Search Results (1,290)

In addition to its role in angiotensin II (Ang II) production, chymase exhibits various functions, including activation of latent transforming growth factor beta 1 (TGF-β1) and pro-matrix metalloproteinases (MMPs). However, the extent to which these Ang II-independent functions contribute to pathological conditions remains unclear. In this study, we investigated the Ang II-independent roles of chymase in cardiac remodeling and dysfunction. Eighteen male Syrian hamsters, aged 6 weeks and weighing 90–110 g, were used. Exogenous Ang II was administered to a hamster model that mirrors the human chymase-dependent Ang II production pathway, via subcutaneous osmotic mini pumps (2 mg/kg/day) for 4 weeks. A chymase-specific inhibitor, TY-51469 (10 mg/kg/day), was given daily starting 1 day after commencement of Ang II infusion. Evaluation showed that while systolic blood pressure increased significantly, only diastolic dysfunction developed over time. Ang II treatment led to elevated cardiac expression of chymase, TGF-β1, and MMP-2, and increased the number of chymase-positive mast cells, resulting in notable cardiac hypertrophy and fibrosis. TY-51469 effectively suppressed these molecular changes and improved both cardiac structure and diastolic dysfunction, despite continued Ang II exposure. These results suggest that chymase promotes cardiac remodeling and dysfunction not only through Ang II generation but also by activating profibrotic and matrix-degrading factors, such as TGF-β1 and MMP-2. Full article

Using multiple species in native plant communities may improve control efficiency compared with single-species use. We conducted field investigations to assess the effects of Artemisia argyi, Portulaca oleracea, and their mixtures on the growth and reproduction of Mikania micrantha, followed by a greenhouse de Wit replacement series to compare different combinations of M. micrantha, A. argyi, and P. oleracea in terms of multispecies competition, phytoallelopathy, and photosynthesis. Field investigation showed that compared with M. micrantha monoculture (Group D), aboveground biomass, total stem length, flower biomass, inflorescence biomass, seed biomass, and seed number of M. micrantha increased in the P. oleracea community (Group B), though only seed number was significantly higher (p < 0.05). In contrast, in the A. argyi community (Group A) and the mixed community of A. argyi and P. oleracea (Group C), all these indicators decreased significantly (p < 0.05), in the order: Group C < Group A < Group D < Group B. This indicates that the mixed community (Group C) most strongly suppressed M. micrantha growth and reproduction. The effects of A. argyi, P. oleracea, and their mixture on the growth of M. micrantha in the greenhouse experiments mirrored the trends observed in field investigations. Calculated indices (relative yield, relative yield total, competitive balance index, and change in contribution) of A. argyi, P. oleracea, and their mixed population on M. micrantha demonstrated a higher competitive ability and higher influence of the combination of the two species compared with either A. argyi or P. oleracea alone. The interspecific phytoallelopathy experiment demonstrated strong allelopathic potential of A. argyi versus M. micrantha (p < 0.05) but showed no significant effect on P. oleracea. The net photosynthetic rate (Pn) of M. micrantha was generally lower in communities with both competitors compared with single-species communities. Our results suggest that, compared with a single plant population, the mixed population of A. argyi and P. oleracea exhibited a markedly enhanced ecological control capability through increased relative competitive ability, strengthened allelopathic inhibition, and markedly reduced photosynthetic efficiency of M. micrantha. Full article

Classical social cognitive conceptions suppose that the existence of common representations between agents constitutes the basis that represents the world from others’ perspectives. Alternatively, recent contributions support that the ability to distinguish self- from other’s representation would rather be at the origins of social inferences abilities. In the present study we compared the effects of two types of imitation training: mirror imitation (for which gesture could be represented in common referential) and anatomically congruent imitation (which requires not only a representation of the gesture of the model but also distinguishing between one’s own and others’ representations). We observed that a 4 min training of anatomically congruent imitation, but not of mirror imitation, improved performance on a visual perspective-taking test. This short training did not significantly impact self-reported measures of social cognitive skills. These results suggest that a unique transversal cognitive mechanism of co-representing and switching between self-related and other-related representations could be involved at both the motor and the mental-state levels. Opportunities for innovative social cognitive interventions at the motor level are discussed. Full article

Background/Objectives: The role of direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs) in preventing recurrent thrombosis in patients with antiphospholipid syndrome (APS) remains uncertain. Using real-world data, we aimed to evaluate the effectiveness and internal validity of a digital twin (DT) approach for modeling thrombotic recurrence risk in APS patients treated with DOACs or VKAs. Methods: We conducted a multicenter observational study that included thrombotic APS patients treated with DOACs or VKAs. Clinical data were used to generate DT via conditional generative adversarial networks (CGANs), incorporating a directed acyclic graph (DAG) to preserve causal relationships. Validation metrics included absolute standardized mean differences (ASMD), mean ASMD (MASMD), and Spearman correlation matrices to assess structural fidelity. Treatment effects were estimated in a CGAN-conditioned cohort matched on key covariates. Results: Eighty-nine thrombotic APS patients were included: 70 (78.7%) received VKAs and 19 (21.3%) received DOACs. Thrombotic recurrences occurred in 5 DOAC patients (26.3%) and 17 AVK patients (24.3%). The CGAN-generated synthetic cohort closely mirrored the original data (MASMD = 0.073 ± 0.041), with 85.4% of pairwise correlations differing by <0.1 in absolute value. In the conditioned DT cohort, predicted recurrence was 24.2% for DOACs and 19.9% for VKAs. Recurrence risk increased with antibody burden, reaching 41.3% in triple-positive patients and 46.8% in those with index arterial thrombosis treated with DOACs. Conclusions: DT technology accurately replicated the clinical structure of APS patients, supporting its application for simulating counterfactual scenarios and estimating individualized treatment effects. Full article

The mechanical behavior of dental composites depends on the sample size and stress configuration. This makes it difficult to extrapolate laboratory data to clinical restorations with significant variations in size and geometry. Intrinsic parameters, such as fracture toughness, are therefore of great importance, even if they are less common and more difficult to measure. The aim of this study was to apply principles of fractography and fracture mechanics to exploit the results obtained from a three-point bending test. The objectives include calculating a material-specific constant, validating the experimental findings, and establishing a correlation with fracture toughness. Forty representative composites with wide variation in filler quantity (65–83% by weight and 46.4–64% by volume), type (compact glasses and pre-polymerized), and composition were examined. Fracture toughness/K_Ic was evaluated in a notchless triangular prism test. Fracture type, origin, and mirror size were determined on 280 flexural fracture specimens (n = 20). The amount of filler strongly influences all measured parameters, with the effect strength varying in the sequence: mechanical work (η_P² = 0.995), modulus of elasticity (η_P² = 0.991), flexural strength (η_P² = 0.988), fracture toughness (η_P² = 0.979), and mirror constant (η_P² = 0.965). Fracture surfaces allowed the delineation of the fracture mirror and the application of fracture mechanics approaches. The mirror constant was derived from the radius of the fracture mirror, measured in the direction of constant stress, using Orr’s equation, and correlates well with K_Ic (0.81). Larger confidence intervals were observed for the mirror constant data, while for 5 of 14 materials, the mirror constant was overestimated compared to K_Ic. The overestimation was attributed to the lower refractive index of the urethane methacrylate composition. Full article

The popularity of keeping ornamental fish has grown increasingly, as their vibrant presence can provide a calming influence. Accurately assessing the health of ornamental fish is important but challenging. For this, researchers have focused on developing fish tracking methods that provide trajectories for health assessment. However, issues such as mirror images, occlusion, and motion prediction errors can significantly reduce the accuracy of existing algorithms. To address these problems, we propose a novel ornamental fish tracking method based on deep learning techniques. We first utilize the You Only Look Once (YOLO) v5 deep convolutional neural network algorithm with Distance Intersection over Union–Non Maximum Suppression (DIoU-NMS) to handle occlusion problems. We then design an object removal algorithm to eliminate fish mirror image coordinates. Finally, we adopt an improved DeepSORT algorithm, replacing the original Kalman Filter with an advanced Noise Scale Adaptive (NSA) Kalman Filter to enhance tracking accuracy. In our experiment, we evaluated our method in three simulated real-world fish tank environments, comparing it with the YOLOv5 and YOLOv7 methods. The results show that our method can increase Multiple Object Tracking Accuracy (MOTA) by up to 13.3%, Higher Order Tracking Accuracy (HOTA) by up to 10.0%, and Identification F1 Score by up to 14.5%. These findings confirm that our object removal algorithm effectively improves Multiple Object Tracking Accuracy, which facilitates early disease detection, reduces mortality, and mitigates economic losses—an important consideration given many owners’ limited ability to recognize common diseases. Full article

The modeling of the enthalpy of mixing in binary alloys is essential to thermodynamic assessments and computational alloy design, particularly in data-scarce systems where experimental measurements are limited or incomplete. In this work, we propose a machine learning framework for the prediction of mixing enthalpy in binary alloys under conditions of limited data availability. The method integrates symmetry-augmented embeddings, which enforce physical invariances such as element permutation and compositional mirroring, ensuring consistency across chemically equivalent representations and capturing chemically meaningful similarities between elements, thereby supporting knowledge transfer across alloy systems. To account for data uncertainty and improve trust in predictions, we incorporate Bayesian neural networks, enabling the estimation of predictive confidence, especially in composition ranges lacking experimental data. The model is trained jointly across multiple binary alloy systems, allowing it to share structural insights and improve prediction quality in data-limited concentration intervals. The method achieves a reduction in mean absolute error by more than a factor of eight compared with the classical Miedema model (0.53 kJ·mol⁻¹ vs. 4.27 kJ·mol⁻¹) while maintaining consistent accuracy even when trained on only 25% of the experimental measurements, confirming its robustness thanks to cross-alloy knowledge transfer and symmetry-based data augmentation. We evaluate the method on a benchmark dataset containing both fully and partially characterized binary alloy systems and demonstrate its effectiveness in interpolating and extrapolating enthalpy values while providing reliable uncertainty estimates. The results highlight the value of incorporating domain-specific symmetries and uncertainty-aware learning in data-driven material modeling and suggest that this approach can support predictive thermodynamic assessments even in under-sampled systems. Full article

In welding applications, line-structured-light vision is widely used for seam tracking, but intense noise from arc glow, spatter, smoke, and reflections makes reliable laser-stripe segmentation difficult. To address these challenges, we propose EUFNet, an uncertainty-driven symmetrical two-stage segmentation network for precise stripe extraction under real-world welding conditions. In the first stage, a lightweight backbone generates a coarse stripe mask and a pixel-wise uncertainty map; in the second stage, a functionally mirrored refinement network uses this uncertainty map to symmetrically guide fine-tuning of the same image regions, thereby preserving stripe continuity. We further employ an uncertainty-weighted loss that treats ambiguous pixels and their corresponding evidence in a one-to-one, symmetric manner. Evaluated on a large-scale dataset of 3100 annotated welding images, EUFNet achieves a mean IoU of 89.3% and a mean accuracy of 95.9% at 236.7 FPS (compared to U-Net’s 82.5% mean IoU and 90.2% mean accuracy), significantly outperforming existing approaches in both accuracy and real-time performance. Moreover, EUFNet generalizes effectively to the public WLSD benchmark, surpassing state-of-the-art baselines in both accuracy and speed. These results confirm that a structurally and functionally symmetric, uncertainty-driven two-stage refinement strategy—combined with targeted loss design and efficient feature integration—yields high-precision, real-time performance for automated welding vision. Full article

We present a comprehensive study on the femtosecond laser direct welding of glass and metal, focusing on optimizing processing parameters and understanding the influence of material properties and beam shaping on welding quality. Using microscopy, we identified optimal pulse energy, focal position, and line-spacing for achieving high-quality welds. We further investigated the effects of laser beam shaping and material property differences in various glass-to-metal pairs, including borosilicate, fused silica, and Zerodur glasses welded with mirror-polished metals such as Cu, Mo, Al, Ti, and AISI316 steel. Our results show that Ti and AISI316 steel exhibit the lowest adhesion to borosilicate and fused silica glasses, while Zerodur glass achieves good adhesion with all tested metals. To understand the weldability differences among material pairs, we employed a time-dependent finite-element method to analyze the laser heating-induced thermal stress. Our findings indicate that the welding quality is significantly influenced by the choice of materials and beam shaping, with the vortex beam showing potential for improved welding outcomes. This study provides valuable insights for optimizing glass-to-metal welding processes for various industrial applications. Full article

Venoms of the Palearctic vipers in the Macrovipera genus cause severe procoagulant clinical effects, yet the precise molecular targets remain incompletely defined. To fill this toxicological knowledge gap, we tested five Macrovipera venoms—M. lebetina cernovi, M. l. obtusa, M. l. turanica (Turkmenistan and Uzbekistan localities), and M. schweizeri—using plasma clotting assays, Factors VII, X, XI, and XII and prothrombin zymogen activation assays, and SDS-PAGE to visualise Factor V (FV) cleavage. All venoms induced extremely rapid clot formation (10.5–12.5 s) compared with the negative control (spontaneous clotting) of 334.6 ± 3.6 s) and the positive control (kaolin trigger) of 55.8 ± 1.9 s. Activation of FVII or FXI was negligible, whereas consistent FX activation and species-variable FXII activation, both moderate, were observed. Prothrombin remained inert in the absence of cofactors, but the presence of FV or FVa elicited potent thrombin generation. SDS-PAGE confirmed proteolytic conversion of the 330 kDa FV zymogen into the ~105 kDa heavy and ~80 kDa light chains of FVa by the venoms of all species. This data demonstrates that Macrovipera venoms rely on a dual enzyme strategy: (i) activation of FV to FVa by serine proteases and (ii) FVa-dependent prothrombin activation by metalloproteases. These results reveal that prothrombin activation is the dominant procoagulant pathway and overshadows the historically emphasised FX activation. This mechanism mirrors, yet is evolutionarily independent from, the FXa:FVa prothrombinase formation seen in Australian elapid venoms, highlighting convergent evolution of cofactor-hijacking strategies among snakes. The discovery of potent FVa-mediated prothrombin activation in Macrovipera challenges existing paradigms of viperid venom action, prompts re-evaluation of related genera (e.g., Daboia), and underpins the design of targeted antivenom and therapeutic interventions. Full article

The integration of AI into online platforms is reshaping consumer experience and behavior. While existing research has largely focused on the role of AI in search services and experience services, few studies have examined the role of AI in the context of credence services. This study fills this gap by investigating an AI-powered preview tool in the context of online medical aesthetic platforms. Specifically, this study investigates how the AI Magic Mirror influences consumer purchase intentions in medical aesthetic services. Using secondary data analysis and two experimental studies, we examine the main effects, as well as mediation and moderation effects. The findings consistently demonstrate that the AI Magic Mirror significantly increases consumer purchase intentions. This relationship is positively mediated by perceived value and negatively mediated by perceived risk. In addition, the main effect is stronger for procedures with higher fit uncertainty and is more pronounced for those with lower popularity. These results provide theoretical insights into AI application in credence service contexts and offer practical implications for the design of AI-enhanced online service platforms. Full article

The Karp and Rabin (KR) fingerprint is a special hash-like function widely utilized for efficient string matching. Recently, Sharma et al.leveraged its linear and symmetric properties to facilitate private database queries. However, their approach mainly protects encrypted or secret-shared databases rather than public databases, where only the query privacy is required. In this paper, we focus explicitly on privacy-preserving queries over public read-only databases. We propose a novel fingerprint-based keyword query scheme using the distributed point function (DPF), which effectively hides users’ data access patterns across two symmetric mirror servers. Moreover, we provide a rigorous analysis of the false positive probability inherent in fingerprinting and discuss strategies for its minimization. Our scheme achieves efficiency close to plaintext methods, significantly reducing deployment complexity. Full article

Second-language (L2) written processing has often been linked to cognitive disfluency, resembling fluency disruptions caused by perceptual challenges, such as visual degradation. This study used Virtual Reality to investigate whether cognitive disfluency in L2 mirrors perceptual disfluency by simulating adverse weather conditions (sunny vs. rainy) and applying visual masking. Spanish–English bilinguals completed a language decision task, identifying orthotactically unmarked words as either Spanish (L1) or English (L2) while experiencing these perceptual manipulations. Results showed that visual masking significantly increased reaction times, particularly for L1 words, suggesting that masking can diminish the native language advantage. Spanish words under masking elicited slower responses than unmasked ones, whereas L2 word recognition remained comparatively stable. Additionally, rainy weather conditions consistently slowed responses across both languages, indicating a general effect of environmental disfluency. A significant interaction between language and masking emerged, highlighting distinct cognitive effects for different disfluency types. These findings suggest that cognitive disfluency in L2 does not equate to perceptual disfluency; each affects processing differently. The use of Virtual Reality enabled the controlled manipulation of realistic environmental variables, offering valuable insights into how perceptual and linguistic challenges jointly influence bilingual language processing. Full article

Stress fields imparted with an ultrafast laser can correct low spatial frequency surface figure error of mirrors through ultrafast laser stress figuring (ULSF): the formation of nanograting structures within the bulk substrate generates localized stress, creating bending moments that equilibrize via wafer deformation. For ULSF to be used as an optical figuring process, the ultrafast laser generated stress must be effectively permanent or risk unwanted figure drift. Two isochronal annealing experiments were performed to measure ultrafast laser-generated stress stability in fused silica and Corning ultra-low expansion (ULE) wafers. The first experiment tracked changes to induced astigmatism up to 1000 °C on 25.4 mm-diameter wafers. Only small changes were measured after each thermal cycle up to 500 °C for both materials, but significant changes were observed at higher temperatures. The second experiment tracked stress changes in fused silica and ULE up to 500 °C but with 4 to 16× higher signal-to-noise ratio. Change in trefoil on 100 mm-diameter wafers was measured, and the induced stress in fused silica and ULE was found to be stable after thermal cycling up to 300 °C and 200 °C, respectively, with larger changes at higher temperatures. Full article

Access to clean cooking remains a major challenge in rural and off-grid areas where traditional fuels are costly, harmful, or scarce. Solar cooking offers a sustainable solution, but many existing systems suffer from fixed positioning and low efficiency. This study presents a low-cost, dual-axis solar tracking parabolic dish cooker designed for such regions, featuring adjustable pot holder height and portability for ease of use. The system uses an Arduino UNO, LDR sensors, and a DC gear motor to automate sun tracking, ensuring optimal alignment throughout the day. A 0.61 m parabolic dish with ≥97% reflective silver-coated mirrors concentrates sunlight to temperatures exceeding 300 °C. Performance tests in April, June, and November showed boiling times as low as 3.37 min in high-irradiance conditions (7.66 kWh/m²/day) and 6.63 min under lower-irradiance conditions (3.86 kWh/m²/day). Compared to fixed or single-axis systems, this design achieved higher thermal efficiency and reliability, even under partially cloudy skies. Built with locally available materials, the system offers an affordable, clean, and effective cooking solution that supports energy access, health, and sustainability in underserved communities. Full article