Search Results (4,225)

Background/Objectives: The EGFR T790M mutation drives lung cancer resistance by sterically hindering inhibitors and restoring ATP affinity. As C797S mutations render covalent inhibitors obsolete, novel non-covalent strategies are critical. This study identifies inhibitors that redefine the mutant methionine sulfur as a primary stabilizing anchor rather than a liability. Methods: A generative AI framework (DrugEx) sampled 100,000 molecules, prioritized via QSAR classification (ROC-AUC: 0.91 ± 0.01) and Applicability Domain (AD) mapping. The workflow was de-risked through retrospective benchmarking against the DUD-E database (35,590 molecules), achieving a 1% Enrichment Factor of 5.19. Lead candidates underwent 100 ns all-atom molecular dynamics (MD) simulations. Mechanistic stability was quantified via Free Energy Landscape (FEL) analysis and ensemble-averaged MM-GBSA binding free energy calculations. Results: Candidate 106 demonstrated exceptional mutation tolerance by redistributing interactions toward the Met790 sulfur atom. MD analysis confirmed potency is dictated by successful recruitment of the thioether environment, locking the complex within a narrow thermodynamic basin. Candidate 106 maintained stable binding (−11.0 kcal/mol) corroborated by an equipotent MM-GBSA ΔG_bind of −50.51 kcal/mol in the mutant system, driven by persistent π-sulfur contacts (85% occupancy). Conclusions: These results indicates that potential T790M resistance bypass is achievable by exploiting the gatekeeper methionine’s electronic environment. This modeled mutation-aware framework provides a candidate non-covalent strategy to be validated in future wet-lab campaigns. Full article

Carbon fiber-reinforced thermoplastic composites (CFRTPs) have attracted increasing attention in aerospace, transportation, marine engineering, and other advanced manufacturing fields owing to their high specific mechanical properties, impact resistance, weldability, reprocessibility, and potential recyclability. However, the high melt viscosity of thermoplastic matrices, the permeability limitations associated with different reinforcement architectures, and the chemical inertness of carbon fiber surfaces continue to restrict resin impregnation, interfacial bonding, defect control, and forming stability. This review systematically summarizes recent advances in CFRTP manufacturing from the perspective of material-derived processing challenges and interface engineering. First, representative thermoplastic matrix systems and reinforcement architectures are discussed, with emphasis on their effects on processability, crystallization behavior, resin flow, and load transfer. Subsequently, six major forming processes, including hot stamping, injection molding, pultrusion, filament winding, automated fiber placement, and additive manufacturing, are critically compared in terms of processing principles, typical defects, technical limitations, and application boundaries. Particular attention is given to process-induced quality issues such as voids, wrinkling, springback, fiber breakage, warpage, insufficient consolidation, and weak interlayer bonding. Finally, interface engineering strategies, including chemical surface modification, interfacial structural design, and functional interlayer design, are reviewed as practical routes to improve wetting, shorten impregnation pathways, and enhance fiber–matrix load transfer in high-viscosity thermoplastic systems. This review highlights that CFRTP manufacturing should be understood as a coupled materials–processing–interface problem rather than a single forming operation. Future development is discussed with emphasis on reproducible manufacturing, processability-oriented materials, scalable interface engineering, predictive modeling, and standardized structural validation. Full article

Graphene oxide (GO) membranes present a compelling alternative to the permeability-selectivity trade-off inherent in conventional polymer membranes. However, the incomplete mechanistic understanding and the absence of scalable, defect-controlled fabrication processes continue to hinder their practical deployment. This review synthesizes and integrates transport mechanisms, computational modeling, fabrication, and translational constraints across graphene-based membrane architectures into a comprehensive design-oriented framework. Five key aspects of this synthesis are highlighted. Firstly, the available evidence supports a three-regime transport model, which unifies viscous near-frictionless flow, activated molecular hopping, and solution–diffusion. This reframes selectivity as a tunable function of the C/O ratio and interlayer chemistry. Secondly, a quantitative parity analysis of literature data reveals that classical molecular dynamics tends to overestimate GO laminate water permeance by a representative factor of approximately 3–8× across the matched comparisons examined. This discrepancy can be corrected using a tortuosity–porosity factor derived from wet-state XRD. Machine-learning force fields (GAP, MACE), while still in an early stage of development with limited reported applications, narrow the residual discrepancy to within 1.5–2× in the studies reviewed. Thirdly, a tiered computational roadmap identifies nuclear quantum effects as critical for proton-transport applications but unresolved for water permeance in GO laminate geometry. Fourthly, performance across water nanofiltration, gas separation, ion recovery, and osmotic energy harvesting is benchmarked against commercial references, with explicit caveats regarding the heterogeneity of testing conditions across cited studies, alongside a technology readiness assessment. Lastly, a standardized 500-h hydraulic stability protocol is proposed to facilitate cross-laboratory comparison. Collectively, this synthesis provides a structured, albeit not exhaustively validated, basis for the discussion of next-generation membrane design. Full article

Existing anti-aging drugs are often limited by toxicity and resistance. In contrast, natural substances derived from food resources, edible plants, and agricultural by-products offer advantages such as low toxicity and suitability for dietary intake. Utilizing these resources aligns with sustainable development goals by promoting the valorization of food waste and functional food development; however, their complex composition makes traditional discovery inefficient and resource-intensive. Machine learning (ML) provides a powerful, sustainable in silico solution. By analyzing vast datasets, computational models can rapidly screen thousands of candidates, significantly reducing the chemical waste and time associated with traditional wet-lab screening. This review focuses on the current status of food-derived anti-aging bioactives and the emerging ML-based perspectives in this field. Key natural compounds and plant extracts are discussed, highlighting their dietary origins and mechanisms. Furthermore, we explore how advanced algorithms accelerate the identification of novel bioactives. Importantly, we address current translational gaps, including the need for explainable AI, ADME (Absorption, Distribution, Metabolism, and Excretion) prediction, and the standardization of complex mixtures. Overcoming these bottlenecks is essential for the sustainable development of effective, food-based anti-aging ingredients. Full article

Diabetic oral mucosal wounds exhibit impaired healing and require biomaterials with strong wet adhesion, favorable biocompatibility, and adequate mechanical stability. In this study, an in situ photocurable adhesive hydrogel (ATDS) based on sulfonated chitosan was developed for diabetic oral mucosal wound repair. ATDS exhibited a tensile strength of 50 kPa, an elongation at break of 320%, and an adhesive strength of 0.605 MPa, while also displaying a porous microstructure without obvious cytotoxicity. Compared with hyaluronic acid (HA) gel, which was completely lost by day 3, ATDS provided more durable wound coverage in the oral environment. In a diabetic rat model of oral mucosal defect, ATDS significantly accelerated wound closure, with wounds nearly completely healed by day 6, promoted re-epithelialization as early as day 3, and increased epidermis thickness by approximately 50% compared with the control group. In addition, ATDS enhanced angiogenesis and reduced the expression of the inflammatory cytokines TNF-α and IL-1β. Collectively, these findings demonstrate that ATDS effectively promotes diabetic oral mucosal wound healing through its barrier-protective, pro-angiogenic, and anti-inflammatory effects, highlighting its potential as a promising biomaterial for oral tissue engineering and regenerative applications. Full article

Accurate atmospheric temperature and humidity profiles are fundamental to weather monitoring and prediction. Geostationary imagers such as the Advanced Meteorological Imager (AMI) provide continuous observations and enable profile retrievals through radiative transfer–based algorithms; however, these products remain affected by systematic biases associated with the limited number of spectral channels and reliance on background fields from numerical weather prediction models. This study presents a data-driven post-processing framework to generate reanalysis-consistent profiles by refining AMI-retrieved temperature, mixing ratio, and relative humidity profiles using Light Gradient Boosting Machine (LGBM) models trained with ERA5 reanalysis data. Using four years (2020–2023) of hourly observations, the refined profiles were evaluated against both ERA5 and independent radiosonde measurements. Relative to ERA5, the refinement yields modest but consistent reductions in root mean square error (RMSE), including approximately 0.04 g kg⁻¹ (6–7%) for mixing ratio and 1.9 percentage points (≈14%) for relative humidity, while temperature shows a smaller error reduction of about 0.02 K (2–3%). When compared with radiosondes, temperature RMSE shows a marginal increase overall (<1%) with a larger increase in the lower troposphere, whereas improvements are observed for mixing ratio (2–3%) and relative humidity (6–7%). Seasonal and diurnal analyses reveal systematic error structures in the original AMI profiles, particularly wet-bias patterns in summer moisture fields, which are partially mitigated by the refinement. Feature-importance analysis using Shapley Additive Explanations (SHAP) identifies the dominant contribution of AMI water vapor channels, consistent with their known vertical sensitivity. Overall, this long-term evaluation demonstrates the feasibility of machine learning-based refinement for geostationary imager atmospheric profiles, while also highlighting inherent limitations related to the information content of current-generation imagers. Full article

Against the global low-carbon transition drive, mudstone biogas represents a vital unconventional natural gas resource. Yet unclear micro-scale gas-displacing-water mechanisms hinder its productive exploitation. This work combines multi-pressure NMR displacement tests and mono-multifractal coupling analysis to quantify dynamic pore evolution, overcoming limits of conventional static pore characterization. Mudstone core samples from the Sanhu area of Qaidam Basin are analyzed to decode biogas charging controls and build a multi-factor coupling model. Results reveal that micro–mesopores dominate reservoirs; brittle minerals form rigid pore frameworks, while water-wet clays impede gas flow. Gas–water displacement proceeds in staged preferential flow, with two threshold pressures (8 MPa, 15 MPa) slowing efficiency gains. Fractal indices reliably reflect gas–water partitioning. Pore geometry and mineral assemblages jointly govern biogas accumulation; intervals with well-connected pores, weak water wettability and low clay content favor gas charging. The findings support the exploration of analogous continental mudstone gas reservoirs. Full article

Regulated deficit irrigation (RDI) is an important water-saving strategy in arid regions. To quantify the effects of RDI on foxtail millet yield and water use efficiency and determine an optimal RDI strategy, a three-year field trial was carried out over dry, normal, and wet rainfall years in the Loess Plateau. Full irrigation throughout the whole growth period served as the control, whereas mild, moderate, and severe deficit irrigation treatments were conducted at the jointing–booting stage, heading–flowering stage, and across the whole growing period, respectively. The results indicate that the effects of RDI on foxtail millet yield varied with crop growth stage and deficit severity. During the heading–flowering stage, mild RDI showed statistically similar grain yield and WUE relative to those under full irrigation. In normal and wet years, moderate and severe RDI had no statistically significant effects on grain yield and WUE. Additionally, moderate and severe RDI significantly improved irrigation water use efficiency by 19.94–28.50% and 34.35–47.72%, respectively. The primary reason is that RDI at this stage maintained root development and led to only limited suppression of plant growth. In contrast, moderate and severe RDI at the jointing–booting stage or throughout the whole growth period significantly inhibited root establishment and plant development, reduced dry matter accumulation, and consequently led to substantial yield losses. The inhibitory effect became more pronounced with increasing deficit severity. Specifically, severe RDI at the jointing–booting stage and throughout the entire growth period significantly reduced yield by 19.35–54.98% and 31.47–100%, respectively. Furthermore, to identify the optimal RDI regime adaptable to variable rainfall years, a multi-model comprehensive evaluation system based on yield and WUE was established by integrating three individual evaluation models, including the membership function method, TOPSIS, and grey relational analysis, with the Fuzzy–Borda combined evaluation model. The result showed that the heading–flowering stage is the critical period for implementing RDI in foxtail millet in the Loess Plateau. Mild RDI during this stage is preferred because it maintains stable yield and WUE while substantially reducing irrigation amount over various rainfall years. Additionally, moderate and severe RDI can also maintain stable yield while significantly improving irrigation water use efficiency in normal and wet years. Full article

Background: Melioidosis correlates strongly with rainfall, and there is substantial diversity in climate between melioidosis-endemic locations. The Northern Territory of Australia epitomises the “wet/dry” tropics, with a prolonged dry season from May to October. We analysed dry season cases of melioidosis during 35 consecutive years and compared these with wet season cases. We aimed to provide insights into how dry season cases of melioidosis may occur in this region and explore non-rainfall exposures that are usually not considered in the wet season. Methods: Case epidemiological and clinical data were extracted from the Darwin Prospective Melioidosis Study. Weather parameters, including daily rainfall, were analysed using generalised additive models and conditional logistic regressions to assess associations between dry season cases and preceding rainfall. Results: Of 1520 melioidosis cases between 1989 and 2024, there were 325 (21%) in the dry season. While the well-recognised clinical diversity of melioidosis was also seen amongst dry season cases, pneumonia was proportionally less common and cutaneous melioidosis was more common than in the wet season. A total of 23% of dry season patients had no identified clinical risk factors for melioidosis, compared to 14% in the wet season. Mortality was 8% in the dry season and 11% in the wet season. There was a range of plausible explanations for many of the dry season cases, including unseasonal rainfall prior to infection. Infections in urban settings were notable, with anthropogenic factors such as irrigation and construction resulting in persistence of Burkholderia pseudomallei in the environment during the dry season. A total of 3% of cases remained unexplained. Conclusions: Not all dry season cases are explained by infection occurring the previous wet season or by unseasonal rainfall in the dry. Identification of cases in the dry season support the need for year-round prevention strategies during potential exposure to contaminated water or soil. Further prospective studies are needed to better define the infecting events resulting in melioidosis, especially in the dry season. These studies should include timely history taking from the case and their family and selected environmental sampling for B. pseudomallei. Full article

Reliable wellbore cleaning remains difficult in deviated, horizontal, extended-reach, deep, and ultra-deep wells because the downhole distribution and mechanical state of cuttings beds cannot usually be observed directly. This review examines cuttings-bed dynamics, multiscale modeling, compressible multiphase constraints, and field-validation pathways for drill cuttings transport and wellbore cleaning. Bibliometric mapping of 625 Web of Science records was combined with critical assessment of 204 technically screened studies and 56 engineering-oriented OnePetro records. Bed height, cuttings concentration, pressure response, equivalent circulating density/bottomhole-pressure (ECD/BHP) margin, solids residence time, and packoff tendency are identified as bridge variables linking particle-scale behavior with operational risk. Recent studies strengthen wet-bed erosion and friction characterization, non-spherical and geometry-resolved CFD–DEM, hybrid prediction, compressible pressure–solids coupling, and field observability. Study-level comparison shows that ML approaches differ markedly in data provenance, validation design, physical integration, uncertainty reporting, and transfer evidence. An uncertainty-aware, field-calibratable workflow is proposed that links synchronized measurements, complementary models, latent-state estimates with prediction intervals, section-specific probabilistic thresholds, operational response, and post-action verification. Quantitative benchmark criteria are defined for particle realism, wet-bed mechanics, tool-induced flow, compressible transport, transient field models, and advisory outputs. Full article

Understanding long-term hydroclimatic variability in the central Alps is essential when placing recent changes in precipitation and streamflow within a broader temporal context. This study reconstructs warm-season hydroclimatic variability in the central Alps using tree-ring-based hydroclimatic proxies from the Old World Drought Atlas (OWDA). Seasonal April–May–June–July–August (AMJJA) precipitation at Innsbruck, Austria, and seasonal May–June–July–August (MJJA) streamflow at the St. Jodok gauge were reconstructed using OWDA self-calibrating Palmer Drought Severity Index (scPDSI) predictors and moving-window Stepwise Linear Regression (SLR) models. Calibration windows of 30, 40, and 50 years were developed to account for temporal variability in predictor–climate relationships, and reconstruction uncertainty was quantified using multi-model ensemble bounds. An independent Deep Learning reconstruction was also developed for precipitation to provide an assessment of reconstruction skill and long-term climate trends. Specifically, the results demonstrate a robust reconstruction skill, with mean calibration R² values of 0.65 for streamflow and 0.59 for precipitation. The streamflow reconstruction indicates that recent sustained increases represent the strongest positive anomaly in approximately 650 years, while reconstructed precipitation suggests recent decades are among the wettest sustained intervals of the last ~2000 years. Both records reveal a pronounced transition from severe late 19th-century drought conditions to persistent modern pluvial conditions. Agreement between regression and Deep Learning reconstructions supports the robustness of the identified long-term wetting trend and highlights the exceptional nature of recent hydroclimatic conditions in the central Alps. Full article

Reliable assessment of rockburst tendency is essential for maintaining the stability and operational safety of deep underground excavations. However, the complex coupling among stress conditions, lithological characteristics, and structural features of rock masses introduces significant uncertainty into rockburst prediction. Conventional evaluation approaches relying on individual indices frequently produce inconsistent classifications and are often insufficient to represent actual rockburst behavior. To address this issue, a hybrid evaluation framework integrating unascertained measure theory, cloud-based uncertainty analysis, and a game-theoretic weighting strategy was developed in this study. Four representative parameters, including the strain energy storage index (W_et), geostress index (S), rock quality designation (RQD), and rock mass integrity factor (K_v), were adopted to characterize the energy-storage capability, stress environment, and structural condition of the surrounding rock mass. The conventional unascertained measure approach was further enhanced using the normal cloud model to describe the uncertain mapping relationship between quantitative measurements and qualitative rockburst classifications. In addition, a combination weighting scheme incorporating AHP, entropy weight (EW), and CRITIC methods was established to improve the stability and rationality of index weighting. The developed framework was subsequently applied to a deep phosphate mine in China. The calculated comprehensive weights of the four evaluation parameters were 0.1982, 0.3446, 0.2173, and 0.2399, respectively, demonstrating that the stress-related parameter has the greatest influence on rockburst evaluation. The results indicate that the investigated rock masses generally exhibit moderate-to-strong rockburst tendency. The shallow and moderately deep zones exhibited relatively high rockburst potential, while the ultra-deep dolomite formations mainly showed a moderate tendency due to the development of joints and fractures, which weakened the integrity of the deep rock mass. The proposed framework provides an effective and practical approach for preliminary hazard assessment, rockburst risk zoning, and prevention strategy design in deep mining engineering. Full article

The human microbiota is essential for maintaining physiological homeostasis, and microbial dysbiosis is increasingly implicated in the pathogenesis of complex diseases. Identifying potential microbe-disease associations (MDAs) can therefore facilitate mechanistic investigation, biomarker discovery, and therapeutic development. However, wet-laboratory validation is costly and time-consuming, while existing computational methods often struggle with sparse association networks and complex nonlinear interactions. We propose a novel deep learning approach named the Dual Dynamic Hypergraph Convolution Framework for Human Microbe-Disease Association Prediction (DDHMDA). Specifically, DDHMDA first utilizes graph convolutional networks to encode local topological features. Subsequently, it dynamically constructs a dual hypergraph architecture: a differentiable K-means similarity hypergraph to capture intra-modal global clustering patterns, and an attention-based cross-modal interaction hypergraph to model inter-modal interactions synergistically. Under leakage-free pair-level five-fold cross-validation (denoted as CV3), DDHMDA achieved AUC/AUPR values of 0.9789 ± 0.0177/0.9843 ± 0.0129 on HMDAD and 0.9651 ± 0.0042/0.9740 ± 0.0031 on Disbiome. DDHMDA also obtained the best overall CV3 performance among the eight evaluated methods. Furthermore, ablation experiments and case studies validate the practical effectiveness of individual modules and the biological interpretability in discovering novel MDAs. Therefore, DDHMDA would be a reliable tool for identifying potential MDAs. Full article

Edible coatings are widely studied as food-compatible formulations for reducing moisture loss, oxidation, microbial spoilage, oil uptake, and quality deterioration. Their translation from laboratory formulation to industrial use, however, depends not only on film-forming composition but also on controlled deposition, retained dose, surface coverage, drying history, defect formation, hygienic operation, and reproducible performance on heterogeneous food surfaces. An OpenAlex-supported evidence-map audit (2014–2026) was used to separate direct food-coating validation from adjacent engineering models. This review reframes edible coatings as engineered deposited interfaces and proposes a claim-controlled, evidence-tiered framework linking food-grade biopolymer fluids, processability, atomization, droplet impact, wet-film evolution, dry-film structure, image-based metrology, multiphase modeling, and food-performance endpoints. This review outlines the prerequisites for future digital-twin-ready edible coating workflows by linking functional biopolymer fluids, deposition technologies, droplet physics, intelligent image metrology, Computational Fluid Dynamics (CFD), Volume of Fluid (VOF), uncertainty reporting, food-performance endpoints, safety, Life-Cycle Assessment (LCA), Techno-Economic Analysis (TEA) and patent-aware innovation. Digital twins are treated as a future integration target that depends on validated inputs, standardized reporting, deposition metrology and food-specific model validation. The central argument is that progress in edible coatings requires fewer isolated formulation claims and stronger validated links between deposited-interface properties and food-relevant function. A minimum reporting checklist is proposed to support reproducible comparison of deposition routes, coating structures, and translation potential. Full article

With the continuing advancement of global environmental change and rapid urbanization, rural human settlements are facing multiple pressures, including ecological degradation, spatial decline, population outflow, and functional weakening. Based on the vulnerability analysis framework, studies on rural vulnerability provide an important perspective for assessing villages’ risk exposure, disturbance response, and functional degradation when coping with internal and external disturbances. However, existing studies often rely on single-dimensional or linearly weighted evaluations, making it difficult to comprehensively reveal the coupling relationships among multiple discriminative variables and the spatial differentiation patterns of vulnerability. Taking rural areas in Hancheng City, Shaanxi Province, as the research object, this study selects 12 indicators from three dimensions—natural ecological constraints, settlement spatial organization, and public service support—to provide proxy representations of conditions related to potential rural vulnerability. K-means clustering was used to identify potential vulnerability configuration types under multidimensional indicator combinations. A Python-based XGBoost model was then employed as an interpretable surrogate model to assist in characterizing the clustering boundaries, while SHAP analysis was used to explain the key discriminative variables associated with type membership. The results show that the potential rural vulnerability configurations in Hancheng City present a significant west–central–east spatial differentiation pattern. Elevation, village core density, topographic wetness index, distance to town centers, accessibility of daily service facilities, distance to major roads, and normalized difference vegetation index are the main discriminative variables distinguishing different potential vulnerability configuration types. Among them, village core density shows a particularly strong explanatory role. Different key discriminative variables also exhibit evident nonlinear response characteristics across different potential types. Under the indicator system and the $K$ = 4 clustering scheme adopted in this study, the potential rural vulnerability configurations in Hancheng City can be summarized into four types: service-concentrated settlement type, complex terrain-constrained type, human–land coupling transitional type, and natural ecological isolation type. The findings reveal the spatial differentiation characteristics, variable combination relationships, and typological discriminative features of potential rural vulnerability configurations in Hancheng City. They can provide a case-based reference for identifying potential vulnerability, conducting spatial zoning diagnosis, and supporting classified governance in similar county-level rural areas within the loess hilly and gully region. In practical terms, this framework can serve as a diagnostic tool for local governments and planners in classified rural governance. It can be used to identify priority areas for public service and infrastructure investment, review key risk-control areas in complex terrain zones, delineate low-intensity use and protection boundaries in ecologically isolated areas, and guide differentiated resource allocation for different types of villages. Full article