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Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by joint inflammation, leading to pain, swelling, stiffness, and joint damage. Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) hold significant promise as therapeutic agents owing to their potent paracrine activities. This study investigated the anti-inflammatory effects of human umbilical cord-derived MSC (hUCSC)-EVs overexpressing miR-124 in collagen-induced arthritis (CIA). hUCSCs were transfected with miR-124, and hUCSC-EVs were isolated and characterized. Arthritis was induced in mice by collagen injection, followed by intravenous administration of miR-124 EVs, miR-control, or vehicle. Cytokine levels, including interleukin (IL)-6, IL-1β, tumor necrosis factor (TNF)-α, IL-10, and transforming growth factor (TGF)-β, were measured in the joints using real-time PCR by enzyme-linked immunosorbent assay (ELISA). miR-124-hUCSC-EVs showed enrichment of miR-124. In arthritic mice, intravenous treatment with miR-124 EVs significantly reduced the clinical score, increased anti-inflammatory cytokines IL-10 and TGF-β, and lowered inflammatory cytokines IL-6, TNF-α, and IL-1β in the joints. This study shows that miR-124 EVs have anti-inflammatory activity in arthritis by suppressing pro-inflammatory cytokines through miR-124 overexpression. MSC-EVs overexpressed with inflammation-modulating miRNAs offer a promising cell-free approach for treating inflammatory arthritis.

目的/意义人工智能（Artificial Intelligence，AI）技术已在学术和工程应用领域掀起了研究高潮，在地球物理参数和农业气象遥感参数反演方面也表现出了强大的应用潜力。目前大部分AI技术在地学和农学的应用还是“黑箱”，没有物理意义或缺乏可解释性及通用性。为了促进AI在地学和农学的应用和培养交叉学科的人才，本研究提出基于AI耦合物理和统计方法的地球物理参数反演范式理论。方法首先基于物理能量平衡方程进行物理逻辑推理，从理论上构造反演方程组，然后基于物理推导构建泛化的统计方法。通过物理模型模拟获得物理方法的代表性解以及利用多源数据获得统计方法代表性的解作为深度学习的训练和测试数据库，最后利用深度学习进行优化求解。［结果和讨论］判定形成具有通用性和物理可解释的范式条件包括：（1）输入与输出变量（参数）之间必须存在因果关系；（2）输入和输出变量（参数）之间理论上可以构建闭合的方程组（未知数个数少于或等于方程组个数），也就是说输出参数可以被输入参数唯一确定。如果输入参数（变量）和输出参数（变量）之间存在很强的因果关系，则可以直接使用深度学习进行反演。如果输入参数和输出参数之间存在弱相关性，则需要添加先验知识来提高输出参数的反演精度。此外，本研究以农业气象遥感中的关键参数地表温度、发射率、近地表空气温度和大气水汽含量联合反演作为案例对理论进行了证明，分析结果表明本理论是可行的，并且可以辅助优化设计卫星传感器波段组合。结论本理论和判定条件的提出在地球物理参数反演史上具有里程碑意义。

Aiming at the problem that the accuracy and real-time performance of current aircraft remote sensing image target detection algorithms cannot be balanced, a target detection algorithm based on single shot MultiBox detector (SSD) is proposed for anchor frame scale densification and anchor frame strategy matching. The algorithm uses an improved deep residual network to replace the original feature extraction network of the SSD algorithm. Combined with the small-scale and dense features of aircraft remote sensing images, this paper redesigns the size and proportion of anchor frame and adds a feature layer containing two scales. Then, the anchor frame densification operation is performed on each feature layer to make the anchor frame laying density of the feature layer basically equal, and to improve the probability of matching the anchor frames of different scales to the real target. On the issue of the large gap in the number of positive sample anchor frames of different scales, an anchor frame strategy matching method that makes the number of positive sample anchor frames of different scales tend to the overall positive sample average is proposed, which improves the effectiveness of training and robustness of target detection to a certain extent. Related experiments are conducted on the aircraft remote sensing dataset, the average precision reaches 91.15%, and the frame per second is 33.4. The results show that the improved algorithm can not only increase the detection accuracy on the basis of adding fewer training parameters, but also retain the real-time detec-tability of the SSD algorithm.

The real-time monitoring and evaluation of water quality provides a scientific basis for water resource management and promotes regional sustainable development. This study established a database using Landsat-8 satellite data and water quality data from the Ningxia Yellow River basin in China, spanning 2021 to 2023, and this paper proposes a custom residual convolutional neural network model with a hybrid attention mechanism, referred to as PCWA-ResCNN. The accuracy of the model in predicting turbidity, permanganate, ammonia nitrogen, and dissolved oxygen concentration was more than 95%. Compared to convolutional neural networks and long short-term memory models, this model performed better in predicting water quality parameters with significantly improved prediction performance. In terms of spatial distribution, the pollution degree in the middle reaches of the basin is relatively serious. However, the overall water quality is good, being mainly Class I and Class II water quality. The hybrid model established in this paper can better capture the complex nonlinear relationship between the observed values and the surface water reflectance, showing strong robustness. This model can be used for the water quality monitoring of complex inland rivers and lakes, and it can also provide effective support for relevant government departments to formulate scientific and reasonable water quality management policies. [ABSTRACT FROM AUTHOR]

Aircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors' scheme, which achieves superior performance. [ABSTRACT FROM AUTHOR]

Since Reform and Opening Up, the Chinese government has been under pressure regarding policy on the residence of foreign migrants to the country. In response, the Chinese government released a document entitled the Regulations of the PRC on the Administration of Permanent Residence of Foreigners in February 2020, which has roused fierce social opposition, as reflected in Chinese netizens' attitudes towards foreign immigrants and citizenship identity. Based on online discussions in the Zhihu community, in this paper, we investigated the exclusive and inclusive boundaries of Chinese netizens' citizenship identity through quantitative and thematic textual analyses. The exclusive boundary includes racial and historical discourses that raise insurmountable walls between Chinese people and foreign immigrants, while the inclusive boundary is reflected in citizenship rights and access criteria. The issues of 'super-national treatment' (privileges enjoyed by foreign nationals with permanent Chinese residency) and 'low access criteria' (the relative ease with which foreign immigrants can obtain permanent residence in China) for foreign immigrants have weakened the openness of the inclusive boundary, causing netizens who were once willing to accept foreign immigrants to oppose the presence of non-native groups in the country. This study deepens our understanding of Chinese netizens' citizenship identity and contributes to citizenship identity theory. [ABSTRACT FROM AUTHOR]

Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the "graph" constructed with the head entity and relationship to predict the tail entity, and then uses the "inverse graph" constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE's parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization. [ABSTRACT FROM AUTHOR]

The study of citizenship in China has become popular not only as a research topic but also as a praxis that seeks to influence citizenship-related policies and address structural injustice. Analysis of the trajectory of developing citizenship research over the decades indicates that scholarship focusing on socioeconomic injustice has affected various policy actions, and the struggle associated with the cultural dimension of citizenship seems to have intensified. This special issue deconstructs the state's monolithic interpretation of culture and examines the cultural underpinnings of citizenship discourse and practice in China. We take a broad view of cultural traditions, which have been deeply rooted throughout history and have shaped the underlying relationships between the state, society, and citizens in modern China. The articles in this special issue reveal the culture-informed citizenship practices enacted by various actors, including the government forces, cultural minorities, and grassroots activists. They suggest that spirituality is a site of experiencing and negotiating citizenship in the Chinese context. [ABSTRACT FROM AUTHOR]

With reference to the heads of departments of moral education (HDMEs) in Shanghai's junior secondary schools, this paper explores middle leaders' logics for leading school-organized extra-curricular activities (SEAs). This qualitative study, guided by Woulfin's lived logic framework, found that the interviewed HDMEs actively reinterpreted the institutional logics with three logics—expressive, instrumental, and hierarchical—by manipulating policy circulation, responding to the performative accountability and micropolitics in the hierarchy, and using correlative thinking. The lived logics of leading SEAs reveal that, as heads of a marginalized department in schools, the HDMEs struggled to seek visibility by using correlative thinking, promoting the importance of their work, and aligning with more helpful senior leaders. The study responds to theories on school middle leadership and implementation logic. It could deepen our understanding of the paradoxes in China's development and governance, especially in areas concerning both measurable performance and unmeasurable issues (e.g. ideology and sustainable development). [ABSTRACT FROM AUTHOR]