Search Results (2,228)

Diabetic nephropathy (DN) is a severe diabetic complication with substantial clinical burden. The complex pathogenesis of DN has hindered the development of targeted therapies, creating an urgent need to develop novel strategies that directly address its underlying inflammatory and fibrotic mechanisms. Coreopsis tinctoria (CE) is an edible plant rich in polyphenols, but its mechanism against DN remains understood. An integrated framework combining network pharmacology and machine learning was developed to prioritize active polyphenols and their targets. A multi-layer perceptron classifier, trained on 3.16 million compound–target pairs from Binding DB, predicted interactions between 36 CE polyphenols and 12,030 DN-associated genes. The top 100 targets were subjected to KEGG enrichment analysis, and the identified pathways were validated in a high-fat diet/STZ-induced DN rat model. The MLP model achieved superior performance (AUC-ROC = 0.9219, AP = 0.9592). Five lead polyphenols (flavonoids/chalcones) showed high predicted activity. KEGG analysis revealed enrichment in PI3K-Akt, calcium signaling, metabolic pathways, and cellular senescence. In vivo, CE treatment (150–600 mg/kg/day) dose-dependently improved glucose/lipid metabolism and renal function, and ameliorated histopathological damage, including glomerular hypertrophy, fibrosis, and mesangial expansion. Mechanistically, CE suppressed NFκB/TGFβ/Smad signaling, restored PPARγ and Nrf2/HO-1/FoxO1 antioxidant defenses, and inhibited apoptosis via Bcl-2/Bax regulation. CE exerts multi-target renoprotective effects through coordinated modulation of metabolic, inflammatory, fibrotic, and antioxidant pathways, supporting its potential as a functional food ingredient for DN management. Full article

Predicting thermal breakthrough and optimizing injection-production parameters are essential for sustainable geothermal development. Traditional hydrothermal coupled simulations in porous media entail substantial computational costs, which limits their use in dense multi-parameter screening. This study develops a physics-constrained surrogate workflow for the Qihe geothermal doublet system by using COMSOL to generate hydrothermal simulation data and a deep neural network (DNN) to emulate the simulator response within a predefined operating domain. The DNN was trained on physics-driven synthetic outputs rather than independent field observations, and a 2.0 °C decrease in production temperature was used as the thermal breakthrough criterion. Under scenario-wise validation, the surrogate model achieved a test-set R² of 0.9995 and an RMSE of 0.0351 °C, indicating accurate approximation of the deterministic simulator response within the bounded parameter space. The surrogate-based global scan identified a favorable operating region near a well spacing of 462 m, a reinjection temperature of 20 °C, and a reinjection rate of 150 m³/h. To evaluate whether this result was affected by sparse well-spacing sampling, additional COMSOL simulations were performed at 430, 440, 450, 460, 462, 470, 480, 490, and 500 m under the same reinjection temperature and rate. These simulator-based validation cases showed a continuous thermal response with increasing well spacing. The 2.0 °C thermal breakthrough time increased from 46 yr at 430 m to 61 yr at 500 m, while the 50-year cumulative heat extraction increased from 6594.2 to 6722.9 TJ. The 430 and 440 m cases experienced thermal breakthrough before the 50-year design life, whereas the 450 m case was close to the design boundary. The 460 and 462 m cases did not reach the 2.0 °C decline threshold within the 50-year design life and retained relatively high heat-extraction efficiency per unit well spacing. Therefore, the engineering recommendation is revised from a single precise optimum to a locally validated spacing interval of approximately 460–462 m under the present equivalent-porous-medium assumption. The proposed workflow does not replace hydrothermal simulation; instead, it provides a rapid screening tool that narrows the design space before targeted simulator verification and field calibration. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

Mycotoxins are fungal secondary metabolites with dual significance: they threaten health via food contamination yet hold potential as biopesticides. Their isolation from complex matrices remains a critical challenge. This review analyzes classical methods (liquid–liquid extraction, SPE including QuEChERS, chromatography). Traditional techniques suffer from poor selectivity, multi-step processing, large toxic solvent volumes, and matrix effects. As alternatives, emerging strategies based on rational design are considered: directed cocrystallization, supercritical fluid extraction, smart MOF/COF membranes, and AI integrated with physicochemical modeling. The concept of “precision” extraction enabling prediction of target isolation at the molecular level is developed. Recommendations for standardizing experimental reporting to create machine-readable datasets for neural networks are provided. The review concludes that while most still require experimental validation for mycotoxins, these approaches point toward selective, sustainable mycotoxin isolation technologies for analytical control and pure standard production. Full article

Oat (Avena sativa L.) silage fermentation often fails due to insufficient lactic acid bacteria (LAB) and low water-soluble carbohydrate content. We investigated the effects of Compound Radix Pulsatillae (CRP; 40 g/kg FM) alone or combined with a commercial LAB inoculant (containing L. plantarum, L. buchneri, and Enterococcus faecium, CRP_LA) on oat silage after 60 days. Compared to control (CK), both CRP and CRP_LA increased dry matter and water-soluble carbohydrate retention while reducing fiber components and ammonia nitrogen (p < 0.05). CRP_LA exhibited superior fermentation quality (lowest pH 4.82, highest lactic acid 47.83 g/kg DM). Using 16S rRNA sequencing and UPLC-MS/MS metabolomics integrated with weighted gene co-expression network analysis (WGCNA), we identified a brown module strongly associated with CRP_LA treatment. Six hub metabolites, belonging to flavonoids, terpenoids, alkaloids, phenolic acids, and nucleotide derivatives, were significantly elevated in CRP_LA silage and showed strong correlations with Lactobacillus abundance and fermentation quality parameters. Correlation-based network analysis revealed that these hub metabolites positively correlated with Lactobacillus abundance, lactic acid, and water-soluble carbohydrate retention, while negatively correlating with spoilage microorganisms (Enterobacter, Acinetobacter, Leuconostoc) and ammonia nitrogen. This multi-omics study provides a metabolite-centric molecular map of the silage microecosystem reshaped by CRP and LAB co-fermentation. The identified hub metabolites—with predicted antimicrobial, antioxidant, and plant-protective functions—represent potential quality markers for functional silage additive development. Mechanistic validation via targeted metabolite supplementation or pathway-specific gene expression analysis is warranted in future studies. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Unmanned Aerial Vehicle (UAV) image object detection holds significant application value in the low-altitude economy, traffic monitoring, intelligent agriculture, and disaster rescue. However, due to the top-down perspective, UAV images typically suffer from challenges such as small target scales, dense object distribution, severe occlusions, and complex backgrounds. These issues often limit the recall and localization accuracy of general-purpose detectors when they are directly applied to UAV small-object detection scenarios. To address these aforementioned challenges, this paper proposes an Adaptive Dynamic Aggregation YOLO network, termed ADA-YOLO. The novelty of ADA-YOLO lies in its highly efficient combinatorial design specifically tailored for UAV small object detection, while retaining the efficient backbone of YOLOv8, we systematically reconstruct the neck and detection head to improve accuracy. Specifically, a high-resolution P2 detection branch is incorporated to construct a P2–P5 multi-scale prediction structure. Furthermore, the lightweight DySample dynamic upsampling module is adopted to replace traditional upsampling methods, and an Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to alleviate semantic conflicts and noise interference during multi-scale feature fusion. This synergistic combination explicitly addresses multi-scale representation challenges and enhances small-object detection performance in complex scenes. Comparative experiments with the baseline YOLOv8n on the VisDrone2019 dataset demonstrate that ADA-YOLO achieves an improvement of 11.3% in mAP@0.5 and 8.2% in mAP@0.5:0.95. The improved model achieves these performance gains with a modest parameter increase and acceptable computational complexity. Finally, ablation experiments further validate the effectiveness of each individual module and their synergistic gains. Full article

Floods remain among the most devastating natural disasters globally, disproportionately impacting low-resource regions where real-time flood forecasting is constrained by limited computational infrastructure and the scarcity of fine-resolution predictive models. Although state-of-the-art global climate models achieve high predictive accuracy, their scale and computational complexity restrict their applicability in localized and resource-constrained settings. This study proposes a deep climate model distillation framework that transfers knowledge from a high-capacity Fourier Neural Operator (FNO)-based global climate model inspired by FourCastNet into lightweight, regionally adaptive student networks suitable for edge deployment. The framework combines climate variables, satellite observations, and hydrological measurements to improve localized flood prediction. Knowledge transfer is achieved through a multi-objective distillation strategy that combines supervised learning, soft-target alignment, and intermediate feature matching. Experimental evaluation across multiple flood-prone regions in Sub-Saharan Africa and South Asia shows that the distilled student model achieves an average classification accuracy of 0.89, an AUC of 0.91, and an F1-score of 0.88, retaining approximately 96.7% of the teacher model’s predictive performance. In continuous discharge estimation, the model attains a mean absolute error of 0.17, RMSE of 0.24, and an R² score of 0.85. The proposed distillation approach yields an 8× reduction in inference latency and over a 20× reduction in model size, enabling real-time execution on low-power edge devices such as the Raspberry Pi 4 and NVIDIA Jetson Nano. The student model further demonstrates robust regional and temporal generalization, with limited performance degradation in unseen geographic areas and during extreme flood years. Full article

Modern power grids with high renewable energy penetration are vulnerable to fast voltage disturbances caused by grid faults. Among these, voltage sags are critical because they develop within milliseconds and require rapid reactive current support to maintain grid stability and power reliability. Reinforcement learning has previously shown potential for reactive current injection control during voltage sag events due to its fast response and adaptability to changing system conditions. However, existing approaches rely on separate policies for specific subsets of the operating space, which limits their ability to provide optimal actions when the system operates across broader or combined state regions. To address this limitation, this paper proposes a unified Soft Actor–Critic (SAC) target policy trained over the full state and action space by integrating multi-source transfer learning with potential-based reward shaping approach. Results show that the proposed multi-source transfer approach enables the target agent to converge faster and reach a higher reward solution than the baseline SAC and single-source transfer approach. The trained policy also improved prediction accuracy, achieving reactive-current errors below 0.2 A with respect to the ground-truth reference generated through extensive simulations over the full observation and action space. The reference follows the grid-code requirement for minimum reactive current injection during faults and provides a benchmark for evaluating prediction accuracy. This can help distributed generation sources respond more effectively during severe perturbations such as voltage sags, support voltage recovery, and reduce the risk of cascaded disconnections that could lead to unwanted blackouts. Additionally, the inference execution time is also sufficiently fast to satisfy the response-time requirement of voltage sag events, confirming the real-time feasibility of the proposed controller. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

Existing drug–target interaction (DTI) prediction methods still face challenges caused by sparse interaction data, complex multi-source relationships, and imbalanced information contributions among different nodes. In this study, we propose NAFF-DTI, a node-level adaptive feature fusion network based on multi-view graphs. The model uniformly represents drug similarity, target similarity, and known drug–target interactions as multiple relational views, and learns node representations through graph encoding and cross-view representation learning. To more effectively utilize heterogeneous relational information, NAFF-DTI introduces cross-view feature discrepancy modeling and a node-level adaptive fusion mechanism to dynamically adjust the contribution of different views according to node structural characteristics. Experimental results show that NAFF-DTI achieves the best AUC and AUPR on all five benchmark datasets. Compared with the strongest baseline for each dataset and metric, NAFF-DTI achieves average relative improvements of 3.81% in AUC and 3.23% in AUPR. It can also improve the utilization of multi-source information, maintain relatively stable prediction under different data distributions, and prioritize biologically plausible candidate drug–target associations from the unannotated candidate space. These results indicate that NAFF-DTI can provide computational support for DTI candidate prioritization and repurposing-oriented hypothesis generation. Full article

Background: Glucocorticoid-induced osteoporosis (GIOP) is marked by impaired osteogenesis and reduced bone formation. Isobavachin (IBA), a flavonoid from Psoralea corylifolia, shows multiple potentials in anti-inflammatory and bone metabolism regulations, but its effects against GIOP remain unclear. This study investigated the osteoprotective effects and potential mechanism of IBA using zebrafish GIOP model. Methods: osteoprotective effects of IBA was assessed by fluorescence imaging in a prednisolone-induced zebrafish model, following with osteogenic gene expressions measured by RT-qPCR. Potential targets and pathways of IBA was filtered and predicted by network pharmacology, molecular docking, and molecular dynamics (MD) simulations, and finally validated with a pharmacological rescue experiment using a PI3K-specific inhibitor. Results: IBA improved bone mineralization and upregulated osteogenesis-related genes. Network pharmacology identified the PI3K-Akt pathway as a key pathway, with ESR1, GSK3B, MTOR, and CCND1 as core targets. PI3K inhibition attenuated the osteoprotective effects of IBA and suppressed downstream osteogenic gene expression. Conclusions: IBA alleviates GIOP by modulating the ESR1-associated PI3K-Akt signaling pathway and may serve as a multi-target therapeutic candidate for osteoporosis. Full article

Zero-shot remote sensing is attractive for scene classification because new regions, sensors, and label taxonomies often appear before sufficient annotated data are available for supervised adaptation. We present OATS-RS, an inference-centric framework that keeps a remote sensing vision–language model (VLM) backbone frozen and improves zero-shot decisions through ontology-aware prompt construction, hierarchical and contrastive scoring, adaptive multi-view aggregation, unlabeled transductive refinement, ambiguity-aware local re-ranking, and selective prediction. The method targets the common remote sensing regime in which neighboring classes such as annual crop, permanent crop, forest, pasture, herbaceous vegetation, river, and sea or lake overlap strongly in red–green–blue (RGB) appearance, meaning that they require more than a single class-name prompt. On the supplied final EuroSAT RGB evaluation with a GeoRSCLIP Contrastive Language–Image Pre-training (CLIP)-family Vision Transformer Base with 32 × 32-pixel patches (ViT-B-32) backbone, the complete pipeline obtains top-1 accuracy of 0.522, balanced accuracy of 0.522, macro-averaged F1 score (macro-F1) of 0.535, and top-3 accuracy of 0.887. The strongest classes are industrial area, residential area, river, highway, and pasture, whereas the weakest classes remain herbaceous vegetation and several fine-grained vegetation categories. Selective prediction increases accepted-example accuracy to 0.538 at 0.934 coverage, but the expected calibration error (ECE) remains high at 0.384. These results support a qualified conclusion: ontology-guided zero-shot inference can already recover useful semantic shortlists for structured remote-sensing scenes, but fine-grained natural-class disambiguation, calibrated confidence, multi-dataset transfer, component-level ablations, and measured runtime remain essential before dependable deployment claims can be made. Full article

Developing functional foods that address both oxidative stress and physiological challenges like dysphagia is a critical frontier in personalized nutrition. This study investigates the multi-component synergy of Fu Ling Yin Zi (FLYZ), a traditional dietary therapy, and translates its functional properties into a 3D-printed dysphagia-friendly food. Using response surface methodology, the optimal FLYZ formulation was established at a 5:1:5 ratio of Poria cocos (Schw.) Wolf., Amygdalus communis Vas, and Citrus reticulata. Network pharmacology and molecular docking suggested that FLYZ’s active compounds (e.g., nobiletin, stigmasterol, tangeretin, l-SPD, glabridin, estrone) may mitigate oxidative stress via multiple targets (PTGS2, AKT1, TNF, ESR1, MMP9, and MAOA), with pathway analysis pointing to a potential role of the AKT1/GSK3β/HIF-1α axis. Subsequent in vitro cellular assays demonstrated that FLYZ enhanced antioxidant enzyme activities, reduced intracellular ROS, and modulated the expression of associated genes, supporting a potential link to this pathway. To actualize these functional benefits for patients with swallowing difficulties, a novel 3D-printing ink incorporating FLYZ and walnut oil within a hydrogel matrix (3% xanthan gum, 3% pectin, 1.5% carrageenan) was developed. The printed constructs exhibited excellent shape fidelity and, based on standardized IDDSI fork and spoon tests, were categorized as level 4 (pureed/extremely thick). Furthermore, a simulated in vitro digestion model showed that the colloidal network significantly protected FLYZ’s polyphenols and flavonoids, markedly improving their bioaccessibility and post-digestion antioxidant capacity. Collectively, this work establishes an integrated approach that combines predictive molecular profiling with advanced 3D food printing, thereby supporting the development of future foods tailored for personalized nutrition. Full article

Cross-material continuous tool wear prediction is difficult because a model must preserve the physical wear scale, not only align high-dimensional sensor features. This limitation is critical in milling, where the target variable is the continuous flank wear width ($\mathrm{VB}$) and material shift can distort the mapping from sensor response to wear magnitude. We address this problem by recasting cross-domain tool wear prediction as monotone wear-scale adaptation. We propose Multi-Physics Monotone Score Transport (MPMST), a monotone score transport framework that constructs a tool-wear-oriented score from sensor-derived candidate cues, transports the target-domain score onto the source-domain wear scale, and then predicts wear through isotonic regression. We also evaluate One-Physics Monotone Score Transport (OPMST), a force-only variant that uses the same score-transport pipeline with a restricted cue family. On Mondragon Unibertsitatea–Tool Condition Monitoring (MU-TCM) with two cross-material transfer tasks, the validation-driven MPMST configuration reduces mean absolute error by approximately 63% relative to Correlation Alignment (CORAL) and by approximately 31% relative to a physics-informed Gaussian process baseline. The results support monotone score construction and score transport as practical mechanisms for continuous tool wear prediction under domain shift, while also showing that MU-TCM is strongly force dominated. Full article