Search Results (1,290)

Objectives: To extract and identify extracellular vesicles (EVs) derived from the traditional Chinese herb Euonymus alatus (EA), characterize their molecular profiles using multi-omics analysis, and assess their regulatory effects and potentially associated signaling pathways in an interferon-γ (IFN-γ)-stimulated inflammatory thyroid follicular cell model mimicking key inflammatory features of autoimmune thyroiditis (AIT). Methods: Differential centrifugation combined with density gradient centrifugation was used to purify EA-derived EVs, and the isolates were characterized using a BCA protein assay, transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blotting (WB). Molecular constituents were examined via label-free proteomics and RNA-seq. Meanwhile, Nthy-ori-3-1 cells were exposed to IFN-γ to establish an inflammatory thyroid follicular cell model; these activated cells were subsequently treated with the purified EVs (EV group). The supernatants were collected and the levels of interleukin-1β (IL-1β) and interleukin-18 (IL-18) were determined by ELISA. Comparative transcriptomic and proteomic profiling was employed to identify differentially expressed molecules and related signaling pathways between the model and the EV group. Results: EA-derived EVs were successfully isolated and showed typical EV characteristics. Proteomics identified 3607 proteins enriched in metabolic, immune, and antioxidant processes, while RNA-seq generated 72.16 million clean reads and revealed genes enriched in metabolic and immune pathways. In the IFN-γ-stimulated Nthy-ori-3-1 cells, ELISA results showed that EV treatment significantly reduced the secretion of IL-1β and IL-18 compared to the model group. EV intervention resulted in 138 differential proteins (42 upregulated, 96 downregulated) and 262 differential genes (81 upregulated, 181 downregulated). GO analysis revealed significant enrichment in terms related to inflammatory response, immune response, and regulation of oxidative stress. KEGG pathway analysis demonstrated enrichment in the IL-17, NOD-like receptor, and PPAR signaling pathways, as well as ferroptosis, and Wnt pathway (unique for upregulated genes). Conclusions: EA-derived EVs are rich in molecules that regulate metabolic pathways and immune responses. They may ameliorate inflammation in this inflammatory thyroid follicular cell model, with multi-omics analyses revealing correlative enrichment of multiple signaling pathways. These findings provide preliminary experimental evidence and novel candidate targets for AIT treatment. Full article

Urban greening is central to climate-responsive planning, but month-to-month variation in urban net primary productivity (UNPP) remains insufficiently resolved. We estimated monthly UNPP from the MOD17A2H Gpp band, a gross primary productivity (GPP) product, across Chengdu’s five central districts during 2013–2022 and examined its associations with the normalized difference vegetation index (NDVI), land surface temperature (LST), precipitation, and night-time light intensity. Ordinary least squares regression used 119 overlapping citywide monthly means from January 2013 to November 2022. The model explained 86.1% of UNPP variance (adjusted R² = 0.856). LST showed the largest standardized association (β = 0.596), followed by NDVI (β = 0.286) and precipitation (β = 0.135); night-time light intensity was not significant (β = 0.017, p = 0.632). HC3-robust inference retained the NDVI and LST associations, whereas the precipitation association weakened. District summaries showed the largest stage-wise UNPP increase in Qingyang and the largest NDVI increase in Chenghua. Greener spatial patterns after 2018 coincided temporally with the Park City Initiative but do not establish a causal policy effect. These findings support temporally aligned monitoring while identifying the need for field-based species, management, and impervious surface data. Full article

Radar emitter recognition based on pulse description words (PDWs) serves as a fundamental prerequisite for target identification and tracking in electronic support measures (ESM). Although self-supervised learning (SSL) has been widely applied to text and image tasks, few effective SSL paradigms are available for the feature representation of radar PDWs. In this paper, a novel masked representation alignment-based self-supervised learning (SSL-MRA) method is proposed for radar emitter feature learning and recognition. Firstly, a dual-branch Transformer encoder is designed to extract contextual representations from both masked and unmasked tokens. Secondly, a cross-attention Transformer-based predictor is constructed to recover the masked representations from unmasked features. Furthermore, a codebook-based tokenizer is developed to learn discrete representations of masked inputs. Based on the masked prediction mechanism, two pretext tasks are established for the pre-training of SSL-MRA. Specifically, a masked discrete representation alignment task is adopted to replace traditional reconstruction-based pre-training, which implements codebook classification for semantic discretization. Meanwhile, a masked prediction representation alignment task is constructed to constrain the high-dimensional semantic consistency of feature embeddings. Experimental results on both simulated and real measured datasets demonstrate that the proposed method achieves superior feature representation capability. It yields an average recognition accuracy of 94.95% on source-domain data, outperforming all baseline methods. Moreover, the proposed SSL-MRA also achieves better cross-domain transfer performance than the mainstream masked autoencoder method. Full article

Near-surface trace gases exhibit significant spatial heterogeneity, whereas existing observational techniques are unable to simultaneously resolve their horizontal distributions at multiple altitudes with high spatial resolution. To address this limitation, this study developed a lightweight unmanned aerial vehicle (UAV)-borne two-dimensional differential optical absorption spectroscopy (2D-DOAS) system that integrates multi-altitude hovering with multi-azimuth spectral scanning for high-resolution characterization of near-surface NO₂, SO₂, HCHO, and O₄-related optical parameters. Spectral retrievals were performed using the QDOAS software, and the developed system was first validated through synchronous observations with a commercial ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) instrument. The NO₂ differential slant column density (DSCD) retrievals from the two systems showed strong agreement, with correlation coefficients greater than 0.90 at all six elevation angles, demonstrating the reliability of the developed system. Following validation, a 20-day field campaign was conducted in Huaibei using multi-altitude hovering observations at 30–110 m above ground level combined with synchronous measurements in 24 azimuth directions at 15° intervals. The observations revealed pronounced horizontal and vertical variability in trace-gas distributions, with enhanced DSCDs associated with local industrial emissions, prevailing winds, and atmospheric transport. These results demonstrate that the proposed UAV-borne 2D-DOAS system provides a reliable and flexible tool for high-resolution monitoring of near-surface trace gases, identification of spatial patterns associated with potential emission sources, and investigation of atmospheric transport processes. Full article

Rapid, non-destructive prioritization of maize kernels can help allocate limited re-inspection capacity in post-harvest quality control. Existing deep learning approaches mainly optimize overall classification accuracy and do not explicitly address which samples should be inspected first when verification resources are constrained. This study proposes RiMS-FiLM, an end-to-end multimodal screening framework that integrates kernel RGB appearance with lightweight physical attributes (weight and size) to generate re-inspection priority scores. Feature-wise Linear Modulation adaptively conditions visual representations on physical measurements, while a prototype-guided module organizes fused embeddings into class-structured operational quality-priority regions. The risk tiers are derived from the original GrainSet-Maize defect categories and are used as screening-priority labels rather than laboratory-validated toxicological endpoints. Internal evaluation on the stratified split of the single public GrainSet-Maize dataset over five random seeds yielded a Macro-F1 of 0.9917 ± 0.0027 and captured 94.65 ± 0.21% of high-priority samples within a 20% re-inspection budget. It also reduced severe high-to-low priority errors compared with representative fusion and selective-screening baselines. These results demonstrate the potential of low-cost multimodal sensing for resource-constrained, non-destructive grain quality screening and re-inspection planning. Full article

Background: Clear cell renal cell carcinoma (ccRCC), the predominant histological subtype of kidney malignancies, is characterized by metabolic dysregulation and therapeutic resistance. Although the intracellular antioxidant paraoxonase 2 (PON2) is frequently dysregulated across multiple cancers, its biological role in ccRCC—particularly the contribution of its enzymatic activity and interplay with the tumor immune microenvironment—remains poorly defined. Methods: Multi-cohort analysis integrating TCGA, ICGC, GEO, and CPTAC datasets was combined with single-cell transcriptomic profiling, survival and Cox regression modeling, immune infiltration estimation, and in vitro loss- and gain-of-function experiments in ccRCC cell lines. Results: PON2 was markedly elevated in ccRCC tissues, predominantly localizing to malignant epithelial and endothelial cells as determined by single-cell transcriptomic profiling. High PON2 expression correlated with advanced T/M stages and independently predicted poor prognosis. Functional experiments showed that PON2 silencing was associated with reduced tumor cell proliferation, invasion, and glycolysis, while rescue experiments using wild-type—but not S311C mutant—PON2 partially restored these phenotypes. PON2 expression was associated with glycolysis-related metabolic changes and CEBPB-related expression of downstream glycolytic targets, including LDHA and PGK1. Furthermore, PON2-high tumors showed transcriptomic features consistent with an immunosuppressive microenvironment, with higher computational estimates of CD8⁺ T-cell infiltration accompanied by elevated immune checkpoint expression (PD-L1, CTLA4) and higher estimated abundance of immunosuppressive populations (Tregs, MDSCs). Conclusions: Collectively, our findings identify PON2 as a candidate biomarker associated with ccRCC progression and CEBPB-related glycolysis and features consistent with an immunosuppressive microenvironment. Full article

Reducing disparities in industrial pollution emission intensity among cities is key to alleviating spatial environmental inequities within urban agglomerations and promoting inclusive regional sustainable development. Existing research primarily analyzes the impact of commodity market integration on pollution from a macroeconomic perspective, but there is a lack of research on the impact of factor market integration within urban agglomerations on disparities in industrial pollution emission intensity among cities across provincial borders. We use matched data from the China Industrial Enterprise Database and the China Polluting Enterprises Database covering the period 2000–2013, using the inverse of the dispersion of total factor productivity (TFP) to measure the degree of factor market segmentation, thereby assessing the level of factor market integration. By constructing datasets of city pairs within urban agglomerations and distinguishing between city pairs within one province and city pairs across provinces, we arrive at three main results: (1) There are significant differences in the level of integration of factor markets across China’s 11 major urban agglomerations, with the Pearl River Delta and Yangtze River Delta ranking highest, whilst inland urban agglomerations lag significantly behind. (2) For every 1% increase in factor market integration, the difference in industrial pollution emission intensity between cities decreases by approximately 0.58%. The emission reduction effect for inter-provincial city pairs is significantly weaker than that for intra-provincial city pairs, confirming the existence of a significant provincial boundary effect. (3) In regions with well-developed transportation infrastructure and where cities are relatively close to one another, the integration of factor markets across provincial borders plays a more significant role in reducing disparities in industrial pollution emission intensity among cities. These findings not only offer insights for pollution control across administrative boundaries within urban agglomerations but also provide empirical evidence for helping promote the sustainable development of urban agglomerations. Full article

Synthetic biology, an evolving interdisciplinary field encompassing revolutionary technologies, requires a comprehensive consideration of bioethics, and biosafety risks to drive scientific and technological advancements. To address these concerns, it is crucial to pay attention to synthetic biology governance, ethics principles, and legal regulations, and develop appropriate biosafety risk assessment frameworks. This study endeavors to delve into the strategies of biosafety as well as the governance of risk assessment to foster innovation in the field of synthetic biology. It provides proposals to facilitate the healthy progression of synthetic biology and enhance biosafety management infrastructure. Emphasizing the critical facets of bio-risk assessment in synthetic biology, this work particularly underscores the biosafety risks associated with various factors and methods of the biosafety risk assessment. Within this context, the assessment of biosafety risk in the implementation of synthetic biology technology requires a comprehensive evaluation. Professional technical capabilities, the accessibility of the required resources and organizational scope should be considered when it comes to the actors of synthetic biology. Full article

Organic protective coatings are extensively employed to mitigate metal corrosion, yet accurate quantitative evaluation of their performance degradation during service still poses a considerable challenge. This study aims to develop a multi-view deep learning framework for five-grade quantitative assessment of organic coating protective performance using routine electrochemical and mechanical parameters. This study aims to develop a multi-view deep learning framework for five-grade quantitative assessment of organic coating protective performance using routine electrochemical and mechanical parameters. Based on abundant laboratory-accelerated corrosion test data, independent single-view sub-models were constructed for three complementary descriptors, including the mid-frequency phase angle (θ_{10 Hz}), open-circuit potential (OCP), and adhesion strength (A_s). Prediction outputs from individual sub-models were fused through correlation-weighted voting, where weighting factors were determined by quantitative parameter-degradation correlations across various coating systems. The proposed framework achieves reliable five-level grading (excellent, good, fair, poor, failure) of coating protective performance. This methodology provides an effective data-driven framework for the predictive assessment of organic coating protective performance in practical engineering applications. Full article

4Cr13 martensitic stainless steel is widely used for plastic molds, but welding repair can produce heterogeneous weld/HAZ microstructures and a strength–toughness trade-off. This study evaluates how filler-wire composition and post-weld heat treatment affect the mechanical performance of repaired 4Cr13 steel. TIG welding was performed using high-molybdenum (HM), low-carbon (LC), and base-metal (BM) wires, followed by either post-weld tempering or quenching and tempering. Post-weld quenching and tempering reduced the hardness gradient and improved the overall hardness uniformity. The average weld-metal hardness followed HM > BM > LC, while the BM joint showed the smallest hardness variation because its nominal composition matched that of the base metal. After quenching and tempering, the tensile strengths of the HM, LC, and BM joints reached 1299.5, 1315.6, and 1300.0 MPa, respectively, compared with 1290.8 MPa for the similarly treated base metal. The corresponding impact energies were 58.9, 116.3, and 36.8 J, with LC showing the highest toughness. Fracture observations were consistent with these mechanical-property differences. The results demonstrate that filler-wire composition and post-weld heat-treatment route should be selected jointly according to the required balance of hardness uniformity, strength, ductility, and impact toughness, providing an experimental basis for repair-welding process selection for 4Cr13 steel. Full article

Accurate and efficient detection of coffee leaf diseases and pests is essential for safeguarding coffee yield and quality. To address the challenges posed by small lesions, complex backgrounds, and highly variable symptom morphologies under field conditions, this study proposes CDMS-YOLO, a lightweight detection model based on YOLOv11n. CDMS-YOLO integrates the C3k2-Convolutional Gated Linear Unit (C3k2-CGLU) module, the DySample dynamic upsampling operator, the Mobile Inverted Bottleneck Convolution detection head (Detect_MBConv) detection head, and Scale-based Dynamic Loss (SD Loss) to enhance disease feature extraction, multiscale detail recovery, and bounding-box regression. Experiments on a coffee leaf disease and pest dataset with complex backgrounds show that CDMS-YOLO achieves a precision of 93.0%, a recall of 90.1%, and an mAP50 of 95.1%, representing improvements of 4.8, 2.1, and 3.0 percentage points, respectively, over YOLOv11n. Under a stricter leakage-controlled data split, CDMS-YOLO achieves an mAP50 of 93.6% and an mAP50-95 of 77.3%, outperforming YOLOv11n by 3.1 and 2.6 percentage points, respectively, indicating that the performance advantage is maintained under a more conservative evaluation protocol. The model contains only 2.84 M parameters and requires 7.2 GFLOPs. On the NVIDIA Jetson Orin Nano, CDMS-YOLO achieves an inference speed of 135.41 FPS, compared with 203.75 FPS for YOLOv11n. Thus, its frame rate is approximately 33.5% lower than that of YOLOv11n; nevertheless, it still satisfies real-time detection requirements. Overall, CDMS-YOLO provides an effective balance between detection accuracy, model complexity, and edge-deployment efficiency, offering a practical approach for intelligent detection and precision management of coffee leaf diseases and pests. Full article

Cardiovascular diseases remain a major global health burden, making the development of accurate and interpretable prediction models important for clinical decision support. In this study, the metaheuristic Ivy Algorithm was employed to perform two-stage hyperparameter optimization for five machine learning classifiers, namely ID3, SVM, RF, XGBoost, and LightGBM. The proposed framework was evaluated on the publicly available Cleveland and Statlog heart disease datasets using outer stratified 10-fold cross-validation. The results showed that IVYA-based optimization improved the predictive performance of all five classifiers to varying degrees. Among them, IVYA-LightGBM achieved the best overall performance, with mean AUC, Accuracy, Precision, Recall, and F1-score values of 0.945, 0.907, 0.931, 0.864, and 0.893, respectively. Paired Wilcoxon signed-rank tests based on the fold-wise results indicated that the improvements in AUC were statistically significant in most model–dataset comparisons. In addition, under consistent experimental settings, IVYA was compared with five widely used metaheuristic optimization algorithms and achieved the highest AUC, Recall, and F1-score, while requiring the shortest average runtime. To enhance model interpretability, SHAP analysis was further incorporated to quantify the contributions of different clinical features to the model predictions and improve the transparency of the prediction process. Overall, IVYA-LightGBM achieved a favorable balance among predictive performance, computational efficiency, and interpretability. Nevertheless, further validation on larger and more diverse clinical datasets is required before practical clinical application. Full article

Background: In China, the current national immunisation schedule extends only to 4 years of age, and no polio booster dose is recommended for adolescents or adults, with data on immunogenicity and safety in these populations lacking. This phase IV trial evaluated the immunogenicity and safety of a single booster dose of Sabin-strain inactivated poliovirus vaccine (sIPV) in Chinese adolescents and adults. Methods: This phase IV, open-label trial enrolled 120 healthy participants, comprising 60 adolescents aged 7–17 years and 60 adults aged 18–50 years, who received a single booster dose of sIPV. Participants were enrolled and vaccinated in Gansu Province, China, between 21 October and 1 November 2025. Immunogenicity was assessed by neutralising antibody titres against poliovirus types 1, 2, and 3 before and 30 days post-vaccination. Safety was evaluated through monitoring of adverse events throughout the 30-day follow-up period. Results: Following a single sIPV booster dose, seropositivity rates reached 100% for all three poliovirus serotypes in both age groups. Post-vaccination geometric mean titres (GMTs) for types 1, 2, and 3 were 1748.29, 3961.77, and 2089.08 in adolescents, and 1902.58, 4434.39, and 2256.16 in adults, respectively, with no significant between-group differences. Seroconversion rates for type 3 were significantly higher in adults than in adolescents (98.33% vs. 86.67%, p = 0.032). Geometric mean increases (GMIs) were significantly higher in adults for type 1 (72.12 vs. 28.10, p = 0.008) and type 3 (316.14 vs. 45.56, p < 0.001). The vaccine was well tolerated; vaccine-related adverse events were reported in 17.50% of participants, all mild to moderate, with no vaccine-related serious adverse events. Conclusions: A single booster dose of sIPV induced robust neutralising antibody responses across all three poliovirus serotypes in both adolescents and adults, with a favourable safety profile. Adults with lower baseline immunity derived proportionally greater fold-increases in antibody titres. Full article

Exogenous glutathione (GSH) can alleviate cadmium (Cd) stress, yet physiological improvement and changes in internal Cd burden may not occur in parallel. Salix variegata seedlings were exposed for 10 d to 0 or 100 μM Cd with 0–1500 μM GSH. Across the full dose series, GSH responses were non-monotonic: 10 μM GSH increased root length by 67.3% under Cd stress, 250 μM GSH increased chlorophyll a, chlorophyll b, and total chlorophyll by 21.4%, 39.0%, and 25.7%, respectively, whereas 1500 μM GSH inhibited several growth and pigment traits. A common 2 × 2 subset (0/250 μM GSH × 0/100 μM Cd) was used to integrate root/leaf physiology with root/shoot element status. At 250 μM GSH, H₂O₂ and MDA decreased in both roots and leaves, while antioxidant and osmotic responses differed between tissues. Root Cd concentration increased by 64.1%, root Cd accumulation by 165%, and total Cd accumulation by 117%, despite a 24.1% decrease in shoot Cd concentration. Root Fe concentration and total Fe accumulation increased by 173.2% and 170%, respectively. These results show that GSH responses in S. variegata are dose- and tissue-dependent and that improved physiological status can coexist with greater Cd burden and altered essential-metal homeostasis. Full article

Peach (Prunus persica) production and the northward expansion of its cultivation boundaries are severely constrained by recurrent extreme climatic events. Based on time-series transcriptomic analysis of two independent parallel treatments (cold stress and ABA application) and qRT-PCR validation, multiple candidate transcription factor genes co-responsive to both cold stress and ABA were identified (PpERF48, PpERF017, PpMYB6, PpWRKY46, PpWRKY40, and PpbHLH35). Among these, PpMYB6 was further characterized through bioinformatic analysis, and transgenic peach callus and Arabidopsis thaliana lines overexpressing PpMYB6 were generated, revealing its dual role in modulating plant growth and conferring low-temperature stress tolerance. Yeast one-hybrid and dual-luciferase reporter assays confirmed that PpMYB6 directly binds to and activates the PpCBF2 promoter. Yeast two-hybrid library screening identified DWARF8 as a candidate interacting protein, pointing to a working hypothesis by which PpMYB6 may negatively regulate vegetative growth via the gibberellin pathway. Together, this study characterizes a novel molecular module associated with cold tolerance and growth balance in peach, providing a promising candidate gene for molecular breeding of cold-resistant cultivars and rootstocks. Full article