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ObjectiveThere exist a high genetic correlation among various morphological characteristics of Mongolian horses. Utilizing advanced technology to obtain body structure parameters related to athletic performance could provide data support for breeding institutions to develop scientific breeding plans and establish the groundwork for further improvement of Mongolian horse breeds. However, traditional manual measurement methods are time-consuming, labor-intensive, and may cause certain stress responses in horses. Therefore, ensuring precise and effective measurement of Mongolian horse body dimensions is crucial for formulating early breeding plans.MethodVideo images of 50 adult Mongolian horses in the suitable breeding stage at the Inner Mongolia Agricultural University Horse Breeding Technical Center was first collected. Fifty images per horse were captured to construct the training and validation sets, resulting in a total of 2 500 high-definition RGB images of Mongolian horses, with an equal ratio of images depicting horses in motion and at rest. To ensure the model's robustness and considering issues such as angles, lighting, and image blurring during actual image capture, a series of enhancement algorithms were applied to the original dataset, expanding the Mongolian horse image dataset to 4 000 images. The YOLOv8n-pose was employed as the foundational keypoint detection model. Through the design of the C2f_DCN module, deformable convolution (DCNV2) was integrated into the C2f module of the Backbone network to enhance the model's adaptability to different horse poses in real-world scenes. Besides, an SA attention module was added to the Neck network to improve the model's focus on critical features. The original loss function was replaced with SCYLLA-IoU (SIoU) to prioritize major image regions, and a cosine annealing method was employed to dynamically adjust the learning rate during model training. The improved model was named DSS-YOLO (DCNv2-SA-SIoU-YOLO) network model. Additionally, a test set comprising 30 RGB-D images of mature Mongolian horses was selected for constructing body dimension measurement tasks. DSS-YOLO was used for keypoint detection of body dimensions. The 2D keypoint coordinates from RGB images were fused with corresponding depth values from depth images to obtain 3D keypoint coordinates, and Mongolian horse's point cloud information was transformed. Point cloud processing and analysis were performed using pass-through filtering, random sample consensus (RANSAC) shape fitting, statistical outlier filtering, and principal component analysis (PCA) coordinate system correction. Finally, body height, body oblique length, croup height, chest circumference, and croup circumference were automatically computed based on keypoint spatial coordinates.Results and DiscussionThe proposed DSS-YOLO model exhibited parameter and computational costs of 3.48 M and 9.1 G, respectively, with an average accuracy mAP0.5:0.95 reaching 92.5%, and a dDSS of 7.2 pixels. Compared to Hourglass, HRNet, and SimCC, mAP0.5:0.95 increased by 3.6%, 2.8%, and 1.6%, respectively. By relying on keypoint coordinates for automatic calculation of body dimensions and suggesting the use of a mobile least squares curve fitting method to complete the horse's hip point cloud, experiments involving 30 Mongolian horses showed a mean average error (MAE) of 3.77 cm and mean relative error (MRE) of 2.29% in automatic measurements.ConclusionsThe results of this study showed that DSS-YOLO model combined with three-dimensional point cloud processing methods can achieve automatic measurement of Mongolian horse body dimensions with high accuracy. The proposed measurement method can also be extended to different breeds of horses, providing technical support for horse breeding plans and possessing practical application value.

The daily behaviors of dairy cows, including standing, drinking, eating, and lying down, are closely associated with their physical health. Efficient and accurate recognition of dairy cow behaviors is crucial for timely monitoring of their health status and enhancing the economic efficiency of farms. To address the challenges posed by complex scenarios and significant variations in target scales in dairy cow behavior recognition within group farming environments, this study proposes an enhanced recognition method based on YOLOv5. Four Shuffle Attention (SA) modules are integrated into the upsampling and downsampling processes of the YOLOv5 model's neck network to enhance deep feature extraction of small-scale cow targets and focus on feature information, while maintaining network complexity and real-time performance. The C3 module of the model was enhanced by incorporating Deformable convolution (DCNv3), which improves the accuracy of cow behavior characteristic identification. Finally, the original detection head was replaced with a Dynamic Detection Head (DyHead) to improve the efficiency and accuracy of cow behavior detection across different scales in complex environments. An experimental dataset comprising complex backgrounds, multiple behavior categories, and multi-scale targets was constructed for comprehensive validation. The experimental results demonstrate that the improved YOLOv5 model achieved a mean Average Precision (mAP) of 97.7%, representing a 3.7% improvement over the original YOLOv5 model. Moreover, it outperformed comparison models, including YOLOv4, YOLOv3, and Faster R-CNN, in complex background scenarios, multi-scale behavior detection, and behavior type discrimination. Ablation experiments further validate the effectiveness of the SA, DCNv3, and DyHead modules. The research findings offer a valuable reference for real-time monitoring of cow behavior in complex environments throughout the day. [ABSTRACT FROM AUTHOR]

The Inner Mongolia Autonomous Region is a crucial area in China with a significant advantage in animal husbandry, particularly in cattle and sheep farming. However, the disposal of the large quantities of manure produced during farming has severely impacted the industry's healthy development. Proper treatment of the manure can convert it into organic fertilizer beneficial to farmland; otherwise, it will cause substantial environmental pollution. This study focuses on existing composting equipment and addresses the issues of cattle and sheep manure mixture ratios and compost maturity evaluation. Through experiments on the mixture of cattle and sheep manure, the optimal ratio for converting cattle and sheep manure into organic fertilizer was determined. Additionally, a fuzzy mathematical evaluation model was employed, along with experimental data, to establish a comprehensive evaluation system for aerobic compost maturity based on multiple indicators, revealing the variation patterns of maturity levels under different mixture ratios. The test results revealed that the composting equipment effectively controls the composting process, shortens the composting cycle, ensures the complete decomposition of organic matter, and meets national standards for livestock and poultry manure treatment. Regarding temperature and humidity, oxygen concentration, seed germination rate, pH value, electrical conductivity (EC), nitrogen, phosphorus, potassium content, and carbon-to-nitrogen ratio, the mixed compost of cattle and sheep manure in various ratios met the relevant standards for agricultural application. Various ratios of organic fertilizers containing cattle and sheep manure significantly promoted the growth of maize, wheat, and mung bean crops. Specifically, the compost decomposition cycle was shortest when sheep and cattle dung were mixed at a ratio of 2:1, while it was longest for all cattle dung. Finally, a fuzzy mathematical comprehensive evaluation model was established by selecting four indicators: water content, carbon-to-nitrogen ratio, apparent score, and germination index. The study demonstrates that the equipment and method offer significant advantages in efficiently treating cattle and sheep manure and producing organic fertilizer, thereby providing strong support for the sustainable development of animal husbandry. [ABSTRACT FROM AUTHOR]

Accurate and efficient access to Mongolian horse body size information is an important component in the modernization of the equine industry. Aiming at the shortcomings of manual measurement methods, such as low efficiency and high risk, this study converts the traditional horse body measure measurement problem into a measurement keypoint localization problem and proposes a top-down automatic Mongolian horse body measure measurement method by integrating the target detection algorithm and keypoint detection algorithm. Firstly, the SimAM parameter-free attention mechanism is added to the YOLOv8n backbone network to constitute the SimAM–YOLOv8n algorithm, which provides the base image for the subsequent accurate keypoint detection; secondly, the coordinate regression-based RTMPose keypoint detection algorithm is used for model training to realize the keypoint localization of the Mongolian horse. Lastly, the cosine annealing method was employed to dynamically adjust the learning rate throughout the entire training process, and subsequently conduct body measurements based on the information of each keypoint. The experimental results show that the average accuracy of the SimAM–YOLOv8n algorithm proposed in this study was 90.1%, and the average accuracy of the RTMPose algorithm was 91.4%. Compared with the manual measurements, the shoulder height, chest depth, body height, body length, croup height, angle of shoulder and angle of croup had mean relative errors (MRE) of 3.86%, 4.72%, 3.98%, 2.74%, 2.89%, 4.59% and 5.28%, respectively. The method proposed in this study can provide technical support to realize accurate and efficient Mongolian horse measurements. [ABSTRACT FROM AUTHOR]

The traditional manual methods for measuring Mongolian horse body parameters are not very safe, have low levels of automation, and cannot effectively ensure animal welfare. This research proposes a method for extracting target Mongolian horse body parameters based on deep learning and machine vision technology. Firstly, Swin Transformer is used as the backbone feature extraction network of Mask R-CNN model, and the CNN-based differentiated feature clustering model is added to minimize the loss of similarity and spatial continuity between pixels, thereby improving the robustness of the model while reducing error pixels and optimizing the rough mask boundary output. Secondly, an improved Harris algorithm and a polynomial fitting method based on contour curves are applied to determine the positions of various measurement points on the horse mask and calculate various body parameters. The accuracy of the proposed method was tested using 20 Mongolian horses. The experimental results show that compared with the original Mask R-CNN network, the PA (pixel accuracy) and MIoU (mean intersection over union) of the optimized model results increased from 91.46% and 84.72% to 98.72% and 95.36%, respectively. The average relative errors of shoulder height, withers height, chest depth, body length, croup height, shoulder angle, and croup angle were 4.01%, 2.98%, 4.86%, 2.97%, 3.06%, 4.91%, and 5.21%, respectively. The research results can provide technical support for assessing body parameters related to the performance of horses under natural conditions, which is of great significance for improving the refinement and welfare of Mongolian horse breeding techniques. [ABSTRACT FROM AUTHOR]

In 2014, the Chinese government unveiled the New Urbanization Plan and Document No. 46, which profoundly influenced the development trajectory of the regional economy and sports industry. Using the coupling coordination model, this study aimed to assess the development progress of the sports industry and urban clusters economy. This study sampled Greater Bay Area urban clusters (GBAUC) and Yangtze River Delta urban clusters (YRDUC). The statistics covered one year after the release of the policies to date. We developed a total of 15 macro indicators to evaluate the sports industry and urban cluster economy as two distinct, yet interdependent, economic systems. Using the entropy weight method, we determined the standardized values and weights for the two systems before calculating the coupling coordination degree (D). Between 2015 and 2021, the sampled sports industry and urban clusters economy exhibited coordinated high growth across all economic metrics, with multiple sports industry metrics exhibiting double-digit growth. In 2015, both showed extreme imbalance: D of GBAUC = 0.092, D of YRDUC = 0.091. In 2017, both improved to bare coordination: D of GBAUC = 0.600, D of YRDUC = 0.566. In 2019, both reached well coordination: D of GBAUC = 0.851, D of YRDUC = 0.814. By 2021, both achieved quality coordination: D of GBAUC = 0.990, D of YRDUC = 1. This study provides the first evidence from the sports industry that China's new urbanization model and Document No. 46 are highly effective for synergistic regional economic growth. [ABSTRACT FROM AUTHOR]

Traditional cow manure composting equipment often lacks ventilation, heating, and real-time monitoring and adjustment capabilities, hindering manure maturation and proper utilization of compost, moisture reduction, and optimal environmental conditions for microbial activity. To address these limitations, we optimized and redesigned the structure and control elements of conventional composting equipment. In doing so, we developed a novel cow manure composting apparatus based on the target composting quantity method. The new design features a refined ventilation and heating structure, as well as an enhanced control system using PLC and MCGS. This equipment provides improved composting effects while offering real-time monitoring of composting data, effectively promoting the composting process. Further evaluation of the modified equipment was conducted using fermented cow dung, demonstrating its feasibility. The experiment yielded positive results, indicating that the composted cow manure satisfied all necessary standards across all evaluation parameters. These parameters included the moisture content of the compost, the duration of high temperature, the pH, and the seed germination index. The seed germination rate reaches 85%, and the water content of manure is 9.3%, which is much higher than the industry standard. [ABSTRACT FROM AUTHOR]

Due to its flexibility and versatility, the electric distributed drive micro-tillage chassis can be used more often in the future in Intelligence agriculture scenarios. However, due to the complex working conditions of the agricultural operation environment, it is a challenging task to distribute the torque demand of four wheels reasonably and effectively. In this paper, we propose a drive torque allocation strategy based on deep reinforcement learning to ensure straight-line retention and energy saving, using a distributed electric traction chassis for greenhouses as the research object. The torque assignment strategy can be represented as a Markovian decision process, and the approximate action values and policy functions are obtained through an Actor–Critic network, and the Twin Delayed Deep Deterministic Policy Gradient (TD3) is used to incorporate the vehicle straight-line retention rate into the cumulative reward to reduce energy consumption. The training results under plowing working conditions show that the proposed strategy has a better straight-line retention rate. For typical farming operation conditions, the proposed control strategy significantly improves the energy utilization and reduces the energy by 10.5% and 3.7% compared to the conventional average torque (CAT) distribution strategy and Deep Deterministic Policy Gradient (DDPG) algorithm, respectively. Finally, the real-time executability of the proposed torque distribution strategy is verified by Soil-tank experiments. The TD3 algorithm used in this study has stronger applicability than the traditional control algorithm in dealing with continuous control problems, and provides a research basis for the practical application of intelligent control algorithms in future greenhouse micro-tillage chassis drive control strategies. [ABSTRACT FROM AUTHOR]

• FAM20C phosphorylates regulatory proteins involved in bone development. • A novel Osx-Cre; FAM20Cflox/flox knockout (oKO) mouse model is established. • oKO has lower bone mineralization, along with greater hypertrophic cartilage. • oKO has up-regulation of osteoclast differentiation genes Sirpb1a-c and Mapk13. • Osteogenesis-related genes Osx, Alp, Ocn, Mepe, and Col2 are downregulated in oKO. Family with sequence similarity 20 member C (FAM20C) is a Golgi casein kinase that phosphorylates extracellularly-secreted regulatory proteins involved in bone development and mineralization, but its specific role in bone development is still largely unknown. In this study, to examine the specific mechanisms that FAM20C influences bone development, we cross-bred Osx-Cre with FAM20Cflox/flox mice to establish a Osx-Cre; FAM20Cflox/flox knockout (oKO) mouse model; FAM20C was KO in pre-osteoblasts. oKO development was examined at 1–10 weeks, in which compared to control FAM20Cflox/flox, they had lower body weights and bone tissue mineralization. Furthermore, oKO had lower bone volume fractions, thickness, and trabecular numbers, along with higher degrees of trabecular separation. These mice also had decreased femoral metaphyseal cartilage proliferation layer, along with thickened hypertrophic layer and increased apoptotic cell counts. Transcriptomic analysis found that differentially-expressed genes in oKO were concentrated in the osteoclast differentiation pathway, in line with increased osteoclast presence. Additionally, up-regulation of osteoclast-related, and down-regulation of osteogenesis-related genes, were identified, in which the most up-regulated genes were signal regulatory protein β-1 family (Sirpb1a-c) and mitogen-activated protein kinase 13. Overall, FAM20C KO in pre-osteoblasts leads to abnormal long bone development, likely due to subsequent up-regulation of osteoclast differentiation-associated genes. [ABSTRACT FROM AUTHOR]

Introduction: The FIFA World Cup is not only the most lucrative athletic event globally, but it also functions as a platform for promoting peace owing to FIFA's new vision. Nevertheless, the determinants of TV viewership ratings, especially in the Chinese market, which is a critical revenue stream for FIFA TV broadcasting, are still unsolved. Using a distributional regression, this study aimed to quantify the dynamics of viewership ratings for the FIFA World Cup 2022 in mainland China., Methods: The CCTV viewership ratings were modeled using 12 covariables related to Chinese TV consumer behavior. Given the data structure, a Logit Normal regression model was chosen to fit the location and dispersion parameters of viewership ratings to explanatory variables., Results: In the fitted heteroscedastic model, the viewership ratings dynamics in mainland China were driven by the match kick-off time: μ ^ = logistic [ - 4.874 + 0.043 × match kick - off time ] . In addition, the model captures the factors that influence the variations in viewership ratings: σ ^ = exp [ - 14.26 - 1.346 (if, FIFA World Cup champion = "Yes") + 0.004 × FIFA world ranking]. Thus, it shows that the FIFA World Cup champions tamp down such variations, leading to a more stable viewing behavior among Chinese consumers., Conclusions: Time- and team-sensitive strategies are proposed to aid in crafting uncertainty-suppressing business decisions for the FIFA World Cup 2026. Ultimately, in the more insecure 2020s, a broader live coverage of the FIFA World Cup would be invaluable for promoting peace., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Wang, Su, Agudamu, Gong, Bu and Zhang.)

The pathogenesis of hepatitis B virus （HBV）-associated hepatocellular carcinoma （HCC） is complicated with the crosstalk of multiple factors and the multi-step processes. The main mechanisms underlying the HBV-induced HCC include:①integration of HBV DNA into the host hepatocyte genome to alter gene function at the insertion site，resulting in host genome instability and expression of carcinogenic truncated proteins;②HBV gene mutations at S，C，and X coding regions in the genome;③HBV X gene-encoded HBx protein activates proto-oncogenes and inhibits tumor suppressor genes，leading to the HCC occurrence. In this article，the recent research progress on the molecular mechanism of HBV-induced HCC is comprehensively reviewed，so as to provide insights into the prevention，early prediction and postoperative adjuvant therapy of HCC.

Promyelocytic leukemia (PML) protein, a tumor suppressor, plays an important role in patients with acute promyelocytic leukemia (APL) receiving arsenic trioxide (As 2 O 3 ) therapy. APL is a M3 subtype of acute myeloid leukemia (AML), which is characterized by expression of PML-RARα (P/R) fusion protein, leading to the oncogenesis. As 2 O 3 is currently used as the first-line drug for patients with APL, and the mechanism may be:As 2 O 3 directly binds to PML part of P/R protein and induces multimerization of related proteins, which further recruits different functional proteins to reform PML nuclear bodies (PML-NBs), and finally it degraded by SUMOylation and ubiquitination proteasomal pathway. Gene mutations may lead to relapse and drug resistance after As 2 O 3 treatment. In this review, we discuss the structure and function of PML proteins; the pathogenesis of APL induced by P/R fusion protein; the involvement of PML protein in treatment of APL patient with As 2 O 3 ; and explain how PML protein mutations could cause resistance to As 2 O 3 therapy.