Integration and learning: a case study of the international higher physical education talent-cultivation model

IntroductionThis study delves into the multifaceted components of talent-training models within China’s physical education domain through Sino-foreign cooperative initiatives. Employing a mixed-methods approach, it systematically evaluates the innovative systems developed by pilot units and outlines their experiential insights.MethodsUsing a mixed-methods approach, this research extensively evaluates the situation of pilot units by collecting and analyzing data from closed-ended and open-ended questionnaires as well as interview responses. The study categorizes and analyzes the data to comprehensively understand cooperative talent-training models.ResultsThe findings are classified into three main themes: Learning, Integration, and Binary Evaluation and Practice Reform. Under the Learning theme, the study observed a selective integration of foreign educational paradigms into the local context, respecting the distinctiveness of Chinese education and aligning with national policies promoting unique educational systems. Additionally, the Integration theme underscores the necessity of meticulously assimilating introduced educational resources into China’s educational fabric, highlighting the need for adaptability when integrating foreign educational elements. Furthermore, the Binary Evaluation and Practice Reform theme reveal the establishment of a dualistic evaluation and reform system tailored to cooperative education specifics, outlining challenges associated with ideological and cultural disparities when integrating certain foreign education aspects into the Chinese context.DiscussionThis research provides insightful exploration into the complexities of collaborative talent-training models in Physical Education. It not only elucidates the assimilation of foreign paradigms but also highlights nuanced challenges and prospects for developing tailored educational systems within specific regional and national contexts

Exosomes Derived From miR-133b-Modified Mesenchymal Stem Cells Promote Recovery After Spinal Cord Injury

Dysregulation of microRNAs (miRNAs) has been found in injured spinal cords after spinal cord injury (SCI). Previous studies have shown that miR-133b plays an important role in the differentiation of neurons and the outgrowth of neurites. Recently, exosomes have been used as novel biological vehicles to transfer miRNAs locally or systemically, but little is known about the effect of the delivery of exosome-mediated miRNAs on the treatment of SCI. In the present study, we observed that mesenchymal stem cells, the most common cell types known to produce exosomes, could package miR-133b into secreted exosomes. After SCI, tail vein injection of miR-133b exosomes into rats significantly improved the recovery of hindlimb function when compared to control groups. Additionally, treatment with miR-133b exosomes reduced the volume of the lesion, preserved neuronal cells, and promoted the regeneration of axons after SCI. We next observed that the expression of RhoA, a direct target of miR-133b, was decreased in the miR-133b exosome group. Moreover, we showed that miR-133b exosomes activated ERK1/2, STAT3, and CREB, which are signaling pathway proteins involved in the survival of neurons and the regeneration of axons. In summary, these findings demonstrated that systemically injecting miR-133b exosomes preserved neurons, promoted the regeneration of axons, and improved the recovery of hindlimb locomotor function following SCI, suggesting that the transfer of exosome-mediated miRNAs represents a novel therapeutic approach for the treatment of SCI

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them

A Method for Improving the Accuracy of Link Prediction Algorithms

Link prediction is a key tool for studying the structure and evolution mechanism of complex networks. Recommending new friend relationships through accurate link prediction is one of the important factors in the evolution, development, and popularization of social networks. At present, scholars have proposed many link prediction algorithms based on the similarity of local information and random walks. These algorithms help identify actual missing and false links in various networks. However, the prediction results significantly differ in networks with various structures, and the prediction accuracy is low. This study proposes a method for improving the accuracy of link prediction. Before link prediction, k-shell decomposition method is used to layer the network, and the nodes that are in 1-shell and the nodes that are not linked to the high-shell in the 2-shell are deleted. The experiments on four real network datasets verify the effectiveness of the proposed method

ZmDof30 Negatively Regulates the Promoter Activity of the Pollen-Specific Gene Zm908

The maize (Zea mays) pollen-predominant gene Zm908, a novel small-peptide gene, was reported to play critical roles in pollen germination and pollen tube growth in our previous work. In this study, we aimed to explore the regulatory mechanism of Zm908. The putative promoter of Zm908 was cloned and analyzed. The activity analysis of a series of promoter truncations in different tissues of transgenic tobacco plants indicated that the Zm908 promoter is pollen-specific and that the –126 to –68 region is crucial for pollen expression. The 5′ deletion analysis of the –126 to –68 region revealed that the –126 to –102 region functions as a transcriptional suppression element. ZmDof30, which is predominantly expressed in pollen and whole anthers, was cloned and characterized. ZmDof30-GFP localized to the nuclei of maize protoplasts and possessed no transcriptional activation activity in a yeast system. ZmDof30 could bind to the AAAG elements in p184 sequence containing the –126 to +58 region of the Zm908 promoter in vitro and in vivo, and negatively regulated p184 activity in tobacco leaves. Collectively, ZmDof30 may function as a Zm908 transcriptional repressor in pollen, and these results may provide a better understanding of the regulation of the Zm908 gene. Additionally, the pollen-specific Zm908 promoter may be valuable for genetically engineering male sterility