Search Results (1,907)

Annurca apple (Malus domestica cv. Annurca) is a traditional Southern Italian cultivar increasingly recognized as a polyphenol-rich food matrix with functional and nutraceutical relevance. Its distinctive post-harvest reddening process, together with tissue-specific differences among flesh, peel and core, influences its phytochemical profile, antioxidant capacity and biological activity. In addition, Annurca apple by-products, including peel and core fractions, are emerging as sustainable sources of food-derived bioactives. This review critically summarizes current evidence on Annurca apple-derived polyphenols, bioactive fractions and by-products, focusing on their molecular mechanisms, context-dependent redox behavior and translational potential. Available studies indicate that Annurca-derived compounds and matrices should not be interpreted merely as antioxidant sources but rather as modulators of redox-sensitive cellular pathways whose effects depend on fruit fraction, ripening stage, extract composition, dose, biological model and cellular context. In normal cellular models, Annurca-derived extracts and fractions have shown cytoprotective effects against oxidative damage, mercury-induced erythrocyte alterations, phosphatidylserine externalization, advanced glycation end product (AGE)-induced cytotoxicity and oxidative stress-induced senescence. These effects involve mechanisms related to ROS modulation, erythrocyte membrane homeostasis, calcium-dependent PLSCR1 regulation, antiglycative activity, endothelial protection and senescence-associated pathways. Conversely, in cancer cell models, Annurca polyphenols may exert pro-oxidant and pro-apoptotic effects, mainly involving ROS/JNK signaling, inhibition of cell survival, modulation of epithelial–mesenchymal plasticity and reduced migration. Additional evidence on lipid metabolism, skin and hair biology, neuroprotective targets and by-product valorization supports broader functional and nutraceutical applications. However, most evidence remains preclinical and often relies on concentrated extracts or specific formulations. Future studies should address extract standardization, phytochemical fingerprinting, bioaccessibility, gut microbiota metabolism, circulating metabolites, dose relevance and clinical validation to clarify whether Annurca-derived bioactives can be developed as evidence-based functional ingredients or nutraceutical candidates. Full article

Wearable and attachable healthcare devices provide valuable insights across a broad range of applications, from health monitoring and disease diagnosis to the assessment of daily human activities. For these devices, both the formation of high-performance electrodes and mechanical, electrical, and physiological stability at human-device interfaces are critically important. Conductive gels, which combine deformable polymer networks with electrical conductivity, show considerable potential as functional materials for such applications. However, existing reviews have often considered material properties, interfacial behavior, and device reliability separately. This review examines hydrogels, ionogels, and organogels by linking material design, stimulus- and analyte-responsive sensing, biological and device interfaces, and healthcare applications. This integrated perspective emphasizes evaluating conductive gels within complete healthcare devices, where material properties, biological interfaces, and device integration jointly determine practical performance. Such evaluation needs to address signal quality and the stability of the assembled system during deformation, environmental exposure, and prolonged use. Future progress will require reproducible device fabrication and standardized validation tailored to the intended application, with systematic assessment of long-term biosafety and alignment with relevant regulatory requirements. Integration with flexible electronics, intelligent data processing, and feedback control may further enable reliable and responsive healthcare platforms. Full article

Moisture content is among the most routinely measured properties of foods, yet water quantity alone does not determine processing behavior, stability, texture, fluid release or sensory function. In heterogeneous food matrices, water occupies environments differing in molecular restriction, compartmentalization, exchange, connectivity and translational mobility. Time-domain nuclear magnetic resonance (TD-NMR) provides a rapid and non-destructive means of interrogating these properties through proton signal intensity, longitudinal and transverse relaxation, relaxation-time distributions, self-diffusion and multidimensional correlation measurements. This review develops a framework extending food-water analysis from quantity to molecular state, mobility, translational accessibility and function. Particular emphasis is placed on distinguishing local molecular environments reflected by relaxation from longer-range molecular displacement characterized by diffusion. Applications in meat, plant-based foods, dairy systems, cereals, gels, frozen and dried foods, and edible oils illustrate how TD-NMR descriptors can be connected with water holding, texture, juiciness, processing behavior, oxidation and shelf life. Integration with structural imaging, mechanical measurements, chemometrics and machine learning further positions TD-NMR as a molecular-state sensing platform for predictive food analysis, product design and intelligent manufacturing. Full article

Accelerometers built on micro-electromechanical systems (MEMS) play a critical role in structural monitoring and machinery diagnostics; however, their accuracy suffers from intrinsic nonlinearities—dead zones, hysteresis, saturation, and colored noise. Conventional physics-based correction methods are interpretable yet cannot capture complicated hysteretic behavior, while purely neural-network approaches generalize poorly and lack a physical foundation. This paper proposes a hybrid architecture that combines a residual convolutional neural network with a physics-guided low-pass filter prior, fused through an attention-gated mechanism. The CNN learns only the residual nonlinearity; the filter supplies a steady, band-limited baseline. We validate the model on two simulated scenarios—a noise-dominant track and a nonlinear-dominant track—across three random seeds. The resulting Hybrid LPF-CNN outperforms a standalone CNN by 15.2% and an LSTM by 33.5% on the severely nonlinear track, reaching a mean R² of 0.9970 and an RMSE of 0.0179 g. On the noise-dominant track, it reaches R² = 0.9407 and RMSE = 0.0800 g, surpassing both CNN and LSTM baselines. The model is also stable across seeds ($\sigma =0.0001$ in R²) and gives a legible breakdown of the correction it applies. Our systematic architectural search revealed that a dual-encoder design with attention-gated fusion—where raw and filtered signals are processed separately and combined via a learnable spatial gate—provides the optimal balance between stability, accuracy, and interpretability. Even basic physics priors substantially improve the performance, stability, and interpretability of deep learning models for sensor error correction. Full article

This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multifrequency dither, and dictionary-based waveform recognition for target detection and classification. Rather than forming conventional spatial images or relying primarily on Doppler processing, RaDICAL encodes target geometry directly into a composite receiver waveform and performs hypothesis testing by matching measured signals to a library of predicted responses. This study develops the SUCA-based signal model for point and extended targets and formulates recognition as a waveform-domain matching problem against the representation dictionary using normalized complex correlation and QR-domain processing. A reproducible MATLAB-based study evaluates waveform separability, probability of detection versus dictionary SNR, physical power balance, receiver operating characteristic (ROC) behavior, and detection performance versus illuminator EIRP. The results show that deterministic dither produces distinctive composite waveforms with strong hypothesis separability. The ROC simulations characterize binary detection of the structured waveform, while the recognition simulations quantify discrimination among competing physical dictionary hypotheses. Because the proposed receiver coherently processes multiple spatial and temporal waveform samples, no direct single-sample SNR advantage over a classical matched-filter detector is claimed. These results support the feasibility of waveform-domain passive sensing using deterministic spatial–frequency encoding and dictionary-based recognition. Unlike conventional representation-learning methods that derive embeddings from image or feature datasets, the proposed waveform representations are generated deterministically from physical electromagnetic models. Full article

Plant–animal communication involves diverse forms of information transfer that can mediate ecologically important interactions, including pollination, seed dispersal, herbivory, and plant defense. This review synthesizes current knowledge of the chemical, visual, gustatory, electrical, acoustic, and vibrational information involved in these interactions, with particular emphasis on volatile organic compounds, herbivore-derived elicitors and effectors, multimodal signaling, sensory perception, learning, and evolutionary processes, including coevolution where reciprocal evidence is available. A central distinction is made between detectable traits or cues and functional biological signals, emphasizing the need for evidence linking signal production or modification to receiver detection, behavioral or physiological responses, and ecologically meaningful outcomes. Acoustic and vibrational phenomena are considered critically because their communicative significance remains less firmly established than that of chemical and visual signaling. The review further examines how climate change and other anthropogenic pressures may alter signal production, transmission, perception, and phenological synchrony, thereby reorganizing plant–animal interactions. Genetic manipulation of hormonal, volatile, and specialized-metabolite pathways is also evaluated as a potential tool for modifying plant signaling, together with its possible unintended ecological consequences. By integrating plant physiology, sensory ecology, animal behavior, evolution, and environmental change, this review identifies major evidence gaps and highlights the importance of plant–animal communication for biodiversity conservation, pollination, biological control, and sustainable agriculture. Full article

Duchenne muscular dystrophy (DMD) is an X-linked disorder initiated by dystrophin deficiency, but disease progression reflects more than sarcolemmal fragility. Recurrent myofiber injury sustains sterile inflammation through damage-associated innate immune signaling and downstream pathways including NF-κB and inflammasome activation. Persistent inflammation alters macrophage and fibro-adipogenic progenitor (FAP) behavior, promotes extracellular matrix remodeling, and creates a fibrotic niche that progressively limits effective repair. Regeneration is further compromised by both intrinsic muscle stem cell dysfunction and extrinsic constraints imposed by the remodeled microenvironment. This review integrates mechanistic and translational evidence linking these processes, with particular attention to FAP-centered stromal remodeling, immune–stem cell crosstalk, and tissue-specific differences between skeletal and cardiac muscle. We also evaluate emerging interventions targeting inflammatory priming, inflammasome activity, fibrotic remodeling, and regenerative competence while distinguishing established pathological mechanisms from predominantly preclinical therapeutic evidence. This perspective may help define stage-specific and niche-directed strategies that complement dystrophin restoration. Full article

Dolycoris baccarum (Hemiptera: Pentatomidae) is a cosmopolitan stink bug that causes substantial damage to several leguminous crops, including sesame. Herbivorous insect feeding can alter key physiological processes in plants, particularly photosynthesis and chlorophyll fluorescence, thereby affecting plant growth and productivity. This study investigated the effects of D. baccarum feeding on photosynthetic traits, including carbon assimilation rate (A) and stomatal conductance to water vapor (g_sw), as well as chlorophyll fluorescence signal in different sesame varieties using a photosynthesis system and a portable gas exchange and fluorescence analyzer. Preference of D. baccarum among sesame varieties was evaluated through a choice test using seed pods, and feeding damage was quantified using the acid fuchsin test. Feeding by D. baccarum significantly reduced photosynthetic performance and chlorophyll fluorescence signal in sesame plants. The insect exhibited clear varietal preferences, and feeding damage differed significantly among sesame varieties. A positive association was observed between feeding activity and damage intensity, whereas photosynthetic parameters (A and g_sw, and chlorophyll fluorescence signal) showed negative associations with yield following insect infestation. These findings indicate that D. baccarum feeding adversely affects both the physiological performance and productivity of sesame and highlight the potential for identifying resistant or tolerant sesame varieties for breeding programs. Further studies should examine the physical characteristics and nutrient composition of sesame seed pods and seeds to better understand the host selection behavior of D. baccarum and support the development of sesame varieties with improved resistance or tolerance to stink bug infestation. Full article

Efficient monitoring and recognition of excavator operational activities are critical for improving productivity, safety, and intelligent construction management. Traditional vision-based activity recognition systems remain sensitive to challenging construction site conditions, including occlusion, dust, variable illumination, and limited visibility. This paper presents an excavator activity recognition framework that, for the first time, applies Gramian Angular Field (GAF) encoding to multi-sensor excavator operational data, transforming them into spatial image representations and enabling deep convolutional neural networks (CNNs) to extract discriminative features. The proposed approach integrates synchronized excavator sensor signals including bucket positional coordinates, body orientation, fuel consumption, engine RPM, and joint angles to characterize operational behavior during four representative activities: digging, dumping, idle, and levelling. Unlike conventional sensor-based methods that directly process sequential time-series data, our GAF-based framework transforms operational signals into structured spatial representations that preserve temporal correlations while enabling effective feature learning through image-based deep learning architectures. Experimental results demonstrate that the proposed GAF-CNN-LSTM framework achieves 8.24 percentage points higher classification accuracy compared with LSTM networks, and 6.24 percentage points higher than 1D CNN baselines trained on the same sensor data. The method effectively captures discriminative operational signatures across multiple excavator activities in the collected dataset. This work bridges the limitations of both vision-based and traditional sensor-based approaches, providing a promising framework for excavator activity monitoring in construction and fleet management contexts, pending further validation under operational deployment conditions. Full article

Cardiac pacemaker and valve cells operate within a heterogeneous interstitial environment, but the identities and observable behaviors of many constituent cells remain incompletely defined. We integrated complementary imaging observations from a single experimental platform into an exploratory analysis of cultured cells isolated from the adult mouse sinoatrial node (SAN) and atrioventricular valves of S100B-EGFP reporter mice. Cellular morphology, endogenous NAD(P)H fluorescence, tetramethylrhodamine methyl ester (TMRM) fluorescence, correlative light and electron microscopy (CLEM), time-lapse imaging, transmission electron microscopy (TEM), and 5-ethynyl-2′-deoxyuridine (EdU) labeling were used to describe selected reporter-positive and reporter-negative cells. S100B-promoter-associated EGFP+ cells displayed heterogeneous forms, including cells with small bodies and long processes. In representative fields, EGFP+ cells showed greater NAD(P)H autofluorescence intensity and lower TMRM signal than adjacent EGFP− cells. CLEM related selected fluorescence phenotypes to ultrastructure in the same cells and documented mitochondrial, vesicular, nuclear, and membrane-associated profiles. One live sequence captured movement of a TMRM-positive, mitochondrion-like structure through a thin intercellular bridge toward an EGFP− cell, followed by bridge disassembly. Additional images showed mitochondrial and vesicular profiles near cell surfaces or in extracellularly situated regions, elongated NAD(P)H-bright structures, reporter-positive cytoplasmic fragments, nuclear size heterogeneity, a narrow connection between nuclear profiles, and unequal EdU labeling among nuclear fragments. These findings are descriptive and hypothesis-generating. They do not establish definitive cell identity, phenotype prevalence, active or selective secretion, tunneling-nanotube identity, mitochondrial metabolic competence, regulated nuclear remodeling, recipient-cell uptake, or physiological consequence. The study provides an integrated morphological framework and a set of candidate events for future quantitative investigation of S100B-associated cardiac interstitial-cell biology. Full article

Background: Problematic digital-media use and gaming disorder have emerged as important public health concerns, particularly among adolescents. Along with psychosocial factors, genetic susceptibility and neuroendocrine pathways may contribute to individual vulnerability to problematic digital-media use and gaming-related problems. Objectives: This review aims to summarize current evidence on the genetic polymorphisms and endocrine pathways associated with problematic digital-media use and gaming disorder, with emphasis on dopaminergic reward mechanisms and hypothalamic–pituitary–adrenal (HPA) axis regulation. Methods: A structured literature search was conducted in PubMed/MEDLINE, Scopus, and Google Scholar for studies published up to 30 June 2026. Relevant human studies addressing genetic, epigenetic, endocrine, or related neurobiological mechanisms of problematic digital-media use and gaming-related behaviors were identified and narratively synthesized according to the principal biological pathways examined. Results: The available evidence suggests that genetic variants affecting dopaminergic neurotransmission (DRD2, COMT, DAT1, and DRD4), stress regulation (CRHR1, FKBP5), serotonergic signaling (SLC6A4), sleep-circadian regulation and other pathways (CHRNA4, NTRK3) may increase susceptibility to problematic Internet and gaming use. Excessive digital media exposure has been associated with alterations in dopaminergic signaling, stress and sleep–circadian regulation, including changes in hypothalamic–pituitary–adrenal axis activity, cortisol secretion and melatonin secretion. These alterations may be related to differences in reward processing, sleep, and emotional regulation, as well as to compulsive digital behaviors. Bidirectional associations have also been reported between problematic digital-media use and depression, anxiety disorders, and attention-deficit/hyperactivity disorder (ADHD). Conclusions: Genetic and endocrine factors may contribute to a broader biopsychosocial vulnerability to problematic digital-media use and gaming-related problems. However, current evidence is insufficient to establish individual genetic variants or endocrine measures as clinically actionable biomarkers. Future research should prioritize large, adequately powered longitudinal studies with harmonized phenotypes, objective behavioral measures, genome-wide approaches, and independent replication to clarify the clinical relevance of biological markers and their potential role in risk prediction and personalized intervention. Full article

Background: Executive function is a critical component of cognitive health that supports goal-directed behavior, attentional control, and cognitive flexibility. These processes are sensitive to aging and lifestyle factors and may be modifiable through nutritional strategies. Hericium Erinaceus (lion’s mane; LM) is an edible mushroom containing bioactive compounds shown to stimulate neurotrophins such as brain-derived neurotrophic factor (BDNF), suggesting potential cognitive benefits. Methods: This pilot, proof-of-concept study examined the effects of four weeks of daily LM supplementation on executive function and circulating plasma BDNF in healthy adults. Participants (n = 10) were healthy adults (18–55 years) and consumed 2 g/day of a certified organic LM supplement composed of mycelial biomass and fruiting body for 28 days. Executive function was assessed using the NIH Toolbox Flanker Inhibitory Control and Attention Test and the dimensional change card sort (DCCS) test. Plasma BDNF concentrations were measured using a quantitative ELISA. Paired-samples t-tests were used to assess changes from pre to post, and effect sizes were calculated using Cohen’s d. Results: No statistically significant differences were observed in executive function or plasma BDNF following supplementation (p > 0.05). However, small-to-moderate effect sizes were observed for flanker inhibitory control and attention (Cohen’s d = 0.48), DCCS (Cohen’s d = 0.25) and plasma BDNF (Cohen’s d = 0.31), suggesting modest improvements from baseline. Conclusions: The results of this small, pilot, proof-of-concept study demonstrate, within the confines of this study, that LM is safe and well tolerated in healthy adults. The small-to-moderate effects on cognitive function combined with the modest increase in plasma BDNF suggest a promising signal for LM supplementation that warrants further investigation in larger and longer-term randomized double-blind placebo-controlled studies. Full article

Intelligent sensor technologies now provide unprecedented multimodal access to human affective and cognitive processes, spanning physiological, ocular, facial, kinematic, ambient, and behavioral streams. Yet, despite dramatic advances in acquisition and deep representation learning, the pipeline from raw signals to psychologically meaningful, actionable understanding remains fragile. Deep-only architectures excel at pattern extraction but struggle with contextual reasoning, uncertainty communication, and human-facing explanation; symbolic-only frameworks resist noisy, high-dimensional streams. This perspective argues that the field’s next inflection point lies not in richer sensors alone but in the interpretive layer that turns signals into states. We propose an integrative view in which neuro-symbolic fusion couples continuous sensor evidence to psychologically grounded symbolic primitives, LLMs act as auditable semantic reasoners rather than opaque classifiers, and explainability is treated as a design constraint rather than a post hoc addition. Synthesizing a decade of work on affective computing, fuzzy learner modeling, neuro-adaptive multimodal interaction, and explainable human–AI collaboration, we motivate a research agenda organized around grounded representations, uncertainty-calibrated LLM reasoning, and reflexive explanation. The aim is a class of sensing systems whose intelligence is measured not by accuracy alone but by the quality of the states they help humans understand. Full article

Tungsten inert gas (TIG) welding of AA5083 aluminum alloy is sensitive to heat input and shielding conditions, which can lead to defects such as porosity and burn-through that compromise weld quality. Conventional non-destructive testing methods are generally performed after welding and therefore provide limited capability for real-time process monitoring. This study presents an acoustic emission (AE)-based frequency-domain framework for real-time classification of weld conditions during TIG welding of AA5083. Acoustic signals were acquired at a sampling rate of 10 kHz and transformed into frequency-domain representations using the Fast Fourier Transform (FFT). Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and ensemble-learning classifiers were evaluated using the resulting spectral features. To address the high dimensionality of the FFT representation, Principal Component Analysis (PCA) was incorporated into the modeling workflow, with the retained components determined from the model-development data. Five-fold cross-validation and grid-search-based hyperparameter tuning were used during model development, while an independent test set was reserved for final evaluation. The best-performing classifiers achieved a classification accuracy of approximately 0.99 for distinguishing good-weld, porosity, and burn-through conditions. Weld-condition labels were independently validated using visual inspection and radiographic testing. FFT and Short-Time Fourier Transform (STFT) analyses were used to characterize global and time-dependent spectral behavior, respectively, while SHapley Additive exPlanations (SHAP) were employed to identify frequency regions contributing to model predictions. A comparative analysis with time-domain statistical features further demonstrated the stronger discriminative capability of the FFT-derived representation under the investigated conditions. The proposed framework combines high classification performance with interpretable frequency-domain information and provides a basis for in-process weld-condition monitoring and quality control of TIG-welded AA5083. Full article

Mitochondria play a key role in maintaining cellular homeostasis through energy metabolism, ROS regulation, calcium handling, and apoptosis control. Mitochondrial dysfunction contributes to neurological vulnerability in the aging brain, where neuronal energy demand, axonal transport, synaptic activity, and neurovascular repair depend on mitochondrial integrity. Animal studies in chronic brain disease and acute brain-injury models have examined acupuncture-related changes in phenotypic outcomes and mitochondrial measures, including behavioral, physiological, and molecular readouts. Their findings span mitochondrial ultrastructure, biogenesis, dynamics, mitophagy, and downstream signaling. Clinical studies have examined acupuncture in Parkinson’s disease, stroke, and Alzheimer’s disease. Animal studies report acupuncture-related changes in mitochondrial structure and indices of quality control alongside improvements in neurological, behavioral, or injury-related outcomes. These mitochondrial changes may influence oxidative stress, calcium regulation, apoptosis, mitochondrial transport, and intercellular mitochondrial support. Clinical studies mainly report symptom and functional outcomes, while a small number of imaging studies also describe changes in cerebral glucose metabolism or neural activity. These preliminary clinical and metabolic signals may inform further investigation into whether mitochondrial processes are associated with clinical responses to acupuncture. Current experimental work also has important limitations: most studies focus on a limited set of mitochondrial quality-control markers, while the mitochondrial unfolded protein response, axonal mitochondrial trafficking, and dynamic mitochondrial transfer are rarely assessed. Future clinical studies should pair neurological outcomes with feasible measures of mitochondrial function and energy metabolism. Tracking these measures over time may help clarify how changes in mitochondrial function and energy metabolism during acupuncture treatment relate to functional recovery. Full article