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The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model’s receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm’s capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming.

Abstract Most nonalcoholic steatohepatitis (NASH) patients develop severe fibrosis through extracellular matrix (ECM) accumulation, which can lead to hepatocellular carcinoma (HCC). Fibroblast growth factor 9 (FGF9) is involved in serial types of cancer; however, the specific role of FGF9 in NASH‐driven HCC is not fully understood. This study finds that FGF9 is increased in patients with NASH‐associated HCC. Furthermore, NASH‐driven HCC mice models by feeding wildtype mice with high‐fat/high‐cholesterol (HFHC) diet and low dose carbon tetrachloride (CCl4) treatment is established; and identified that hepatic FGF9 is increased; with severe fibrosis. Additionally, AAV‐mediated knockdown of FGF9 reduced the hepatic tumor burden of NASH‐driven HCC mice models. Hepatocyte‐specific FGF9 transgenic mice (FGF9Alb) fed with a HFHC diet without CCl4 treatment exhibited an increased hepatic ECM and tumor burden. However, XAV‐939 treatment blocked ECM accumulation and NASH‐driven HCC in FGF9Alb mice fed with HFHC diet. Molecular mechanism studies show that FGF9 stimulated the expression of ECM related genes in a β‐catenin dependent manner; and FGF9 exerts its effect on β‐catenin stability via the ERK1/2‐GSK‐3β signaling pathway. In summary, the data provides evidence for the critical role of FGF9 in NASH‐driven HCC pathogenesis; wherein it promotes the tumors formation through the ECM pathway.

Background: Although many genes related to epithelial-mesenchymal transition (EMT) have been explored in hepatocellular carcinoma (HCC), their prognostic significance still needs further analysis.Methods: Differentially expressed EMT-related genes were obtained through the integrated analysis of 4 Gene expression omnibus (GEO) datasets. The univariate Cox regression and Lasso Cox regression models are utilized to determine the EMT-related gene signature. Based on the results of multivariate Cox regression, a predictive nomogram is established. Time-dependent ROC curve and calibration curve are used to show the distinguishing ability and consistency of the nomogram. Finally, we explored the correlation between EMT risk score and immune immunity.Results: We identified a nine EMT-related gene signature to predict the survival outcome of HCC patients. Based on the EMT risk score’s median, HCC patients in each dataset were divided into high and low-risk groups. The survival outcomes of HCC patients in the high-risk group were significantly worse than those in the low-risk group. The prediction nomogram based on the EMT risk score has better distinguishing ability and consistency. High EMT risk score was related to immune infiltration.Conclusion: The nomogram based on the EMT risk score can reliably predict the survival outcome of HCC patients, thereby providing benefits for medical decisions.

Objective: The purpose of this study was to establish an N6-methylandenosine (m6A)-related long non-coding RNA (lncRNA) signature to predict the prognosis of hepatocellular carcinoma (HCC).Methods: Pearson correlation analysis was used to identify m6A-related lncRNAs. We then performed univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct an m6A-related lncRNA signature. Based on the cutoff value of the risk score determined by the X-title software, we divided the HCC patients into high -and low-risk groups. A time-dependent ROC curve was used to evaluate the predictive value of the model. Finally, we constructed a nomogram based on the m6A-related lncRNA signature.Results: ZEB1-AS1, MIR210HG, BACE1-AS, and SNHG3 were identified to comprise an m6A-related lncRNA signature. These four lncRNAs were upregulated in HCC tissues compared to normal tissues. The prognosis of patients with HCC in the low-risk group was significantly longer than that in the high-risk group. The M6A-related lncRNA signature was significantly associated with clinicopathological features and was established as a risk factor for the prognosis of patients with HCC. The nomogram based on the m6A-related lncRNA signature had a good distinguishing ability and consistency.Conclusion: We identified an m6A-related lncRNA signature and constructed a nomogram model to evaluate the prognosis of patients with HCC.

Summary: RING finger protein186 (RNF186) is dramatically upregulated in steatotic livers. The physiological role of RNF186 in non-alcoholic fatty liver disease (NAFLD) remains obscure. Here, we found that hepatocyte-specific RNF186 knockout (RNF186LKO) mice were protected from HFD-induced obesity. RNF186 ablation in liver suppressed inflammatory responses and ER stress and alleviated insulin tolerance, leading to improved glucose and lipid metabolism under HFD conditions. RNA-seq and western blot analyses revealed a significant downregulation of peroxisome proliferator-activated receptor γ, stearoyl-CoA desaturase 1, and cluster of differentiation 36 in the liver of RNF186 knockout mice consuming HFD. RNF186 deletion in liver results in less weight gain during HFD feeding and is associated with reduced liver fat, inflammation, and improved glucose and insulin tolerance. In contrast, upregulation of RNF186 in C57BL/6J mice livers impaired lipid metabolism and insulin tolerance. The collective results suggest that RNF186 may be a potential regulator of NAFLD in obesity.