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Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management. The aim of this study was to detect important cattle physiological states using a neural network model and wearable electronic sensors. A novel long short-term memory (LSTM) recurrent neural network model that uses two-way information was developed to accurately classify cattle behaviour and compared with baseline LSTM. Deep residual bidirectional LSTM and baseline LSTM were used to classify six behavioural patterns of cows with window sizes of 64, 128 and 256 (6.4 s, 12.8 s and 25.6 s, respectively). The results showed that when using deep residual bidirectional LSTM with window size 128, four classification performance indicators, namely, accuracy, precision, recall, and F1-score, achieved the best results of 94.9%, 95.1%, 94.9%, and 94.9%, respectively. The results showed that the deep residual bidirectional LSTM model can be used to classify time-series data collected from twelve cows using inertial measurement unit collars. Six aim cattle behaviour patterns can be classified with high accuracy. This method can be used to quickly detect whether a cow is suffering from bovine dermatomycosis. Furthermore, this method can be used to implement automated and precise cattle behaviour classification techniques for precision livestock farming.

Herein, a combined multipoint picking scheme was proposed, and the sizes of the end of the bud picker were selectively designed. Firstly, the end of the bud picker was abstracted as a fixed-size picking box, and it was assumed that the tea buds in the picking box have a certain probability of being picked. Then, the picking box coverage and the greedy algorithm were designed to make as few numbers of picking box set as possible to cover all buds to reduce the numbers of picking. Furthermore, the Graham algorithm and the minimum bounding box were applied to fine-tune the footholds of each picking box in the optimal coverage picking box set, so that the buds were concentrated in the middle of the picking boxes as much as possible. Moreover, the geometric center of each picking box was taken as a picking point, and the ant colony algorithm was used to optimize the picking path of the end of the bud picker. Finally, by analyzing the influence of several parameters on the picking performance of the end of the bud picker, the optimal sizes of the picking box were calculated successfully under different conditions. The experimental results showed that the average picking numbers of the combined multipoint picking scheme were reduced by 31.44%, the shortest picking path was decreased by 11.10%, and the average consumed time was reduced by 50.92% compared to the single-point picking scheme. We believe that the proposed scheme can provide key technical support for the subsequent design of intelligent bud-picking robots.

Feeding behavior is known to affect the welfare and fattening efficiency of yaks in feedlots. With the advancement of machine vision and sensor technologies, the monitoring of animal behavior is progressively shifting from manual observation towards automated and stress-free methodologies. In this study, a real-time detection model for individual yak feeding and picking behavior was developed using YOLO series model and StrongSORT tracking model. In this study, we used videos collected from 11 yaks raised in two pens to train the yak face classification with YOLO series models and tracked their individual behavior using the StrongSORT tracking model. The yak behavior patterns detected in trough range were defined as feeding and picking, and the overall detection performance of these two behavior patterns was described using indicators such as accuracy, precision, recall, and F1-score. The improved YOLOv8 and Strongsort model achieved the best performance, with detection accuracy, precision, recall, and F1-score of 98.76%, 98.77%, 98.68%, and 98.72%, respectively. Yaks which have similar facial features have a chance of being confused with one another. A few yaks were misidentified because their faces were obscured by another yak’s head or staff. The results showed that individual yak feeding behaviors can be accurately detected in real-time using the YOLO series and StrongSORT models, and this approach has the potential to be used for longer-term yak feeding monitoring. In the future, a dataset of yaks in various cultivate environments, group sizes, and lighting conditions will be included. Furthermore, the relationship between feeding time and yak weight gain will be investigated in order to predict livestock weight.

Yak behavior is a valuable indicator of their welfare and health. Information about important statuses, including fattening, reproductive health, and diseases, can be reflected and monitored through several indicative behavior patterns. In this study, an improved YOLOv7-pose model was developed to detect six yak behavior patterns in real time using labeled yak key-point images. The model was trained using labeled key-point image data of six behavior patterns including walking, feeding, standing, lying, mounting, and eliminative behaviors collected from seventeen 18-month-old yaks for two weeks. There were another four YOLOv7-pose series models trained as comparison methods for yak behavior pattern detection. The improved YOLOv7-pose model achieved the best detection performance with precision, recall, mAP0.5, and mAP0.5:0.95 of 89.9%, 87.7%, 90.4%, and 76.7%, respectively. The limitation of this study is that the YOLOv7-pose model detected behaviors under complex conditions, such as scene variation, subtle leg postures, and different light conditions, with relatively lower precision, which impacts its detection performance. Future developments in yak behavior pattern detection will amplify the simple size of the dataset and will utilize data streams like optical and video streams for real-time yak monitoring. Additionally, the model will be deployed on edge computing devices for large-scale agricultural applications.

Among all stroke types, ischemic stroke (IS) occurs most frequently, resulting in neuronal death and tissue injury within both the central infarct region and surrounding areas. This study explored the neuroprotective mechanisms of scutellarin, a flavonoid compound, through an integrated strategy that merged in silico analyses (including network pharmacology and molecular docking simulations) with both in vitro and in vivo experimental verification. We identified 1887 IS-related targets and 129 scutellarin targets, with 23 overlapping targets. PPI network analysis revealed five core targets, and molecular docking demonstrated strong binding affinities between scutellarin and these targets. Bioinformatic analyses, including GO functional annotation and KEGG pathway mapping, indicated that the PI3K/AKT cascade represents the primary signaling mechanism. An in vitro experimental system was developed using PC12 cells under oxygen-glucose deprivation conditions to investigate how scutellarin regulates neuronal cell death via the PI3K/AKT pathway. Western blot quantification demonstrated that treatment with scutellarin enhanced the expression of p-PI3K, p-AKT, and Bcl-2 proteins, while simultaneously reducing levels of apoptotic markers Bax and cleaved caspase-3. Furthermore, pharmacological intervention with the selective PI3K inhibitor LY294002 attenuated these molecular alterations, resulting in diminished expression of p-PI3K, p-AKT, and Bcl-2, accompanied by elevated levels of Bax and cleaved caspase-3. In a rat model of middle cerebral artery occlusion, scutellarin administration demonstrated comparable neuroprotective effects, maintaining neuronal survival and modulating apoptotic protein expression via PI3K/AKT pathway activation. Collectively, this study demonstrates the therapeutic potential of scutellarin in cerebral ischemia through PI3K/AKT pathway modulation, suggesting its possible application in treating ischemic disorders.

Due to the low detection precision and poor robustness, the traditional pig-posture and behavior detection method is difficult to apply in the complex pig captivity environment. In this regard, we designed the HE-Yolo (High-effect Yolo) model, which improves the Darknet-53 feature extraction network and integrates DAM (Dual attention mechanism) of channel attention mechanism and space attention mechanism, to recognize the posture behaviors of the enclosure pigs in real-time. First, the pig data set is clustered and optimized by the K-means algorithm to obtain a new anchor frame size. Second, the DSC (Depthwise separable convolution) and h-switch activation function are innovatively introduced into the Darknet-53 feature extraction network, and the C-Res (Contrary residual structure) unit is designed to build Darknet-A feature extraction network, so as to avoid network gradient explosion and ensure the integrity of feature information. Subsequently, DAM integrating the spatial attention mechanism and the channel attention mechanism is established, and it is further combined with the Incep-abate module to form DAB (Dual attention block), and HE-Yolo is finally built by Darknet-A and DAB. A total of 2912 images of 46 enclosure pigs are divided into the training set, the verification set, and the test set according to the ratio of 14 : 3:3, and the recognition performance of HE-Yolo is verified according to the parameters of the precision P, the recall R, the AP (i.e., the area of P-R curve) and the MAP (i.e., the average value of AP). The experiment results show that the AP values of HE-Yolo reach 99.25%, 98.41%, 94.43%, and 97.63%, respectively, in the recognition of four pig-posture behaviors of standing, sitting, prone and sidling of the test set. Compared with other models such as Yolo v3, SSD, and faster R–CNN, the mAP value of HE-Yolo is increased by 5.61%, 4.65%, and 0.57%, respectively, and the single-frame recognition time of HE-Yolo is only 0.045 s. In the recognition of images with foreign body occlusion and pig adhesion, the mAP values of HE-Yolo are increased by 4.04%, 4.94%, and 1.76%, respectively, while compared with other models. Under different lighting conditions, the mAP value of HE-Yolo is also higher than that of other models. The experimental results show that HE-Yolo can recognize the pig-posture behaviors with high precision, and it shows good generalization ability and luminance robustness, which provides technical support for the recognition of pig-posture behaviors and real-time monitoring of physiological health of the enclosure pigs.

The intestine is a key organ for the absorption of amino acids. L-theanine (LTA) is a structural analog of glutamine and a characteristic non-protein amino acid found in tea (Camellia sinensis) that regulates lipid and protein metabolism. The present study explored the role of LTA in intestinal amino acid absorption, protein synthesis, and its mechanisms. Overall, our findings suggest that LTA supplementation not only affects serum alkaline phosphatase (AKP), total protein (TP), and urea nitrogen (BUN) levels, but it also upregulates the mRNA and protein expression of amino acid transporters (EAAT3, EAAT1, 4F2hc, y+LAT1, CAT1, ASCT2, and B0AT1), and activates the mTOR signaling pathway. The downstream S6 and S6K1 proteins are regulated, and the expression of amino acid transporters is regulated. These findings suggest that LTA increases intestinal AA absorption, promotes protein metabolism, and increases nitrogen utilization by upregulating AAT expression, activating the mTOR signaling pathway, and phosphorylating the mTOR downstream proteins S6 and S6K1.

Epigallocatechin gallate (EGCG) and L-theanine (LTA) are important bioactive components in tea that have shown promising effects on nutrient metabolism. However, whether EGCG alone or combined with LTA can regulate the glucose, lipid, and protein metabolism of healthy rats remains unclear. Therefore, we treated healthy rats with EGCG or the combination of EGCG and LTA (EGCG+LTA) to investigate the effects of EGCG on nutrient metabolism and the role of LTA in the metabolism-regulatory effects of EGCG. The results showed that compared with the control group, EGCG activated insulin and AMP-activated protein kinase (AMPK) signals, thus regulating glucose, lipid, and protein metabolism. Compared with EGCG, EGCG+LTA enhanced hepatic and muscle glycogen levels and suppressed phosphorylation of AMPK, glycogen synthase 2, mammalian target of rapamycin, and ribosomal protein S6 kinase. In addition, EGCG+LTA inhibited the expression of liver kinase B1, insulin receptor and insulin receptor substrate, and promoted the phosphorylation level of acetyl-CoA carboxylase. Furthermore, both EGCG and EGCG+LTA were harmless for young rats. In conclusion, EGCG activated AMPK and insulin pathways, thereby promoting glycolysis, glycogen, and protein synthesis and inhibiting fatty acid (FA) and cholesterol synthesis. However, LTA cooperated with EGCG to promote glycogen metabolism and suppressed the effect EGCG on FA and protein synthesis via AMPK signals.

Text-to-image (T2I) diffusion models typically require substantial computational resources and cloud infrastructure, posing significant challenges for edge deployment in terms of latency, cost, and user privacy. We present JuZhou 1.0, an ultra-lightweight T2I foundation model designed for fully offline, on-device execution. JuZhou 1.0 achieves its efficiency through four key designs: (1) a compact image-generation backbone consisting of a 0.385B-parameter denoising U-Net and a 1.90M-parameter distilled decoder, totaling approximately 0.387B parameters; (2) Rectified Flow training combined with DMD2 distillation, reducing inference to 4 sampling steps; (3) Chinese semantic alignment trained on 9M curated image-text pairs, enabling direct Chinese prompting without external translation at inference time; and (4) a training and distillation pipeline completed on domestically developed Sugon K100 AI accelerators without relying on NVIDIA GPUs for training or distillation. Despite its compact scale, the 28-step base model of JuZhou 1.0 achieves an overall GenEval score of 0.69, outperforming published baselines including SDXL (2.6B, 0.55), SD3-Medium (2B, 0.62), and IF-XL (4.3B, 0.61). We further validate the full poetry-to-image pipeline on Android and the core CLIP-U-Net-VAE generation branch on iOS. On a smartphone powered by the Snapdragon 8 Elite Gen 5 Mobile Platform, the 4-step U-Net denoising branch runs in approximately 1.6 seconds, while the full Android poetry-to-image pipeline takes 4.5 seconds with on-device prompt refinement on Xiaomi 17 Pro Max. These results position JuZhou 1.0 as a practical approach to mobile text-to-image generation and provide a concrete reference for Chinese-native generation, domestic-compute training, and fully offline on-device deployment after one-time installation.

Intra-articular injection of therapeutics is an effective strategy for treating osteoarthritis (OA), but it is hindered by rapid drug diffusion, thereby necessitating high-frequency injections. Hence, the development of a biofunctional hydrogel for improved delivery is required. In this study, we introduce a liposome-anchored teriparatide (PTH (1–34)) incorporated into a gallic acid-grafted gelatin injectable hydrogel (GLP hydrogel). We show that the GLP hydrogel can form in situ and without affecting knee motion after intra-articular injection in mice. We demonstrate controlled, sustained release of PTH (1–34) from the GLP hydrogel. We find that the GLP hydrogel promotes ATDC5 cell proliferation and protects the IL-1β-induced ATDC5 cells from further OA progression by regulating the PI3K/AKT signaling pathway. Further, we show that intra-articular injection of hydrogels into an OA-induced mouse model promotes glycosaminoglycans synthesis and protects the cartilage from degradation, supporting the potential of this biomaterial for OA treatment. Osteoarthritis is a common disease that causes pain and difficulty moving joints. Here the authors present an injectable gelatin-based hydrogel that slowly releases teriparatide drug to avoid frequent injections, offering a potential solution for patients with osteoarthritis.