Search Results (141)

Cancer metastasis, the spread of tumour cells from the primary site to distant organs, is responsible for over 90% of cancer deaths, yet effective treatments remain elusive due to incomplete understanding of the molecular drivers involved. Podocalyxin (PODXL), a protein overexpressed in many aggressive cancers, links the cell membrane to the internal skeleton through its interaction with Ezrin, an actin cytoskeleton cross-linker. Despite its therapeutic relevance, the PODXL–Ezrin interface remains structurally uncharacterised and pharmacologically intractable. Here, we employed an integrated computational approach combining protein–protein docking, molecular dynamics (MD) simulations, and virtual screening to investigate the structural basis of the PODXL–Ezrin interaction. Using AlphaFold-predicted structures, we modelled PODXL and Ezrin complexes, revealing that PODXL’s cytoplasmic domain stabilises upon Ezrin binding, with Arg495 mediating temporally distinct electrostatic interactions essential for initial complex assembly. Particularly, we characterised the R495W missense mutation in PODXL’s Ezrin-binding domain, demonstrating that substitution of arginine with bulky, hydrophobic tryptophan may allosterically destabilise Ezrin’s dormant conformation. This mutation slightly increases the intramolecular distance between the F3 subdomain and C-terminal domain from 2.59 Å to 3.40 Å, thus leading to potential partial unmasking of the Thr567 phosphorylation site that could plausibly prime Ezrin for activation. Molecular dynamics simulations in the WT state with a total simulation time of 100 ns revealed enhanced structural rigidity and reduced radius of gyration fluctuations in the mutant complex, consistent with a potential “locked,” activation-prone state that amplifies oncogenic signalling. Through virtual screening, we identified NSC305787 as a selective destabiliser of the R495W mutant complex by disrupting key Trp495–pre-C-terminal loop Ezrin interactions and causing steric hindrance to PIP2 recruitment. Our findings identified mutation-dependent changes in drug binding that can guide the development and repurposing of compounds for targeting PODXL-related cancers and improve patient outcomes in PODXL-altered malignancies. Full article

Knowledge graphs (KGs) offer a structured and collaborative approach to integrating diverse knowledge from various domains. However, constructing knowledge graphs typically requires significant manual effort and heavily relies on pretrained models, limiting their adaptability to specific sub-domains. This paper proposes an innovative, efficient, and locally deployable knowledge graph construction framework that leverages low-rank adaptation (LoRA) to fine-tune large language models (LLMs) in order to reduce noise. By integrating iterative optimization, consistency-guided filtering, and prompt-based extraction, the proposed method achieves a balance between precision and coverage, enabling the robust extraction of standardized subject–predicate–object triples from raw long texts. This makes it highly effective for knowledge graph construction and downstream reasoning tasks. We applied the parameter-efficient open-source model Qwen3-14B, and experimental results on the SciERC dataset show that, under strict matching (i.e., ensuring the exact matching of all components), our method achieved an F1 score of 0.358, outperforming the baseline model’s F1 score of 0.349. Under fuzzy matching (allowing some parts of the triples to be unmatched), the F1 score reached 0.447, outperforming the baseline model’s F1 score of 0.392, demonstrating the effectiveness of our approach. Ablation studies validate the robustness and generalization potential of our method, highlighting the contribution of each component to the overall performance. Full article

Artificial emotional intelligence is a sub-domain of human–computer interaction research that aims to develop deep learning models capable of detecting and interpreting human emotional states through various modalities. A major challenge in this domain is identifying meaningful correlations between heterogeneous modalities—for example, between audio and visual data—due to their distinct temporal and spatial properties. Traditional fusion techniques used in multimodal learning to combine data from different sources often fail to adequately capture meaningful and less computational cross-modal interactions, and struggle to adapt to varying modality reliability. Following a review of the relevant literature, this study adopts an experimental research method to develop and evaluate a mathematical cross-modal fusion model, thereby addressing a gap in the extant research literature. The framework uses the Tucker tensor decomposition to analyse the multi-dimensional array of data into a set of matrices to support the integration of temporal features from audio and spatiotemporal features from visual modalities. A cross-attention mechanism is incorporated to enhance cross-modal interaction, enabling each modality to attend to the relevant information from the other. The efficacy of the model is rigorously evaluated on three publicly available datasets and the results conclusively demonstrate that the proposed fusion technique outperforms conventional fusion methods and several more recent approaches. The findings break new ground in this field of study and will be of interest to researchers and developers in artificial emotional intelligence. Full article

The Fornberg–Whitham equation, which contains high-order nonlinear derivatives, is widely recognized as a prominent model for describing shallow water dynamics. We explore physics-informed neural networks (PINsN) in conjunction with transfer learning technology to investigate numerical solutions for the FW equation. The proposed domain adaptation in transfer learning for PINN transforms the complex problem defined over the entire spatiotemporal domain into simpler problems defined over smaller subdomains. Training a neural network on these subdomains provides extra supervised learning data, effectively addressing optimization challenges associated with PINNs. Consequently, it enables the resolution of anisotropic and long-term predictive issues in these types of equations. Moreover, it enhances prediction accuracy and accelerates loss convergence by circumventing local optima in the specified scenarios. The method efficiently handles both forward and inverse FW equation problems, excelling in cost-effective accurate predictions for inverse problems. The efficiency and accuracy of our proposed approaches are demonstrated through examples and results. Full article

Background: Menopause is often accompanied by menopausal symptoms and reduced quality of life. Studies on the combined effects of time-restricted eating and exercise in this population are lacking. This approach may provide additive preventive benefits by aligning nutritional timing with exercise to improve health and well-being in menopausal women. We aimed to assess whether a combined intervention is more effective than exercise alone in reducing menopausal symptoms and improving quality of life. Methods: This study examined the effects of a time-restricted eating protocol (16:8) combined with a resistance and endurance circuit training program in menopausal women. Symptoms were assessed using the Menopause Rating Scale (MRS), and quality of life was evaluated with the Menopause-Specific Quality of Life Questionnaire (MENQOL). Participants (n = 54) were quasi-randomly assigned to a combination group (exercise + time-restricted eating; n = 24) or an exercise group (exercise only; n = 30), with allocation influenced by participant preference. Results: The reduction in the total MRS score, as well as in the psychological and somatic MRS subdomains, was significantly greater in the combination group than in the exercise group (p = 0.008, p = 0.009, p = 0.007, respectively). No significant difference was observed in the urogenital domain. For MENQOL, post-intervention scores in the physical and psychosocial subdomains were significantly lower in the combination group compared with the exercise group (p = 0.013, p = 0.002, respectively), while no significant differences were found in the vasomotor and sexual subdomains. Conclusions: These findings suggest that integrating time-restricted eating with exercise results in greater alleviation of menopausal symptoms and improvements in quality of life compared to exercise alone in menopausal women. Full article

Microplastics pose a growing environmental concern, necessitating accurate and scalable methods for their detection and classification. This study presents a novel deep learning framework that integrates a transformer-based architecture with domain adaptation techniques to classify microplastics using reflectance micro-FTIR spectroscopy. A key challenge addressed in this work is the domain shift between laboratory-prepared reference spectra and environmentally sourced spectra, which can significantly degrade model performance. To overcome this, three domain-adaptation strategies—Domain Adversarial Neural Networks (DANN), Deep Subdomain-Adaptation Networks (DSAN), and Deep CORAL—were evaluated for their ability to enhance cross-domain generalization. Experimental results show that while DANN was unstable, DSAN and Deep CORAL improved target domain accuracy. Deep CORAL achieved 99% accuracy on the source and 94% on the target, offering balanced performance. DSAN reached 95% on the target but reduced source accuracy. Overall, statistical alignment methods outperformed adversarial approaches in transformer-based spectral adaptation. The proposed model was integrated into a reflectance micro-FTIR workflow, accurately identifying PE and PP microplastics from unlabelled spectra. Predictions closely matched expert-validated results, demonstrating practical applicability. This first use of a domain-adaptive transformer in microplastics spectroscopy sets a benchmark for high-throughput, cross-domain analysis. Future work will extend to more polymers and enhance model efficiency for field use. Full article

Autonomous driving has emerged as a rapidly advancing field in both industry and academia over the past decade. Among the enabling technologies, computer vision (CV) has demonstrated high accuracy across various domains, making it a critical component of autonomous vehicle systems. However, CV tasks are computationally intensive and often require hardware accelerators to achieve real-time performance. Field Programmable Gate Arrays (FPGAs) have gained popularity in this context due to their reconfigurability and high energy efficiency. Numerous researchers have explored FPGA-accelerated CV solutions for autonomous driving, addressing key tasks such as lane detection, pedestrian recognition, traffic sign and signal classification, vehicle detection, object detection, environmental variability sensing, and fault analysis. Despite this growing body of work, the field remains fragmented, with significant variability in implementation approaches, evaluation metrics, and hardware platforms. Crucial performance factors, including latency, throughput, power consumption, energy efficiency, detection accuracy, datasets, and FPGA architectures, are often assessed inconsistently. To address this gap, this paper presents a comprehensive literature review of FPGA-accelerated, vision-based autonomous driving systems. It systematically examines existing solutions across sub-domains, categorizes key performance factors and synthesizes the current state of research. This study aims to provide a consolidated reference for researchers, supporting the development of more efficient and reliable next generation autonomous driving systems by highlighting trends, challenges, and opportunities in the field. Full article

Background/Objectives: The YSK olfactory function (YOF) test is a culturally adapted psychophysical tool that assesses threshold, discrimination, and identification. This study evaluated whether functional near-infrared spectroscopy (fNIRS) synchronized with routine YOF testing, combined with machine learning, can predict YOF subdomain performance in healthy adults, providing an objective neural correlate to complement behavioral testing. Methods: In this prospective diagnostic-accuracy (feasibility/validation) study in healthy adults with algorithm development, 100 healthy adults completed the YOF test while undergoing prefrontal/orbitofrontal fNIRS during odor blocks. Feature sets from ΔHbO/ΔHbR included time-domain descriptors, complexity (Lempel–Ziv), and information-theoretic measures (mutual information); the identification task used a hybrid attention–CNN. Separate models were developed for threshold (binary classification), discrimination (binary classification), and identification (binary classification). Performance was summarized with accuracy, area under the curve (AUC), F1-score, and (where applicable) sensitivity/specificity, using participant-level cross-validation. Results: The threshold classifier achieved accuracy 0.86, AUC 0.86, and F1 0.86, indicating strong discrimination of correct vs. incorrect threshold responses. The discrimination model yielded accuracy 0.75, AUC 0.76, and F1 0.75. The identification model (attention–convolutional neural network [CNN]) achieved accuracy 0.88, sensitivity 0.86, specificity 0.91, and F1 0.88. Feature-attribution (e.g., SHapley Additive exPlanations [SHAP]) provided interpretable links between fNIRS features and task performance for threshold and discrimination. Conclusions: Olfactory-evoked fNIRS signals can accurately predict YOF subdomain performance in healthy adults, supporting the feasibility of non-invasive, portable, near–real-time olfactory monitoring. These findings are preliminary and not generalizable to clinical populations; external validation in diverse cohorts is warranted. The approach clarifies the scientific essence of the method by (i) aligning psychophysical outcomes with objective hemodynamic signatures and (ii) introducing a feature-rich modeling pipeline (ΔHbO/ΔHbR + Lempel–Ziv complexity/mutual information; attention–CNN) that advances prior work. Full article

Phishing, especially brand impersonation attacks, is a critical cybersecurity threat that harms user trust and organization security. This paper establishes a lightweight model for real-time detection that relies on URL-only sequences, addressing limitations for multimodal methods that leverage HTML, images, or metadata. This approach is based on a Temporal Convolutional Network with Attention (TCNWithAttention) that utilizes character-level URLs to capture both local and long-range dependencies, while providing interpretability with attention visualization and Shapley additive explanations (SHAP). The model was trained and tested on the balanced GramBeddings dataset (800,000 URLs) and validated on the PhiUSIIL dataset of real-world phishing URLs. The model achieved 97.54% accuracy on the GramBeddings dataset, and 81% recall on the PhiUSIIL dataset. The model demonstrated strong generalization, fast inference, and CPU-only deployability. It outperformed CNN, BiLSTM and BERT baselines. Explanations highlighted phishing indicators, such as deceptive subdomains, brand impersonation, and suspicious tokens. It also affirmed real patterns in the legitimate domains. To our knowledge, a Streamlit application to facilitate single and batch URL analysis and log feedback to maintain usability is the first phishing detection framework to integrate TCN, attention, and SHAP, bridging academic innovation with practical cybersecurity techniques. Full article

In structural topology optimisation, intermediate densities are typically interpreted as local distributions of heterogeneous materials, bridging the gap between a solid and a void through optimised arrangements of cellular or composite microstructures. These multiscale configurations, governed by interactions between micro- and macroscales, are commonly addressed via hierarchical approaches. However, such methods often suffer from high computational cost and limited practical applicability. This work proposes an alternative strategy that reformulates the hierarchical problem by replacing pointwise microscale variations with a subdomain-based formulation. Each subdomain is associated with a periodic microstructure, reducing the number of local problems and significantly decreasing computational demands. A multiscale topology optimisation framework is developed using Asymptotic Expansion Homogenisation, enabling effective macrostructural properties and supporting inverse homogenisation for microscale design. The proposed method is implemented in a user-developed code and validated through several benchmark problems. The results show that the subdomain approach yields discrete and manufacturable microstructures that better reflect real-world composite applications, while also achieving substantial improvements in computational efficiency. Full article

This study investigates steady-state heat conduction in materials with stepwise discontinuities in thermal conductivity, a phenomenon frequently encountered in layered composites, thermal barrier coatings, and electronic packaging. The problem is formulated for a 2D two-domain region, where each subdomain has a distinct constant conductivity. Both the direct problem—determining the temperature field from known conductivities—and the inverse problem—identifying conductivities and the internal heat source from limited temperature measurements—are addressed. To this end, three deterministic finite-difference-type models are developed: two for the standard formulation and one for a meshless formulation based on Moving Least Squares (MLS), all derived within a local framework that efficiently enforces interface conditions. In addition, two Monte Carlo models are proposed—one for the standard and one for the meshless setting—providing pointwise estimates of the solution without requiring computation over the entire domain. Finally, an algorithm for solving inverse problems is introduced, enabling the reconstruction of material parameters and internal sources. The performance of the proposed approaches is assessed through 2D benchmark problems of varying geometric complexity, including both structured grids and irregular node clouds. The numerical experiments cover convergence studies, sensitivity of inverse reconstructions to measurement noise and input parameters, and evaluations of robustness across different conductivity contrasts. The results confirm that the hybrid difference-meshless Monte Carlo framework delivers accurate temperature predictions and reliable inverse identification, highlighting its potential for engineering applications in thermal design optimization, material characterization, and failure analysis. Full article

We aimed to investigate the interaction mechanism of transport protein Human serum albumin (HSA) with a synthesized compound, QP-11, with tested antimalarial properties to monitor the changes in the protein because of QP-11 binding. The interaction between the antimalarial compound QP-11 and HSA was thoroughly investigated through a multidimensional approach, utilizing UV-VIS spectroscopy, fluorescence, time-resolved fluorescence, and CD (Circular dichroism), alongside molecular docking techniques. Our findings unveiled a robust 1:1 binding pattern, signifying a strong affinity between QP-11 and HSA. Employing static quenching, evidenced by time-resolved fluorescence spectroscopy, QP-11 was observed to induce fluorescence quenching of HSA, particularly binding to subdomain IIA. Thermodynamic parameters indicated that van der Waals forces and hydrogen bonding predominantly facilitated the binding, with increased temperature compromising complex stability. The 3D fluorescence and CD results demonstrated QP-11-induced conformational changes in HSA. Both experimental and in silico analyses suggested a spontaneous, exothermic binding reaction. The profound impact of the QP-11–HSA interaction underscores the potential for QP-11 in antimalarial drug development, encouraging further exploration for dose design and enhanced pharmacodynamic and pharmacokinetic properties. Full article

The Cultural STEM Night (CSN) initiative was developed to address the persistent lack of culturally relevant STEM teaching materials, which often contributes to student disengagement—particularly among underrepresented populations. This study examined the impact of the CSN program on enhancing STEM affinity and cultural intelligence (CQ) among American and Korean teacher candidates. Over six weeks, participants engaged in synchronous workshops, virtual cultural exchanges, and collaborative STEM lesson design integrating Korean cultural contexts. Quantitative analysis of pre- and post-program surveys using the STEM Affinity Test and Cultural Intelligence Scale revealed statistically significant improvements across all subdomains of STEM affinity (identity, interest, self-concept, value, and attitudes) and in most dimensions of CQ (metacognitive, cognitive, and behavioral). However, motivational CQ did not show significant gains, likely due to limited student interaction time during the event. Qualitative data from written reflections and focus group discussions supported these findings, indicating increased instructional adaptability, cultural awareness, and confidence in designing inclusive STEM lessons. These results demonstrate the transformative potential of interdisciplinary, culturally immersive programs in teacher education. The CSN model, supported by digital collaboration tools, offers a scalable and effective approach to preparing educators for diverse classrooms. Findings underscore the importance of integrating culturally responsive teaching into STEM education to promote equity, engagement, and global competence. Full article

Recent studies have increasingly focused on molecular interactions between small molecules and proteins, especially binding mechanisms and thermodynamics, using multispectroscopic and molecular dynamics approaches. This study elucidated the molecular interaction mechanism between bovine serum albumin (BSA) and trans-resveratrol (Res) through an integrated approach combining multispectroscopic analyses and molecular dynamics simulations. The fluorescence quenching study revealed a static quenching mechanism between BSA and Res, which was further confirmed via ultraviolet–visible (UV-Vis) absorption spectroscopy. In particular, K_SV decreased from 5.01 × 10⁴ M⁻¹ at 298 K to 3.99 × 10⁴ M⁻¹ at 318 K. Furthermore, the calculated K_q values significantly exceeded 1 × 10¹² M⁻¹ s⁻¹. With increasing Res concentration, the peak fluorescence intensities of Tyr and Trp residues both exhibited a blue shift. The α-helix content of the BSA–Res complex was 59.8%, slightly lower than that of BSA (61.3%). Res was found to bind to site I in subdomain IIA of BSA. The molecular dynamics simulation also identified the specific binding of Res to site I of BSA, while thermodynamic studies revealed that the binding process occurs spontaneously and is primarily mediated by hydrogen bonding interactions. These findings not only enrich the theoretical framework of small-molecule–protein interactions but also provide a crucial scientific foundation for the development and utilization of natural products. Full article

This paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristics, vehicle multi-source information redundancy characteristics, and vehicle state transition characteristics; (3) from the perspective of key estimation algorithms, including model-based Kalman filtering algorithms, data-driven machine learning algorithms, and optimization estimation algorithms combining mechanism-based and data-driven approaches. This manuscript helps interested readers to comprehensively understand the research progress, technical features, and future trends of vehicle state estimation technology from the perspective of overall architecture and subdomains. Full article