Search Results (551)

Although the role of the mammalian hippocampus in representing locomotor speed has been widely investigated, how the avian hippocampus represents continuous flight speed under free-flight conditions in the outdoor environment remains unclear. In this study, we used homing pigeons as a model system and synchronously recorded hippocampal formation (HF) local field potentials (LFPs), global positioning system (GPS) trajectories, and inertial measurement unit (IMU) data during natural homing flights. We aimed to determine whether and how the avian HF encodes flight speed. Flight-speed-related neural features were extracted from both frequency-domain and time-domain signals, including the 50–70 Hz power spectral density (PSD) ratio and theta-demodulated amplitude (DAmp). We then constructed models for discrete flight-speed state decoding and continuous flight-speed prediction. The results showed that the 50–70 Hz PSD ratio in the HF was significantly negatively correlated with flight speed, whereas DAmp was significantly positively correlated with flight speed. Both features exhibited consistent speed-related trends across different spatial release sites. Support vector machine (SVM)-based classification showed that PSD, DAmp, and their combined features could effectively decode four flight-speed states, including non-flight, low-speed, medium-speed, and high-speed states, with the combined features achieving the best performance. Further Gaussian process regression (GPR) analysis demonstrated that the combined features predicted continuous flight speed more accurately than either single feature. These findings provide evidence that the avian hippocampal formation encodes continuous flight speed during natural navigation through the complementary integration of frequency-domain and time-domain features, extending the known role of the avian hippocampal formation from static spatial mapping to dynamic self-motion representation. Full article

GPR35 is a member of the G-protein-coupled receptor family and existing studies have shown that its expression may be negatively correlated with fat deposition in mice. However, the exact role of GPR35 in goat intramuscular preadipocytes’ adipogenic differentiation remains unknown. This study is the first to demonstrate that GPR35 inhibits the maturation and differentiation of goat intramuscular preadipocytes via activating the cAMP/PKA signaling pathway under in vitro culture conditions. The functional experimental results showed that GPR35 overexpression suppressed the adipogenic differentiation, lipid synthesis, and proliferation capacity of goat intramuscular preadipocytes, and concurrently downregulated the expression of adipogenic differentiation marker genes and cell proliferation marker genes. Conversely, GPR35 knockdown promoted lipid accumulation, reduced intracellular cAMP levels, and ultimately enhanced the differentiation and proliferation capacity of intramuscular preadipocytes. Treatment with the cAMP inhibitor SQ22536 and PKA phosphorylation inhibitor H89 successfully reversed the phenotypic changes induced by GPR35 overexpression, including abnormal lipid droplet morphology, decreased triglyceride (TG) content, and inhibition of the expression of genes related to adipocyte differentiation/proliferation/metabolism (such as CDK2, PCNA, and HSL). These results confirm that GPR35 mainly regulates intramuscular fat deposition in goats through the cAMP/PKA pathway, providing a new target for elucidating the molecular mechanism of lipid metabolism and goat molecular breeding. Full article

Vegetation above the soil surface can have a considerable influence on ground-penetrating radar (GPR) measurements, especially when shallow buried objects are to be detected. Plant water content, biomass, and the structural arrangement of leaves and stems can attenuate, scatter, or obscure reflections from both the soil surface and buried targets. This study therefore examines how different vegetation types and soil moisture conditions affect the GPR response of a shallow buried reference target under controlled laboratory conditions. For the analysis, a GPR operating in a down-looking configuration is used, which is moved across the study area on an equidistant grid. The evaluation is based on the analysis of multiple intensity pixels and heuristic statistics to characterise the radar reflections. The Normalised Difference Vegetation Index (NDVI) is used to describe the vegetation; this index approximates, in particular, the water content of the plants, as well as the relationship between biomass and dry matter content. The analysis reveals a relationship between water content, biomass volume, and the signal-to-clutter ratio (SCR) in relation to the detectability of targets. The condition of the vegetation significantly influences radar target reflection and thus the detectability of subsurface targets. In particular, higher water content in vegetation correlates with increased scattering within the vegetation layer, thereby preventing ground reflection and target reflection. Full article

The Raša Fault is a prominent seismically active strike-slip fault within the Dinaric Fault System in SW Slovenia, seismotectonically estimated to be capable of producing earthquakes up to Mw = 7.4. Since the surface exposure of fault-related markers is discontinuous, and the near-surface expression of deformation is poorly constrained, there is a need to improve the detection of fault-related features in complex sedimentary environments. In such settings, signal attenuation, complex stratigraphy, and irregular fault-zone geometries often obscure subtle deformation features, limiting the interpretability of standard 2-D geophysical profiles. A pseudo 3-D Ground-Penetrating Radar (GPR) survey, along with complementary Electrical Resistivity Tomography (ERT) surveys and reprocessing of LiDAR (light detection and ranging) data to obtain high-resolution Digital Elevation Models (DEMs), was conducted in selected environments dominated by low-resistivity karstic deposits and highly heterogeneous fluvial sediments to assess and improve the capability to detect and characterize subtle shallow deformations associated with the Raša Fault. Tectonic geomorphological mapping facilitated the recognition of potentially active fault traces and the identification of Quaternary sedimentary and erosional features, where recent deformations are usually preserved and can be dated in further paleoseismological investigations. The analysis of dense GPR data and complementary ERT profiles enabled us to clearly image the fault deformation pattern and obtain quantitative information about the subsurface, showing details of faulting and related deformation structures not evident at the surface. Furthermore, it enabled the detection of fault zone complexity, revealing it as an irregular and laterally changing area with sediment infillings, rather than a single vertical discontinuity. The complexity of faulting in the near surface depends on many factors, including the competence and age of the faulted material, as well as the local geomorphology. This study has demonstrated the applicability of pseudo 3-D GPR surveying, combined with ERT profiles, for subsurface mapping of active strike-slip faults in karstic and fluvial sedimentary environments. The methodology can be recommended in particular for rapid and cost-effective investigation of sites with subtle surface evidence of active faulting in order to determine near-surface fault splaying. Full article

The gut–immune–metabolic axis has emerged as a central regulator of human health, with growing evidence indicating that microbiota-derived metabolites improve gut microbial ecology, enhance intestinal barrier integrity, reduce systemic inflammation, and maintain metabolic homeostasis. This review synthesizes current mechanistic and clinical evidence on the role of postbiotics in regulating intestinal barrier integrity, immune responses, oxidative stress, and metabolic–endocrine homeostasis. The literature was identified through the PubMed/MEDLINE, Scopus, and Web of Science, integrating evidence from experimental, mechanistic, animal and clinical studies on the therapeutic potential of postbiotics to modulate the gut–immune–metabolic axis. Preclinical studies suggest that postbiotics may enhance epithelial barrier function by improving tight junction integrity through multiple pathways such as PI3K/Akt signaling, stimulating mucin-2 (MUC2) production, and reducing intestinal permeability. They modulate immune responses through interactions with Toll-like receptors, nucleotide-binding oligomerization domain receptors, and G-protein-coupled receptors (GPR41/43), influencing key signaling pathways, including NF-κB and Nrf2, and altering cytokine profiles, such as IL-10, TNF-α, and IFN-γ. Similarly, preclinical investigations have demonstrated that short-chain fatty acids (SCFAs) and other microbial metabolites may improve insulin sensitivity, regulate hepatic gluconeogenesis, stimulate glucagon-like peptide 1 (GLP-1) secretion, and modulate lipid metabolism through the FXR and TGR5 signaling pathways. Emerging human studies suggest potential benefits of postbiotics in regulating gut, immune, and metabolic health; nevertheless, clinical evidence remains limited and is influenced by variability in postbiotic composition, dosage, formulation, and metabolite profiles. Therefore, standardized production approaches and well-designed large-scale randomized clinical trials are required to confirm therapeutic efficacy and establish evidence-based applications of postbiotics. Full article

The accurate and nondestructive quantification of subgrade moisture content is a core demand for highway construction quality control and long-term performance maintenance. In order to study the response relationship between subgrade moisture content and ground-penetrating radar (GPR) signal eigenvalues, this study constructs the volumetric moisture content–dielectric constant relationship of Guangxi high-plasticity clay and carries out gprMax forward numerical simulations. Fourteen radar signal eigenvalues are extracted from preprocessed signals via time-domain waveform analysis, Hilbert transform analysis, and power spectral density analysis. Seven key eigenvalues are screened out through Pearson correlation coefficient-based dimensionality reduction. Three machine learning algorithms—artificial neural network (ANN), random forest (RF), and light gradient boosting machine (LightGBM)—are adopted to optimize the subgrade moisture-content inversion model, which is finally validated through indoor model box tests and field subgrade tests. The results show that: (1) The linear fitting formula is the most suitable for describing the volumetric moisture content–dielectric constant relationship of Guangxi clay, with a coefficient of determination (R²) of 0.979 and a mean absolute error (MAE) of 0.31. (2) The feature matrix after dimensionality reduction effectively alleviates the degradation of model generalization ability and interpretability. (3) The LightGBM model achieves the highest prediction accuracy for clay volumetric moisture content, with an R² of 0.99926 and an MAE of 0.172%. (4) For gravimetric moisture-content inversion, the maximum relative error is 1.6% in the indoor model box test and 1.7% in the field test, both within the 2% tolerance of engineering requirements. This study verifies the feasibility of the proposed subgrade moisture-content identification method based on GPR signal eigenvalues. The proposed method provides an efficient technical path for the large-area and nondestructive detection of subgrade moisture and has promising application prospects in subgrade construction quality acceptance, daily maintenance monitoring and hidden disease early warning. Full article

While standard dietary frameworks and public health guidelines predominantly evaluate honey based on its high free-sugar content to regulate caloric intake, its comprehensive role as a complex and dense biological signaling matrix is often less characterized. This review integrates raw chemical profiling with physiological bioaccessibility to evaluate the effect of the floral source on the functional quality of monofloral honeys. Through a structured literature-based comparative synthesis, the specific enzymatic behaviors, gastrointestinal metabolic fates, and chemical biomarkers of four representative monofloral honeys—Manuka, Chestnut, Acacia, and Argan—were systematically mapped and contrasted. Honey bioactives undergo extensive metabolic transformation during gastrointestinal transit, actively communicating with the mucosal environment rather than acting as a static nutrient solution The synthesized literature highlights distinct botanical-specific mechanisms: Manuka honey matrices exhibit well-characterized topical antimicrobial kinetics, while data on Chestnut honey suggest a potential role in cellular redox homeostasis via the Nrf2 pathway and a highly specific, though preclinical, signaling axis through kynurenic acid–GPR35 interactions. Furthermore, in vitro profiles of Acacia honey support its potential to modulate digestive carbohydrate enzymes, providing a plausible framework for its lower postprandial glycemic responses. Conversely, Argan honey bioactivity remains restricted to preliminary baseline antioxidant screenings. We also note that sophisticated sugar adulteration and climate change create critical “phytochemical drift,” endangering the therapeutic consistency of honey. In summary, the identification of honey as a “functional interactome” offers a strong scientific foundation for evidence-based dietetics. The targeted use of verified medical-grade monofloral honeys must be supported by advanced authenticity testing, metabolomics, and in vivo pharmacokinetics in future clinical dietetic interventions. Full article

Ground penetrating radar (GPR) is widely used for non−destructive evaluation of pavement structures, yet the automatic detection of internal cracks remains challenging due to variations in crack geometry, infilling materials, and antenna configurations that significantly alter signal responses. Most existing machine learning approaches focus on improving detection accuracy but pay limited attention to the inherent symmetries and invariances present in GPR data. This study proposes a symmetry−informed learning framework for robust pavement crack detection across different antenna orientations and material conditions. Laboratory concrete slabs containing cracks with varying widths (2–30 mm) and depths (10–110 mm) were constructed and tested under five representative crack states: air−filled, dry sand, fresh water, saturated sand, and bitumen−filled. GPR data were collected using a 2.3 GHz system under perpendicular and parallel broadside antenna orientations to capture rotational variability. A deep learning model was developed with symmetry−aware training strategies that exploit rotational consistency and material−invariant feature learning. Comparative experiments were conducted to evaluate detection performance and cross−condition generalization. Results demonstrate that incorporating symmetry improves model robustness and generalization across unseen orientations and filling conditions. The proposed framework highlights the importance of symmetry−informed learning for reliable AI−driven GPR inspection of pavement infrastructure. Full article

Ground-penetrating radar (GPR) was utilized for subsurface soil investigation in the Yellow River Delta, aiming to provide a scientific basis for the remediation performance of saline soils. The study particularly focuses on the red clay layer, a typical and characteristic soil horizon in this region. GPR antennas with central frequencies of 400 MHz and 900 MHz were adopted to investigate shallow soils within 1 m of the ground surface across three experimental plots (pits, undisturbed soils, and tilled soils) and 18 scattered measurement sites, followed by systematic analysis and interpretation of the acquired GPR profiles. During data acquisition, reasonable survey lines were deployed across the patchy bare areas of cultivated lands covering the experimental plots and measurement points to collect raw GPR data. Meanwhile, subsurface soil data were collected via test pits and borehole sampling along the survey lines. Raw GPR data were further preprocessed and postprocessed to characterize soil horizons and interpret subsurface stratigraphic structures. Finally, the correlations between the relative dielectric permittivity, reflection coefficient, and reflected wave amplitude of each soil layer were systematically analyzed. The results demonstrate that the 400 MHz antenna enables effective identification of soil layers within 1 m depth, while the 900 MHz antenna provides high-resolution detection for soil layers above 0.5 m. The red clay layer presents a distinct strong-amplitude reflection on GPR profiles, and the average relative dielectric permittivity of soils across the study area reaches 30.57. GPR profiles reveal that soil horizons with an absolute reflection coefficient greater than 0.01 yield detectable continuous reflection signals and allow uninterrupted stratigraphic interpretation. An empirical formula was established to calculate soil relative dielectric permittivity from soil moisture content, with a correlation coefficient of 0.9173. However, this formula ignores the influences of soil salinity and other trace soil elements. This study realizes rapid and accurate characterization of the depth and thickness of shallow soil layers, providing technical support for soil remediation of saline–alkali land in the Yellow River Delta. The findings also provide a valuable reference for evaluating the remediation effects, optimizing arable land utilization, preventing and mitigating soil salinization risks, and promoting the sustainable economic development of the study area. Full article

Background: Short-chain fatty acids (SCFAs) are metabolites produced by the gut microbiota after fiber fermentation. Some SCFAs, such as acetate, propionate, and butyrate, have been recognized as essential for human health, especially for the brain; however, the cellular mechanisms activated by these molecules in food-intake-related organs, such as the olfactory bulb, remain unclear. Objective: This study evaluates the effects of acetic acid (AA) and sodium butyrate on the physiology of Ca²⁺ metabolism in olfactory bulb cells (OBCs). Methods: Primary OBC cultures of the postnatal rat (P7-21) were made, and Ca²⁺ imaging experiments were performed to record the intracellular Ca²⁺ responses (iCaR) elicited by the application of AA and sodium butyrate (100 nM–1 mM). Immunocytochemical analyses were performed to identify GFAP⁺ cells and GPR41 and GPR43 receptors in OBCs. Endpoint RT-PCR analyses were made to identify GPR41 and GPR43 transcripts in OBCs. Results: Fewer than 10% of the OBCs tested responded to the application of AA and sodium butyrate with iCaR. Pharmacological studies (20 µM 2-aminoethyl diphenylborinate (2-APB); 10 nM GLPG0974, 120 nM AR420626) showed that iCaR were independent of inositol triphosphate (IP₃)-signaling pathways and that OBCs expressed both GPR41 and GPR43 receptors. Endpoint RT-PCR studies performed in both olfactory bulbs and primary OBC cultures confirmed the expression of the GPR41 receptor. Conclusions: This study shows that AA and butyrate induce intracellular Ca²⁺ responses activated by GPR41 and GPR43 receptors in a discrete cellular population of the rat olfactory bulb. Full article

Background/Objectives: Fluoride (F) is widely used in public water fluoridation to prevent dental caries, and an optimal level of F has been linked to improved glucose metabolism in animal models. Per- and polyfluoroalkyl substances (PFAS), including fluorotelomer alcohols (FTOHs), are persistent environmental contaminants with potential effects on pancreatic function. Methods: This in vitro and in vivo study investigated the effects of 8:2 FTOH and F (NaF) on pancreatic β-cells, focusing on Ca²⁺ homeostasis, insulin secretion, and the GPR40 pathway. Results: Results showed that 8:2 FTOH alters Ca²⁺ influx in a dose-dependent, biphasic manner, enhancing it at low doses and inhibiting it at high doses, while F increased Ca²⁺ signaling at high doses. High-dose 8:2 FTOH also downregulated GPR40 protein in βTC-6 pancreatic cells and modulated pathways related to lipid metabolism, endoplasmic reticulum stress, and insulin regulation in the mouse pancreas by proteomic analyses (in vivo). Conclusions: These findings exploratory indicate that both PFAS and F can impact β-cell function through complex mechanisms, potentially affecting Ca²⁺ homeostasis. This work highlights the hormesis effect of F and provides novel insights into the pancreatic effects of environmentally relevant PFAS exposures, emphasizing the need for further mechanistic studies at low, human-relevant doses. Full article

Soil salinization severely restricts ecosystem stability and the sustainable development of agricultural productivity. However, current understanding of the spectral–salinity quantitative relationships under the influence of surface cracking still remains limited. To address this gap, this study collected hyperspectral reflectance data (350–2500 nm) from salt-affected soil in both cracked and uncracked surface conditions across the Songnen Plain, and applied fractional-order differentiation (FOD) processing with orders ranging from 0 to 2 and a step size of 0.1. Based on this, 14 types of FOD spectral indices were constructed, incorporating one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) structures. For each spectral index, the optimal fractional order and corresponding band combinations were first selected through Pearson correlation analysis for pH and EC under both surface conditions; subsequently, feature selection was performed using XGBoost-SHAP explainable analysis among the 14 optimal indices across different dimensions. Furthermore, the predictive performance of four modeling methods, including partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression (SVR), and random forest regression (RFR), was evaluated. The results showed that FOD transformations significantly enhanced correlations with EC and pH compared to raw reflectance. All prediction models demonstrated higher prediction accuracy under cracked surface conditions than uncracked surface conditions, indicating that desiccation cracks positively modulate spectral signals to enhance salinity information expression. Across different surface states, model performance generally followed the ranking: PLSR > GPR > SVR > RFR, with PLSR achieving the best predictions for EC and pH under cracked surfaces (R² of 0.88 and 0.76, RMSE of 0.29 dS/m and 0.35). This study not only deepens the understanding of fractional-order spectral response mechanisms in saline–alkali soils but also provides methodological support for regional monitoring of soil salinization. Full article

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for the safe and reliable operation of battery management systems. While point prediction methods have been extensively studied, principled uncertainty quantification (UQ) remains underexplored, particularly in the early-cycle regime where degradation signals are subtle and training data are scarce. This paper presents a systematic evaluation of ten UQ configurations for early-cycle battery RUL prediction using the MIT-Stanford dataset of 124 cells. A composite-kernel Gaussian process regression model combining a radial basis function and a white noise kernel, denoted GPR-CK(RBF + W), is used as the core predictor. We compare Bayesian native UQ, split conformal prediction, jackknife+, bootstrap resampling, and a Conformalized Adaptive Intervals (CAI) method across a Primary test set and a distribution-shifted Secondary test set collected one year later. On the Primary test set, the composite-kernel GPR variants achieve full (100%) prediction-interval coverage at the 95% nominal level, while the proposed CAI calibration yields the sharpest coverage-valid intervals. Under a one-year distribution shift, GPR-CK(RBF + W) empirically retains 97.5% coverage, which we report as empirical robustness rather than a guaranteed coverage level. A leave-one-out calibration factor (δ = 1.39 with the white noise kernel versus δ = 3.43 without it) isolates explicit noise modeling as the decisive factor for calibration. Feature dimensionality analysis further reveals a three-phase sensitivity pattern, identifying three features as the optimal operating point balancing predictive accuracy and UQ quality. Full article

Lactate was traditionally considered a metabolic by-product of anaerobic glycolysis, mainly associated with tissue hypoxia and muscle fatigue. However, increasing evidence has redefined lactate as a multifunctional metabolic intermediate and signaling molecule involved in exercise-induced systemic adaptations. During physical activity, circulating lactate levels rise markedly when skeletal muscle production exceeds systemic clearance, allowing lactate to act as an exercise-responsive metabolite, or exerkine, and as a mediator of cardiometabolic adaptation. In the cardiovascular system, lactate serves not only as an efficient substrate for myocardial energy production but also as a regulator of vascular tone, endothelial function, angiogenesis, inflammation, and cardiac remodeling. These effects occur through receptor-dependent and receptor-independent mechanisms, including activation of hydroxycarboxylic acid receptor 1 (HCAR1/GPR81), modulation of intracellular redox balance, and histone or non-histone protein lactylation. This review summarizes current evidence on lactate in cardiovascular physiology and disease, focusing on myocardial lactate metabolism, HCAR1/GPR81 signaling, protein lactylation, extracellular vesicle communication, gut microbiota interactions, and therapeutic implications in heart failure, atherosclerosis, and diabetic cardiomyopathy. Although lactate is also produced under resting, postprandial, and pathological conditions, exercise is characterized by the amplitude and kinetics of lactatemia, coordinated hormonal and hemodynamic responses, and transient high-concentration signaling. These features support exercise-derived lactate as a context-dependent cardiovascular exerkine. Full article

Maintaining reliable communication while ensuring collision-free motion is a central challenge for mobile relay robots operating in indoor disaster environments, where abrupt non-line-of-sight (NLOS) degradation and narrow structural bottlenecks can severely disrupt multi-hop connectivity. To address this problem, this paper proposes a Gaussian Process-Driven Adaptive Tube Model Predictive Control (GP-ATMPC) framework for communication-preserving relay navigation. Gaussian process regression (GPR) is used to construct a probabilistic spatial radio map from sparse received signal strength indicator (RSSI) measurements, providing both the predicted channel mean and its uncertainty over unvisited regions. Motion uncertainty is represented by an adaptive ellipsoidal error tube whose radius varies with translational motion, angular motion, and localization uncertainty. Based on this tube model, both obstacle and communication constraints are tightened over the full closed-loop state tube via a tube-tightened lower confidence bound (LCB) that jointly accounts for radio-prediction and motion-tracking uncertainty. Across two indoor disaster environments and 50 Monte Carlo runs each, the proposed method attains the highest connectivity satisfaction rate among controllers that preserve a safe motion margin, with significantly fewer end-to-end connectivity violations than nominal and heuristic adaptive-margin MPC by a paired Wilcoxon test, while maintaining millisecond-level online solve times. A reactive connectivity-first baseline reaches slightly higher raw connectivity but at three to four times the near-collision rate and without feasibility or stability guarantees. The radio-prediction layer is further validated in a higher-fidelity Gazebo environment and on real indoor RSSI measurements, where it reconstructs the measured channel with a mean absolute error of about 2.1 dB. These results indicate that coupling spatial radio prediction with adaptive tube-based robust control provides an effective framework for resilient communication-aware relay navigation in degraded indoor environments. Full article