Search Results (1,708)

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

Weeds are a primary factor affecting sugarcane production and productivity in Thailand. During the late-rainy season, when cultivation is carried out under rainfed conditions, weed competition becomes increasingly severe, prompting farmers to perform secondary weed control using post-emergence herbicides. Therefore, to guide farmers on the appropriate use of herbicides for effective weed management and long-term control during the critical period of sugarcane growth, this study evaluates the effectiveness of pre- and post-emergence herbicides. Conducted in Northeast Thailand using a randomized complete block design (RCBD) with four replications, the experiment revealed that several pre-emergence herbicides, namely pendimethalin + imazapic (825 + 75 g a.i. ha⁻¹), indaziflam (62.5 g a.i. ha⁻¹), and sulfentrazone (875 g a.i. ha⁻¹), and a combination of indaziflam + sulfentrazone (46.88 + 750 g a.i. ha⁻¹) were applied one day after sugarcane planting, demonstrating high weed control efficacy. These treatments significantly reduced the summed dominance ratio (SDR) of both total weed (41.65–78.54%) and dominant weeds (70.13–86.04%), including Digitaria ciliaris (Retz.) Koel., Dactyloctenium aegyptium (L.), Brachiaria distachya (L.) Stapf, and Cyperus rotundus, compared with the no-weeding treatment. In summary, effective weed management in sugarcane fields under late-rainy season can be achieved through the application of pendimethalin + imazapic at 825 + 75 g a.i. ha⁻¹, which produced the highest sugarcane yield (a 139.00% increasing compared with no weeding) and net profit (a 79.75% increasing compared with hand weeding) in loamy sand soil conditions, where D. ciliaris, D. aegyptium, and C. rotundus were dominant weeds. Similarly, indaziflam at 62.5 g a.i. ha⁻¹ yielded the best results (a 71.68% increasing compared with no weeding) and net profit (a 121.04% increasing compared with no weeding) in sandy loam soil, where B. distachya was the only dominant weed. This weed management strategy is potentially transferable to sugarcane production systems in other regions that share comparable soil properties, climatic conditions, and dominant weed species. Full article

This study investigated the occupational hazard effects of heavy metal(loid)s (HMs) from soil in several critical mining activity areas at the Majdanpek copper mine in Serbia. Soil contamination and associated ecological and health risks to workers were evaluated through an apportionment of sources and a quantitative evaluation of ecological and health risks. The majority of soil samples had increased concentrations of Cd, Cu, Pb, Zn, Hg, As, Mo, and Sb. The results of the multivariate statistical analysis suggested the existence of multiple sources. The positive matrix factorization further explained these associations between HMs and defined three main pollution sources: natural (Factor 1), mixed source (Factor 2), and mining pollution (Factor 3). According to the RI, the average value was 1215, with more than half of the samples (57.4%) showing very high pollution levels, while 3.3% of the samples had an RI lower than 150. The ecological risk was dominated by Cd, Cu, and Hg, with Factor 3 contributing the most to the RI values. Assessment of worker exposure to soil revealed that outdoor workers had a higher potential for adverse health effects, with mean HI and TCR being 0.18 and 2.9 × 10⁻⁵, respectively. The identified sources had similar impacts on non-carcinogenic and carcinogenic risks, with a decreasing trend: Factor 3 > Factor 2 > Factor 1. Indoor workers were exposed to neither non-carcinogenic or carcinogenic risks, whereas outdoor workers suffered from possible health issues regarding TCR. Source-specific health risk assessment indicated mining pollution as the only risk contributing factor. A Monte Carlo simulation of risks revealed that the probability of developing carcinogenic issues for outdoor workers was within the safety threshold (TCR < 10⁻⁴). The findings of this study emphasize the need for regulation and control strategies for worker health risks from HM-contaminated soil in mining areas. Full article

In this study, the decarburization behavior in basic oxygen furnace (BOF) steelmaking was investigated based on the multi-zone reaction mechanism. The contributions of the main reaction zones to decarburization were clarified, and the effects of key factors—including the effective reaction amount in the main reaction zones, the post combustion ratio (PCR) in auxiliary reaction zones, and the carbon content of scrap steel—on decarburization behavior were quantitatively analyzed. The results indicate that decarburization predominantly occurs in the jet impact reaction zone (approximately 76% of the total decarburization), followed by the emulsion and metal droplet reaction zone (approximately 14%) and the bulk metal and slag reaction zone (approximately 10%). Variations in the effective reaction amount for the main reaction zones significantly affect both the decarburization rate and the endpoint carbon content, with the direct oxidation decarburization reaction in the jet impact reaction zone being the dominant factor. In addition, the PCR in the gas homogenization zone of the auxiliary reaction zones determines the distribution ratio of effective reaction oxygen, while the melting behavior of scrap steel in the metal homogenization zone plays a critical role in the precise control of the endpoint carbon content. This study provides a quantitative elucidation of the effects of different reaction zones on decarburization behavior, offering a foundation for the precise control of endpoint carbon content in BOF steelmaking. Full article

This study develops an integrated framework for groundwater pollution source identification by coupling Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), and the Mantel test, with the Qujiang River Basin as a case study. The framework enables a full-process assessment, encompassing qualitative identification, quantitative apportionment, and spatial validation of pollution drivers. Results indicate that groundwater chemistry is primarily influenced by three categories of sources: natural rock weathering, agricultural and domestic activities, and industrial wastewater discharge. Anthropogenic sources account for 73.7% of the total contribution, with mixed agricultural and domestic inputs dominating (38.5%), followed by industrial effluents (35.2%), while natural weathering contributes 26.3%. Mantel test analysis further shows that agricultural and domestic pollution correlates strongly with intensive farmland distribution in the midstream area, natural sources correspond to carbonate outcrops and higher elevations in the upstream, and industrial contributions cluster in downstream industrial zones. By integrating PCA, PMF, and Mantel analysis, this study offers a robust and transferable framework that improves both the accuracy and spatial interpretability of groundwater pollution source identification. The proposed approach provides scientific support for regionalized groundwater pollution prevention and control under complex hydrogeological settings. Full article

Today, as planners and urban designers increasingly rely on computational modeling to study complex urban systems, a methodological shift toward virtual experimentation is discernible because the real-world factors are difficult to control. This paper investigates the effect of the realism of computer simulations on distance perception in urban squares and streets. This study used Autodesk 3ds Max^® for modeling and V-Ray for rendering to create systematic variations in distances, with 172 participants providing distance estimates for 216 images. Results indicated that realism had a significant effect on distance perception, increasing estimation accuracy from r = 0.8 to r = 0.94. Lower realism was always associated with an underestimation of the distance, whereas higher realism manifested both overestimation and underestimation. Underestimation is dominant at long distances (>20 m), attributable to a lack of cues, common in low realism; overestimation happens only for short distances (≤20 m) due to high realism. These findings underscore the importance of simulation fidelity for urban designers and planners, enhancing the validity of virtual tools in design, research, and decision-making. Full article

Pile-anchor foundations, serving as one of the anchoring solutions to ensure the safety and stability of floating offshore wind turbines, are primarily subjected to inclined loading induced by anchor chain forces, resulting in significantly different bearing behavior compared to conventional vertically loaded pile foundations. However, experimental research on the inclined pullout performance of anchor piles remains insufficient. To address this gap, this study employs a self-developed servo-controlled loading system to investigate the pullout bearing characteristics of anchor piles in dry and saturated sand, considering factors such as pullout angle and loading point depth. The research results show that from the load–displacement curve of the model pile, it can be found that with the increase in displacement, the load it bears first gradually increases to the peak, then decreases, and then gradually stabilizes. The loading angle has a significant impact on the bearing performance of pile-anchor foundations. As the loading angle increases, the failure mode shows pullout failure. When the loading angle increases from 30° to 60°, the bearing performance of the pile foundation decreases by approximately 63%. When the depth of the loading point increases from 0.22 times the pile length to 0.78 times the pile length, the diagonal anchor tensile bearing capacity of the model pile increases by approximately 45%. When the depth of the loading point is the same, the distribution patterns of bending moment and shear force are basically similar. However, the smaller the loading angle, the larger the value. This is because the horizontal load component plays a dominant role. The compression of the piles above and below the loading point, as well as the bending moment, shear force and axial force under saturated sand conditions, are similar to those in dry sand, but their values are reduced by about 50%. It can be seen that the soil conditions have an influence on the bearing characteristics of pile foundations. Full article

For axial closed-circuit piston pumps, cavitation often causes a reduction in volumetric efficiency during operation, and flow passage pressure loss is a key factor inducing cavitation. To address this issue, this study aims to suppress pump cavitation and improve volumetric efficiency through a synergistic approach involving low-pressure loss flow passages and parameter optimization. First, a flow field model based on a full cavitation model is established to meet the requirements for accurate cavitation simulation of piston pumps. The segmented approximation method is used to decompose complex flow passages inside the pump, a mathematical model of flow passage pressure loss is developed, and the traditional flow rate model is optimized by introducing a pressure loss correction term, thus reducing flow rate prediction error. Further, variance-based sensitivity analysis is used to quantify the effects of pressure loss influencing parameters on cavitation. Results indicate that inlet pressure is the core independent parameter affecting cavitation, and interaction effects between parameters are dominated by antagonism. Accordingly, a single-parameter control and a multi-parameter optimization strategy is proposed based on the sensitivity of different parameters, providing technical support and a quantitative basis for pump cavitation suppression and volumetric efficiency improvement. Full article

Atmospheric sulfur (S) deposition, a key component of acid deposition, poses risks to ecosystems, human health, and sustainable development. In China, decades of coal-dominated energy use caused severe S pollution, but recent emission-control policies and energy restructuring have sought to reverse this trend. However, the effectiveness and regional differences in these measures remain insufficiently quantified. Here, we combined continuous observations from 43 monitoring sites (2013–2023), satellite-derived SO₂ vertical column density, and multi-source environmental datasets to construct a high-resolution record of wet S deposition. A random forest model, validated with R² = 0.52 and RMSE = 1.2 kg ha⁻¹ yr⁻¹, was used to estimate fluxes and spatial patterns, while ridge regression and SHAP analysis quantified the relative contributions of emissions, precipitation, and socioeconomic factors. This framework allows us to assess both the environmental and health-related sustainability implications of sulfur deposition. Results show a nationwide decline of more than 50% in wet S deposition during 2013–2023, with two-thirds of sites and 95% of grids showing significant decreases. Historical hotspots such as the North China Plain and Sichuan Basin improved markedly, while some southern provinces (e.g., Guizhou, Hunan, Jiangxi) still exhibited high deposition (>20 kg ha⁻¹ yr⁻¹). Over 90% of the reduction was attributable to emission declines, confirming the dominant effect of sustained policy-driven measures. This study extends sulfur deposition records to 2023, demonstrates the value of integrating ground monitoring with remote sensing and machine learning, and provides robust evidence that China’s emission reduction policies have delivered significant environmental and sustainability benefits. The findings offer insights for region-specific governance and for developing countries balancing economic growth with ecological protection. Full article

Indoor environmental quality in offices, comprising thermal, acoustic, lighting, and air quality domains, is known to influence human comfort, yet the relative importance of their sub-factors—particularly in severe cold regions—remains unclear. This study addresses this gap by integrating objective (Criteria Importance Through Intercriteria Correlation, CRITIC) and subjective (Analytic Hierarchy Process, AHP) weighting methods, supported by field measurements and questionnaire surveys in open-plan offices in three provinces in northeastern China. Cluster analysis categorized acoustic sub-factors into outdoor traffic, outdoor entertainment, people conversation, burst sound, and people movement. Results show that temperature is the dominant thermal comfort driver (39.7% CRITIC; 45.5% AHP), exceeding air velocity and humidity, which had nearly equal influence. Indoor sound exerted greater impact than outdoor sound, with people conversation ranked highest among indoor noise sources, and burst sound and movement showing similar but slightly lower weights. Natural light outweighed artificial light in importance (54.2% CRITIC; 61.0% AHP), while air freshness and pollution were nearly equally influential. Compared to CRITIC, AHP produced more dispersed weights, reflecting subjective bias toward pronounced differences. These findings provide a quantitative basis for prioritizing environmental design interventions—such as controlling indoor conversational noise, optimizing natural lighting, and stabilizing temperature—to enhance comfort in offices in severe cold regions. Full article

Cave systems serve as key interfaces connecting surface and underground carbon cycles, and research on their carbon dynamics provides a unique perspective for revealing the mechanisms of carbon transport and transformation in karst critical zones. In this study, we established a multi-factor monitoring framework spanning the atmosphere–soil–cave continuum and associated meteorological conditions, continuously recorded cave microclimate parameters (temperature, relative humidity, atmospheric pressure, and cave winds) and CO₂ concentrations across atmospheric–soil–cave interfaces, and employed stable carbon isotope (δ¹³C) tracing in Mahuang Cave, a typical karst cave in southwestern China, from 2019 to 2023. The results show that the seasonal amplitude of atmospheric CO₂ and its δ¹³C is small, while soil–cave CO₂ and δ¹³C fluctuate synchronously, exhibiting “high concentration-light isotope” signatures during the rainy season and the opposite pattern during the dry season. Cave CO₂ concentrations drop by about 29.8% every November. Soil CO₂ production rates are jointly controlled by soil temperature and volumetric water content, showing a threshold effect. The δ¹³C response exhibits nonlinear behavior due to the combined effects of land-use type, vegetation cover, and soil texture. Quantitative analysis establishes atmospheric CO₂ as the dominant source in cave systems (66%), significantly exceeding soil-derived contributions (34%). At diurnal, seasonal, and annual scales, carbon-source composition, temperature and precipitation patterns, ventilation effects, and cave structure interact to control the rhythmic dynamics and spatial gradients of cave microclimate, CO₂ levels, and δ¹³C signals. Our findings enhance the understanding of carbon transfer processes across the karst critical zone. Full article

Elucidating the dominant factors governing heavy metal accumulation and their spatial heterogeneity in soils is fundamental to implementing science-based environmental management protocols. In this study, a Geodetector model, spatial interpolation, bivariate local Moran’s I (BLMI), and hotspot analysis were adopted to reveal the spatial pattern and driving mechanisms of soil cadmium (Cd) across six townships in southern Shimen County, Hunan Province. Results showed that Cd accumulation in the study area was predominantly controlled by natural factors, though anthropogenic contributions were also significant. Strata (q = 0.068), soil type (q = 0.045), and atmospheric deposition (q = 0.046) emerged as the most influential factors. The interaction between different driving factors exhibited a synergistic enhancing effect. Spatial interpolation revealed elevated Cd concentrations primarily clustered in central and western regions, particularly concentrated in Jiashan Town. BLMI analysis confirmed significant spatial correlations between Cd distribution and driving factors, and hotspot areas showing strong spatial coherence with strata and soil type. This study provides valuable insights for understanding the driving mechanisms of soil heavy metal pollution and informs targeted contamination control strategies. Full article

Snowmelt runoff is a major soil erosion trigger in mid-to-high latitude and altitude regions. Through runoff plot observations and simulations in the northeastern black soil region, this study reveals the key regulatory mechanism of water migration on snowmelt erosion. Results demonstrate that the interaction between thawed upper and frozen lower soil layers creates a significant hydraulic gradient during snowmelt. Impermeability of the frozen layer causes meltwater accumulation and moisture supersaturation (>47%, exceeding field capacity) in the upper layer. Freeze–thaw action accelerates vertical moisture migration and redistributes shallow moisture by increasing porosity. This process causes soils with high initial moisture to reach supersaturation faster, triggering earlier and more frequent erosion. Gray correlation analysis shows that soil moisture migration’s contribution to erosion intensity is layered: migration in shallow soil (0–10 cm) correlates most strongly with surface erosion; migration in deep soil (10–15 cm) exhibits a U-shaped contribution due to freeze–thaw front boundary effects. A regression model identified key controlling factors (VIP > 1.0): changes in bulk density, porosity, and permeability of deep soil significantly regulate erosion intensity. The nonlinear relationship between erosion intensity and moisture content (R² = 0.82) confirms supersaturation dominance. Physical structure and mechanical properties of unfrozen layers regulate erosion dynamics via moisture migration. These findings clarify the key mechanism of moisture migration governing snowmelt erosion, providing a critical scientific foundation for developing targeted soil conservation strategies and advancing regional prediction models essential for sustainable land management under changing winter climates. Full article

Rocket launches constitute a major anthropogenic source of disturbance in the near-Earth space environment, inducing significant ionospheric perturbations through both chemical and dynamic mechanisms. This study presents a systematic analysis of ionospheric disturbances—specifically, electron density depletion and traveling ionospheric disturbances (TIDs)—triggered by four rocket launches from China’s Jiuquan Satellite Launch Center between 2023 and 2025. Using high-rate, multi-constellation GNSS data from 370 ground stations and BeiDou GEO satellites, we extracted total electron content (TEC) signals and applied advanced detection methods, including the Multi-Rolling-Multi-Image-Tracking (MRMIT) algorithm for depletion identification and a parametric integration framework for quantitative comparison. Our results reveal that all launches produced rapid TEC depletions, evolving along the rocket trajectory and peaking within approximately 30 min. Launch mass was the dominant factor controlling depletion intensity, while propellant chemistry (UDMH-based vs. liquid oxygen/methane) and local time/background TEC levels modulated the recovery rate and spatial extent. Additionally, distinct TIDs exhibiting wave-like and V-shaped structures were observed, propagating outward from the trajectory with latitudinal variations in amplitude and waveform. These findings highlight the critical roles of rocket attributes and ambient ionospheric conditions in shaping disturbance characteristics. The study underscores the value of multi-source GNSS networks and novel methodologies in monitoring anthropogenic space weather effects, with implications for GNSS performance and sustainable space operations. Full article

Background/Objectives: Frank’s sign, a diagonal earlobe crease, may reflect systemic microvascular dysfunction. We investigated whether Frank’s sign serves as a clinical marker of white matter hyperintensity (WMH) burden in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), a monogenic model of pure cerebral small vessel disease. Methods: We analyzed 81 genetically confirmed CADASIL patients (61.8 ± 12.6 years, 40.7% female) and 54 age/sex-matched controls (70.3 ± 6.6 years, 48.1% female). Frank’s sign was detected using deep learning from brain MRI-reconstructed 3D facial surfaces. WMH volumes were automatically quantified and adjusted for confounders using Random Forest regression residuals. We compared Frank’s sign prevalence between groups, assessed within-CADASIL associations, and evaluated dose–response relationships across WMH tertiles. Results: Frank’s sign prevalence was significantly higher in CADASIL versus controls (66.7% vs. 42.6%, p = 0.020), with strengthened association after multivariate adjustment (OR = 4.214, 95% CI: 1.128–15.733, p = 0.032). Within CADASIL, Frank’s sign-positive patients showed 72% greater WMH burden (51.5 ± 27.1 vs. 30.0 ± 26.1 mL, p < 0.001) and lower Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total scores (57.7 ± 19.6 vs. 71.2 ± 22.8, p = 0.006), but similar lacunes, microbleeds, and hippocampal volumes. A robust dose–response relationship emerged across WMH tertiles, with Frank’s sign prevalence increasing from 37.0% (lowest) to 74.1% (highest tertile; adjusted OR = 3.571, 95% CI: 1.134–11.253, p = 0.030). Conclusions: Frank’s sign represents an accessible biomarker of WMH burden in CADASIL, demonstrating disease-specificity and dose–response characteristics independent of vascular risk factors. The automated MRI-based detection method of Frank’s sign enables retrospective analysis of existing neuroimaging databases, transforming a bedside observation into a quantifiable neuroimaging biomarker for genetic small vessel disease stratification. Full article