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Abstract Background Morbidity and mortality associated with cardiovascular diseases, such as myocardial infarction, stem from the inability of terminally differentiated cardiomyocytes to regenerate, and thus repair the damaged myocardial tissue structure. The molecular biological mechanisms behind the lack of regenerative capacity for those cardiomyocytes remains to be fully elucidated. Recent studies have shown that c-Jun serves as a cell cycle regulator for somatic cell fates, playing a key role in multiple molecular pathways, including the inhibition of cellular reprogramming, promoting angiogenesis, and aggravation of cardiac hypertrophy, but its role in cardiac development is largely unknown. This study aims to delineate the role of c-Jun in promoting early-stage cardiac differentiation. Methods The c-Jun gene in mouse embryonic stem cells (mESCs) was knocked out with CRISPR-Cas9, and the hanging drop method used to prepare the resulting embryoid bodies. Cardiac differentiation was evaluated up to 9 days after c-Jun knockout (ko) via immunofluorescence, flow cytometric, and qPCR analyses. Results Compared to the wild-type control group, obvious beating was observed among the c-Jun-ko mESCs after 6 days, which was also associated with significant increases in myocardial marker expression. Additionally, markers associated with mesoderm and endoderm cell layer development, essential for further differentiation of ESCs into cardiomyocytes, were also up-regulated in the c-Jun-ko cell group. Conclusions Knocking out c-Jun directs ESCs toward a meso-endodermal cell lineage fate, in turn leading to generation of beating myocardial cells. Thus, c-Jun plays an important role in regulating early cardiac cell development.

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.

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.

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.

IntroductionThe 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.MethodsThe 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.ResultsIn 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×matchkick-offtime]. 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.ConclusionsTime- 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.

Baling cellulosic biomass into round bales is an effective way to reduce the cost of storage and transportation. To improve the quality of bales and reduce the baling energy consumption, this paper introduces the steel roller layout parameters of the round baler into the biomass baling process. Alfalfa was used as an experimental material for five levels pitch value of roller circumferential layout baling experiments. The results showed that the introduction of chamber non-roundness (pitch value of roller circumferential layout) destroyed the formation of the entanglement high density ring cylindrical shell lamination of the outer layer of bales, which was beneficial to the compression of bales core material. When the pitch value was 30 mm, the maximum baling pressure, radial pressure transfer loss, and the baling energy consumption of baler were reduced by 30.4%, 33.4%, and 13.7%, respectively. When the pitch value was 60 mm, the relaxation ratio and radial density difference were reduced by 6.3% and 35.8%, respectively, and the radial density uniformity of alfalfa bale was increased by 32.0%. The experimental results provide a theoretical basis and technical support for the chamber structural optimization design of the round baler.

ObjectiveAging is associated with decreased nicotinamide adenine dinucleotide (NAD) levels, which in turn cause dysfunctional mitochondria and indirectly affect a myriad of diseases. Intracellular nicotinamide phosphoribosyltransferase (iNAMPT) serves as a central rate-limiting enzyme in NAD synthesis, making it an indispensable health mediator. This meta-analysis examined the effect of exercise training on the expression of iNAMPT in humans.MethodsWe searched PubMed, Scopus, ClinicalTrials.gov, and the International Clinical Trials Registry Platform for studies published between the inception of the database and July 5, 2023. Using the common-effect model, evidence for the change in iNAMPT following exercise training was synthesized as Cohen’s d.ResultsThe search yielded five eligible studies. The overall effect size is 0.81, with a 95% confidence interval of 0.55 to 1.07. Therefore, a random adult will have a 71.7% probability that iNAMPT will be up-regulated following exercise training. In general, exercise training resulted in a 1.46-fold increase in iNAMPT. Our probability statistics indicate that subgroups of interest may differ practically. Specifically, there is a 79.3% probability of increased iNAMPT in men, compared to a 69.0% probability in the overall population; young adults have a 75.6% probability of having an increased iNAMPT, whereas aged adults have a 68.7% probability; and, iNAMPT has a 75.1% probability increase after aerobic exercise and a 66.4% probability increase after resistance exercise.ConclusionExercise training is effective for increasing iNAMPT levels in skeletal muscles. This essential enzyme regulates not only cellular energetics but also healthspan. Therefore, exercise should be promoted as a natural slow-aging lifestyle.

BackgroundAcute coronary syndrome (ACS) is the most common cause of death in patients with coronary artery disease. The aim of the study was to identify the predictors of both comprehensive clinical risk and severity of coronary lesions by comprehensive use of GRACE and SYNTAX scores in patients with ACS.MethodsClinical data of 225 ACS patients who underwent coronary angiography between 2015 and 2016 were collected. Multiple logistic regression analysis (stepwise) was used to identify the predictors. The predictive ability of predictors and the model were determined using receiver operating characteristics analyses.ResultsMultivariable logistic regression analyses showed that high aspartate aminotransferase (AST) predicted the comprehensive clinical risk with odds ratios (ORs) and 95% confidence intervals (CIs) of 1.011 (1.002–1.021). High total cholesterol (TC) and red blood cell distribution width (RDW) predicted the severity of coronary lesions with ORs and 95% CIs of 1.517 (1.148–2.004) and 1.556 (1.195–2.028), respectively. Low prealbumin predicted both severity of coronary lesions and comprehensive clinical risk of ACS patients with ORs and 95% CIs of 0.743 (0.672–0.821) and 0.836 (0.769–0.909), respectively. The model with a combination of prealbumin and AST had the highest predictive efficacy for comprehensive clinical risk, and the combination of prealbumin, TC, and RDW had the highest predictive efficacy for the severity of coronary lesions. The sensitivity and specificity, and the optimal cut-off values of these four indexes were determined.ConclusionsFour predictors for the comprehensive clinical risk and severity of coronary lesions of ACS were identified, which provided important information for the early diagnosis and appropriate treatment of ACS.

In recent years, with the gradual expansion of the planting area for Chinese crab apple trees, the traditional method of picking crab apples has become inadequate due to its large workload, low efficiency, and high cost. To address this issue, this paper presents the design of a trunk-type vibration picker specifically for crab apple picking. The design process began with a modal analysis and a harmonic response analysis of a crab apple tree using Ansys Workbench. Through these analyses, the optimal excitation frequency for the crab apple tree was determined to be 12.7 Hz. Subsequently, the picker was designed, and its key components underwent modal and stress analyses. A comparison revealed no resonance between the picker and the key components, ensuring the feasibility of the picker. To further evaluate the performance of the picker, a rigid–flexible coupling analysis was conducted using Adams. This analysis determined the acceleration response curves of the lateral branch points of the crab apple tree at various excitation frequencies: 9.7 Hz, 12.7 Hz, 15.7 Hz, and 18.7 Hz. Finally, field trials were carried out to validate the reliability of the data obtained from Adams. The results showed that the lateral branch point acceleration increased with higher excitation frequencies. However, when the optimal excitation frequency of 12.7 Hz was reached, the lateral branch point acceleration decreased. This indicated that the most effective vibration frequency for picking crab apples was 12.7 Hz.

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.

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.

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.

Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

Automatic estimation of the poses of dairy cows over a long period can provide relevant information regarding their status and well-being in precision farming. Due to appearance similarity, cow pose estimation is challenging. To monitor the health of dairy cows in actual farm environments, a multicow pose estimation algorithm was proposed in this study. First, a monitoring system was established at a dairy cow breeding site, and 175 surveillance videos of 10 different cows were used as raw data to construct object detection and pose estimation data sets. To achieve the detection of multiple cows, the You Only Look Once (YOLO)v4 model based on CSPDarkNet53 was built and fine-tuned to output the bounding box for further pose estimation. On the test set of 400 images including single and multiple cows throughout the whole day, the average precision (AP) reached 94.58%. Second, the keypoint heatmaps and part affinity field (PAF) were extracted to match the keypoints of the same cow based on the real-time multiperson 2D pose detection model. To verify the performance of the algorithm, 200 single-object images and 200 dual-object images with occlusions were tested under different light conditions. The test results showed that the AP of leg keypoints was the highest, reaching 91.6%, regardless of day or night and single cows or double cows. This was followed by the AP values of the back, neck and head, sequentially. The AP of single cow pose estimation was 85% during the day and 78.1% at night, compared to double cows with occlusion, for which the values were 74.3% and 71.6%, respectively. The keypoint detection rate decreased when the occlusion was severe. However, in actual cow breeding sites, cows are seldom strongly occluded. Finally, a pose classification network was built to estimate the three typical poses (standing, walking and lying) of cows based on the extracted cow skeleton in the bounding box, achieving precision of 91.67%, 92.97% and 99.23%, respectively. The results showed that the algorithm proposed in this study exhibited a relatively high detection rate. Therefore, the proposed method can provide a theoretical reference for animal pose estimation in large-scale precision livestock farming.

Atherosclerosis (AS) is a life-threatening vascular disease. RNA N6-methyladenosine (m6A) modification level is dysregulated in multiple pathophysiologic processes including AS. In this text, the roles and molecular mechanisms of m6A writer METTL3 in AS progression were explored in vitro and in vivo. In the present study, cell proliferative, migratory, and tube formation capacities were assessed through CCK-8, Transwell migration, and tube formation assays, respectively. RNA m6A level was examined through a commercial kit. RNA and protein levels of genes were measured through RT-qPCR and western blot assays, respectively. VEGF secretion level was tested through ELISA assay. JAK2 mRNA stability was detected through actinomycin D assay. The relationship of METTL3, IGF2BP1, and JAK2 was investigated through bioinformatics analysis, MeRIP, RIP, RNA pull-down, and luciferase reporter assays. An AS mouse model was established to examine the effect of METTL3 knockdown on AS development in vivo. The angiogenetic activity was examined through chick chorioallantoic membrane assay in vivo. The results showed that METTL3 was highly expressed in ox-LDL-induced dysregulated HUVECs. METTL3 knockdown inhibited cell proliferation, migration, tube formation, and VEGF expression/secretion in ox-LDL-treated HUVECs, hampered AS process in vivo, and prevented in vivo angiogenesis of developing embryos. METTL3 positively regulated JAK2 expression and JAK2/STAT3 pathway in an m6A dependent manner in HUVECs. IGF2BP1 positively regulated JAK2 expression through directly binding to an m6A site within JAK2 mRNA in HUVECs. METTL3 knockdown weakened the interaction of JAK2 and IGF2BP1. METTL3 exerted its functions through JAK2/STAT3 pathway. In conclusion, METTL3 knockdown prevented AS progression by inhibiting JAK2/STAT3 pathway via IGF2BP1.