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Gamma-aminobutyric acid (GABA) significantly affects plant responses to heavy metals in hydroponics or culture media, but its corresponding effects in plant–soil systems remain unknown. In this study, different GABA dosages (0–8 g kg−1) were added to the rhizosphere of Coreopsis grandiflora grown in Cd-contaminated soils. Cd accumulation in the shoots of C. grandiflora was enhanced by 38.9–159.5% by GABA in a dose-dependent approach because of accelerated Cd absorption and transport. The increase in exchangeable Cd transformed from Fe-Mn oxide and carbonate-bound Cd, which may be mainly driven by decreased soil pH rather than GABA itself, could be a determining factor responsible for this phenomenon. The N, P, and K availability was affected by multiple factors under GABA treatment, which may regulate Cd accommodation and accumulation in C. grandiflora. The rhizospheric environment dynamics remodeled the bacterial community composition, resulting in a decline in overall bacterial diversity and richness. However, several important plant growth-promoting rhizobacteria, especially Pseudomonas and Sphingomonas, were recruited under GABA treatment to assist Cd phytoextraction in C. grandiflora. This study reveals that GABA as a soil amendment remodels the rhizospheric environment (e.g., soil pH and rhizobacteria) to enhance Cd phytoextraction in plant–soil systems.

Discovering the implicit pattern and using it as heuristic information to guide the policy search is one of the core factors to speed up the procedure of robot motor skill acquisition. This paper proposes a compound heuristic information guided reinforcement learning algorithm PI2-CMA-KCCA for policy improvement. Its structure and workflow are similar to a double closed-loop control system. The outer loop realized by Kernel Canonical Correlation Analysis (KCCA) infers the implicit nonlinear heuristic information between the joints of the robot. In addition, the inner loop operated by Covariance Matrix Adaptation (CMA) discovers the hidden linear correlations between the basis functions within the joint of the robot. These patterns which are good for learning the new task can automatically determine the mean and variance of the exploring perturbation for Path Integral Policy Improvement (PI2). Compared with classical PI2, PI2-CMA, and PI2-KCCA, PI2-CMA-KCCA can not only endow the robot with the ability to realize transfer learning of trajectory planning from the demonstration to the new task, but also complete it more efficiently. The classical via-point experiments based on SCARA and Swayer robots have validated that the proposed method has fast learning convergence and can find a solution for the new task.

Breast milk is widely considered to be the most natural, safe, and complete food for infants. However, current breastfeeding rates fall short of the recommendations established by the World Health Organization. Despite this, there are few studies that have focused on the promotion of human lactation through nutrient supplementation. Therefore, the aim of this study was to investigate the effect of methionine on milk synthesis in human mammary epithelial cells (MCF-10A cells) and to explore the underlying mechanisms. To achieve this, MCF-10A cells were cultured with varying concentrations of methionine, ranging from 0 to 1.2 mM. Our results indicated that 0.6 mM of methionine significantly promoted the synthesis of milk protein. An RNA-seq analysis revealed that methionine acted through the PI3K pathway. This finding was validated through real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. In addition, PI3K inhibition assays confirmed that methionine upregulated the expression of both mTOR and p-mTOR through activation of PI3K. Taken together, these findings suggest that methionine positively regulates milk protein synthesis in MCF-10A cells through the PI3K-mTOR signaling pathway.

The enhancement of milk production is essential for dairy animals, and nutrient supplements can enhance milk production. This work summarizes the influence of nutrient supplements—including amino acids, peptides, lipids, carbohydrates, and other chemicals (such as phenolic compounds, prolactin, estrogen and growth factors)—on milk production. We also attempt to provide possible illuminating insights into the subsequent effects of nutrient supplements on milk synthesis. This work may help understand the strategy and the regulatory pathway of milk production promotion. Specifically, we summarize the roles and related pathways of nutrients in promoting milk protein and fat synthesis. We hope this review will help people understand the relationship between nutritional supplementation and milk production.