A Moderate Supplementation of Native Whey Protein Promotes Better Muscle Training and Recovery Adaptations Than Standard Whey Protein - A 12-Week Electrical Stimulation and Plyometrics Training Study.

The purpose of this study was to assess if native whey protein (NW) supplementation could promote recovery and training adaptations after an electrostimulation (ES) training program combined to plyometrics training. Participants were allocated into three groups, supplemented 5 days/week, either with 15 g of carbohydrates + 15 g of NW (n = 17), 15 g of carbohydrates + 15 g of standard whey protein (SW; n = 15), or placebo (PLA; 30 g of carbohydrates; n = 10), while undergoing a 12-week ES training program of the knee extensors. Concentric power (Pmax) was evaluated before, immediately after, as well as 30 min, 60 min, 24 h, and 48 h after the 1st, 4th and last ES training session. The maximal voluntary contraction torque (MVC), twitch amplitude, anatomical cross-sectional area (CSA) and maximal voluntary activation level (VA) were measured before (T0), and after 6 (T1) and 12 weeks of training (T2). P max recovery kinetics differed between groups (p < 0.01). P max started to recover at 30 min in NW, 24 h in SW and 48 h in PLA. Training adaptations also differed between groups: MVC increased between T0 and T2 in NW (+11.8%, p < 0.001) and SW (+7.1%, p < 0.05), but not PLA. Nevertheless, the adaptation kinetics differed: MVC increased in NW and SW between T0 and T1, but an additional gain was only observed between T1 and T2 in NW. VA declined at T1 and T2 in PLA (-3.9%, p < 0.05), at T2 in SW (-3.5%, p < 0.05), and was unchanged in NW. CSA increased, but did not differ between groups. These results suggest that NW could promote a faster recovery and neuromuscular adaptations after training than SW. However, the mechanisms underlying this effect remain to be identified.

Review and research on feature selection methods from NMR data in biological fluids. Presentation of an original ensemble method applied to atherosclerosis field.

Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals with different chemical shifts and intensities representing different metabolites' types and levels, respectively. Relevant predictive NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The different PCs representing the (n - 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation). >From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA) were used to build final population and sub-population regression models predicting metabolite levels in BM-B.