Search Results (28,087)

Cold-active cellulases are highly desirable for temperature-sensitive biomass valorization and food processing, yet they remain scarce in conventional industrial fungal platforms. In this study, a novel cold-induced cellobiohydrolase, VvCBHI-II, was mined from the mushroom Volvariella volvacea and successfully engineered into the industrial workhorse Trichoderma reesei via site-specific homologous replacement. Structural homology modeling revealed that the substitution of the flexible B3 loop with a β-sheet creates a more open substrate-binding cleft in VvCBHI-II. Consequently, the purified VvCBHI-II exhibited robust endoglucanase-like characteristics with superior catalytic efficiency on amorphous cellulose. At 10 °C, the engineered cellulase complex demonstrated an 8.1-fold increase in filter paper activity compared to the wild-type strain. Mechanistic structural analyses indicated that the open cleft architecture elongates and weakens the hydrogen-bonding network with the cellobiose product, facilitating rapid product dissociation and alleviating severe cold-induced product inhibition. In practical applications, the engineered cold-active enzyme complex exhibited an exceptional saccharification capacity on natural pear pomace at 10 °C. Furthermore, when applied to simulated fruit juice processing, it significantly maximized the extraction yield, elevated the sweetness response, and substantially mitigated undesirable bitterness and astringency. This study elucidates the structural-functional paradigm of cold-adapted cellobiohydrolases and provides a promising strategy for formulating highly efficient, energy-saving biocatalysts for the food and biorefinery industries. Full article

Pozzolanic concrete demonstrates intricate, highly nonlinear material interactions that pose significant challenges for the accurate prediction of compressive strength (CS). This study introduces a novel, interpretable ensemble machine learning (ML) framework for predicting CS based on 759 mixture records encompassing cement, aggregates, supplementary cementitious materials (pozzolans), water/binder (W/B), superplasticizer, water, and curing age. Descriptive analysis and ANOVA were used to identify key predictors, followed by an 80/20 train–test split with 10-fold cross-validation to ensure robust and generalizable modeling. To further enhance model reliability, 5% of outliers were removed using an isolation forest algorithm, after which data were normalized and ensemble hyperparameters optimized. Among the evaluated models, the extra trees algorithm with standard scaling demonstrated the most stable generalization, achieving a coefficient of determination (R²) of 0.978 and a root mean square error (RMSE) of 4.197 MPa on the test set, and R² = 0.966 (RMSE = 5.053 MPa) under 10-fold cross-validation. Feature importance, SHAP, and partial dependence analyses consistently demonstrated that W/B, curing age, and cement are the principal determinants of CS. Finally, multi-objective optimization generated high-strength, low-impact mixtures, confirming the framework’s effectiveness as a transparent decision-support tool for performance- and sustainability-oriented pozzolanic concrete design. This study is novel in combining interpretable ensemble ML with multi-objective optimization to simultaneously achieve precise CS prediction and the formulation of sustainable, performance-optimized pozzolanic concrete mixtures. Full article

Effective hemostasis in deep and irregular wounds remains a critical clinical challenge. To address this, we developed a bioresorbable chitosan composite hydrogel reinforced with short eggshell membrane (ESM) nanofibers, which were obtained through cryogenic grinding. The resulting ESM/CCS hydrogel, crosslinked with citric acid, exhibited significantly enhanced properties compared to pure CCS hydrogel, including a 63% increase in mechanical strength, a two-fold improvement in shape memory, a 25.31% reduction in hemolysis, over 2% higher cytocompatibility, and more than 48% greater hemostatic efficiency. Structural characterization confirmed the successful integration of bioactive chitosan with collagen mimetic ESM nanofibers. This biomimetic approach synergistically combines mechanical reinforcement with biological functionality, highlighting its strong potential as an advanced hemostatic dressing for complex wound management. Full article

This paper presents a compact output-stage current-limiting architecture intended for reliable overcurrent protection in CMOS analog and mixed-signal circuits. In modern integrated systems, the output stages of blocks such as operational amplifiers, drivers, buffers, and reference circuits may be exposed to overload conditions, low-impedance loads, or short circuits that can lead to excessive power dissipation and device degradation. The proposed architecture employs scaled replicas of the output transistors together with local negative feedback to sense the delivered load current and independently limit both sinking and sourcing currents. The circuit is demonstrated by integration into a two-stage folded-cascode operational amplifier with a class-AB output stage and evaluated through circuit-level simulations in 130 nm CMOS technology. The results confirm a well-defined current limit across the supply and temperature corners that are relevant to high-reliability applications, spanning 2 V and 5 V supplies and a temperature range from −55 °C to 175 °C. The proposed current-limiting scheme constrains both pull-down and pull-up currents to approximately 9–12 mA across the investigated operating domain. Monte Carlo analysis further shows bounded dispersion and symmetric single-mode distributions, indicating predictable operation under device mismatch. These results demonstrate that the proposed architecture provides a compact and scalable solution for deterministic current limiting in reliability-critical CMOS systems. Full article

This study presents two independently optimised Extreme Gradient Boosting (XGBoost) regression models, one for predicting corrosion current density ($i_{corr}$) and one for predicting corrosion potential ($E_{corr}$) parameters of carbon steel rebar embedded in mortar and immersed in simulated pore solution. An experimental dataset consisting of 216 measurements was curated from a systematic potentiodynamic scan study covering six chloride contamination levels, two carbonation states (non-carbonated and carbonated), four moisture conditions for mortar (65%, 85%, 95% relative humidity, and submerged), and three conditioning durations for simulated pore solution (36 h, 72 h and 20 days). Hyperparameters of the XGBoost models were optimised using a Bayesian optimisation framework with the Tree-structured Parzen Estimator (TPE) sampler over 300 trials. Model performance was assessed using 5-fold cross-validation and a random 80:20 train–test split. The optimised models achieved cross-validation $R^2$ scores of 0.936 and 0.953 for $i_{corr}$ and $E_{corr}$, respectively. On the hold-out test set, $R^2$ values of 0.933 and 0.945 were obtained with test RMSE values of 0.2 log₁₀(µA/cm²) and 41.9 mV, respectively. The contribution of each input feature to model predictions was quantified and visualised using the SHapley Additive exPlanations (SHAP) methodology. SHAP analysis reveals that chloride content has the highest impact on $i_{corr}$, followed by carbonation state and the low-humidity condition, while for $E_{corr}$, chloride content and the Submerged condition have the greatest impact. An interactive web application was developed using Streamlit, enabling researchers and practitioners to obtain corrosion parameter predictions. The findings provide data-driven insights into the relative importance of environmental factors governing rebar corrosion, with direct implications for the development of accurate corrosion prediction models for reinforced concrete service life assessment. Full article

Seasonal human coronaviruses (HCoVs) continue to undergo adaptive evolution under structural and immune-mediated constraints. We investigated the molecular epidemiology and spike (S) protein structural variation of circulating coronaviruses in Riyadh, Saudi Arabia, during the 2025–2026 winter season, with particular emphasis on genotype persistence and glycosylation architecture in HCoV-OC43. Among 293 nasopharyngeal aspirates (NPAs) collected from hospitalized patients with acute respiratory illness, HCoV-OC43 was detected in 26 cases (8.87%), whereas other seasonal coronaviruses were not identified. Partial sequencing of the S gene revealed 97.84–98.23% nucleotide identity relative to the prototype strain VR-759, with amino acid substitutions distributed at discrete positions rather than within extended variable domains, indicating structural conservation. Phylogenetic reconstruction demonstrated that all Riyadh isolates clustered within genotype C, together with previously circulating local strains, supporting sustained endemic persistence and in situ evolution. In silico analysis of the S protein glycosylation landscape identified four invariant N-linked glycosylation motifs (N-X-S/T) at residues 46, 121, 134, and 190, reflecting strong structural constraints on glycan-dependent folding and antigenic configuration. A genotype-associated K68N substitution generated an additional N-glycosylation motif (68NGTD) in multiple Riyadh isolates, potentially modifying local glycan shielding without disrupting the overall glycosylation framework. The preservation of core glycosylation sites alongside selective motif acquisition suggests evolutionary fine-tuning of S surface topology rather than large-scale structural remodeling. Collectively, these findings indicate that genotype C persistence in Riyadh is accompanied by conserved S architecture and subtle glycosylation adjustments that may modulate immune recognition while maintaining structural integrity. Continued high-resolution molecular surveillance will be critical for defining the functional consequences of S microevolution in endemic HCoVs. Full article

Naked oat (Avena nuda L.) is an important dual-purpose crop for grain and forage in cold regions; however, its high fatty acid content renders seeds prone to deterioration during storage. This study aimed to investigate the protective effects of zinc oxide nanoparticles (ZnO NPs) on artificially aged naked oat seeds and elucidate the underlying molecular mechanisms. Non-aged seeds (Naged) were subjected to artificial aging at 45 °C and 100% relative humidity for 24 h (Aged), followed by priming with 30 mg L⁻¹ ZnO NPs for 6 h (Daged). Antioxidant enzyme activities were determined spectrophotometrically, and transcriptome sequencing was performed on an Illumina platform to identify differentially expressed genes (DEGs) and enriched pathways. We found that ZnO NPs increased catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) activities by 3–4-fold, restored germination rate from 75% to 98%, and enhanced seed vigor index. A total of 21,403 DEGs were detected, with 15,841 stably expressed in response to nano-priming. Reactive oxygen species (ROS) burst rapidly induced up-regulation of AP2/EREBP transcription factor family members, which subsequently activated antioxidant enzyme genes to maintain cellular redox homeostasis. Metabolic pathway analysis demonstrated that the phenylpropanoid pathway was reprogrammed, characterized by down-regulated lignin biosynthesis and up-regulated flavonoid production, thereby enhancing ROS scavenging capacity. Additionally, the pentose phosphate pathway was activated to provide additional NADPH for antioxidant defense, and up-regulated ADP-glucose pyrophosphorylase (AGPase) facilitated starch accumulation. Notably, the 40S ribosomal protein S13 exhibited the highest connectivity in protein–protein interaction networks, was up-regulated 2.1-fold, and was enriched in post-translational modification processes. These findings suggest that nano-priming with ZnO NPs represents a promising biotechnological strategy for enhancing seed vigor and storability in naked oat, with potential applications in sustainable agriculture and the seed industry. Full article

Robust vehicle detection in real-world traffic surveillance remains challenging due to degraded imagery caused by motion blur, adverse weather, and low illumination, which significantly increases detector sensitivity to hyperparameter configurations. This study proposes a “Frugal AI” distributed multi-GPU framework that optimizes hyperparameters via a stochastic simplex-based search coupled with five-fold cross-validation. Utilizing three low-cost NVIDIA GTX 1050 Ti GPUs, the framework performs parallel candidate exploration with an asynchronous model-level exchange mechanism to escape local optima without the overhead of gradient synchronization. Seven CNN backbones—VGG16, VGG19, GoogLeNet, MobileNetV2, ResNet18, ResNet50, and ResNet101—were evaluated within YOLOv2 and Faster R-CNN detectors. To address memory constraints (4 GB VRAM), YOLOv2 was selected for extensive benchmarking. Performance was measured using a harmonic precision–recall-based cost metric to strictly penalize imbalanced outcomes. Experimental results demonstrate that under identical wall-clock time budgets, the proposed framework achieves an average 1.38% reduction in aggregated cost across all models, with the highly sensitive VGG19 backbone showing a 4.00% improvement. Benchmarking against Bayesian optimization, genetic algorithms, and random search confirms that our method achieves superior optimization quality with statistical significance ($p < 0.05$). Under a rigorous IoU = 0.75 threshold, the optimized models consistently yielded F1-scores 0.8444 $\pm$ 0.0346. Ablation studies further validate that the collaborative model exchange is essential for accelerating convergence in rugged loss landscapes. This research offers a practical, scalable, and cost-efficient solution for deploying robust AI surveillance in resource-constrained smart city infrastructure. Full article

The double hidden-layer neural network has increasingly been applied in tree height modeling due to its superior performance. To improve the precision of tree height estimation, this study compared the performance of a double hidden-layer neural network with that of a nonlinear mixed-effects model, aiming to provide a new method for tree height prediction. Taking the Larix olgensis forest plantation in Jilin Province as the research object, a double hidden-layer back propagation (BP) neural network was established for tree height prediction by adopting trial and error, k-fold cross-validation, and near-domain optimization strategies. In constructing the nonlinear mixed-effects model, the overall and local differences in forest growth data, as well as the autocorrelation among the various levels of data, were considered. Accordingly, after determining the base model, random effects were introduced, the correlation variance–covariance matrix was calculated, and random parameters were estimated to compare the predictive performance of the two aforementioned models. For the mixed-effects model, the coefficient of determination R² was 0.8590, the root mean square error (RMSE) was 1.6230, and the mean absolute error (MAE) was 2.2658. For the double hidden-layer BP neural network, the R² reached 0.9068 (an increase of 5.56%), the RMSE was 1.3197 (a decrease of 18.69%), and the MAE was 1.2736 (a decrease of 43.79%). The results demonstrate that the double hidden-layer BP neural network is superior to the nonlinear mixed-effects model for tree height prediction. Therefore, the results provide a more accurate method for tree height prediction. Full article

Due to the ultra-trace concentrations of perfluoroalkyl compounds (PFCs) existing in environmental aqueous matrices, it is imperative to develop sensitive and high-enrichment-efficiency approaches for the determination of these emerging pollutants. In this study, a nitrogen-doped carbon dot-functionalized hollow fiber membrane (NCDs@HFM) was fabricated and employed in solid-phase microextraction (SPME) mode for the simultaneous identification of eight perfluoroalkyl carboxylates (PFCAs). The NCDs@HFM offers several advantages, including multiple active binding sites, chemical durability, a large specific surface area and environmental compatibility. Owing to these properties, the NCDs@HFM-based SPME demonstrated high extraction efficiency for PFCAs, where enrichment factors for target molecules could reach 35–61 fold under the optimum conditions. This established method was then integrated with liquid chromatography–tandem mass spectrometry (LC-MS/MS) for the qualitative and quantitative analysis of eight representative PFCAs in drinking and environmental water samples. The limits of detection (LODs, S/N = 3) and quantitation (LOQs, S/N = 10) of the method were at the scale of 0.0018–0.015 μg/L and 0.006–0.050 μg/L, respectively. This proposed method exhibited good precision, with RSDs below 13.2% and satisfactory accuracy, with recoveries ranging from 70.6% to 122.5%. The developed method was successfully applied in the identification of eight typical PFCAs in drinking and environmental water samples. This method exhibits several merits, including low cost, high sensitivity, good reliability and reusability, representing a promising alternative for measuring trace levels of PFCAs in aqueous matrices. Full article

Aflatoxin B1 (AFB1) poses a serious threat to global food and feed safety. Laccase-based enzymatic degradation represents a promising green strategy for AFB1 removal; however, its industrial application is severely limited by the rapid thermal inactivation of wild-type enzymes under high-temperature processing conditions (>70 °C). Here, we engineered the thermal stability of a laccase from Bacillus amyloliquefaciens B10 through an integrated strategy combining computational structural biology with semi-rational design. By coupling molecular dynamics (MD) simulations with folding free-energy (ΔΔG) calculations, we identified key flexible regions associated with thermal instability and subsequently implemented iterative saturation mutagenesis. The best single mutant, R196C, retained more than 96% relative activity after heat treatment at 80 °C for 10 min. Further iterative mutational stacking progressively enhanced thermostability: the R90E/R196C double mutant showed 1.25-fold higher activity at 80 °C than R196C, and the R90E/R196C/H54F triple mutant showed a further 1.16-fold increase over the double mutant. The final quadruple mutant, R90E/R196C/H54F/R253I, achieved 86.9% AFB1 degradation at 80 °C after 24 h. High-temperature MD simulations (100 ns at 353.15 K) indicated that the enhanced thermostability was associated with reduced conformational flexibility, lower radius of gyration (Rg) and solvent-accessible surface area (SASA), and a coil-to-β-sheet transition that contributed to stabilization of the protein core. In addition, efficient secretory expression of the engineered enzyme was achieved in Pichia pastoris, reaching 3.0 U/mL, while the crude enzyme maintained more than 70% activity at 80 °C. Collectively, these results provide a practical basis for the rational engineering and scalable production of thermostable biocatalysts for AFB1 detoxification-related applications of AFB1 control, and offer broader insights into the targeted enhancement of thermal stability in industrial enzymes. Full article

Varroa destructor (Bee mite) and Deformed Wing Virus are primary causes of honeybee colony collapse. This study developed an automated AI-based vision inspection system for detecting bee mites and deformed bees using the YOLO algorithm. The system integrates an RGB camera, a beecomb rotation motor, and an image transmission module to enable automated dual-sided image acquisition of the beecomb. The image characteristics of normal bees, bee mites, and deformed bees were analyzed, and YOLO-based object detection models were developed to classify them. Six YOLO models—based on YOLOv8 and YOLOv11 architectures across three model sizes (nano, small, and large)—were evaluated on 405 test images (6441 objects). The proposed system reduced the inspection time from 240 s required for manual method to 20 s per beecomb, achieving 12-fold efficiency improvement. Comparative analysis showed model-task specialization: YOLOv8l excelled in detecting small bee mites (F1: 92.5%, mAP[0.5]: 92.1%), while YOLOv11s achieved the highest performance for morphologically diverse deformed bees (F1: 95.1%). Error analysis indicated that detection performance was influenced by morphological characteristics. Deformed bee detection errors correlated with overlap in wing-to-body ratio: DB Type II exhibited 18.6% miss rate, while DB Type III achieved perfect detection. In bee mite detection, a sensitivity–specificity trade-off was observed: YOLOv11l had the lowest false negatives (2.5%) but highest false positives, while YOLOv8l demonstrated superior discrimination. These results demonstrate the practical potential of the proposed system for field deployment in apiaries, supporting early pest diagnosis and improved colony health management. The model-task specialization framework provides guidance for architecture selection based on object characteristics. Future work will focus on multi-location validation and real-time monitoring integration. Full article

Internal phosphorus loading is a key process sustaining eutrophication in stratified Baltic Sea coastal systems, yet its long-term dynamics in the Archipelago Sea remain poorly quantified due to limited deep-water monitoring and the absence of sediment time series. This study provides a multidecadal assessment of internal loading from the early 1980s to 2025 using two complementary indicators: (i) seasonal accumulation of total phosphorus in the surface layer (ΔTP) and (ii) the covariation between near-bottom oxygen depletion and dissolved inorganic phosphorus (DIP) release. Temporal associations with external phosphorus inputs from marine fish farming—highly variable during the study period—were analyzed to evaluate whether cumulative loading trajectories coincided with phases of intensified ΔTP. New measurements of drifting filamentous macroalgae from 2025 were additionally used to assess their seasonal contribution to the internal phosphorus pool and their relevance for mitigation. Results show a pronounced multidecadal strengthening of internal loading signals in the mid and inner Archipelago Sea. At the Seili station, ΔTP increased by approximately 6.8 µg L⁻¹ (≈3.4-fold) since the early 1980s. This trend coincided with long-term deterioration of near-bottom oxygen conditions and increasing DIP concentrations, consistent with enhanced sediment phosphorus release. Although cumulative aquaculture loading exhibited simple correlations with ΔTP, detrended analyses indicate that these relationships largely reflect shared long-term trends rather than direct causal linkages. Drifting filamentous macroalgae formed a substantial seasonal phosphorus reservoir (≈146 t P). Overall, internal phosphorus input to the Archipelago Sea has intensified markedly—by an estimated ~70% since the 1980s—highlighting the growing importance of sediment–water feedbacks and legacy phosphorus. Effective mitigation therefore requires strategies that address both internal recycling processes and external nutrient inputs. Targeted removal of drifting filamentous macroalgae may provide a complementary nutrient-export pathway in coastal management. Full article

Population aging in middle-income economies produces macroeconomic and distributional consequences that aggregate frameworks cannot detect. This paper develops a multi-household CGE model calibrated to a 26-sector Social Accounting Matrix for Thailand (2024) and traces the labor, saving, and fiscal channels of aging across eleven counterfactual scenarios. Three findings emerge. First, aging’s primary macroeconomic cost operates through capital accumulation, not output contraction: investment falls seven times faster than the GDP under a savings-driven closure, because middle-aged households—the economy’s dominant net savers—compress lifecycle saving in response to aging. The saving channel alone amplifies the labor supply shock four-fold (range: 3.5–4.5). Second, aging can raise elderly welfare. When elderly households retain labor market attachment, wage gains from tighter factor markets outweigh declining capital returns—a welfare reversal invisible to representative agent and OLG frameworks by construction. The critical labor income threshold is ${\alpha }_L^{\ast }=35.5\%$ (range: 34.8–36.2%), confirmed across all participation increments tested (elderly welfare gain: THB 341–521 million). Third, no single instrument satisfies efficiency and equity simultaneously. Pension transfers crowd out investment nonlinearly above 12 percent of tax revenue (range: 10–14%); health demand expansion is the decisive complement that converts redistribution into a near-Pareto improvement. Policy complementarity is an empirical necessity, not a theoretical refinement. Collectively, these results reframe demographic aging as a factor price redistribution mechanism whose welfare incidence is determined by the cohort-level income composition—with direct implications for aging policy in middle-income economies facing rapid demographic transitions under tighter fiscal constraints than for advanced economies encountered at equivalent demographic stages. Full article

Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder associated with adverse maternal and fetal outcomes. However, current diagnostic strategies have a limited capacity to identify women at risk early in pregnancy. In this longitudinal prospective pilot study, 200 pregnant Mexican women were recruited at 11–14 weeks and underwent follow-up throughout pregnancy. Of these, 34 women (19 with GDM and 15 with normal glucose tolerance [NGT]) completed follow-up and were included in the final analyses. Most withdrawals were due to logistical constraints, although the reduced final sample size should be considered when interpreting generalizability. Nine serum proteins (ADIPOQ, AFM, FABP4, IGFBP-5, PAPP-A, PAPP-A2, RBP4, RETN, SHBG) were measured simultaneously using an antibody array and subsequently validated by ELISA. FABP4 showed the greatest increase in the first trimester (4.9-fold, p = 0.0105) and the highest apparent discriminative performance (AUC = 0.91), which declined in the second and third trimesters. Exploratory, hypothesis-generating multivariable analyses suggested a stronger association when FABP4 was combined with gravidity and serum triglycerides (AUC up to 0.97). Overall, FABP4 emerged as a promising candidate biomarker for early GDM detection in Mexican women; however, these findings are preliminary and require validation in larger, independent cohorts to support early risk stratification. Full article