Search Results (717)

Structural health monitoring in resource-constrained environments demands crack segmentation models that match the accuracy of heavyweight convolutional networks while conforming to the power, memory, and latency limits of watt-level edge devices. This study presents a lightweight dual-attention network, which is a four-stage U-Net compressed to one-quarter of the channel depth and augmented—exclusively at the deepest layer—with a compact dual-attention block that couples channel excitation with spatial self-attention. The added mechanism increases computation by only 19%, limits the weight budget to 7.4 MB, and remains fully compatible with post-training INT8 quantization. On a pixel-labelled concrete crack benchmark, the proposed network achieves an intersection over union of 0.827 and an F1 score of 0.905, thus outperforming CrackTree, Hybrid 2020, MobileNetV3, and ESPNetv2. While refined weight initialization and Dice-augmented loss provide slight improvements, ablation experiments show that the dual-attention module is the main factor influencing accuracy. With 110 frames per second on a 10 W Jetson Nano and 220 frames per second on a 5 W Coral TPU achieved without observable accuracy loss, hardware-in-the-loop tests validate real-time viability. Thus, the proposed network offers cutting-edge crack segmentation at the kiloflop scale, thus facilitating ongoing, on-device civil infrastructure inspection. Full article

Coumarins are known for interacting with proteins and exhibiting diverse biological activities. This study investigates the interaction between coumarin-343 (C343) and human (HSA) and bovine (BSA) serum albumins. Fluorescence spectroscopy and theoretical simulations, including density functional theory (DFT) and molecular docking, were used to analyze the ligand–protein complex formation. The fluorescence quenching data revealed that C343 binds to both proteins, with binding constants of 2.1 × 10⁵ mol·L⁻¹ (HSA) and 6.5 × 10⁵ mol·L⁻¹ (BSA), following a 1:1 stoichiometry. Binding site markers identified drug site I (DS1), located in subdomain IIA, as the preferential binding region for both proteins. Computational results supported these findings, showing high affinity for DS1, with binding energies of −69.02 kcal·mol⁻¹ (HSA) and −67.22 kcal·mol⁻¹ (BSA). While complex formation was confirmed for both proteins, differences emerged in the induced circular dichroism (ICD) signals. HSA displayed a distinct ICD profile compared to BSA in both intensity and absorption maximum. Molecular Docking revealed that the C343 conformation differed between HSA and BSA, explaining the variation in ICD signals. These results highlight the importance of protein structure in modulating ligand interactions and spectral responses. Full article

Gas turbine (GT) modeling and optimization have been widely studied at the design level but still lacks focus on real-world operational cases. The concept of a digital twin (DT) allows for the interaction between operation data and the system dynamic performance. Among many DT studies, only a few focus on GT for thermal power plants. This study proposes a digital twin prototype framework including the following modules: process modeling, parameter estimation, and performance optimization. Provided with real-world power plant operational data, key performance parameters such as turbine inlet temperature (TIT) and specific fuel consumption (SFC) were initially unavailable, therefore necessitating further calculation using thermodynamic analysis. These parameters are then used as a target label for developing artificial neural networks (ANNs). Three ANN models with different structures are developed to predict TIT, SFC, and turbine power output (GTPO), achieving high R² scores of 94.03%, 82.27%, and 97.59%, respectively. Physics-informed neural networks (PINNs) are then employed to estimate the values of the air–fuel ratio and combustion efficiency for each time index. The PINN-based estimation resulted in estimated values that align with the literature. Subsequently, an unconventional method of detecting alarms by using conformal prediction were also proposed, resulting in a significantly reduced number of alarms. The developed ANNs are then combined with particle swarm optimization (PSO) to carry out performance optimization in real time. GTPO and SFC are selected as the primary metrics for the optimization, with controllable parameters such as AFR and a fine-tuned inlet guide vane position. The results demonstrated that GTPO could be optimized with the application of conformal prediction when the true GTPO is detected to be higher than the upper range of GTPO obtained from the ANN model with a conformal prediction of a 95% confidence level. Multiple PSO variants were also compared and benchmarked to ensure an enhanced performance. The proposed PSO in this study has a lower mean loss compared to GEP. Furthermore, PSO has a lower computational cost compared to RS for hyperparameter tuning, as shown in this study. Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms. Full article

The microtubule-associated protein tau plays an essential role in regulating the dynamic assembly of microtubules and is implicated in axonal elongation and maturation, axonal transport, synaptic plasticity regulation, and genetic stability maintenance. Nevertheless, the assembly of tau into neurofibrillary tangles in neurons is a pathological hallmark of a group of neurodegenerative diseases known as tauopathies. Despite enormous efforts and rapid advancements in the field, effective treatment remains lacking for these diseases. In this review, we provide an overview of the structure and phase transition of tau protein. In particular, we focus on the involvement of liquid–liquid phase separation in the biology and pathology of tau. We then discuss several potential strategies for combating tauopathies in the context of phase separation: (i) modulating the formation of tau condensates, (ii) delaying the liquid-to-solid transition of tau condensates, (iii) reducing the enrichment of aggregation-prone species into tau condensates, and (iv) suppressing abnormal post-translational modifications on tau inside condensates. Deciphering the structure–activity relationship of tau phase transition modulators and uncovering the conformational changes in tau during phase transitions will aid in developing therapeutic agents targeting tau in the context of phase separation. Full article

Phage display has advanced the discovery of peptides that selectively bind to a wide variety of cell surface molecules, offering new modalities to modulate disease-related protein–protein interactions (PPIs). These cell-binding peptides occupy a unique pharmaceutical space between small molecules and large biologics, and their growing popularity has opened up new avenues for targeting cell surface proteins that were previously considered undruggable. This work provides an overview of methods for identifying cell-selective peptides using phage display combinatorial libraries, covering in vitro, ex vivo, and in vivo biopanning approaches. It addresses key considerations in library design, including the peptide conformation (linear vs. cyclic) and length, and highlights examples of clinically approved peptides developed through phage display. It also discusses the on-phage chemical cyclization of peptides to overcome the limitations of genetically encoded disulfide bridges and emphasizes advances in combining next-generation sequencing (NGS) with phage display to improve peptide selection and analysis workflows. Furthermore, due to the often suboptimal binding affinity of peptides identified in phage display selections, this article discusses affinity maturation techniques, including random mutagenesis and rational design through structure–activity relationship (SAR) studies to optimize initial peptide candidates. By integrating these developments, this review outlines practical strategies and future directions for harnessing phage display in targeting challenging cell surface proteins. Full article

Age-related neurodegeneration is characterized by oxidative stress and iron-dependent cell death, yet the neuroprotective mechanisms of folic acid in modulating ferroptosis remain unclear. This study systematically investigated the role of folic acid in inhibiting ferroptosis and attenuating neuronal damage in aging, with a focus on the solute carrier family 7 member 11 (SLC7A11)-glutathione (GSH)-glutathione peroxidase 4 (GPX₄) antioxidant pathway, using aged rats supplemented with folic acid (<0.1, 2.0, and 4.0 mg/kg·diet) for 22 months, with young adult rats as controls. Brain iron accumulation and ferroptosis-related proteins (SLC7A11, GPX₄, Ferritin heavy chain 1 (FTH1)) were evaluated. In vitro, HT-22 hippocampal neuronal cells were pre-treated with folic acid (0, 10, 20 μmol/L) for 72 h before combining with Erastin (10 μmol/L)-induced ferroptosis for an additional 24 h. Intracellular Fe²⁺, lipid peroxidation (LPO), malondialdehyde (MDA), reactive oxygen species (ROS), along with cystine, GSH, and ferroptosis-related protein levels were quantified. Stable sh-SLC7A11 knockdown and control (sh-NC) cell lines were used to validate the dependency of folic acid’s protective effects on SLC7A11 expression. Folic acid supplementation in aged rats dose-dependently reduced aging-related brain iron accumulation and enhanced the expression of SLC7A11, GPX₄, and FTH1. In Erastin-induced HT-22 cells, folic acid significantly mitigated ferroptosis hallmarks. Mechanistically, folic acid increased extracellular cystine uptake and intracellular GSH synthesis, thereby activating the SLC7A11-GSH-GPX₄ antioxidant pathway. Notably, molecular docking technique suggested that compared to GPX₄, folic acid stabilized SLC7A11’s active conformation. sh-SLC7A11 knockdown completely abolished folic acid-mediated protection against ferroptosis, as evidenced by restored loss of cystine, GSH and GPX₄ production. This study innovatively emphasized the critical role of folic acid supplementation in inhibiting ferroptosis by up-regulating the SLC7A11-GSH-GPX₄ antioxidant pathway, primarily through enhancing cystine availability and SLC7A11 expression. These findings established folic acid as a potential dietary intervention for aging-related neurodegenerative diseases characterized by neuronal ferroptosis, providing preclinical evidence for folic acid based neuroprotection. Full article

The Lower–Middle Jurassic of the Kuqa Depression consists of terrestrial clastic deposits containing coal seams and thick lacustrine mudstones, and is of great significance for oil and gas exploration. Based on the comprehensive analysis of core, well-logging, outcrop, and seismic data, the sequence stratigraphy, depositional systems, and the controlling factors of the basin filling in the depression are systematically documented. Four primary depositional systems, including braided river delta, meandering river delta, lacustrine, and swamp deposits, are identified within the Ahe, Yangxia, and Kezilenuer Formations of the Lower–Middle Jurassic. The basin fills can be classified into two second-order and nine third-order sequences (SQ1–SQ9) confined by regional or local unconformities and their correlative conformities. This study shows that the sedimentary evolution has undergone the following three stages: Stage I (SQ1–SQ2) primarily developed braided river, braided river delta, and shallow lacustrine deposits; Stage II (SQ3–SQ5) primarily developed meandering river, meandering river delta, and extensive deep and semi-deep lacustrine deposits; Stage III (SQ6–SQ9) primarily developed swamp (SQ6–SQ7), meandering river delta, and shore–shallow lacustrine deposits (SQ8–SQ9). The uplift of the Tianshan Orogenic Belt in the Early Jurassic (Stage I) may have facilitated the development of braided fluvial–deltaic deposits. The subsequential expansion of the sedimentary area and the weakened sediment supply can be attributed to the planation of the source area and widespread basin subsidence, with the transition of the depositional environments from braided river delta deposits to meandering river delta and swamp deposits. The regional expansion or rise of the lake during Stage II was likely triggered by the hot and humid climate conditions, possibly associated with the Early Jurassic Toarcian Oceanic Anoxic Event. The thick swamp deposits formed during Stage III may be controlled by the interplay of rational accommodation, warm and humid climatic conditions, and limited sediment supply. Milankovitch cycles identified in Stage III further reveal that coal accumulation was primarily modulated by long-period eccentricity forcing. Full article

The development of metallic coatings as Ni-Co alloys, with particular emphasis on their homogeneity, processability, and sustainability, is of the utmost significance. To address these challenges, the utilization of phenylsalicylimines (PSIs) as additives within deep eutectic solvents (DES) was investigated, assessing their influence on the electrodeposition process of these metals at an intermediate temperature of 60 °C, while circumventing aqueous reaction conditions. The findings demonstrated that the incorporation of PSIs markedly enhances coating uniformity, resulting in an optimal cobalt content of 37% and an average thickness of 24 µm. Electrochemical evaluations revealed improvements in charge and mass transfer, thereby optimizing process efficiency. Moreover, computational studies confirmed that PSIs form stable complexes with Co (II), modulating the electrochemical characteristics of the system through the introduction of the diethylamino electron-donating group, which significantly stabilizes the coordinated forms with both components of the DES. Additionally, the coatings displayed exceptional corrosion resistance, with a rate of 0.781 µm per year, and achieved an optimal hardness of 38 N HRC, conforming to ASTM B994 standards. This research contributes to the development of electroplating bath designs for metallic coating deposition and lays the groundwork for the advancement of sophisticated technologies in functional coatings that augment corrosion resistance and mechanical properties. Full article

With the rapid development of wearable electronics, traditional rigid thermal management materials face limitations in flexibility, conformability, and multi-physics adaptability. Low-dimensional carbon materials such as graphene and carbon nanotubes combine ultrahigh thermal conductivity with outstanding mechanical compliance, making them promising building blocks for flexible thermal regulation. This review summarizes recent advances in integrating these materials into textile architectures, mapping the evolution of this emerging field. Key topics include phonon-dominated heat transfer mechanisms, strategies for modulating interfacial thermal resistance, and dimensional effects across scales; beyond these intrinsic factors, hierarchical textile configurations further tailor macroscopic performance. We highlight how one-dimensional fiber bundles, two-dimensional woven fabrics, and three-dimensional porous networks construct multi-directional thermal pathways while enhancing porosity and stress tolerance. As for practical applications, the performance of carbon-based textiles in wearable systems, flexible electronic packaging, and thermal coatings is also critically assessed. Current obstacles—namely limited manufacturing scalability, interfacial mismatches, and thermal performance degradation under repeated deformation—are analyzed. To overcome these challenges, future studies should prioritize the co-design of structural and thermo-mechanical properties, the integration of multiple functionalities, and optimization guided by data-driven approaches. This review thus lays a solid foundation for advancing carbon-based smart textiles toward next-generation flexible thermal management technologies. Full article

Prion diseases such as chronic wasting disease involve the conformational conversion of the cellular prion protein (PrP^C) into its misfolded, β-rich isoform (PrP^Sc). While chemical denaturants such as guanidine hydrochloride (GdnHCl) and urea are commonly used to study this process in vitro, their distinct molecular effects on native and misfolded PrP conformers remain incompletely understood. In this study, we employed 500 ns all-atom molecular dynamics simulations and essential collective dynamics analysis to investigate the differential effects of GdnHCl and urea on a composite PrP^C/PrP^Sc system, where white-tailed deer PrP^C interfaces with a corresponding PrP^Sc conformer. GdnHCl was found to preserve interfacial alignment and enhance β-sheet retention in PrP^Sc, while urea promoted partial β-strand dissolution and interfacial destabilization. Both denaturants formed transient contacts with PrP, but urea displaced water hydrogen bonds more extensively. Remarkably, we also observed long-range dynamical coupling across the PrP^C/PrP^Sc interface and between transiently bound solutes and distal protein regions. These findings highlight distinct, denaturant-specific mechanisms of protein destabilization and suggest that localized interactions may propagate non-locally via mechanical or steric pathways. Our results provide molecular-scale insights relevant to prion conversion mechanisms and inform experimental strategies using GdnHCl and urea to modulate misfolding processes in vitro. Full article

This study investigated the therapeutic potential of Bacillus thuringiensis extracellular polysaccharide BPS-2 in dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) murine models. BPS-2 demonstrated significant efficacy in ameliorating UC-associated pathologies through three principal mechanisms: (1) attenuating histopathological damage while preserving colon epithelial integrity, (2) modulating immune marker expression patterns in colon tissues, and (3) restoring gut microbiota homeostasis. BPS-2 exhibited multi-faceted protective effects on the gut by mitigating oxidative stress responses and enhancing short-chain fatty acid biosynthesis, leading to an improved gut microbial community structure. Molecular docking analysis displayed strong binding affinity (ΔG = −7.8 kcal/mol) between the BPS-2U fragment and the Nuclear Factor κB (NF-κB) p50/p65 heterodimer, suggesting the potential disruption of NF-κB signaling pathways. Complementary molecular dynamics simulations revealed exceptional conformational stability in the p65-BPS-2U complex. These findings establish BPS-2 as a natural food additive that modulates the microbiota-barrier–inflammation axis through dietary intervention, offering a novel strategy to alleviate UC. Full article

Intrinsically disordered regions (IDRs), defined as protein segments lacking stable tertiary structures, are ubiquitously present in the human proteome and enriched with disease-associated mutations. IDRs harbor molecular recognition features (MoRFs) and post-translational modification sites (e.g., phosphorylation), enabling dynamic intermolecular interactions through conformational plasticity. Furthermore, IDRs drive liquid–liquid phase separation (LLPS) of biomacromolecules via multivalent interactions such as electrostatic attraction and pi–pi interactions, generating biomolecular condensates that are essential throughout the cellular lifecycle. These condensates separate intracellular space, forming a physical barrier to avoid interference between other molecules, thereby improving reaction specificity and efficiency. As a dynamically equilibrated process, LLPS formation and maintenance are regulated by multiple factors, endowing the condensates with rapid responsiveness to environmental cues and functional versatility in modulating diverse signaling cascades. Consequently, disruption of LLPS homeostasis can derail its associated biological processes, ultimately contributing to disease pathogenesis. Moreover, precisely because liquid–liquid phase separation (LLPS) is co-regulated by multiple factors, it may provide novel insights into the pathogenic mechanisms of disorders such as autism spectrum disorder (ASD), which result from the cumulative effects of multiple etiological factors. Full article

Background/Objectives: Chloroquine (CQ) and hydroxychloroquine (HCQ) have been the subject of debate in the treatment of COVID-19 due to the lack of conclusive evidence regarding their efficacy and safety. Our study aims to investigate the molecular interaction between the enantiomers of CQ and HCQ with angiotensin-converting enzyme 2 (ACE2), focusing on the binding mechanism, affinity, and selectivity. Methods: We used in silico methods, including molecular docking, molecular dynamics, and binding free energy calculations using the MM-PBSA method, to evaluate the interaction between the enantiomers of CQ and HCQ with ACE2. Results: We identified three main interaction sites on ACE2 (α, β, and γ) with distinct characteristics based on the pocket size, hydrophilic/hydrophobic characteristics, and affinity energy. We observed that protonation states and ionic strength significantly influence the binding affinity and specificity. In particular, the selectivity of the β-site, characterized by its smaller size and hydrophilic residues, is preferential for species with the (R) configuration, whereas the α and γ binding sites, with a larger size and amphiphilic residues, have greater affinity for the (S) enantiomer of CQ and HCQ. Furthermore, ionic strength can affect ligand binding by modulating electrostatic interactions, molecular conformation, solvation, and the stability of the complex. Conclusions: Our findings reveal that protonation states and the ionic strength substantially impact the binding affinity and specificity, regulated by spatial and polar–electrostatic complementarity, as well as hydrophobic contributions. These results suggest that understanding the interaction between CQ and HCQ enantiomers with ACE2 could be useful for the design of novel therapies against COVID-19. Full article

Prion diseases are classically attributed to the accumulation of protease-resistant prion protein (PrP^Sc); however, recent evidence suggests that alternative misfolded prion conformers and systemic inflammatory factors may also contribute to neurodegeneration. This study investigated whether recombinant moPrP^Res, generated by incubating wild-type mouse PrP^C with bacterial lipopolysaccharide (LPS), can induce prion-like disease in FVB/N female mice, whether LPS alone causes neurodegeneration, and how LPS modulates disease progression in mice inoculated with the Rocky Mountain Laboratory (RML) strain of prions. Wild-type female FVB/N mice were randomized into six subcutaneous treatment groups: saline, LPS, moPrP^Res, moPrP^Res + LPS, RML, and RML + LPS. Animals were monitored longitudinally for survival, body weight, and clinical signs. Brain tissues were analyzed histologically and immunohistochemically for vacuolar degeneration, PrP^Sc accumulation, reactive astrogliosis, and amyloid-β plaque deposition. Recombinant moPrP^Res induced a progressive spongiform encephalopathy characterized by widespread vacuolation and astrogliosis, yet with no detectable PrP^Sc by Western blot or immunohistochemistry. LPS alone triggered a distinct neurodegenerative phenotype, including cerebellar amyloid-β plaque accumulation and terminal-stage spongiosis, with approximately 40% mortality by the end of the study. Co-administration of moPrP^Res and LPS resulted in variable regional pathology and intermediate survival (50% at 750 days post-inoculation). Interestingly, RML + LPS co-treatment led to earlier clinical onset and mortality compared to RML alone; however, vacuolation levels were not significantly elevated and, in some brain regions, were reduced. These results demonstrate that chronic endotoxemia and non-infectious misfolded PrP conformers can independently or synergistically induce key neuropathological hallmarks of prion disease, even in the absence of classical PrP^Sc. Targeting inflammatory signaling and toxic prion intermediates may offer novel therapeutic strategies for prion and prion-like disorders. Full article

Spatial scenes, as fundamental units of geospatial cognition, encompass rich objects and spatial relationships, and their generation techniques hold significant application value in disaster simulation and emergency drills, delayed spatial reconstruction and analysis, and other fields. However, existing studies still face limitations in modeling complex spatial relationships during scene generation, leading to insufficient semantic consistency and geographical accuracy. The advancement of Geospatial Artificial Intelligence (GeoAI) offers a new technical pathway for the intelligent modeling of spatial scenes. Against this backdrop, we propose SceneDiffusion, a scene generation model embedded with spatial constraints, and construct a geospatial scene dataset incorporating spatial relationship descriptions and geographic semantics, aiming to enhance the understanding and modeling capabilities of GeoAI models for spatial information. Specifically, SceneDiffusion employs a spatial scene representation framework to uniformly characterize objects and their topological, directional, and distance relationships, enhances the interactive modeling of objects and relationships through a Spatial relationship Attention-aware Graph (SAG) module, and finally generates high-quality scene images conforming to geographic semantics using a Layout information-guided Conditional Diffusion (LCD) module. Both qualitative and quantitative experiments demonstrate the superiority of SceneDiffusion, achieving a 56.6% reduction in FID and a 35.3% improvement in SSIM compared to baseline methods. Ablation studies confirm the importance of multi-relational modeling with attention mechanisms. By generating scenes that satisfy spatial distribution constraints, this work provides technical support for applications such as emergency scene simulation and virtual scene construction, while also offering insights for theoretical research and methodological innovation in GeoAI. Full article