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Lignin, the most abundant renewable source of aromatic compounds, plays a pivotal role in advancing sustainable biorefineries and reducing dependence on fossil resources. Recent progress in integrated lignin valorization pathways has unlocked opportunities to convert this complex biopolymer into high-value chemicals, materials, and energy carriers, despite its structural heterogeneity and recalcitrance posing major challenges. This review highlights the significant advancements in depolymerization strategies, including catalytic, oxidative, and biological approaches, which are reinforced by innovations in catalyst design and reaction engineering that enhance selectivity and efficiency. It also discusses emerging technologies, such as hybrid chemo-enzymatic systems, solvent fractionation, and continuous-flow reactors, for their potential to improve scalability and sustainability. Furthermore, this review examines the integration of lignin valorization with upstream pretreatment and downstream recovery, emphasizing process intensification, co-product synergy, and techno-economic optimization to achieve commercial viability. Despite these developments, critical gaps remain in understanding the molecular complexity of lignin, developing universally applicable catalytic systems, and optimizing economic and environmental performance. To guide future research, it poses two key questions: how to design catalysts for selective depolymerization across diverse lignin sources, and how to configure biorefineries for maximum lignin utilization while ensuring sustainability? Addressing these challenges will be essential for lignin’s role in next-generation biorefineries and a circular bioeconomy. Full article

Background: Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot increasingly associated with the development and progression of coronary artery disease (CAD). Transthoracic echocardiography is the most widely used modality for EAT assessment; however, substantial heterogeneity exists regarding the timing of measurement within the cardiac cycle, with EAT thickness variably assessed during systole or diastole. Whether these measurements provide equivalent information for identifying obstructive CAD remains unclear. This systematic review and meta-analysis evaluated the association between echocardiographically measured EAT thickness and angiographically confirmed obstructive CAD, with specific focus on systolic versus diastolic assessments. Methods: PubMed, Scopus, and EMBASE were systematically searched through December 2025 for observational studies comparing EAT thickness in patients with and without obstructive CAD confirmed by invasive coronary angiography. Random-effects models were used to pool standardized mean differences (SMDs) for systolic and diastolic EAT thickness. Heterogeneity was assessed using the I² statistic, publication bias by funnel plots and Egger’s regression test, and robustness by meta-regression and leave-one-out sensitivity analyses. Results: Twenty-two studies including more than 6500 patients were analyzed. Both systolic and diastolic EAT thickness were significantly greater in patients with obstructive CAD than in non-CAD controls. Systolic EAT showed a large, pooled effect size (SMD 1.27; 95% CI 0.96–1.59; p < 0.001), while diastolic EAT demonstrated a similarly strong association (SMD 1.59; 95% CI 1.10–2.07; p < 0.001). Heterogeneity was substantial (I² > 90%), but the direction of effect was consistent across all studies. Meta-regression analyses indicated that demographic, clinical, metabolic, geographic, and methodological characteristics, including ultrasound software/vendor category and timing of EAT measurement, did not significantly moderate the association between EAT thickness and obstructive CAD. No significant publication bias was detected, and sensitivity analyses confirmed the robustness of the results. Conclusions: Echocardiographically measured EAT thickness is strongly and consistently associated with obstructive CAD, irrespective of whether measurements are obtained during systole or diastole. Although both approaches show robust discriminatory capacity at the population level, differences in effect magnitude suggest that they may not be fully interchangeable. Moreover, in the absence of standardized and broadly applicable cut-off values, the interpretation and clinical management of EAT measurements as individual risk predictors require further investigation. Full article

Triple-negative breast cancer (TNBC), which is characterized by a lack of the estrogen receptor, the progesterone receptor, and HER2 expression, is the most aggressive breast cancer subtype and has a poor prognosis and high recurrence rates because of frequent chemotherapy resistance. As a crucial epigenetic regulator, DNA methylation modulates gene expression through aberrant methylation patterns, contributing to tumor progression and therapeutic resistance. Early diagnosis and treatment of TNBC are vital for its prognosis. The development of DNA methylation testing technology and the application of liquid biopsy provide technological support for early diagnosis and treatment. Additionally, preclinical and early-phase clinical studies suggest that epigenetic therapies targeting DNA methylation may hold promise for TNBC treatment, pending larger clinical trials. Furthermore, research on DNA methylation-based prognostic models enables personalized precision treatment for patients, helping to reduce unnecessary therapies and improve overall survival. The emerging role of DNA methylation patterns in predicting the therapeutic response and overcoming drug resistance is highlighted. In this narrative review, we integrate current research findings and clinical perspectives. We propose that DNA methylation presents promising research prospects for the diagnosis, treatment and prognosis prediction of TNBC. Future efforts should focus on translating methylation-driven insights into clinically actionable strategies, ultimately advancing precision oncology for this challenging disease. Full article

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article

The development of coal resources has created a large number of underground mined-out spaces, which can be utilized for cross-seasonal thermal storage through underground reservoirs to achieve seasonal heat storage. However, there is currently limited research on the cross-seasonal thermal storage capabilities and thermal storage performance evaluation of coal mine underground reservoirs. This study aims to evaluate the operational stability and long-term performance of a Coal Mine Underground Reservoir Energy Storage System (CMUR-ESS) under realistic geological conditions of the Shendong Coalfield. A multi-physics coupling model, integrating thermal-fluid processes, was developed based on the actual structure of the No. 5-2 coal seam goaf in the Dalinta Mine. Numerical simulations were conducted over five annual cycles, each comprising injection, storage, production, and transition stages. Results demonstrate that the system achieves progressive thermal accumulation, with the volume fraction of water above 70 °C increasing from 75.0% in the first cycle to 88.9% by the fifth cycle at the end of the storage stage. Production temperatures also improved, with peak and final temperatures rising by 6.2% and 6.8%, respectively, after five cycles. The analysis confirms enhanced heat retention and reduced thermal loss over time, indicating robust long-term stability and sustainability of the CMUR-ESS for seasonal energy storage applications. The results of this study can provide a reference for the design and evaluation of CMUR-ESS. Full article

Feline calicivirus (FCV) is a highly variable RNA virus that infects domestic cats and circulates endemically within feline populations, causing a wide spectrum of clinical manifestations, from asymptomatic infections to severe disease. Genomic analysis of 69 FCV strains revealed a high prevalence of the virus across multiple provinces in China. In vitro infection of CRFK cells with laboratory isolates FCV-BJ616 and FCV-BJDX40 resulted in significant cytotoxic effects. Serum proteomic analysis identified 221 upregulated and 123 downregulated proteins following infection with FCV-BJ616, and 233 upregulated and 165 downregulated proteins following infection with FCV-BJDX40. Among these, 215 proteins exhibited shared differential expression. Functional analyses revealed enriched pathways, including TNF signaling and ferroptosis. Notably, upregulation of Acyl-CoA Synthetase Long-Chain Family Member 4 (ACSL4) was correlated with lung injury, while downregulation of S100 Calcium Binding Protein A2 (S100A2) was associated with poor prognosis in FCV-associated oral disease. The differential expression of ACSL4 and S100A2 was further validated through Western blot analysis. These results suggest that ACSL4 and S100A2 are promising candidate biomarkers for monitoring FCV infection and disease progression, laying a foundation for future diagnostic and prognostic applications. Full article

Achieving sustainable growth in the global economy and promoting low-carbon development can be achieved by concluding trade agreements that advance trade liberalisation progressively. The study looks at how far environmental rulers go in trade deals between different countries, by examining what these agreements actually say. Combining this analysis with trade-embedded carbon data from 35 sub-sectors across 60 countries from 2009 to 2023, the effect of the depth of environmental rulers in trade deals on trade-embedded carbon is the focus of this empirical study and its underlying mechanisms. Research findings indicate that strengthening environmental clauses significantly reduces carbon emissions embedded in trade. This result remained consistent after undergoing a series of robustness tests and employing instrumental variable methods to address endogeneity issues. Mechanism tests reveal that the carbon reduction effect of environmental clauses can be achieved through two channels: green technology cooperation between countries and increased carbon productivity. Heterogeneity tests indicate that provisions in trade agreements that are more environmentally focused can have a greater effect on reducing embedded carbon in non-technology-intensive areas and pollution-intensive sectors, particularly for developing countries. Provisions relating to the environment in bilateral trade agreements demonstrate greater effectiveness in curbing trade-embedded carbon. This paper concludes that a more in-depth knowledge of the way environmental provisions are created in trade agreements, an accurate assessment of the impact, effectiveness and applicable scenarios of these provisions, and the promotion of targeted policy measures for future provisions relating to the environment and trade agreements and the global transition to green, low-carbon trade, will provide policy references and development guidance. Full article

Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality, and its initiation and progression are closely associated with multiple molecular mechanisms. Neutrophil extracellular traps (NETs) are mesh-like structures composed of DNA, histones, and antimicrobial proteins that are released by neutrophils during inflammation or infection. They play a crucial role in innate immune defense. However, when the dynamic balance of NETs is disrupted by excessive formation, persistent accumulation, or impaired clearance, NETs are no longer merely bystanders. Instead, they actively drive pathological processes in multiple CVDs and serve as a critical link between inflammation and cardiovascular injury. Given the central role of NETs in CVD pathogenesis, including atherosclerosis, myocardial ischemia–reperfusion injury, pulmonary arterial hypertension, atrial fibrillation, and heart failure, therapeutic strategies targeting NETs, such as inhibiting aberrant formation, enhancing clearance, or neutralizing toxic components, have emerged as promising approaches. In recent years, traditional Chinese medicine (TCM) and natural products have shown potential therapeutic value by modulating NET formation and promoting NET degradation, owing to their multitarget, multipathway regulatory effects. This article reviews the mechanisms by which NETs operate in CVDs and explores potential pathways through which TCM and natural active ingredients prevent and treat CVDs by regulating NETs. This review provides theoretical support for further research and clinical application. Full article

ISO 22002-100:2025 introduces stringent and more technically explicit prerequisite programme (PRP) requirements for allergen management, food fraud mitigation, and the control of chemical and packaging-related contaminants across the food, feed, and packaging supply chain. This review examines how advanced chromatographic methods provide the analytical basis required to meet these requirements and to support alignment with GFSI-recognized certification schemes. Recent applications of liquid and gas chromatography coupled with mass spectrometry for allergen quantification, authenticity assessment, and the determination of packaging migrants, auxiliary chemical residues, lubricants, and indoor pest-control pesticides are presented to demonstrate their relevance as verification tools. Across these PRP-related controls, chromatographic methods enable trace-level detection, structural specificity, and reproducible measurement performance, thereby shifting PRP compliance from a documentation-based activity to a process verified through measurable analytical evidence. The review highlights significant progress in method development and simultaneous multi-target analytical approaches while also identifying remaining challenges related to matrix-appropriate validation, harmonization, and analytical coverage for chemical contamination, which is now formally defined as a measurable PRP requirement under ISO 22002-100:2025. Overall, the findings demonstrate that chromatographic analysis has become essential to demonstrating PRP effectiveness under ISO 22002-100:2025, supporting the broader shift toward evidence-based, scientifically robust food safety assurance. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

Bleeding and thromboembolism are among the leading causes of mortality worldwide. Thrombosis encompasses both arterial forms—primarily associated with atherosclerosis and leading to heart attacks or strokes—and venous forms. Microvascular thrombosis typically arises in the context of sepsis or systemic inflammation, and it became particularly prominent during the COVID-19 pandemic, substantially contributing to increased mortality. Given this burden, the rapid development of new therapies using advanced techniques and materials to prevent and treat these conditions is essential. This review summarizes recent advances in the design of antithrombotic polymers, discussing mechanisms of action, surface-modification strategies, and current clinical and preclinical applications. It also outlines criteria for evaluating hemocompatibility, describes in vitro and in vivo testing methods, and highlights key barriers to translating these materials into clinical practice. The review concludes by identifying promising directions for future research, including multifunctional approaches that combine antifouling properties, controlled drug release, and bioresistance strategies with the greatest potential to reduce thromboembolic complications associated with medical materials. It further evaluates the progress made to date in combating thrombotic diseases and identifies remaining gaps in the development and clinical implementation of new antithrombotic materials. Full article

This work presents a hierarchical deep reinforcement learning (DRL) framework based on Soft Actor–Critic (SAC) for the autonomy of rock-breaking machinery in surface mining comminution processes. The proposed approach explicitly integrates mobile navigation and hydraulic manipulation as coupled subprocesses within a unified decision-making architecture, designed to operate under the unstructured and highly uncertain conditions characteristic of open-pit mining operations. The system employs a hysteresis-based switching mechanism between specialized SAC subagents, incorporating automatic entropy tuning to balance exploration and exploitation, twin critics to mitigate value overestimation, and curriculum learning to manage the progressive complexity of the task. Two coupled subsystems are considered, namely: (i) a tracked mobile machine with a differential drive, whose continuous control enables safe navigation, and (ii) a hydraulic manipulator equipped with an impact hammer, responsible for the fragmentation and dismantling of rock piles through continuous joint torque actuation. Environmental perception is modeled using processed perceptual variables obtained from point clouds generated by an overhead depth camera, complemented with state variables of the machinery. System performance is evaluated in unstructured and uncertain simulated environments using process-oriented metrics, including operational safety, task effectiveness, control smoothness, and energy consumption. The results show that the proposed framework yields robust, stable policies that achieve superior overall process performance compared to equivalent hierarchical configurations and ablation variants, thereby supporting its potential applicability to DRL-based mining automation systems. Full article

This review provides a comprehensive overview of recent progress in chitin-based nanomaterials and their composite engineering. Particular focus is placed on techniques for constructing self-assembled chitin nanofibers (ChNFs) with tightly bundled fibrillar structures, as well as strategies for fabricating composites in which the ChNFs serve as reinforcing components, combined with natural polymeric matrices. In addition, high-crystalline scaled-down (SD-)ChNFs were fabricated through partial deacetylation of the ChNFs, followed by electrostatic repulsive disassembly of the abovementioned bundled fibrils in aqueous acetic acid, which were further used to reinforce composites comprising the other polysaccharides. Mixing the SD-ChNFs with low-crystalline chitin substrates further enabled the fabrication of all-chitin composites (AChCs) that exploit crystallinity contrast to achieve enhanced tensile strength. Moreover, the AChC films exhibited high cell-adhesive properties and promoted the formation of three-dimensional cell-networks, highlighting their potential for biomedical applications. Full article

Hydrogen has emerged as a cornerstone of global decarbonisation strategies, offering a flexible pathway to reduce dependence on fossil fuels and accelerate progress towards net-zero targets. However, the development of a globally integrated hydrogen economy remains uneven, reflecting disparities in renewable energy potential, infrastructure readiness, investment capacity, and policy commitment. To better understand these differences and the barriers they create, this study undertakes a comprehensive comparative assessment of the global hydrogen supply chain encompassing resources, production, storage, transport, and end-use applications. Further, a structured analytical framework comprising ten principles and twenty-nine sub-factors was developed to evaluate national hydrogen policies, technological readiness, and enabling conditions across twenty-six countries. The results show that the United States, China, Japan, South Korea, and Germany lead global progress, while many countries remain at an early stage of engagement. These findings further inform persistent regional asymmetries and emphasise the need for stronger international coordination. Drawing on these findings, the paper advances targeted policy and research recommendations to lower production costs, expand storage and transport capacity, and harmonise regulatory frameworks, thereby defining a coherent pathway towards a secure, cost-competitive, and equitable global hydrogen economy by 2050. Full article