Analysis of intellectual and thematic progress of the Journal of Environmental Management - Section V: Environmental Policy, Economics, and Social Science.

Abstract This study aimed to improve copy-move forgery detection by combining traditional forensic techniques with deep learning methods. A hybrid detection framework is proposed that integrates Error Level Analysis (ELA), Haar wavelet decomposition, and Sobel edge detection with a VGG16 Convolutional Neural Network (CNN). The model was trained and evaluated using the CASIA v2.0 tampered image dataset from the Chinese Academy of Sciences. Results show that the ELA CNN Sobel model achieved a training accuracy of 99.96% and a validation accuracy of 91.21%, outperforming the Haar Wavelet Sobel model, which recorded 81.30% training accuracy and 65.90% validation accuracy. The use of Sobel filtering enhanced edge localization, allowing the CNN to detect manipulation boundaries more accurately. These findings demonstrate that combining ELA with Sobel filtering improves CNN performance and generalization, especially in compressed or low-quality images. This hybrid preprocessing approach offers practical value for image forensics, content authentication, and misinformation prevention. Future work should focus on optimizing preprocessing time and increasing dataset diversity to enhance real-time detection and robustness.

To determine factors influencing progression-free survival (PFS) in patients with advanced non-small cell lung cancer (NSCLC) treated with anlotinib and to create a predictive model using a nomogram. A descriptive study. Place and Duration of the Study: Clinical Research Centre, Hefei Cancer Hospital, Chinese Academy of Sciences, Hebei, China, from July 2020 to 2024. Data of patients with advanced NSCLC receiving anlotinib treatment were retrieved from medical records. Potential predictors of PFS were first identified using univariate Cox proportional hazards regression analysis, and subsequently, multivariate Cox regression analysis was performed to identify key factors influencing PFS. A nomogram was constructed for PFS prediction. The study included 145 patients. Six factors were identified as significant predictors: combined use of proton pump inhibitors (PPI), nutritional risk score, monocyte percentage, pathological grading, combined use of immune checkpoint inhibitors (ICI), and body temperature. The combined use of PPI and Pathological Grading Ⅳ was found to be an independent risk factor for PFS, whereas the combined use of ICI, a nutritional risk score of 0, a monocyte percentage below 10.04%, and a body temperature under 36.2°C was identified as an independent protective factor. The areas under the ROC curve (AUC) at 1, 3, 6, and 12 months were 0.765 (95% CI: 0.672-0.859), 0.816 (95% CI: 0.759-0.873), 0.784 (95% CI: 0.718-0.849), and 0.773 (95% CI: 0.673-0.874), respectively. The model demonstrated a C-index of 0.71 (95% CI: 0.65-0.76), and the calibration curves indicated a high degree of alignment between the predicted and actual outcomes. A nomogram was successfully created to predict PFS. Six significant factors affecting PFS in patients with advanced NSCLC treated with anlotinib were identified. A reliable nomogram was successfully constructed for the prediction of PFS, offering a tool to support personalised treatment planning. Anlotinib, Non-small cell lung cancer, Predictors, Cox regression, Nomogram.

Objective:This study aims to systematically explore the development pattern and influence of nanozyme technology in the field of oncology diagnosis and treatment through bibliometric analysis methods, while identifying which countries are the innovation drivers in this field.Methods:On October 16, 2024, a search of the Web of Science Core Collection (WoSCC) was conducted to review and analyze the application of nanozymes in oncology. A total of 977 studies published from 2007 to 2024 were retrieved and examined.Results:We found that global research interest and publication volume related to this topic continue to rise. China leads in international cooperation, publication volume, and citation frequency, highlighting its outstanding position in this field. The Chinese Academy of Sciences is the largest contributing institution in terms of publication volume. Notably, ACS Applied Materials & Interfaces and Advanced Healthcare Materials are the 2 most popular journals in this field. In authors, Yang, Piaoping leads with 25 articles, and Qu, Xiaogang is the most cited author. The keyword co-occurrence network reveals 3 hotspots: “nanozymes,” “photodynamic therapy,” and “peroxidase-like activity”; “cell-death,” “strategies,” “recent progress,” and “catalyst” are identified as trend topics for future exploration.Conclusion:Nanozymes hold significant promise for cancer diagnostics and therapy, with China, the United States, and Singapore leading the way in innovative research within this domain. Moving forward, research efforts should focus on fostering global collaboration and interdisciplinary synergy, innovate in the development of new nanozyme materials like SAzymes to improve catalytic performance and safety, investigate the potential role of nanozymes in cancer immunotherapy, and expedite the progression of nanozyme technologies from experimental settings to clinical use.

Given the increasing threat of lightning to modern electronic systems and human activities, the accurate identification and classification of lightning electromagnetic pulses has become a critical research focus, prompting the present study. A dataset was established by collecting lightning electromagnetic signals generated by various types of lightning under diverse environmental conditions via the lightning location system of the Institute of Electrical Engineering, Chinese Academy of Sciences. Subsequently, A deep learning model integrating a convolutional neural network was developed for feature extraction and pattern recognition using the multi-station data. Experimental results demonstrate that the proposed model significantly improves LEMP identification accuracy (exceeding 97%) compared to existing single-station methods. Moreover, it effectively uncovers complex hidden features within the data, outperforming conventional approaches in both accuracy and robustness. In conclusion, the proposed deep learning model offers a reliable technical foundation for lightning monitoring and localization based on LEMP signals.

Abstract The Soft X-ray Imager (SXI) is a compact X-ray telescope with a wide $15.5^{\circ } \times 26.5^{\circ }$ 15.5 ∘ × 26.5 ∘ field-of-view (FOV) and is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. SMILE is a joint science mission between the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS) and is due for launch in 2026. SXI is capable of viewing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit. SXI is designed to detect the soft X-rays from the Solar Wind Charge eXchange (SWCX) process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere. SWCX provides a mechanism for boundary detection within the magnetosphere, such as the position of Earth’s magnetopause, because the solar wind heavy ions essentially do not enter regions of closed magnetic field lines. The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours. The SXI has a large grasp (FOV times effective area) and a CCD-based detector system with an energy resolution comparable to contemporary X-ray astrophysics missions. SXI will, therefore, acquire high precision spectra capable of discriminating the composition of many of the solar wind heavy ions within the regions of interest. SXI is led by the University of Leicester (UL) in the United Kingdom (UK) with collaborating organisations on hardware, software and science support within the UK, Europe, China and the United States.

Lymphoma continues to pose a major global health challenge. While the application of nanoparticles (NPs) in lymphoma research has shown considerable promise, a comprehensive evaluation of research trends and the underlying knowledge structure remains lacking. This study aims to systematically evaluate the current progress and future trends in NP applications for lymphoma. We performed a bibliometric analysis using CiteSpace, VOSviewer, and R software on 1040 publications retrieved from the Web of Science Core Collection (WoSCC) between 2004 and 2024. Data on publications, countries, institutions, journals, authors, references, and keywords were analyzed. Research output demonstrated a consistent upward trend, with China and the United States emerging as the leading contributors. The Chinese Academy of Sciences had the highest output, while the University of North Carolina led in citation impact. The International Journal of Nanomedicine published the most studies, whereas Nature Reviews Drug Discovery ranked first in citations. Partha Partim Manna was the most prolific author, and Jindrich Kopecek's work had the strongest influence. Our analysis revealed crucial research areas, including enhanced drug delivery, nanovaccines, nanodiagnostics and monitoring, green synthesis, and the optimization of chimeric antigen receptor (CAR)-T cell therapy. This bibliometric analysis suggests that several directions in NP applications for lymphoma may hold promise for near-term progress. NP-mediated chemotherapy and targeted therapy could improve drug delivery and help overcome resistance, while NP-based imaging agents may enhance sensitivity and enable real-time monitoring. The integration of NPs with CAR-T therapy may also offer strategies to improve efficacy and safety. Additional avenues, such as nanovaccines and green synthesis, highlight opportunities for immune modulation and biocompatibility. Despite notable translational challenges, multifunctional NP platforms may gradually contribute to advances in lymphoma management.

Glioblastoma (GBM) is an extremely aggressive and lethal brain tumor marked by extensive invasiveness. Although nanomaterials have garnered increasing attention in GBM research, a comprehensive bibliometric evaluation remains lacking. Here, we perform a bibliometric analysis to map the scientific landscape of nanomaterials in GBM, identifying prevailing research hotspots and emerging directions. A total of 5241 publications on nanomaterials in GBM were retrieved from the Web of Science Core Collection for bibliometric analysis. VOSviewer and CiteSpace were employed to visualize research output by country/region, author, institution, citation patterns, and keyword co-occurrence. Over the past 25years, research on nanomaterials in GBM has grown rapidly, with 5,241 publications showing a steady upward trend. China leads in both output and citations, with the Chinese Academy of Sciences as the most influential institution and the National Natural Science Foundation of China providing major funding. The Journal of Controlled Release is the primary publication venue, and Shi Bingyang is the most productive and influential scholar. Key research hotspots include blood-brain barrier-targeting strategies, precision nanoparticle design, and integration with emerging therapies. Persistent themes such as exosm, targeted therapy, cell death, challenge, tumor microenvironment, acid, microenvironment, immunotherapy, and t cell-continue to drive innovation in the field. Current research on nanomaterials for GBM treatment focuses on three major challenges: overcoming therapeutic resistance, elucidating the tumor microenvironment, and enhancing blood-brain barrier penetration. Addressing these obstacles is expected to advance nanomedicine from experimental applications to a transformative approach for clinical GBM diagnosis and therapy.

Camphora tenuipilis, a unique aromatic plant in the traditional Xishuangbanna dish “Duo Sheng” (raw minced meat dish), lacks scientific evidence to support its traditional use and potential application as a natural preservative/antioxidant. This study aims to fill this gap by analyzing the chemical composition and bioactivities of its leaf essential oils (EOs), verifying its traditional use, and exploring the bioactivities specific to different chemotypes. Leaf samples were collected from the Xishuangbanna Tropical Botanical Garden (XTBG), Chinese Academy of Sciences, and local markets. Gas chromatography–mass spectrometry (GC-MS) analysis identified 53 compounds, leading to the classification of the EOs into five chemotypes: linalool, geraniol, citral, elemicin, and methyl cinnamate. Notably, the elemicin-type EO (YC02, with an elemicin content of 94.56 ± 0.98%) exhibited the strongest antioxidant properties. The EOs demonstrated antibacterial activity against four foodborne pathogens: Bacillus cereus, Bacillus subtilis, Escherichia coli, and Staphylococcus aureus; except for YC04, the other EOs effectively inhibited pathogen growth to varying extents. Cytotoxicity tests revealed half-maximal inhibitory concentrations (IC50) for HaCaT cells ranging from 0.163 to 0.847 mg/mL. This study scientifically validates the traditional use of C. tenuipilis in “Duo Sheng” and supports its potential as a natural food preservative, antioxidants, and antimicrobial agents.

Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), is a chronic relapsing gastrointestinal inflammatory disorder, whose underlying mechanisms remain elusive. Novel cell death modalities such as ferroptosis and pyroptosis are implicated in its pathogenesis. This study employed bibliometric analysis to elucidate research trends in IBD and novel forms of cell death. A total of 682 records were retrieved from the Web of Science Core Collection on 20 June 2024, using relevant keywords. Bibliometric analysis and visualisation were conducted with tools including Citespace, VOSviewer, the bibliometrix R package, Scimago Graphica, and an online bibliometric platform. Topic modelling was performed via the latent Dirichlet allocation (LDA) algorithm, and burst detection was applied to forecast emerging research trends. Over the past 20 years, there has been a remarkable increase in research output and influence in this field. The number of publications grew exponentially after 2019, and the median impact factor of highly cited papers also showed an upward trend. China and the United States were the major contributors. Chinese Academy of Sciences and Zhejiang University had the highest publication volumes among institutions. Key authors like Zhang Caiying and Markus F. Neurath were identified. Nature, Cell, and Gastroenterology were the most influential journals. Research focus evolved from traditional inflammatory mechanisms to diverse cell death mechanisms. Ten themes were identified through topic modelling, with their significance changing over time. MeSH and gene/protein/pathway analyses highlighted the importance of necroptosis, ferroptosis, and pyroptosis. Biclustering analysis explored specific research topics, and 22 gene/pathway/protein named entities with significant research potential were identified by burst detection. This is the first bibliometric analysis of novel cell death in the context of IBD. It offers a holistic overview of research dynamics, trends, and hotspots in this field, which can enhance the comprehension of the field modalities and offer valuable guidance for future research. Future studies should focus on the interactions between different cell death forms and accelerate the translation of basic research findings into clinical applications.

Near space, as a critical atmospheric domain with unique physical, electromagnetic, and biological characteristics, remains a frontier with extensive unresolved scientific questions in atmospheric physics, electromagnetic environment dynamics, and biological adaptability mechanisms. In response to these knowledge gaps, the Chinese Academy of Sciences (CAS) initiated a strategic pilot science and technology project dedicated to systematically investigating the aforementioned three core domains of near space. This project has allowed for a series of near-space scientific experiments to be successfully conducted on the Qinghai–Tibet Plateau, utilizing large zero-pressure high-altitude balloons to carry diverse scientific payloads. From an engineering perspective, all experiments achieved complete success: high-altitude balloons with volumes ranging from 2000 m3 to 50,000 m3 safely transported scientific exploration equipment and payload cabins (with payload masses of 100 kg to 400 kg) to the near space; all scientific instruments maintained stable operational status throughout the missions; payload cabins reliably provided essential support functions, including power supply, data storage, real-time data transmission, and video monitoring for the scientific payloads; and both the scientific equipment and payload cabins were successfully recovered. These efforts are expected to enhance the scientific understanding of Earth’s near space environment and provide a technical foundation for subsequent large-scale near space exploration initiatives.

The PPP-B2b service of BeiDou-3 enables real-time precise point positioning (RT-PPP) through correction information contained in B2b signals, circumventing the reliance on ground-based network infrastructures. This study comprehensively evaluates the accuracy of PPP-B2b correction parameters and their impact on positioning and tropospheric zenith total delay (ZTD) estimation. The PPP-B2b DCB products exhibit good consistency with the Chinese Academy of Sciences (CAS) reference, with average differences below 1.2 ns and standard deviations within 0.11 ns, indicating comparable performance to CAS products. For BDS-3 satellites, PPP-B2b achieves a radial orbit accuracy of 0.07 m and a clock standard deviation of 0.17 ns, outperforming the Centre National d’Études Spatiales (CNES) real-time products in both aspects. For GPS satellites, the corresponding accuracies are 0.06 m and 0.20 ns. Kinematic PPP experiments using combined GPS and BDS-3 observations yield horizontal and vertical accuracies of 4.3 cm and 2.8 cm, respectively, comparable to CNES results, while the BDS-3-only solution performs better than CNES but is still slightly inferior to the CODE. The ZTD estimation accuracy reaches 1.8 cm for GPS+BDS-3 combinations and 2.3 cm for BDS-3-only cases. Overall, PPP-B2b delivers centimeter-level performance in real-time positioning and ZTD estimation, demonstrating strong potential as an independent, space-based precise service, though further improvement is required for GPS-only applications.

Abstract 2025 International Conference on Surface Science Technology and Applications (ICSSTA 2025) Conference Announcement Hangzhou, China July 18- July 21, 2025 Conference Theme: Innovation in Surface Science and Technology and International Exchange and Cooperation Surface Science and Technology, as an interdisciplinary and comprehensive technology, mainly focuses on the study of various phenomena and processes occurring on the surface and interface at the atomic or molecular level. It has involved many basic and applied disciplines, including physics, chemistry, mathematics, material science, biology and engineering, and is one of the most active frontier disciplines in the world at present. It has now laid the foundation of technology development in modern industry. In order to bridge the gap between the basic science of surface and interface and the wide applications of surface technology, the 1 st International Conference on Surface Science Technology and Applications was held in 2023 in Chongqing, China. Following the successful ICSSTA 2023, the 2 nd International Conference on Surface Science Technology and Application” (ICSSTA 2025), jointly organized by Southwest Institute of Technology and Engineering, Beihang University, Harbin Institute of Technology, Zhejiang University and other leading Chinese research institutions, will be held in Hangzhou, China from July 18 to 21, 2025. Under the theme “Innovation in Surface Science and Technology and International Exchange and Cooperation”, the conference will feature plenary and keynote presentations by academicians from the Chinese Academy of Sciences, Chinese Academy of Engineering, European Academy of Sciences, and renowned international experts. This gathering will facilitate extensive and in-depth exchanges among scholars, industry leaders, and researchers worldwide, focusing on the latest scientific achievements and industrial developments in surface science technologies. Through interdisciplinary dialogues and strategic partnership building, the conference aims to propel the high-quality advancement of this critical discipline. List of Conference Organizers are available in this PDF.

Classification of dark night environment level standards of National Astronomical Observatories, Chinese Academy of Sciences (NAOC), has been released

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest China (NWC). We analyzed 1569 articles (NCP = 788; NWC = 781) from the Web of Science Core Collection (1995–2025) using visualization tools such as CiteSpace and VOSviewer to investigate annual numbers of publications, leading scholars and research institutions, and then to map keyword co-occurrence and co-citation structures. Our results showed that keyword clustering exhibited high structural quality (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), supporting reliable thematic interpretation. The bibliometric analysis indicates a steady growth in annual publications since 1995, with the Chinese Academy of Sciences and China Agricultural University as leading contributors. From 1995 to 2005, studies centered on irrigation, yield and field-scale WUE, emphasizing the optimization of irrigation strategies and crop productivity. During 2006–2015, the thematic focus has broadened to encompass nitrogen use efficiency, crop quality and eco-environmental performance, thereby moving toward integrated evaluation frameworks that capture ecological synergies. Since 2016, the literature now emphasizes system integration, regional adaptability, climate-response mechanisms and the ecological co-benefits of agricultural practices. Future studies are expected to incorporate indicators such as crop quality, water footprint and carbon isotope indicators to support the sustainable development of agricultural water use. This study offers insights and recommendations for developing a comprehensive crop WUE evaluation framework in China, which will support sustainable agricultural water management and the realization of national “dual carbon” targets.

BackgroundPrimary liver cancer (PLC) ranks as the third leading cause of cancer-related mortality worldwide, posing a serious global public health burden. Hepatocellular carcinoma (HCC) is the most common subtype, accounting for approximately 75%–85% of all PLC cases. In recent years, environmental pollution has emerged as a potential risk factor for PLC. However, a systematic bibliometric analysis of global research trends in this field remains lacking. This study aims to perform a comprehensive bibliometric analysis to explore global trends in the field of environmental pollution and PLC research from 2000 to 2025.MethodsWe conducted a bibliometric analysis using the Web of Science Core Collection database, covering studies published from January 2000 to March 2025. CiteSpace was used to analyze publication trends, global collaborations, and key research areas through network visualizations and co-occurrence analyses.ResultsA total of 562 publications were included in this study, with China and the United States identified as the leading contributors. Prominent institutions in this field include the National Institutes of Health (USA), National Cancer Institute (USA), and the Chinese Academy of Sciences. Among journals, Environmental Health Perspectives, Hepatology, and Cancer Research were the most frequently cited, indicating a close connection between environmental science and oncology. Keyword analysis revealed that research focuses not only on traditional pollutants such as air pollution and heavy metals, but also on emerging exposures including volatile organic compounds and drinking water contaminants. Mechanistic studies remain at the core of this field, with frequently occurring terms such as “oxidative stress”, “gene expression”, and “inflammation”. Meanwhile, clinical research-related keywords like “epidemiology” and “follow-up” have become increasingly prominent in recent years, indicating a growing emphasis on population-based risk assessment.ConclusionsThis study highlights the growing research interest in the link between environmental pollution and PLC. Cross-disciplinary collaborations between environmental science, medicine, and public health are increasingly influencing the development of this field. Future research should focus on elucidating the carcinogenic mechanisms of pollutants and enhancing translational applications in public health.

ObjectivesTo map global research on AI-driven histopathological image interpretation in oncology (2000–2024).MethodologyScopus-based bibliometrics using Boolean queries; data exported, cleaned, and analyzed in VOSviewer.ResultsThe dataset included 1874 publications, surging after 2015. China (n = 561) and the United States (n = 467) led output; leading institutions included Harvard Medical School and the Chinese Academy of Sciences. Top journals were Scientific Reports, Cancers, and IEEE Access; the corpus H-index was 112. Collaboration networks intensified, especially U.S.–Asia. Keyword mapping showed four clusters: (1) deep learning for breast cancer/CNN diagnostics; (2) transfer learning/feature extraction; (3) prognostic modeling/tumor microenvironment; and (4) digital infrastructure/explainable AI. Overlay analyses traced a shift from classical machine learning to transformers and multimodal models integrating molecular and clinical data; emerging themes include semantic segmentation, self-supervised learning, and therapy-response prediction. Applications spanned breast, prostate, colorectal, head–neck, gynecologic, and gastrointestinal/liver cancers. Models primarily used whole-slide images (e.g., TCGA) and multi-omics; algorithms included CNNs, deep learning, classical machine learning, and weakly supervised approaches. Evidence ranged from proof-of-concept to multicenter validation and workflow integration; adoption remains constrained by data standardization, interpretability, and regulation. Clinically, AI improved diagnostic accuracy/efficiency and supported personalization via multi-omics. Bibliographic coupling revealed three clusters: clinical/translational journals; engineering/computational outlets; and interdisciplinary venues linking algorithmic innovation with digital pathology.ConclusionsAI histopathology is advancing toward clinical-grade deployment, propelled by collaboration and methodological innovation, yet limited by data standards, explainability, and regulatory requirements.

Abstract Background: Immunometabolism plays a vital role in the immunopathogenesis of people living with human immunodeficiency virus type 1 (HIV-1) (PLWH). However, the precise relationship between metabolic profiles and T-cell dysfunction in this population remains unclear. This study aims to investigate the metabolic reprogramming and underlying mechanisms contributing to T-cell dysfunction in PLWH. Methods: This study re-analyzed single-cell RNA sequencing data from the Genome Sequence Archive of the Beijing Institute of Genomics Data Center, Chinese Academy of Sciences. The dataset comprised samples from healthy donors (HD), HIV-1-infected treatment-naive patients (TN), and patients undergoing antiviral therapy. Various analytical approaches—including functional analysis, transcription factor analysis, network analysis, and enrichment analysis—were performed to assess T-cell functional and metabolic characteristics, as well as to identify potential targets within metabolic-epigenetic or non-epigenetic regulatory axes involved in T-cell dysfunction. Results: By analyzing the transcriptional profiles, a total of 58,752 CD4 + T cell subsets and 68,907 CD8 + T cell subsets were identified and annotated. Among these, the naive subset CD8-CCR7 was significantly reduced in TN patients compared to HD ( P < 0.05), whereas CD4-CCR7 showed a decreasing trend. Conversely, the effector subset CD8+ activated effector/memory T cells (CD8-EMRA) were significantly increased in TN patients ( P < 0.05), while cytolytic CD4+ T cells (CD4-CTL) displayed an increasing trend. ART did not effectively restore these alterations. Additionally, naive subsets and CD8-EMRA cells were associated with disease progression. Further analysis revealed that naive subsets exhibited hyper-activation and increased differentiation, whereas effector subsets showed excessive activation and a strong interferon (IFN) response in PLWH compared to HD ( P < 0.05). Intriguingly, we observed substantial metabolic alterations linked to immune dysfunction within the four T-cell subsets. Specifically, elevated levels of the methyltransferases absent, small, or homeotic-like 1 (ASH1L) and SET domain containing the 1B gene (SETD1B) may promote the differentiation and exhaustion of the CD4-CCR7 subset via the ASH1L/SETD1B-H3K4me3-FOXP1 axis. These enzymes are also associated with the exhaustion of CD8-CCR7 cells in TN patients through ASH1L/SETD1B-H3K4me3 axis. Additionally, isocitrate dehydrogenase 2 (IDH2)-mediated production of α-ketoglutarate (α-KG) may contribute to the dysfunction of CD8-CCR7 cells by activating janus kinase (JAK)-signal transducer and activator of transcription (STAT)3-dependent interferon signaling during HIV-1 infection. Conversely, increased activity of SET domain containing protein 2 methyltransferase is closely linked to hyperactivation, a strong type I interferon response, and cellular senescence in CD4-CTL cells from TN patients. Furthermore, heightened expression of solute carrier family 7 member 5 correlates with exhaustion of effector subsets in TN individuals. The IDH2-STAT1 axis may also play a crucial role in driving the over-activation and exhaustion of CD8-EMRA cells through interferon signaling pathways. Conclusion: These findings indicate amino acid- and IDH2-related metabolism may cause the dysfunction of both naive and effector subsets by metabolic-epigenetic or non-epigenetic regulatory axes in PLWH.

IntroductionEstimation of forest carbon (C) storage is essential for understanding the global C cycle, mitigating climate change, and developing carbon markets. However, systematic research on forest C storage estimation needs improving.MethodsHerein, a bibliometric and content review of literature published between 2008 and 2025 was conducted to synthesize temporal and spatial trends and to identify methodological advances and gaps in forest C-storage estimation.ResultsThe results revealed that environmental sciences accounted for the largest share of publications (n = 718). The most productive institution and country were the Chinese Academy of Sciences (n = 208) and the United States (n = 691), respectively. Research progress in the field was categorized into three distinct stages since 2008. The early stage (2008–2012) was dominated by eddy covariance, satellite remote sensing, and airborne radar. The middle stage (2013–2017) was characterized by greater use of process-based and statistical simulation models. In the later stage (2018–2025), techniques such as random forest (RF), machine learning and biomass mapping became more widely used. Over this period, model performance improved substantially, especially the coefficient of determination (R2) increased from 0.62 to 0.97 for the TRIPLEX-Flux C-exchange model and from 0.63 to 0.97 for RF models.DiscussionSpatially, most studies addressed local-to-regional scales, whereas large-scale or global assessments remain limited. This synthesis clarifies methodological trajectories and persistent gaps that can guide the development and wider deployment of forest C-storage estimation approaches and support evidence-based climate policy and C-market design.

Preface to the Second International Conference on Space Science & Technology (ICSST 2025) With the vision of deepening international academic exchange and broadening global space cooperation, the Second International Conference on Space Science & Technology (ICSST 2025) was convened in two phases. Phase I, the Suzhou Venue (22-24 May 2025), was held in Suzhou, Jiangsu Province, China. It was sponsored by Beijing Institute of Technology, organized by the journal Space: Science & Technology , Aerospace Information Research Institute (CAS), and Soochow University, co-organized by Beijing Minospace Technology Co., Ltd, Harbin Institute of Technology Satellite Technology Co., Ltd, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, and Tsinghua University / China-Russia International Joint Research Center for Aerospace Innovation Technologies. List of Committee Member is available in this PDF.