Search Results (118)

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article

Although cranial growth has been extensively explored, forensic and biological anthropology lack a formal incorporation of how cranial growth processes impact the adult phenotype and downstream biological profile estimations. Objectives: This research uses an ontogenetic framework to identify when interlandmark distances (ILDs) stabilize during growth to reach adult levels of variation and to evaluate patterns of cranial sexual size dimorphism. Methods: Multivariate adaptive regression splines (MARS) were conducted on standardized cranial ILDs for 595 individuals from the Subadult Virtual Anthropology Database (SVAD) and the Forensic Data Bank (FDB) aged between birth and 25 years. Cross-Validated R-squared (CVRSq) values evaluated ILD variation explained by age while knot placements identified meaningful changes in ILD growth trajectories. Results: Results reveal the ages at which males and females reach craniometric maturity across splanchnocranium, neurocranium, basicranium and cross-regional ILDs. Changes in growth patterns observed here largely align with growth milestones of integrated soft tissue and skeletal structures as well as developmental milestones like puberty. Conclusions: Our findings highlight the variability in growth by sex and cranial region and move forensic anthropologists towards recognizing cranial growth as a mosaic, continuous process with overlap between subadults and adults rather than consistently approaching subadult and adult research separately. Full article

This study delivers a rigorous and in-depth bibliometric examination of 693 scholarly publications addressing the intersection of metaverse technologies and banking, retrieved from the Web of Science Core Collection. Through advanced scientometric tools, including VOSviewer and Bibliometrix, the research systematically unpacks the evolving intellectual and thematic contours of this interdisciplinary frontier. The co-occurrence analysis of keywords reveals a landscape shaped by seven core thematic clusters, encompassing immersive user environments, digital infrastructure, experiential design, and ethical considerations. Factorial analysis uncovers a marked bifurcation between experience-driven narratives and technology-centric frameworks, with integrative concepts such as technology, information, and consumption serving as conceptual bridges. Network visualizations of authorship patterns point to the emergence of high-density collaboration clusters, particularly centered around influential contributors such as Dwivedi and Ooi, while regional distribution patterns indicate a tri-continental dominance led by Asia, North America, and Western Europe. Temporal analysis identifies a significant surge in academic interest beginning in 2022, aligning with increased institutional and commercial experimentation in virtual financial platforms. Our findings argue that the incorporation of metaverse paradigms into banking is not merely a technological shift but a systemic transformation in progress—one that blurs the boundaries between speculative innovation and tangible implementation. This work contributes foundational insights for future inquiry into digital finance systems, algorithmic governance, trust architecture, and the wider socio-economic consequences of banking in virtualized environments. Whether a genuine leap toward financial evolution or a sophisticated illusion, the metaverse in banking must now be treated as a systemic phenomenon worthy of serious scrutiny. Full article

Virtual interactivity in marketing has evolved significantly in recent decades, becoming an essential tool for effectively connecting with consumers in digital environments. Through interactive digital platforms, brands can generate a two-way dialogue with their customers, allowing them to obtain instant feedback and adapt their strategies quickly and effectively. This not only improves but also contributes to strengthening brand love and fostering long-term loyalty. In this context, this research aimed to analyze the effect of virtual interactivity on customer loyalty and WOM through multiple sequential mediation paths, considering the elements of relationship marketing and examining the cognitive (trust–commitment), communicational (communication–conflict management), and experiential (brand love–WOM) mechanisms that operate in digital consumer–brand interactions. A cross-sectional and explanatory study was conducted, considering 417 frequent customers of brands who also follow brands on social media. Participants included women (60.7%) and men (39.3%), ranging in age from 19 to 52 years (M = 30.9 and SD = 6.0). Data were collected using a self-report form on virtual interactivity, relationship marketing, WOW, customer loyalty, and brand love, yielding an appropriate measurement model (α = between 0.791 and 0.908; CR = between 0.791 and 0.916; AVE = between 0.679 and 0.845) which was statistically analyzed using PLS-SEM. The hypotheses confirmed the proposed model, observing the effect of virtual interactivity on important brand factors. This research provides valid results linked to the digital world in banking contexts. Full article

The accumulation of senescent cells is a major contributor to aging and various age-related diseases, making developing senolytic compounds that are capable of clearing these cells an important area of research. However, progress has been hampered by the limited number of known senolytics and the incomplete understanding of their mechanisms. This study presents a powerful senolytic predictor built using phenotypic data and machine learning techniques to identify compounds with potential senolytic activity. A comprehensive training dataset consisting of 111 positive and 3951 negative compounds was curated from the literature. The dataset was used to train machine learning models, incorporating traditional molecular fingerprints, molecular descriptors, and MoLFormer molecular embeddings. By applying MoLFormer-based oversampling and testing different algorithms, it was found that the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models with MoLFormer embeddings exhibited the best performance, achieving Area Under the Curve (AUC) scores of 0.998 and 0.997, and F1 scores of 0.948 and 0.941, respectively. This senolytic predictor was then used to perform virtual screening of compounds from the DrugBank and TCMbank databases. In the DrugBank database, 98 structurally novel candidate compounds with potential senolytic activity were identified. For TCMbank, 714 potential senolytic compounds were predicted and 81 medicinal herbs with possible senolytic properties were identified. Moreover, pathway enrichment analysis revealed key targets and potential mechanisms underlying senolytic activity. In an experimental screening of predicted compounds, panaxatriol was found to exhibit senolytic activity on the etoposide-induced senescence of the IMR-90 cell line. Additionally, voclosporin was found to extend the lifespan of C. elegans more effectively than metformin, demonstrating the value of our model for drug repurposing. This study not only provides an efficient framework for discovering novel senolytic agents, but also highlights the predicted novel senolytic compounds and herbs as valuable starting points for future research into senolytic drug development. Full article

The adoption of Intelligent Virtual Assistants (IVAs) in the banking sector presents new opportunities to enhance customer service efficiency, reduce operational costs, and modernize service delivery channels. However, the factors driving IVA adoption and usage, particularly in specific regional contexts such as Portugal, remain underexplored. This study examined the determinants of IVA adoption intention and actual usage in the Portuguese banking sector, drawing on the Technology Acceptance Model (TAM) as its theoretical foundation. Data were collected through an online questionnaire distributed to 154 banking customers after they interacted with a commercial bank’s IVA. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived usefulness significantly influences the intention to adopt, which in turn significantly impacts actual usage. In contrast, other variables—including trust, ease of use, anthropomorphism, awareness, service quality, and gendered voice—did not show a significant effect. These results suggest that Portuguese users adopt IVAs based primarily on functional utility, highlighting the importance of outcome-oriented design and communication strategies. This study contributes to the understanding of technology adoption in mature digital markets and offers practical guidance for banks seeking to enhance the perceived value of their virtual assistants. Full article

The prostaglandin E2 receptor EP3 is emerging as a promising therapeutic target in cardiovascular diseases because of its involvement in vascular inflammation, platelet aggregation, and vasoconstriction. However, selective EP3 ligands with validated biological activities are scarce. In this study, we combined computational and experimental strategies to identify and validate novel EP3 receptor ligands with therapeutic potential. We implemented a high-throughput, structure- and ligand-based virtual screening pipeline, enabling efficient exploration of approved drugs and natural compounds from DrugBank and FooDB libraries. Top-scoring candidates were prioritised based on binding energy and pharmacophoric similarity. Selected hits were subjected to in silico ADME/Tox profiling using QikProp to identify molecules with favourable pharmacokinetic and safety parameters. TUCA, masoprocol, and pravastatin sodium have emerged as lead candidates and were validated in vitro using endothelial migration and platelet aggregation assays. TUCA exhibited the most consistent inhibitory effect on endothelial migration, whereas masoprocol and hydrocortisone significantly reduced platelet aggregation. These findings establish a multidimensional workflow for the rational identification of EP3 ligands and support their potential use in cardiovascular therapeutics. Full article

Background/Objectives: The increasing threat of antibiotic resistance and the declining efficiency of traditional drug discovery pipelines highlight the urgent need for novel drug targets and effective enzyme inhibitors against infectious diseases. Oligopeptidase B (OPB), a serine protease with trypsin-like specificity that processes low-molecular-weight peptides and oligopeptides, is present in bacteria and certain parasites but absent in mammals. This unique distribution makes OPB an attractive and selective target for antimicrobial drug development. Methods: Three-dimensional models of OPB from Serratia marcescens and Stenotrophomonas maltophilia, previously identified by our research group, were constructed via homology modeling using the best available OPB template from the RCSB Protein Data Bank. The S. marcescens OPB model was subjected to high-throughput virtual screening (HTVS) against the Natural Products Atlas (npatlas) database. Top-ranking compounds were further evaluated using Glide standard precision (SP) and extra precision (XP) docking protocols. Binding affinities were refined using molecular mechanics with generalized born and surface area (MM–GBSA) calculations. Molecular dynamics (MD) simulations assessed binding stability, while absorption distribution metabolism excretion and toxicity (ADMET) profiling evaluated drug-likeness and pharmacokinetic properties. Results: Ten natural product compounds demonstrated stronger binding affinities than antipain, a well-known oligopeptide-based protease inhibitor, as indicated by their more favorable MM–GBSA scores of −60.90 kcal/mol (S. marcescens) and −27.07 kcal/mol (S. maltophilia). Among these, dichrysobactin and validamycin E consistently exhibited favorable binding profiles across both OPB models. MD simulations confirmed the stability of their interactions with OPB active sites, maintaining favorable binding conformations throughout the simulation period. ADMET analysis suggested that while both compounds show promise, lead optimization is required to enhance their drug-like characteristics. Conclusions: This study identifies dichrysobactin and validamycin E as promising OPB inhibitors with potential antimicrobial activity. These findings support their further development as selective and potent agents against bacterial pathogens, including resistant strains, and underscore the need for experimental validation to confirm their efficacy and safety. Full article

Background/Objectives: The purpose of this study was to evaluate the association between risk-taking behaviors, vestibular symptoms/impairment and perception–action coupling behavior in recently concussed adolescents. Methods: This study utilized a cross-sectional design to evaluate the early effects of concussion on 12–18-year-old adolescents (n = 47) recruited from a concussion specialty clinic at their presenting clinical appointment. The Perception–Action Coupling Task (PACT) was used to assess action boundary perception by evaluating the participant’s ability to quickly and accurately determine whether a virtual “ball” fits in a virtual “hole”. Accuracy, response time and inverse efficiency were evaluated at the 0.8 and 1.2 ratios of ball–hole pairings, where 0.8 indicates the ball was slightly smaller than the hole and 1.2 indicates the ball was slightly larger than the hole. The Balloon Analog Risk Task (BART) is a computerized test which measures risk-taking behavior by “pumping” up a balloon. Each pump provides a small amount of virtual money into their bank; the goal is to make as much money as possible without popping the virtual balloon. The Vestibular Ocular Motor Screening (VOMS) tool is a brief screening tool designed to identify ocular or vestibular dysfunction following sport-related concussion, where horizontal/vertical vestibular ocular reflex (VOR) and visual motion sensitivity (VMS) are the primary vestibular outcomes. Pearson correlation matrices were developed to evaluate the association between BART, VOMS and PACT outcomes within the study cohort of concussed adolescents. Results: PACT inverse efficiency at the 1.2 ball–hole ratio was significantly correlated with all three VOMS outcomes (r = 0.33–0.37). The standard deviation of pump reaction time during BART was significantly correlated with accuracy (r = −0.47) and inverse efficiency (r = 0.42) at the 1.2 ratio. The standard deviation of the total number of pumps during BART was significantly correlated with PACT response time at the 1.2 ratio (r = 0.34). Horizontal VOR correlated with balloons collected (r = −0.30) and balloons popped (r = −0.30). Conclusions: The results of this study suggest that risk-taking behaviors and vestibular symptoms/impairment are associated with worse action boundary perception in adolescents following concussion. This relationship is more pronounced in male adolescents than females. Full article

Precision Farming (PF) in vineyards represents an innovative approach to vine cultivation that leverages the advantages of the latest technologies to optimize resource use and improve overall field management. This study investigates the application of PF techniques in a vineyard, focusing on sensor-based decision-making for autonomous driving. The goal of this research is to define a repeatable methodology for virtual testing of autonomous driving operations in a vineyard, considering realistic scenarios, efficient control architectures, and reliable sensors. The simulation scenario was created to replicate the conditions of a real vineyard, including elevation, banking profiles, and vine positioning. This provides a safe environment for training operators and testing tools such as sensors, algorithms, or controllers. This study also proposes an efficient control scheme, implemented as a state machine, to autonomously drive the tractor during two distinct phases of the navigation process: between rows and out of the field. The implementation demonstrates improvements in trajectory-following precision while reducing the intervention required by the farmer. The proposed system was extensively tested in a virtual environment, with a particular focus on evaluating the effects of micro and macro terrain irregularities on the results. A key feature of the control framework is its ability to achieve adequate accuracy while minimizing the number of sensors used, relying on a configuration of a Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU) as a cost-effective solution. This minimal-sensor approach, which includes a state machine designed to seamlessly transition between in-field and out-of-field operations, balances performance and cost efficiency. The system was validated through a wide range of simulations, highlighting its robustness and adaptability to various terrain conditions. The main contributions of this work include the high fidelity of the simulation scenario, the efficient integration of the control algorithm and sensors for the two navigation phases, and the detailed analysis of terrain conditions. Together, these elements form a robust framework for testing autonomous tractor operations in vineyards. Full article

Targeting the tumor microenvironment (TME) is an attractive strategy for developing new drugs with anticancer activity against triple-negative breast cancer (TNBC). Interleukins (ILs) are key players in the TME cytokine network promoting cancer progression. Recent studies have highlighted the involvement of IL-20 receptor subunit alpha (IL-20RA) signalling in several cancers, including BC, in which IL-20RA is highly expressed, correlating with poor prognosis and influencing tumoral characteristics such as proliferation, cell death, invasiveness, and TME activity. Therefore, elucidating the role of the IL-20RA signalling pathway could form the basis for developing new therapeutic strategies. This study aimed to identify selective bioactive ligands able to affect IL-20RA activity. Virtual screening of over 310,000 compounds from both the DrugBank and ZINC15 databases identified four potential hit compounds tested for their anticancer activity against TNBC in vitro cell lines. Notably, Ritonavir, a well-known Human Immunodeficiency Virus Type 1 (HIV-1) protease inhibitor, significantly inhibited cell proliferation (about 40% at 50 µM, p < 0.001). IL-20 preincubation counteracted Ritonavir’s cytostatic effect while IL-20RA knockdown restored proliferation in Ritonavir-treated TNBC cells. In conclusion, these findings demonstrated that Ritonavir reduced TNBC cell proliferation through IL-20RA activity modulation, suggesting its potential repurposing as a therapeutic agent for TNBC management. Full article

Background: Colorectal cancer (CRC) is a complex and increasingly prevalent malignancy with significant challenges in its treatment and prognosis. This study aims to explore the role of the SLC4A4 transporter as a biomarker in CRC progression and its potential as a therapeutic target, particularly in relation to tumor acidity and immune response. Methods: The study utilized computational approaches, including receptor-based virtual screening and high-throughput docking, to identify potential SLC4A4 inhibitors. A model of the human SLC4A4 structure was generated based on CryoEM data (PDB ID 6CAA), and drug candidates from the DrugBank database were evaluated using two computational tools (DrugRep and CB-DOCK2). Results: The study identified the compound (5R)-N-[(1r)-3-(4-hydroxyphenyl)butanoyl]-2-decanamide (DB07991) as the best ligand, demonstrating favorable binding affinity and stability. Molecular dynamics simulations revealed strong protein–ligand interactions with consistent RMSD (~0.25 nm), RMSF (~0.5 nm), compact Rg (4.0–3.9 nm), and stable SASA profiles, indicating that the SLC4A4 structure remains stable upon ligand binding. Conclusions: The findings suggest that DB07991 is a promising drug candidate for further investigation as a therapeutic agent against CRC, particularly for targeting SLC4A4. This study highlights the potential of computational drug repositioning in identifying effective treatments for colorectal cancer. Full article

Global supply chains, especially in commodity trading, are plagued by fragmentation, lack of transparency, and trust deficits among participants. These issues lead to inefficiencies, increased costs, and an over-reliance on intermediaries. The present Communication describes a blockchain-based platform that leverages Self-Sovereign Identity (SSI) and Verifiable Credentials (VCs) to address these challenges in supply chain management. Developed in collaboration with coffee industry stakeholders, our approach proposes a platform with an integrated marketplace for seller discovery, enables precise order definition with detailed terms and conditions, and actively guides both buyers and sellers throughout the shipping process, managing financial guarantees and ensuring a secure transaction flow. The platform is compatible with both traditional banking infrastructure and modern crypto-based systems, enabling seamless financial transactions. In cases where disputes arise, we empower users to easily collect all communications and documents to present to legal authorities, expediting the resolution process. The platform is implemented using the Internet Computer Protocol (ICP) for secure, on-chain storage and application hosting, and is integrated with the Ethereum blockchain to leverage its extensive decentralized finance (DeFi) ecosystem, significant liquidity, and robust stablecoin infrastructure, thereby facilitating secure financial transactions. Moreover, we introduce an SSI-based authentication and authorization framework that spans across the entire platform, including both the Ethereum Virtual Machine (EVM) and Internet Computer Protocol (ICP), enabling unified role-based access control through verifiable credentials. A value-added of the present Communication, the framework is demonstrated by means of a detailed case study in the coffee industry, highlighting the technical challenges addressed during implementation. While quantitative efficiency metrics will be established through upcoming real-world testing with industry partners, the platform’s design aims to streamline operations by reducing intermediary dependencies and automating key processes. Finally, the Communication provides insights into its adaptability to other industries facing comparable supply chain challenges, presenting an approach focused on enhancing trust and reducing reliance on intermediaries. Full article

Background: Histone deacetylase 6 (HDAC6) plays a crucial role in neurological, inflammatory, and other diseases; thus, it has emerged as an important target for therapeutic intervention. To date, there are no FDA-approved HDAC6-targeting drugs, and most pipeline candidates suffer from poor target engagement, inadequate brain penetration, and low tolerability. There are a few HDAC6 clinical candidates for the treatment of mostly non-CNS cancers as their pharmacokinetic liabilities exclude them from targeting HDAC6-implicated neurological diseases, urging development to address these challenges. They also demonstrate off-target toxicity due to limited selectivity, leading to adverse effects in patients. Selective inhibitors have thus been the focus of development over the past decade, though no selective and potent HDAC6 inhibitor has yet been approved. Methods: This study involved an integrated virtual screening against HDAC6 using the DrugBank database to identify repurposed drugs capable of inhibiting HDAC6 activity. The primary assessment involved the determination of the ability of molecules to bind with HDAC6. Subsequently, interaction analyses and 500 ns molecular dynamics (MD) simulations followed by essential dynamics were carried out to study the conformational flexibility and stability of HDAC6 in the presence of the screened molecules, i.e., penfluridol and pimozide. Results: The virtual screening results pinpointed penfluridol and pimozide as potential repurposed drugs against HDAC6 based on their binding efficiency and appropriate drug profiles. The docking results indicate that penfluridol and pimozide share the same binding site as the reference inhibitor with HDAC6. The MD simulation results showed that stable protein–ligand complexes of penfluridol and pimozide with HDAC6 were formed. Additionally, MMPBSA analysis revealed favorable binding free energies for all HDAC6–ligand complexes, confirming the stability of their interactions. Conclusions: The study implies that both penfluridol and pimozide have strong and favorable binding with HDAC6, which supports the idea of repositioning these drugs for the management of neurodegenerative disorders. However, further in-depth studies are needed to explore their efficacy and safety in biological systems. Full article

This pioneering study evaluates the digital divide and advances in virtual education (VE) and e-learning research in the Global South Countries (GSCs). Using metadata from bibliographic and World Bank data on research and development (R&D), we conduct quantitative bibliometric performance analyses and evaluate the connection between R&D expenditures on VE/e-learning research advances in GSCs. The results show that ‘East Asia and the Pacific’ (EAP) spent significantly more on (R&D) and achieved the highest scientific literature publication (SLP), with significant impacts. Other GSCs’ R&D expenditure was flat until 2020 (during COVID-19), when R&D funding increased, achieving a corresponding 42% rise in SLPs. About 67% of ‘Arab States’ (AS) SLPs and 60% of citation impact came from SLPs produced from global north and other GSCs regions, indicating high dependence. Also, 51% of high-impact SLPs were ‘Multiple Country Publications’, mainly from non-GSC institutions, indicating high collaboration impact. The EAP, AS, and ‘South Asia’ (SA) regions experienced lower disparity. In contrast, the less developed countries (LDCs), including ‘Sub-Sahara Africa’, ‘Latin America and the Caribbean’, and ‘Europe (Eastern) and Central Asia’, showed few dominant countries with high SLPs and higher digital divides. We advocate for increased educational research funding to enhance innovative R&D in GSCs, especially in LDCs. Full article