Search Results (1,573)

Bone immunity represents a dynamic interface where skeletal homeostasis intersects with systemic immune regulation. We synthesize emerging paradigms by contrasting two functionally distinct microenvironments: the marrow cavity, a hematopoietic and immune cell reservoir, and cancellous bone, a metabolically active hub orchestrating osteoimmune interactions. The marrow cavity not only generates innate and adaptive immune cells but also preserves long-term immune memory through stromal-derived chemokines and survival factors, while cancellous bone regulates bone remodeling via macrophage-osteoclast crosstalk and cytokine gradients. Breakthroughs in lymphatic vasculature identification challenge traditional views, revealing cortical and lymphatic networks in cancellous bone that mediate immune surveillance and pathological processes such as cancer metastasis. Central to bone immunity is the neuro–immune–endocrine axis, where sympathetic and parasympathetic signaling bidirectionally modulate osteoclastogenesis and macrophage polarization. Gut microbiota-derived metabolites, including short-chain fatty acids and polyamines, reshape bone immunity through epigenetic and receptor-mediated pathways, bridging systemic metabolism with local immune responses. In disease contexts, dysregulated immune dynamics drive osteoporosis via RANKL/IL-17 hyperactivity and promote leukemic evasion through microenvironmental immunosuppression. We further propose the “brain–gut–bone axis” as a systemic regulatory framework, wherein vagus nerve-mediated gut signaling enhances osteogenic pathways, while leptin and adipokine circuits link marrow adiposity to inflammatory bone loss. These insights redefine bone as a multidimensional immunometabolic organ, integrating neural, endocrine, and microbial inputs to maintain homeostasis. By elucidating the mechanisms of immune-driven bone pathologies, this work highlights therapeutic opportunities through biomaterial-mediated immunomodulation and microbiota-targeted interventions, paving the way for next-generation treatments in osteoimmune disorders. Full article

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a functional imaging modality that can quantify tissue permeability and blood flow. Due to vasculature changes resulting from radiation therapy (RT), DCE-MRI quantitative parameters should be significantly different in regions receiving a high radiation dose compared to regions receiving a low radiation dose. This study sought to determine whether a significant difference exists in post-head-and-neck-cancer (HNC)-RT DCE-MRI quantitative parameters (K^trans and v_e) between regions of the mandible receiving a high radiation dose and regions of the mandible receiving a low radiation dose. Methods: DCE-MRI was acquired from HNC subjects post-RT. The DCE-MRI quantitative parameters K^trans and v_e were obtained through Tofts model fitting. Four mandible sections (left ramus, left body, right ramus, and right body) were delineated on subject mandible contours. Two Friedman tests comparing the mean K^trans and v_e in low-dose (≤60 Gy) areas of the four mandible regions were computed. If the Friedman test determined that a significant difference for a parameter between mandible regions exists, post hoc Wilcoxon signed-rank tests were completed comparing the four mandible regions. If the Friedman test determined that there was no significant difference between mandible regions, a Wilcoxon signed-rank test was used to determine whether a significant difference exists in the parameter between high-dose (>60 Gy) and low-dose (≤60 Gy) mandible regions. Results: 48 HNC subjects were included in the analysis. The Friedman tests showed no significant difference in v_e means between mandible regions (${\chi }_{\left(3\right)}^2$ = 1.63, p = 0.44) and a significant difference in K^trans means between mandible regions (${\chi }_{\left(3\right)}^2$ = 10.29, p = 0.005). Post hoc testing between K^trans mandible regions found that the left body and right body differed significantly from the left ramus and right ramus. The Wilcoxon signed-rank test comparing the mean v_e between high- and low-dose mandible regions found a significant difference (W = 214, p = 0.00013). Conclusions: no inherent difference in the DCE-MRI quantitative parameter v_e was observed within subject mandibles, but a significant difference was observed between v_e means in high- and low-radiation-dose mandible regions. These results provide evidence of the utility of DCE-MRI to monitor mandible vasculature changes resulting from head and neck cancer radiation therapy. Monitoring post-HNC-RT mandible vasculature changes is important to initiate earlier toxicity management and ultimately improve HNC survivors’ quality of life. Full article

Adipose tissue renewal requires precise coordination of stem/progenitor cell proliferation, preadipocyte commitment, and terminal adipocyte differentiation. T-cadherin (CDH13), an atypical GPI-anchored cadherin, is expressed in adipose tissue and functions as a receptor for high-molecular-weight (HMW) adiponectin—a key adipokine produced by adipose tissue and involved in metabolic regulation. While T-cadherin is implicated in cardiovascular and metabolic homeostasis, its role in adipogenesis still remains poorly understood. In this study, we used the 3T3-L1 preadipocyte model to investigate the function of T-cadherin in adipocyte differentiation. We analyzed T-cadherin expression dynamics during differentiation and assessed how T-cadherin overexpression or knockdown affects lipid accumulation, expression of adipogenic markers, and key signaling pathways including ERK, PI3K–AKT, AMPK, and mTOR. Our findings demonstrate that T-cadherin acts as a negative regulator of adipogenesis. T-cadherin overexpression ensured a proliferative, undifferentiated cell state, delaying early adipogenic differentiation and suppressing both lipid droplet accumulation and the expression of adipogenic markers. In contrast, T-cadherin downregulation accelerated differentiation, enhanced lipid accumulation, and increased insulin responsiveness, as indicated by PI3K–AKT pathway activation at specific stages of adipogenesis. These results position T-cadherin as a key modulator of adipose tissue plasticity, regulating the balance between progenitor expansion and terminal differentiation, with potential relevance to obesity and metabolic disease. Full article

The harsh conditions of the space environment necessitate advanced materials capable of withstanding extreme temperature fluctuations and ultraviolet (UV) radiation. While epoxy-based composites are widely utilized in aerospace due to their favorable strength-to-weight ratio, they are prone to degradation, especially under prolonged high-energy UV-C exposure. This study investigated the mechanical and chemical stability of epoxy composites reinforced with nanosilica at 0, 2, 5, and 10 wt% before and after UV-C irradiation. Dynamic mechanical analysis (DMA) revealed that increased nanosilica content enhanced the storage modulus below the glass transition temperature (Tg) but reduced both Tg and the damping factor. Following UV-C exposure, all samples showed a decrease in storage modulus and Tg; however, composites with higher nanosilica content maintained better property retention. Frequency sweeps corroborated these findings, indicating improved instantaneous modulus but accelerated relaxation with increased nanosilica. Fourier-transform infrared (FTIR) spectroscopy of UV-C-exposed samples demonstrated significant oxidation and carboxylic group formation in neat epoxy, contrasting with minimal spectral changes in nanosilica-modified composites, signifying improved chemical resistance. Overall, nanosilica incorporation substantially enhances the thermomechanical and oxidative stability of epoxy composites under simulated space conditions, highlighting their potential for more durable performance in low Earth orbit applications. Full article

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, incorporating both continuous and discrete decision variables—such as charging power and cooling modes—into a unified optimization framework. An environment-adaptive optimization strategy is also developed. To enhance learning efficiency and policy safety, a contrastive learning–enhanced policy gradient (CLPG) algorithm is proposed to distinguish between high-quality and unsafe charging trajectories. A manifold-aware action generation network (MAN) is further introduced to enforce dynamic safety constraints under varying environmental and battery conditions. Simulation results demonstrate that the proposed framework reduces charging time to 18.3 min—47.7% faster than the conventional CC–CV method—while achieving 96.2% energy efficiency, 99.7% capacity retention, and zero safety violations. The framework also exhibits strong adaptability across wide temperature (−20 °C to 45 °C) and aging (SOH down to 70%) conditions, with real-time inference speed (6.76 ms) satisfying deployment requirements. This study provides a safe, efficient, and adaptive solution for intelligent high-power EV fast-charging. Full article

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

Breast cancer is one of the major health threats to women worldwide; thus, a need has arisen to reduce the number of instances and deaths through new methods of diagnostic monitoring and treatment. The present review is the synthesis of the recent clinical studies and technological advances in the application of magnetic resonance imaging (MRI) to monitor the pharmacological treatment of breast cancer. The specific focus is on high-risk groups (carriers of BRCA mutations and recipients of neoadjuvant chemotherapy) and the use of novel MRI methods (dynamic contrast-enhanced (DCE) MRI, diffusion-weighted imaging (DWI), and radiomics tools). All the reviewed studies show that MRI is more sensitive (up to 95%) and specific than conventional imaging in detecting malignancy particularly in dense breast tissue. Moreover, MRI can be used to assess the response and residual disease in a tumor early and accurately for personalized treatment, de-escalate unneeded interventions, and maximize positive outcomes. AI-based radiomics combined with deep-learning models also expand the ability to predict the therapeutic response and molecular subtypes, and can mitigate the risk of overfitting models when using complex methods of modeling. Other developments are hybrid PET/MRI, image guidance during surgery, margin assessment intraoperatively, three-dimensional surgical templates, and the utilization of MRI in surgery planning and reducing reoperation. Although economic factors will always play a role, the diagnostic and prognostic accuracy and capability to aid in targeted treatment makes MRI a key tool for modern breast cancer. The growing complement of MRI and novel curative approaches indicate that breast cancer patients may experience better survival and recuperation, fewer recurrences, and a better quality of life. Full article

Wavefront aberrations caused by thermal flows or arising from the quality of optical components can significantly impair wireless communication links. Such aberrations may result in an increased error rate in the received signal, leading to data loss in laser communication applications. In this study, we explored a newly developed combined stochastic gradient descent optimization algorithm aimed at compensating for optical distortions. The algorithm we developed exhibits linear time and space complexity and demonstrates low sensitivity to variations in input parameters. Furthermore, its implementation is relatively straightforward and does not necessitate an in-depth understanding of the underlying system, in contrast to the Stochastic Parallel Gradient Descent (SPGD) method. In addition, a developed switch-mode approach allows us to use a stochastic component of the algorithm as a rapid, rough-tuning mechanism, while the gradient descent component is used as a slower, more precise fine-tuning method. This dual-mode operation proves particularly advantageous in scenarios where there are no rapid dynamic wavefront distortions. The results demonstrated that the proposed algorithm significantly enhanced the total collected power of the beam passing through the 10 μm diaphragm that simulated a 10 μm fiber core, increasing it from 0.33 mW to 2.3 mW. Furthermore, the residual root mean square (RMS) aberration was reduced from 0.63 μm to 0.12 μm, which suggests a potential improvement in coupling efficiency from 0.1 to 0.6. Full article

Can brightness illusions modulate ocular accommodation? Previous studies have shown that brightness illusions can influence pupil size as if caused by actual luminance increases. However, their effects on other ocular responses—such as accommodative or focusing dynamics—remain largely unexplored. This study investigates the influence of brightness illusions, under two ambient lighting conditions, on accommodative and pupillary dynamics (physiological responses), and on perceived brightness and visual comfort (subjective responses). Thirty-two young adults with healthy vision viewed four stimulus types (blue bright and non-bright, yellow bright and non-bright) under low- and high-contrast ambient lighting while ocular responses were recorded using a WAM-5500 open-field autorefractor. Brightness and comfort were rated after each session. The results showed that high ambient contrast (mesopic) and brightness illusions increased accommodative variability, while yellow stimuli elicited a greater lag under photopic condition. Pupil size decreased only under mesopic lighting. Perceived brightness was enhanced by brightness illusions and blue color, whereas visual comfort decreased for bright illusions, especially under low light. These findings suggest that ambient lighting and visual stimulus properties modulate both physiological and subjective responses, highlighting the need for dynamic accommodative assessment and visually ergonomic display design to reduce visual fatigue during digital device use. Full article

To address the challenges of multi-scale distribution, low contrast and background interference in rock crack identification, this paper proposes an improved Mask R-CNN model (CBAM-BiFPN-Mask R-CNN) that integrates the convolutional block attention mechanism (CBAM) module and the bidirectional feature pyramid network (BiFPN) module. A dataset of 1028 rock surface crack images was constructed. The robustness of the model was improved by dynamically combining Gaussian blurring, noise overlay, and color adjustment to enhance data augmentation strategies. The model embeds the CBAM module after the residual block of the ResNet50 backbone network, strengthens the crack-related feature response through channel attention, and uses spatial attention to focus on the spatial distribution of cracks; at the same time, it replaces the traditional FPN with BiFPN, realizes the adaptive fusion of cross-scale features through learnable weights, and optimizes multi-scale crack feature extraction. Experimental results show that the improved model significantly improves the crack recognition effect in complex rock mass scenarios. The mAP index, precision and recall rate are improved by 8.36%, 9.1% and 12.7%, respectively, compared with the baseline model. This research provides an effective solution for rock crack detection in complex geological environments, especially the missed detection of small cracks and complex backgrounds. Full article

Few-shot object detection methods perform well in natural scenes, where meta-learners can effectively extract target features from limited support data. However, PCB defect detection faces unique challenges: scarce defect samples, low-resolution targets, and severe overfitting risks. Additionally, PCBs’ dense circuitry creates low-contrast defects that blend into background noise, while traditional meta-learning struggles to generate realistic synthetic defects under actual manufacturing constraints. To overcome these limitations, we propose SM-FSOD, a second-order meta-learning model featuring a defect-aware prototype network. Unlike conventional approaches, it dynamically emphasizes critical defect features when constructing class prototypes from few-shot samples. Our extensive few-shot experiments on the DeepPCB and DsPCBSD+ datasets demonstrate the performance of the SM-FSOD model. Comparative tests on the DeepPCB dataset show that, compared with the strong Meta-RCNN baseline, our model achieves a maximum performance improvement of 14.0% under the challenging five-shot setting, while still attaining a 7.6% accuracy gain in the more relaxed 50-shot setting. Similarly, evaluation on the DsPCBSD+ dataset reveals that our proposed method maintains an average accuracy improvement of 2.3% to 9.6% compared to the competitive DeFRCN model in complex scenarios, indicating the strong adaptability of SM-FSOD across various application environments. Ablation studies further demonstrate that incorporating the improved MOMP and DGPT modules individually yields average accuracy gains of 3.6% and 4.5%, respectively, under the five-shot setting compared to the baseline, confirming that these enhancements can orthogonally improve the detection precision in few-shot PCB scenarios. Full article

Modern fire alarm systems are essential for public safety, yet they often fail to exploit the wealth of historical alarm data and the complex spatiotemporal dependencies inherent in urban environments. Graph Neural Networks (GNNs) are currently among the most popular methods for handling complex spatiotemporal dependencies. While a range of dynamic GNN approaches have been proposed, many existing GNN-based predictors still rely on a static topology, which limits their ability to fully capture the evolving nature of risk propagation. Furthermore, even among dynamic graph methods, most focus on temporal link prediction or social interaction modeling, with limited exploration in safety-critical applications such as fire alarm prediction. DeaGAT dynamically updates inter-building edge weights through an attention mechanism, enabling the graph structure to evolve in response to shifting risk patterns. A margin-based contrastive learning objective further enhances the quality of node embeddings by distinguishing subtle differences in risk states. In addition, DeaGAT jointly models static building attributes and dynamic alarm sequences, effectively integrating long-term semantic context with short-term temporal dynamics. Extensive experiments on real-world datasets, including comparisons with state-of-the-art baselines and comprehensive ablation studies, demonstrate that DeaGAT achieves superior accuracy and F1-score, validating the effectiveness of dynamic graph updating and contrastive learning in enhancing proactive fire early-warning capabilities. Full article

RNA polymerases are essential enzymes that catalyze DNA transcription into RNA, vital for protein synthesis, gene expression regulation, and cellular responses. Non-template-dependent RNA polymerases, which synthesize RNA without a template, are valuable in biological research due to their flexibility in producing RNA without predefined sequences. However, their substrate polymerization mechanisms are not well understood. This study examines Poly (A) polymerase (PAP), a nucleotide transferase superfamily member, to explore its substrate selectivity using computational methods. Previous research shows PAP’s polymerization efficiency for nucleoside triphosphates (NTPs) ranks ATP > GTP > CTP > UTP, though the reasons remain unclear. Using 500 ns Gaussian accelerated molecular dynamics simulations, stability analysis, secondary structure analysis, MM-PBSA calculations, and Markov state modeling, we investigate PAP’s differential polymerization efficiencies. Results show that ATP binding enhances PAP’s structural flexibility and increases solvent-accessible surface area, likely strengthening protein–substrate or protein–solvent interactions and affinity. In contrast, polymerization of other NTPs leads to a more open conformation of PAP’s two domains, facilitating substrate dissociation from the active site. Additionally, ATP binding induces a conformational shift in residues 225–230 of the active site from a loop to an α-helix, enhancing regional rigidity and protein stability. Both ATP and GTP form additional π–π stacking interactions with PAP, further stabilizing the protein structure. This theoretical study of PAP polymerase’s substrate selectivity mechanisms aims to clarify the molecular basis of substrate recognition and selectivity in its catalytic reactions. These findings offer valuable insights for the targeted engineering and optimization of polymerases and provide robust theoretical support for developing novel polymerases for applications in drug discovery and related fields. Full article

Progress in clinical diagnosis increasingly relies on innovative technologies and advanced disease biomarker detection methods. While cell labeling remains a well-established technique, label-free approaches offer significant advantages, including reduced workload, minimal sample damage, cost-effectiveness, and simplified chip integration. These approaches focus on the morpho-biophysical properties of cells, eliminating the need for labeling and thus reducing false results while enhancing data reliability and reproducibility. Current label-free methods span conventional and advanced technologies, including phase-contrast microscopy, holographic microscopy, varied cytometries, microfluidics, dynamic light scattering, atomic force microscopy, and electrical impedance spectroscopy. Their integration with artificial intelligence further enhances their utility, enabling rapid, non-invasive cell identification, dynamic cellular interaction monitoring, and electro-mechanical and morphological cue analysis, making them particularly valuable for cancer diagnostics, monitoring, and prognosis. This review compiles recent label-free cancer cell detection developments within clinical and biotechnological laboratory contexts, emphasizing biophysical alterations pertinent to liquid biopsy applications. It highlights interdisciplinary innovations that allow the characterization and potential identification of cancer cells without labeling. Furthermore, a comparative analysis addresses throughput, resolution, and detection capabilities, thereby guiding their effective deployment in biomedical research and clinical oncology settings. Full article

BioHealing Optimization (BHO) is a bio-inspired metaheuristic that operationalizes the injury–recovery paradigm through an iterative loop of recombination, stochastic injury, and guided healing. The algorithm is further enhanced by adaptive mechanisms, including scar map, hot-dimension focusing, RAGE/hyper-RAGE bursts (Rapid Aggressive Global Exploration), and healing-rate modulation, enabling a dynamic balance between exploration and exploitation. Across 17 benchmark problems with 30 runs, each under a fixed budget of $1.5·{10}^5$ function evaluations, BHO achieves the lowest overall rank in both the “best-of-runs” (47) and the “mean-of-runs” (48), giving an overall rank sum of 95 and an average rank of 2.794. Representative first-place results include Frequency-Modulated Sound Waves, the Lennard–Jones potential, and Electricity Transmission Pricing. In contrast to prior healing-inspired optimizers such as Wound Healing Optimization (WHO) and Synergistic Fibroblast Optimization (SFO), BHO uniquely integrates (i) an explicit tri-phasic architecture (DE/best/1/bin recombination → Gaussian/Lévy injury → guided healing), (ii) per-dimension stateful adaptation (scar map, hot-dims), and (iii) stagnation-triggered bursts (RAGE/hyper-RAGE). These features provide a principled exploration–exploitation separation that is absent in WHO/SFO. Full article