Search Results (2,240)

Background: Cardiovascular diseases remain important causes of morbidity and potential premature mortality in children. Although clinical imaging and electrophysiologic testing have advanced, early, minimally invasive biomarkers that can both detect myocardial injury and help differentiate among overlapping pediatric phenotypes are still limited. Circulating microRNAs (miRNAs; miRs) are becoming attractive biomarker candidates because many are abundant in the heart, actively released into the circulation, and remarkably stable in plasma. The study aimed to assess the expression of miR-1-3p, miR-let-7b-5p, miR-21-5p, and miR-26b-5p in children with cardiovascular disease. Methods: Children aged 10–18 years with cardiac arrhythmias, myocarditis, or cardio-myopathies were recruited. The control group consisted of healthy age- and sex-matched children. For each participant, peripheral venous blood was collected for plasma isolation and miRNA profiling. The expression of miR-1-3p, miR-let-7b-5p, miR-21-5p, miR-26b-5p, and UniSp6 molecules was analyzed using the comparative cycle threshold delta Ct (ΔCt) method. A p-value ≤ 0.05 was considered statistically significant. Results: miR-26b-5p was significantly downregulated in patients with cardiac disease compared with healthy controls. miR-21-5p and miR-26b-5p were downregulated in patients with ventricular arrhythmia. Moreover, miR-26b-5p was downregulated in arrhythmia in general. We found no significant difference in the expression of miR-1-3p, miR-let-7b-5p, miR-21b-5p, and miR-26b-5p between patients with and without myocarditis, as well as with and without hypertrophic cardiomyopathy. Conclusions: miR-26b-5p may distinguish young patients with cardiovascular disease and those with arrhythmias from healthy individuals. miR-21-5p and miR-26b-5p may also be seen as potential biomarkers of ventricular arrhythmia. Further studies involving a larger sample size are required to obtain sufficient data and validate these findings. Full article

Unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) has been increasingly utilized for fine-scale surface characterization and quantitative retrieval due to its capability of capturing dense spectral information at ultra-high spatial resolution. However, UAV-HSI analysis remains challenging due to high dimensionality, noise and within-class variability, as well as limited cross-flight consistency under varying acquisition conditions. Deep learning (DL) has therefore attracted growing attention by enabling spectral-spatial representation learning and more robust inference under residual degradations and domain shifts. This review summarizes DL approaches for UAV-HSI analytics and organizes the literature along a complete workflow, from imaging principles, preprocessing, and correction to DL architectures, core tasks, and representative applications, to provide guidance for future research and applications. The reviewed papers demonstrate that DL exhibits great potential and a promising future in UAV-HSI analysis. Full article

Membrane distillation (MD) is an attractive technology for desalination driven by renewable energy and low-grade heat sources. However, specific practical guidelines for intermittent operations, typical of such alternative energy sources, are still limited—particularly with respect to established shutdown measures to mitigate adverse effects on the overall system performance. The present study compares continuous and intermittent air-gap MD desalination at a lab-scale by evaluating performance parameters and scaling development. Apart from a slightly lower distillate productivity and a similar distillate quality under intermittent conditions, no direct difference in MD performance between continuous and intermittent experiments was detected. Nevertheless, online monitoring by image analysis with optical coherence tomography revealed more advanced scaling development during intermittent operation, with larger scaling volumes and cover ratios, particularly after implementing a membrane rinsing and preservation protocol with demineralized water. Membrane autopsies revealed that intermittency led to alterations in the development of the crystal morphology of predominantly CaCO₃ scaling. These changes were attributed to enhanced nucleation and modified growth kinetics triggered by recurring shutdown and start-up phases. Overall, the findings showed that intermittency had an adverse effect in terms of scaling behavior, highlighting the need for operating protocols tailored to each specific MD application. Full article

Surface-enhanced Raman spectroscopy (SERS) amplifies Raman scattering by placing molecules in the near-field of plasmonic nanostructures, enabling label-free molecular fingerprinting. While attractive for living cell phenotyping, many cellular SERS works rely on internalized colloidal nanoparticles, leading to variable uptake/localization, aggregation-driven hotspot fluctuations, and potential cellular perturbation. Here, we report a chip-like Au/SiO₂ nanolaminate SERS substrate that supports direct culture and label-free measurements of living cells on spatially defined hotspots without nanoparticle uptake. The periodic nanolaminate forms dense nanogaps and is engineered for 785 nm excitation, providing uniform enhancement over a large, culture-compatible area with high hotspot uniformity. By engineering the cell–substrate nano–bio interface, the platform enables reproducible acquisition of intrinsic cellular vibrational fingerprints under physiological conditions without Raman tags. Using MCF-7 and MDA-MB-231 breast cancer cells, we collected hundreds of spectra per line, and MDA-MB-231 exhibited broader spectral variations, indicating greater heterogeneity. Principal component analysis and linear discriminant analysis achieved 99% classification accuracy for MCF-7 and MDA-MB-231, and bright-field imaging confirmed preserved adhesion and canonical morphologies. This chip-based, label-free living cell SERS platform enables scalable, nonperturbative phenotyping and may support rapid malignancy classification and treatment response screening across subtle cancer states. Full article

Multifrequency operation in micromachined ultrasonic transducers, enabled by targeted excitation of specific vibrational modes, has emerged as an attractive approach for achieving tunable performance and configurability, well-suited for advanced ultrasound imaging and therapeutic applications. This paper presents a dual-electrode rectangular piezoelectric micromachined ultrasonic transducer (PMUT) designed for efficient dual-frequency operation through mode-selective actuation. The proposed architecture employs segmented electrodes that are spatially aligned with the strain distributions of two distinct flexural modes, enabling selective excitation of Mode 1 (fundamental) and Mode 3 (higher order) through appropriate electrode actuation. Finite element simulations and impedance analysis were used to guide the electrode configuration and validate the mode-selective behavior. The dual-mode PMUT was fabricated alongside a conventional single-electrode PMUT using identical membrane dimensions and material stack for direct comparison. Comprehensive electrical and underwater acoustic characterization confirmed that the conventional PMUT is limited to single-frequency operation at the fundamental resonance. In contrast, the proposed design achieved a substantial improvement in higher-order performance, with a threefold increase in acoustic pressure at Mode 3 compared to the conventional device. These results demonstrate that mode-aligned electrode segmentation enables efficient dual-mode operation without added fabrication complexity, making the design highly suitable for multifrequency ultrasonic applications such as biomedical imaging and sensing. Full article

Seminaphtofluorones (SNAFRs) are a family of benzannulated xanthene dyes that exhibit strong fluorescence in both neutral and anionic states and can reach emission wavelengths in the deep-red to near-infrared region. Their optical response is highly sensitive to regioisomerism and functionalization, making them attractive candidates for systematic structure–property investigations. Here, we computed the photophysical properties of six SNAFR regioisomers for both neutral and anionic species and correlate the calculated results with available experimental data. From the six dyes, we further chose two of them, SNAFR4 and SNAFR6, to further investigate how phenyl-ring functionalization modulates SNAFR properties by introducing methyl (–CH₃) and carboxyl (–COOH) substituents at the ortho (o), meta (m), and para (p) positions. The calculations indicate that substitution induces measurable changes in geometries, as well as in excitation and emission energies, with particularly pronounced effects for the anionic derivatives. Overall, these results provide a computational framework for the rational tuning of SNAFRs’ optical properties and the design of derivatives with tailored optical characteristics for fluorescence imaging and applications in photodynamic therapy. Full article

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of sentiment, even though sentiment information in text is closely linked to clues of sarcasm. Additionally, few consider how text and images align semantically. To address these issues, we propose a sentiment-guided mutual learning network (SGMLN) for multimodal sarcasm detection. SGMLN utilizes sentiment information to inform the combination of text and image features, and employs mutual learning to facilitate knowledge sharing among classifiers. We design a sentiment-guided attention layer that injects sentiment into both modalities, producing features that capture sarcasm more effectively. Sentic-BERT extracts sentiment-aware vectors from text, using word-level sentiment as a mask. In mutual learning, a logistic distribution function measures differences between classifiers, improving knowledge transfer between modalities. This step boosts multimodal understanding and model performance. By introducing sentiment-aware representations and semantic alignment, SGMLN bridges the gap between text and images, making them more consistent. Experiments on public datasets demonstrate that our model is effective and outperforms alternatives. Full article

Sparse light field imaging often limits the quality of 3D scene reconstruction due to insufficient viewpoint coverage, resulting in incomplete or inaccurate reconstructions. This work introduces a hybrid CNN–LSTM-based framework to address this issue by generating novel camera poses and the corresponding synthesized novel views, effectively densifying the light field representation. The CNN extracts spatial features from the sparse input views, while the LSTM predicts temporal and positional dependencies, enabling smooth interpolation of novel poses and views. The proposed method integrates these synthesized views with the original sparse dataset to produce a comprehensive set of images. Our approach was evaluated on several datasets, including challenging datasets. The inference capability of our method was tested extensively, and it showed good generalization across diverse datasets. The effectiveness of the framework was evaluated not only with local light field fusion (LLFF) but also with NeRF and 3D Gaussian Splatting, which are considered state-of-the-art reconstruction methods. Overall, the enriched dataset generated by our method led to consistent improvements in 3D reconstruction quality, including higher depth estimation accuracy, reduced artifacts, and enhanced structural consistency. Most importantly, LSTM-based approaches have so far attracted limited attention in the context of generating novel views. While LSTMs have been widely applied in sequential data domains such as natural language processing, their use for image generation conditioned on camera poses remains largely unexplored, which underscores the novelty and significance of the proposed work. This approach provides a scalable and generalizable solution to the sparsity problem in light fields, advancing the capabilities of computational imaging, photorealistic rendering, and immersive 3D scene reconstruction. The results firmly establish the proposed method as a robust and versatile tool for improving reconstruction quality in sparse-view settings. Full article

Recently, more sustainable and biodegradable packaging materials have begun to attract attention in food packaging due to major, rising concerns related to plastic usage. This study aims to develop and characterize biodegradable food packaging materials, namely poly(lactic acid) (PLA) and poly(ε-caprolactone) (PCL) enriched with pomegranate peel extract (PoPE). Firstly, the optimal extract selected was a 24 h maceration of PoPE with 60% ethanol, after production with different solvents and methods. PLA- and PCL-based films were produced via melt compounding with the addition of PoPE at different concentrations (1, 3, 5 and 10%, w/w). FTIR confirmed that the PoPE did not modify the chemical backbones of PLA or PCL, with only a more pronounced O–H band in PCL, suggesting mainly non-covalent/physical interactions. UV–Vis spectroscopy showed tunable warm coloration and strong UV shielding with reduced transparency; for PLA ~3–5 wt.%, PoPE enabled near-complete UV blocking, while PCL achieved very high UV protection even at low loadings. PoPE improved toughness in PLA (3–5 wt.%) and maintained ductility in PCL (1–10 wt.%). PoPE-added PLA and PCL films maintained thermal stability up to 10 wt.% according to TGA results. DSC/XRD indicated a matrix-dependent crystallization response. PLA remained largely amorphous, whereas PoPE promoted PCL crystallinity without changing polymer crystal polymorphs. SEM images revealed homogenous dispersion of PoPE in the films. Full article

As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability. Adversarial example purification is attractive for such deployments because it removes input perturbations without modifying the already deployed models. However, most existing purification methods are inherently goal-free: denoising-based approaches apply blind heuristic operators, while reconstruction-based methods rely on stochastic sampling guided by natural image priors. These methods typically suppress perturbations at the cost of weakening semantic details or inducing structural distortions. To address this limitation, we propose a novel goal-directed purification framework, termed adversarial energy field optimization for adversarial example purification (AEFOP). AEFOP formulates purification as a constrained optimization problem by defining a learnable adversarial energy which quantifies how far an input deviates from the benign region. This allows adversarial examples to be explicitly pushed from high-energy regions toward low-energy benign regions along an interpretable descent trajectory. Specifically, we build an adversarial energy network and optimize the energy field via a two-stage strategy: adversarial energy field shaping, which enforces distance-like energy behavior and correct gradient directions, and task-driven energy field calibration, which unrolls the descent process to calibrate the field with classification-consistency and semantic-preservation objectives. Extensive experiments across multiple attack scenarios demonstrate that AEFOP achieves superior purification accuracy and high visual quality while requiring only a few gradient steps during inference, offering a practical and efficient robustness layer for vision-based AI services in education. Full article

Image restoration under adverse weather conditions has attracted increasing attention because of its importance for both human perception and downstream vision applications. Existing methods, however, are often designed for a single degradation type. We present WeatherMAR, a multi-weather restoration framework that formulates adverse-weather restoration as a paired-domain completion problem in a shared continuous token space. Specifically, WeatherMAR concatenates degraded and clean token sequences into a joint paired-domain sequence and performs restoration through masked autoregressive modeling, in which self-attention enables direct cross-domain interaction. To strengthen conditional learning while avoiding trivial paired correspondences, we introduce complementary bidirectional masking together with an optional reverse objective used only during training to encourage degradation-aware representations. WeatherMAR further employs a conditional diffusion objective for continuous token prediction and adopts a progress-to-step schedule to improve inference efficiency. Extensive experiments on standard multi-weather benchmarks, including Snow100K, Outdoor-Rain, and RainDrop, show that WeatherMAR achieves the best PSNR/SSIM on Snow100K-S (38.14/0.9684), the best SSIM on Outdoor-Rain (0.9396), and the best PSNR on Snow100K-L (32.58) and RainDrop (33.12). These results demonstrate that paired-domain token completion provides an effective solution for adverse-weather restoration. Full article

Head and neck squamous cell carcinoma (HNSCC) continues to pose a major global health challenge, with over 600,000 new cases diagnosed annually and persistently poor survival outcomes despite advances in surgery, radiotherapy, and immunotherapy. Growing evidence implicates metabolic reprogramming, including enhanced glycolysis, glutaminolysis, lipid synthesis, and one-carbon/redox flux as a central driver of HNC initiation, progression, and therapy resistance. In contrast, metabolic crosstalk within the hypoxic, acidic tumor microenvironment (TME) further shapes immune evasion and stromal support. Recent innovations in mass spectrometry platforms (LC-MS, GC-MS, NMR) have attracted attention in clinical therapeutics, and spatial metabolomics imaging techniques now enable high-resolution in situ mapping of metabolites, revealing intratumoral heterogeneity and offering new insights into tumor-immune–stromal interactions and potential biomarkers for precision oncology. In this review, we integrate critical methodological considerations from sample collection and data-analysis workflows to analytical pitfalls with a balanced, pathway-focused analysis of HNSCC dysmetabolism, examine tumor immune stromal metabolic interactions, and highlight translational opportunities through emerging biomarkers, targeted inhibitors, and cutting-edge approaches such as single-cell and AI-driven metabolomics to chart a roadmap toward precision oncology for HNSCC. Full article

Background: Breast cancer poses not only a physical health threat but also significant emotional and identity challenges for women, particularly regarding femininity and body image. Understanding how patients adapt psychologically can guide effective psychosocial interventions. Objective: This study aimed to evaluate psychological adaptation, coping strategies, illness acceptance, and body image in women with breast cancer and identify factors associated with better adjustment. Methods: A cross-sectional study was conducted among 30 women aged 22–66 undergoing treatment at the Wielkopolskie Centrum Onkologii, Poland. Standardized tools included the Mini-MAC scale (coping strategies), Acceptance of Illness Scale (AIS), and Body Image Scale (BIS). Descriptive statistics and correlations were analyzed. Results: Most participants exhibited a constructive coping style, with positive redefinition and fighting spirit being predominant. Some women simultaneously showed elements of a destructive coping style, including helplessness and hopelessness, indicating complex emotional reactions. Overall, participants demonstrated high illness acceptance, despite notable body image-related discomfort, particularly shame, reduced perceived attractiveness, and appearance-related anxiety. While age did not correlate significantly with coping or body image, a significant negative association was found between age and illness acceptance, with younger women showing better adjustment. Conclusions: Psychological adaptation to breast cancer is multidimensional and individualized, dependent on personality traits, internal resources, and social support. Findings highlight the need for holistic, patient-centered psychosocial care, addressing both emotional adaptation and body image-related distress, including support for intimacy and prosthetic interventions. Individualized strategies can improve quality of life and functional outcomes during and after cancer treatment. Full article

Zirconia-based hybrid blends at various molecular or nanometer scales have attracted significant interest from a technological perspective. In particular, several inorganic-organic hybrids are being applied in the biomedical field. In this context, inorganic ZrO₂ and hybrids composed of ZrO₂, and polyethylene glycol (PEG) have been synthesized through the sol–gel process and characterized from both morphological and spectroscopic viewpoints to explore their potential as hybrid biomaterials. Atomic Force Microscopy (AFM) has enabled a quantitative assessment of the surface roughness of bioactive sol–gel-based materials. The findings indicated an increase in material porosity in relation to the amount of PEG present in the systems, underscoring the important role of PEG in influencing the morphological characteristics of zirconia-based blends. AFM images display the typical globular structure of PEG spread across the surface of all systems. All hybrid systems seem to be uniform, and no phase separation is evident, thereby validating that the produced materials are hybrid nanostructured ones. The simultaneous presence of both inorganic and organic phases was verified using Fourier-transform infrared spectroscopy (FT-IR). FT-IR deconvolution in 850–550 cm⁻¹ region showed that PEG progressively perturbs the Zr–O–Zr network, increasing disorder and establishing more flexible inorganic domains at high PEG content. Increasing polymer amount enhanced cell viability against NIH-3T3 cell line, while antibacterial activity decreased, with pure ZrO₂ showing the strongest inhibition against Escherichia coli (E. coli). Full article

Event-based face detection has attracted significant interest due to the unique advantages of event cameras, including high temporal resolution, high dynamic range, and low power consumption. However, the lack of annotated public datasets remains a major challenge for training effective event-based face detection models. In this paper, we propose a spiking feature-driven synthetic event generation framework that utilizes a spiking neural network (SNN) in conjunction with a pretrained convolutional backbone to generate synthetic event representations from a single RGB image. To incorporate motion-induced ON/OFF polarity information, we introduce a movement-aware polarity integration (MPI) module that assumes four directional facial movements. An event-similarity score is further employed to select representations most consistent with real event data for training. Unlike conventional approaches relying on video-based simulators, our method enables efficient synthetic event dataset construction without requiring video inputs or additional simulation training. Experimental results on the N-Caltech101 dataset demonstrate a face detection accuracy of 99.91%, outperforming existing event-based face detection methods. Full article