Search Results (17,030)

Environmental contaminants pose threats to exposed organisms and negatively impact the nervous, cardiovascular, immune, and reproductive systems. Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals that are ubiquitous in the environment. Given that mixtures of environmental contaminants have the potential to exacerbate toxicity, we reviewed the current literature on pesticides, microplastics, or metal exposure in combination with PFAS on aquatic vertebrates and invertebrates. The objectives were to evaluate the toxicological effects of mixtures of the selected contaminants with PFAS on aquatic organisms to better understand biological responses in animals. Based on our review, data suggest that PFAS can modify the toxicity of co-occurring pollutants. For example, synergistic effects on toxicity include chlorpyrifos + perfluorohexanoic acid (PFHxA), which increased reactive oxygen species (ROS) and upregulated neurotoxicity-related genes in zebrafish, and perfluorooctanoic acid (PFOA) + atrazine, which increased the presence of malformations and oxidative stress. However, antagonistic interactions were also observed, for example, reduced herbicide toxicity in PFOA + 2,4-dichlorophenoxyacetic acid (2,4-D) mixtures. PFAS combined with microplastics often intensified oxidative stress and developmental or reproductive effects, though polyethylene microplastics attenuated perfluorooctane sulfonic acid (PFOS)-induced immunotoxicity in fish like seabass. Interactions with metals also varied, with copper and cadmium enhancing oxidative stress while mercury mixtures with PFAS showed antagonism, underscoring the complexity of mixture effects in real environments. A computational approach demonstrated that PFOS can engage in intermolecular interactions with pesticides, microplastic monomers, and metals, suggesting chemical-level effects that could modify toxicity or bioavailability. Future studies should focus on elucidating the mechanisms underlying these complex interactions, investigating effects at different trophic levels and in a broader range of species, and should consider environmentally relevant mixtures. Full article

Background: Epimedium is a natural herb with immunomodulatory potential, but its vaccine adjuvant properties remain poorly understood. Objective: The aim of this study was to elucidate the adjuvant effects of Epimedium and the underlying molecular mechanisms. Methods: Network pharmacology was used to identify bioactive compounds and targets of Epimedium from the TCMSP database, and immunomodulation-related targets from GeneCards and OMIM. PPI networks, KEGG/GO enrichment, molecular docking, and molecular dynamics (MD) simulations were performed. In vivo, female BALB/c mice were immunized with the Staphylococcus aureus (S. aureus) vaccine subunit HI antigen, either alone or with low- or high-dose icariin (ICA). Serum antibody responses (IgG, IgG1, IgG2a, IgG2b) were measured by ELISA. Survival against lethal S. aureus USA300 challenge was monitored. Results: Network pharmacology predicted 488 targets and 13 pathways. Core targets included IL6, TP53, EGFR, CTNNB1, HIF1A, HSP90AA1, JUN, MTOR, SRC, and AKT1. KEGG/GO analysis indicated involvement of T cell receptor and NOD-like receptor signaling pathways in inflammatory responses. Molecular docking and MD simulations confirmed stable ligand-target binding. Experimental validation showed that ICA significantly enhanced HI-specific antibody responses and induced a Th2-biased humoral immune response (IgG1/IgG2a ratio > 1), which is particularly relevant for vaccines targeting extracellular pathogens such as S. aureus. ICA also improved survival after lethal bacterial challenge. Conclusions: This study identifies potential bioactive compounds, core targets, and key pathways of Epimedium as a vaccine adjuvant. Experimentally, ICA, as a representative component, enhanced HI-specific antibody responses and conferred protection against lethal S. aureus challenge. Together, these findings offer a computational–experimental basis that may guide further mechanistic investigation. Full article

Thermal hazards in industrial environments often remain undetected until critical failure or injury occurs. Conventional handheld infrared cameras require manual operation and limit continuous situational awareness. This study presents the design and field validation of a wearable helmet-mounted real-time thermal system based on the MLX90640 infrared array sensor and a Raspberry Pi 5 edge computing platform. Experimental validation was performed across multiple scenarios of 400 measurements based on industrial distances of 100 cm and 150 cm. The performance of the system was tested against a pre-calibrated hotspot infrared thermometer using linear regression analysis and standard error metrics to determine proportional agreement. The results indicate a strong proportional relationship between the two systems at both industrial distances, with R² values ranging from 0.9885 to 0.9973 at 100 cm and from 0.9586 to 0.9867 at 150 cm. A moderate increase in mean absolute error (MAE) was observed as the measurement distance increased. Statistically significant increases in error were identified in mechanically dynamic scenarios where statistically significant increases in measurement error were observed (p-value < 0.05), indicating distance-dependent sensitivity under moving mechanical conditions. The higher absolute errors at longer distances mainly result from field-of-view expansion, reduced target occupancy, and mixed-pixel hotspot effects rather than weakened proportional trend stability. An industrial distance-aware linear regression model was developed to describe behavior and support calibrations under different deployment conditions. Despite minor absolute deviations during dynamic operations, the system maintained strong trend-tracking performance, suggesting suitability for daily preliminary hazard monitoring in industrial safety maintenance. Full article

The purpose of this research is to introduce a scalable system of an improved security for devices connected to a wireless network using the Advanced Encryption Standard with a Post-Quantum Cryptography standard FIPS203, Module Lattice–Key Encapsulation Mechanism (ML-KEM). This implementation is to address concerns regarding compromised network security or bad actors sniffing packets through a data bus to collect unintended compromised data. The ML-KEM is used to create a shared secret that is used as the symmetric key that will enable the encryption and decryption method for the ciphertexts between the client and the host. This research provides a baseline implementation of added security against Quantum Computers by using an encapsulation method for key pairs, digital signatures for data integrity, and added difficulties for side-channel attacks from unauthorized users. Devices that are older than the WiFi6-compliant standard also have additional vulnerability of not having the WiFi Protected Access (WPA) third-generation security, which this work addresses. This paper proposes an added layer of encryption security that is sufficient to protect information within the network that has been compromised by an unauthorized user. Based on the findings, new features, utilities and improvements are recommended that can modernize the needs of the industry. Full article

Background: Inflammatory bowel disease (IBD) is frequently complicated by secondary bone loss driven by chronic inflammation and gut–bone axis dysregulation. Although dried ginger has pharmacological activities relevant to intestinal inflammation, the effects of dried ginger milk extract (DGME), a lipophilic constituent-enriched preparation, on IBD-associated bone loss (IBD-BL) remain unknown. This study evaluated the preventive and therapeutic effects of DGME on IBD-BL and explored the underlying mechanisms. Methods: Mice with DSS-induced IBD-BL were treated with DGME (250, 125, or 62.5 mg/kg) or sulfasalazine. Colitis severity, bone microarchitecture, osteoclast activity and Th17 cells were assessed by histology, micro-computed tomography, histomorphometry and flow cytometric analysis. UHPLC-Q-TOF MS, network pharmacology, 16S rRNA sequencing, fecal metabolomics, and in vitro assays were used for mechanistic investigation. Results: DGME ameliorated colitis, improved trabecular bone microarchitecture, and reduced osteoclast-related bone destruction. These effects were associated with selective suppression of pathogenic bone marrow TNF-α⁺ Th17 cells and downregulation of Il17a, Rorc, Tnfα, Ccr2, Ccr6, Cxcr4, Csf1, and Tnfsf11. Compared with aqueous extract, DGME was enriched in 19 lipophilic constituents. Multi-omics analyses showed that DGME remodeled gut microbiota and metabolite profiles, characterized by enrichment of Lactobacillus, Anaerotruncus, vanillin, and spermidine. Both vanillin and spermidine suppressed Th17 effector genes and inhibited MEK/ERK signaling in vitro. Conclusions: DGME alleviated IBD-BL by suppressing pathogenic TNF-α⁺ Th17 responses and remodeling the gut microbiota–metabolite axis. This study not only extends the therapeutic application of dried ginger from intestinal inflammation to IBD-BL, but also identifies vanillin and spermidine as candidate functional mediators linked to MEK/ERK inhibition. Full article

Aiming at the issues of low recognition accuracy and high model computational complexity for power quality disturbances (PQDs) in strong-noise environments, this paper proposes a novel lightweight PQD-recognition method that integrates a hybrid architecture of variational mode decomposition (VMD), convolutional neural network (CNN), and transformer. Firstly, a hybrid optimization algorithm named the monkey–genetic hybrid optimization algorithm (MGHOA) is proposed to optimize VMD parameters for denoising disturbance signals, thereby enhancing recognition accuracy in noisy environments. Secondly, to fully extract disturbance signal features and reduce the computational complexity of the model, a lightweight CNN–transformer model is designed. Depthwise separable convolution (DSC) is employed to extract local features and the multi-head attention mechanism of transformer is utilized to mine the long-distance dependence and global features, thereby enhancing the feature representation. Thirdly, a multitask joint-learning method is proposed to collaboratively optimize classification accuracy and temporal localization tasks, enhancing the discrimination of similar disturbances. Additionally, a dual-pooling global feature fusion strategy is designed to further enhance the model’s ability to discriminate complex disturbances. Comparative experiments on 16 typical PQD types demonstrate that the proposed method achieves excellent performance in recognition accuracy, model robustness, and computational efficiency. The integration of the MGHOA–VMD module improves recognition accuracy by 1.08%, while the multitask joint-learning method contributes an additional 0.55% improvement. When achieving recognition accuracy comparable to complex models, the training time of the proposed method is 36.51% of that required by DeepCNN and merely 5.90% of that required by bidirectional long short-term memory (BiLSTM), with a 31.22% reduction in parameter scale. This work provides a novel solution for intelligent power quality disturbance recognition. Full article

Transcranial direct current stimulation (tDCS) over motor–premotor regions may modulate motor performance, though underlying mechanisms remain unclear. Twenty-four athletes (9 females, 15 males) were randomly assigned to receive anodal tDCS (2 mA, 20 min) over the left sensorimotor cortex (n = 12) or sham stimulation (n = 12). Participants performed a bimanual isometric force-matching task at 30% maximal voluntary contraction, with visual feedback initially provided and then removed. Force undershoot, root mean square error (RMSE), spectral power (1–3 Hz), and inter-hand coherence were analyzed. A computational model was developed to test whether enhanced proprioceptive feedback processing could account for observed effects. Following tDCS, force undershoot decreased significantly (p = 0.002, d = −1.15) and RMSE improved (p = 0.010, d = −0.91). Spectral power in the 1–3 Hz band increased (p = 0.012, d = 0.87), suggesting enhanced corrective oscillations. These within-group changes were absent in the sham group (all p > 0.20), although Group × Epoch interactions did not reach significance (all p > 0.05), likely due to limited statistical power. Inter-hand coherence remained unchanged. The computational model demonstrated that enhanced proprioceptive feedback gain qualitatively reproduces the observed behavioral pattern. Anodal tDCS over the left sensorimotor/premotor region may enhance bimanual force control under conditions requiring proprioceptive feedback. Replication with larger samples is needed to confirm between-group specificity. Full article

Protective behavior in dairy cattle is one of the important potential indicators of their health and welfare status, and the precise detection of this behavior is of great significance for improving pasture management. However, existing methods face challenges, including capturing rapid motions, excessive background interference, and sample imbalance in complex agricultural environments. In response to these challenges, we proposed a Multi-Stage Attention SlowFast (MSA-SlowFast) model based on the improved SlowFast network to explore the model’s ability to distinguish between normal and protective behavior of dairy cattle. It achieves performance improvement through three core modules: the Multi-Path Balanced Head (MPBHead) for alleviating category imbalance, the Spatio-Temporal Convolutional Block Attention Module (ST-CBAM) for enhancing key feature extraction, and the 7 (BAF) for promoting multi-path feature complementarity. Additionally, we proposed novel timing-aware oversampling methods and dynamic loss adjustment mechanisms to further improve the detection performance of minority-class protective behaviors. Finally, a spatio-temporal-oriented dairy cattle protective behaviors dataset is constructed. Experimental results demonstrate that the proposed MSA-SlowFast model achieves 79.41% mAP, surpassing the standard SlowFast (70.58%) and Slow-only (68.21%). Further validation shows that the model exhibits high detection confidence in four specific actions labeled as protective behavior: 0.97 for tail swaying, 0.90 for head shaking, 0.92 for ear flapping, and 0.90 for leg kicking. These preliminary results show that the method proposed in this study has certain feasibility and reference value for the detection of protective behavior of dairy cattle under our constructed dataset. Full article

Oxidative stress is a key contributor to the pathogenesis of immune-mediated diseases through its effects on cellular metabolism, mitochondrial function, immune signaling pathways, and inflammatory tissue injury. Disruption of redox homeostasis promotes metabolic reprogramming and persistent activation of innate and adaptive immune responses, contributing to disease progression across multiple inflammatory and autoimmune disorders. Recent advances in high throughput molecular technologies have generated large scale multi-omics datasets that enable comprehensive investigation of redox-associated mechanisms at a systems level. Integration of these datasets with computational analytical approaches has facilitated the identification of multidimensional molecular signatures associated with disease development and progression. This systematic review evaluates studies applying computational frameworks to analyze redox-related molecular data in immune-mediated diseases including multiple sclerosis, systemic lupus erythematosus, lupus nephritis, rheumatoid arthritis, Sjögren’s syndrome, and inflammatory bowel disease. Across the reviewed studies, oxidative stress associated with molecular signatures were consistently linked to immune activation, mitochondrial metabolism, and inflammatory signaling pathways. Computational analyses also identified regulatory genes involved in antioxidant defense and metabolic regulation, as well as pathways associated with regulated cell death. These findings highlight the translational potential of computational redox analysis for biomarker discovery, disease stratification, and development of targeted therapeutic strategies aimed at restoring redox balance and improving clinical management of immune-mediated diseases. Full article

This paper presents a real-time magnetic adhesion force estimation framework for wall-climbing robots equipped with Halbach permanent magnet arrays (PMAs) and air-gap–adjustable mechanisms. Accurately computing the magnetic adhesion force between a PMA and a large ferromagnetic surface is challenging due to the nonlinear magnetization behavior of soft magnetic materials and the strongly coupled, highly nonuniform magnetic fields generated by Halbach arrays. Conventional analytical models fail to capture these effects, while finite element methods (FEM) incur prohibitive computational cost for real-time applications. To address this, we propose an analytical magnetic-force estimation model based on the magnetostatic MoI (Method of Images), which replaces the unknown magnetization inside the steel plate with an equivalent image magnet distribution that satisfies boundary conditions at the air–steel interface. The method avoids solving complex magnetization in soft magnetic media and enables a unified force computation for arbitrarily oriented magnet elements. Additionally, complex Halbach PMA geometries are approximated through cuboid-element segmentation into cuboid magnet array, allowing efficient force evaluation. Comparative studies demonstrate that the proposed method achieves accuracy comparable to FEM while reducing computation time by several orders of magnitude. Experimental validation using a linear Halbach array and a large steel plate proved that the framework can reliably estimate magnetic adhesion force across varying air-gap distances, meeting the real-time requirements of air-gap–adjustable wall-climbing robots. Full article

Subsoiling can break the plough pan and improve the root growth environment. The effect of the traditional subsoiler is poor, as it relies only on the chisel tine, but pneumatic subsoiling can improve the soil structure more efficiently through the negative pressure generated by the jet hole. This research used computational fluid dynamics and the discrete element method to optimize the biomimetic structure of the jet hole, model the pneumatic subsoiling process at a depth of 330 mm, and observe the movement of soil particles as airflow passes through. The effect of the jet hole at different positions and sizes on the plough pan soil was analyzed, and fluid domains and measurement areas were set up to observe the upward movement, diffusion, stabilization, and settling of soil particles under the action of airflow. The results of the soil bin experiment validated the accuracy of the simulation model through draft force and vertical force, and the average error between the simulation and experimental data was 2.8%. The study revealed that the increase in the rate of soil porosity reached a maximum of 3.65% when the jet hole was positioned above the chisel tine with a radius of 4 mm. The biomimetic jet hole pneumatic subsoiler designed in this study, along with the established CFD-DEM coupled simulation model capable of predicting pneumatic subsoiling performance, can provide references for the design and application of a pneumatic subsoiler. Furthermore, it also provides a theoretical basis for understanding the mechanism of airflow on soil during pneumatic subsoiling operations. Full article

Combining plant-derived bioactives could produce effective anti-inflammatory interventions for myofascial inflammation. This study evaluated in vitro synergy and computational mechanisms of curcumin–evodiamine activity against TNF-α, IL-1β, iNOS and COX-2, with frontier molecular orbital analysis to inform putative mechanisms. Evodiamine and curcumin were identified/quantified by HPLC–PDA and LC–MS (λmax 226 nm and 426 nm; RT 8.61 and 9.53 min; [M−H]⁻m/z 302.2 and 367.2). Purities were 98.08 ± 1.92% and 98.04 ± 1.86%. Noncytotoxic concentrations in RAW264.7 cells were determined, then LPS-stimulated cells were treated with evodiamine (0.01 µM), curcumin (0.01 µM) and a 1:1 mixture (0.001 µM). Molecular docking against TNF-α, IL-1β, iNOS and COX-2 and HOMO–LUMO calculations were performed. Curcumin and the combination significantly reduced TNF-α and NO; curcumin and the combination reduced IL-1β, whereas evodiamine alone showed limited effects. Docking predicted stronger binding for curcumin and evodiamine than ibuprofen across targets (e.g., curcumin ΔG −10.18 kcal·mol⁻¹ for TNF-α; evodiamine ΔG −10.02 kcal·mol⁻¹ for COX-2). Frontier orbital energies indicated differing electronic profiles (ibuprofen ΔE 8.62 eV; evodiamine 9.65 eV; curcumin 9.89 eV), suggesting complementary reactivity. The curcumin–evodiamine combination exhibits in vitro anti-inflammatory activity with supportive docking and orbital data, providing mechanistic rationale for further development. Full article

Cement stabilized macadam (CSM) bases are prone to cracking and void damage under long-term traffic loading and environmental actions, which accelerates structural deterioration. Although grouting is an effective method for treating such concealed defects, laboratory-based evaluation of repair effectiveness remains limited. In this study, field-cored CSM specimens were recombined in a cylindrical mold to simulate four void conditions (1/4, 2/4, 3/4, and 4/4), and repaired using an inorganic cementitious composite grouting material based on ultra-fine cement and high-belite sulphoaluminate cement (HBSAC), and modified with ethylene-vinyl acetate (EVA) latex, wollastonite (WO) whiskers, and polyvinyl alcohol (PVA) fibers. The repair effectiveness was evaluated through ultrasonic testing, capacitance measurement, uniaxial compression with acoustic emission (AE) monitoring, and computed tomography (CT). The results show that the longitudinal wave velocity of all repaired groups increases continuously with curing time, with a maximum increase of 21.98% at 28 days. The normalized capacitance response exhibits clear time- and layer-dependent variation, with the 4/4 group showing the most pronounced spatial heterogeneity. In the uniaxial compression tests, the peak load increases from 181 kN in the control group to 201–286 kN in the repaired groups, while the tensile-related AE event proportion increases from 77.35% in the 1/4 group to 89.38% in the 4/4 group. CT analysis shows that the proportion of micropores smaller than 1 mm³ increases from 66.3% to 82.7%, whereas the proportion of pores larger than 100 mm³ decreases from 46.5% to 21.6% after repair. These results demonstrate that the composite grouting material provides effective filling, structural reconstruction, and mechanical enhancement for void-damaged CSM, and that the proposed multi-source characterization framework is suitable for evaluating grouting repair performance. Full article

The increasing complexity of next-generation wireless systems challenges the scalability and generalization capabilities of traditional model-driven physical layer (PHY) design, which relies on analytically derived channel models and optimization frameworks. This paper presents a comprehensive survey and critical review of deep learning (DL) architectures enabling the transition toward AI-native PHY design. A unified optimization perspective is developed in which all PHY tasks—including channel estimation, channel state information (CSI) feedback, massive MIMO processing, signal detection, channel coding, beamforming, resource allocation, and semantic-aware transmission—are formulated under a common empirical risk minimization (ERM) framework. Neural architectures such as autoencoders, convolutional and recurrent networks, transformers, and reinforcement learning models are examined through their underlying optimization formulations, loss functions, training methodologies, and representation learning mechanisms. The review compares model-driven and AI-native approaches in terms of performance metrics, computational complexity, robustness, generalization capability, and practical deployment constraints, including hardware limitations, energy efficiency, and real-time feasibility. The analysis highlights the conditions under which AI-native architectures provide adaptability and performance improvements while identifying trade-offs in complexity, latency, and interpretability. The study concludes by outlining prioritized research directions toward fully adaptive and self-optimizing wireless communication systems. Full article

Imaging-based multi-omics derived from digital histopathology provides a valuable approach for characterizing tumor heterogeneity from routine clinical specimens. However, robust multi-cancer histopathological analysis remains challenging due to pronounced intra-tumor variability, inter-organ morphological overlap, and sensitivity to staining and acquisition variations, which can limit the generalizability of deep learning models. These limitations are largely driven by insufficient representation learning, particularly in multi-organ and multi-class diagnostic settings. In this study, we propose a hierarchically regularized representation learning framework for multi-cancer histopathological image analysis that models imaging-based features across multiple organs and diagnostic categories. The framework integrates complementary mechanisms to capture fine-grained cellular morphology, long-range tissue architecture, and organ-aware diagnostic semantics within a unified computational model. A hierarchical supervision strategy guides the network to reduce entanglement between organ-level structural characteristics and disease-specific diagnostic patterns in the learned representations. The method operates without pixel-level annotations or handcrafted morphological priors, supporting scalable experimental evaluation. We demonstrate the approach on balanced lung and colon cancer histopathology cohorts, achieving 96.5% accuracy on lung cancer classification and 96.8% accuracy on colon cancer classification. Ablation and robustness analyses further validate the contributions of hierarchical regularization and consistency learning. Overall, this work provides a demonstrated proof-of-concept framework for representation-centric imaging-based analysis in multi-organ histopathology under the evaluated dataset conditions. Full article