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Enhancing rural resilience is crucial due to the significant vulnerabilities faced by rural areas, such as weak economic foundations, scarce resources, and deficient infrastructure. This paper aims to provide a comprehensive review of rural network resilience (RNR) to underscore its importance in understanding the mechanisms and pathways of rural sustainable development. To establish the theoretical foundations of RNR, we trace the evolution of resilience concepts and their applications across disciplines. The proposed analytical framework integrates evaluation methods from network science to assess RNR's structural characteristics and outlines simulation techniques for predicting resilience under various risk scenarios. Furthermore, the paper examines the key influencing factors that shape RNR within complex rural regional systems, exploring the intricate evolutionary mechanisms involved. To bridge existing research gaps, a synergistic development perspective is introduced, emphasizing the interconnected, multi-layered nature of rural networks across societal, economic, and ecological domains. Additionally, a county-level analytical framework tailored for county-level rural resilience analysis is presented to offer strategic guidance for enhancing RNR and driving sustainable rural revitalization. This transdisciplinary synthesis propels RNR as an emerging field with significant policy implications. [ABSTRACT FROM AUTHOR]

During the Ediacaran–Cambrian transition, a series of stratal continuous and well-preserved siliceous rock and phosphorite assemblages developed in Qingzhen, Guizhou Province, China, facilitating research on the biological evolution, marine chemistry, and palaeoecological environment of this period. Therefore, we investigated the paleontology, trace and rare earth elements, total organic carbon, total sulfur content, and Zn isotopes of the phosphorus-bearing rock series in the Taozichong Formation of the Cambrian period in Qingzhen. Geochemical analysis reveals that the sedimentary rocks in this area were formed in the oxygen-rich seawater environment and were not affected by high-temperature hydrothermal activity. The upwelling ocean current provided abundant rare earth elements and other nutrient elements, as well as conditions for the prosperity of biota in Qingzhen. In addition, the δ66Zn value (−0.21‰–0.41‰ range and 0.17‰ mean) in the Qingzhen phosphorous rock series was much lower than that in seawater, indicating a strong level of biological productivity. The variation trend of δ13C, δ18O, and δ66Zn exhibited four stages and three obvious drift events. The results suggest that climate change during this period led to the intermittent flourishing and extinction of organisms, which triggered the negative drift of δ13C and δ18O in the ocean, resulting in a coordinated response of δ66Zn. The unique ecological environment of the Taozichong Formation in Qingzhen also provides favorable conditions for the population continuation of Ediacaran-type benthic soft-bodied metazoon dominated by discoid fossils, Shaanxilithes, worm fossils, and sponge fossils in the Cambrian strata, as well as participation in the global biological explosion events. The study area provides new insights for rebuilding the global Ediacaran–Cambrian ecosystem during the transition period. [ABSTRACT FROM AUTHOR]

Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic flow due to the high non‐linearity, complexity and dynamicity of the data. Most existing forecasting methods focus on designing complicated graph neural network architectures to capture the spatio‐temporal features of traffic data with the help of predefined graphs. However, traffic data exhibit a strong spatial dependency, which means that there are often complex correlations between nodes in a road network topology graph. Moreover, the spatial correlations of the road network change over time. To capture the properties of the road network topology graph, a novel spatio‐temporal adaptive graph convolutional networks model (STAGCN) based on deep learning is proposed. Utilize adaptive graph generation block to capture the static and dynamic structures of the traffic road network respectively, and they are integrated to construct an adaptive road network topology graph. Then the spatio‐temporal features of the traffic data are captured using spatio‐temporal convolution blocks. Experiments were conducted on publicly available traffic datasets for freeway traffic in California, USA, and the results showed that the prediction accuracy of the STAGCN model outperformed multiple baseline methods. [ABSTRACT FROM AUTHOR]

DNA methyltransferase 3A (DNMT3A) has been regarded as a potential epigenetic target for the development of cancer therapeutics. A number of DNMT3A inhibitors have been reported, but most of them do not have good potency, high selectivity and/or low cytotoxicity. It has been suggested that a non-conserved region around the target recognition domain (TRD) loop is implicated in the DNMT3A activity under the allosteric regulation of the ATRX-DNMT3-DNMT3L (ADD) domain, but the molecular mechanism of the regulation of the TRD loop on the DNMT3A activity needs to be elucidated. In this study, based on the reported crystal structures, the dynamics of the TRD loop in different multimerization with/without the bound guest molecule, namely the ADD domain or the DNA molecule, was investigated using conventional molecular dynamics (MD) and umbrella sampling simulations. The simulation results illustrate that the TRD loop exhibits relatively higher flexibility than the other components in the whole catalytic domain (CD), which could be well stabilized into different local minima through the binding with either the ADD domain or the DNA molecule by forming tight hydrogen-bond and salt-bridge networks involving distinct residues. Moreover, the movement of the TRD loop away from the catalytic loop upon activation could be triggered simply by the detachment of the ADD domain, but not necessarily induced by the ADD domain relocation on the CD. All these dynamic structural details could be a supplement to the previously reported crystal structure, which underlines the importance of the structural flexibility for the critical residues in the TRD loop, arousing more interest in the rational design of novel DNMT3A inhibitors targeting this region. [ABSTRACT FROM AUTHOR]

Treatments of inflammatory bowel disease (IBD) are diverse, but their efficacy is limited, and it is therefore urgent to find better therapies. Controlling mucosal inflammation is a must in IBD drug treatment. The occurrence of anti-tumor necrosis factor α (TNF-α) monoclonal antibodies has provided a safer and more efficacious therapy. However, this kind of treatment still faces failure in the form of loss of response. β-Carboline alkaloids own an anti-inflammatory pharmacological activity. While Kumujan B contains β-carboline, its biological activity remains unknown. In this study, we attempted to determine the anti-inflammatory effects of Kumujan B using both the TNF-α- induced in vitro inflammation and DSS-induced in vivo murine IBD models. Our data show that Kumujan B attenuated the expression of interleukin 1β (IL-1β) and interleukin 6 (IL-6) induced by TNF-α in mouse peritoneal macrophages. Kumujan B suppressed c-Jun N-terminal protein kinases (JNK) signaling, especially c-Jun, for anti-inflammatory response. Furthermore, Kumujan B promoted K11-linked ubiquitination and degradation of c-Jun through the proteasome pathway. In an in vivo study, Kumujan B inhibited the expression of IL-1β, IL-6, and TNF-α and improved the colon barrier function in dextran sulfate sodium salt (DSS)-induced experimental mice colitis. Kumujan B exhibited in vivo and in vitro anti-inflammatory effects, making it a potential therapeutic candidate for treating IBD., 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., (Copyright © 2024 Li, Di, Li, Zhao, Wu, Xiao, Tang, Huang, Chen, Chen, Gao, Gao, Xiao and Chen.)