Search Results (8,103)

Targeted therapy with BRAFi has significantly improved outcomes for patients with BRAF-mutated metastatic melanoma. However, resistance mechanisms, particularly metabolic adaptations, such as increased glutaminolysis, present substantial clinical challenges. This study investigated the metabolic changes underlying BRAFi resistance in melanoma cells. Using pharmacological agents, including dabrafenib (BRAFi), pimasertib (MEKi), dasatinib (cKITi), and CB-839 (glutaminase inhibitor), we explored metabolic adaptations in melanoma cell lines harboring various mutations. Our methodologies included cell culture, qPCR, polysome profiling, animal studies in nude mice, and analyses of patient samples to evaluate the therapeutic potential of targeting glutaminolysis. Our findings confirmed that melanoma cells, with resistance to targeted therapies, exhibit metabolic adaptations, including enhanced glutaminolysis, increased mitochondrial content, and elevated antioxidative capacities. We evaluated the efficacy of CB-839 and demonstrated its ability to reduce the proliferation of resistant melanoma cells both in vitro and in vivo. Mechanistic studies revealed that CB-839 suppressed ATP production and TCA cycle intermediates in resistant cells while inducing oxidative stress in sensitive cells, thereby inhibiting their proliferation. High glutaminase expression in primary patient tumor samples was associated with poor prognosis. We identified a metabolic signature in tumors from patients responsive or unresponsive to BRAFi prior to treatment, which could serve as a predictive factor for BRAFi response. This study underscores the metabolic alterations driving resistance to BRAFi in melanoma cells and highlights the therapeutic potential of targeting glutaminolysis with CB-839. The identification of metabolic signatures in patient samples provides valuable insights for personalized treatment strategies, aiming to overcome resistance mechanisms and improve patient outcomes in melanoma management. Full article

Soybean (Glycine max L.) is one of the world’s most important sources of plant-based protein, with a protein content exceeding 35–40% (dry basis), along with other essential nutritional benefits. Ideally, soybeans are field-dried to approximately 13% moisture content (wet basis, wb); however, adverse weather conditions can necessitate harvesting at elevated moisture levels sometimes exceeding 20% (wb). In such cases, mechanized drying systems, particularly in northern U.S. regions, become essential for safe storage and quality preservation. This study investigated the effects of drying temperature, airflow rate, and initial moisture content on drying kinetics and kernel integrity using mathematical modeling. Drying behavior was modeled using fractional calculus and compared to the empirical Page model, while kernel cracking and breakage were analyzed using logistic regression. Both fractional and Page models exhibited strong agreement with experimental data (R² = 0.903–0.993). The fractional model achieved superior predictive accuracy, improving RMSE and MAE by 83.7% and 81.2%, respectively, compared to the Page model. Cracking and breakage were more strongly influenced by drying temperature than by initial moisture content, with the greatest quality degradation occurring at high temperatures. Optimal drying conditions were identified as temperatures below 27 °C and initial moisture contents between 19 and 20% (wb), which best preserved kernel quality. Logistic models more accurately predicted breakage than cracking, confirming their effectiveness in assessing mechanical damage during drying. The results affirm the suitability of fractional order models for accurately capturing drying kinetics, while logistic models offer robust performance for evaluating physical quality degradation. These modeling approaches provide a framework for efficient and quality-preserving soybean drying strategies in regions reliant on off-field drying systems. Full article

This study focuses on predicting β-carotene content using hyperspectral images captured in the near-infrared (NIR) region during the drying process. Several machine learning models are compared, including Partial Least Squares Regression (PLSR), Stacked Autoencoders (SAEs) combined with Random Forest (RF), and Convolutional Neural Networks (CNNs) in three configurations: 1D-CNN, 2D-CNN, and 3D-CNN. The models are evaluated using R², Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The PLSR model showed excellent results with R² = 0.97 for both training and testing, indicating minimal overfitting. The SAE-RF model also performed well, with R² values of 0.82 and 0.83 for training and testing, respectively, showing strong consistency. The CNN models displayed varying results: 1D-CNN achieved moderate performance, while 2D-CNN and 3D-CNN exhibited signs of overfitting, especially on testing data. Overall, the findings suggest that although CNNs are capable of capturing complex patterns, the PLSR and SAE-RF models deliver more reliable and robust predictions for β-carotene content in hyperspectral imaging. Full article

Droughts are becoming more frequent, severe, and impactful across the globe. Agroecosystems, which are human-made ecosystems with high water demand that provide essential ecosystem services, are vulnerable to extreme droughts. Although water use efficiency in agriculture has increased in rec ent decades, drought management should be based on long-term, proactive strategies rather than crisis management. The AgrHyS network of sites in French Brittany collects high-resolution soil moisture data from agronomic stations and catchments to improve understanding of temporal soil moisture dynamics and enhance water use efficiency. Frequent mapping of soil moisture and plant water stress is crucial for assessing water stress risk in the context of global warming. Although satellite remote sensing provides reliable, periodic global data on surface soil moisture, it does so at a very coarse spatial resolution. The intrinsic spatial heterogeneity of surface soil moisture requires a higher spatial resolution in order to address upcoming challenges on a local scale. Drones are an excellent tool for upscaling point measurements to catchment level using different onboard cameras. In this study, we evaluated the potential of multispectral images, thermal images and LiDAR data captured in several concurrent drone flights for high-resolution mapping of soil moisture spatial variability, using in situ point measurements of soil water content and plant water stress in both agricultural areas and natural ecosystems. Statistical models were fitted to map soil water content in two areas: a natural marshland and a grassland-covered agricultural field. Our results demonstrate the statistical significance of topography, land surface temperature and red band reflectance in the natural area for retrieving soil water content. In contrast, the grasslands were best predicted by the transformed normalised difference vegetation index (TNDVI). Full article

Although significant progress has been made in the conservation of Père David’s deer (Elaphurus davidianus) populations, rapid population growth in coastal wetlands has caused severe habitat degradation. This highlights the urgent challenge of balancing ungulate population dynamics with wetland restoration efforts, particularly considering the limited data available on post-disturbance ecosystem recovery in these environments. In this study, we evaluated soil quality and bacterial community dynamics at an abandoned feeding site and a nearby control site within the Dafeng Milu National Nature Reserve during 2020–2021. The goal was to provide a theoretical basis for the ecological restoration of Père David’s deer habitat in coastal wetlands. The main findings are as follows: among the measured indicators, bulk density (BD), soil water content (SWC), sodium (Na⁺), total carbon (TC), total nitrogen (TN), total phosphorus (TP), available potassium (AK), microbial biomass nitrogen (MBN), and the Chao index were selected to form the minimum data set (MDS) for calculating the soil quality index (SQI), effectively reflecting the actual condition of soil quality. Overall, the SQI at the feeding site was lower than that of the control site. Based on the composition of bacterial communities and the functional prediction analysis of bacterial communities in the FAPROTAX database, it is shown that feeding sites are experiencing sustained soil carbon loss, which is clearly caused by the gathering of Père David’s deer. Co-occurring network analyses demonstrated the structure of the bacterial community at the feeding site was decomplexed, and with a lower intensity than the control. In RDA, Na⁺ is the main soil property that affects bacterial communities. These findings suggest that the control of soil salinity is a primary consideration in the development of Père David’s deer habitat restoration programmes, followed by addressing nitrogen supplementation and carbon sequestration. Full article

To promote the recycling and reuse of waste concrete, this study investigated the comprehensive impact of recycled aggregate (RA) content and particle size on the mechanical properties of concrete. A novel equivalence parameter (λ_eq) of RA was developed to consider the influence of RA content and size on the mechanical properties of concrete. Empirical equations were developed using linear regression to describe the test results and predict the impact of content and size of RA on the mechanical properties of concrete. The results showed that the comprehensive impact on the mechanical strength of recycled concrete shows a certain regularity when the content and particle size of RA change simultaneously. The measured mechanical properties and regression equations provided a reference and basis for engineering applications, such as the processing of RA in a crushing plant, the design of mix proportions in concrete using RA, and the rapid assessment of mechanical properties on-site. This study provides a design method and technical path for green construction. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

Rapid, accurate, and sustainable methods for assessing soil properties are essential for environmental management. This study proposes a green analytical approach for the direct determination of soil texture and organic matter using benchtop (1250–2500 nm) and portable (900–1700 nm) near-infrared (NIR) spectrophotometers combined with multivariate calibration. Partial least squares (PLS1 and PLS2) regression models were developed using regional calibration samples and applied to additional samples from the same area. Both individual (PLS1) and simultaneous (PLS2) predictions of clay, sand, silt, and organic matter contents were evaluated. Synergy interval PLS (siPLS) algorithms were used to optimize variable selection. For clay, RMSEP was 2.1% (benchtop) and 2.0% (portable), with RPD values around 2.0. Simultaneous prediction of sand content yielded better results (RPD = 1.3 benchtop; 0.8 portable). Silt prediction showed low accuracy (RPD < 1.0). Organic matter was best predicted by siPLS1 using the benchtop device (RPD = 1.5), followed by portable PLS2 (RPD = 1.2). Benchtop and portable NIR approaches proved satisfactory for direct determination of soil properties. PLS1 models offered greater specificity, while siPLS enhanced accuracy through variable selection. PLS2 models enabled efficient simultaneous predictions. Both devices meet white analytical chemistry principles, aligning performance with sustainability, thus demonstrating that accurate and environmentally responsible soil analysis can be achieved without compromising analytical efficiency. Full article

Deep eutectic solvents (DESs) have been studied to obtain extracts from medicinal plants, aiming for a more environmentally friendly process. Aligned with this initiative, the use of predictive thermodynamic models for screening the best solvent represents a theoretical action to reduce experimental time and cost. Therefore, this study aimed to perform and validate a relative solubility screening of 5-methoxy-6,7-methylenedioxycoumarin and prenyletin-methyl-ether at 313 K in choline chloride, menthol, and betaine-based DES, using the COSMO-RS model in COSMOThermX software. The density of DES was also predicted with a maximum error of 7.31% for this property. Ultrasound-assisted extraction (UAE) with DES at 313 K, 30 min, and a solid/liquid ratio of 1:20 (w/w) was performed to confirm the theoretical solubility results experimentally, as the extracts were analyzed through ultrafast liquid chromatography (UFLC) for coumarin content. For the results, the coumarin molecules presented intense peaks in the nonpolar region of their σ-profile, and the relative solubility screening indicated the DES Men/Lau (2:1), known for its hydrophobic nature and low polarity, as the best DES to solubilize these coumarins. Nevertheless, the UFLC results, and the complementary solubility screening of pigments, showed an interaction preference of this DES with chlorophylls instead of coumarins. This result was corroborated by spectrophotometric analysis of the extracts in UV-Vis, demonstrating that experimental validation is still mandatory in extraction processes and that predictive methodologies such as COSMO-RS should be used as guiding tools and analyzed in a greater context, considering the complexity of plant matrices in the beginning of simulations. Full article

Improved irrigation guidelines are needed to maximize crop water use efficiency. Combining field data with simulation models can provide information for better irrigation management. The objective of the present study was to evaluate the effects of two flood irrigation treatments on fiber yield (FY) and quality during the 2023 and 2024 growing seasons in Maricopa, Arizona, USA. Two irrigation treatments, denoted as F100% and F80%, were arranged in a randomized complete block design with three replicates. Then, AquaCrop was used to simulate cotton yield (Y_Tot), water use (ET_obs), and total soil water content (WC_Tot) for the two irrigation treatments. Six statistical metrics, including the coefficient of determination (R²), the normalized root-mean-square error (NRMSE), the mean absolute error (MAE), simulation error (S_e), the index of agreement (D_index), and the Nash–Sutcliffe efficiency coefficient (NSE), were employed to assess model performance. The results of the field trial demonstrated that reducing the irrigation rate to 80% of ET_c negatively impacted cotton FY and ET water productivity (ETWP); the FY declined by 45.2% (ETWP = 0.097 kg·ha⁻¹) in 2023 and by 38.1% (ETWP = 0.133 kg·ha⁻¹) in 2024. Conversely, F100% produced a more uniform and stronger fiber than F80%, with the uniformity index (UI) and fiber strength (STR) measuring 81.7% and 29.5 g tex⁻¹ in 2023 and 82.2% and 30.0 g tex⁻¹ in 2024, indicating that UI and STR were well correlated with soil water during both growing seasons. AquaCrop showed an excellent performance in simulating cotton CC during the two growing seasons. The R², NRMSE, D_index, and NSE were between 0.97 and 0.99, 8.45% and 14.36%, 0.98 and 0.99, and 0.96 and 0.98, respectively. Moreover, the AquaCrop model accurately simulated Y_Tot during these seasons, with R², NRMSE, D_index, and NSE for pooled yield data of 0.93, 8.05%, 0.95, and 0.78, respectively. The model consistently overestimated Y_Tot, ET_obs, and WC_Tot, but within an acceptable S_e (S_e < 15%) during both growing seasons, except for WC_Tot under the 80% treatment in 2023 (S_e = 26.4%). Consequently, AquaCrop can be considered an effective tool for irrigation management and yield prediction in arid climates such as Arizona. Full article

The increasing adoption of industrialized offsite construction (IOC) offers substantial benefits in efficiency, quality, and sustainability, yet presents persistent challenges related to data fragmentation, real-time monitoring, and coordination. This systematic review investigates the transformative role of artificial intelligence (AI)-enhanced digital twins (DTs) in addressing these challenges within IOC. Employing a hybrid re-view methodology—combining scientometric mapping and qualitative content analysis—52 relevant studies were analyzed to identify technological trends, implementation barriers, and emerging research themes. The findings reveal that AI-driven DTs enable dynamic scheduling, predictive maintenance, real-time quality control, and sustainable lifecycle management across all IOC phases. Seven thematic application clusters are identified, including logistics optimization, safety management, and data interoperability, supported by a layered architectural framework and key enabling technologies. This study contributes to the literature by providing an early synthesis that integrates technical, organizational, and strategic dimensions of AI-driven DT implementation in IOC context. It distinguishes DT applications in IOC from those in onsite construction and expands AI’s role beyond conventional data analytics toward agentive, autonomous decision-making. The proposed future research agenda offers strategic directions such as the development of DT maturity models, lifecycle-spanning integration strategies, scalable AI agent systems, and cost-effective DT solutions for small and medium enterprises. Full article

Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for classifying different tenderness levels and quantitatively assessing key anthocyanin components in Zijuan tea fresh leaves. First, NIR spectra and visual feature data were collected, and anthocyanin components were quantitatively analyzed using UHPLC-Q-Exactive/MS. Then, four preprocessing techniques and three wavelength selection methods were applied to both individual and fused datasets. Tenderness classification models were developed using Particle Swarm Optimization–Support Vector Machine (PSO-SVM), Random Forest (RF), and Convolutional Neural Networks (CNNs). Additionally, prediction models for key anthocyanin content were established using linear Partial Least Squares Regression (PLSR), nonlinear Support Vector Regression (SVR) and RF. The results revealed significant differences in NIR spectral characteristics across different tenderness levels. Model combinations such as TEX + Medfilt + RF and NIR + Medfilt + CNN achieved 100% accuracy in both training and testing sets, demonstrating robust classification performance. The optimal models for predicting key anthocyanin contents also exhibited excellent predictive accuracy, enabling the rapid and nondestructive detection of six major anthocyanin components. This study provides a reliable and efficient method for intelligent tenderness classification and the rapid, nondestructive detection of key anthocyanin compounds in Zijuan tea, holding promising potential for quality control and raw material grading in the specialty tea industry. Full article

Amphidinols (AM) are a diverse group of bioactive polyketides produced by dinoflagellates of the genus Amphidinium, known for their hemolytic, antifungal, and cytotoxic activities. This work presents the assessment of AM profiles in a comprehensive number of strains, whose species boundaries were previously established through detailed taxonomic analysis. Using UHPLC-MS/MS, we characterized the spectrum of AM analogs in 54 Amphidinium strains isolated from diverse geographical locations. In addition, toxicity was assessed using brine shrimp assays, which revealed significant inter- and intraspecific variability. Despite the broad diversity in AM content, no clear correlation was observed between total AM levels and toxicity across all strains. Multivariate analysis grouped the strains into clusters distinguished by distinct AM profiles and toxicity levels, suggesting that AM production alone does not predict toxicity. Our findings highlight the complexity of Amphidinium bioactivity, emphasizing the influence of strain-specific factors and other bioactive compounds. This work highlights the importance of integrating chemical, genetic, and biological assessments to understand better the factors that govern toxicity in this genus, with implications for ecological studies and the monitoring of harmful dinoflagellates. Full article

Leaf nutrient resorption efficiency (NuRE) is critical for plant nutrient conservation, yet its relationship with leaf and root economic traits remains poorly understood in mangroves. We quantified nitrogen (N) and phosphorus (P) resorption across ten mangrove species (five trees and five shrubs) in Hainan, China, and related NuRE to key leaf (leaf mass per area, LMA; leaf dry mass content, LDMC; and green leaf nitrogen and phosphorus contents, N_gr and P_gr, respectively) and root (specific root length, SRL; root tissue density, RTD; root diameter, RD; and root nitrogen content, N_root) traits. We found that species with a lower leaf structural investment (LMA = 103–173 g m⁻², LDMC = 19–27%) presented a 6–45% greater N and P resorption efficiency than those with a higher structural investment (LMA = 213–219 g m⁻², LDMC = 26–31%). Contrary to global meta-analyses, higher green leaf N and P contents also predicted a greater NuRE, implying enhanced internal recycling under chronic nutrient limitation. Root traits (SRL, RTD, RD, and N_root) had no significant influence on NuRE, indicating decoupled above- versus belowground strategies. Trees and shrubs diverged in size but converged in NuRE–leaf trait relationships. These findings refine plant economics theory and guide restoration by prioritizing species with acquisitive, high-NuRE foliage for nutrient-poor coasts. Full article

Peach (Prunus persica) leaves, usually discarded in traditional Chinese medicine, were explored as a source of laxative agents. Using zebrafish larvae for bioactivity-guided fractionation, we isolated a single active flavanone that was identified by NMR and HR-MS as Sakuranetin. In vivo assays demonstrated that Sakuranetin (10–25 µM) accelerated intestinal transit in a dose-dependent fashion; at 25 µM, 64.8% of the fluorescent intestinal content was expelled. Untargeted LC-MS metabolomic analysis revealed significant perturbations in serine biosynthesis and N-glycan precursor biosynthesis, suggesting energetic rewiring of enterocytes. RNA-Seq analysis highlighted gnat1 as the most responsive gene, and molecular docking predicted a stable Sakuranetin–Gnat1 complex with a binding free energy of −8.7 kcal/mol. Concurrent down-regulation of rho transcripts indicated suppression of inflammatory signaling that often accompanies constipation. Our findings identified Sakuranetin as a potent promoter of gut motility and position the otherwise wasted peach leaves as an untapped botanical resource for developing anti-constipation therapeutics. Full article