Search Results (92,503)

While immunophilins are well-recognized therapeutic targets, several members of this family of peptidyl-proline isomerases (PPIases) have yet to be subjected to ligand discovery efforts. In this study, we demonstrate a cost-effective means to identify ligands to the insufficiently investigated two-domain PPIase human Cyclophilin40 (Cyp40). Central to this effort was the use of beads, wherein a confocal nanoscanning (CONA) approach was used to rapidly probe candidates. Here, we describe how one can adapt the physical nature of microsized beads as a means to strategically reduce cost and ultimately make the discovery of small molecule hit and lead compounds more accessible to everyone irrespective of financial status (democratization). Full article

Hydrogels are water-rich polymeric networks mimicking the body’s extracellular matrix, making them highly biocompatible and ideal for precision medicine. Their “tunable” and “smart” properties enable the precise adjustment of mechanical, chemical, and physical characteristics, allowing responses to specific stimuli such as pH or temperature. These versatile materials offer significant advantages over traditional drug delivery by facilitating targeted, localized, and on-demand therapies. Applications range from diagnostics and wound healing to tissue engineering and, notably, cancer therapy, where they deliver anti-cancer agents directly to tumors, minimizing systemic toxicity. Hydrogels’ design involves careful material selection and crosslinking techniques, which dictate properties like swelling, degradation, and porosity—all crucial for their effectiveness. The development of self-healing, tough, and bio-functional hydrogels represents a significant step forward, promising advanced biomaterials that can actively sense, react to, and engage in complex biological processes for a tailored therapeutic approach. Beyond their mechanical resilience and adaptability, these hydrogels open avenues for next-generation therapies, such as dynamic wound dressings that adapt to healing stages, injectable scaffolds that remodel with growing tissue, or smart drug delivery systems that respond to real-time biochemical cues. Full article

With increasing global pressure to decarbonize electricity systems, particularly in regions outside international carbon trading frameworks, it is essential to develop adaptive optimization tools that account for regulatory policies and system-level uncertainty. An emission-constrained power dispatch strategy based on an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm is proposed. The algorithm incorporates dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements improve the algorithm’s convergence reliability and global search capacity in complex constrained environments. The proposed method is implemented in Taiwan’s 345 kV transmission system, covering a decadal planning horizon (2023–2033) with scenarios involving varying load demands, wind power integration levels, and carbon tax schemes. Simulation results show that the AGFMO approach achieves greater reductions in total dispatch cost and CO₂ emissions compared with conventional swarm-based techniques, including PSO, GACO, and FMO. Embedding policy parameters directly into the optimization framework enables robustness in real-world grid settings and flexibility for future carbon taxation regimes. The model serves as decision-support tool for emission-sensitive operational planning in power markets with limited access to global carbon trading, contributing to the advanced modeling of control and optimization processes in low-carbon energy systems. Full article

Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics (accumulation and dissipation), significantly enhancing delay estimation accuracy under oversaturated conditions. Secondly, we propose a novel intersection-level signal optimization method that addresses key practical challenges: (1) pedestrian stages, overlap phases; (2) coupling effects between signal cycle and queue length; and (3) stochastic vehicle arrivals in undersaturated conditions. Unlike conventional approaches, this method proactively shortens signal cycles to reduce queues while avoiding suboptimal solutions that artificially “dilute” delays by extending cycles. Thirdly, we introduce an adaptive coordination control framework that maintains arterial-level green-band progression while maximizing intersection-level adaptive optimization flexibility. To bridge theory and practice, we design a cloud–edge–terminal collaborative deployment architecture for scalable signal control implementation and validate the framework through a hardware-in-the-loop simulation platform. Case studies in real-world scenarios demonstrate that the proposed method outperforms existing benchmarks in delay estimation accuracy, average vehicle delay, and travel time in coordinated directions. Additionally, we analyze the influence of coordination constraint update intervals on system performance, providing actionable insights for adaptive control systems. Full article

Object detection in single synthetic aperture radar (SAR) imagery has always been essential for SAR interpretation. Over the years, the saliency-based detection method is considered as a strategy that can overcome some inherent deficiencies in traditional SAR detection and arouses widespread attention. Considering that the conventional saliency method usually suffers performance loss in saliency map generation from lacking specific task priors or highlighted non-object regions, this paper is devoted to achieving excellent salient object detection in single SAR imagery via a two-channel framework integrating Bayesian inference and adaptive iteration. Our algorithm firstly utilizes the two processing channels to calculate the object/background prior without specific task information and extract four typical features that can enhance the object presence, respectively. Then, these two channels are fused to generate an initial saliency map by Bayesian inference, in which object areas are assigned with high saliency values. After that, we develop an adaptive iteration mechanism to further modify the saliency map, during which object saliency is progressively enhanced while the background is continuously suppressed. Thus, in the final saliency map, there will be a distinct difference between object components and the background, allowing object detection to be realized easily by global threshold segmentation. Extensive experiments on real SAR images from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and SAR Ship Detection Dataset (SSDD) qualitatively and quantitatively demonstrate that our saliency map is superior to those of four classical benchmark methods, and final detection results of the proposed algorithm present better performance than several comparative methods across both ground and maritime scenarios. Full article

Molecular diagnoses of Entamoeba histolytica and Strongyloides stercoralis in human samples are becoming increasingly common. To contribute to the ongoing standardization of molecular diagnostic approaches targeting these parasites, we compared three published E. histolytica- and S. stercoralis-specific real-time PCR assays in test comparisons without a reference standard. Latent class analysis (LCA) was used to calculate diagnostic accuracy estimations for the three compared assays per parameter. The comparison was conducted using stool samples from Ghanaian individuals. In the course of the assessment of 873 stool samples, the number of detected positive PCR results ranged from 10 to 15 for S. stercoralis and from 4 to 54 for E. histolytica depending on the applied assay. Diagnostic accuracy estimates of real-time PCR sensitivity for S. stercoralis and E. histolytica ranged from 89% to 100% and from 75% to 100%, respectively; diagnostic estimates of specificity ranged from 99% to 100% and from 94% to 100%, respectively. Diagnostic accuracy-adjusted prevalence estimates were 1.2% for S. stercoralis and 0.5% for E. histolytica. High cycle threshold values of real-time PCR > 35 showed a particularly reduced likeliness of reproducibility when applying competitor real-time PCR assays. There were no clear-cut differences in terms of diagnostic accuracy favoring either small-subunit ribosomal ribonucleic acid (SSU rRNA) gene sequences or the S. stercoralis dispersed repetitive sequence for S. stercoralis PCR. The same applied to the comparison of real-time PCRs targeting SSU rRNA gene sequences and the SSU rRNA episomal repeat sequence (SREPH) of E. histolytica. In conclusion, interchangeability of the compared real-time PCR assays was higher for the assessed S. stercoralis assays compared with the assessed E. histolytica assays. Regional diagnostic accuracy testing seems advisable before literature-adapted assays for rare tropical pathogens like S. stercoralis and E. histolytica are applied in different study regions. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Background/Objectives: As part of the human adaptation to dairy consumption, the presence of the rs4988235-T variant in the MCM6 gene primarily determines lactase persistence in adult European populations, increasing the expression of the lactase-encoding LCT gene. Carriers of the C/C variant are lactose intolerant, while carriers of the T/T or T/C variant have persistent lactase enzyme activity and are able to digest lactose in adulthood. While the association between lactose intolerance and increased cardiovascular risk (CVR) is well-known, the underlying causes have only been partly explored. The present study aimed to investigate the association of rs4988235 polymorphism with significant lipids affecting cardiovascular health and estimated CVR. Methods: The rs4988235 polymorphism was genotyped in 397 subjects from the general Hungarian population and 368 individuals from the Roma population. To characterize the overall lipid profile, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), apolipoprotein AI (ApoAI), and apolipoprotein B100 (ApoB100) levels were measured, and their ratios (TG/HDL-C, LDL-C/HDL-C, and ApoB100/ApoAI) were calculated. Cardiovascular risk was estimated using the Framingham Risk Score (FRS), Pooled Cohort Equations (PCE), Revised Pooled Cohort Equations (RPCE), and the Systematic Coronary Risk Evaluations (SCORE and SCORE2) algorithms. Adjusted linear and logistic regression analyses were performed, with p < 0.05 considered significant. Results: The Roma population had a significantly higher prevalence of the C/C genotype than the general population (65.5% vs. 40.3%, respectively). The results of the adjusted linear regression analysis showed a significant association between the C/C genotype and higher LDL-C level (B = 0.126, p = 0.047) and ApoB100 level (B = 0.046, p = 0.013), as well as a higher LDL-C/HDL-C ratio (B = 0.174, p = 0.021) and a higher ApoB100/ApoAI ratio (B = 0.045, p = 0.002), as well as a lower HDL-C level (B = −0.041, p = 0.049). The C/C genotype was also significantly associated with an increased cardiovascular risk (CVR) as estimated by the SCORE (B = 0.235, p = 0.034), SCORE2 (B = 0.414, p = 0.009), PCE (B = 0.536, p = 0.008), and RPCE (B = 0.289, p = 0.045) but not the FRS. After adjusting the statistical model further for ApoAI and ApoB100 levels, the significant correlation with the risk estimation algorithms disappeared (SCORE: p = 0.099; SCORE2: p = 0.283; PCE: p = 0.255; and RPCE: p = 0.370). Conclusions: Our results suggest that the C/C genotype of rs4988235 is associated with significantly higher ApoB100 and lower ApoAI levels and consequently higher ApoB100/ApoAI ratios, potentially contributing to an increased risk of cardiovascular disease. The results of the statistical analyses suggest that the association between lactose intolerant genotype and cardiovascular risk may be mediated indirectly via modification of the apolipoprotein profile. Full article

Cisplatin resistance remains a major therapeutic challenge in oral squamous cell carcinoma (OSCC), leading to treatment failure and poor outcomes. This study aimed to generate and characterize cisplatin-resistant OSCC models to elucidate resistance mechanisms. Two resistant OSCC cell lines (SCC-9R and HSC-3R) were developed through gradual dose escalation. Parental and resistant cells were analyzed via RNA-seq and gene set enrichment analysis, and validated through RT-qPCR, Western blot, immunofluorescence, and gelatin zymography. Functional assays, including 2D and 3D migration and invasion models, assessed phenotypic changes. A multi-omics analysis revealed molecular alterations in resistant cells, including 305 differentially expressed genes (DEGs) in HSC-3R (187 upregulated) and 782 in SCC-9R (298 upregulated) versus parental lines, with enrichment for extracellular matrix organization (p < 0.001) and consistent epithelial–mesenchymal transition (EMT) activation (p < 0.001), demonstrated by the upregulation of ZEB1, ZEB2, Vimentin, and TWIST1, and E-cadherin suppression. Functional validation confirmed an aggressive phenotype, including increased migration (p < 0.05), invasion (p < 0.01), and elevated MMP-2 (p < 0.01) and MMP-9 (p < 0.001) activity. Findings were verified in 3D spheroid models. Overall, cisplatin resistance in OSCC involves EMT, inflammatory signaling, and metabolic adaptation. The consistency of these features across both models supports the robustness of this in vitro system and reveals targets for therapeutic intervention. Full article

High-resolution (HR) remote sensing images contain rich, sensitive information regarding the distribution of geospatial objects and natural resources. With the widespread application of HR remote sensing images, there is an urgent need to protect the data security of HR remote sensing images during transmission and sharing. Existing encryption approaches typically employ a global encryption strategy, overlooking the varying texture complexity across different sub-regions in HR remote sensing images. This oversight results in low efficiency and flexibility for encrypting large-scale remote sensing image data. To address these limitations, this paper presents a texture-adaptive hierarchical encryption method that combines region-specific security levels. The method first decomposes remote sensing images into grid-based sub-blocks and classifies them into three texture complexity types (i.e., simple, medium, and complex) through gradient and frequency metrics. Then, chaotic systems of different dimensions are adaptively adopted to encrypt the sub-blocks according to their texture complexity. A more complex chaotic system encrypts a sub-block with a more complex texture to ensure security while reducing computational complexity. The experimental results on publicly available high-resolution remote sensing datasets demonstrate that the proposed method achieves adequate information concealment while maintaining an optimal balance between encryption security and computational efficiency. The proposed method is more competitive in encrypting large-scale HR remote sensing data compared to conventional approaches, and it shows significant potential for the secure sharing and processing of HR remote sensing images in the big data era. Full article

Ventricular arrhythmias such as ventricular fibrillation (VF) and ventricular tachycardia (VT) are among the leading causes of sudden cardiac death worldwide, making their timely and accurate detection a critical task in modern cardiology. This study presents an advanced framework for the automatic detection of critical cardiac arrhythmias—specifically ventricular fibrillation (VF) and ventricular tachycardia (VT)—by integrating deep learning techniques with neuro-fuzzy systems. Electrocardiogram (ECG) signals from the MIT-BIH and AHA databases were preprocessed through denoising, alignment, and segmentation. Convolutional neural networks (CNNs) were employed for deep feature extraction, and the resulting features were used as input for various fuzzy classifiers, including Fuzzy ARTMAP and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Among these classifiers, ANFIS demonstrated the best overall performance. The combination of CNN-based feature extraction with ANFIS yielded the highest classification accuracy across multiple cardiac rhythm types. The classification performance metrics for each rhythm type were as follows: for Normal Sinus Rhythm, precision was 99.09%, sensitivity 98.70%, specificity 98.89%, and F1-score 98.89%. For VF, precision was 95.49%, sensitivity 96.69%, specificity 99.10%, and F1-score 96.09%. For VT, precision was 94.03%, sensitivity 94.26%, specificity 99.54%, and F1-score 94.14%. Finally, for Other Rhythms, precision was 97.74%, sensitivity 97.74%, specificity 99.40%, and F1-score 97.74%. These results demonstrate the strong generalization capability and precision of the proposed architecture, suggesting its potential applicability in real-time biomedical systems such as Automated External Defibrillators (AEDs), Implantable Cardioverter Defibrillators (ICDs), and advanced cardiac monitoring technologies. Full article

Machine vision technologies play a critical role in the advancement of modern human-centric manufacturing systems. This study investigates their practical applications in improving both safety and productivity within industrial environments. Particular attention is given to areas such as quality assurance, worker protection, and process optimization, illustrating how intelligent visual inspection systems and real-time data analysis contribute to increased operational efficiency and higher safety standards. The research methodology combines an in-depth analysis of industrial case studies, including one from the frozen dough industry, with a systematic review of the current literature on machine vision technologies in manufacturing. The findings highlight the potential of such systems to reduce human error, maintain consistent product quality, minimize material waste, and promote safer and more adaptable work environments. This study offers valuable insights into the integration of advanced visual technologies within human-centered production environments, while also addressing key challenges and future opportunities for innovation and technological evolution. Full article

Nowadays, the use of technology has become increasingly indispensable, leading to prolonged exposure to computers and other screen devices. This situation is common in work areas related to Information and Communication Technologies (ICTs), where people spend long hours in front of a computer. This exposure has been associated with the development of musculoskeletal disorders, among which nonspecific back pain is particularly prevalent. This observational study presents the design of a telerehabilitation strategy based on 3D animations, which is aimed at enhancing the musculoskeletal health of individuals working or studying in ICT-related fields. The intervention was developed through the Moodle platform and designed using the ADDIE instructional model, incorporating educational content and therapeutic exercises adapted to digital ergonomics. The sample included university students in the field of computer science who were experiencing symptoms associated with prolonged computer use. After a four-week intervention period, the results show favorable changes in pain perception and knowledge of postural hygiene. These findings suggest that a distance-based educational and therapeutic strategy may be a useful approach for the prevention and treatment of back pain in academic settings. Full article

Drought stress limits seedling growth, hindering morphological development and population establishment. Artocarpus nanchuanensis, a critically endangered species endemic to the karst regions of southwest China, exhibits poor population structure and limited natural regeneration in the wild, with water deficit during the seedling stage identified as a major factor contributing to its endangered status. Elucidating the physiological and molecular mechanisms underlying drought tolerance in A. nanchuanensis seedlings is essential for improving their drought adaptability and facilitating population recovery. In this study, 72 two-year-old seedlings were divided into two groups: drought (PEG) and ethephon (PEG + Ethephon), and subjected to drought-rehydration experiments. The results showed that exogenous application of 100 mg·L⁻¹ ethephon significantly improved stomatal conductance and photosynthetic pigment content in A. nanchuanensis seedlings. Under drought stress, the PEG + Ethephon group exhibited rapid stomatal closure, maintaining water balance and higher photosynthetic pigment levels. After rehydration, the PEG + Ethephon group significantly outperformed the PEG group in terms of photosynthetic rate. Ethephon treatment reduced H₂O₂ and MDA levels, enhanced antioxidant enzyme activity (SOD, CAT, POD, GR), and increased osmotic regulator activity (soluble sugars, soluble proteins, and proline), improving ROS-scavenging capacity and reducing oxidative damage. Ethephon application significantly enhanced ethylene accumulation in seedlings, while drought stress stimulated the concentrations of key ethylene biosynthetic enzymes (SAMS, ACS, and ACO), thereby further contributing to improved drought resistance. Transcriptomic data revealed that drought stress significantly upregulated key ethylene biosynthesis genes, with expression levels increasing with stress duration and rapidly decreasing after rehydration. WGCNA analysis identified eight key drought-resistance genes, providing valuable targets for future research. This study provides the first mechanistic insight into the physiological and molecular responses of A. nanchuanensis seedlings to drought and rehydration, underscoring the central role of endogenous ethylene in drought tolerance. Ethephon treatment effectively enhanced ethylene accumulation and biosynthetic enzyme activity, thereby improving drought adaptability. These findings lay a theoretical foundation for subsequent molecular functional studies and the conservation biology of this endangered species. Full article

This study explores the potential of National Spatial Data Infrastructure (NSDI) to strengthen governance and policy processes in Pakistan. Drawing on the UNESCAP principles of good governance and the EGU policy cycle model, this research applies a dual-method approach combining thematic document analysis of 23 national policy frameworks and a stakeholder survey (n = 28). The results reveal that while many policies reference spatial data conceptually, critical components such as standardised datasets, spatial dashboards, and institutional coordination mechanisms remain underdeveloped. Spatial references are largely confined to early policy stages, with limited integration in evaluation and maintenance, thereby limiting adaptive governance. Conversely, survey findings reflect strong recognition of NSDI’s value across governance principles, policy integration, and spatial awareness dimensions. The composite endorsement score highlights institutional demand for geospatial tools, data standards, and capacity-building platforms. The study concludes that embedding NSDI within policy and planning systems can bridge critical governance gaps, enhance implementation fidelity, and support inter-agency coordination for long-term policy effectiveness. Full article