Search Results (2,393)

To address yield uncertainties from contrasting hydrothermal events in black soil regions, this study developed a universal estimation framework integrating multi-dimensional features. The universal yield estimation framework leveraged data from contrasting flood (2024) and drought (2025) scenarios in Youyi Farm in the Northeast Black Soil Region. And we fused multi-dimensional environmental features, including remote sensing, soil, and micro-topography factors, to identify “Regime Shifts” in yield-driving mechanisms across contrasting years. We evaluated four ML algorithms (RF, XGBoost, MLP, and TabNet) using Recursive Feature Elimination with Cross-Validation (RFECV) for variable optimization. Results showed the following: (1) The Universal RF model achieved superior robustness (R² = 0.80), overcoming inter-annual fluctuations. (2) Mechanistic analysis identified a “Regime Shift” in yield drivers, transitioning from micro-topography-governed “drainage limitation” during flooding to soil-texture-dominant (SAND) “linear limitation” during drought. (3) Dynamic growth-stage differential features successfully corrected asymmetric spectral responses, resolving slope inversion and overestimation driven by “non-productive greenness” during flooding. (4) Spatio-temporal yield mapping revealed a transition from topography-constrained linear distributions (2024) to soil-moisture-driven “patchy mosaic” structures (2025). Moran’s I increased from 0.21 to 0.45, reflecting intensified yield clustering and intensified spatial clustering under drought. This study provides a robust tool for food security monitoring and site-specific management in climate-vulnerable intensive agricultural zones. Full article

Chub Mackerel (Scomber japonicus) is an important pelagic fishery resource in the Northwest Pacific, and its fishing-ground distribution is strongly influenced by dynamic marine environmental conditions. This study aimed to evaluate how environmental information at different temporal scales affects the prediction of high-catch fishing grounds and to identify environmental-variable contributions. Fishery logbook data from Chinese light purse seine vessels during 2014–2022 were combined with marine environmental variables to construct four feature sets: instantaneous features (E1), multi-temporal-scale fusion features (E2), short-term features with 7-day rolling means (E3), and long-term features with 30-day rolling means (E4). Deep Forest, random forest, XGBoost, LightGBM, and CatBoost were evaluated using nested spatial group cross-validation, and SHapley Additive exPlanations (SHAP) was applied to interpret model predictions. The results showed that, after historical environmental information was added, AUC values increased for most models, and the multi-temporal-scale fusion features performed better in metrics related to high-catch sample identification; therefore, the hypothesis proposed in this study was supported in the overall trend. Model comparisons showed that Deep Forest performed relatively stably under E2, E3, and E4, whereas RF performed relatively well under E1. Short-term environmental features helped improve overall fishing-ground discrimination, whereas multi-temporal-scale fusion was more favorable for identifying high-catch samples. Time-lag correlation and SHAP analyses indicated that short-term environmental changes, longer-term background conditions, and seasonal signals jointly provided information for model prediction. This study may provide a reference for real-time fishing-ground prediction and fishery management. Full article

Background: Metastasis and poor chemotherapy response have stagnated therapeutic progress in osteosarcoma (OS) for the past three decades. Defining the transition from localized to metastatic OS before overt dissemination is fundamental for improving survival. However, effective early diagnostic tools remain scarce, largely due to limited exploitation of the metastasis-associated tumor microenvironment’s own record of prior environmental and stress exposures encoded in cell-intrinsic transcriptional states. Here, we employed a supervised machine learning framework with iterative resampling and multi-stage model selection to identify molecular markers associated with metastasis in osteosarcoma and to develop a computational signature, Auto-RS. Methods: Transcriptomic and clinical data from 139 OS patients with ≥5 years of follow-up were analyzed. A LASSO–Cox framework was applied to derive a gene expression-based risk score, Auto-RS, from which a nomogram integrating age and sex was generated for individualized prognosis. Model interpretability was assessed across six independent single-cell OS patient datasets, and drug sensitivity predictions were inferred by integrating Auto-RS with the Precily algorithm to uncover actionable therapeutic vulnerabilities. Results: Auto-RS, constructed from the expression of four autophagy genes (BNIP3, MYC, PEA15, and SAR1A), served as an independent prognostic factor for overall survival (HR = 1.091; 95% CI, 1.047–1.136; p < 0.001). Time-dependent ROC analysis showed that Auto-RS was the most accurate single predictor (AUC = 0.88), exceeding metastasis (0.83), sex (0.45), and age (0.39). A basic prognostic model (BpM) incorporating metastasis status yielded a C-index of 0.741 (95% CI, 0.679–0.803). The addition of Auto-RS (CpM) improved discrimination (C-index = 0.788; 95% CI, 0.731–0.845), whereas a model without metastasis information (ApM) retained predictive ability (C-index = 0.709; 95% CI, 0.640–0.778). Single-cell analysis confirmed that Auto-RS features aligned with known metastatic trajectories, reflecting the transition from proliferative to invasive tumor states and highlighting coordinated programs among cancer-associated fibroblasts and immune cells. Drug sensitivity integration through Precily identified gemcitabine and cytarabine as FDA-approved agents predicted in silico to show greater sensitivity in the high-risk subgroup. Conclusions: We identified autophagy-mediated transcriptional ‘stress fingerprints’ that are tightly associated with OS metastasis. The Auto-RS signature, composed of BNIP3, MYC, PEA15, and SAR1A, enables early therapeutic stratification of patients independent of overt metastatic status. Moreover, Auto-RS delineates key molecular underpinnings of OS metastasis at single-cell resolution. As a practical laboratory tool, Auto-RS may represent a step toward improved risk stratification, where advances in metastasis prediction and therapeutic guidance converge to improve outcomes in OS. Full article

Obesity is increasingly recognized as a complex systemic disorder rather than a simple consequence of excess energy intake and fat accumulation. This review presents a systems biology framework that examines how obesity-driven disruption of inter-organ communication networks contributes to chronic disease susceptibility, with particular emphasis on colorectal cancer (CRC). Disrupted signaling among the brain, adipose tissue, liver, skeletal muscle, gut, and immune system generates maladaptive feedback loops that promote chronic metabolic inflammation (metaflammation), loss of physiological resilience, and progressive metabolic dysfunction. Within this framework, obesity is redefined as a network disease characterized by neuroendocrine dysregulation, adipose tissue remodeling, immune dysfunction, impaired organ crosstalk, and alterations in the gut microbiome. A central feature of this dysregulation is persistent low-grade inflammation driven by immune-metabolic reprogramming and sustained activation of inflammatory pathways. Obesity-associated metaflammation is further linked to accelerated biological aging through mechanisms involving cellular senescence, mitochondrial dysfunction, oxidative stress, and impaired metabolic resilience. These interconnected processes create a tumor-promoting environment by enhancing oncogenic signaling, disrupting intestinal barrier integrity, altering microbial and metabolic signaling, impairing immune surveillance, and promoting epithelial dysfunction, thereby increasing susceptibility to CRC. The review also examines how behavioral, circadian, environmental, and socioeconomic factors influence metabolic health and cancer risk. Finally, emerging translational opportunities, including biomarker-guided risk stratification, precision prevention, metabolic network restoration, and integrative lifestyle and pharmacological interventions, are discussed. Collectively, this review reframes obesity as a whole-body regulatory disorder and provides an integrated conceptual framework linking metabolism, inflammation, aging, and colorectal carcinogenesis to inform future prevention and therapeutic strategies. Full article

The interaction of electromagnetic waves with atmospheric aerosols plays a significant role in communication systems and multimedia information transmission. Understanding the interaction of vortex light beams with an aerosol-laden atmosphere is indispensable for establishing a framework of the environmental channel. During the interaction, different optical effects such as absorption and scattering will result in energy attenuation, and this yields the deterioration of the transmission feature of the vortex beam signal. In this study, we present a theoretical analysis of Gaussian vortex beams (GVBs) scattering by diverse aerosol (unformed carbon, dust, sulphate, silicate, soot, and nitrate) particles in the atmosphere on the basis of the well-established generalized Lorenz–Mie theory (GLMT). Combined with the lognormal distribution model for aerosol particles, the attenuation and transmission characteristics of GVBs for different aerosol particles are analyzed. The extinction efficiency $\left(Q_{ext}\right)$ factor of GVB, caused by the absorption and scattering of various aerosols, becomes smaller compared to that of a basic Gaussian beam (GB). Increasing the OAM mode index, the energy attenuation and transmission caused by aerosol absorption and scattering further decrease. Moreover, this research provides a basis to analyze the optical characteristics of the twisted beams in different atmospheric channels, such as wireless communication networks over aerosol-laden systems and material interactions. Full article

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, $R^2=0.999$, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high $R^2$ should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization. Full article

Red blood cells (RBCs) are sensitive biomarkers of human health, influenced by urbanization and agricultural exposures. Using scanning electron microscopy (SEM) within an exposome framework, we examined RBC morphology in residents of an industrialized area of Thessaloniki, Greece, and in a rural population primarily exposed to agricultural stressors. Blood samples and questionnaires covering demographics, lifestyle, and environmental factors were statistically analyzed. SEM revealed moderate morphological alterations without significant differences between groups. Observed features were associated with longer residence duration and suboptimal nutrition, suggesting subclinical cellular stress. Integrating these findings into exposome research may clarify cumulative industrial and agricultural impacts on RBC morphology. Full article

In resource-constrained UAV swarm systems, multimodal sensory data are often affected by complex environmental factors, resulting in modality missing, signal degradation, and asynchrony, which significantly reduce the reliability of multimodal learning and incremental model updates. To address this issue, we propose a Compensatory Collaboration Modality-Balanced Sample Selection framework (CC-MBS), which improves robustness through modality quality modeling and cross-UAV collaborative compensation. Specifically, a modality confidence vector is introduced to quantify modality reliability from missing rate, degradation, and asynchrony. A lightweight collaboration mechanism is designed to exchange low-dimensional confidence information instead of high-dimensional features or model parameters. Based on the compensated confidence, a modality-aware sample selection strategy is further developed to prioritize high-value samples under limited memory. Experimental results in simulated UAV-swarm-inspired benchmark settings show that CC-MBS outperforms representation-based methods such as ShaSpec and its parameter aggregation variants (AVG, PFM, POW) in both modality compensation accuracy and communication–computation efficiency under missing conditions. In addition, it achieves stronger robustness than MBS and training-dynamics-based methods such as EL2N and GraNd in sample selection. These results demonstrate that CC-MBS effectively improves robustness and data efficiency for multimodal incremental learning under incomplete modalities. Full article

The respiratory system is directly exposed to various environmental factors, and specifically allergens and environmental pollutants, which are ligands/agonists of the aryl hydrocarbon receptor (AhR) and promote chronic lung diseases in humans. AhR, a ligand-activated transcription factor, is involved in the metabolism of xenobiotics, assigning their carcinogenic and toxic effects, and is also involved in normal homeostasis, organogenesis, and immune system function. Exogenous and endogenous AhR ligands are both high-molecular-weight compounds with a planar structure and low-molecular-weight compounds of diverse chemical structures. After entering the cell, the ligands bind to AhR and induce the activation of signaling cascades. The lung immune system responds to pathogens and environmental toxins first with a pro-inflammatory innate immune response, and then with an anti-inflammatory adaptive immune response. An imbalance between these immune systems may have an effect on the course of the disease. Activation of AhR by exogenous or endogenous ligands can affect this balance and lead to dysregulation of the immune response, leading to inflammatory complications in the lungs. Individual features of AhR expression or components of the AhR-dependent signaling pathway may also play a role in the superposition of the functions of these two links of immunity. This review summarizes advances in the comprehension of AhR’s role in immunomodulation and inflammatory responses in the lungs following data in experimental rodent models, in vitro studies utilizing lung structural cells and isolated immune cell lines, and humans. The molecular mechanisms of AhR’s regulation of immunity and inflammation and the potential of AhR as a therapeutic target for inflammatory lung disease are also considered. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

Disentangling the coupled, nonlinear impacts of climate change and anthropogenic activities on vegetation dynamics is critical yet challenging for global change research. The Qinghai–Tibetan Plateau (QTP), a highly climate-sensitive and ecologically strategic region, serves as a vital arena for examining such complex socio-ecological attributions. Based on multi-source environmental datasets from 2000 to 2020, this study developed an integrated, spatially explicit framework coupling residual trend analysis (RESTREND) and GeoDetector to quantify individual drivers and nonlinear climate–human interactions. The QTP exhibited a significant, widespread greening trend during 2000–2020, featuring prominent spatial clustering with “High–High” clusters in the southeast and “Low–Low” clusters in the northwest. Attribution modeling revealed that vegetation dynamics were governed not by isolated variables, but by multifaceted, nonlinear synergies among precipitation, temperature, topography, vegetation type, and land-use change. Key interactive pairs, particularly elevation–temperature and slope–precipitation, dramatically increased explanatory power over single-factor models. Crucially, climate–human synergies explained substantially more variance than climate variables alone, bounded by a distinct elevational threshold: human activities dominated vegetation dynamics at mid-elevations (2500–3500 m), while climate factors took over as the primary controller at high altitudes (above 3500 m). Quantitatively, human activities induced vegetation improvement across 38.6% of the plateau, maintained stability in 35.8%, and caused degradation in 25.6%. By successfully merging trend decomposition with spatial stratified heterogeneity analysis, this study provides a transferable approach to uncoupling complex environmental interactions. These insights highlight the intensifying human footprint on alpine ecosystems and advocate for zone-specific adaptive management: mitigating human disturbances at mid-elevations and fostering climate adaptation in higher zones to preserve plateau resilience. Full article

Fibropapillomatosis (FP) is a disease that threatens the health and safety of sea turtles globally, with green sea turtles having the highest FP prevalence. FP is associated with Chelonid alphaherpesvirus 5, but the primary etiological agent remains unknown as expression and severity of the virus are influenced by host susceptibility, immunological status, development of epithelial lesions, and environmental factors. This review explores current understanding of FP in green sea turtles, focusing on etiology, pathological features, diagnostic approaches, and rehabilitation strategies. Emphasis is placed on the compounded nature of the disease, integrating factors that influence disease expression. Screening challenges are examined through the limitations of clinical, histological, and molecular methods, particularly in distinguishing latent from active infection. Rehabilitation practices, including surgical intervention and long-term supportive care, are evaluated in the context of treatment outcomes and recurrence risk. Collectively, the evidence supports the interpretation of FP as a disease shaped by host–pathogen–ecology interactions. Improved integration of diagnostic tools and greater focus on natural drivers are essential for advancing understanding of the disease and informing conservation and management efforts. Full article

In cold arid regions, the relationships between dissolved organic matter (DOM) characteristics and heavy metal distributions across ice, water, and sediment interfaces remain insufficiently resolved. This study characterized DOM spectral features and examined their associations with measured metal distributions in a typical frozen eutrophic lake using excitation–emission matrices coupled with parallel factor analysis (EEMs-PARAFAC), ultraviolet-visible absorption spectroscopy (UV-Vis), and Fourier-transform infrared spectroscopy (FTIR). Protein-like substances dominated ice DOM, whereas water and sediment-derived DOM contained more humified fluorescent components. Fluorescence indices confirmed a primarily biological origin across all media, with ice showing the highest autochthonous microbial contribution (BIX = 1.23) but the lowest humification (HIX = 0.26), suggesting a greater contribution of recently produced protein-like fluorescent DOM in the ice samples. Water DOM showed the highest average HIX (1.88), followed by sediment-derived DOM (0.61) and ice DOM (0.26). The measured hydrochemical conditions, including weak alkalinity, elevated total dissolved solids (TDS), and locally low dissolved oxygen, provide environmental context for differences in metal distributions. Exploratory Spearman analysis at 17 matched water stations identified the strongest DOM–metal associations for HIX-As (rho = 0.474, p = 0.054) and FI-Zn (rho = 0.471, p = 0.056), indicating that DOM optical properties provide testable indicators of metal-distribution patterns but should be combined with direct binding and speciation measurements for mechanistic confirmation. Because ice was collected in January 2021, whereas water and sediment were collected in October 2020, cross-medium differences are interpreted as between-campaign associations rather than synchronous partitioning. These findings provide a basis for targeted winter monitoring and future binding, speciation, and freeze-concentration experiments in shallow eutrophic lakes. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Target tracking for Unmanned Aerial Vehicles (UAVs) can be significantly impacted by environmental factors such as lighting variations, background clutter, and target occlusion. To address these challenges, we developed a target tracking method that integrates both frequency-domain and spatial perception capabilities in UAV vision (FSTrack). Specifically: (1) we utilized the Swin Transformer as the core network to extract features from both the template and search images; (2) we introduced a Transformer-based module to enhance both frequency and spatial information, improving tracking accuracy under varying illumination conditions; (3) we designed a spatio-temporal feature fusion module with multiple multi-head self-attention mechanisms to precisely model the tracking state, thus increasing reliability in cluttered and occluded environments; and (4) we created a hybrid loss function to boost accuracy in both classification and regression tasks. Our experimental results on the UAV123, DTB70, and UAVDT datasets show that our approach not only surpasses current state-of-the-art methods in success rates and precision but also operates more swiftly. Full article