Search Results (14,694)

Wind fields on tropical islands are among the most complex systems in atmospheric science, simultaneously influenced by large-scale monsoons, tropical cyclones, local sea-land circulation, and island topography. These interactions result in extremely complex responses to climate change, posing significant challenges for detailed assessment. This study examines how multi-scale processes have shaped the long-term evolution of the near-surface wind speed over Hainan, China’s largest tropical island. We developed a new high-resolution (5 km, hourly) regional climate reanalysis spanning 1961–2022, based on the WRF model and ERA5 data. Our analysis reveals three key findings: First, the long-term trend of wind speed over Hainan exhibits significant spatial heterogeneity, characterized by “coastal stilling and inland strengthening.” Wind speeds in coastal areas have decreased by −0.03 to −0.09 m/s per decade, while those in the mountainous interior have paradoxically increased by up to +0.06 m/s per decade. This pattern arises from the interaction between the weakening East Asian Winter Monsoon and the island’s complex terrain. Second, the frequency of extreme wind events has undergone seasonal reorganization: days with strong winds linked to the winter monsoon have significantly decreased (−0.214 days per decade), whereas days linked to warm-season tropical cyclones have increased (+0.097 days per decade), indicating asynchronous evolution of climate extremes. Third, the risk from 100-year extreme wind events is undergoing geographical redistribution, shifting from the coast to the mountainous interior (with an increase of 0.4–0.7 m/s in inland areas), posing a direct challenge to existing engineering design standards. Taken together, these findings demonstrate that local topography can significantly influence large-scale climate change signals, underscoring the critical role of high-resolution modeling in understanding the climate response of such complex systems. Full article

Objectives: Connective tissue diseases (CTDs) include a diverse group of systemic autoimmune conditions, among which interstitial lung disease (ILD) is acknowledged as a major determinant of prognosis. High-resolution computed tomography (HRCT) is the gold standard for ILD assessment. The distribution of HRCT patterns across CTDs remain incompletely defined. The objective of this systematic review is to synthesize available evidence regarding the prevalence of specific radiological patterns within CTD-ILDs and to assess whether specific patterns occur at different frequencies among individual CTDs. Methods: The inclusion criteria encompassed original human studies published in English between 2015 and 2024, involving adult participants (≥18 years) with CTD-ILDs assessed primarily by HRCT and designed as retrospective, prospective, or cross-sectional trials with extractable data. We systematically searched PubMed, Scopus, and Web of Science (January 2025). Risk of bias was evaluated using the Newcastle–Ottawa Scale (NOS) for cohort and case–control studies, and the JBI Critical Appraisal Checklist for cross-sectional studies. Data were extracted and categorized by HRCT pattern for each CTD, and then summarized descriptively and statistically. Results: We analyzed 23 studies published between 2015 and 2024, which included 2020 patients with CTD-ILDs. The analysis revealed non-specific interstitial pneumonia (NSIP) as the most prevalent pattern overall (36.5%), followed by definite usual interstitial pneumonia (UIP) (24.8%), organizing pneumonia (OP) (9.8%) and lymphoid interstitial pneumonia (LIP) (1.25%). HRCT distribution varied by CTD: NSIP predominated in systemic sclerosis, idiopathic inflammatory myopathies, and mixed connective tissue disease; UIP was most frequent in rheumatoid arthritis; LIP was more common in Sjögren’s syndrome. While global differences were statistically significant, pairwise comparisons often lacked significance, likely due to sample size constraints. Discussion: Limitations include varying risk of bias across study designs, heterogeneity in HRCT reporting, small sample sizes, and inconsistent follow-up, which may reduce precision and generalizability. In addition to the quantitative synthesis, this review offers a detailed description of each radiologic pattern mentioned above, illustrated by representative examples to support the recognition in clinical settings. Furthermore, it includes a brief overview of the major CTDs associated with ILD, summarizing their epidemiological data, risk factors for ILD and clinical presentation and diagnostic recommendations. Conclusions: NSIP emerged as the most common HRCT pattern across CTD-ILDs, with UIP predominating in RA. Although inter-disease differences were observed, statistical significance was limited, likely reflecting sample size constraints. These findings emphasize the diagnostic and prognostic relevance of HRCT pattern recognition and highlight the need for larger, standardized studies. Full article

Against the backdrop of a complex global economic landscape, foreign trade serves as a critical link integrating China’s marine economy with the global market, playing an indispensable role in advancing high-quality marine economic development in China and realizing the strategic goal of building a strong maritime nation. Utilizing panel data covering 11 coastal provinces and municipalities in China from 2013 to 2022, this research adopts a double machine learning approach to examine the effects and mechanisms through which the high-quality development of foreign trade (HQD) shapes high-quality marine economic development (THQ) in China. The empirical results demonstrate that (1) high-quality development of foreign trade significantly promotes high-quality marine economic development in China, with a 1-unit increase in the former corresponding to a 1.437-unit rise in the latter. This finding withstands multiple robustness checks. (2) Mechanism analysis indicates that this promotion occurs through three channels: strengthening marine environmental regulation, enhancing marine labor productivity, and upgrading the marine industrial structure. (3) Heterogeneity analysis shows that the effect of high-quality foreign trade is stronger in China’s eastern marine economic region. Simultaneously, the trade development environment emerges as a key factor exerting a significantly positive influence on marine economic quality during China’s foreign trade advancement. The empirical findings propose the following optimization countermeasures for high-quality marine economic development: strengthening marine environmental regulation, enhancing marine labor productivity, and promoting the upgrading of the marine industrial structure. Full article

Regarding the impact of hull roughness on ship resistance and propulsive performance, most existing studies rely heavily on numerical hulls or simplified models, while systematic analysis focusing on the heterogeneous roughness of actual ships remains insufficient. Taking the 2433 TEU container ship SITC CAGAYAN as the research object, this study adopts a method that combines CFD numerical simulation with actual ship operation data. It employs a resistance prediction model based on the “roughness influence factor” to explore the mechanism by which local roughness affects ship resistance. Meanwhile, this study innovatively proposes the index of “fuel consumption increment per unit wetted surface area” and the concept of “fuel consumption factor,” thereby realizing the quantitative characterization of the impact of local rough areas on fuel consumption. The purpose of this study is to provide theoretical support and technical pathways for the optimization of ship energy efficiency and the development of green shipping. Full article

The rapid economic growth in major coal-consuming countries has often come at the cost of environmental quality and social well-being. This study is urgently needed to provide empirical evidence on how such growth impacts sustainable development, helping policymakers balance economic progress with environmental protection and social welfare in an era of increasing climate concerns. Despite growing attention on sustainability, few studies have examined how key economic-environmental transformation drivers, such as coal consumption, financial development, globalization, urbanization, and economic growth, affect social sustainability. This study addresses this gap by analyzing the impact of these drivers on social sustainability in the world’s leading coal-consuming countries, as classified by Global Firepower. Using data from ten major coal-consuming nations between 1991 and 2022, sourced from the International Monetary Fund (IMF), KOF Swiss Economic Institute, the BP Statistical Review of World Energy, the World Bank’s World Development Indicators (WDIs), and the United Nations Development Programme (UNDP), the study applies advanced estimation techniques, including the Augmented Mean Group (AMG) and Feasible Generalized Least Squares (FGLS), to address cross-sectional dependence and slope heterogeneity. The results indicate that coal consumption has a negative and significant effect on social sustainability. In contrast, financial development, globalization, urbanization, and economic growth all show positive and significant effects. These findings highlight the urgent need for deliberate policy reforms to support a socially inclusive energy transition. Policymakers in major coal-consuming countries should invest in clean energy, fund worker retraining and community health, promote green innovation, and encourage private sector and stakeholder collaboration for a just, sustainable transition. Such measures are vital for coal-dependent countries to balance economic progress with social well-being. This study is the first to quantify social sustainability using the HDI, addressing a gap in the literature concerning the relationship between coal consumption and social development, thereby providing a quantitative basis for formulating policies that balance equity and decarbonization. Full article

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption. Full article

As a new type of production factor, releasing data dividends is of great significance in improving corporate energy efficiency. Based on the data of listed enterprises in China from 2011 to 2022, the establishment of government open data platforms in each prefecture-level city is taken as a policy shock event, and the impact of government open data on corporate energy efficiency is empirically examined through a multi-period DID model. The results show that government open data improves enterprise energy efficiency by approximately 2.5% (relative to the mean), and capacity utilization and biased technological progress are the main pathways of action. In addition, the application of big data technology can better fulfill the role of data factors in improving enterprise energy efficiency. Heterogeneity analysis finds that government open data has a stronger effect on enterprise energy efficiency improvement in areas with high manufacturing concentration, environmental tax rate leveling, and high Internet penetration. The study suggests that enterprises should apply big data technology and build a mechanism for integrating data assets and energy management so as to fulfill the important role of data elements in the green development of enterprises. Full article

Background: Bronchiectasis is a chronic respiratory condition characterized by permanent bronchial dilation, recurrent infections, and progressive lung damage. A subset of patients, known as frequent exacerbators, experience multiple exacerbations annually, leading to accelerated lung function decline, hospitalizations, and reduced quality of life. The aim of this study is to identify distinct phenotypes and treatable traits in bronchiectasis frequent exacerbators, since it could be crucial for optimizing patient management. Research question: Could clinically distinct phenotypes and treatable traits be identified among frequent exacerbators with bronchiectasis to guide personalized management strategies? Methods: We analysed a cohort of 56 bronchiectasis frequent exacerbator patients using 21 clinically relevant variables, including pulmonary function tests, radiological patterns, and microbiological data. Hierarchical clustering and k-means algorithms were applied to identify subgroups. Key outcomes included cluster-specific characteristics, treatable traits, and their implications for management. Results: Four distinct clusters were identified: 1. Mild, idiopathic bronchiectasis (Cluster 1): Predominantly mild disease (FACED), idiopathic etiology (93.3%), and cylindrical bronchiectasis with moderate obstruction (60%). 2. Rheumatological and NTM-associated bronchiectasis (Cluster 2): Patients with systemic inflammatory diseases (50%) and NTMever (50%) but minimal infections by Pseudomonas aeruginosa. 3. Mild, post-infective bronchiectasis (Cluster 3): Exclusively mild disease, mixed idiopathic and post-infective etiologies, and preserved lung function. 4. Severe, chronic infection phenotype (Cluster 4): Severe disease with high colonization rates of Pseudomonas aeruginosa (71.4%), advanced structural damage (57.1% varicose, 50% cystic bronchiectasis), and frequent exacerbations. Interpretation: This analysis highlights the heterogeneity of bronchiectasis and its frequent exacerbator phenotype. The treatable traits framework underscores the importance of aggressive infection control and management of airway inflammation in severe cases, while milder clusters may benefit from preventive strategies. These findings support the integration of precision medicine in bronchiectasis care, focusing on phenotype-specific interventions to improve outcomes. Full article

Background/Objectives: Computed tomography (CT)-based radiomic analysis is an emerging technique that enables non-invasive assessment of tumor characteristics. In gastrointestinal stromal tumors (GISTs), radiomics may reflect biological behavior such as proliferative activity, often indicated by Ki-67 expression. To our knowledge, this is the first systematic review and meta-analysis synthesizing evidence on the ability of CT radiomics to predict the Ki-67 index in GISTs, addressing an important gap in the literature. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines to evaluate the predictive performance of CT radiomics for Ki-67 expression in GISTs. A literature search of PubMed, Scopus, Science Direct, and the Cochrane Library was performed up to December 2024 using predefined terms. Extracted data included study design, patient demographics, imaging protocols, radiomic features, and diagnostic performance. Study quality was assessed using the QUADAS-2 tool. A random-effects meta-analysis summarized the pooled area under the ROC curve (AUC), sensitivity, and specificity. Subgroup and sensitivity analyses explored heterogeneity sources. Publication bias was assessed using Egger’s test and funnel plots. Results: Six studies involving 1632 patients were included. The pooled sensitivity and specificity for predicting Ki-67 expression were 0.71 and 0.76, respectively, with a summary AUC of 0.79. Subgroup analyses showed consistent results across different imaging protocols and radiomic feature sets, though the Ki-67 cutoff (8% vs. 10%) affected diagnostic performance. Moderate heterogeneity and potential publication bias in specificity were observed. Conclusion: CT-based radiomics demonstrates moderate accuracy for non-invasively predicting Ki-67 index in GISTs. While not a substitute for histology, it may support personalized preoperative planning and guide future immunotherapy strategies. In the future, radiomic signatures—particularly when integrated with molecular or immune-related biomarkers—could help refine patient selection and monitoring strategies for emerging therapies, including immunotherapy. Full article

Federated learning enables collaborative model training across distributed devices while preserving data privacy. However, in real-world environments such as smart cities, heterogeneous and resource-constrained edge devices often render existing methods impractical. Low-power sensors and cameras struggle to complete full-model training, while high-performance devices remain idly waiting for others. Knowledge distillation approaches rely on public datasets that are rarely available or poorly aligned with urban data, which limits their effectiveness in deployment. These limitations lead to inefficiencies, unstable convergence, and poor adaptability in diverse urban networks. Partial training alleviates some challenges by allowing clients to train submodels tailored to their capacity, but existing methods still incur high computational costs for identifying important parameters and suffer from uneven parameter updates, reducing model effectiveness. To address these challenges, we propose Parameter-Level Dynamic Submodel Extraction (PLDSE), a lightweight and adaptive framework for federated learning. PLDSE estimates parameter importance using gradient-based scores on a server-side validation set, reducing overhead while accurately identifying critical parameters. In addition, it integrates a rolling scheduling mechanism to rotate unselected parameters, ensuring full coverage and consistent model updates. Experiments on CIFAR-10, CIFAR-100, and Fashion-MNIST demonstrate superior accuracy and faster convergence, with PLDSE achieving 62.82% on CIFAR-100 under low heterogeneity and 61.51% under high heterogeneity, outperforming prior methods. Full article

Background: Glaucoma is a progressive optic neuropathy marked by retinal ganglion cells (RGCs), apoptosis, vascular insufficiency, oxidative stress, mitochondrial dysfunction, excitotoxicity, and neuroinflammation. While intraocular pressure (IOP) reduction remains the primary intervention, many patients continue to lose vision despite adequate pressure control. Emerging neuroprotective agents—citicoline, coenzyme Q10 (CoQ10), pyruvate, nicotinamide, pyrroloquinoline quinone (PQQ), homotaurine, berberine, and gamma-aminobutyric acid (GABA)—target complementary pathogenic pathways in experimental and clinical settings. Methods: This literature review synthesizes current evidence on glaucoma neuroprotection, specifically drawing on the most relevant and recent studies identified via PubMed. Results: Citicoline enhances phospholipid synthesis, stabilizes mitochondrial membranes, modulates neurotransmitters, and improves electrophysiological and visual field outcomes. CoQ10 preserves mitochondrial bioenergetics, scavenges reactive oxygen species, and mitigates glutamate-induced excitotoxicity. Pyruvate supports energy metabolism, scavenges reactive oxygen species, and restores metabolic transporter expression. Nicotinamide and its precursor nicotinamide riboside boost NAD⁺ levels, protect against early mitochondrial dysfunction, and enhance photopic negative response amplitudes. PQQ reduces systemic inflammation and enhances mitochondrial metabolites, while homotaurine modulates GABAergic signaling and inhibits β-amyloid aggregation. Berberine attenuates excitotoxicity, inflammation, and apoptosis via the P2X7 and GABA-PKC-α pathways. Preclinical models demonstrate synergy when agents are combined to address multiple targets. Clinical trials of fixed-dose combinations—such as citicoline + CoQ10 ± vitamin B3, citicoline + homotaurine ± vitamin E or PQQ, and nicotinamide + pyruvate—show additive improvements in RGCs’ electrophysiology, visual function, contrast sensitivity, and quality of life without altering IOP. Conclusions: A multi-targeted approach is suitable for glaucoma’s complex neurobiology and may slow progression more effectively than monotherapies. Ongoing randomized controlled trials are essential to establish optimal compound ratios, dosages, long-term safety, and structural outcomes. However, current evidence remains limited by small sample sizes, heterogeneous study designs, and a lack of long-term real-world data. Integrating combination neuroprotection into standard care holds promise for preserving vision and reducing the global burden of irreversible glaucoma-related blindness. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis. T cell exhaustion (TEX) is a key factor in tumor immune evasion and therapeutic resistance. In this study, we integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to characterize TEX-related transcriptional features in HCC. Methods: We first computed TEX scores for each sample using a curated 65-gene signature and classified them into high-TEX and low-TEX groups by the median score. Differentially expressed genes were identified separately in scRNA-seq and bulk RNA-seq data, then intersected to retain shared candidates. A 26-gene prognostic signature was derived from these candidates via univariate Cox and LASSO regression analysis. Results: The high-TEX group exhibited increased expression of immune checkpoint molecules and antigen presentation molecules, suggesting a tumor microenvironment that is more immunosuppressive but potentially more responsive to immunotherapy. Functional enrichment analysis and protein–protein interaction (PPI) network construction further validated the roles of these genes in immune regulation and tumor progression. Conclusions: This study provides a comprehensive characterization of the TEX landscape in HCC and identifies a robust gene signature associated with prognosis and immune infiltration. These findings highlight the potential of targeting TEX-related genes for personalized immunotherapeutic strategies in HCC. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

Background/Objectives: Drug discovery is a lengthy and expensive process, taking an average of 10 years and more than USD 2 billion from target discovery to drug approval. It is even more challenging in complex diseases due to disease heterogeneity and limited knowledge about the underlying mechanisms. We present a novel computational framework that integrates network analysis, statistical mediation, and deep learning to identify causal target genes and repurposable small-molecule candidates. Methods: We applied weighted gene co-expression network analysis (WGCNA) and bidirectional mediation analysis (causal WGCNA) to transcriptomic data from idiopathic pulmonary fibrosis (IPF) patients to identify genes causally linked to the disease phenotype. These genes were used as a phenotypic signature for deep learning-based compound screening using the DeepCE model. Results: Using RNA-seq data from 103 IPF patients and 103 controls, we identified seven significantly correlated modules and 145 causal genes. Five of these genes (ITM2C, PRTFDC1, CRABP2, CPNE7, and NMNAT2) were predictive of disease severity in IPF. Our compound screening identified several promising candidates, such as Telaglenastat (GLS1 inhibitor), Merestinib (MET kinase inhibitor), and Cilostazol (PDE3 inhibitor), with significant inverse correlation with the IPF-specific gene signature. Conclusions: This study demonstrates the utility of combining causal inference and deep learning for drug discovery. Our framework identified novel gene targets and therapeutic candidates for IPF, offering a scalable strategy for phenotype-driven drug discovery and repurposing. Full article

Detecting trees accurately from terrestrial laser scanning (TLS) point clouds is crucial for processing terrestrial LiDAR data in individual tree analyses. Due to the heterogeneity of savanna ecosystems, our understanding of how various segmentation methods perform on savanna trees remains limited. Therefore, we compared two segmentation algorithms based on the ecological theory of resource distribution, which enables the prediction of the branching geometry of plants. This approach suggests that the shortest path along the vegetation from a point on the tree to the ground remains within the same tree. The algorithms were tested on a 15.2 ha plot scanned at 0.025° resolution during the dry season, using a Riegl VZ1000 Terrestrial Laser Scanner (TLS) in October 2019 at the Skukuza Flux Tower in Kruger National Park, South Africa. Individual tree segmentation was performed on the cloud using the comparative shortest-path (CSP) algorithm, implemented in LiDAR 360 (v 5.4), and the shortest path-based tree isolation method (SPBTIM), implemented in MATLAB (R2022a). The accuracy of each segmentation method was validated using 125 trees that were segmented and manually edited. Results were evaluated using recall (r), precision (p), and the F-score (F). Both algorithms detected (recall) 90% of the trees. The SPBTIM achieved a precision of 91%, slightly higher than the CSP’s 90%. Overall, both methods demonstrated an F-score of 0.90, indicating equal segmentation accuracy. Our findings suggest that both techniques can reliably segment savanna trees, with no significant difference between them in practical application. These results provide valuable insights into the suitability of each method for savanna ecosystems, which is essential for ecological monitoring and efficient TLS data processing workflows. Full article