Search Results (2,095)

Wet-snow avalanches constitute a major geomorphic hazard in southeastern Tibet, where warm, humid climatic conditions and a steep, high-relief terrain generate failure mechanisms that are distinct from those in cold, dry snow environments. This study investigates the snowpack conditions underlying avalanche initiation in this region by integrating UAV-based multi-sensor surveys with field validation. Ground-penetrating radar (GPR), infrared thermography, and optical imaging were employed to characterize snow depth, stratigraphy, liquid water content (LWC), snow water equivalent (SWE), and surface temperature across an inaccessible avalanche channel. Calibration at representative wet-snow sites established an appropriate LWC inversion model and clarified the dielectric properties of avalanche-prone snow. Results revealed SWE up to 1092.98 mm and LWC exceeding 13.8%, well above the critical thresholds for wet-snow instability, alongside near-isothermal profiles and weak bonding at the snow–ground interface. Stratigraphic and UAV-based observations consistently showed poorly bonded, water-saturated snow layers with ice lenses. These findings provide new insights into the hydro-thermal controls of wet-snow avalanche release under monsoonal influence and demonstrate the value of UAV-based surveys for advancing the monitoring and early warning of snow-related hazards in high-relief mountain systems. Full article

Background: Endocrine autoimmune diseases (AIDs) exhibit special polygenic characteristics in human leucocyte antigen (HLA) region. Current understanding of their association with lipid metabolism remains constrained by imprecise polygenic risk score (PRS) modeling. Advanced analytical approaches are needed to elucidate the association between genetic susceptibility and lipid metabolic dysregulation. Methods: We proposed a genetic distance-based clumping gPRS to account for linkage disequilibrium in the HLA region. gPRS and pathway gPRS were constructed for individuals diagnosed with type I diabetes (T1D), Graves’ disease (GD), Hashimoto thyroiditis (HT) and Addison’s disease (AD) in the UK Biobank, with sex considered as a stratification factor. Latent correlations between gPRS and phenotypes were explored using Kendall’s tau test, two-trait LD score regression (LDSC) and gene annotation. Results: Lipid metabolism served an important function through immune and inflammatory biomarkers across multiple traits. Males with low genetic risk tended to have lower high-density lipoprotein cholesterol level, while the correlation presented the opposite pattern in females. Increased genetic susceptibility to AIDs was associated with elevated levels of low-density lipoprotein cholesterol, triglycerides in low-density lipoprotein (LDL) and very-low-density lipoprotein (VLDL) across all traits. Moreover, levels of polyunsaturated fatty acids, including omega-3 and omega-6, decreased with higher PRS in males and females, while those of monounsaturated fatty acids exhibited an increasing trend. Conclusion: Our study constructed more precise polygenic risk scores of AIDs, highlighting inflammation-mediated lipid metabolism as a potential pathogenic mechanism in endocrine AIDs, offering valuable insights into shared etiology for future comprehensive investigations. Full article

Background/Objectives: Copper is an essential trace element for physiological processes related to reproduction, but its impact on the hypothalamic–pituitary–ovarian (HPOA) axis and its specific mechanism remain unclear. Methods: In vivo study: 21-day-old female Sprague Dawley (SD) rats were randomly assigned to five groups (n = 10 per group), with all groups fed a basal diet and supplemented with CuSO₄·5H₂O to achieve copper ion concentrations of 0, 15, 30, 45, or 60 mg/kg in the diet. During the second phase of proestrus, blood samples, hypothalamic tissues, pituitary tissues, and ovarian tissues were collected. In vitro study: Primary mixed hypothalamic neurons were isolated and cultured from fetal SD rats on embryonic day 17. After identification by NSE immunofluorescence staining, six copper ion concentration groups (0, 15.6, 31.2, 46.8, 62.4, and 78 μmol/L) were established. The optimal copper concentration for cell viability and GnRH secretion was screened using CCK-8 assay (Sangon, Shanghai, China) and ELISA (Mlbio, Shanghai, China). On this basis, the cells were treated with different concentrations of PKC agonist (PMA) and PKC inhibitor (chelerythrine). Cell viability was evaluated by CCK-8 assay, the expression level of PKC was detected by Western blot, and the optimal concentration with no obvious toxicity was selected for subsequent mechanism research. Results: Dietary copper dose-dependently regulated rat puberty onset; the 45 mg/kg copper group had the earliest onset, and showed significantly increased levels of reproduction-related hormones (GnRH, FSH, LH, E₂) in serum and HPOA axis. Hypothalamic transcriptomics revealed significantly enriched GnRH signaling pathways and GABAergic synaptic pathways. Mechanistically, this copper dose upregulated hypothalamic KISS-1, GPR54, and PKC (mRNA/protein), and downregulated GABA/GABA-R. Adding 46.8 μmol/L copper (as Cu²⁺, equivalent to optimal in vivo level) could activate the KISS-1/GPR54-GnRH system in hypothalamic neurons; regulating PKC activity could synchronously affect the expression of KISS-1, GPR54, GnRH, and GABA/GABA-R, with additional copper enhancing this effect in vitro experiments. Conclusions: This study demonstrates for the first time that dietary copper at 45 mg/kg promotes puberty onset in SD rats. The mechanism involves activation of the hypothalamic PKC pathway, which inhibits GABAergic neurotransmission while activating the KISS-1/GPR54-GnRH system, thereby enhancing HPOA axis activity and gonadotropin secretion. Full article

Ultra-high-performance concrete (UHPC) is recognized for its exceptional strength and durability. However, the adoption of UHPC frequently leads to higher material and environmental costs. Accurate prediction of compressive strength is crucial for optimizing material design and reducing construction costs. In this study, a dataset of 800 samples was compiled from published articles. Four models, including random forest (RF), Gaussian Process Regression (GPR), Gradient Boosting (GB) and Artificial Neural Network (ANN), were applied. Results show that ANN and GPR achieved the best accuracy and stability. GB also performed well with good adaptability. RF captured general trends but produced larger errors in the high-strength range. Feature importance analysis highlighted curing age and cement content as the most influential factors, with a combined contribution above 65%. The water-to-binder ratio also affected strength through matrix densification. Extended evaluation with regression error characteristic (REC) curves and environmental impact index (EII) revealed the balance between performance and environmental impact. Higher compressive strength often required higher energy, CO₂, and resource use. The range of 150–250 MPa showed a better balance between performance and sustainability. This study confirms the robustness of machine learning models for strength prediction and provides guidance for green and low-carbon ultra-high-performance concrete design. Full article

Adult neurogenesis is well established in canonical niches—the dentate gyrus and the subventricular zone, where aerobic exercise reliably enhances progenitor proliferation, survival, and synaptic integration via increased cerebral blood flow, neurotrophins (e.g., BDNF, IGF-1), neurotransmitter regulation, and reduced neuroinflammation. Nutraceuticals (e.g., polyphenols, omega-3, creatine, vitamins) further support neuroplasticity and neuronal survival through convergent trophic, anti-inflammatory, and metabolic pathways. By contrast, the hypothalamus, a metabolically pivotal, non-canonical niche, remains comparatively understudied. Here, we synthesize anatomical and functional features of hypothalamic neural stem cells, primarily tanycytes (α1, α2, β1, β2), which line the third ventricle and differentially contribute to neuronal activity regulation, metabolic signaling, and cerebrospinal fluid–portal vasculature coupling, thereby linking neurogenesis to endocrine control. Notably, tanycytes can form neurospheres in vitro, enabling mechanistic interrogation. Although evidence for adult hypothalamic neurogenesis in humans is debated due to methodological constraints, animal data suggest potential relevance to disorders characterized by neuronal loss, metabolic dysregulation, and impaired neuroendocrine function. We propose that an integrative framework is timely: exercise and diet likely interact in the hypothalamic niche through shared mediators (BDNF, IGF-1, CNTF, GPR40) and exercise-derived signals (e.g., lactate, IL-6) that may be complemented by defined nutraceuticals. Yet critical uncertainties persist, including the extent of bona fide hypothalamic neurogenesis, nucleus-specific responses (arcuate nucleus, paraventricular nucleus, ventromedial hypothalamic nucleus), and the mechanistic integration of lifestyle signals in this region. To address these gaps, we outline actionable priorities: (i) single-cell and lineage-tracing studies of tanycyte subtypes under distinct training modalities (aerobic, high-intensity interval training, resistance); (ii) combinatorial interventions pairing structured exercise with nutraceuticals to test synergy on progenitor dynamics and inflammation; and (iii) multi-omics and translational studies to identify biomarkers and establish clinical relevance. Clarifying these interactions will determine whether lifestyle and supplementation strategies can synergistically modulate hypothalamic neurogenesis and inform therapies for neurological, neuropsychiatric, and metabolic disorders. Full article

This paper compares three nondestructive methods used to detect and locate defects such as delaminations or voids in externally bonded fiber reinforced polymer (FRP) concrete structures: infrared thermography, ground-penetrating radar, and measurement of acoustic wave velocity. One of the main goals was to check whether it was possible to distinguish overlapping defects. For this purpose, eight concrete samples were made with a bonded carbon fiber reinforced polymer (CFRP) strip with the following dimensions 100 × 100 × 500 mm. Two samples had no defects, four had single defects varying in location (at the edge of the strip or in the centre) simulating delamination or voids in the concrete cover, and the remaining samples had overlapping defects. Both infrared thermography and acoustic wave velocity measurement methods allow the detection of defects/voids in the adhesive layer and a concrete defect (void in the concrete cover). However, ground penetration failed to detect defects in the adhesive layer. Only infrared thermography allows for the differentiation of overlapping defects. On the basis of the conducted research, the methodology, differences, advantages, and limitations of each method were described, along with recommendations based on the authors’ experience. Full article

With the increasing use of horizontal wells in oil and gas development, measurement-while-drilling (MWD) systems require higher magnetic azimuth accuracy to ensure precise trajectory control. This study proposes a three-stage magnetic azimuth error compensation method based on multi-station analysis (MSA). First, the OPTICS clustering algorithm is utilized to identify and remove noise points, and ellipse fitting is applied to suppress radial magnetic interference. Second, an improved MSA model incorporating wellbore trajectory constraints is developed to minimize axial interference and enhance correction stability. Finally, a Gaussian Process Regression (GPR) model, using accelerometer and magnetometer data as features, is introduced to model and compensate for residual nonlinear errors. Experimental validation under simulated complex magnetic conditions shows that OPTICS-based preprocessing significantly improves ellipse fitting and reduces hard magnetic interference. The improved MSA lowers the mean azimuth error to approximately 2.5°, while integrating GPR further decreases it to below 0.04°. The proposed GPR model achieves an R² of 0.99972 and an RMSE of 0.02928° on the test set, confirming its strong nonlinear compensation capability. Overall, the proposed framework effectively suppresses magnetic interference and enhances azimuth accuracy, providing a robust solution for high-precision MWD applications in horizontal wells. Full article

This study is devoted to investigating the asymmetric effects of geopolitical risks (GPRs) on Vietnam’ exports during the period from January 2010 to December 2024. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing model, the study documented that in the short-run, GPRs have asymmetric effects on Vietnam’s exports. Specifically, negative changes in GPRs have a significantly negative influence on the exports while positive changes in the GPRs have no significant effects on exports. In the long-run, the same effects of GPRs on exports are also found from the NARDL model. Specifically, negative changes in GPRs have a significantly adverse effect on exports, while positive changes in GPRs have no significant influence on exports in the long-run. Moreover, the empirical findings reveal that, in the long-run, the real exchange rate (RER) has a significantly positive impact on exports, suggesting that the depreciation of the VND (Vietnamese Dong) boosts Vietnam’s exports. Finally, the findings obtained from the error correction model show that 34.82 percent of the divergence from the long-run equilibrium caused by a shock in month n will be corrected and adjusted back toward equilibrium in month n + 1. Full article

Submesoscale processes, characterized by strong vertical velocities that generate sea surface temperature (SST) fronts as well as O(1) Rossby number (Ro), are critical to ocean mixing and biogeochemical transport, yet their observation is hampered by cost and spatial limitations. Hence, this study proposes an adaptive sampling framework for unmanned surface vehicles (USVs) that integrates Gaussian process regression (GPR) with submesoscale physical characteristics for efficient, targeted sampling. Three composite-kernel GPR models are developed to predict SST, zonal velocity U, and meridional velocity V, providing predictive fields to support adaptive path planning. A robust coupled gradient indicator (CGI) is further introduced to identify SST frontal zones, where the maximum CGI values are used to select candidate waypoints. Connecting these waypoints yields adaptive paths aligned with frontal structures, while a Ro threshold (0.5–2) automatically triggers spiral-intensive sampling to collect more useful data. Simulation results show that the planned paths effectively capture SST gradient and submesoscale dynamics. The final environment reconstruction achieved the desired accuracy after model retraining, and deployment analysis informs optimal platform deployment. Overall, the proposed framework couples environmental prediction, adaptive path planning, and intelligent sampling, offering an effective strategy for advancing the observation of submesoscale ocean processes. Full article

Ground Penetrating Radar (GPR) is a powerful tool for imaging shallow stratigraphic and structural features. This study shows that it is particularly effective also in detecting near-surface evidence of active faulting. In the Southern Apennines (Italy), one of the most seismically active regions of the Mediterranean area, the shallow expression of active faults is often poorly constrained due to limited or ambiguous surface evidence. Low-frequency GPR profiles were acquired in the Calore River Valley (Campania Region), an area historically affected by large earthquakes and characterized by debated seismogenic sources. The surveys employed multiple antenna frequencies (30, 60, and 80 MHz) and both horizontal and vertical acquisition geometries, enabling penetration depths ranging from ~5 m to ~50 m. The acquired GPR profiles, integrated with high-precision georeferencing, were able to reveal the presence of shallow steeply dipping active normal faults striking E–W to ENE–WSW, here named the Postiglione Fault System. Therefore, this study highlights the methodological potential of low-frequency GPR for investigating active faults in carbonate substratum and fine-to-coarse-grained sedimentary units and thus contributing to refining the seismotectonic framework and improving seismic hazard assessment of seismically active areas such as the Southern Apennines. Full article

In Korea, apple (Malus domestica) is one of the major fruit crops. The area occupied by apple orchards has exhibited a consistent upward trend, increasing from 26,398 hectares in 2003 to 33,313 hectares in 2024, and production reached 460,088 tons in 2024. However, stable apple production is currently threatened by global challenges such as climate change and the decline in rural labor, which hinders timely and efficient orchard management. Under these circumstances, developing automated and data-driven technologies capable of rapidly predicting and responding to apple growth conditions is essential to enhancing management efficiency and ensuring consistent fruit quality and yield stability. In this study, unmanned aerial vehicle (UAV)-based multispectral imagery was acquired and used to analyze time series data. Vegetation indices (VIs) derived from this imagery were then applied to build models predicting fruit diameter and length, which reflect apple size. A total of nine VIs were calculated from the acquired data and utilized as input variables for model development. Based on these variables, four machine learning models—Gaussian process regression (GPR), the K-Nearest Neighbors (KNNs), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB)—were developed to predict the fruit diameter and length. Both RFR and XGB showed tendencies of overfitting, and although the KNNs demonstrated relatively stable performance (diameter: R² ≥ 0.82, RMSE ≤ 7.61 mm, RE ≤ 12.53%; length: R² ≥ 0.76, RMSE ≤ 8.85 mm, RE ≤ 15.08%), this model failed to follow the prediction line consistently. In contrast, GPR maintained stable performance in both the validation and calibration stages (diameter: R² ≥ 0.79, RMSE ≤ 8.23 mm, RE ≤ 13.56%; length: R² ≥ 0.72, RMSE ≤ 9.48 mm, RE ≤ 16.16%) and followed the prediction line relatively well, indicating that it is the most suitable model for predicting apple size. These results demonstrate that UAV-based multispectral imagery, combined with machine learning techniques, is an effective tool for predicting the size of apples, and it is expected to contribute to orchard management at different growth stages and improve apple productivity in the future. Full article

The increasing integration of High-Voltage Direct Current (HVDC) systems and renewable energy challenges traditional grid strength assessment. This paper proposes a comprehensive framework that combines a composite strength index with an interpretable importance analysis to address this issue. First, a composite index is developed using the AHP-CRITIC method to fuse structural and fault withstand metrics. Then, to identify the factors influencing this index, SHapley Additive exPlanations (SHAP) is employed, accelerated by a high-fidelity Gaussian Process Regression (GPR) surrogate model that overcomes the computational burden of large-scale simulations. This GPR-SHAP approach provides both global parameter rankings and local, scenario-specific explanations, overcoming the limitations of conventional sensitivity analysis. Validated on a detailed model of the Southwest Power Grid in China, the framework successfully quantifies grid strength and pinpoints key vulnerabilities. Verification through a typical scenario demonstrates that implementing coordinated increases in both generation and load (each by 1000 MW) in the Chengdu area, as guided by local SHAP explanations, significantly improves the grid strength index from 33.73 to 47.61. It provides operators with a dependable tool to transition from experience-based practices to targeted, proactive stability management. Full article

The current pharmacological approach for the treatment of opioid use disorder (OUD), as a result of prescription misuse or illicit opioids, utilises opioid ligands that have either an agonist or antagonist profile. In this context, methadone and buprenorphine act as opioid agonists, whereas naltrexone functions as an opioid antagonist. To decrease the reinforcing effects of illicit opioids, higher doses of methadone and buprenorphine have been recommended, but this is associated with increased side effects. Therefore, several preclinical efforts have been carried out over the last decades to find drugs that act on receptors other than opioid receptors. A large body of preclinical evidence has shown the ability of N-methyl-D-aspartate receptor (NMDAR) antagonists like ketamine to treat opioid addiction behaviours in animals. Indeed, ketamine by itself is an addictive drug; thus, the treatment of OUD is still a matter to be solved. Growing data position glycine transporter 1 as a possible therapeutic target for the treatment of substance use disorder. This transporter regulates the reuptake of glycine, which can modulate the function of both NMDARs and GPR158, a metabotropic glycine receptor (mGlyR); thus, it is worth investigating in the management of OUD. To gain insight into the role of glycinergic transmission in OUD, alongside NMDAR-mediated glutamatergic transmission, dopaminergic and GABAergic transmission were also reviewed. Full article

Gut microbiota have a significant impact neurotransmitters, short-chain fatty acids (SCFAs), immune signaling molecules, and gut hormones. These signaling molecules interact with receptors on the gut wall, immune cells, or the enteric nervous system (ENS), and reach the central nervous system (CNS) via the Vagus nerve (VN). SCFAs interact with G protein-coupled receptors (GPCRs), Toll-like receptors (TLRs), and proliferator-activated receptors (PPARs), influencing inflammatory reactions, gut motility, nutrient absorption, hormone secretion, neurochemical signaling, and brain functions. Olfactory receptor OR51E1 influences blood pressure, vascular reactivity, and arterial stiffness. Activation of the brainstem nucleus tractus solitarius (NTS) by glucagon-like peptide 1 (GLP-1) influences mood, cognition, and gastrointestinal motility. Prolactin-releasing peptide (PrRP) binds to its receptor (PrRPR), suppressing food intake, and regulating stress, cardiovascular reactions, and circadian rhythms. In-depth studies on how gut microbiota control cognitive behavior, mood, and neuropsychiatric disorders are lacking. G protein receptor 119 (GPR119) suppresses appetite and may find an application in the treatment of type 2 diabetes and obesity. The binding of butyrate to nuclear factor kappa B (NF-κB) and proliferator-activated receptor γ (PPARγ) regulates the production of pro-and anti-inflammatory cytokines. This suppresses protein CD36, preventing the uptake of oxidized low-density lipoprotein (ox-LDL) and cardiovascular diseases (CVDs). This review focuses on a few prominent health conditions related to CVDs, i.e., metabolic syndrome (MetS), cancer, and brain functions. Information in this review is based on animal and preclinical studies published in repositories such as PubMed, the National Institutes of Health (NIH), NIH PubChem, ScienceDirect, MDPI, Frontiers, Cell Press, and the CAS Content Collection. Full article

To effectively control the deformation of high concrete face rockfill dams, this study proposes an intelligent prediction model (CO–SA–GPR) that integrates the Cheetah Optimizer (CO) and Simulated Annealing (SA) algorithm to optimize Gaussian Process Regression (GPR) for accurately estimating the compaction density of sandy gravel materials. Firstly, a theoretical derivation of the specification-similar gradation scaling method was conducted, clarifying the relationship between gradation parameters before and after scaling. On this basis, the CO and SA algorithms were employed to adaptively optimize the hyperparameters of the GPR model, obtaining the global optimal solution through intelligent search, thereby enhancing the model’s prediction accuracy and robustness. Application of the established model to actual engineering predictions shows that in estimating the maximum and minimum dry densities, the CO–SA–GPR model achieves R² values as high as 0.9752 and 0.9741, with RMSE as low as 0.0022 and 0.0028, respectively, significantly outperforming comparative models. The proposed model enables accurate prediction of compaction density from laboratory scaled-down tests to prototype gradations, providing a reliable new method for quality control in high rockfill dam construction and offering important theoretical and technical reference values for similar coarse-grained soil engineering. Full article