An enhanced chemical profiling characterization strategy integrating off-line two-dimensional counter-current chromatography × UHPLC-MS/MS and feature-based molecular networking: Comprehensive characterization of the fibrous root of Bletilla striata as a case study.

Lysophosphatidic acid receptor 2 (LPAR2), a G protein-coupled receptor, has been implicated in the progression of fibrosis and is therefore a promising novel drug target for the treatment of fibrosis and related diseases. In this paper, a reliable homology model of LPAR2 was obtained by using three templates (PDB IDs: 4Z34, 7TD0, and 7VIE) and evaluations. A new binding site for a series of selective LPAR2 inhibitors were identified through molecular docking with the reference compound 50. Subsequently, a three-dimensional quantitative structure-activity relationship (3D QSAR) analysis was conducted on a series of N-sulfonyl heterocyclic antagonists of LPAR2. The derived optimal CoMFA model (q2 = 0.792, r2 = 0.999, [Formula: see text] = 0.998, [Formula: see text] = 0.978) and CoMSIA model (q2 = 0.713, r2 = 0.996, [Formula: see text] = 0.978, [Formula: see text] = 0.958) demonstrated strong statistical robustness and high external predictability. The 3D contour maps generated from these models were analyzed and compared with the binding mode of the reference compound. This provided insights into the structural requirements of these LPAR2-selective inhibitors. Furthermore, the predictive capability of these models was validated by accurately predicting the antagonistic activities of other types of LPAR2-selective inhibitors (CoMFA-SE, [Formula: see text] = 0.862; CoMSIA-SEHDA, [Formula: see text] = 0.934), confirming the robustness of the optimal 3D QSAR models. The new binding site and the optimal 3D QSAR models will be helpful to design novel molecules and predict their inhibitory activity against LPAR2.

KRAS is the most common altered oncogene of the RAS family in human tumors, emerging as an attractive target for various human tumors. KRAS is found to be responsible for activating mutations upto 90% in pancreatic cancers and 50% in colorectal cancers. The present study involved retrieving a set of compounds from the ZINC database based on the best pharmacophore hypothesis/model and the optimal 3D-QSAR model ([Formula: see text]), indicating strong correlation and predictive ability, along with low standard deviation (SD = 0.3987) and RMSE (0.38). In addition, 3D contour maps were examined to identify favorable and unfavorable regions for enhancing drug activity. The selected compounds were further screened through virtual molecular docking studies using PDB ID: 7O83. Three compounds — ZINC1911582104, ZINC1911585886, and ZINC1569989248 demonstrated comparable XP docking scores of -9.281, -8.112, and -7.261, respectively, relative to the standard drug Sotorasib (docking score: -9.232). Also, ADME studies suggested drug-like characteristics of the compounds ZINC1911582104, ZINC1911585886 and ZINC1569989248. Further, molecular dynamics simulation studies supported the stability of the ligand-protein complex during the course of the simulation. As a result, this research may help scientists in the fight against the undruggable KRAS target.

ABSTRACT Accurate 3D lung tumor segmentation on CT is crucial for stereoscopic diagnosis and virtual preoperative planning. However, this task faces significant challenges: traditional segmentation methods are often limited by parameter sensitivity and manual intervention, while purely deep learning approaches are constrained by the scarcity of annotated medical data and struggle with irregular, low‐contrast tumor margins. To overcome these limitations, we propose a fully automatic hybrid pipeline that combines AI‐based localization with an interpretable segmentation algorithm. The method first uses a pretrained MONAI 3D RetinaNet detector to localize tumors and generate seeds, followed by a four‐point ensemble region growing process with adaptive thresholding and dynamic constraints, and finally reconstructs the tumor using the Marching Cubes algorithm. Evaluated on selected cases from the LIDC‐IDRI dataset, the proposed method achieved a mean Dice score of 0.935 for tumor segmentation, exceeding other models like I‐3D DenseUNet (0.831), 3D MultiResUNet (0.866), and a hybrid CNN (0.720). By synergizing deep learning for robust localization and a constrained region‐growing algorithm for precise boundary delineation, our method provides stable 3D tumor contours and interactive models, showing great promise for assisting thoracoscopic virtual preoperative planning.

ABSTRACT Human decision-making during autonomous underwater vehicle (AUV) operations is fundamentally shaped by uncertainty in environmental information. This exploratory pilot study investigates how during path planning, task dynamics can influence operator performance and risk tolerance, relative to a 2D visualisation of bathymetric data uncertainty. Using bathymetric data obtained from field trials of prototype AUVs, we visualise uncertainty in these data using Gaussian Processes (GP), manipulating the hyperparameters. Participants (n = 18) completed 108 free-form path planning trials, overlaid on a 2D contour map of bathymetric uncertainty while avoiding marine dangers. Additional uncertainty was introduced via an AI agent with one of three face realism conditions, designed to sow doubt in how participants’ perceived performance on that trial. Bayesian modelling suggests that the visualisation parameter Contour lowered redraw rates, with a modest U-curve relationship. The GP parameter Variance followed an inverted U-curve effect on redraw rates, with moderate values reducing ambiguity and improving performance. AI agent appearance shaped trust behaviour, while environmental complexity reduced risk tolerance. Results of our experimental pilot study show that visual uncertainty, social agent appearance, and task complexity systematically shape human trust, risk tolerance, and decision-making behaviour during path planning.

Inspection and capacity prediction of corroded steel bridge girders through 3D scanning, contour mapping, and experimental testing

GRFormer: 3D reconstruction of liver and tumor via gridding and transformer-based point cloud completion.

Rice, as a primary grain crop, is prone to breakage during harvesting and processing when mechanical forces exceed the grain's strength limit. Grain breakage not only reduces its edible and market value but also increases the risk of pests and diseases during storage. Studies have shown significant differences in the morphological structure and mechanical properties of grains at different spike positions (upper, middle, and lower) on the same rice spike. However, research on the influence of spike position on grain breakage remains limited. Using brown rice from the upper, middle, and lower spike positions as the research subject, this study precisely reconstructed three-dimensional (3D) contour models of the embryo, endosperm, and outer epidermis for each spike position based on X-ray computed tomography (CT) and image processing technology. Discrete element simulation parameters were calibrated using response surface optimization design. A punching test setup was established to extract and characterize internal crack patterns under varying impact forces. The results showed that the critical static loads for grains at different spike positions were 102.4 ± 16.2 N (upper), 94.5 ± 10.6 N (middle), and 93.0 ± 11.3 N (lower). Due to differences in breakage models, the optimal calibrated parameters varied by spike position, but errors remained below 5%. The volume of impact-induced cracks increased with force. Under the same force, upper grains were less prone to breakage than middle and lower grains, indicating that impact resistance followed the order: upper > middle > lower. These findings provide a key reference for optimizing agricultural machinery design to minimize grain damage. © 2026 Society of Chemical Industry.

A framework for 3D direct sampling-based environmental contours of wind, wave, and current using ABKP model and R-vine copula

Mononuclear Ru(II)Polypyridyl complexes of type [Ru(A)2MTZIP] (ClO4)2.2H2O, where MTZIP = (2-(4-methyl thiazol-5-yl)-1 H-imidazole [4,5-f] [1, 2] phenanthroline and A = phen = 1,10 Phenanthroline (1), bpy = 2, 2'- bipyridine (2), dmp = 4,4'-dimethyl-1,10 -Ortho Phenanthroline (3) & dmb = 4, 4' -dimethyl 2, 2'- bipyridine (4) were synthesized and their biological activity were examined. Biophysical techniques (absorption, emission methods, and viscosity) ascertains that intercalation is the primary mechanism by which Ru (II) polypyridyl complexes bind to DNA.The intrinsic binding constant (kobs) data show 1 > 2>3 > 4, indicating the phen complex (1.0 × 106) has a higher binding capacity than the bpy, dmp, and dmb (4.5 × 105,3.5 × 105, 2.0 × 105), demonstrating the auxiliary ligand effect on the specificity of DNA binding. The HOMO-LUMO gap (Eg) values for the complexes 1-4 are in the range of 5.6375-5.7398eV, ligand MTZIP (7.3026eV), indicative of a more reactivity for complexes. The 3D contour maps show that in complexes LUMO is primarily concentrated on or near the Ru (II) metal ion and auxiliary ligands; on the contrary, the HOMO has constraints to the intercalating ligand's nitrogen. The phen complex has greater chemical reactivity than the bpy, dmp and dmb complexes, so complex 1 is more vulnerable to nucleophilic attack. All four complexes were active against Gram-positive bacterial pathogens (Staphylococcus and Bacillus strains) as well as Gram-negative bacterial species (E. Coli and Klebsiella) and antifungal activity (candida, aspergillus). All the complexes showed good anticancer activity, but the phen complex with IC50 = 22.89 ± 0.814 against the MCF-7 cell line.

Density-Constrained Contour Analysis for Unmanned Aerial Vehicle-Based 3D Building Reconstruction and Measurement

The disturbed flow contributes to juxta-anastomotic intimal hyperplasia (IH) in arteriovenous fistulas (AVFs). This study developed an in vitro method aiming to understand the hemodynamic impact on endothelial cells (ECs) in AVFs. A tubular bifurcation AVF model was constructed, and the disturbed flow was induced near the bifurcation by pulsatile flow. Hemodynamics was simulated using computational fluid dynamics (CFD) and visualized as 2D contour plots. Human Umbilical Vein Endothelial Cells (HUVECs) were cultured on a tailored polycarbonate membrane (PCM) and placed in the model. HUVECs on the PCM allowed precise mapping to the hemodynamic plots. CFD identified four regions: the outer wall with high time-averaged wall shear stress (TAWSS MAX) and transverse wall shear stress (TransWSS MAX), the inner wall with low and oscillatory wall shear stress (L/O), and the pulsatile flow (PF). HUVECs in PF were aligned in the direction of flow. The cells in other regions showed more focal adhesion junctions and fewer glycocalyces. HUVECs on inner wall had the lowest expression of Krüppel-like factor 2 and endothelial nitric oxide synthase, while the outer wall showed the highest expression of platelet-derived growth factor and transforming growth factor-β. We developed an in vitro AVF model and validated the effects of different hemodynamic profiles on ECs by matching CFD plots with cell positions on a tailored PCM. This study shows that the in vitro AVF model can be a promising tool to assess the impact of interventions aimed at improving ECs function in AVFs. In Vitro Model Development: An innovative in vitro model was developed to simulate arteriovenous fistula conditions, allowing for direct assessment of endothelial cell behavior under varied hemodynamic conditions. Linking Hemodynamics to Cell Response: The research successfully correlated computational fluid dynamics results with specific endothelial cell positions, facilitating a clearer understanding of the impact of hemodynamics on cell morphology and function. Arteriovenous Fistula Failure Understanding: The study enhances the understanding of arteriovenous fistula failure mechanisms, specifically the role of intimal hyperplasia caused by disturbed flow.

This work studies the exact solutions of the integrable (3+1)-dimensional combined potential Kadomtsev-Petviashvili (pKP) equation with the B-type Kadomtsev-Petviashvili (BKP) equation, which is used to characterize several nonlinear oscillations occurring in hydrodynamics, plasma physics, and nonlinear optics. A bilinear representation of the pKP-BKP model is used to study the properties of different wave solutions. A variety of ansatzes are utilized to derive lump cross-kink waves, lump cross-periodic waves, rogue waves, as well as two, three, and multi-wave solutions pertinent to the model. In addition, a traveling wave transformation is applied to transform the problem into an ordinary differential equation. The new auxiliary equation methodology yields solutions including rational, exponential, hyperbolic, and trigonometric functions. Graphical visualizations using 2D plots, contour plots, and 3D plots show the dynamics of the obtained solutions. These solutions are of great importance in nonlinear fiber optics and telecommunications, which contribute to our understanding of the fundamental physical models.

In this study, the nonlinear wave dynamics of the Double-Chain DNA model, a molecular system that captures the longitudinal and transverse interactions between two complementary nucleotide chains, are investigated. To construct explicit analytical structures, the generalized Riccati equation mapping method and the modified generalized Riccati mapping neural network method are applied in a complementary manner. Through these approaches, several classes of exact solutions, including hyperbolic, trigonometric, rational, and hybrid waveforms, are systematically derived. Consequently, the spectrum of admissible bright, dark, kink, and anti-kink wave structures supported by the model is significantly expanded. In addition to the derivation of exact solutions, the governing system is reduced to a planar dynamical framework to examine its qualitative behavior. Lyapunov exponent analysis is performed to quantify the long-term stability characteristics of the system and to identify parameter regimes exhibiting strong sensitivity to initial conditions. The analytical findings are further illustrated through 3D surface plots, contour diagrams, and 2D profiles, confirming the geometric and physical consistency of the obtained wave structures.

Vortex research is critical for environmental and engineering applications. Quantitative evaluation on vortex properties were centred on two-dimensional (2D) contours (based on 2D or three-dimensional (3D) flow fields) in previous studies. In this study we proposed a 3D isosurface-based method with higher ability and accuracy for vortex extraction to characterise 3D vortices. The key lies in the usage of a fast triangle-triangle intersection method together with a triangular cutting plane. Vortices were extracted as polygons from intersection lines between the 3D isosurface and the triangular cutting plane. Vortex population density, morphological descriptors (circularity, radius, convex solidity, and axis ratio), and orientation for different thresholds and wall distances were analysed. As the threshold increases, the number of extracted vortices decreases logarithmically, and vortices become smaller, more circular and convex. Similar to the threshold case, a logarithmic relationship between vortex density and wall distance exists. And as the wall distance increases, vortices also become more circular and convex, and inclination angle increases and stabilises in the wake layer. Compared with findings based on 2D contours in previous studies, the isosurface-based method in this study represented 3D vortices in a clearer perspective. Due to the usage of threshold varied with the wall distance, vortex radius increased with the increasing wall distance in previous studies. However, results based on 3D isosurface show that in the fully developed channel flow vortex size is strongly correlated with the velocity gradient, and vortex size only increases in the near-wall region, beyond which it remains nearly constant. The proposed method may help to delve into turbulent structures along with their interactions and evolutionary mechanisms.

The operational environment of construction machinery is predominantly unstructured, characterized by rapid changes, high complexity, and irregularly distributed objects. This poses significant challenges for 3D semantic perception, particularly due to the high cost of acquiring point cloud semantic labels. To address this, a novel 3D semantic perception scheme is proposed for such unstructured environments. This scheme integrates image semantic segmentation results with point cloud clustering via perspective projection. The projection parameters are refined using Particle Swarm Optimization (PSO), and the semantic consistency of the fused results is further enhanced by a Kd-tree-based radius nearest neighbor (RNN) matching algorithm. Consequently, a weakly supervised framework is established that achieves accurate 3D semantic understanding using only 2D image labels, eliminating the need for annotated 3D point clouds. The feasibility and effectiveness of the scheme are validated through a dedicated unstructured scene dataset and real-world testing. Results demonstrate its capability to effectively perceive 3D semantic information and reconstruct target contours, achieving a mean Pixel Accuracy (mPA) of 84.72% and a mean Intersection over Union (mIoU) of 75.85%.

Abstract Due to the characteristics of small operable space and large processing area, the development of automated painting equipment for ship blocks bottom painting at home and abroad is still in the exploratory stage. In this paper, we design a mobile robot for the bottom painting of ship blocks bottom painting, and improve the intelligent spraying system for the whole ship blocks. The mapping and path planning research for the bottom of ship segments puts forward a static mapping idea: SLAM algorithm maps the basic environment of the workshop (only once), the sensor collects point cloud data of segments and converts them into 2D contour maps, and finally the two are fused to realize the drawing of a complete map of the workshop. Then a Full-Coverage Path-Planning method is applied, aiming at avoiding collisions and planning a coverage path that can fully traverse the whole bottom of the segment.

Transonic shock buffet is a complex flow phenomenon characterized by self-sustained shock oscillations, which severely limits the flight envelope of modern civil aircraft. While Shock Control Bumps (SCBs) have been widely studied for drag reduction, their potential for delaying the buffet boundary on swept wings has yet to be fully explored. This study employs numerical analysis to investigate the efficacy of three-dimensional (3D) contour SCBs in delaying the buffet boundary of the NASA Common Research Model (CRM) wing. The buffet boundary is identified using both the lift-curve slope change and trailing-edge pressure divergence criteria. The results reveal that 3D SCBs generate streamwise vortices that energize the boundary layer, thereby not only weakening local shock strength but, more critically, suppressing the spanwise expansion of shock-induced separation. Collectively, the reduction in shock strength and the containment of spanwise separation delay the buffet boundary, thereby improving the aerodynamic efficiency of the wing. Two configurations, designed at different lift conditions (SCB-L at CL=0.460 and SCB-H at CL=0.507), demonstrate a trade-off between buffet delay and off-design drag reduction. The SCB-H configuration achieves a buffet boundary lift coefficient improvement of 6.3% but exhibits limited drag reduction at lower angles of attack, whereas the SCB-L offers a balanced improvement of 4.0%, with a broader effective drag-reduction range. These results demonstrate that effective suppression of spanwise flow is key to delaying swept-wing buffet and establish a solid reference framework for the buffet-oriented design of SCBs.

Objective: To explore a 3D visualization method for tongue articulation based on ultrasound images and to construct a corresponding visualization database. Methods: A high-fidelity statistical tongue model was constructed from MRI data and parameterized with six independent, physiologically interpretable control parameters to capture tongue-shape variation. To provide speech-specific data for model fitting, mid-sagittal ultrasound images were collected for each phoneme in the corpus, and tongue contours were manually annotated. A fully connected neural network was then trained to map the ultrasound-derived tongue contours to the model's control parameters. The estimated parameters were further refined through manual adjustment to obtain 3D tongue shapes that accurately matched the observed contours. Finally, model-fitting accuracy was quantitatively evaluated, and statistical analyses were conducted to examine tongue-shape differences among easily confusable phonemes. Results: For the majority of phonemes, the similarity between the 3D model's mid-sagittal contour and the ultrasound-derived tongue contour exceeded 90%, and the average root mean square error(RMSE) was reduced by approximately 28% compared with conventional tongue models, thereby enabling the detection of subtle articulatory distinctions among phonemes. Conclusion: The constructed 3D tongue articulation visualization database for Mandarin phonemes provides a valuable tool for speech rehabilitation in individuals with hearing impairment and for visualization-based instruction in second-language learning, demonstrating strong potential for dissemination and application.

Neural organoids (NOs) have emerged as important tissue engineering models for microphysiological systems, brain sciences, and biocomputing. Establishing reliable relationships between stimulation and recording traces of electrical activity is essential for monitoring the functionality of NOs, especially in paradigms such as neural plasticity, learning, or stimulus discrimination. While researchers have demonstrated neuromodulation in NOs, they have primarily used 2D microelectrode arrays (MEAs) with limited access to the entire 3D contour of the NOs. Here, we report neuromodulation using tiny mimics of macroscale EEG caps, or shell MEAs. Specifically, we observe that stimulating current within a specific range (20 to 30 µA) induced a statistically significant increase in neuron firing rate when comparing the activity 5s before and after stimulation. We detect neuromodulatory behavior using both three- and 16-electrode shells and generated 3D spatiotemporal maps of neuromodulatory activity around the entire surface of the NO. Our studies demonstrate a methodology for investigating 3D spatiotemporal neuromodulation in organoids of broad relevance to biomedical engineering models of neural functionality, plasticity, and learning.