Search Results (54,447)

Background: Protein arginine deiminase 4 (PAD4) has emerged as a promising therapeutic target for acute promyelocytic leukemia (APL) because of its role in epigenetic regulation and leukemogenesis. All-trans retinoic acid, a standard differentiation agent in APL therapy, has been shown to upregulate PAD4 expression during leukemic cell maturation. Interestingly, first-generation PAD4 inhibitors also promote differentiation, but simultaneously trigger compensatory PAD4 overexpression, underscoring the unresolved complexity of PAD4 modulation in leukemia therapy. Methods: In this study, we employed mass cytometry and transcriptomic–proteomic integrated analysis to investigate the underlying mechanisms of YW3-56, a dual-function PAD4 inhibitor against protein expression and enzymatic function, in NB4 leukemia cells. Functional validation was conducted using Western blot and metabolic assays. Results: Mass cytometry analysis revealed that YW3-56 reduced leukemia stemness (CD44/CD133), while enhancing myeloid differentiation (CD11b/CD14) and immunogenic activation (CD80/CD86). Multiomics analysis revealed a YW3-56-induced metabolic shift characterized by downregulation of glycolytic enzymes and upregulation of the tricarboxylic acid cycle and pentose phosphate pathway components, indicating a reversal of the Warburg effect. Mechanistically, this metabolic reprogramming was driven by reduced AKT expression and phosphorylation at Thr308, impaired GLUT1 expression and membrane localization, and decreased glucose uptake, which collectively promoted the differentiation of NB4 cells. Additionally, YW3-56 suppressed the downstream mTOR pathway, inducing caspase-3/PARP-mediated apoptosis and inhibiting cell proliferation. Conclusions: Our study demonstrated that YW3-56 exerts multimodal antileukemic effects in APL by simultaneously targeting PAD4-mediated epigenetic regulation, AKT-driven metabolic reprogramming and cellular differentiation, highlighting PAD4-AKT signaling as a promising target for APL combination therapy. Full article

Embankment piping and leakage are primary causes of flood control infrastructure failure, accounting for more than 90% of embankment failures worldwide and posing significant threats to public safety and economic stability. Current manual inspection methods are labor-intensive, hazardous, and inadequate for emergency flood season monitoring, while existing automated approaches using thermal infrared imaging face limitations in cost, weather dependency, and deployment flexibility. This study addresses the critical scientific challenge of developing reliable, cost-effective automated detection systems for embankment safety monitoring using Unmanned Aerial Vehicle (UAV)-based visible light imagery. The fundamental problem lies in extracting subtle textural signatures of piping and leakage from complex embankment surface patterns under varying environmental conditions. To solve this challenge, we propose the Embankment-Frequency Network (EmbFreq-Net), a frequency-enhanced deep learning framework that leverages frequency-domain analysis to amplify hazard-related features while suppressing environmental noise. The architecture integrates dynamic frequency-domain feature extraction, multi-scale attention mechanisms, and lightweight design principles to achieve real-time detection capabilities suitable for emergency deployment and edge computing applications. This approach transforms traditional post-processing workflows into an efficient real-time edge computing solution, significantly improving computational efficiency and enabling immediate on-site hazard assessment. Comprehensive evaluations on a specialized embankment hazard dataset demonstrate that EmbFreq-Net achieves 77.68% mAP@0.5, representing a 4.19 percentage point improvement over state-of-the-art methods, while reducing computational requirements by 27.0% (4.6 vs. 6.3 Giga Floating-Point Operations (GFLOPs)) and model parameters by 21.7% (2.02M vs. 2.58M). These results demonstrate the method’s potential for transforming embankment safety monitoring from reactive manual inspection to proactive automated surveillance, thereby contributing to enhanced flood risk management and infrastructure resilience. Full article

Driver monitoring systems (DMS), advanced driver assistance ssystems (ADAs), and technologies for autonomous driving, along with other upcoming innovations, have been developed as possible solutions to minimize accidents resulting from human error. This paper presents a thorough review of DMSs and user experience (UX). The objective is to investigate, combine, and evaluate the key elements involved in the development and application of DMSs, as well as the UX factors relevant to the current landscape of the field, serving as a reference for future investigations. The review encompasses a bibliographic analysis performed at different stages, offering valuable insights into the evolution of the topic. It examines the processes of development and implementation of driver monitoring systems. Furthermore, this work facilitates future research by consolidating and presenting a valuable collection of identified datasets, both public and private, for various research purposes. From this evaluation, critical components for DMSs can be identified, establishing a foundation for future research by providing a framework for the adoption and integration of these systems. Full article

Background: The pathological response to neoadjuvant chemotherapy (NAC) is an established predictor of long-term outcomes in breast cancer. However, conventional binary assessment based solely on pathological complete response (pCR) fails to capture prognostic heterogeneity across molecular subtypes. This study aimed to develop an interpretable deep learning model that integrates multiple clinical and pathological variables to predict both recurrence and metastasis development following NAC treatment. Methods: We conducted a retrospective analysis of 832 breast cancer patients who received NAC between 2013 and 2022. The analysis incorporated five key variables: tumor size changes, nodal status, Ki-67 index, Miller–Payne grade, and molecular subtype. A Multi-Layer Perceptron (MLP) model was implemented on the PyTorch platform and systematically benchmarked against SVM, Random Forest, and XGBoost models using five-fold cross-validation. Model performance was assessed by calculating the area under the curve (AUC), accuracy, precision, recall, and F1-score, and by analyzing confusion matrices. Results: The MLP model achieved AUC values of 0.86 (95% CI: 0.82–0.93) for HER2-positive cases, 0.82 (95% CI: 0.70–0.92) for triple-negative cases, and 0.76 (95% CI: 0.66–0.82) for HR+/HER2-negative cases. SHAP analysis identified post-NAC tumor size, Ki-67 index, and Miller–Payne grade as the most influential predictors. Notably, patients who achieved pCR still had a 12% risk of developing recurrence, highlighting the necessity for ongoing risk assessment beyond binary response evaluation. Conclusions: The proposed deep learning system provides precise and interpretable risk assessment for NAC patients, facilitating individualized treatment approaches and post-treatment monitoring plans. Full article

This research explores how coalitions of leaders who score high in the Dark Triad traits—narcissism, Machiavellianism, and psychopathy—rebuild moral architectures in organizations to consolidate power, suppress dissent, and secure their rule. Contrary to work that has focused predominantly on individual toxic leaders, this research examines the collective processes that emerge when multiple high-DT-scoring leaders coalesce and unify their moral leadership front. Adopting a qualitative, article-based document analysis methodology, this study synthesizes and critiques evidence from 55 peer-reviewed articles published between 2015 and 2025. Thematic analysis identified three fundamental dynamics through which Dark Triad leaders collectively exercise dominance. The first, the Ethics Cartel, involves the construction of a shared moral façade that legitimates power and shields wrongdoing. The second, Mutual Cover, outlines forms of mutual protection in which leaders shield one another from accountability and scrutiny. The third, Cultural Capture, outlines processes through which organizational culture is increasingly reconfigured such that “ethics” are structured to favor leadership over employees or wider stakeholders. This study illustrates how these coalitions cross over into individual transgressions, creating systemic risk that warps the fabric of organizational culture. Employees are confronted with a work culture that positions ethics as a means of developing survival adaptive mechanisms, such as silence, withdrawal, or compliance. These processes not only harm psychological safety and break trust but also disable accountability mechanisms established to maintain integrity. This study contributes to the study of leadership and organizational ethics by framing ethics not as merely an individual moral stance but as a collective instrument of power. It calls for more attention to the risks that follow collaboration among toxic leaders and for governance arrangements that address the organizational and systemic consequences of these unions. By situating these findings within the broader debate on power, people, and performance, this paper aligns with the focus of the Special Issue “Power, People, and Performance: Rethinking Organizational Leadership and Management” by showing how collective Dark Triad leadership distorts organizational performance outcomes while reshaping power relations in ways that undermine people’s trust and well-being. These insights extend Alowais & Suliman’s findings, highlighting the systemic feedback loops sustaining ethical distortion. Full article

Repurposing lithium-ion (Li-ion) batteries for second-life applications, such as stationary energy storage, offers significant economic and environmental benefits as these cells reach the end of their initial service life. Accurate and scalable classification of used Li-ion cell chemistries is essential for efficient sorting and safe repurposing, especially when manufacturer metadata is unavailable. This study presents a robust, automated machine learning (AutoML) framework, implemented in MATLAB R2024b and its toolboxes, for classifying three commercial 18,650 cell chemistries (LFP, NMC, and NCA) using long-term cycle life data. The workflow integrates structured data ingestion, segmentation, and multi-tiered feature engineering, extracting over 75 diagnostic features per cycle, including statistical, cumulative, segment-specific, and differential curve metrics. Feature selection is performed using principal component analysis and sequential forward selection, while Bayesian optimisation within AutoML identifies the optimal classification model. The resulting K-Nearest Neighbours classifier achieves over 99% test accuracy, demonstrating the effectiveness of the approach. This framework enables research-grade, metadata-independent classification and provides a scalable foundation for future industrial battery sorting and second-life applications. Full article

This study investigates how software developers discuss usability on Stack Overflow through an analysis of posts from 2008 to 2024. Despite recognizing the importance of usability for software success, there is a limited amount of research on developer engagement with usability topics. Using mixed methods that combine quantitative metric analysis and qualitative content review, we examine temporal trends, comparative engagement patterns across eight non-functional requirements, and programming context-specific usability issues. Our findings show a significant decrease in usability posts since 2010, contrasting with other non-functional requirements, such as performance and security. Despite this decline, usability posts exhibit high resolution efficiency, achieving the highest answer and acceptance rates among all topics, suggesting that the community is highly effective at resolving these specialized questions. We identify distinctive platform-specific usability concerns: web development prioritizes responsive layouts and form design; desktop applications emphasize keyboard navigation and complex controls; and mobile development focuses on touch interactions and screen constraints. These patterns indicate a transformation in the sharing of usability knowledge, reflecting the maturation of the field, its integration into frameworks, and the migration to specialized communities. This first longitudinal analysis of usability discussions on Stack Overflow provides insights into developer engagement with usability and highlights opportunities for integrating usability guidance into technical contexts. Full article

This study successfully established a novel discriminative model that distinguishes between Thai and foreign hemp seed extracts based on gas chromatography/mass spectrometry (GC/MS) metabolic profiling combined with machine learning algorithms such as hierarchy clustering analysis (HCA), principal component analysis (PCA), and partial least square-discriminant analysis (PLS-DA). The findings highlighted significant metabolic features, such as vitamin E, clionasterol, and linoleic acid, related with anti-aging properties via elastase inhibition. Our biological validation experiment revealed that the individual compound at 2 mg/mL exhibited a moderate elastase inhibitory activity, 40.97 ± 1.80% inhibition (n = 3). However, a binary combination among these metabolites at 1 mg/mL of each compound demonstrated a synergistic effect against elastase activities up to 89.76 ± 1.20% inhibition (n = 3), showing 119% improvement. Molecular docking experiments aligned with biological results, showing strong binding affinities and enhanced inhibitory effects in all combinations. This integrated approach provided insights into the bioactive compounds responsible for anti-aging effects and established a dependable framework for quality control and standardization of hemp seed-based skincare products. Additionally, the developed models enable effective discrimination between Thai and foreign strains, which is valuable for sourcing and product consistency. Overall, this research advances our understanding of hemp seed phytochemicals and their functional potential, paving the way for optimized natural anti-aging formulations and targeted functional foods. Full article

Cultural ecosystem services (CES) represent fundamental expressions of human-environment interactions. A comprehensive assessment of CES supply and demand offers a robust scientific foundation for optimizing the transformation of ecosystem service values to improve human well-being. This study integrates multi-source datasets and employs Maximum Entropy (MaxEnt) modeling with the ArcGIS platform to analyze the spatial distribution of CES supply and demand in Hunan Province, a typical mountain tourism regions in China. Furthermore, geographical detector methods were used to identify and quantify the driving factors influencing these spatial patterns. The findings reveal that: (1) Both CES supply and demand demonstrate pronounced spatial heterogeneity. High-demand areas are predominantly concentrated around prominent scenic locations, forming a “multi-core, clustered” pattern, whereas high-supply areas are primarily located in urban centers, water systems, and mountainous regions, exhibiting a gradient decline along transportation corridors and river networks. (2) According to the CES supply-demand pattern, Hunan Province can be classified into demand, coordination, and enhancement zones. Coordination zones dominate (45–70%), followed by demand zones (20–30%), while enhancement zones account for the smallest proportion (5–20%). (3) Urbanization intensity and land use emerged as the primary drivers of CES supply-demand alignment, followed by vegetation cover, distance to water bodies, and population density. (4) The explanatory power of two-factor interactions across all eight CES categories surpasses that of any individual factor, highlighting the critical role of synergistic multi-factorial influences in shaping the spatial pattern of CES. This study provides a systematic analysis of the categories and driving factors underlying the spatial alignment between CES supply and demand in Hunan Province. The findings offer a scientific foundation for the preservation of ecological and cultural values and the optimization of spatial patterns in mountain tourist areas, while also serving as a valuable reference for the large-scale quantitative assessment of cultural ecosystem services. Full article

Identifying malware families is vital for predicting attack campaigns and creating effective defense strategies. Traditional signature-based methods are insufficient against new and evasive malware, highlighting the need for adaptive, multimodal solutions. This paper proposes a deep learning framework that fuses visual and static features through a ConvNeXt-Tiny backbone with cross-attention integration and incorporates calibration strategies such as snapshot ensembling, test-time augmentation, and per-class bias adjustment. The model is evaluated on two publicly available datasets: Malimg and Fusion Malware. The results demonstrate an outstanding accuracy of 99.69% on Malimg and 98.67% on Fusion, with macro F1 scores of 99.22% and 98.12%, respectively. Bias calibration improved the detection of difficult families on Malimg, and error analysis of Fusion identified challenges with polymorphic and underrepresented families. Overall, combining multimodal fusion with lightweight calibration enhances robustness and interpretability for real-world malware detection and attribution. Full article

The impingement of a molten droplet on a solid surface, forming a “splat,” is a fundamental phenomenon observed across numerous industrial surface engineering techniques. For example, thermal spray deposition is widely used to create metal, ceramic, polymer, and composite coatings that are vital for aerospace, biomedical, electronics, and energy applications. Significant progress has been made in understanding droplet impact behavior, largely driven by advancements in high-resolution and high-speed imaging techniques, as well as computational resources. Although droplet impact dynamics, splat morphology, and interfacial bonding mechanisms have been extensively reviewed, a comprehensive overview of the mechanical behaviors of single splats, which are crucial for coating performance, has not been reported. This review bridges that gap by offering an in-depth analysis of bonding strength and residual stress in single splats. The various experimental techniques used to characterize these properties are thoroughly discussed, and a detailed review of the analytical models and numerical simulations developed to predict and understand residual stress evolution is provided. Notably, the complex interplay between bonding strength and residual stress is then discussed, examining how these two critical mechanical attributes are interrelated and mutually influence each other. Subsequently, effective strategies for improving interfacial bonding are explored, and key factors that influence residual stress are identified. Furthermore, the fundamental roles of splat flattening and formation dynamics in determining the final mechanical properties are critically examined, highlighting the challenges in integrating fluid dynamics with mechanical analysis. Thermal spraying serves as the primary context, but other relevant applications are briefly considered. Cold spray splats are excluded because of their distinct bonding and stress generation mechanisms. Finally, promising future research directions are outlined to advance the understanding and control of the mechanical properties in single splats, ultimately supporting the development of more robust and reliable coating technologies. Full article

Hop (Humulus lupulus L.) is a well-known medicinal and brewing plant, yet studies on the biological activity of its complete extracts remain limited. A comprehensive characterization of a full hop ethanolic extract (HLE) was conducted, integrating untargeted HPLC–MS profiling, anti-inflammatory evaluation in an inflammation-induced Caco-2 model, and transport assessment across intestinal epithelial monolayers. After ultrafiltration to remove pyrogenic components, HLE reduced IL-6 secretion in a concentration-dependent manner and decreased IL-8 levels, while mitigating IL-1β–induced barrier disruption as reflected by TEER recovery. HPLC–MS analysis of the basolateral compartment revealed selective permeability of medium-sized bitter-acid derivatives and the presence of three features not detected in the original extract, suggesting metabolic transformation during epithelial passage. Overall, the complete extract exhibited moderate but biologically relevant anti-inflammatory and barrier-protective effects in intestinal epithelial cells. The use of the whole extract, without isolating individual fractions, represents a practical and physiologically meaningful approach that may facilitate its application in the formulation of functional foods or dietary supplements. Full article

Background: Visceral adipose tissue (VAT) around internal organs is strongly related to metabolic disorders. While its metabolic effects are well-established, its influence on musculoskeletal function, particularly lower-body strength and endurance in women, remains underexplored. Lower-body strength is essential for mobility, independence, and fall prevention. The 30 s chair stand test (30CST) is a reliable measure of lower-body function, and bioelectrical impedance analysis (BIA) offers a non-invasive method for evaluating VAT. Despite its potential, BIA remains underutilized in clinical practice. Integrating these tools could provide critical insights into how VAT affects functional health and guide evidence-based interventions. Objective: To examine the relationship between visceral adiposity, quantified by visceral fat rating (VFR) via BIA, and lower-body strength and endurance assessed by the 30CST in women. Methods: A cross-sectional study of 131 Saudi women examined VAT using BIA with VFR as a VAT marker. Lower-body strength and endurance were evaluated using the 30CST. Spearman’s rank correlation was employed to explore relationships between VFR and 30CST. Results: The median age was 56 (IQR 45–61). The median VFR was 10 (IQR 7–12), and the median 30CST score was 8 (IQR 7–10). In the entire sample, a significant negative correlation was observed between VFR and 30CST performance (r = −0.4106, p < 0.0001). Women with obesity (n = 73) had significantly higher VFR (12, IQR 10–13) compared to women without obesity (n = 58), who had a median VFR of 7 (IQR 6–9) (p < 0.0001). In contrast, women with obesity had significantly lower 30CST (8, IQR 6–9) compared to those without obesity (9, IQR 8–11) (p = 0.0004). Additionally, the entire sample had significant negative correlations between 30CST and age, weight, BMI, %BF, FM, and FFM (p < 0.05). Conclusions: Elevated visceral fat is associated with lower lower-body strength and endurance in women, highlighting the value of routine visceral fat assessment for guiding musculoskeletal health evaluation and management. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mathematical approach that integrates human physiological parameters with drug-specific characteristics (including both active pharmaceutical ingredients and excipients), and it has emerged as one of the core technologies for optimizing the efficiency and reliability of drug development. Methods: This study synthesizes applications of PBPK models in FDA-approved drugs (2020–2024), systematically analyzing model utilization frequency, indication distribution, application domains and choice of modeling platforms, to reveal their substantive contributions to regulatory submissions. Additionally, we conducted an in-depth analysis of the PBPK models for 2024, classifying models into three tiers based on critical assessment of FDA reviewer comments. Results: Among 245 FDA-approved new drugs during this period, 65 NDAs/BLAs (26.5%) submitted PBPK models as pivotal evidence. Oncology drugs accounted for the highest proportion (42%). In application scenarios, drug–drug interaction (DDI) was predominant (81.9%), followed by dose recommendations for patients with organ impairment (7.0%), pediatric population dosing prediction (2.6%), and food-effect evaluation. Regarding modeling platforms, Simcyp^® emerged as the industry-preferred modeling platform, with an 80% usage rate. In terms of regulatory evaluation, a core concern for reviewers is whether the model establishes a complete and credible chain of evidence from in vitro parameters to clinical predictions. Conclusions: Detailed regulatory reviews demonstrate that although some PBPK models exhibit certain limitations and shortcomings, this does not preclude them from demonstrating notable strengths and practical value in critical applications. Benefiting from the strong support these successful implementations provide for regulatory decision-making, the technology is gaining increasing recognition across the industry. Looking forward, the integration of PBPK modeling with artificial intelligence (AI) and multi-omics data will unprecedentedly enhance predictive accuracy, thereby providing critical and actionable insights for decision-making in precision medicine and global regulatory strategies. Full article

Humans show neural specificity in processing animal-related information, especially regarding companion animals. However, the underlying cognitive mechanisms remain poorly understood. This study’s main objective is to investigate human neural specificity in processing companion animal-related information, compared to other animal types and inanimate objects. Forty participants viewed four image types (companion animals, neutral animals, positive objects, neutral objects) during functional magnetic resonance imaging (fMRI) scans and judged image categories. T-test results showed: 1. Processing companion animal-related information elicited specific brain activation in the right Inferior Parietal Lobe (right IPL), right Middle Occipital Gyrus (right MOG), left Superior Frontal Gyrus (left SFG), and left Precuneus (left PCu) (<0.05). 2. Generalized Psychophysiological Interaction (gPPI) analysis revealed specific functional connectivity changes between relevant brain regions during companion animal info processing (<0.05). 3. Dynamic Causal Modelling (DCM) analysis showed significant intrinsic connectivity differences between pet owners and non-pet owners: specifically, left IPL to left PCu and right ACC to right MOG (posterior probability, Pp > 0.95). The results of this study demonstrate that humans exhibit distinct neural specificity when processing information related to companion animals compared with livestock and inanimate objects. This neural specificity involves brain regions linked to higher-order cognitive functions (e.g., visual processing, emotion, and attachment), all of which are integral components of the human attachment network. These regions are part of the human attachment network, and their functional role likely relates to attachment mechanisms. These findings help clarify companion animals’ impact on human neural activity during human–animal interactions and guide applications like animal-assisted therapy. Full article