Search Results (1,894)

Noncontact, high-fidelity data acquisition has enabled terrestrial laser scanning (TLS) to be widely adopted for bridge geometry measurement and condition monitoring. In TLS applications, point cloud registration directly affects data quality and the correctness of subsequent results. For long-span bridges in large-scale scenes, complex geometry and sparse sampling pose challenges to surface-based, data-driven registration methods, and may degrade registration accuracy. A data-driven approach for high-precision point cloud registration, referred to as the Iterative Closest-Surface (IC-Surface) method, is presented in this study. The method extracts neighboring surface patches via a bounding box and applies random sampling-based plane fitting to derive surface features for registration, effectively mitigating the impact of sparse points and outliers in long-span bridges. Regular points are generated on the source patch and projected onto the corresponding target patch to establish high precision correspondences, yielding a stable and accurate transformation. This method effectively overcomes the limitations of the Iterative Closest Point (ICP), which struggles with unreliable correspondences and outliers. Comparative experiments were conducted using synthetic data, large bridge segments, and full-bridge datasets against commonly used registration methods. The results show that the IC-Surface method maintains high accuracy and stability across varying levels of outliers and overlap ratios. In complex scenes, IC Surface achieves higher registration accuracy than both ICP and the sphere target method, with distance errors reduced from 3 mm to 1 mm and inter-plane angle errors reduced from 0.016 rad to 0.009 rad. These findings demonstrate the method’s broad applicability in digital construction and operation and maintenance assessments of long-span bridges. Full article

Background/Objectives: Obstructive sleep apnea (OSA) affects approximately 1 billion adults worldwide with extensive comorbidities, including cardiovascular disease, metabolic disorders, and cognitive decline, yet pharmacological therapies remain limited. Conventional bottom-up omics approaches identify numerous genes overlapping with other diseases, hindering therapeutic translation. This study introduces a top-down, comorbidity-driven approach to identify actionable molecular targets and develop rational dual kinase inhibitors for OSA management. Methods: We implemented a five-tier modeling workflow: (1) comorbidity network analysis, (2) disease module identification through NetworkAnalyst, (3) mechanistic pathway reconstruction of the CK1δ-(HIF1A)-PINK1 signaling cascade, (4) molecular docking analysis of Nigella sativa alkaloids and reference inhibitors (IC261, PF-670462) against CK1δ (PDB: 3UYS) and PINK1 (PDB: 5OAT) using AutoDock Vina, and (5) rational design and computational validation of novel dual inhibitors (ICL, PFL) integrating pharmacophoric features from natural alkaloids and established kinase inhibitors. Results: Extensive network analysis revealed a discrete OSA disease module centered on two interconnected protein kinases—CK1δ and PINK1—that mechanistically bridge circadian disruption and neurodegeneration. Among natural alkaloids, Nigellidine showed strongest CK1δ binding (−8.0 kcal/mol) and Nigellicine strongest PINK1 binding (−8.6 kcal/mol). Rationally designed dual inhibitors demonstrated superior binding: ICL (−7.2 kcal/mol PINK1, −8.9 kcal/mol CK1δ) and PFL (−10.8 kcal/mol CK1δ, −11.2 kcal/mol PINK1), representing −2.6–2.8 kcal/mol improvements over reference compounds. Conclusions: This study establishes a comorbidity-driven translational framework identifying the CK1δ-PINK1 axis as a therapeutic target in OSA. The rationally designed dual inhibitors represent third-generation precision therapeutics addressing OSA’s multi-dimensional pathophysiology, while the five-tier workflow provides a generalizable template for drug discovery in complex multimorbid diseases. Full article

The fungal pathogen Blumeria graminis forma specialis tritici (B.g. tritici) infects bread wheat (Triticum aestivum L.) to cause wheat powdery mildew disease. Elucidating the molecular mechanism underlying wheat susceptibility to the pathogenic fungus B.g. tritici could facilitate wheat genetic improvement. In this study, we identified the wheat TaSWI3B gene as a novel Susceptibility gene positively regulating wheat susceptibility to B.g. tritici. The TaSWI3B gene encodes the SWI3B subunit of the SWI/SNF chromatin remodeling complex. The overexpression of the TaSWI3B gene enhances wheat powdery mildew susceptibility, whereas TaSWI3B silencing results in attenuated wheat powdery mildew susceptibility. Importantly, we found that TaSWI3B could be enriched at the promoter regions of the salicylic acid (SA) biosynthesis activator gene TaSARD1, facilitating nucleosome occupancy and thereby suppressing TaSARD1 transcription and inhibiting SA biosynthesis. Silencing of TaSARD1 and TaICS1 encoding a key enzyme in SA biosynthesis could attenuate the SA biosynthesis and powdery mildew resistance potentiated by knockdown of TaSWI3B expression. Collectively, these results suggest that the SWI3B subunit of the wheat SWI/SNF chromatin remodeling complex negatively regulates SA biosynthesis by suppressing TaSARD1 transcription at the epigenetic level and thus facilitates wheat powdery mildew susceptibility. Full article

The stability of ice structures in cold regions and polar environments has been increasingly challenged by global warming and climate change, making the accurate estimation of ice flexural properties essential. However, the flexural failure process of ice is highly complex, and the calculated flexural properties are influenced by multiple factors. Hence, several data-driven artificial intelligence models were developed to predict flexural strength, using classification and regression tree (CART), AdaBoost, and Random Forest methods, while the Elitist Ant System (EAS) was applied to optimize model parameters. The EAS procedure converged rapidly within ten iterations and effectively enhanced overall model performance. Compared with the single CART model, ensemble approaches exhibited higher prediction accuracy and better generalization, with AdaBoost achieving the best performance (R² = 0.736). Feature-importance analysis indicated that the testing method and specimen geometry had the greatest influence on the results, highlighting the importance of careful control of experimental conditions. The proposed ensemble–metaheuristic framework provides an efficient tool for predicting the mechanical behavior of ice and offers useful support for stability assessments of ice structures under changing climatic conditions. Full article

River and lake ice are sensitive indicators of climate change and important components of hydrological and ecological systems in cold regions. In this study, we develop a simple and transferable “surface water + land surface temperature (LST)” framework on Google Earth Engine to map potential winter ice area across China from 1990 to 2020. The framework enables consistent, large-scale, long-term monitoring without relying on complex remote sensing models or region-specific thresholds. Our results show that, despite a pronounced northwestward shift in the freezing-zone boundary, more than 400 km in the Northeast Plain and about 13 km per year along the eastern coast, the total ice-covered area increased by approximately 1.1% per year. At the same time, the average ice season became slightly shorter. This indicates asynchronous spatial and temporal responses of potential winter ice to warming. We identify a persistent “Northwest–Northeast dual-core” spatial pattern with strong positive spatial autocorrelation, characterized by increasing ice cover in Tibet, Qinghai, Xinjiang, Inner Mongolia, and Northeast China, and decreasing ice cover mainly in Beijing and Yunnan, where intense urbanization and low-latitude warming dominate. Random Forest modeling further shows that water area fraction, nighttime lights, built-up area, altitude, and water–heat indices are the main controls on potential winter ice. These findings highlight the combined influence of hydrological and thermal conditions and urbanization in reshaping potential winter ice patterns under climate change. Full article

Oxidative stress is a key driver of chronic inflammatory diseases. Anodendron affine is a native Formosan plant species in Taiwan that remains largely underexplored phytochemically and bioactivity. To reveal the bioactive constituents and assess its potential as a source of anti-inflammatory antioxidants, we performed bioactivity-guided fractionation and evaluated the inhibition of superoxide anion (O₂^•−) generation in formyl-L-methionyl-L-leucyl-L-phenylalanine-stimulated human neutrophils. Molecular docking simulations were employed to model interactions with Formyl peptide receptor 1 (FPR1) and the Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex, including neutrophil cytosol factor 1 (p47phox) and NADPH oxidase 2 (NOX2), to propose a theoretical mechanism of action. Phytochemical investigation led to the isolation of two new compounds, methyl 4,5-O-diferuloyl-3-methoxyquinate (1) and 16-pregnen-3,12,20-trione (2), together with four known compounds. Notably, 4-hydroxy-3-prenylbenzoic acid (5) exhibited potent inhibitory activity (IC₅₀ = 17.65 ± 0.97 μM), surpassing the activity of the positive control, ibuprofen (IC₅₀ = 27.85 ± 3.56 μM). Docking studies suggested that anodendrosin H (4) and 4-hydroxy-3-prenylbenzoic acid (5) exhibit high predicted binding affinity to p47phox and NOX2. Based on these results, compounds 1, 4, and 5 from A. affine were identified as potential lead candidates for the development of novel anti-inflammatory therapeutics. Full article

Background: Many inhibitors have been discovered to target hypoxia-induced carbonic anhydrase IX (CAIX) due to its critical role in lung cancers. This study discovers a novel compound, 3-(E-3,4-dihydroxycinnamaoyloxyl)-2-hydroxypropyl-9Z,12Z-octadeca-9,12-dienoate, which is produced by the seagrass Cymodocea serrulata and has binding sites at CAIX that are distinct from those of current inhibitors. Methods: Compound and reference drug treatment for cell lines; Cell viability: MTT; Staining: Ao/PI/DAPI; MMP shifts and cell cycle: FACS; Gene and protein expression of CAIX, BAX, BAD: qPCR and Western blotting. Results: The compound binds to the CAIX protein, raises extracellular pH, and kills A549 cells [IC₅₀: 11.61 µM], producing results that are lower than those of the reference drug doxorubicin [13.7 µM]. The substance depolarised the electrical potential of the mitochondrial membrane, caused S-phase arrest, and fragmented DNA. Additionally, it downregulated CAIX by 0.9 times while increasing apoptotic mRNA, BAX and BAD by 5.2 and 3.08 times, respectively, as demonstrated by qPCR. Between 0 and 24 h, the untreated hypoxic cells had a ΔpHe of 0.15, but the compound-treated cells had a ΔpHe of 0.6 indicative of intracellular acidosis. MD simulations verify the stability of the CAIX–C1 complex for more than 100 ns, and in silico studies show a strong binding affinity of the molecule to CAIX [−7.55 kcal/mol]. Conclusions: This implies that the amount of extracellular alkalosis was increased by the combination of treatment and hypoxia induction. As a result, when the cells were deprived of O₂, the compound provided less defense against ROS. The compound binds to the glutamine and alanine amino acids at positions 242 and 392, respectively, at the central Zn atom of CAIX, which sets it apart from conventional sulphonamide CAIX inhibitors. This naturally occurring compound may be a potent CAIX inhibitor with newer binding sites, which could help treat hypoxic lung cancers. Full article

Droplet microarrays are increasingly used for miniaturized, high-throughput biochemical assays, yet their fabrication commonly relies on complex lithographic processes, custom masks, or specialized coatings. Here we present a simple method for generating hydrophilic arrays on hydrophobic plastic substrates by combining ultrasonic atomization with off-the-shelf perforated masks. A fine mist of poly(vinyl alcohol) (PVA) solution is directed through commercial diamond sieves onto polypropylene (PP) sheets and polystyrene (PS) sheets, forming hydrophilic spots surrounded by the native hydrophobic background. Static contact angle measurements confirm a strong local contrast in wettability (from 100.85 ± 0.91° on untreated PP to 39.96 ± 0.71° on patterned spots, from 95.68 ± 3.61° on untreated PS to 52.00 ± 0.85° on patterned spots), while Image analysis shows droplet CVs of 6–8% in aqueous dye solutions for 1.2–2.0 mm masks; in complex media (LB), droplet uniformity decreases. By mounting the moving mask on a motorized stage, we generate one-dimensional reagent gradients simply by controlling the moving mask motion during atomization. We further demonstrate biological compatibility by culturing Escherichia coli in LB droplets containing resazurin, and by performing localized antibiotic screening using a moving mask-guided streptomycin gradient. The resulting droplet-wise viability data yield an on-chip dose–response curve with an IC₅₀ of 5.1 µg · mL⁻¹ (95% CI: 4.5–5.6 µg·mL⁻¹), obtained from a single array. Covering droplets with Electronic Fluorinated Fluid maintains volumes within 5% of their initial value over 24 h. Compared with conventional droplet microarray fabrication, the proposed method eliminates custom mask production and cleanroom steps, is compatible with standard plastic labware, and intrinsically supports spatial gradients. These attributes make masked ultrasonic atomization a practical platform for high-throughput microfluidic assays, especially in resource-limited settings. Full article

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power $P_e$ as a function of motor speed $n$ and mechanical power $P_m$. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using $R^2$, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4 $\left(+P_m^2\right)$ offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4 $\left(+P_m^2\right)$ polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

Background/Objectives: Leishmaniasis constitutes one of the most fatal parasitic diseases globally, adversely impacting the health of individuals residing in both intertropical and temperate zones. In these geographical areas, the administration of treatment is often inconsistent and largely ineffective with the available pharmaceuticals, as these exhibit more pronounced side effects than the therapeutic advantages they purport to provide. Methods: Consequently, the current investigation seeks to engage in molecular modeling of novel pharmacological candidates incorporating 1,2,3 disubstituted triazole moieties, coordinated with Cu^II metal centers, in pursuit of promising bioactive properties. Results: Two complexes were prepared and X-ray analysis revealed a comparable structural configuration surrounding the copper (II) atom. The planar square coordination geometry was elucidated through the assessment of the ${\tau }_4=0$ (tau four) parameters. The comprehensive characterization encompasses HRMS-ESI (+), NMR, elemental analyses, mid-infrared, and UV-vis spectroscopic techniques. Time-dependent density functional theory (TD-DFT) analyses will substantiate the findings obtained through UV-vis spectroscopy. Crucially, the biological assays against Leishmania (L.) amazonensis revealed that Complex 1 exhibited outstanding potency against the intracellular amastigote form, demonstrating a half-maximal inhibitory concentration (IC₅₀) of 0.4 µM. This activity was 6-fold higher than that of amphotericin B (IC₅₀ = 2.5 µM) and 33-fold higher than pentamidine (IC₅₀ = 13.3 µM). Furthermore, Complex 1 showed a promising selectivity index (SI = 9.7) against amastigotes, surpassing the reference drugs and meeting the criteria for a lead compound. While less active on promastigotes, both complexes demonstrated high stability in DMSO solution, a prerequisite for biological testing. Conclusions: These results unequivocally identify Complex 1 as a highly promising candidate for the development of new antileishmanial therapies, warranting further in vivo studies. Full article

This study investigated the site-specific ionic composition and wet deposition loads of rainwater collected from eight actively cultivated agricultural regions across South Korea, with the aim of quantifying spatial and seasonal variability and interpreting how regional agricultural characteristics and surrounding site conditions influence major ion concentrations and deposition patterns. Rainfall samples were obtained using automated samplers and analyzed via high-performance ion chromatography for major cations (Na⁺, $\mathrm{N}{\mathrm{H}}_4^{+}$, K⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, $\mathrm{N}{\mathrm{O}}_3^{-}$, $\mathrm{S}{\mathrm{O}}_4^{2-}$, $\mathrm{N}{\mathrm{O}}_2^{-}$). The results revealed significant seasonal fluctuations in ion loads, with $\mathrm{N}{\mathrm{H}}_4^{+}$ (peak 1.13 kg/ha) and K⁺ (peak 0.25 kg/ha) reaching their highest levels during summer due to increased fertilizer use and crop activity. Conversely, Cl⁻ peaked in winter (2.11 kg/ha in December), particularly in coastal regions, likely influenced by de-icing salts and sea-salt aerosols. Correlation analysis showed a strong positive association among $\mathrm{N}{\mathrm{H}}_4^{+}$, $\mathrm{N}{\mathrm{O}}_3^{-}$, and $\mathrm{S}{\mathrm{O}}_4^{2-}$ (r = 0.89 and r = 0.84, respectively), indicating shared atmospheric transformation pathways from agricultural emissions. Ternary diagram analysis further revealed regional distinctions: coastal regions such as Gimhae and Muan exhibited Na⁺ and Cl⁻ dominance, while inland areas like Danyang and Hongcheon showed higher proportions of Ca²⁺ and Mg²⁺, reflecting differences in aerosol sources, land use, and local meteorological conditions. These findings underscore the complex interactions between agricultural practices, atmospheric processes, and local geography in shaping rainwater chemistry. The study provides quantitative baseline data for evaluating non-point source pollution and developing region-specific nutrient and soil management strategies in agricultural ecosystems. Full article

Cedrus atlantica wood tar (CAWT) is traditionally used as a medicinal product, especially in low- and middle-income countries. Despite its traditional use, scientific support for its efficacy remains limited. This study evaluated the biological properties of CAWT using an integrated approach that combined qualitative and quantitative phytochemical analysis, disc diffusion and microdilution tests for antimicrobial assays (disc diffusion and microdilution), antioxidant activity (DPPH and ferric-reducing power assays), in silico ADMET/toxicity, docking, and MD/MMGBSA and provided a balanced comparison with reference antioxidants. This study demonstrated that CAWT is rich in secondary metabolites linked to biological activity, including polyphenols (307.39 ± 58.45 mg GAE/g), tannins (124.42 ± 6.14 mg TAE/g), and flavonoids (15.62 ± 2.53 mg QE/g). For free radical scavenging, CAWT inhibited DPPH with an IC50 of 19.781 ± 2.51 µg/mL and showed ferric-reducing activity with an IC50 of 83.7 ± 2.88 µg/mL for its antimicrobial activity against Pseudomonas aeruginosa; inhibition zones reached 35.66 ± 0.58 mm. In silico analysis, Swiss ADMET and pkCSM predicted ≥94% intestinal absorption, no cytochrome P450 liabilities, and low acute toxicity for six dominant terpenoids. Docking pinpointed trans-cadina-1(6),4-diene and α/β-himachalene as high-affinity ligands of LasR and gyrase B (ΔG ≈ −8 kcal mol⁻¹). A 100 ns GROMACS run confirmed stable hydrophobic locking of the lead LasR complex (RMSD 0.22 nm), while MM/GBSA calculated a dispersion-dominated binding free energy of −37 kcal mol⁻¹. Overall, CAWT showed in vitro antioxidant activity (DPPH and ferric-reducing assays) and inhibitory effects in disc diffusion assays, while in silico predictions for major terpenoids suggested favorable oral absorption and low acute toxicity. However, chemical composition analysis and bio-guided fractionation are necessary to confirm the antimicrobial activity and to validate the compounds responsible for the observed effects. Full article

Establishing a correlation between the Cone Penetration Test (CPT) and the Standard Penetration Test (SPT) is of significant importance for geotechnical engineering practice. A novel correlation between CPT-q_t and SPT-N₆₀ based on a genetic algorithm (GA) and linear regression was proposed in this study. Based on the soil behavior type index (I_c), a GA was first applied to divide the dataset into different I_c intervals. Subsequently, linear regression was performed separately for the data in each interval to establish a correlation between CPT-q_t and SPT-N₆₀. Concurrently, the Segmented Information Criterion (SIC) was introduced to perform dual-objective optimization of complexity and prediction accuracy. The results indicate that the proposed model achieved an R² of 0.60 and an RMSE of merely 8.10. Specifically, the R² values improved by 33% and 5% compared to the traditional models and the AI models, respectively. On the validation dataset, the proposed model achieved an R² of 0.67 and an RMSE of 4.33, demonstrating higher accuracy compared to the traditional models. In summary, a method for investigating the CPT-SPT correlation is proposed in this study, characterized by simplicity, efficiency, and enhanced reliability. Additionally, a novel criterion (SIC) for mitigating overfitting is introduced. These two research findings can provide more reliable input parameters for SPT-based design, thereby supporting geotechnical engineering applications, and offer a valuable reference for relevant studies in other regions. Full article

The urgent need for effective therapies against cancer and antimicrobial-resistant pathogens motivates the development of novel metal-based complexes. Herein, we report the synthesis and characterization of four novel cobalt(II) complexes with biologically relevant β-diketo ester ligands. The complexes were characterized via UV-Vis, FTIR, mass spectrometry, and elemental analysis. Their biological activities were evaluated through antimicrobial and cytotoxic assays. Complex B1 exhibited the strongest antimicrobial activity, with minimum inhibitory concentrations (MICs) of 0.23 mg/mL against Staphylococcus aureus and Proteus mirabilis, and 0.01 mg/mL against Mucor mucedo, exceeding the performance of ketoconazole. Cytotoxicity studies on SW480 colorectal cancer cells and HaCaT normal keratinocytes identified B3 as the most potent anticancer agent (IC₅₀ = 11.49 µM), selectively targeting tumor cells. Morphological analysis indicated apoptosis as the primary mode of cell death. Mechanistic studies were performed to elucidate interactions with biomolecules. UV-Vis and fluorescence spectroscopy, viscosity measurements, and molecular docking revealed that B3 binds strongly to calf thymus DNA via hydrophobic interactions and groove binding, and exhibits selective binding to bovine serum albumin (site II, subdomain IIIA). These results highlight the potential of cobalt(II) complexes as multifunctional agents with significant antimicrobial and antitumor activities and provide detailed insight into their molecular interactions with DNA and serum proteins. Full article

During constant-force operations in complex marine environments, underwater manipulators are affected by hydrodynamic disturbances and unknown, time-varying environment stiffness. Under classical impedance control (IC), this often leads to large transient contact forces and steady-state force errors, making high-precision compliant control difficult to achieve. To address this issue, this study proposes a Bayesian recursive least-squares-based fuzzy adaptive impedance control (BRLS-FAIC) strategy with displacement correction for underwater manipulators. Within a position-based impedance-control framework, a Bayesian Recursive Least Squares (BRLS) stiffness identifier is constructed by incorporating process and measurement noise into a stochastic regression model, enabling online estimation of the environment stiffness and its covariance under noisy, time-varying conditions. The identified stiffness is used in a displacement-correction law derived from the contact model to update the reference position, thereby removing dependence on the unknown environment location and reducing steady-state force bias. On this basis, a three-input/two-output fuzzy adaptive impedance tuner, driven by the force error, its rate of change, and a stiffness-perception index, adjusts the desired damping and stiffness online under amplitude limitation and first-order filtering. Using an underwater manipulator dynamic model that includes buoyancy and hydrodynamic effects, MATLAB simulations are carried out for step, ramp, and sinusoidal stiffness variations and for planar, inclined, and curved contact scenarios. The results show that, compared with classical IC and fuzzy adaptive impedance control (FAIC), the proposed BRLS-FAIC strategy reduces steady-state force errors, shortens force and position settling times, and suppresses peak contact forces in variable-stiffness underwater environments. Full article