Search Results (9,014)

The Sugars Will Eventually be Exported Transporters (SWEET) family plays a crucial role in the carbohydrate distribution, phloem loading, and stress response of plants, yet the evolutionary characteristics and functional diversification of SWEET genes in the endangered timber species Phoebe bournei (Hemsl.) Yen C. Yang remain largely unexplored. In this study, 21 PbSWEET genes were identified and classified into four subfamilies (A-D). Subfamily A exhibited a unique lineage expansion, mainly driven by tandem and segmental duplications. The nonsynonymous-to-synonymous substitution ratio (Ka/Ks) values of all duplicate gene pairs were all less than 1, indicating a strong selective suppression effect; consistent with this evolutionary constraint, the majority of PbSWEET proteins harbor the conserved Medicago truncatula Nodulin 3/saliva (MtN3_slv) domain, with only a few exceptions lacking a complete version. Promoter and hormone response analyses revealed that under abscisic acid (ABA) stress, PbSWEET4 exhibited an immediate burst, whereas PbSWEET10 showed a delayed burst. Physiological data indicated that soluble sugars may be more dominant osmolytes than proline (Pro), a pattern that points to a potential carbon-centric regulatory strategy. PbSWEET4 showed an early burst before sugar/oxidative peaks, suggesting a possible non-canonical signaling role, whereas PbSWEET10 exhibited a late increase coinciding with sugar/malondialdehyde (MDA) peaks, suggesting potential involvement in sugar redistribution. Under methyl jasmonate (MeJA) treatment, PbSWEET10 was rapidly induced, yet sugar accumulation occurred only at 24 h, a temporal decoupling that suggests a possible transcription–metabolism decoupling. Collectively, these correlative patterns point to a possible dual-wave transcriptional mechanism and nominate PbSWEET10 as a candidate for stress response, though these inferences require functional validation. Full article

Ascophyllum nodosum extracts (ANE) are widely used biostimulants associated with improvements in plant growth, productivity, nutrient acquisition, and abiotic stress tolerance. However, the molecular mechanisms linking extract composition to plant signaling and physiological responses remain incompletely resolved. ANE contains a complex mixture of bioactive constituents, including polysaccharides, osmolytes, phenolic compounds, and phytohormone-like molecules. Their composition varies according to biomass source, environmental conditions, and extraction methodology, contributing to variability in biological activity. Current evidence suggests that ANE functions mainly as a signaling modulator rather than a direct nutrient source. ANE treatment has been associated with early cellular responses, including cytosolic Ca²⁺ influx, reactive oxygen species (ROS) generation, and mitogen-activated protein kinase (MAPK)-associated signaling events. However, many proposed mechanisms remain unresolved, and a considerable proportion of the available mechanistic evidence originates from studies using purified ANE-derived polysaccharides or related elicitor systems. ANE-associated responses include modulation of nutrient transport, primary metabolism, hormonal regulation, transcriptional reprogramming, and stress-responsive pathways, contributing to improved root development, nutrient acquisition, and defense-related responses. Nevertheless, limited knowledge of receptor-mediated perception mechanisms, signaling hierarchies, and extract-dependent variability continues to constrain mechanistic understanding and reproducibility. Future research should prioritize receptor identification, bioassay-guided fractionation, integrated multi-omics approaches, and improved standardization of extraction and formulation procedures. These advances will be essential for establishing robust mechanistic models and supporting the development of evidence-based ANE biostimulants for sustainable crop production. Full article

Plants must continuously balance the trade-offs between growth and defense, a constraint that is exacerbated by biotic and abiotic stresses, particularly when they occur together. Ethylene (ET) serves as a central, integrative regulatory node controlling this by linking developmental programs to stress-responsive signaling networks. Advances at the molecular and systems levels have revealed that ET mediates the redistribution of metabolic resources via coordinated regulation of its synthesis, perception, and downstream signaling. The ETR (Ethylene Receptor)-CTR1 (Constitutive Triple Response 1)-EIN2 (Ethylene Insensitive 2)-EIN3(Ethylene Insensitive 3) signaling module lies at the core of this network, integrating multiple hormonal pathways. Through dynamic crosstalk with jasmonic acid (JA), salicylic acid (SA), abscisic acid (ABA), auxin (AUX), and gibberellins (GA), ET enables the fine-tuned coordination of growth inhibition, immune activation, and stress acclimation in response to environmental fluctuations. Processes such as induced systemic resistance, programmed cell death, and architectural plasticity further reinforce this regulatory framework, with ethylene-responsive transcription factors, including ERFs (ethylene responsive factor gene family) and WRKYs, acting as critical convergence points. Emerging insights into ACC (1-aminocyclopropane-1-carboxylic acid) -dependent signaling, chromatin remodeling, and tissue-specific regulation expand the functional scope of ET beyond traditional hormone paradigms. At the same time, the ability of pathogens to manipulate ET signaling underscores its dual role in both promoting immunity and facilitating susceptibility. By integrating molecular, physiological, and ecological perspectives, this review highlights ET as a central coordinator of plant stress resilience and growth optimization, providing a unifying framework for understanding how plants adapt to complex and dynamic environments. Full article

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

The skin serves as the body’s first line of defense against environmental threats, acting as a barrier between external aggressors and internal systems. Current evidence regarding the roles of sulfur dioxide (SO₂) in biology and medicine is limited. Environmental pollutants, including SO₂, can increase the production of reactive oxygen species in the skin, leading to oxidative damage that may worsen various dermatological conditions. Endogenous SO₂, proposed as the fourth member of the gasotransmitter family, functions as a biological signaling molecule. It is generated in various human skin cells, including vascular smooth muscle cells, endothelial cells, mast cells, keratinocytes, macrophages, adipocytes, fibroblasts, dermal immune cell population, etc, where it performs multiple functions at physiologically relevant concentrations. Endogenous SO₂ plays a crucial role in regulating cell signaling and maintaining skin homeostasis through its antioxidant, anti-inflammatory, and cytoprotective effects. Abnormal generation and metabolism of SO₂ are linked to several critical processes in the skin, including vascular biology, immune response, cell proliferation, pigmentation, malignancy, protective barriers, senescence, and resistance to stress. This paper provides a narrative review of the significant roles of SO₂ in skin health and disease. A comprehensive understanding of the complex molecular effects and mechanisms mediated by SO₂ in human skin, along with the development of gas therapy, will be essential for translating fundamental research into clinical applications. Full article

Background/Objectives: Numerous studies have investigated the responses of the gastrointestinal tract to tastants, particularly in specialized enteroendocrine and other chemosensory cells. However, many of these investigations used various taste stimuli often at high concentrations or relied on immortalized cell lines or heterogeneous cell populations, which can limit their physiological relevance and reproducibility. To establish a stable, physiologically representative model system for consistently investigating gut epithelial responses to tastants, our study developed 3D murine intestinal organoids (MIOs). Methods: Murine intestinal organoids were generated from isolated intestinal crypts and cultured under defined conditions to maintain epithelial differentiation. Organoids were stimulated with selected nutrients and tastants, and downstream signaling responses were assessed using hormone secretion assays. Results: The 3D MIO culture system was successfully established, providing a robust in vitro platform for studying extraoral bitter sensing and release of the enteroendocrine hormone cholecystokinin. Moreover, 5 mM denatonium benzoate and 30 mM L-glutamic acid specifically induced cholecystokinin secretion in MIOs, whereas other bitter or non-bitter stimuli did not. Conclusions: Murine intestinal organoids provide a stable model for studying nutrient- and tastant-evoked signaling in the gut. This approach enables precise investigation of underlying mechanisms and may advance our understanding of gut chemosensation and metabolic regulation. Full article

As a precious indigenous goose resource in China, the Zhedong white goose occupies an essential position in the domestic goose industry. However, this breed spontaneously enters a prolonged non-laying period of over two months per year, which greatly limits egg production capacity and restricts the economic development of the goose industry. Herein, this study systematically compared serum physiological indices and serum and fecal metabolome, as well as fecal microbial communities, between laying and non-laying Zhedong white geese, aiming to reveal the key regulatory mechanisms underlying reproductive stage transition. Physiological analyses indicated that non-laying geese had higher serum levels of GnRH, PRL, APOA, and T-AOC, whereas the concentrations of LH, E₂, TNF-α, IL-1, and calcium were significantly reduced; FSH, PROG, and BA levels showed no significant differences between the two groups. Metabolomic analysis identified 277 upregulated and 403 downregulated DAMs in feces, and 386 DAMs in serum. The shared enriched pathways across serum and fecal samples encompassed arginine biosynthesis, histidine metabolism, and pantothenate and CoA biosynthesis, as well as steroid hormone biosynthesis. A total of 120 DAMs overlapped in two specimens, and the non-laying geese presented pronounced depletion of tryptophan-derived metabolites and steroid hormone-related metabolites. Metagenomic results showed no significant difference in gut microbial alpha diversity between groups, while their microbial community structures were clearly differentiated. A total of 774 upregulated and 854 downregulated microbial species were screened in non-laying geese, and these differential microbes were primarily enriched in pathways associated with reproductive hormone signaling, steroid biosynthesis and energy metabolism. Multi-omics correlation analysis verified close associations between differential microbes and reproductive-related metabolites. Certain probiotic strains, including Pediococcus pentosaceus and Lactococcus raffinolactis, were positively correlated with steroid hormones and tryptophan metabolites, and their abundances declined obviously in the non-laying stage. Collectively, this study elaborates the holistic changes in serum biochemistry, gut metabolome and microbiome in geese at different reproductive stages. The dysregulation of amino acid and steroid hormone metabolism, combined with the loss of beneficial intestinal microbes, jointly induces the non-laying phenotype. This study provides new perspectives for understanding the gut–reproductive axis and supplies promising biomarkers to improve the laying performance of geese. Full article

Carbon monoxide (CO) is a gasotransmitter generated by heme oxygenase (HO) isoforms during heme catabolism. The inducible HO-1 produces CO under conditions of redox imbalance, such as oxidative stress and inflammation. On the other hand, HO-2 constitutively generates CO, primarily during the physiological turnover of heme. Extensive evidence indicates that CO exerts autocrine effects by targeting hemoproteins, including soluble guanylyl cyclase, cyclooxygenase, and cytochromes. Furthermore, CO regulates many biological processes within the brain, including mitochondrial biogenesis, potassium channel activity, mitogen-activated protein kinase and phosphatidylinositol-3-kinase/Akt signaling. It also controls the activity of transcription factors, such as hypoxia-inducible factor-1 and peroxisome proliferator-activated receptor-γ. Through these mechanisms, CO modulates inflammatory gene expression, promotes anti-apoptotic signaling, and contributes to local stress responses. Conversely, CO produced in the hypothalamus inhibits the stress-induced release of corticotropin-releasing hormone and arginine vasopressin under pro-inflammatory conditions, resulting in reduced adrenocorticotropin hormone release and cortisol secretion from the anterior pituitary and adrenal cortex, respectively. Moreover, hypothalamic CO acts in a paracrine manner to modulate glucocorticoid release during psychological stress, including restraint or water deprivation. Together, these findings support the view that endogenous CO is a key modulator of the stress axis, exerting pleiotropic effects that integrate neuroendocrine, immune, and metabolic responses. Full article

Quantum machine learning (QML) provides a framework for benchmarking wearable biosignal classification relevant to stress detection. Motivated by the burden of stress-related conditions, this study compares three quantum classifiers with seven classical baselines using heart rate and respiration rate features as inputs under noise-free and noisy conditions. Uncertainty was quantified using Nadeau–Bengio-corrected confidence intervals and percentile bootstrap ($B=1000$). The variational quantum classifier (VQC) achieved an accuracy of $99.47\%/97.30\%$ (noise-free/noisy), the quantum support vector classifier (QSVC) achieved $99.90\%/99.37\%$, and PegasosQSVC achieved $99.80\%/99.70\%$. Additionally, under the assessed proof-of-concept conditions, statistical equivalence between the QSVC and the best-performing classical model was established at $\Delta =1$ pp; PegasosQSVC under noise achieved equivalence at $\Delta =2$ pp with accuracy degradation of less than $0.10$ pp. The time feature was identified as the primary separability driver in a post hoc classical ablation. Tree-based models were robust on physiological features alone. The surveyed methods provide a reproducible, noise-aware benchmark for wearable physiological signal classification; however, the reported high accuracies are based on a deliberately separable proof-of-concept benchmark and do not demonstrate clinical utility or a quantum advantage. Full article

Wireless Body Area Networks (WBANs) have become an essential component of modern Internet of Medical Things (IoMT) healthcare systems, enabling continuous monitoring of patient physiological signals through wearable sensors. Despite their advantages, WBAN environments remain highly prone to cyber threats, privacy breaches, and single points of failure. To address these risks, this work proposes a Hybrid Multi-Metric Anomaly Detection (HM-MAD) framework deployed on the NodeMCU-32S platform with BLE 5.0 connectivity for secure continuous glucose monitoring (CGM) data transmission. The detection model simultaneously analyses physiological signals, system-level parameters, and network-level communication metrics, enabling the reliable identification of multiple cyberattacks. The proposed system focuses on securing data transmission against relay attacks, where attackers induce communication delay without modifying payloads, potentially leading to false glucose readings, improper insulin dosage delivery, unauthorized control or denial-of-service. The Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) model classifies attack types including timing manipulation, replay attacks, power glitches, firmware tampering, and sensor spoofing. Experimental evaluation demonstrates that the proposed CNN + BiLSTM framework achieves 94.6% detection accuracy with an average inference latency of 15 ms, representing a 50% latency reduction compared to Transformer-based intrusion detection models (30 ms), while simultaneously reducing computational overhead by 28% in terms of floating-point operations and memory utilization. These results indicate that the HM-MAD framework provides an effective and scalable solution for protecting resource-constrained IoMT healthcare systems against emerging cyber threats. Full article

Smart contact lens sensors integrate biochemical sensing elements, flexible electronics, power modules, and wireless readout components onto optically transparent contact lens platforms, enabling non-invasive and potentially continuous analysis of tear-derived biomarkers and ocular physiological signals. This review focuses on the translation pathway from contact lens materials and fabrication methods to sensing mechanisms, tear biomarker interpretation, and clinical deployment. We synthesize recent progress in substrate engineering, manufacturing processes, power delivery, and representative sensing strategies for intraocular pressure, glucose, electrolytes, pH, cortisol, cholesterol, and inflammatory cytokines. Instead of treating these systems as isolated examples, we compare optical/colorimetric, electrochemical, field-effect transistor, microfluidic, and wireless resonant approaches in terms of sensitivity, response time, power/readout requirements, and clinical relevance. Finally, we discuss persistent barriers, including biocompatibility, interface stability, tear-sample variability, calibration, sterilization, regulatory validation, data privacy, and compatibility with commercial contact lens manufacturing. Full article

The physiological and metabolic responses of juvenile Chinese softshell turtles, Pelodiscus sinensis, exposed to single or double heatwaves (33 °C for 4 days) were investigated based on liver antioxidant assay and transcriptomic and metabolomic analyses. Heatwave exposure increased superoxide dismutase and catalase activities, but did not significantly alter malondialdehyde and reactive oxygen species levels, indicating enhanced antioxidant defense. Transcriptomic analysis revealed the differential expression of genes involved in carbohydrate metabolism, immune function, cardiovascular regulation, and signal transduction, with more pronounced alterations in single-heatwave-exposed turtles. Additionally, metabolic dysregulation in amino acids was revealed by significantly altered levels of some key amino acids and their derivatives. Compared with single-heatwave-exposed turtles, fewer differentially expressed genes and less metabolic disruptions in double-heatwave-exposed turtles probably indicated less physiological disorders under recurrent heat stress. Although P. sinensis can adopt physiological and metabolic adjustments to mitigate the adverse effects of short-term heatwaves, repeated heatwave exposure might still cause severe physiological consequences in aquatic turtles. These findings may have important implications for the conservation of freshwater turtle species under future climate change scenarios. Full article

Microplastics and nanoplastics (MNPLs) are increasingly recognized as dynamic vectors capable of transporting a wide range of environmental contaminants, as well as acting as physical particulates. Their small size, high surface reactivity and strong sorption capacity allow them to carry metals, pesticides, pharmaceuticals and endocrine-active compounds into biological systems. This narrative review examines how these particle-contaminant complexes influence oxidative stress, inflammatory signaling and endocrine function during early development. Relevant literature was identified through structured searches of PubMed, Scopus, Web of Science and Google Scholar, with a focus on the physicochemical properties of plastics, sorption mechanisms, gut barrier physiology and developmental toxicology. Early developmental stages are particularly sensitive, as immature mucus layers, permeable epithelial junctions and underdeveloped detoxification pathways facilitate the uptake and systemic distribution of MNPLs. Once internalized, these particles and their chemical cargo promote the generation of reactive oxygen species through redox-active contaminants, surface-catalysed reactions and mitochondrial dysfunction. The resulting oxidative imbalance activates stress-responsive pathways, including Nrf2–Keap1 signaling, and promotes lipid peroxidation, DNA damage and cellular dysfunction. MNPLs also stimulate inflammatory cascades by activating pattern-recognition receptors, altering cytokine profiles and disrupting epithelial homeostasis. These responses are intensified in the presence of sorbed pollutants, leading to sustained inflammatory states that can be particularly detrimental during organogenesis and immune maturation. Endocrine function is likewise affected, as MNPLs transport hormonally active chemicals and can interfere with hormone-responsive pathways through oxidative and inflammatory mechanisms. These interactions may disrupt thyroid signaling, metabolic regulation and the development of the reproductive axis, with potential long-term physiological consequences. Integrating evidence from polymer chemistry, contaminant behavior and developmental physiology, this review shows that MNPLs act as biologically active vectors that may increase oxidative, inflammatory and endocrine disturbances during early development. These findings highlight the importance of considering particle–contaminant interactions as a critical component of early-life risk assessment. Full article

D-box binding protein (DBP) is a high-amplitude proline- and acidic amino acid-rich basic leucine zipper (PAR bZIP) transcription factor that functions as a key circadian output regulator downstream of the core molecular clock. Although DBP is widely recognized as a clock-controlled gene, its broader role in converting circadian timing into tissue-specific physiological programs remains incompletely integrated. In this review, we synthesize current evidence supporting DBP as a context-dependent D-box-centered regulatory node. We first summarize the upstream mechanisms that establish rhythmic Dbp expression, including CLOCK–BMAL1-dependent transcription, promoter-level amplification, signaling-dependent modulation, and post-translational control of DBP stability. We then discuss how DBP, together with related PAR bZIP activators and the opposing repressor E4 promoter-binding protein 4/nuclear factor interleukin 3 regulated (E4BP4/NFIL3), regulates D-box-mediated transcriptional output. Finally, we examine tissue-selective DBP functions in hepatic metabolism, pancreatic β-cell secretory competence, neural and behavioral regulation, reproductive neuroendocrine timing, and T helper 9 (Th9)-associated antitumor immunity. Across these systems, DBP does not act as a universal circadian effector; rather, its function depends on chromatin accessibility, cofactor availability, competing transcription factors, and local signaling context. We also highlight the current limits of human translational evidence and propose that DBP-centered signatures may be useful for interpreting circadian output failure in disease. Overall, DBP provides a mechanistically informative framework for understanding how circadian time is transformed into organ-specific physiological function and pathological vulnerability. Full article

The Mega-Tsunami of March 2011 in eastern Japan caused severe damage in the coastal black pine (Pinus thunbergii) forests along the Pacific coast. To evaluate post-disturbance forest recovery, tree-ring samples from 30 trees at Ishinomaki coastal forest were analyzed for the period 2006–2020 using tree-ring indices and stable carbon isotope discrimination (Δ¹³C). The results revealed a strong decline in radial growth immediately after the tsunami, indicating severe growth suppression during the years 2011–2014. Simultaneously, Δ¹³C values decreased, suggesting reduced stomatal conductance and acute physiological stress associated with the initial salinity effect at the root zone. Although isotopic signals indicated gradual physiological adjustment in subsequent years, radial growth recovery occurred more slowly. Most trees returned to pre-disturbance growth levels within approximately 3–5 years and later exceeded pre-disturbance growth levels, likely due to reduced competition following the mortality of nearly 40% of trees after the tsunami. However, recovery trajectories differed markedly among individual trees, with some trees showing prolonged growth suppression beyond 6 years. This variability may reflect highly localized or tree-level factors, including intrinsic differences in individual resilience, while spatial autocorrelation analysis did not indicate significant clustering of recovery time across the stand. We conclude that black pine coastal forests show a high degree of resilience, showing physiological recovery in a short period (3–4 years). Although growth recovery took longer, initial tree mortality promoted the growth of the surviving trees beyond pre-disturbance values. Full article