黑龙江八一农垦大学

Current automated mastitis detection methods typically identify dairy cows only in a fixed standing posture, which limits their scope and practical flexibility. To address potential occlusion issues across different postures, we divided the key anatomical regions into three areas (the eye, the back surface of the udder (BSU) and the lower surface of the udder (LSU)) and developed an infrared thermography (IRT)-based automated diagnostic system for cows in various postures within a lactation barn. First, a three-stage image enhancement method was applied to extract contour and texture features from the IRT images. Next, the You Only Look Once v8 Nano (YOLOv8n) model was improved by integrating Dynamic Snake Convolution to better capture subtle and complex texture patterns. We further optimised the weight distribution of contour and texture features using an efficient multi-scale attention module to reduce the loss of critical information in the deep network. Structural improvements included adding a P2 detection head to focus on contour features in the target regions. Finally, we built three machine learning models to diagnose mastitis using the maximum body temperatures of these critical regions. Results showed that the three-stage image enhancement effectively enriched IRT details, strengthened contour and texture features and improved detection confidence for the eye, BSU and LSU by 0.025, 0.05 and 0.04, respectively. The improved YOLOv8n model achieved top performance, with precision (P) of 94.2%, 97.8% and 96.1%; recall (R) of 96.6%, 94.1% and 89.7%; and average precision at an intersection-over-union of 50% (AP@0.5) of 94.3%, 93.7% and 94.2% for the eye, BSU and LSU, respectively. Compared with the baseline YOLOv8n model, the enhanced version improved P, R and AP@0.5 metrics by 2%∼5.4% across the three regions of interest. Among the diagnostic models, random forest achieved the highest accuracy at 92.31%. This method broadens the application of automated mastitis detection and provides a reference framework for building automatic monitoring systems for mastitis in feeder barns.

Natural rice glutelin exhibits poor water solubility and low interfacial activity due to its compact structure, limiting its food industry application. However, the structure-activity relationship and underlying synergistic mechanism of rice glutelin modification by ultrasonic cavitation combined with protein glutaminase (PG) remain unclear. This study comparatively analyzed the property changes of rice glutelin before and after different modifications, and evaluated its application performance and stability in emulsion preparation. Results showed that ultrasonic cavitation disrupted the compact structure, exposing more enzyme action sites. The combined treatment achieved a deamidation degree as high as 62.50% ± 0.43% while maintaining a minimal hydrolysis degree (<2.00%), indicating a synergistic “high deamidation-low hydrolysis” modification. Structural characterization revealed that the combined treatment significantly reduced the α-helix content, with a concurrent increase in β-sheet and β-turn contents, suggesting substantial structural rearrangement. The protein particle size decreased, and the absolute zeta-potential value increased. These structural changes, including the exposure of hydrophobic groups and free sulfhydryl groups, contributed to improved functional properties. The exposed hydrophobic sites and negatively charged –COO- groups introduced by deamidation synergistically enhanced protein adsorption and rearrangement at the oil-water interface, thereby improving dispersion stability. Specifically, solubility increased by 45.73% ± 0.10%, the emulsifying activity index reached 65.80 ± 0.49 m2/g, and emulsion stability attained 98.80% ± 0.42%. Moreover, the prepared emulsion exhibited optimal rheological properties and stability. In summary, ultrasonic cavitation and PG exert a significant synergistic effect on rice glutelin modification, providing a new theoretical basis and technical support for its high-value utilization, with important implications for advancing plant protein resource development and the functional food industry.

In the present study, we evaluated the effects of deoxynivalenol (DON) on intestinal oxidative stress, immunity, ferroptosis, and endoplasmic reticulum stress in Channa argus. Furthermore, we investigated the mechanism underlying DON-induced cytotoxicity by inhibiting ACSL4 in macrophages. In vivo analysis demonstrated DON inhibits growth and even leading to increased mortality. DON induces intestinal ROS and iron ion accumulation, thereby causing endoplasmic reticulum dysfunction and triggering oxidative stress and ferroptosis. This process is accompanied by decreased levels of ferroptosis-inhibiting proteins (GPX4, FPN1, and FSP1), along with increased ACSL4 and ROS levels. Furthermore, DON-induced ROS accumulation elicits endoplasmic reticulum dysfunction and drives the progression of immunosuppression and inflammation. In vitro analysis demonstrated that inhibition of ACSL4 affects macrophage activity as well as ROS and ATP levels, representing a potential intervention strategy to restore immune cell function. This study demonstrates that targeting ACSL4 may serve as a potential strategy for alleviating DON-mediated intestinal injury in C. argus.

Northeast China's croplands harbor critical soil organic carbon (SOC) reserves essential for soil fertility and food security. Partitioning SOC into particulate (POC) and mineral-associated organic carbon (MAOC) fractions advances mechanistic understanding of C cycling and enables targeted sequestration strategies. To resolve the unresolved latitudinal patterns and controls of these fractions, we analyzed 96 cropland soils spanning 40.03°-48.02° N at 0-20 cm and 20-40 cm depths. SOC and MAOC stocks increased markedly with latitude across both soil layers, whereas POC stock exhibited no visible spatial trend. Though both fractions correlated significantly with total SOC, POC stock demonstrated greater sensitivity to SOC stock changes than MAOC stock. Multivariate analyses revealed divergent drivers: 35-38 % of SOC and MAOC stocks variation in topsoil (and 27-31 % in subsoil) stemmed from interactions among climate, soil properties, and enzyme activity, whereas POC stock variation was dominated by soil properties (46 %) in topsoil and enzyme activity (28 %) in deeper strata. Random forest modeling identified mean annual temperature as the primary driver for topsoil SOC and MAOC stocks and total N for subsoil dynamics. Our findings establish temperature sensitivity and nitrogen availability as pivotal controls over SOC fraction distribution, providing actionable strategies for C management in temperate croplands under changing climates.

Hyperspectral images (HSIs) contain abundant spectral information but also exhibit significant redundancy. Therefore, selecting bands that possess high information content and low interband correlation is essential. Evolutionary algorithms (EAs) have demonstrated strong capabilities in addressing band selection (BS) problems. However, many existing methods rely on single-objective optimization, which limits their ability to jointly optimize multiple characteristics of the selected band subsets. To address this limitation, this study proposes an improved multiobjective EA for BS (IMOEABS). The proposed algorithm employs interclass distance and information entropy (IE) as objective functions to identify bands with strong class discriminability and high information richness. It is built upon the dimension-wise special crowding distance (DSCD) nondominated sorting mechanism, which penalizes small index differences between adjacent bands to suppress redundancy in the decision space. Furthermore, the generation strategy of offspring solutions is guided by the population entropy, enhancing the exploration efficiency of the algorithm. Experimental results on three benchmark datasets demonstrate that IMOEABS achieves superior performance compared to state-of-the-art BS methods, when evaluated using support vector machine (SVM) and K-nearest neighbor (KNN) classifiers. These findings confirm the robustness and effectiveness of IMOEABS in hyperspectral BS tasks.