Xiaoling Chen

This study integrated in vivo finishing pig trials and in vitro primary porcine myotube assays to assess the effects of caffeic acid (CA) in relation to pork quality and its molecular mechanisms. Our results showed that CA increased muscle pH at 24 h and redness, and reduced drip loss and cooking loss. CA enhanced muscle antioxidant capacity and oxidative fibers proportion, while suppressing muscle glycolysis. CA upregulated nuclear factor erythroid 2-related factor 2 (Nrf2) expression and decreased hypoxia-inducible factor-1α (HIF-1α) expression. Myotube tests verified that ML385 abolished the promoting effects of CA on muscle antioxidant capacity and oxidative muscle fiber expression, whereas DMOG attenuated the regulatory effects of CA on glycolytic metabolism and muscle fiber composition. In conclusion, CA improves pork quality, and its mechanisms are closely linked to Nrf2 pathway activation to enhance the antioxidant capacity of skeletal muscle and suppression of HIF-1α-mediated glycolysis, thereby driving a glycolytic-to-oxidative fiber-type switch.

Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, most approaches still rely on static fusion and lack explicit multi-scale alignment. As a result, they struggle to adaptively integrate heterogeneous inputs and suppress cross-modal interference, which constrains representation learning. To overcome these limitations, we propose BuildFunc-MoE, an adaptive multimodal Mixture-of-Experts (MoE) network built on an effective end-to-end Swin-UNet backbone. The model treats high-resolution remote sensing imagery as the primary input and integrates auxiliary geospatial data such as nighttime light imagery, DEM, and point-of-interest information. An Adaptive Multimodal Fusion Gate (AMMFG) first refines auxiliary features into informative fused representations, which are then combined with the primary modality and passed through multi-scale Swin-MoE blocks that extend standard Swin Transformer blocks with MoE routing. This enables fine-grained, dynamic fusion and alignment between primary and auxiliary modalities across feature scales. BuildFunc-MoE further introduces a Shared Task-Expert Module (STEM), which extends the MoE framework to share experts between the main BFI task and auxiliary tasks (road extraction, green space segmentation, and water body detection), enabling parameter-level transfer. This design enables complementary feature learning, where structural and contextual information jointly enhance the discrimination of building functions, thereby improving identification accuracy while maintaining model compactness. Experiments on the proposed Wuhan-BF multimodal dataset show that, under identical supervision, BuildFunc-MoE outperforms the strongest multimodal baseline by over 2% on average across metrics. Both PyTorch and LuoJiaNET implementations validate its effectiveness, while the latter achieves higher accuracy and faster inference through optimized computation. Overall, BuildFunc-MoE offers a scalable solution for fine-grained BFI with strong potential for urban planning and sustainable governance.

BACKGROUND: Enhancing the water‐holding capacity (WHC) of pork is crucial for enhancing meat quality and reducing weight loss. The study aimed to investigate the effect of dietary pterostilbene (PTS) supplementation on the WHC of pork. RESULTS: Our results showed that 200 mg kg ⁻¹ PTS (PTS200) in finishing pigs reduced drip loss of pork. Using low‐field nuclear magnetic resonance, it was found that PTS200 increased the contents of bound water, immobilized water and free water in pork. Scanning electron microscopy revealed that PTS200 reduced myofibril spacing and muscle fiber spacing. Furthermore, PTS200 enhanced the antioxidant capacity of pork. Additionally, PTS200 enhanced calpain‐I and integrin protein expression while reducing desmin protein expression. Further mechanistic studies revealed that PTS may enhance the protein stability of calpain‐I by binding to it, thereby promoting its expression. CONCLUSIONS: Our results indicate that dietary PTS supplementation improves the WHC of pork, and its mechanisms may involve the promotion of antioxidant capacity, the regulation of drip channel‐associated proteins and the increase of calpain‐I expression in pork. © 2025 Society of Chemical Industry.

The demand for real-time land cover mapping from high-resolution remote sensing (HR-RS) imagery motivates lightweight segmentation models running directly on satellites. By processing on-board and transmitting only fine-grained semantic products instead of massive raw imagery, these models provide timely support for disaster response, environmental monitoring, and precision agriculture. Many recent methods combine convolutional neural networks (CNNs) with Transformers to balance local and global feature modeling, with convolutions as explicit information aggregation modules. Such heterogeneous hybrids may be unnecessary for lightweight models if similar aggregation can be achieved homogeneously, and operator inconsistency complicates optimization and hinders deployment on resource-constrained satellites. Meanwhile, lightweight Transformer components in these architectures often adopt aggressive channel compression and shallow contextual interaction to meet compute budgets, impairing boundary delineation and recognition of small or rare classes. To address this, we propose SatViT-Seg, a lightweight semantic segmentation model with a pure Vision Transformer (ViT) backbone. Unlike CNN-Transformer hybrids, SatViT-Seg adopts a homogeneous two-module design: a Local-Global Aggregation and Distribution (LGAD) module that uses window self-attention for local modeling and dynamically pooled global tokens with linear attention for long-range interaction, and a Bi-dimensional Attentive Feed-Forward Network (FFN) that enhances representation learning by modulating channel and spatial attention. This unified design overcomes common lightweight ViT issues such as channel compression and weak spatial correlation modeling. SatViT-Seg is implemented and evaluated in LuoJiaNET and PyTorch; comparative experiments with existing methods are run in PyTorch with unified training and data preprocessing for fairness, while the LuoJiaNET implementation highlights deployment-oriented efficiency on a graph-compiled runtime. Compared with the strongest baseline, SatViT-Seg improves mIoU by up to 1.81% while maintaining the lowest FLOPs among all methods. These results indicate that homogeneous Transformers offer strong potential for resource-constrained, on-board real-time land cover mapping in satellite missions.

This study aimed to investigate the effects of caffeic acid (CA) on the inflammatory response induced by hydrogen peroxide (H ₂ O ₂ ) in porcine small intestinal epithelial cells (IPEC-J2 cells) and to elucidate the potential molecular mechanisms involved. Firstly, we treated IPEC-J2 cells with different concentrations of H ₂ O ₂ to establish the inflammation model caused by oxidative stress. Subsequently, we treated IPEC-J2 cells with CA and/or H ₂ O ₂ to investigate the effect of CA on the inflammatory response of IPEC-J2 cells induced by H ₂ O _2. In addition, IPEC-J2 cells were treated with a nuclear factor kappa-B (NF-κB) inhibitor and a Nucleotide-binding Oligomerization Domain (NOD)-like receptor thermal protein domain associated protein 3 (NLRP3) inhibitor, so as to investigate the molecular mechanism by which CA alleviates H ₂ O ₂ -induced damage in porcine intestinal epithelial cells. The changes in cell morphology, intestinal epithelial cell damage and the expression of genes related to the NF-κB/NLRP3 signalling axis were examined. The results showed that CA attenuated H ₂ O ₂ -induced intestinal epithelial cell injury, and the mechanism may be related to the inhibition of NF-κB-mediated NLRP3 inflammasome activation. These findings were expected to provide a theoretical basis for more reasonable and effective application of CA, and provide new ideas for nutritional regulation measures to alleviate intestinal inflammation in piglets.