Egg White Protein Feeding Facilitates Skeletal Muscle Gain in Young Rats with/without Clenbuterol Treatment.

Based on the Digestible Indispensable Amino Acid Score (DIAAS), egg white protein (EGG) has an excellent score, comparable to that of whey protein but with a lower amount of leucine. We examined the effect of EGG feeding on rat skeletal muscle gain in comparison to that of two common animal-derived protein sources: casein (CAS) and whey (WHE). To explore the full potential of EGG, this was examined in clenbuterol-treated young rats. Furthermore, we focused on leucine-associated anabolic signaling in response to EGG after single-dose ingestion and chronic ingestion, as well as clenbuterol treatment. Because EGG is an arginine-rich protein source, a portion of the experiment was repeated with diets containing equal amounts of arginine. We demonstrated that EGG feeding accelerates skeletal muscle gain under anabolism-dominant conditions more efficiently than CAS and WHE and this stronger effect with EGG is not dependent on the arginine-rich composition of the protein source. We also demonstrated that the plausible mechanism of the stronger muscle-gain effect with EGG is not detectable in the mechanistic target of rapamycin (mTOR) or insulin signaling under our experimental conditions. We conclude that EGG may have a superior efficiency in muscle gain compared to other common animal-based proteins.

Prediction of Cyclic Stress-Strain Property of Steels by Crystal Plasticity Simulations and Machine Learning.

In this study, a method for the prediction of cyclic stress-strain properties of ferrite-pearlite steels was proposed. At first, synthetic microstructures were generated based on an anisotropic tessellation from the results of electron backscatter diffraction (EBSD) analyses. Low-cycle fatigue experiments under strain-controlled conditions were conducted in order to calibrate material property parameters for both an anisotropic crystal plasticity and an isotropic J2 model. Numerical finite element simulations were conducted using these synthetic microstructures and material properties based on experimental results, and cyclic stress-strain properties were calculated. Then, two-point correlations of synthetic microstructures were calculated to quantify the microstructures. The microstructure-property dataset was obtained by associating a two-point correlation and calculated cyclic stress-strain property. Machine learning, such as a linear regression model and neural network, was conducted using the dataset. Finally, cyclic stress-strain properties were predicted from the result of EBSD analysis using the obtained machine learning model and were compared with the results of the low-cycle fatigue experiments.