs and symptoms of maladaptation in which restoration of performance capacity may take several weeks or months. » « Une accumulation de stress lié, ou non, à l'entraînement, résultant en une diminution à long terme de la capacité de performance. Cette dernière est associée ou non avec des signes et des symptômes physiologiques et psychologiques d'inadaptation de l'athlète à l'entraînement. La restauration de ladite capacité de performance peut prendre plusieurs semaines ou mois. » Les recommandations actuelles, concernant le monitoring de l'entraînement et la détection précoce du syndrome du surentrainement, préconisent, entre autre, un suivi psychologique à l'aide de questionnaires (tel que le Profile of Mood State (POMS)), un suivi de la charge d'entraînement perçue par l'athlète (p.ex. avec la session rating of perceived exertion (RPE) method selon C. Foster), un suivi des performances des athlètes et des charges d'entraînement effectuées ainsi qu'un suivi des problèmes de santé (blessures et maladies). Le suivi de paramètres sanguins et hormonaux n'est pas recommandé d'une part pour des questions de coût et de faisabilité, d'autre part car la littérature scientifique n'a, jusqu'ici, pas été en mesure de dégager des évidences à ce sujet. A ce jour, peu d'études ont suivi ces paramètres de manière rigoureuse, sur une longue période et chez un nombre d'athlète important. Ceci est précisément le but de notre étude.

Determination of Rod and Cone Influence to the Early and Late Dynamic of the Pupillary Light Response.

PURPOSE: This study aims to identify which aspects of the pupil light reflex are most influenced by rods and cones independently by analyzing pupil recordings from different mouse models of photoreceptor deficiency. METHODS: One-month-old wild type (WT), rodless (Rho-/-), coneless (Cnga3-/-), or photoreceptor less (Cnga3-/-; Rho-/- or Gnat1-/-) mice were subjected to brief red and blue light stimuli of increasing intensity. To describe the initial dynamic response to light, the maximal pupillary constriction amplitudes and the derivative curve of the first 3 seconds were determined. To estimate the postillumination phase, the constriction amplitude at 9.5 seconds after light termination was related to the maximal constriction amplitude. RESULTS: Rho-/- mice showed decreased constriction amplitude but more prolonged pupilloconstriction to all blue and red light stimuli compared to wild type mice. Cnga3-/- mice had constriction amplitudes similar to WT however following maximal constriction, the early and rapid dilation to low intensity blue light was decreased. To high intensity blue light, the Cnga3-/- mice demonstrated marked prolongation of the pupillary constriction. Cnga3-/-; Rho-/- mice had no pupil response to red light of low and medium intensity. CONCLUSIONS: From specific gene defective mouse models which selectively voided the rod or cone function, we determined that mouse rod photoreceptors are highly contributing to the pupil response to blue light stimuli but also to low and medium red stimuli. We also observed that cone cells mainly drive the partial rapid dilation of the initial response to low blue light stimuli. Thus photoreceptor dysfunction can be derived from chromatic pupillometry in mouse models.

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.