重庆三峡职业学院

Heat stress (HS) remains a critical environmental constraint threatening the sustainability and productivity of global livestock and poultry production by including oxidative damage, compromising gut integrity, and disruption of immune homeostasis. The limited conventional nutritional strategies in conferring long-term thermotolerance underscore the urgent need for innovative and biologically effective interventions. In this context, phytobiotics, bioactive plant-derived compounds, have emerged as promising nutrigenomic modulators due to their potent antioxidant, anti-inflammatory, antimicrobial, and gut-protective properties, which collectively reinforce cellular defense mechanisms and enhance physiological adaptability under thermal challenge. This review provides a novel nutrigenomic perspective by elucidating how phytobiotics regulate the expression of key stress-responsive molecular pathways, including heat shock proteins (HSPs), nuclear factor erythroid 2-related factor 2 (Nrf2), nuclear factor kappa B (NF-κB), and mitogen-activated protein kinases (MAPKs), which govern oxidative balance, immune signaling, and apoptosis. Importantly, these mechanistic insights are reinforced by emerging in silico approaches, such as molecular docking and network pharmacology, which offer predictive validation of phytobiotic bioactivity, binding affinities, and target specificity against stress-related biomarkers, thereby accelerating the identification of high-potential candidates. By integrating experimental and computational evidence, this review consolidates current knowledge on the genomic and physiological roles of phytobiotics in mitigating HS and associated pathophysiological disturbances, and provides an effective scientific framework for the development of sustainable, phytobiotic-based nutritional strategies to enhance resilience and productivity in climate-vulnerable animal production systems.

Representation learning on continuous-time dynamic graphs (CTDGs) is a fundamental research problem in graph learning, whose core challenge lies in the persistent evolution of dynamic interactions. Preserving spatial-temporal proximity among nodes is a critical requirement for CTDG representation learning. However, existing methods suffer from two major limitations: first, interaction intensity between node pairs is typically modeled under rigid parametric assumptions (e.g., Hawkes processes), which struggle to accommodate the diverse interaction patterns across different node pairs; second, the neighbor sampling strategies employed by existing works are decoupled from the semantic information of interaction events, leading to suboptimal context node selection for proximity estimation. To address these issues, we propose AISE, a framework integrating a Pattern-Adaptive Intensity Estimation (PAIE) module and a Semantic-Aware Neighbor Sampling (SANS) strategy. PAIE dynamically infers personalized decay coefficients from historical interaction interval sequences under a non-homogeneous Poisson process assumption. SANS jointly leverages event features and temporal proximity scores to select more informative context nodes via importance-weighted sampling. Experiments on multiple public benchmarks show that AISE outperforms existing baselines on both link prediction and node classification tasks.

Probiotics are widely used in companion animal nutrition due to their roles in maintaining gut health, regulating the microbiota, and supporting metabolic homeostasis. Here, we summarize recent advances in the application of probiotics in functional foods for dogs and cats. We outline the strain resources, functional mechanisms, and stabilization technologies of probiotics, with emphasis on their effects on gut barrier function, immune regulation, and metabolic health. In addition, the current challenges, including insufficient host specificity, a lack of rigorous clinical trials, and poor processing stability are discussed. Future directions are proposed for the development of precision probiotics for companion animals. This review provides a theoretical basis for the rational design and standardized application of probiotics in the pet food industry.

Mechanised furrowing and fertiliser application is an effective engineering solution for improving operational efficiency and reducing labour intensity in hilly and mountainous orchards. In this study, a systematic optimisation methodology for a disc-type furrowing tool is proposed by integrating discrete element method (DEM) simulations using EDEM with quadratic orthogonal regression experiments and response surface analysis. Key structural parameters, including bending radius, bending angle, and alpha angle, were selected as design variables, and their individual and interactive effects on furrowing power consumption were investigated. The results of the response surface analysis indicate that appropriate structural optimisation can significantly reduce trenching power consumption. The optimal structural parameters were determined as follows: bending radius of 36.79 mm, bending angle of 126.94°, and alpha angle of 43.65°. Under these conditions, the predicted power consumption was 0.85 kW, representing a reduction of 8.88% compared with the original tool design. Soil bin experiments were conducted to validate the DEM simulation results, and the experimental trends showed good agreement with the simulation predictions, demonstrating the reliability of the proposed optimisation approach. This study provides a practical reference for the engineering design and performance improvement of soil-engaging tools.