Weight loss increases skeletal muscle mitochondrial energy efficiency in obese mice

Weight loss from an overweight state is associated with a disproportionate decrease in whole-body energy expenditure that may contribute to the heightened risk for weight regain.Evidence suggests that this energetic mismatch originates from lean tissue.Although this phenomenon is well documented,the mechanisms have remained elusive.We hypothesized that increased mitochondrial energy efficiency in skeletal muscle is associated with reduced expenditure under weight loss.Wildtype(WT)male C57BL6/N mice were fed with high-fat diet for 10 weeks,followed by a subset of mice that were maintained on the obesogenic diet(OB)or switched to standard chow to promote weight loss(WL)for additional 6 weeks.Mitochondrial energy efficiency was evaluated using high-resolution respirometry and fluorometry.Mass spectrometric analyses were employed to describe the mitochondrial proteome and lipidome.Weight loss promoted~50%increase in the efficiency of oxidative phosphorylation(ATP produced per O_(2) consumed,or P/O)in skeletal muscle.However,Weight loss did not appear to induce significant changes in mitochondrial proteome,nor any changes in respiratory supercomplex formation.Instead,it accelerated the remodeling of mitochondrial cardiolipin(CL)acyl-chains to increase tetralinoleoyl CL(TLCL)content,a species of lipids thought to be functionally critical for the respiratory enzymes.We further show that lowering TLCL by deleting the CL transacylase tafazzin was sufficient to reduce skeletal muscle P/O and protect mice from diet-induced weight gain.These findings implicate skeletal muscle mitochondrial efficiency as a novel mechanism by which weight loss reduces energy expenditure in obesity.

Improvement in the information-oriented construction of temporary soil-retaining walls using sparse modeling

This fundamental study investigates how“super-resolution”technology based on sparse modeling,which has attracted attention in various fields,can be applied to the information-oriented construction of temporary soil-retaining walls.The machine learning process adopted here is based on the analytical results of numerical computations that involve many preliminary assumptions related to soilretaining walls,rather than the collection of images utilized in the image reconstruction technology.Consequently,bases for vectors related to the displacement of retaining walls are generated using efficient inverse analysis and“super-resolution”processing from sparse amounts of physical observation data.The purpose is to improve the properties of the inverse problem by artificial interpolation based on numerical analysis.It has been shown that the inverse analysis related to the displacement of retaining walls can be performed efficiently and that highly accurate predictions can be achieved even with limited physical observations.In general,the inverse analysis of retaining walls is an ill-posed problem.However,if the number of apparent observations reconverted by“super-resolution”technology exceeds the number of unknown parameters,then the displacement distribution of a retaining wall can be estimated efficiently.Another original idea is to break down the inverse problem into two separate problems by addressing the earth pressure distribution acting on the retaining wall.This makes it possible to identify the part to which the nonlinear inverse problem can be applied and to facilitate the efficient estimation and interpretation of the results.