Search Results (1,436)

Unmanned aerial vehicle (UAV) red–green–blue–infrared (RGB-IR) object detection is important for traffic monitoring, security surveillance, and urban management, but remains challenging because aerial targets are often small, densely distributed, and affected by complex backgrounds. In addition, RGB and infrared (IR) modalities contribute unequally under different imaging conditions, making simple feature concatenation or indiscriminate middle-layer fusion insufficient for stable cross-modal utilization. To address this problem, this paper proposes Selective Interaction Mechanism and Prefiltering Complementary Spatial Refinement (SIM-PCSR), a key-layer complementary enhancement method for UAV RGB-IR small-object detection. The proposed method decomposes cross-modal modeling into two stages. SIMAdapter first performs selective interaction on the small-object-sensitive P3 layer before fusion, suppressing redundant responses and enhancing potentially complementary modal evidence. PCSR then refines the fused representation through prefiltering, modal selection, and local window residual refinement, injecting reliable complementary information into the key-layer fused feature in a controlled manner. Experiments on the DroneVehicle dataset show that SIM-PCSR achieves 85.323 mean average precision (mAP)₅₀ and 63.572 mAP_50:95, improving the Fixed Middle Fusion baseline by 0.523 and 0.751 percentage points, respectively. These gains correspond to relative improvements of 0.62% and 1.20% over the baseline. Module ablation, position ablation, repeated-seed evaluation, category-wise analysis, scale-wise analysis, and qualitative visualization jointly demonstrate that explicit selection and organization of cross-modal information can improve UAV RGB-IR small-object detection under modality imbalance and background interference. Full article

This study aimed to systematically compare the functional properties of the four major components (albumin, globulin, prolamin, and glutelin) of protein from Yunnan deep-veined walnuts to screen for protein-based carrier materials with good processing adaptability and the ability to efficiently encapsulate the active ingredient rutin. In addition, the binding and molecular interactions between the preferred protein and rutin were analyzed. The results indicated that albumin exhibited superior performance compared to the other three components in solubility, emulsifying properties, foaming properties, and gel properties, and demonstrated the strongest processing applicability. Further analysis revealed that albumin possessed an excellent amino acid composition (essential amino acid content accounting for 42.30%) and antioxidant activity (with the highest ABTS scavenging rate reaching 85.71 ± 0.26%), which indicated its considerable potential as a functional carrier. Loading rutin onto albumin yielded a walnut albumin–rutin complex (WA@Rut), which significantly enhanced the thermal stability of albumin (with the thermal denaturation temperature elevated to 108.72 °C) and the storage stability of rutin (66.16 ± 5.05% retention after 22 days of storage). Combined analyses of FT-IR spectroscopy, intrinsic fluorescence spectroscopy, molecular docking, and molecular dynamics simulations confirmed that rutin primarily bound to albumin via hydrogen bonding and electrostatic interactions, and formed a stable complex structure. SEM images revealed that the composite surface was smooth and exhibited a flake-like morphology. In conclusion, walnut albumin is a protein resource with significant functional potential in Yunnan deep-veined walnuts, and it exhibits strong processing applicability and enables efficient encapsulation and protection of active ingredients. This study provides novel strategies and theoretical foundations for the high-value utilization of walnut protein. Full article

Remote sensing image dehazing remains a formidable challenge due to complex atmospheric scattering and large-scale spatially varying degradation, which severely compromise fine-grained surface details. While recent diffusion-based restoration frameworks, such as DiffIR, have achieved remarkable efficiency by injecting compact diffusion priors into deterministic networks, they typically rely on a monolithic global Image Prior Representation (IPR). However, such a global design is suboptimal for the dehazed results of remote sensing imagery, where haze distribution exhibits strong spatial heterogeneity and scale dependency. To address this limitation, this paper presents the Hierarchical and Scale-Adaptive Diffusion Prior (HS-DiffIR) framework. Specifically, Hierarchical Image Prior Representation decomposes the holistic diffusion latent into multi-scale priors aligned with the hierarchical stages of the restoration network. Such a design facilitates fine-grained, scale-aware guidance by projecting the compact global latent into layer-specific representations, thereby bypassing the computational burden of high-dimensional generative modeling. Complementing this, the Scale-Adaptive Injection mechanism utilizes lightweight learnable coefficients to dynamically modulate the influence of diffusion priors across different feature scales, allowing the network to adaptively balance global semantic consistency and local detail recovery under dense-haze conditions. Evaluations on remote sensing benchmarks confirm that HS-DiffIR generally outperforms the DiffIR baseline. The method yields superior quantitative metrics (particularly PSNR) at a marginal computational cost while demonstrating robust detail restoration in regions subject to severe, spatially variant haze. Full article

Micro- and nanoplastics (MNPs) are emerging environmental contaminants of increasing relevance to human health. Growing evidence suggests that, following ingestion, inhalation, or, less convincingly, dermal exposure, MNPs may cross biological barriers, enter lymphatic and vascular compartments, and reach the liver. Owing to portal blood flow, sinusoidal architecture and Kupffer cell activity, the liver appears to be one of the principal sites of early particle sequestration. Human biomonitoring, ex vivo and postmortem studies have detected MNPs in blood and multiple organs, including the liver, although the currently available evidence remains limited and methodologically heterogeneous. Their identification relies on multistep analytical procedures that integrate sample pretreatment with FTIR, Raman spectroscopy, LD-IR, Py-GC-MS and supplementary imaging methods. However, each of these techniques presents significant limitations, particularly in the analysis of nanoplastics. Experimental studies indicate that MNPs may induce hepatic injury through oxidative stress, mitochondrial impairment, endoplasmic reticulum stress, inflammation, DNA damage, dysregulated lipid metabolism and disruption of the gut–liver axis, consequently contributing to steatosis, cholestatic anomalies and fibrosis. Consequently, MNPs should be considered potential contributors to liver pathology, although more comprehensive human data are still required. Full article

Ultra-high-voltage (UHV) converter transformer equipment is critical for UHVDC transmission systems. This paper proposes a Cross-modal Transformer framework for fault diagnosis by fusing dissolved gas analysis (DGA) and infrared (IR) thermography data. The framework encodes DGA measurements into temporal tokens and processes IR images through a ResNet-18 backbone to generate spatial tokens. A Cross-modal Transformer module enables deep semantic interaction via bidirectional cross-attention, allowing DGA tokens to attend to relevant IR regions and vice versa. A modality-gating mechanism adaptively reweights the two modalities under measurement degradation, including partial and fully missing-modality scenarios. The novelty lies in adapting these components into a leakage-controlled DGA-IR diagnostic framework for UHV converter transformers, with explicit interaction between gas-evolution tokens and spatial thermal tokens. Evaluation is performed under a leakage-controlled grouped chronological split that isolates equipment units, converter stations, and fault episodes across train, validation, and test partitions. Labels are drawn exclusively from maintenance inspection and operational records, independent of the IEC 60599 ratio features seen by the model. Under this protocol, the proposed framework consistently improves accuracy and macro-F1 over encoder-matched simple-fusion baselines (Transformer-DGA + ResNet-18 with concatenation, late fusion, and gated averaging). Additional missing-modality, noise, and ablation experiments indicate that the gains come from bidirectional cross-attention and adaptive gating rather than from stronger unimodal encoders alone. Full article

Infrared focal plane array detectors produce column stripe noise due to inter-detector response variations. Existing single-frame correction methods operate exclusively on the degraded infrared image and cannot reliably distinguish column noise from genuine vertical scene structures. With the increasing availability of co-registered visible-light cameras in modern electro-optical/infrared payloads, we propose to exploit the visible image as a structural guide for infrared destriping. Through a cross-modal correlation analysis, we show that the structural correspondence between RGB and infrared images is spatially non-uniform, motivating a selective rather than uniform fusion strategy. Based on this observation, we propose CMSP (Cross-Modal Scene Prior), a lightweight single-frame denoising architecture that selectively applies RGB guidance where it is beneficial. The proposed AdaptiveSPADE module blends RGB-guided modulation with standard instance normalization through a learned per-pixel confidence map, while a dual-path output head separately estimates pixel-wise residuals and column-constant stripe patterns. Evaluated on three public RGB–IR datasets, CMSP achieves 51.91 dB PSNR on M3FD, outperforming the best baseline by 5.79 dB with only 638 K parameters. A downstream evaluation on real stripe noise demonstrates that CMSP not only removes artifacts but also preserves the fine structures critical for infrared small target detection. Ablation studies confirm that adaptive gating more than doubles the benefit of RGB guidance compared to uniform modulation, and prevents degradation when cross-modal alignment is weak. Full article

Background/Objectives: To directly compare the capabilities of hybrid-type iterative reconstruction (IR) with the newly developed deep learning reconstruction (DLR) for the inner ear on high-definition CT (HDCT) obtained using the super-high-resolution (SHR) mode for external, middle and inner ear evaluations and diagnosis in patients with and without otologic diseases. Methods: Included in this study were 140 patients who had undergone HDCT, consisting of 32 otologic disease patients and 108 non-otologic disease patients, and 280 inner and middle ears and temporal bones were evaluated on a per ear analysis. Signal-to-noise ratios (SNRs) of the temporal bone surrounding the aural vestibule of the ear and in the vestibule as well as the cerebellar hemisphere, overall image and detailed evaluation of the visibility of anatomical landmarks in the middle and inner ear and temporal bone obtained with the two methods were assessed and statistically compared using the paired t-test or Wilcoxon’s signed-rank test. Then, receiver operating characteristic (ROC) analysis was performed to compare diagnostic performance between two reconstruction methods. Results: Each SNR of DLR was significantly higher than that of hybrid-type IR (p < 0.05). Overall image quality and detailed visualization of each anatomical structure obtained with DLR were significantly better than those obtained with hybrid-type IR (p < 0.05). The area under the curve of DLR had no significant difference with hybrid-type IR (p = 0.18). Conclusions: DLR has superior potential to hybrid-type IR for better image quality and visualization of anatomical landmarks in middle and inner ears and temporal bones on HDCT, although diagnostic performance was not affected in clinical practice. Full article

Specialty oilseeds, encompassing herbaceous (sunflower, flaxseed, sesame) and woody (Camellia oleifera, walnut, olive) species, serve as important sustainable sources of plant proteins that are inherently enriched with structurally diverse endogenous polyphenols such as chlorogenic acid, lignans, catechins, and ellagitannins. During processing, these polyphenols drive covalent or non-covalent interactions that profoundly reshape the physicochemical and functional properties of the resulting food systems. While prior reviews have largely remained descriptive or focused on single commodities or model proteins, this work provides the first critical, multiscale synthesis across herbaceous and woody oilseeds. We systematically compare polyphenol diversity, delineate the continuum from reversible non-covalent association (specific residue-level vs. non-specific surface-mediated) to irreversible covalent coupling, and establish a “structure–interaction–function” framework that explicitly defines a condition-dependent “Processing Window”. Within this window, moderate interactions enhance interfacial viscoelasticity, oxidative stability, foaming, and emulsification; excessive cross-linking, however, impairs solubility, digestibility, and sensory quality. By integrating experimental spectroscopy (UV-vis, FT-IR, CD, ITC), microscopic imaging, and computational simulations (molecular docking and dynamics), we map residue-level binding modes directly to macroscopic functional outcomes. Furthermore, the review evaluates the engineering potential of these complex systems in frontier applications such as antioxidant emulsions and active packaging. By explicitly identifying evidence boundaries and quantitative knowledge gaps in endogenous matrices, this work provides a comprehensive theoretical framework for the precision design and valorization of specialty oilseed-derived functional ingredients. Full article

Immune checkpoint receptors (ICRs) play a pivotal role in modulating antitumor immunity and have become central targets in the immunotherapy of genitourinary (GU) malignancies. This review provides a comprehensive overview of the fundamental mechanisms of ICR signaling, the expression and pathophysiological roles of these receptors in GU cancers (kidney, bladder, prostate, testicular, and penile), and the evolving therapeutic landscape. Key ICRs, including PD-1, CTLA-4, LAG-3, TIM-3, and TIGIT, orchestrate complex signaling cascades that can lead to T-cell exhaustion and tumor immune evasion. Their expression varies significantly across GU cancer types, histological subtypes, and tumor stages, influencing prognosis and therapeutic response. Immune checkpoint inhibitors (ICIs) reinvigorate antitumor immunity by disrupting these inhibitory pathways and remodeling the tumor microenvironment (TME); however, resistance mechanisms (primary, adaptive, and acquired) and immune-related adverse events (irAEs) pose significant clinical challenges. Established biomarkers such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI)/deficient mismatch repair (dMMR) status guide ICI use, but their predictive power has limitations. Consequently, emerging tissue-based (e.g., immune cell signatures, multiplex IHC/IF, spatial transcriptomics), liquid biopsy-based (e.g., ctDNA, CTCs, exosomes), and imaging-based (radiomics, AI-driven analysis) biomarkers are under active investigation to refine patient selection and monitor treatment efficacy. The therapeutic armamentarium is rapidly expanding with novel ICIs targeting new receptors, bispecific antibodies, and innovative combination strategies involving ICIs with chemotherapy, targeted therapies, radiotherapy, and other immunotherapies. Furthermore, ICIs are increasingly explored in neoadjuvant, adjuvant, and maintenance settings. This review highlights the dynamic progress in understanding ICR biology and its clinical translation, emphasizing the ongoing efforts to develop more personalized and effective immunotherapeutic strategies for patients with genitourinary tumors. Full article

Near-infrared radiation contributes to photoaging through oxidative stress and matrix metalloproteinase activation. Botanical extracts with antioxidant properties may offer additional protection beyond conventional UV filters. To evaluate the effect of hydrogel formulations containing Rosmarinus officinalis and Crataegus monogyna extracts on the directional reflectance of human skin across various spectral ranges. Directional reflectance was measured on the forearm skin of healthy female volunteers before and after application of a base hydrogel and hydrogels containing plant extracts. Hyperspectral imaging was used across spectral ranges of 335–2500 nm. To assess the application properties, rheological and textural evaluation of extract-based hydrogels was performed. The obtained results are satisfactory and indicate the expected application effectiveness of hydrogels with C. monogyna and R. officinalis extracts. Significant reductions in skin reflectance were observed in the IR spectrum after application of both botanical formulations. Median reflectance decreased by 3.5% with rosemary and 2.3% with hawthorn in the 1000–1700 nm range, and by 17.8% and 20.3% respectively in the 1700–2500 nm range. No statistically significant changes were observed in the UV or visible light ranges. Hydrogels enriched with R. officinalis and C. monogyna extracts reduced infrared reflectance of the skin, suggesting potential as adjunctive agents in photoprotection. These findings support further investigation into extract-based formulations for IR-related skin damage prevention. Full article

Immune checkpoint inhibitors (ICIs) have transformed the therapeutic landscape of oncology but are increasingly associated with cardiovascular immune-related adverse events (irAEs), including myocarditis, heart failure, arrhythmias, and vascular complications. Among these, ICI-associated myocarditis represents the most severe manifestation, often characterized by high mortality and challenging early diagnosis. Detecting subclinical myocardial injury before irreversible cardiomyocyte necrosis occurs remains a major unmet need in contemporary cardio-oncology. This narrative expert review critically examines the biological rationale, preclinical evidence, and emerging clinical data supporting the potential role of heart-type fatty acid-binding protein (H-FABP) as an adjunctive biomarker of early immune-mediated myocardial injury during ICI therapy. H-FABP is a small cytosolic lipid chaperone abundantly expressed in cardiomyocytes and rapidly released into the circulation following subtle membrane destabilization and metabolic stress, frequently preceding detectable troponin elevation in other forms of myocardial injury. Experimental studies support a mechanistic association between H-FABP release, inflammasome activation, cytokine amplification, mitochondrial dysfunction, and immune–metabolic cardiomyocyte stress. Preliminary clinical observations further suggest that H-FABP elevations may occur during ICI treatment even in the absence of overt myocarditis or concomitant increases in high-sensitivity cardiac troponins (hs-cTns). Although H-FABP cannot replace hs-cTn, which remains the cornerstone biomarker for the diagnosis of clinically significant ICI-associated myocarditis, its rapid kinetics and sensitivity to early metabolic membrane injury support its potential role as an investigational adjunctive biomarker for early surveillance and risk stratification. This approach may be particularly relevant in patients receiving high-risk combination ICI regimens or in individuals with pre-existing cardiovascular disease. However, current evidence remains limited, and large prospective multicenter studies integrating H-FABP with hs-cTns, natriuretic peptides, cardiac magnetic resonance imaging, and clinical outcomes are required before routine clinical implementation can be considered. Full article

This study assesses the technical feasibility of using polar orbiting satellite constellations to generate temperature profiles in the middle atmosphere, based on image analysis from the UVSQ-Sat NG nanosatellite. We first identified the phenomena influencing the temperature of this layer of the atmosphere, specifying their amplitudes and spatio-temporal resolutions. We then present the UVSQ-Sat NG nanosatellite and its Nanocam instrument, whose images of the Earth’s limb served as the basis for our processing. Finally, we detail the processing methodology, demonstrating its applicability to any image of the Earth’s limb acquired in the spectral range from near-UV to near-IR, subject to the following strict conditions: a measurement dynamic range greater than 1000 and rigorous control of instrumental noise. This approach paves the way for continuous, global monitoring of the middle atmosphere, which is essential for improving climate and weather models. Full article

Background/Objectives: To investigate the scleral inferior rectus–optic nerve distance (SIROND) and its association with intraocular pressure (IOP) elevation during upward gaze in patients with thyroid eye disease (TED), based on magnetic resonance imaging (MRI) examinations. Methods: This prospective study included 20 eyes (13 patients) diagnosed with active TED. All patients underwent orbital MRI in both primary and upward gaze positions before and 6 months after steroid pulse therapy. The SIROND was measured on MRI. IOP was recorded in both gazes. Changes in SIROND, inferior rectus (IR) muscle volume, proptosis, optic disc and scleral morphology, and IOP were analyzed pre- and post-treatment. Results: SIROND significantly decreased from primary gaze to upward gaze both before and after treatment (p < 0.001). Following steroid pulse therapy, there were significant reductions in IR muscle volume and proptosis (p < 0.05). Correspondingly, SIROND significantly increased in both primary and more in upward gaze post-treatment (p < 0.05). Although IOP during upward gaze was significantly higher than that in primary gaze both before and after treatment (p < 0.001), the gaze-related difference in IOP (p = 0.059), as well as SIROND, tended to be smaller after treatment (p < 0.001). A larger reduction in gaze-related pre-treatment SIROND was associated with greater IOP elevation in upward gaze (p = 0.038). MRI showed no evidence of globe compression by the IR muscle, and optic disc morphology remained unchanged following treatment. Conclusions: SIROND may serve as supportive radiological evidence for IOP elevation induced by upward gaze in patients with TED. Full article

The autoantibody reactome refers to the multidimensional repertoire of antibody reactivities against self-antigens across the human proteome or selected antigenic compartments. This offers a scalable systemic layer for precision immunology across spontaneous autoimmunity and treatment-induced immune toxicity. Autoimmune diseases and immune-related adverse events (irAEs) share major features of dysregulated immunity, yet clinically useful tools for risk stratification, early detection, endotyping, and treatment guidance remain limited and slow. A central challenge is that tissue pathology is highly informative but not uniformly accessible across diseases and organ systems, whereas routine serology captures only a narrow fraction of immune heterogeneity. In this perspective, I argue that a global autoantibody reactome can serve as a central unifying framework linking systemic immune history, tissue pathology, and clinical trajectories across autoimmune disorders and irAEs. Rheumatoid arthritis (RA) provides a strong prototype because its serological diversity, major role of post-translationally modified autoantigens, and marked synovial heterogeneity allow reactome features to be interpreted against tissue biology. Immune checkpoint inhibitor-associated inflammatory arthritis serves as an illustrative rheumatic irAE and a model of treatment-induced immune dysregulation with clear opportunities for longitudinal blood-based profiling. Spatial transcriptomics and proteomics are therefore positioned not as stand-alone solutions, but as mechanistic tools that can decode reactome-defined immune states within tissue microenvironments where tissue is accessible. Clinical translation will require integration of autoantibody reactomes with tissue, circulating proteomic, imaging, genetic, and clinical data through transparent multimodal models, as well as a shift from exploratory resources such as AAgAtlas toward analytically validated and clinically interpretable biomarker panels for risk prediction, endotyping, monitoring, and biomarker-guided intervention. This perspective outlines technical and strategic steps toward clinically actionable decision support, including risk stratification before ICI initiation and treatment guidance for patients who develop ICI-induced inflammatory arthritis, through integration of autoantibody reactome profiling, spatial omics and transparent multimodal AI. Full article

High-resolution satellite imagery is pivotal for accurate analysis in remote sensing applications, including land-use monitoring, urban planning, and environmental assessment. However, obtaining such data is often costly and limited. Consequently, super-resolution techniques, such as deep learning models and fine-tuning strategies like LoRA, offer a promising alternative to the critical research challenge, especially given the diversity and large scale of satellite datasets. While deep learning-based super-resolution models have been very promising recently, their effectiveness, efficiency, and scalability across heterogeneous satellite scenes are not well studied. This work studies the performance of representative deep learning Super-Resolution frameworks, including the Enhanced Super-Resolution Generative Adversarial Network. (ESRGAN), Swin Transformer for Image Restoration (SwinIR), and latent diffusion models (LDM), under unified experimental conditions using the WorldStrat dataset. The main goal is to establish whether adaptation strategies for parameter efficiency can boost reconstruction quality while reducing computational and training costs. Toward this goal, we investigate hybrid sequential pipelines, ensemble averaging, and Low-Rank Adaptation (LoRA)–based fine-tuning. The experiments indicate that these pipelines, which use multi-model methods, achieve only marginal performance gains while incurring substantial increases in computational complexity. LoRA-Based Fine-Tuning, by contrast, has demonstrated superiority in enhancing reconstruction accuracy and quality across all model families, despite using only a small percentage of trainable parameters. LoRA-based models demonstrate superiority over multi-model methods in both efficiency and performance. The presented results confirm that LoRA is an effective and accessible technique for high-fidelity satellite-based super-resolution image synthesis. The manuscript identifies LoRA as one of the enabling technologies advancing the state of the art in Deep Learning-based Super Resolution for large-scale satellite-based image synthesis. Full article