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Deficiency of short-chain enoyl-CoA hydratase (ECHS1), a crucial enzyme in fatty acid metabolism through the mitochondrial β-oxidation pathway, has been strongly linked to various diseases, especially cardiomyopathy. However, the structural and biochemical mechanisms through which ECHS1 recognizes acyl-CoAs remain poorly understood. Herein, cryo-EM analysis reveals the apo structure of ECHS1 and structures of the ECHS1-crotonyl-CoA, ECHS1-acetoacetyl-CoA, ECHS1-hexanoyl-CoA, and ECHS1-octanoyl-CoA complexes at high resolutions. The mechanism through which ECHS1 recognizes its substrates varies with the fatty acid chain lengths of acyl-CoAs. Furthermore, crucial point mutations in ECHS1 have a great impact on substrate recognition, resulting in significant changes in binding affinity and enzyme activity, as do disease-related point mutations in ECHS1. The functional mechanism of ECHS1 is systematically elucidated from structural and biochemical perspectives. These findings provide a theoretical basis for subsequent work focused on determining the role of ECHS1 deficiency (ECHS1D) in the occurrence of diseases such as cardiomyopathy. The study of ECHS1 functional mechanism provides a theoretical basis for subsequent work focused on determining the role of ECHS1 deficiency (ECHS1D) in the occurrence of diseases such as cardiomyopathy.

Isovaleryl-CoA (coenzyme A) dehydrogenase (IVD) plays a pivotal role in the catabolism of leucine, converting isovaleryl-CoA to 3-methylcrotonyl-CoA. Dysfunction of IVD is linked to isovaleric acidemia (IVA), a rare metabolic disorder characterized by the accumulation of toxic metabolites. In this study, we present the cryo-electron microscopy structures of human IVD, resolved both in its apo form and in complex with its substrates, isovaleryl-CoA and butyryl-CoA. Our findings reveal a tetrameric architecture with distinct substrate-binding pockets that facilitate the enzyme's preference for short branched-chain acyl-CoAs. Key residues involved in FAD binding and substrate interaction were identified, elucidating the catalytic mechanism of IVD. Additionally, we investigated the impact of various disease-associated hotspot mutations derived from different regions, demonstrating their effects on enzyme stability and activity. Notably, mutations such as A314V, S281G/F382V, and E411K resulted in substantial loss of function, while others exhibited milder effects, which is consistent with our structural analyses. These insights enhance our understanding of IVD's enzymatic properties and provide a foundation for developing targeted therapies for IVA.

ObjectivesThis study assessed the global burden of glaucoma using data from the Global Burden of Disease (GBD) 2021 study. The analysis of epidemiological trends aimed to inform future public health prevention strategies.DesignRetrospective cross-sectional study.ParticipantsNone.MethodsAnalysis of 1990–2021 GBD data on glaucoma prevalence, disability-adjusted life years (DALYs), age-standardised prevalence rates (ASPR), and age-standardised DALY rates (ASDR). Estimated annual percentage changes (EAPC) were calculated, Joinpoint regression identified trend changes, and Autoregressive Integrated Moving Average (ARIMA) modelling projected the burden for the year 2050.ResultsGlobally, the number of prevalent glaucoma cases increased from 4 072 106.59 (95% uncertainty interval (UI) 3 489 888.7 to 4 752 867.3) in 1990 to 7 587 672.9 (95% UI 6 522 906 to 8 917 725.4) in 2021. Concurrently, DALYs increased from 467 600.4 (95% UI 323 490.5 to 648 641.6) in 1990 to 759 900.2 (95% UI 530 942.9 to 1 049 127.2) in 2021. In contrast, the ASPR and ASDR declined to 90.1 per 100 000 population (95% UI 77.8 to 105.5) and 9.1 per 100 000 population (95% UI 6.3 to 12.5) in 2021, respectively. During the COVID-19 pandemic period (2019–2021), the slowest growth rates in crude case numbers and overall disease burden were observed, accompanied by the most pronounced decline in annual percentage change of ASPR. The highest estimates for both case counts and DALYs were identified in the 70–74 age group, with males demonstrating higher prevalence rates than females. Furthermore, regions with lower Sociodemographic Index (SDI) values bore a disproportionately higher burden of glaucoma.ConclusionThese findings underscore the need to strengthen early screening and treatment of glaucoma, particularly in ageing populations, male groups and low SDI regions. We urge cautious interpretation of COVID-19 related data and vigilance against potential post-pandemic surges in burden. Critical strategies include enhanced screening and intervention for high-risk groups, targeted prevention measures and integration of ophthalmic care into public health emergency frameworks to alleviate the disease burden.

China had approved to construct 49 urban mineral industry demonstration bases from 2010 to 2015, which indicated that the Chinese Resource Recycling Industry (RRI) has been transferred into the social circulation patterns process after experiencing a micro-pattern and park-pattern. With the rapid progress of industrial process, the RRI presents some features just like system relationship is increasingly tight coupling, technology integration and innovation are accelerating, affecting factors are more complex, policy response is more sensitive, etc. All these changes have made the research of industry ecosystem symbiosis more complicated. In this paper, we divide the renewable resource recycling system into “management, marketization, supply, recovery, resource” five sub-systems firstly, then analyze the industry ecosystem symbiotic relationship by integrating the use of complex system theory and catastrophe progression model. Secondly, we establish the comprehensive evaluation index system including symbiosis mode, symbiosis environment and symbiosis level. We conduct Catastrophe Progression Method (CPM) into the comprehensive evaluation of the industrial symbiosis ecosystem. By using the control variables and state variables of CPM, the key factors and indexes changes are described. Finally, the model and method are utilized for evaluating Hunan Miluo Recycling Economy Industrial Park development between 2013 and 2018. The results are found to be coincident with a practical situation, which proves that the catastrophe progression method works well. The method is proved to be feasible in practice and has high objectivity and reference value.

To address the challenges of detecting small targets in complex underwater environments, an efficient and lightweight model, SFESI-YOLOv8n, is proposed. The model improves small target recognition by incorporating a dedicated detection layer and reduces parameter count by removing the large target detection layer. Furthermore, the introduction of the C2f-F module eliminates redundant information from consecutive convolution operations in the bottleneck, further simplifying the model. The integration of a lightweight mixed local context attention (MLCA) mechanism within the small target fusion layer increases sensitivity to small targets. The dynamic upsampler (DySample) employs point sampling to preserve enhanced edge and detail information in feature maps, resulting in clearer feature representations. In addition, the novel In-NWD loss function, utilizing Wasserstein distance and auxiliary bounding boxes, improves small target detection performance. On the UPRC2020 public dataset, SFESI-YOLOv8n achieved an mAP@0.5 of 83.7%, which is a 1.1% improvement over the baseline model. The parameter count and size of the model were reduced by 49.2% and 45.7%, respectively. The frame rate reached 227 FPS, indicating a 9 FPS increase compared to the baseline. On the NVIDIA Jetson TX2 edge device, inference latency decreased from 65 ms to 24 ms with TensorRT acceleration, thereby meeting real-time detection requirements. The SFESI-YOLOv8n model provides a viable and efficient solution for the autonomous detection of small underwater targets, demonstrating significant practical value.

Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW module are designed to improve the backbone network, enhancing the model's ability to perceive micro-lesions. Secondly, a fea-ture-focused diffusion pyramid network FDPN is designed to fully integrate multi-scale context information, further improving the model's ability to perceive micro-lesions. Finally, a lightweight shared detection head GSDHead is designed to reduce the model's parameter count, making it more deployable on re-source-constrained devices. Experimental results show that compared with the base model YOLOv8n, the improved model reduces the parameter count by 20.7%, increases mAP@0.5 by 4.1%, and improves the recall rate by 7.9%. Compared with single-stage mainstream algorithms such as YOLOv5n and YOLOv10n, YOLO-KFG demonstrates significant advantages in both detection accuracy and efficiency.

Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.