Condition physique

Résumé

La condition physique est une combinaison des facteurs physique, technique, tactique, stratégique et mental de la performance sportive. C'est un niveau de qualités physiques qui permet de réaliser une activité physique ou un sport dans les meilleures conditions possibles. Articles connexes

Préparation physique

Notes et références Catégorie:Entraînement sportif Catégorie:Éducation physique et sportive

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https://fr.wikipedia.org/wiki/Condition_physique

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Nuclear receptor/microRNA circuitry links muscle fiber type to energy metabolism

Johan Auwerx, Hui Xie

The mechanisms involved in the coordinate regulation of the metabolic and structural programs controlling muscle fitness and endurance are unknown. Recently, the nuclear receptor PPAR beta/delta was shown to activate muscle endurance programs in transgenic mice. In contrast, muscle-specific transgenic overexpression of the related nuclear receptor, PPAR alpha, results in reduced capacity for endurance exercise. We took advantage of the divergent actions of PPAR beta/delta and PPAR alpha to explore the downstream regulatory circuitry that orchestrates the programs linking muscle fiber type with energy metabolism. Our results indicate that, in addition to the well-established role in transcriptional control of muscle metabolic genes, PPAR beta/delta and PPAR alpha participate in programs that exert opposing actions upon the type I fiber program through a distinct muscle microRNA (miRNA) network, dependent on the actions of another nuclear receptor, estrogen-related receptor gamma (ERR gamma). Gain-of-function and loss-of-function strategies in mice, together with assessment of muscle biopsies from humans, demonstrated that type I muscle fiber proportion is increased via the stimulatory actions of ERR gamma on the expression of miR-499 and miR-208b. This nuclear receptor/miRNA regulatory circuit shows promise for the identification of therapeutic targets aimed at maintaining muscle fitness in a variety of chronic disease states, such as obesity, skeletal myopathies, and heart failure.

American Society for Clinical Investigation2013

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, ,

Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors such as anticipating that vehicles will follow the middle of the roads. While this perspective leads to feasible outputs, it has limited performance due to the difficulty to hand-craft complex interactions in urban environments. On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance. However, generalization, \textit{i.e.}, having accurate predictions on unseen data, is an issue leading to unrealistic outputs. In this paper, we propose to learn a "Realistic Residual Block" (RRB), which effectively connects these two perspectives. Our RRB takes any off-the-shelf knowledge-driven model and finds the required residuals to add to the knowledge-aware trajectory. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty. We also constrain our output with Model Predictive Control (MPC) to satisfy kinematic constraints. Using a publicly available dataset, we show that our method outperforms previous works in terms of accuracy and generalization to new scenes.

2021

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End-to-End Industrial Study of Retiming

Maciej Jerzy Ciesielski, Giovanni De Micheli, Cunxi Yu

Sequential circuits are combinational circuits that are separated by registers. Retiming is considered as the most promising technique for optimizing sequential circuits, that involves moving the edge-triggered registers across the combinational logic without changing the functionality. Despite significant efforts spent on sequential optimization since 1980's, there are few works discussed its performance in an end to-end design flow. The retiming algorithms were mostly evaluated at the logic level. However, it turns out that the retiming results at logic level could be significantly different than evaluating the physical level. This paper provides the findings of how retiming algorithms perform in an end-to-end industrial design flow, with seven industry designs taken from a recent 14nm microprocessor. Experiments are conducted with several complete industrial design flows. The evaluations are made at the end of the physical design flow. The experimental results show that the performance (design quality) of the retiming algorithms vary on the designs. Based these experimental results, we discover a feature that describes the retiming potentials of sequential designs. This model successfully forecast whether the given industrial designs could be significantly improved by retiming in an end-to-end design flow, regarding timing, area, and power.

IEEE2018

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