Search Results (2,404)

In mammals, the suprachiasmatic nucleus (SCN), located in the hypothalamus serves as the master biological clock and precisely regulates circadian rhythms through a complex network structure. As a central pacemaker, the SCN has two primary functions: one is to synchronize the daily rhythms in physiological and behavioral activities; the other is to entrain the endogenous rhythms to the external light–dark cycle. A deep understanding of the SCN network structure is crucial for elucidating the functional mechanisms of the biological clock system. In this review, we systematically summarized the impact of the SCN network structure on functional regulation under light stimulation based on mathematical models. Studies have shown that the coupling between the light-sensitive subgroups in the left and right nuclei of the SCN can enhance the entrainment ability. As an integrated network structure, the SCN may have the characteristics of the small-world network or the scale-free network, as these properties are more conducive to the realization of functions. Additionally, the higher-order coupling mechanism within the SCN can effectively expand the entrainment range. These theoretical research results offer new insights into the relationship between the SCN network and functions and provide crucial theoretical guidance and validation directions for subsequent experimental research. Full article

Citrus diseases severely impact fruit yield and quality. To facilitate in-field, non-destructive, and rapid detection of citrus leaf physiological and pathological conditions, this study proposes a classification method for citrus leaf physiological and pathological statuses that integrates visible/near-infrared multispectral technology with deep learning. First, a handheld spectrometer was employed to acquire spectral images of five sample categories—Healthy, Huanglongbing, Yellow Vein Disease, Magnesium Deficiency and Manganese Deficiency. Mean spectral data were extracted from regions of interest within the 350–2500 nm wavelength range, and various preprocessing techniques were evaluated. The Standard Normal Variate (SNV) transformation, which demonstrated optimal performance, was selected for data preprocessing. Next, we innovatively introduced an adaptive spectral positional encoding mechanism into the Transformer framework. A lightweight, learnable network dynamically optimizes positional biases, yielding the ASTransformer (Adaptive Spectral Transformer) model, which more effectively captures complex dependencies among spectral features and identifies critical wavelength bands, thereby significantly enhancing the model’s adaptive representation of discriminative bands. Finally, the preprocessed spectra were fed into three deep learning architectures (1D-CNN, 1D-ResNet, and ASTransformer) for comparative evaluation. The results indicate that ASTransformer achieves the best classification performance: an overall accuracy of 97.7%, underscoring its excellent global classification capability; a Macro Average of 97.5%, reflecting balanced performance across categories; a Weighted Average of 97.8%, indicating superior performance in classes with larger sample sizes; an average precision of 97.5%, demonstrating high predictive accuracy; an average recall of 97.7%, showing effective detection of most affected samples; and an average F1-score of 97.6%, confirming a well-balanced trade-off between precision and recall. Furthermore, interpretability analysis via Integrated Gradients quantitatively assesses the contribution of each wavelength to the classification decisions. These findings validate the feasibility of combining a handheld spectrometer with the ASTransformer model for effective citrus leaf physiological and pathological detection, enabling efficient classification and feature visualization, and offer a valuable reference for disease detection of physiological and pathological conditions in other fruit crops. Full article

Background/Objectives: Obesity and type 2 diabetes mellitus (T2DM) profoundly disrupt lipid metabolism within local microenvironments of skeletal muscle and its associated connective tissues, including adipose tissue, bone, and fascia. However, the role of local communication between skeletal muscle and its proximal connective tissues in propagating metabolic dysfunction is incompletely understood. This narrative review synthesizes current evidence on these local metabolic interactions, highlighting novel insights and existing gaps. Methods: We conducted a comprehensive literature analysis of primary research published in the last decade, sourced from PubMed, Web of Science, and ScienceDirect. Studies were selected for relevance to skeletal muscle, adipose tissue, fascia, and bone lipid metabolism in the context of obesity and T2DM, with emphasis on molecular, cellular, and paracrine mechanisms of local crosstalk. Findings were organized into thematic sections addressing physiological regulation, pathological remodeling, and inter-organ signaling pathways. Results: Our synthesis reveals that local lipid dysregulation in obesity and T2DM involves altered fatty acid transporter dynamics, mitochondrial overload, fibro-adipogenic remodeling, and compartment-specific adipose tissue dysfunction. Crosstalk via myokines, adipokines, osteokines, bioactive lipids, and exosomal miRNAs integrates metabolic responses across these tissues, amplifying insulin resistance and lipotoxic stress. Emerging evidence highlights the underappreciated roles of fascia and marrow adipocytes in regional lipid handling. Conclusions: Collectively, these insights underscore the pivotal role of inter-tissue crosstalk among skeletal muscle, adipose tissue, bone, and fascia in orchestrating lipid-induced insulin resistance, and highlight the need for integrative strategies that target this multicompartmental network to mitigate metabolic dysfunction in obesity and T2DM. Full article

Background/Objectives: Skin temperature has been considered a physiological variable associated with the risk of pressure injuries. This systematic review analyzed the available evidence regarding the relationship between skin temperature and the development, progression, or prevention of pressure injuries in humans. Methods: A systematic search was conducted in the PubMed, Scopus, and Dimensions databases, including studies published between 2013 and 2023 in English or Spanish. PRISMA 2020 guidelines and EQUATOR network checklists (CONSORT, STROBE, CARE) were applied to assess methodological quality. Risk of bias was evaluated using RoB 2, ROBINS-I, ROBINS-E, and JBI tools. Results: The reviewed studies reported thermal variations in tissues subjected to sustained pressure, some of which preceded the appearance of visible clinical signs of tissue damage. However, methodological heterogeneity, lack of standardized thermal thresholds, and variability in measurement conditions limited the generalizability of the findings. Conclusions: Skin temperature may be associated with relevant pathophysiological mechanisms in the development of pressure injuries. Its measurement could complement traditional clinical tools, such as the Braden scale, enhancing early risk identification. More robust, multicenter, and standardized studies are needed to validate its clinical applicability. Full article

NIN-like proteins (NLPs) are conserved, plant-specific transcription factors that play crucial roles in the nitrate signaling response, plant growth and development, and abiotic stress responses. However, their functions have not been explored in sweet potato. In this study, we identified 7 NLPs in cultivated hexaploid sweet potato (Ipomoea batatas, 2n = 6x = 90), 9 NLPs in the diploid relative Ipomoea trifida (2n = 2x = 30), and 12 NLPs in Ipomoea triloba (2n = 2x = 30) via genome structure analysis and phylogenetic characterization, respectively. The protein physiological properties, chromosome localization, phylogenetic relationships, syntenic analysis maps, gene structure, promoter cis-acting regulatory elements, and protein interaction networks were systematically investigated to explore the possible roles of homologous NLPs in the nitrate signaling response, growth and development, and abiotic stress responses in sweet potato. The expression profiles of the identified NLPs in different tissues and treatments revealed tissue specificity and various expression patterns in sweet potato and its two diploid relatives, supporting differences in the evolutionary trajectories of the hexaploid sweet potato. These results are a critical first step in understanding the functions of sweet potato NLPs and offer more candidate genes for improving nitrogen use efficiency and increasing yield in cultivated sweet potato. Full article

Driving fatigued is a prevalent issue frequently contributing to traffic accidents, prompting the development of automated fatigue detection methods based on various data sources, particularly reliable physiological signals. However, challenges in accuracy, robustness, and practicality persist, especially for cross-subject detection. Multimodal data fusion can enhance the effective estimation of driver fatigue. In this work, we leverage the advantages of multimodal signals to propose a novel Multimodal Attention Network (TMU-Net) for driver fatigue detection, achieving precise fatigue assessment by integrating electroencephalogram (EEG) and electrooculogram (EOG) signals. The core innovation of TMU-Net lies in its unimodal feature extraction module, which combines causal convolution, ConvSparseAttention, and Transformer encoders to effectively capture spatiotemporal features, and a multimodal fusion module that employs cross-modal attention and uncertainty-weighted gating to dynamically integrate complementary information. By incorporating uncertainty quantification, TMU-Net significantly enhances robustness to noise and individual variability. Experimental validation on the SEED-VIG dataset demonstrates TMU-Net’s superior performance stability across 23 subjects in cross-subject testing, effectively leveraging the complementary strengths of EEG (2 Hz full-band and five-band features) and EOG signals for high-precision fatigue detection. Furthermore, attention heatmap visualization reveals the dynamic interaction mechanisms between EEG and EOG signals, confirming the physiological rationality of TMU-Net’s feature fusion strategy. Practical challenges and future research directions for fatigue detection methods are also discussed. Full article

Short-term synaptic plasticity (STSP) and short-term neuronal dynamics (STND) are fundamental properties of neural circuits, essential for information processing and brain function. Emerging evidence suggests that biological sex may influence these properties, yet sex-related differences in STSP and STND remain underexplored. This study investigates sex-specific differences in short-term synaptic plasticity (STSP) and neuronal dynamics (STND) along the dorsoventral axis of the rat hippocampus. Our findings reveal that both STSP and STND exhibit significant variation between female and male subjects. These differences are particularly pronounced in the ventral hippocampus, a region associated with affective and motivational processes. Given the role of short-term activity-dependent neuronal phenomena in modulating information processing and network function, these findings suggest potential functional implications for sex-specific cognitive and emotional regulation. The results highlight the importance of incorporating sex as a biological variable in studies of hippocampal physiology and its relation to behavior. Full article

Nanoparticles have found widespread application across diverse fields, including agriculture and animal husbandry. However, a persistent challenge in laboratory-based studies involving nanoparticle exposure is the limited availability of experimental data, which constrains the robustness and generalizability of findings. This study presents a comprehensive analysis of the impact of zinc oxide nanoparticles (ZnO NPs) in feed on elemental homeostasis in male Wistar rats. Using correlation-based network analysis, a correlation graph weight value of 15.44 and a newly proposed weighted importance score of 1.319 were calculated, indicating that a dose of 3.1 mg/kg represents an optimal balance between efficacy and physiological stability. To address the issue of limited sample size, synthetic data generation was performed using generative adversarial networks, enabling data augmentation while preserving the statistical characteristics of the original dataset. Machine learning models based on fully connected neural networks and kernel ridge regression, enhanced with a custom loss function, were developed and evaluated. These models demonstrated strong predictive performance across a ZnO NP concentration range of 1–150 mg/kg, accurately capturing the dependencies of essential element, protein, and enzyme levels in blood on nanoparticle dosage. Notably, the presence of toxic elements and some other elements at ultra-low concentrations exhibited non-random patterns, suggesting potential systemic responses or early indicators of nanoparticle-induced perturbations and probable inability of synthetic data to capture the true dynamics. The integration of machine learning with synthetic data expansion provides a promising approach for analyzing complex biological responses in data-scarce experimental settings, contributing to the safer and more effective application of nanoparticles in animal nutrition. Full article

Background: Madecassoside is widely utilized in wound healing due to its multiple physiological activities. However, its limited bioavailability and solubility hinder its clinical application. Enzymatic hydrolysis has been employed to enhance the bioavailability and bioactivity of natural products, but its potential for modifying madecassoside remains unexplored. Methods: In this study, we prepared MA1G and MA2G through enzymatic hydrolysis, inspired by the metabolic processes of madecassoside. Network pharmacology was employed to investigate the mechanisms of these madecassoside derivatives (MDs) in wound healing, and molecular docking was performed to evaluate their binding affinities. Transdermal permeation studies, scratch assays, and antioxidant and anti-inflammatory tests were conducted to characterize the biological properties and activities of MDs. Results: Network pharmacology identified TLR4, NF-κB, and STAT3 as key targets for wound healing, and the MDs inhibited the expression of these proteins in vitro. Additionally, the results demonstrated that MDs exhibited robust reactive oxygen species (ROS) scavenging activity (43.05–147.50% reduction) and significantly enhanced cell migration (36.76–77.28% increase). Notably, the biomodified MA2G showed superior transdermal permeability and biological activities. Conclusions: This paper represents the first report directly comparing the biological activities of the parent compound (madecassoside) and its metabolites while simultaneously proposing a novel therapeutic strategy for wound healing. Full article

This study introduces a promising authentication framework utilizing brain–computer interface (BCI) technology to enhance both security protocols and user experience. A key strength of this approach lies in its reliance on objective, physiological signals—specifically, brainwave patterns—which are inherently difficult to replicate or forge, thereby providing a robust foundation for secure authentication. The authentication system was developed and implemented in four sequential stages: signal acquisition, preprocessing, feature extraction, and classification. Objective feature extraction methods, including Fisher’s Linear Discriminant (FLD) and Discrete Wavelet Transform (DWT), were employed to isolate meaningful brainwave features. These features were then classified using advanced machine learning techniques, with Quadratic Discriminant Analysis (QDA) and Convolutional Neural Networks (CNN) achieving accuracy rates exceeding 99%. These results highlight the effectiveness of the proposed BCI-based system and underscore the value of objective, data-driven methodologies in developing secure and user-friendly authentication solutions. To further address usability and efficiency, the number of BCI channels was systematically reduced from 64 to 32, and then to 16, resulting in accuracy rates of 92.64% and 80.18%, respectively. This reduction streamlined the authentication process, demonstrating that objective methods can maintain high performance even with simplified hardware and pointing to future directions for practical, real-world implementation. Additionally, we developed a real-time application using our custom dataset, reaching 99.75% accuracy with a CNN model. Full article

Potassium (K) is a critical macronutrient for sugarcane (Saccharum spp.), playing a vital role in metabolic processes, sucrose accumulation, and yield formation. Herein, this study systematically evaluated the effects of potassium oxide (K₂O) application on sugarcane (cultivar YZ1696) growth at the seedling and tillering stages. Hydroponic experiments demonstrated that 6 mmol/L K₂O optimally promoted seedling growth, whereas field trials revealed that 150 kg/ha K₂O maximized growth rate, yield, and sucrose content. Sugarcane growth exhibited a biphasic response—stimulation followed by inhibition—with increasing K₂O dosage at both developmental stages. Transcriptomic profiling of sugarcane roots under low-potassium (K-deficient), optimal potassium, and high-potassium conditions identified 10,266 differentially expressed genes (DEGs), with the most pronounced transcriptional shifts occurring under K deficiency. Functional enrichment analysis identified DEGs associated with potassium transport, calcium signaling, and carbohydrate metabolism. Notably, potassium uptake was mediated by distinct mechanisms: Shaker family channels (AKT1, AKT2, SPIKE) and the TPK family member KCO1 were induced under optimal K supply, whereas HAK/KUP/KT transporters (HAK1/5/10/21/25) exhibited broad activation across K concentrations, underscoring their key role in K homeostasis. Furthermore, calcium signaling genes (e.g., CIPK23) displayed K-dependent expression patterns. Weighted gene co-expression network analysis identified key gene modules that correlated strongly with agronomic traits, including plant height, yield, and sucrose content. Optimal K conditions favored the expression of yield- and sucrose-associated genes, suggesting a molecular basis for K-mediated productivity enhancement. Our findings revealed the genetic and physiological mechanisms underlying K-dependent sugarcane improvement, providing actionable insights for precise potassium fertilization to maximize the yield and sugar content. Full article

Dendrobium officinale, an orchid of significant medicinal and ornamental value, exhibits poorly characterized hormonal regulation of flower bud differentiation. To address this knowledge gap, we employed an integrated multi-omics approach combining physiological, transcriptomic, metabolomic, and network analyses to elucidate the molecular mechanisms underlying the coordinated action of 6-Benzylaminopurine (6-BA) and Gibberellin A3 (GA₃) in this critical developmental process. Our key findings reveal that combined 6-BA and GA₃ treatment significantly enhances flower bud differentiation and induces stage-specific fluctuations in soluble sugar, protein, and starch levels. Transcriptomic profiling identified 11,994 differentially expressed genes (DEGs), with DEGs specific to the hormone-treated stage showing pronounced enrichment in plant hormone signal transduction and plant–pathogen interaction pathways. Metabolomic analysis uncovered 18 stage-specific differential metabolites (DAMs) during hormone treatment, including GA₃, 6-BA, and OPDA, whose accumulation dynamics were strongly correlated with the progression of differentiation. Weighted gene co-expression network analysis (WGCNA) pinpointed key hub genes within the yellow module, notably transcription factors from the C2H2, bZIP, and NAC families. Their interaction network demonstrated significant correlation with the transcriptional regulation of hormone-responsive genes. Significantly, this study establishes the first molecular framework for 6-BA and GA₃ regulation of flower bud differentiation in D. officinale. We demonstrate a metabolomic–transcriptomic coordination network driven by these hormones, where key hub genes form regulatory modules with transcription factors. Dynamic shifts in endogenous hormones reinforce the flowering signal. These findings provide crucial molecular targets for precision flowering control and molecular breeding strategies in orchids. Full article

Cardiotonic steroids (CTS), including the endogenous compound ouabain, modulate neuronal Na/K-ATPase (NKA) activity in a concentration-dependent manner, affecting neuronal survival and function. While high concentrations of ouabain are neurotoxic, endogenous levels of 0.1–1 nM exert neuroprotective effects and influence intracellular signaling. However, the effects of physiologically relevant ouabain concentrations on excitatory synaptic transmission remain unclear. In this study, we examined how 1 nM ouabain affects synaptic responses in rat hippocampal CA1 neurons. Using whole-cell patch-clamp recordings of evoked excitatory postsynaptic currents (EPSCs) and extracellular recordings of field excitatory postsynaptic potentials (fEPSPs), we found that ouabain enhances excitatory synaptic transmission, increasing EPSC amplitude and fEPSP slope by 35–50%. This effect was independent of NMDA receptor (NMDAR) activity. Ouabain reduced the magnitude of NMDAR-dependent long-term potentiation (LTP), but still augmented fEPSPs when applied after LTP induction. This implies separate additive mechanisms. These observations exhibit that ouabain, at concentrations corresponding to endogenous levels, facilitates basal excitatory synaptic transmission while partially suppressing LTP. We propose that ouabain exerts dual modulatory effects in hippocampal networks via distinct synaptic mechanisms. Full article

Aluminum (Al) toxicity in acidic soils severely limits the productivity of Chinese fir (Cunninghamia lanceolata) plantations. Despite being a crucial timber species in southern China, the regulatory mechanisms underlying phenolic accumulation and Al tolerance pathways under Al stress in Chinese fir remain unidentified. In this study, 5-month-old Chinese fir seedlings were treated with an exogenous phenolic synthesis inhibitor (AIP) and precursor (MJ) to establish the following groups: CK, AIP, MJ, Al, Al+AIP, and Al+MJ. Physiological and biochemical indicator analyses, transcriptome analysis, and protein interaction network predictions were conducted. The findings revealed that phenolic compounds enhance Al tolerance in Chinese fir through two mechanisms: (1) regulation of active oxygen homeostasis (elevating SOD and POD activities, promoting AsA and GSH accumulation, and augmenting total antioxidant capacity); and (2) modulation of cell wall characteristics (increasing pectin content and pectinase activity, and facilitating Al sequestration in the cell wall). Moreover, MJ was found to synergistically enhance these processes, while AIP impeded them. Genes associated with antioxidant enzymes, secondary metabolite synthesis, and cell wall modification were implicated in the regulatory mechanisms. This study provides a theoretical foundation for elucidating the adaptation of Chinese fir to Al toxicity in acidic soil environments, offers insights for enhancing Chinese fir productivity in acidic soils, and presents a novel target for breeding trees with stress resistance. Full article

In the field of cognitive workload assessment for aerospace training, existing methods exhibit significant limitations in unimodal feature extraction and in leveraging complementary synergy among multimodal signals, while current fusion paradigms struggle to effectively capture nonlinear dynamic coupling characteristics across modalities. This study proposes DST-Net (Cross-Modal Downsampling Transformer Network), which synergistically integrates pilots’ multimodal physiological signals (electromyography, electrooculography, electrodermal activity) with flight dynamics data through an Anti-Aliasing and Average Pooling LSTM (AAL-LSTM) data fusion strategy combined with cross-modal attention mechanisms. Evaluation on the “CogPilot” dataset for flight task difficulty prediction demonstrates that AAL-LSTM achieves substantial performance improvements over existing approaches (AUC = 0.97, F1 Score = 94.55). Given the dataset’s frequent sensor data missingness, the study further enhances simulated flight experiments. By incorporating eye-tracking features via cross-modal attention mechanisms, the upgraded DST-Net framework achieves even higher performance (AUC = 0.998, F1 Score = 97.95) and reduces the root mean square error (RMSE) of cumulative flight error prediction to 1750. These advancements provide critical support for safety-critical aviation training systems. Full article