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Introduction Normal aging is often associated with a decline of muscle mass (MM), strength (MS) and quality (MQ: MS/MM), leading to functional incapacities. This aging-related deterioration of muscles may involve a decreased protein intake. Mixed power training has been recently shown to induce positive effects on MM, MS and MQ. However, to our knowledge, no study has examined if muscle adaptations following mixed power training could be influenced by the daily amount of protein ingested in elderly men. Methods Twenty-one men completed the intervention and were divided into 2 groups based on their usual protein intake: PROT 1.1− ( 1.2 g·kg−1·d−1 [n = 11; 73 ± 3 years]). Body composition (DXA: lean and fat masses), MS (1-maximal repetition on leg-press and handgrip strength), MQ (MS/body mass and MS/lower limb lean mass), functional capacities (Short Physical Performance Battery/Senior Fitness Test), dietary intake (3-day food record) and energy expenditure (accelerometer; 7 days) were measured. Mixed power training intervention consisted in power and functional exercises (12 weeks; 3 times/week; 1 h/session). Results Lower limb MS increase in the PROT 1.2+ group was greater from that of the PROT 1.1− group when normalized to lower limbs lean mass (p = 0.036). In addition, a trend for greater gain in lower limb MS normalized to body mass (p = 0.053) was observed in the PROT 1.2+. Conclusion To optimize mixed power training effects on muscle function, healthy older men should ingest daily at least 1.2 g·kg−1·d−1 of protein. These beneficial effects of a higher usual protein intake were observed especially for MQ, which is one of the best predictors of functional capacities in older adults.

International audience; This article is within the context of decision models aimed for maintenance of structures and infrastructures in civil engineering. The contribution relies on the construction of a degradation model oriented toward risk analysis. The proposed model can be defined as a meta-model in the sense that it is based on observations while incorporating key features from the degradation process necessary for the maintenance decision. We propose to stimulate the construction of the degradation model based on the crack propagation of a submerged reinforced concrete structure subject to chloride-induced corrosion. Furthermore, a set of numerical illustrations is performed to demonstrate the advantages and applicability of the proposed approach in risk management and maintenance contexts.

Physics-based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising; however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data-driven state-dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride-induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities.