A dynamic individual yak heifer live body weight estimation method using the YOLOv8 network and body parameter detection algorithm.

Live body weight (LBW) is one of the most important parameters for supervising the growth and development of livestock. The yak (Bos grunniens) is a special species of cattle that lives on the Qinghai-Tibetan Plateau. Yaks are more untamed than regular cattle breeds, thus it is more challenging to measure their LBW. In this study, a YOLOv8 yak detection and LBW estimation models were used to automatically estimate yak LBW in real-time. First, the proper posture (normal posture) and individual yak identification was confirmed and then the YOLOv8 detection model was used for LBW estimation from 2-dimensional (2D) images. Yak LBW was estimated through yak body parameter extraction and a simple linear regression between the estimated yak LBW and the actual measured yak LBW. The results showed that the overall detection performance of yak normal yak posture was described by precision, recall, and mean Average Precision 50 (mAP50) indicators, reaching 81.8, 86.0, and 90.6%, respectively. The best yak identification results were represented by precision, recall, and mAP50 values of 97.8, 96.4, and 99.0%, respectively. The yak LBW estimation model achieved better results for the 12 mo old yaks with shorter hair with R2, root mean square error (RMSE), mean absolute percentage error (MAPE), and Multiple R values of 0.96, 2.43 kg, 1.69%, and 0.98, respectively. The results demonstrate that yak LBW can be estimated and monitored in real-time using this approach. This study has the potential to be used for daily yak LBW monitoring in an unstressed manner and to save considerable labor resources for large-scale livestock farms. In the future, to reduce the limitations caused by the impacts of yak hair and light condition data sets of dairy cows and yaks of different ages will be used to improve and generalize the model.

Classification of cow behavior patterns using inertial measurement units and a fully convolutional network model.

In this study, we aimed to classify 7 cow behavior patterns automatically with an inertial measurement unit (IMU) using a fully convolutional network (FCN) algorithm. Behavioral data of 12 cows were collected by attaching an IMU in a waterproof box on the neck behind the head of each cow. Seven behavior patterns were considered: rub scratching (leg), ruminating-lying, lying, feeding, self-licking, rub scratching (neck), and social licking. To simplify the data and compare classification performance with or without magnetometer data, the 9-axis IMU data were reduced using the square root of the sum of squares to develop 2 datasets. Comparing the classification accuracy of the 3 models using a window size of 64 with 6-axis data and a window size of 128 with both 6-axis and 9-axis data, the best overall accuracy (83.75%) was achieved using the FCN model with a window size of 128 (12.8 s) using all IMU data. This model achieved classification accuracies of 83.2, 96.5, 92.8, 98.1, 82.9, 87.2, and 45.2% for ruminating-lying, lying, feeding, rub scratching (leg), self-licking, rub scratching (neck), and social licking, respectively. As a sequence of varied and intensive movement, the classification accuracy of behavior patterns related to skin disease was lower; better classification of these behavior patterns could be achieved with full IMU data and a larger window size. In the future, additional data will take into account different data types, such as audio and video data, to further enhance performance. In addition, an adaptive sliding window size will be used to improve model performance.

Design of an efficient combined multipoint picking scheme for tea buds.

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.

Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism.

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.

Effects of black tea and black brick tea with fungal growth on lowering uric acid levels in hyperuricemic mice.

Black tea, a traditional drink, can induce urination and quench thirst. Black brick tea with fungal growth, prepared by steaming, pressing, inducing fungal growth, and drying the black tea, is a new type of black tea with different sensory qualities and is suitable for storage. However, the effects of black brick tea with fungal growth on lowering uric acid are still unexplored. Therefore, the potassium oxonate was administered for 7 consecutive days to establish the hyperuricemic mice. Then allopurinol, black tea, and black brick tea with fungal growth were orally administered with hyperuricemic mice for 14 days. Serum uric acid levels, liver xanthine oxidase (XOD) and adenosine deaminase (ADA) activities, and expression of renal urate transporters and inflammatory response were detected. Compared to the model group, both types of black tea lowered serum uric acid by decreasing the uric acid production with inhibiting the activities of XOD and ADA, and increasing uric acid excretion because of downregulating urate transporter 1 (URAT1) and glucose transporter 9 (GLUT9) expressions, and upregulating organic anion transporter 1 (OAT1), organic anion transporter 3 (OAT3), and organic cation transporter 1 (OCT1) expressions. They could also improve renal injury by suppressing the activation of the NOD-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome, and nuclear factor-κB (NF-κB) signaling, thereby reducing renal proinflammatory cytokine levels. Compared with black tea, black brick tea with fungal growth with a higher content of theabrownins had a better effect on lowering serum uric acid. PRACTICAL APPLICATIONS: Black tea accounts for approximately 78% of the total consumed tea in the world. Black brick tea with fungal growth is a new kind of black tea product with different sensory qualities and is suitable for storage. The study found that black brick tea with fungal growth is superior to black tea in reducing serum uric acid levels, which make a significant contribution to promote people's health and stimulate the production and consumption of black brick tea with fungal growth. In addition, it provides a clue for future research to identify the effective components of black brick tea with fungal growth lowering uric acid.

L-Theanine Regulates the Abundance of Amino Acid Transporters in Mice Duodenum and Jejunum via the mTOR Signaling Pathway.

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.

Role of Epigallocatechin Gallate in Glucose, Lipid, and Protein Metabolism and L-Theanine in the Metabolism-Regulatory Effects of Epigallocatechin Gallate.

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.

l-Theanine regulates glucose, lipid, and protein metabolism via insulin and AMP-activated protein kinase signaling pathways.

l-Theanine is an important component found in tea and has positive effects on nutrient absorption and transport. However, whether l-theanine can regulate glucose, lipid, and protein metabolism remains unknown. This study aims to investigate the effects of l-theanine on glucose, lipid, and protein metabolism in male Sprague-Dawley rats and characterize the underlying mechanisms. Compared to the control group, l-theanine increased the contents of hepatic and muscle glycogen, serum total protein (TP), and albumin (Alb), lowered the serum low-density lipoprotein cholesterol (LDL-C) level, decreased the activity of acetyl-CoA carboxylase (ACC), and enhanced carnitine palmitoyl transferase-1 (CPT-1) activity in the liver. Additionally, l-theanine upregulated the mRNA expression of phosphofructokinase (PFKL), CPT-1, insulin receptor (INSR), insulin receptor substrate (IRS), and liver kinase B1 (LKB1) and downregulated the mRNA expression of phosphoenolpyruvate carboxykinase 1 (PCK1), glucose-6-phosphatase catalytic subunit (G6PC), fatty acid synthase (FAS), and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR). Moreover, l-theanine upregulated the expression of PFKL, glycogen synthase 2 (GYS2), ribosomal protein S6 (S6), INSR, IRS, and phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) proteins; downregulated the expression of FAS, sterol regulatory element binding protein-1c (SREBP-1c), and HMGCR proteins; enhanced the phosphorylation of the mammal target of rapamycin (mTOR), ribosomal protein S6 kinase (p70S6K), protein kinase B (AKT), and AMP-activated protein kinase (AMPK); and decreased the phosphorylation of glycogen synthase kinase 3ß (GSK-3ß) and ACC1. Furthermore, 100 mg kg-1l-theanine was more effective at eliciting these effects than 200 and 400 mg kg-1l-theanine. In conclusion, l-theanine can regulate glucose, lipid, and protein metabolism via insulin and AMPK and their downstream signaling pathways.

l-Theanine attenuates liver aging by inhibiting advanced glycation end products in d-galactose-induced rats and reversing an imbalance of oxidative stress and inflammation.

Redox and inflammation imbalances are associated with increased levels of advanced glycation end products (AGEs), leading to the degeneration of body function. l-Theanine, derived from tea, reportedly inhibits AGE formation in vitro. We investigated the effects on AGE content, oxidative stress, and inflammatory factors in d-galactose-induced aging rats for prevention and treatment of age-related liver dysfunction. l-Theanine increased activities of antioxidant enzymes such as superoxide dismutase, catalase, and glutathione peroxidase, thus enhancing total antioxidant capacity, and decreasing malondialdehyde and nitric oxide synthase levels in serum and liver. Levels of the pro-inflammatory factors, interleukin (IL)-1ß, tumour necrosis factor-alpha, and IL-6 were decreased in serum and liver, whereas those of anti-inflammatory factors, IL-4 and IL-10, were increased in the serum. Further, l-theanine inhibited AGE production and decreased the levels of the liver function markers, alanine aminotransaminase and aspartate aminotransferase. It also significantly increased the mRNA expression levels of FoxO1 and downregulated NF-κB(p65) but suppressed the phosphorylation of both FOXO1 and NF-κB (p65). Moreover, l-theanine effectively attenuated d-galactose-induced oedema and vacuole formation, thus protecting the liver. Overall, l-theanine reversed the d-galactose-induced imbalance in oxidative stress and inflammatory responses, reduced AGEs content in aging rats, maintained homeostasis in the body, and ameliorated liver aging.