Search Results (6,229)

Super high-rise buildings of 400 m and above are currently rare globally, making their design and construction data invaluable. Due to their enormous size, the structural safety, architectural effect, and construction cost are key concerns of all parties. This study employs parametric analysis to research the lateral force-resisting system and key local structural issues of a 400 m under-construction super-high-rise structure. The overall analysis results show that the 8-mega-column scheme can relatively well balance architectural effect and structural performance; the 5-belt truss design minimizes the steel consumption. The local research results indicate that the inward inclination of bottom columns leads to increased axial forces in floor beams significantly, necessitating reinforcement; horizontal braces directly connected to the core tube enhance folded belt truss integrity under rare earthquakes; failure of bottom gravity columns in the folded secondary frame increases beam bending moments and axial forces substantially. Steel consumption sensitivity analysis shows that when the structural first-order period is reduced by 0.1 s, adjusting the section sizes of the members in the belt truss minimizes the increase in steel consumption, while adjusting steel beams maximizes it. These findings provide essential design insights for similar super-high-rise projects. Full article

Background and objectives: Antibiotic resistance in Cutibacterium acnes is undermining topical macrolides and clindamycin, prompting renewed interest in zinc oxide nanoparticles (ZnO-NPs) as non-antibiotic alternatives. We aimed to (i) determine the antimicrobial and anti-inflammatory performance of topical ZnO-NP formulations across in vitro, animal and early human models; (ii) identify physicochemical parameters that modulate potency and tolerance; and (iii) delineate translational gaps and priority design elements for randomised trials. Methods: We systematically searched PubMed, Scopus and Web of Science until 1 June 2025 for in vitro, animal and human studies that evaluated ≤100 nm ZnO-NPs applied topically to C. acnes cultures, extracting data on bacterial load, lesion counts, biophysical skin parameters and acute toxicity. Eight eligible investigations (five in vitro, two animal, one exploratory human) analysed particles 20–50 nm in diameter carrying mildly anionic zeta potentials. Results: Hyaluronic acid-coated ZnO-NPs achieved a sixteen-fold higher selective kill ratio over Staphylococcus epidermidis at 32 µg mL¹, while centrifugally spun polyvinyl alcohol dressings reduced C. acnes burden by 3.1 log₁₀ on porcine skin within 24 h, and plant-derived nanogels generated inhibition zones that were 11% wider than benzoyl-peroxide’s 5%. In human subjects, twice-daily 0.5% hyaluronic–ZnO nanogel cut inflammatory-lesion counts by 58% at week four and lowered transepidermal water loss without erythema. Preclinical safety was reassuring, zero mortality among animals at 100 µg mL¹ and no irritation among patients, although high-dose sunscreen-grade ZnO (20 nm) delayed rat wound closure by 38%, highlighting dose-dependent differences. Conclusions: Collectively, the evidence indicates that nanoscale reformulation markedly augments zinc’s antibacterial and anti-inflammatory performance while maintaining favourable acute tolerance, supporting progression to rigorously designed, adequately powered randomised trials that will benchmark ZnO-NPs against benzoyl peroxide and retinoids, optimise dosing for efficacy versus phototoxicity, and establish long-term dermatological safety. Full article

Enzyme entrapment in alginate hydrogel microbeads is an effective method of immobilization for industrial applications, but many fabrication methods for alginate microbeads involve oil, organic solvents, or high temperatures that reduce enzymatic activity. In this study, we employed an oil- and solvent-free gas-shearing technique to prepare alginate microbeads for the entrapment of Candida rugosa lipase (CRL), thereby minimizing thermal- and solvent-induced inactivation. To enhance immobilization efficiency and reusability, the effects of gas flow rate, alginate concentration, and cross-linking metal ions were systematically investigated. CRL entrapped in Ba- and Fe-alginate microbeads showed superior immobilization yield, activity retention, and activity recovery compared with CRL entrapped in conventional Ca-alginate microbeads. Notably, both Ba- and Fe-alginate microbeads exhibited significantly enhanced stability, with half-lives up to 127-fold greater than that of free CRL at 60 °C, and maintained substantially higher pH stability across the tested range. Ba-alginate microbeads provided greater pH stability and substrate affinity, whereas Fe-alginate microbeads demonstrated enhanced thermal stability and catalytic turnover. These findings highlight gas-shearing as a scalable and gentle fabrication method for producing high-performance alginate microbeads with tunable properties, making them suitable for enzyme entrapment in diverse biocatalytic applications. Full article

The global gold extraction industry has been reported to use cyanide-based recovery processes, which pose environmental effects on water resources. The study examined Coptodon zillii liver rhodanese from a gold mining-impacted reservoir with a specific focus on the enzyme’s critical function in cyanide detoxification. Rhodanese was purified using successive chromatographic techniques with 5.4 U/mg specific activity and 3.1-fold purification. The molecular weight of the native enzyme was 36 kDa, and the subunits were 17 kDa, indicative of a dimeric structure. Optimal enzymatic activity was recorded at pH 8.0 and 50 °C. The effect of metal ions was significantly varied: the activity was inhibited by BaCl₂, CaCl₂, NaCl, and MgCl₂, and KCl enhanced performance. The kinetic determinations showed Michaelis-Menten kinetics with a Km of 20.0 mM for sodium thiosulfate and 25.0 mM for potassium cyanide. The enzyme’s minimal activity was identified toward 2-mercaptoethanol, ammonium persulfate, and ammonium sulfate, but with evidence of preference for thiosulfate utilization under the substrate specificity tests. The major interactions between the enzyme and the substrate were revealed by the molecular docking experiments. These showed Glu159, Gln161, and Arg173 formed important hydrogen bonds with thiosulfate, while Arg156 and Val172 were also involved. Other substrates are bound to Gln121 and Trp139 residues with much lower binding energy than thiosulfate. The findings increase our understanding of biochemical adaptation process knowledge in anthropogenically stressed environments, showing strategies of ecological resilience. The characterized enzymatic features showed potent cyanide detoxification potential, and the possible applications are in bioremediation strategies for mining-impacted aquatic ecosystems. Full article

The integration of bacterial biotechnology into construction and geotechnical practices is redefining approaches to material sustainability, infrastructure longevity, and environmental resilience. Over the past two decades, research activity in construction biotechnology has expanded rapidly, with more than 350 publications between 2000 and 2024 and a five-fold increase in annual output since 2020. Beyond bibliometric growth, technical studies have demonstrated the remarkable performance of bacterial systems: for example, microbial-induced calcium carbonate precipitation (MICP) can increase the compressive strength of treated soils by 60–70% and reduce permeability by more than 90% in field-scale trials. In concrete applications, bacterial self-healing has been shown to seal cracks up to 0.8 mm wide and improve water tightness by 70–90%. Similarly, biofilm-mediated corrosion barriers can extend the durability of reinforced steel by significantly reducing chloride ingress, while bacterial biopolymers such as xanthan gum and curdlan enhance soil cohesion and water retention in eco-grouting and erosion control. The novelty of this review lies in its interdisciplinary scope, integrating microbiological mechanisms, materials science, and engineering practice to highlight how bacterial processes can transition from laboratory models to real-world applications. By combining quantitative evidence with critical assessment of scalability, biosafety, and regulatory challenges, this paper provides a comprehensive framework that positions construction biotechnology as a transformative pathway towards low-carbon, adaptive, and resilient infrastructure systems. Full article

Endolysins, phage-derived enzymes capable of lysing bacterial cell walls, hold significant promise as novel antimicrobials against resistant Gram-positive and Gram-negative pathogens. In this study, we undertook an integrative approach combining extensive in silico analyses and experimental validation to characterize the novel endolysin LysPALS22. Initially, sixteen endolysin sequences were selected based on documented lytic activity and enzymatic diversity, and subjected to multiple sequence alignment and phylogenetic analysis, which revealed highly conserved catalytic and binding domains, particularly localized to the N-terminal region, underscoring their functional importance. Building upon these sequence insights, we generated three-dimensional structural models using Swiss-Model, EBI-EMBL, and AlphaFold Colab, where comparative evaluation via Ramachandran plots and ERRAT scores identified the Swiss-Model prediction as the highest quality structure, featuring over 90% residues in favored conformations and superior atomic interaction profiles. Leveraging this validated model, molecular docking studies were conducted in PyRx with AutoDock Vina, performing blind docking of key peptidoglycan-derived ligands such as N-Acetylmuramic Acid-L-Alanine, which exhibited the strongest binding affinity (−7.3 kcal/mol), with stable hydrogen bonding to catalytic residues ASP46 and TYR61, indicating precise substrate recognition. Visualization of docking poses using Discovery Studio further confirmed critical hydrophobic and polar interactions stabilizing ligand binding. Subsequent molecular dynamics simulations validated the stability of the LysPALS22–NAM-LA complex, showing minimal structural fluctuations, persistent hydrogen bonding, and favorable interaction energies throughout the 100 ns trajectory. Parallel to computational analyses, LysPALS22 was heterologously expressed in Escherichia coli (E. coli) and Pichia pastoris (P. pastoris), where SDS-PAGE and bicinchoninic acid assays validated successful protein production; notably, the P. pastoris-expressed enzyme displayed an increased molecular weight (~45 kDa) consistent with glycosylation, and achieved higher volumetric yields (1.56 ± 0.31 mg/mL) compared to E. coli (1.31 ± 0.16 mg/mL), reflecting advantages of yeast expression for large-scale production. Collectively, these findings provide a robust structural and functional foundation for LysPALS22, highlighting its conserved enzymatic features, specific ligand interactions, and successful recombinant expression, thereby setting the stage for future in vivo antimicrobial efficacy studies and rational engineering efforts aimed at combating multidrug-resistant Gram-negative infections. Full article

Wind turbine blades feature complex geometries and operate under harsh conditions, including high curvature gradients, nonlinear deformations, elevated humidity, and particulate contamination. This study presents the design and kinematic analysis of a novel climbing robot based on a 10R folding metamorphic mechanism. The robot employs a hybrid wheel-leg drive and adaptively reconfigures between rectangular and hexagonal topologies to ensure precise adhesion and efficient locomotion along blade leading edges and windward surfaces. A high-order kinematic model, derived from a modified Grubler–Kutzbach criterion augmented by rotor theory, captures the mechanism’s intricate motion characteristics. We analyze the degrees of freedom (DOF) and motion branch transitions for three representative singular configurations, elucidating their evolution and constraint conditions. A scaled-down prototype, integrating servo actuators, vacuum adhesion, and multi-modal sensing on an MDOF control platform, was fabricated and tested. Experimental results demonstrate a configuration switching time of 6.3 s, a single joint response time of 0.4 s, and a maximum crawling speed of 125 mm/s, thereby validating stable adhesion and surface tracking performance. This work provides both theoretical insights and practical validation for the intelligent maintenance of wind turbine blades. Full article

Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article

Background/Objectives: In this study, a vision transformer (ViT) based ensemble architecture was developed for the classification of normal, benign, and malignant diseases from breast ultrasound images. The breast ultrasound images (BUSI) dataset was used for the implementation of the proposed method. This dataset includes 133 normal, 437 benign, and 210 malignant ultrasound images. Methods: ROI segmentation and image preprocessing were applied to the dataset to select only the tumor region and use it in the model. Thus, a better performance was achieved using only the lesion regions. Image augmentation was performed using the Albumentations library to increase the number of images. Feature extraction was performed on the obtained images using three ViT-based models (ViT-Base, DeiT, ViT-Small). The purpose of using three different models is to achieve high accuracy. The extracted features were classified using a multilayer perceptron (MLP). Training was performed using 10-fold stratified cross-validation. Results: The purpose of stratified cross-validation is to include a certain number of images from all three classes in each cross-validation proposed model provided 96.2% precision and 86.3% recall for the benign class and 92.9% recall and 76.4% precision for the malignant class. The normal class achieved 100% success. The area under the curve (AUC) values were 0.97, 0.96, and 1.00 for benign and malignant tumors, respectively, and 1.00 for normal tumors. Conclusions: The ROI-based ViT + MLP + Ensemble architecture provided higher accuracy and explainability compared to traditional convolutional neural network (CNN) based methods in medical image classification. It demonstrated a stable success, especially in minority classes, and presented a potential, reliable, and flexible solution in clinical decision support systems. Full article

Triplophysa yarkandensis, a unique saline-alkali tolerant fish in the Tarim River Basin, exhibits unclear adaptive mechanisms of its digestive system to saline-alkali stressors. This study compared the histological characteristics of the digestive system in fish reared in saline-alkali water (salinity 5.89, alkalinity 125.60) and freshwater. Histological characteristics were analyzed using hematoxylin-eosin staining, and parameters were quantified via Image-Pro Plus software, with statistical comparisons performed using independent sample t-tests. Key findings included a 2.7-fold increase in oropharyngeal club cell density (48.50 ± 2.68 vs. 17.80 ± 2.04, p < 0.01) with denser stratified squamous epithelium in the saline-alkali group; a 74% increase in esophageal goblet cells (104.42 ± 6.67 vs. 59.94 ± 4.68, p < 0.01) alongside a 39% reduction in mucosal fold height; 87%, 24%, and 51% increases in villi number across the foregut, midgut, and hindgut, respectively, with an 84% elevation in midgut goblet cells (p < 0.01); and mild vacuolization in the hepatopancreas. Results indicate that T. yarkandensis adapts via synergistic strategies of enhanced digestive mucus secretion, epithelial structural optimization, and hepatopancreatic metabolic reprogramming. The coordinated villi proliferation and mucus secretion enhance nutrient absorption and osmotic barrier function, providing a theoretical basis for saline-alkali aquaculture. Full article

We developed a machine learning model for disease classification based on the International Classification of Diseases, 10th Revision (ICD-10) codes, analyzing male and female groups using seven features. The three most prevalent ICD-10 classes covered over 98% of the data. Features were selected using the least absolute shrinkage and selection operator, ridge, and elastic net, followed by the mean decrease in accuracy and impurity. A random forest classifier with five-fold cross-validation showed improved performance with more features. Using Shapley additive explanations, age, BMI, respiratory rate, and body temperature were identified as key predictors, with gender-specific variations. Integrating gender-specific insights into predictive modeling supports personalized medicine and enhances early diagnosis and healthcare resource allocation. Full article

Abeliophyllum distichum is rich in polyphenols and flavonoids with various bioactivities; however, studies on enzymatic modifications to enhance its functional properties remain limited. This study investigated the effect of Viscozyme^® L treatment on the secondary metabolite profile of A. distichum leaves. Phytochemical profiling using liquid chromatography–electrospray ionization tandem mass spectrometry revealed a decrease in the total number of detectable compounds, from 26 in the untreated extract to 16 in the enzyme-treated extract. Following Viscozyme^® L treatment, a notable shift in metabolite composition was observed, with significant enrichment of flavonoid glycosides, pyranone derivatives, and amino acid-related metabolites. Quantitative high-performance liquid chromatography analysis showed significant reductions in glycosylated compounds such as rutin (1), acteoside (2), and isoacteoside (3), while the aglycone quercetin (4) content increased more than four-fold compared to the control. These results indicate that Viscozyme^® L facilitates the deglycosylation of flavonoid glycosides into their aglycone forms. This enzymatic transformation suggests a potential strategy to enhance the bioavailability and functional value of A. distichum leaf extracts for nutraceutical and pharmaceutical applications. Full article

Proper estimation of flowing bottomhole pressure at coiled tubing depth (BHP-CTD) is crucial in optimization of nitrogen lifting operations in oil wells. Conventional estimation techniques such as empirical correlations and mechanistic models may be characterized by poor generalizability, low accuracy, and inapplicability in real time. This study overcomes these shortcomings by developing and comparing sixteen machine learning (ML) regression models, such as neural networks and genetic programming-based symbolic regression, in order to predict BHP-CTD with field data collected on 518 oil wells. Operational parameters that were used to train the models included fluid flow rate, gas–oil ratio, coiled tubing depth, and nitrogen rate. The best performance was obtained with the neural network with the L-BFGS optimizer (R² = 0.987) and the low error metrics (RMSE = 0.014, MAE = 0.011). An interpretable equation with R² = 0.94 was also obtained through a symbolic regression model. The robustness of the model was confirmed by both k-fold and random sampling validation, and generalizability was also confirmed using blind validation on data collected on 29 wells not included in the training set. The ML models proved to be more accurate, adaptable, and real-time applicable as compared to empirical correlations such as Hagedorn and Brown, Beggs and Brill, and Orkiszewski. This study does not only provide a cost-efficient alternative to downhole pressure gauges but also adds an interpretable, data-driven framework to increase the efficiency of nitrogen lifting in various operational conditions. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations. Full article

Background: In hemodialysis patients, precise hemoglobin (Hb) monitoring is essential for anemia management. Point-of-care blood gas analyzers (BGAs), such as the ABL800 Flex, offer rapid Hb determinations, but their accordance and comparability with central laboratory measurements remains to be assessed in the hemodialysis setting. Methods: We performed a retrospective analysis (April 2017–February 2024) of 10,802 paired Hb measurements from 291 hemodialysis patients. BGA and laboratory values within 90 min were compared using paired t-tests, non-inferiority testing (margin 0.5 g/dL), a Bland–Altman analysis, and linear regression. Results: The mean ± standard deviation Hb (g/dL) values were 10.14 ± 1.64 (BGA) versus 9.90 ± 1.55 (laboratory). The overall mean difference (BGA—laboratory) was 0.24 ± 0.49 g/dL (95% CI: 0.23–0.25), demonstrating non-inferiority (p < 0.0001). Measurement delay correlated with increasing analysis discrepancies (mean difference in g/dL: 0.22 at <30 min vs. 0.27 at 60–90 min; p < 0.001). We derived the equation of laboratory Hb = 0.90 × BGA Hb + 0.72; a simplified correction (BGA−0.3 g/dL) produced a mean absolute error (MAE) of 0.30 g/dL and root mean square error (RMSE) of 0.50 g/dL, and patient-level 10-fold cross-validation yielded MAE ≈ 0.30 and RMSE ≈ 0.49 g/dL. The Bland–Altman analysis confirmed a small systematic bias of 0.24 g/dL with 95% limits of agreement ranging from −0.73 to +1.21 g/dL. Conclusions: BGA Hb measurements via the ABL800 Flex are non-inferior to central laboratory values across clinical scenarios, with minimal bias. After regression correction, the estimated total error was ≈0.78 g/dL. If hemodialysis centers accept this level of total error and apply confirmatory testing near decision points, BGA could be used to guide anemia management. Full article