Search Results (463)

We synthesized the current evidence from the literature and conducted a 2 × 3 factorial experiment to quantify the impact of thermocycling on the Vickers microhardness (HV) of dental CAD/CAM materials: VITA ENAMIC (VE, polymer-infiltrated ceramic network) and CERASMART (CS, nanofilled resin-matrix). Sixty polished specimens (n = 10 per Material × Cycles cell; 12 × 2 × 2 mm) were thermocycled at 5–55 °C (0, 10,000, 20,000 cycles; 30 s dwell, ≈10 s transfer) and tested as HV0.3/10 (300 gf, 10 s; five indentations/specimen with standard spacing). Assumptions regarding the model residuals were met (Shapiro–Wilk W ≈ 0.98, p ≈ 0.36; Levene F(5,54) ≈ 1.12, p ≈ 0.36), so a two-way ANOVA (Type II) with Tukey’s HSD post hoc (α = 0.05) was applied. VE maintained consistently higher HV than CS at all cycle levels and showed a smaller drop from baseline: VE (mean ± SD): 200.2 ± 10.8 (0), 192.4 ± 13.9 (10,000), and 196.7 ± 9.3 (20,000); CS: 60.8 ± 6.1 (0), 53.4 ± 4.7 (10,000), and 62.1 ± 3.8 (20,000). ANOVA revealed significant main effects from the material (η²p = 0.972) and cycles (η²p = 0.316), plus a Material × Cycles interaction (η²p = 0.201). Results: Thermocycling produced material-dependent changes in microhardness. Relative to baseline, VE varied by −3.9% (10,000) and −1.7% (20,000), while CS varied by −12.2% (10,000) and +2.1% (20,000); from 10,000→20,000 cycles, microhardness recovered by +2.2% (VE) and +16.3% (CS). Pairwise comparisons were consistent with these trends (CS decreased at 10,000 vs. 0 and recovered at 20,000; VE only showed a modest change). Conclusions: Thermocycling effects were material-dependent, with smaller losses and better retention in VE (PICN) than in CS. These results align with the literature (resin-matrix/hybrids are more sensitive to thermal aging; polished finishes mitigate losses). While HV is only one facet of performance, the superior retention observed in PICN under thermal challenge suggests the improved preservation of superficial integrity; standardized reporting of aging parameters and integration with wear, fatigue, and adhesion outcomes are recommended to inform indications and longevity. Full article

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies. Full article

Real-time distribution system state estimation is hampered by limited observability, frequent topology changes, and measurement errors. Neural networks can capture the nonlinear characteristics of power-grid operation through a data-driven approach that possesses important theoretical value and is promising for engineering applications. In that context, we develop a deep learning framework that leverages General Attributed Multiplex Heterogeneous Network Embedding to explicitly encode the multiplex, heterogeneous structure of distribution networks and to support inductive learning that adapts to dynamic topology. A cross-modal attention mechanism further models fine-grained interactions between input measurements and node/edge attributes, enabling the capture of nonlinear correlations essential for accurate state estimation. To ensure physical feasibility, soft power-flow residuals are incorporated into training as a physics-constrained regularization, guiding predictions toward consistency with grid operation. Extensive studies on IEEE/CIGRE 14-, 70-, and 179-bus systems show that the proposed method surpasses conventional weighted least squares and representative neural baselines in accuracy, convergence speed, and computational efficiency while exhibiting strong robustness to measurement noise and topological uncertainty. Full article

Parkinson’s disease (PD) is a neurodegenerative disease that is closely related to genetic and environmental factors, among which pesticide exposure is considered an important risk factor. Fresh jujube and hawthorn, as widely consumed fruits, may contain pesticide residues, but the potential effects of long-term low-dose intake on PD are not yet clear. This study combines network toxicology and molecular docking technology to elucidate the molecular mechanism of PD induced by residual pesticides in fresh jujube and hawthorn. Firstly, common risk pesticides (such as organophosphates and pyrethroids) in fresh jujube and hawthorn were screened through the database. Subsequently, a “pesticide target—PD” interactive network was constructed using network toxicology to predict key targets and related pathways. Finally, molecular docking technology was used to verify the binding ability of pesticide molecules to PD-related proteins. The results indicate that some pesticides (such as chlorpyrifos and cypermethrin) may increase the risk of PD by affecting lipid metabolism and oxidative stress response. This study provides a new approach for assessing the neurotoxicity of pesticide residues and suggests the need to pay attention to the potential impact of dietary pesticide exposure on PD, providing a scientific basis for food safety regulation and PD prevention strategies. Full article

The SOS (salt overly sensitive) hypersensitivity pathway is a key mechanism for maintaining ion homeostasis at the cellular level and conferring plant resistance and tolerance to salt stress. Its components interact directly and indirectly with various proteins and regulatory mechanisms. We conducted the first coevolutionary analysis of two key proteins of the SOS interaction network (SOS1 and NHX1) across a broad taxonomic range of plant species, including halophytes and glycophytes. Due to sequence availability, our analyses primarily support intramolecular coevolutionary patterns, with preliminary indications of possible intermolecular associations. We assessed the functional and topological relevance of coevolving sites. Six coevolving amino acid pairs were identified in SOS1, and two in NHX1. Except for two residues in SOS1, all sites were associated with functionally and topologically conserved positions. In SOS1, the most relevant coevolving pairs were located in the cytoplasmic domain, which controls the activity of the Na⁺/H⁺ antiporter and plays a critical role in maintaining cellular Na⁺ homeostasis, whereas in NHX1, they were in the transmembrane domain. Our findings reveal previously unexplored molecular relationships in these critical ion homeostasis proteins. Understanding these interactions, which have significant implications for biotechnology and sustainable agriculture, can aid crop improvement and enhance agricultural sustainability under saline conditions. Full article

Understanding the atomistic basis of multi-layer mechanisms employed by broadly reactive neutralizing antibodies of the SARS-CoV-2 spike protein without directly blocking receptor engagement remains an important challenge in coronavirus immunology. Class 4 antibodies represent an intriguing case: they target a deeply conserved, cryptic epitope on the receptor-binding domain yet exhibit variable neutralization potency across subgroups F1 (CR3022, EY6A, COVA1-16), F2 (DH1047), and F3 (S2X259). The molecular basis for this variability is not fully understood. Here, we employed a multi-modal computational approach integrating atomistic and coarse-grained molecular dynamics simulations, binding free energy calculations, mutational scanning, and dynamic network analysis to elucidate how these antibodies engage the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and influence its function. Our results reveal that neutralization efficacy arises from the interplay of direct interfacial interactions and allosteric effects. Group F1 antibodies (CR3022, EY6A, COVA1-16) primarily operate via classic allostery, modulating flexibility in RBD loop regions to indirectly interfere with the ACE2 receptor binding through long-range effects. Group F2 antibody DH1047 represents an intermediate mechanism, combining partial steric hindrance—through engagement of ACE2-critical residues T376, R408, V503, and Y508—with significant allosteric influence, facilitated by localized communication pathways linking the epitope to the receptor interface. Group F3 antibody S2X259 achieves potent neutralization through a synergistic mechanism involving direct competition with ACE2 and localized allosteric stabilization, albeit with potentially increased escape vulnerability. Dynamic network analysis identified a conserved “allosteric ring” within the RBD core that serves as a structural scaffold for long-range signal propagation, with antibody-specific extensions modulating communication to the ACE2 interface. These findings support a model where Class 4 neutralization strategies evolve through the refinement of peripheral allosteric connections rather than epitope redesign. This study establishes a robust computational framework for understanding the atomistic basis of neutralization activity and immune escape for Class 4 antibodies, highlighting how the interplay of binding energetics, conformational dynamics, and allosteric modulation governs their effectiveness against SARS-CoV-2. Full article

The ultimate function of a protein is a summation of the activities of all its modules or domains. A major mechanism for regulating protein activity, besides modulation of its levels through translation or degradation, is covalent post-translational modification (PTM) of these modules, including phosphorylation and dephosphorylation of tyrosine, threonine, and/or serine residues. Phosphorylation is a fast, reversible, and highly specific mode of regulating protein function. Unlike proteins that are marked with other PTMs, phosphorylated proteins orchestrate an extensive network of protein interactions because of their ability to bind many protein partners. Protein phosphorylation is crucial for many cellular processes—signaling, transcription, and metabolism—because it precisely controls these processes in time and space. In this review, we will focus on signaling coordinated by tyrosine phosphorylation–dephosphorylation, specifically structural insights that govern the mechanism of recognition of phosphotyrosine (pY)-containing ligands by Src homology 2 (SH2) domains. We update the approaches used to target the SH2 domains and techniques applied in drug discovery, highlighting inhibitors that have reached clinical development. Full article

Recycled coarse aggregates (RCA) offer an alternative to natural coarse aggregates in concrete production, reducing natural aggregate extraction and landfill burdens and potentially lowering embodied energy and CO₂ emissions. This study leverages machine learning algorithms to predict the dynamic yield stress (DYS) and plastic viscosity (PV) of RCA concrete (RCAC). A database of 380 RCAC mixtures, incorporating 11 input features, was analyzed using six machine learning models: Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM). The model performance was compared, followed by sensitivity analyses to identify critical factors influencing DYS and PV. For DYS, the DT model demonstrated the highest predictive performance (testing R²/RMSE/MAE = 0.95/18.25/13.99; others: 0.90–0.93/12.14–26.10/15.40–19.50) due to its robustness on smaller datasets. The XGBoost model led for PV (testing R²/RMSE/MAE = 0.93/7.06/4.58; others: 0.82–0.89/8.69–11.20/6.06–7.51) owing to its sequential residual minimization that captures nonlinear interactions. Sensitivity analyses revealed that polycarboxylate superplasticizer content and water-to-binder ratio significantly influence DYS, while cement content and saturated-surface-dried water absorption of RCA (i.e., measured with open pores filled and the aggregate surface dry) dominate PV. The time-dependent role in affecting PV was also highlighted. By optimizing and comparing different machine learning algorithms, this study advances predictive methodologies for the rheological properties of RCAC, addressing the underexplored use of machine learning for RCAC rheology (DYS and PV) and the limitations of traditional empirical rheology methods, thereby promoting the efficient use of recycled materials in sustainable concrete design. Full article

With the widespread application of wearable sensors in health monitoring and human–computer interaction, deep learning-based human activity recognition (HAR) research faces challenges such as the effective extraction of multi-scale temporal features and the enhancement of robustness against noise in multi-source data. This study proposes the TGA-HAR (TCN-GRU-Attention-HAR) model. The TGA-HAR model integrates Temporal Convolutional Neural Networks and Recurrent Neural Networks by constructing a hierarchical feature abstraction architecture through cascading Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU) layers for complex activity recognition. This study utilizes TCN layers with dilated convolution kernels to extract multi-order temporal features. This study utilizes BiGRU layers to capture bidirectional temporal contextual correlation information. To further optimize feature representation, the TGA-HAR model introduces residual connections to enhance the stability of gradient propagation and employs an adaptive weighted attention mechanism to strengthen feature representation. The experimental results of this study demonstrate that the model achieved test accuracies of 99.37% on the WISDM dataset, 95.36% on the USC-HAD dataset, and 96.96% on the PAMAP2 dataset. Furthermore, we conducted tests on datasets collected in real-world scenarios. This method provides a highly robust solution for complex human activity recognition tasks. Full article

Background/Objective: Sleep is significant for human mental and physical health. Sleep disorders represent a crucial risk to human health, and a large portion of the world population suffers from them. The efficient identification of sleep disorders is significant for effective treatment. However, the precise and automatic detection of sleep disorders remains challenging due to the inter-subject variability, overlapping symptoms, and reliance on single-modality physiological signals. Methods: To address these challenges, a Neurophysiological Residual Gated Attention Multimodal Transformer Encoder (NRGAMTE) model was developed for robust sleep disorder detection using multimodal physiological signals, including Electroencephalogram (EEG), Electromyogram (EMG), and Electrooculogram (EOG). Initially, raw signals are segmented into 30-s windows and processed to capture the significant time- and frequency-domain features. Every modality is independently embedded by a One-Dimensional Convolutional Neural Network (1D-CNN), which preserves signal-specific characteristics. A Modality-wise Residual Gated Cross-Attention Fusion (MRGCAF) mechanism is introduced to select significant cross-modal interactions, while the learnable residual path ensures that the most relevant features are retained during the gating process. Results: The developed NRGAMTE model achieved an accuracy of 94.51% on the Sleep-EDF expanded dataset and 99.64% on the Cyclic Alternating Pattern (CAP Sleep database), significantly outperforming the existing single- and multimodal algorithms in terms of robustness and computational efficiency. Conclusions: The results shows that NRGAMTE obtains high performance across multiple datasets, significantly improving detection accuracy. This demonstrated their potential as a reliable tool for clinical sleep disorder detection. Full article

Neurodevelopmental disorders (NDDs) represent significant public health challenges due to their multifactorial etiology and clinical heterogeneity. Current treatments remain limited, highlighting the need for novel therapeutic strategies. This study aimed to identify neuroprotective natural compounds targeting NDD-associated pathways and describe an integrative computational pipeline combining in silico screening, network pharmacology, and molecular docking approaches to accelerate NDD drug discovery. An integrative computational pipeline was developed through sequential phases: (1) systematic screening of the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) for natural compounds meeting drug-likeness criteria and toxicity thresholds; (2) biological activity prediction; (3) network pharmacology analysis integrating compound targets and NDD-associated genes; (4) protein–protein interaction network construction and functional enrichment; and (5) molecular docking validation of top compounds against prioritized targets. From 2634 initial compounds, 10 met all selection criteria. Network analysis revealed significant interactions between compound targets and NDD-associated genes, with enrichment in neurodevelopment, cognition, and synaptic regulation pathways. Three key targets emerged as hubs: CSNK2B, GRIN1, and MAPK1. Molecular docking demonstrated high-affinity binding of caryophyllene oxide, linoleic acid, and tangeretin, supported by stable interactions with catalytic residues. This study identifies caryophyllene oxide, linoleic acid, and tangeretin as promising multi-target compounds for NDD intervention, with verified interactions against key neurodevelopmental targets. The integrative computational pipeline effectively bridges traditional medicine knowledge with modern drug discovery, offering a strategy to accelerate neurotherapeutic development while reducing experimental costs. These findings warrant further experimental validation of the prioritized compounds. Full article

This study aimed to investigate the molecular binding mechanisms of bromocriptine toward histamine-associated targets, exploring both antagonist-like and other potential interaction modes that may support therapeutic repurposing. Network pharmacology was applied to identify histamine-related pathways and prioritize potential protein targets. CXCR4, GHSR, and OXTR were selected based on combined docking scores and pharmacophore modeling evidence. Molecular dynamics (MD) simulations over 100 ns assessed structural stability, flexibility, compactness, and solvent exposure. Binding site contact analysis and MM/PBSA free binding energy calculations were conducted to characterize binding energetics and interaction persistence. Bromocriptine exhibited stable binding to all three receptors, engaging key residues implicated in receptor modulation (e.g., Asp187 in CXCR4, Asp99 in GHSR, Arg232 in OXTR). The MM/PBSA ΔG_binding values of bromocriptine were −22.67 ± 3.70 kcal/mol (CXCR4 complex), −22.11 ± 3.55 kcal/mol (GHSR complex), and −21.43 ± 2.41 kcal/mol (OXTR complex), stronger than standard agonists and comparable to antagonists. Contact profiles revealed shared and unique binding patterns across targets, reflecting their potential for diverse modulatory effects. Bromocriptine demonstrates high-affinity binding to multiple histamine-associated GPCR targets, potentially exerting both inhibitory and modulatory actions. These findings provide a molecular basis for further experimental validation and therapeutic exploration in histamine-related conditions. Full article

To address the challenges of dynamic coupling interference and time-frequency feature degradation in current approaches to Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) behavior recognition, this study proposes a novel behavior recognition method integrating multi-feature analysis with a dual-path residual network. The proposed method mitigates interference by using phase difference methods to eliminate signal errors and cross-correlation, as well as adaptive equalization algorithms to decouple interfering signals. To identify the target tags participating in behavioral interactions, we construct a three-dimensional feature space and apply an improved weighted isolated forest algorithm to detect active tags during interactions. Subsequently, Doppler shift analysis extracts behavioral features, and multiscale wavelet-packet decomposition generates time-frequency representations. The dual-path residual network then fuses global and local features from these time-frequency representations for behavioral classification, thereby identifying interaction behaviors such as ‘taking away’, ‘putting back’, and ‘hesitation’ (characterized by picking up, then putting back). Experimental results demonstrate that the proposed scheme achieves behavioral recognition accuracy of 94% in complex scenarios, significantly enhancing the overall robustness of interaction behavior recognition. Full article

High-resolution remote sensing images often suffer from inadequate fusion between global and local features, leading to the loss of long-range dependencies and blurred spatial details, while also exhibiting limited adaptability to multi-scale object segmentation. To overcome these limitations, this study proposes RST-Net, a semantic segmentation network featuring a dual-branch encoder structure. The encoder integrates a ResNeXt-50-based CNN branch for extracting local spatial features and a Shunted Transformer (ST) branch for capturing global contextual information. To further enhance multi-scale representation, the multi-scale feature enhancement module (MSFEM) is embedded in the CNN branch, leveraging atrous and depthwise separable convolutions to dynamically aggregate features. Additionally, the residual dynamic feature fusion (RDFF) module is incorporated into skip connections to improve interactions between encoder and decoder features. Experiments on the Vaihingen and Potsdam datasets show that RST-Net achieves promising performance, with MIoU scores of 77.04% and 79.56%, respectively, validating its effectiveness in semantic segmentation tasks. Full article

Signed networks, which incorporate both cooperative and antagonistic interactions, naturally give rise to symmetric behaviors in multi-agent systems. One such behavior is bipartite containment tracking, where follower agents converge to a symmetric configuration determined by multiple groups of leaders with opposing influence. Moreover, a timely response is critical to ensuring high performance in containment tracking tasks, particularly for high-order multi-agent systems operating in dynamic and uncertain environments. To this end, this paper investigates the predefined-time bipartite containment tracking problem for high-order multi-agent systems affected by external disturbances. A robust tracking control scheme is developed based on the backstepping method to ensure that the tracking errors converge to a predefined residual set within a user-specified time. The convergence time is explicitly adjustable through a design parameter, and the proposed scheme effectively avoids the singularities often encountered in conventional predefined-time control approaches. The stability and robustness of the proposed scheme are rigorously established through Lyapunov-based analysis, and extensive simulation results are provided to validate our theoretical findings. Full article