Search Results (3,357)

Seasonal human coronaviruses (HCoVs) continue to undergo adaptive evolution under structural and immune-mediated constraints. We investigated the molecular epidemiology and spike (S) protein structural variation of circulating coronaviruses in Riyadh, Saudi Arabia, during the 2025–2026 winter season, with particular emphasis on genotype persistence and glycosylation architecture in HCoV-OC43. Among 293 nasopharyngeal aspirates (NPAs) collected from hospitalized patients with acute respiratory illness, HCoV-OC43 was detected in 26 cases (8.87%), whereas other seasonal coronaviruses were not identified. Partial sequencing of the S gene revealed 97.84–98.23% nucleotide identity relative to the prototype strain VR-759, with amino acid substitutions distributed at discrete positions rather than within extended variable domains, indicating structural conservation. Phylogenetic reconstruction demonstrated that all Riyadh isolates clustered within genotype C, together with previously circulating local strains, supporting sustained endemic persistence and in situ evolution. In silico analysis of the S protein glycosylation landscape identified four invariant N-linked glycosylation motifs (N-X-S/T) at residues 46, 121, 134, and 190, reflecting strong structural constraints on glycan-dependent folding and antigenic configuration. A genotype-associated K68N substitution generated an additional N-glycosylation motif (68NGTD) in multiple Riyadh isolates, potentially modifying local glycan shielding without disrupting the overall glycosylation framework. The preservation of core glycosylation sites alongside selective motif acquisition suggests evolutionary fine-tuning of S surface topology rather than large-scale structural remodeling. Collectively, these findings indicate that genotype C persistence in Riyadh is accompanied by conserved S architecture and subtle glycosylation adjustments that may modulate immune recognition while maintaining structural integrity. Continued high-resolution molecular surveillance will be critical for defining the functional consequences of S microevolution in endemic HCoVs. Full article

Accurately quantifying carbon dioxide (CO₂) emissions from heavy-duty diesel trucks (HDTs) is crucial for developing effective transportation emission reduction strategies. In this study, we adopted a bottom–up approach and, in conjunction with the “International Vehicle Emissions” (IVE) model, constructed a high-resolution 1 × 1 km CO₂ emission inventory for the urban area of Kunming, China. Using data from 1.24 million track points collected from 5996 heavy-duty diesel trucks, we implemented a map matching algorithm based on a simplified hidden Markov model (HMM) to efficiently process large-scale GPS data. Furthermore, we improved upon traditional spatial allocation methods by dynamically integrating track point density with static road network density. The results indicate that although higher driving speeds correspond to lower CO₂ emission rates, heavy-duty diesel trucks typically operate within an observed speed range of 40–60 km/h, with an average emission factor of approximately 500 g/km. Vehicles compliant with the “National III” emission standards remain the primary source of CO₂ emissions in this region. Correlation analysis reveals a significant positive relationship (p < 0.01) between emissions from heavy-duty diesel trucks and both traffic volume and mileage. Notably, daytime vehicle restriction policies led to a temporal redistribution of emissions rather than a net reduction in emissions; this resulted in increased activity levels of heavy-duty diesel trucks at night, leading to a surge in nighttime emissions. In terms of spatial distribution, the “dual-density” allocation method proposed in this study more accurately captured emission hotspots, revealing that CO₂ emissions are primarily concentrated in the southeastern part of the city—a distribution pattern largely influenced by the city’s industrial layout. Full article

Assimilated carbon allocation to belowground processes may influence soil respiration (Rs). Because Rs includes autotrophic respiration (Ra) and heterotrophic respiration (Rh), different root and microbial responses complicate the separation of these effects. In a temperate deciduous broadleaf forest, we used sap flux density and estimated photosynthesis as indicators of plant activity. Total soil respiration and heterotrophic respiration were measured using automated chambers, and autotrophic respiration was estimated as Rs minus Rh. We examined the overall responses and time lags of respiration components. Ra showed positive relationships with sap flux density and estimated photosynthesis (R² = 0.37 and 0.30, p < 0.05), whereas Rh showed weaker relationships (R² = 0.20 and 0.15, p < 0.05). In lagged cross-correlation analyses using high-resolution data, Rs and Ra showed maximum responses 13 h after plant activity changes, whereas Rh showed no lag response (p > 0.05). These results suggest that associations with plant activity were clearer for Ra than Rh, and that the detected lagged response of soil respiration was more consistent with partitioned Ra than Rh. However, because Ra was estimated as Rs minus Rh, these patterns should be interpreted cautiously. Considering the responses and time lags of respiration components may improve ecosystem carbon cycling predictions. Full article

Background/Objectives: The impact of antimicrobial susceptibility testing methodology on the categorization and positioning of cefiderocol and aztreonam-avibactam against metallo-β-lactamase (MBL)-producing Gram-negative bacilli remains unclear. This study aimed to evaluate the in vitro activity of cefiderocol and aztreonam-avibactam against clinical MBL-producing isolates and to assess the agreement between different cefiderocol susceptibility testing methods. Methods: A total of 299 non-duplicate clinical MBL-producing Gram-negative isolates were collected from clinical samples between 2022 and 2025. Antimicrobial susceptibility testing was performed using broth microdilution, disc diffusion, and gradient strip diffusion according to European Committee on Antimicrobial Susceptibility Testing (EUCAST) criteria. Carbapenemase genes were identified by immunochromatography and multiplex PCR. Categorical agreement and error rates between cefiderocol testing methods were analyzed. Results:Klebsiella pneumoniae was the predominant species, mainly producing NDM alone or in combination with OXA-48-like carbapenemases. Aztreonam-avibactam demonstrated complete activity against all Enterobacterales isolates (262/262, 100%) and high activity against Pseudomonas spp. (33/37, 89%). Cefiderocol susceptibility among Enterobacterales varied markedly depending on the testing method. Disc diffusion yielded 14% susceptibility (37/262), which increased to 52% (136/262) after ATU resolution, whereas broth microdilution showed 85% susceptibility (224/262). This resulted in low categorical agreement (42%) and a high rate of major errors (58%), with no very major errors detected. Cefiderocol activity did not differ substantially across carbapenemase types and was highest against VIM-producing Pseudomonas spp. Conclusions: Aztreonam-avibactam showed consistent in vitro activity against MBL-producing Enterobacterales, whereas cefiderocol activity was strongly influenced by the susceptibility testing methodology. Disc diffusion substantially underestimated cefiderocol susceptibility compared with broth microdilution. These findings highlight the critical impact of testing methodology on cefiderocol categorization and support the therapeutic role of last-line agents in the management of MBL-producing Gram-negative infections, with direct implications for clinical microbiology laboratories and antimicrobial stewardship programs. Full article

Uniform circular arrays (UCAs) are widely used in underwater source localization due to their omnidirectional coverage. However, random sensor position errors caused by installation inaccuracies and environmental disturbances convert UCAs into non-uniform circular arrays (NCAs), severely degrading the performance of high-resolution direction of arrival (DOA) estimation algorithms. To address this issue, this paper proposes a robust DOA estimation method that integrates empirical mode decomposition (EMD) denoising with prior-weighted iterative least squares (PWLS) correction. The method first applies EMD to adaptively denoise received signals by selecting intrinsic mode functions based on a combined energy-correlation criterion. An initial DOA estimate is then obtained using the MUSIC algorithm. Finally, a PWLS correction algorithm leverages prior knowledge of deviated sensors to iteratively fit the circle center and gradually pull sensor positions toward the ideal circumference, using a differentiated relaxation mechanism to suppress outliers while preserving geometric features. Systematic Monte Carlo simulations compare five correction algorithms under multi-frequency and wideband signals. The results show that both multi-frequency and wideband signals reduce estimation errors to below 0.1°, with the proposed PWLS achieving the best accuracy under multi-frequency signals, while all algorithms approach zero error under wideband signals. The PWLS algorithm converges in about 10 iterations with high computational efficiency, providing a reliable solution for practical underwater NCA applications. Full article

Objective: This study aims to investigate the protective effect of Shengmai San (SMS) against high-glucose (HG)-induced injury in neonatal rat ventricular myocytes (NRVMs) and to elucidate the underlying pharmacological molecular mechanisms. We hypothesize that SMS ameliorates HG-induced calcium homeostasis imbalance in NRVMs by improving mitochondrial energy metabolism disorder, and this protective effect is associated with the downregulation of oxidized and phosphorylated CaMKII expression to inhibit CaMKII signaling pathway overactivation. Herein, we verify this hypothesis by assessing mitochondrial function, calcium transients, sarcoplasmic reticulum (SR) calcium handling and CaMKII phosphorylation levels in NRVMs. Methods: First, ultra-high performance liquid chromatography–high resolution mass spectrometry was used to identify the chemical components of SMS to clarify its material basis. Primary NRVMs were then cultured under low-glucose (LG) or HG conditions, with 2% SMS-medicated serum (SMS-MS) as the experimental intervention, and NAC (ROS scavenger) and KN93 (CaMKII inhibitor) as positive controls. Following intervention, we sequentially detected key indicators corresponding to the proposed pathological pathway: intracellular reactive oxygen species (ROS) levels (oxidative stress), mitochondrial ROS, mitochondrial function indices including oxygen consumption rate (OCR) (energy metabolism), calcium transients and diastolic intracellular free calcium concentration (global calcium homeostasis), sarcoplasmic reticulum (SR) calcium leak (calcium handling disorder), and, finally, the phosphorylation, oxidation levels of CaMKII and RyR2 phosphorylation (Ser2814) (p-RyR2) (key regulatory pathway) via Western blot to systematically elucidate the mechanistic link between SMS intervention and HG-induced NRVM injury. Results: Quantitative analysis revealed that high-glucose (HG) induction significantly reduced calcium transient amplitude and prolonged the decay time constant (tau) in NRVMs at 72 h (p < 0.01 vs. LG), with these parameters normalizing by 120 h—an effect indicative of a compensatory adaptive response. The 2%SMS-MS markedly ameliorated HG-induced calcium transient abnormalities at 72 h (p < 0.01 vs. HG). Additionally, 2%SMS-MS significantly enhanced mitochondrial basal oxygen consumption rate, spare respiratory capacity, ATP production, and maximal respiration in HG-exposed NRVMs (p < 0.01 vs. HG). SMS also significantly reduced intracellular reactive oxygen species (ROS) levels (p < 0.01 vs. HG), mitochondrial ROS levels (p < 0.01 vs. HG), diastolic intracellular free calcium concentration (p < 0.01 vs. HG), and SR calcium leak (p < 0.05 vs. HG). Western blot analysis revealed that 2%SMS-MS intervention effectively downregulated the expression of oxidized CaMKII (Ox-CaMKII) (p < 0.01 vs. HG), phosphorylated CaMKII (p-CaMKII) (p < 0.01 vs. HG), and RyR2 phosphorylation (Ser2814) (p < 0.05 vs. HG), which may be the potential mechanism in maintaining calcium homeostasis in HG-induced NRVMs. Conclusions: This study suggests that SMS enhances mitochondrial energy metabolism and exerts a protective effect against high-glucose-induced calcium homeostasis imbalance in NRVMs, which supports our proposed hypothesis. Its potential mechanism indicates that the protective effects of SMS are associated with its ability to downregulate the expression of oxidized and phosphorylated CaMKII. These findings highlight SMS as a potential therapeutic candidate for alleviating HG-related myocardial injury and provide evidence for its application in the prevention of early diabetic cardiomyopathy. Full article

High spatio-temporal resolution satellite imagery has become increasingly accessible thanks to advancements in the aerospace industry which, combined with a growing computational power, has enabled the spring of novel techniques regarding recognition in remote sensing (RS) images. However, there is still a lack of studies around 3D convolutions for spatio-temporal data applied to classification problems in RS. Hence, this study investigates the feasibility of 3D convolutional neural networks (3DCNNs) within a spatio-temporal perspective for scene classification and semantic segmentation in RS images, focusing on the identification of mining sites. We firstly developed a dataset covering several parts of Brazil based on MapBiomas products and Planet imagery, then we evaluated the effectiveness of 3DCNNs in capturing temporal information from a sequence of monthly captured images. Moreover, not only for scene classification but also for semantic segmentation, we compared 3D and 2D approaches. As for scene classification, a 3DCNN was better than the corresponding 2D model, while a 2D U-Net was better than a U-Net3D for semantic segmentation. The main explanation for this lies in the fact that a less costly annotation and training time strategy was adopted, but this may have harmed spatio-temporal approaches for semantic segmentation but not for scene classification. However, U-Net3D presented the highest Precision of all models, meaning that it is highly accurate when it predicts a positive. Moreover, 3DCNN (U-Net3D) presented significantly better performance with respect to semantic segmentation compared to other spatio-temporal approaches like ConvLSTM+U-Net and TempCNN. Sensitivity analysis revealed that the near-infrared (NIR) band played a decisive role in distinguishing mining areas, emphasizing its importance in highlighting subtle spectral variations associated with land-cover disturbances. Full article

This paper presents an onboard, markerless perching system for a quadrotor UAV, validated in outdoor flight experiments, to reduce hovering energy during long-endurance unmanned missions. Existing autonomous landing research predominantly focuses on planar surfaces, cooperative environments with visual markers, or specialized hardware, limiting scalability to scenarios requiring detection and perching on thin rod-like targets in uncooperative outdoor settings. This study proposes a markerless perching system for autonomously perching a drone on a hurdle’s horizontal bar. The system employs a single-axis gimbal camera, altitude LiDAR, and ToF sensor, integrating perception, post-processing, and control. On the perception side, we augment a YOLOv12n-based segmentation model with a high-resolution P2 pathway for small-object detection and apply module compression for real-time inference on edge devices. Robustness is improved by jointly utilizing the full hurdle and horizontal bar while constructing negative samples to suppress false positives. On the control side, a state machine controller leverages centroid coordinates, orientation, and distance measurements to achieve a stable long-range approach and precise close-range alignment. Experiments on a Jetson Orin NX-based system demonstrate successful perching in all six outdoor flight tests. Ablation studies quantitatively analyze each component’s contribution to perching success rate and completion time. This research validates perching technology’s practical applicability through outdoor markerless perching on thin 3D structures. Full article

This paper presents ANAID (Autonomous Naturalistic obstacle-Avoidance Interaction Dataset), a new multimodal dataset designed to support research on autonomous driving, particularly with regard to obstacle avoidance and naturalistic driver–vehicle interaction. Data were collected using a Hyundai Tucson Hybrid equipped with a Comma-3X autonomous-driving development kit, combining high-resolution front-facing video with detailed CAN-bus telemetry. The dataset comprises four data collection campaigns, each corresponding to a single continuous driving session, yielding a total of 208 videos and 240,014 synchronized frames. In addition to the video data, the dataset provides vehicle state measurements (speed, acceleration, steering, pedal positions, turn signals, etc.) and an additional annotation layer identifying evasive maneuvers derived from steering-related signals. Data were recorded across four driving campaigns on an urban circuit at Universidad Europea de Madrid, capturing diverse real-world scenarios such as roundabouts, intersections, pedestrian areas, and segments requiring obstacle avoidance. A multi-stage processing pipeline aligns telemetry and visual data, extracts frames at 20 FPS, and detects evasive maneuvers using threshold-based time-series analysis. ANAID provides a fully aligned and non-destructive representation of naturalistic driving behavior, enabling research on control prediction, driver modeling, anomaly detection, and human–autonomy interaction in realistic traffic conditions. Full article

The geomorphological characterization of coastal–marine environments is essential for environmental management and biodiversity conservation. The objective of this study was to model the geomorphological diversity of the Punta de Coles National Reserve, located in Puerto de Ilo, Moquegua, Peru, using GNSS geodetic receivers, integrating topographic and bathymetric data to continuously represent both the emerged and submerged relief. The methodology involved establishing two “C”-order geodetic control points, implementing a closed polygon with 13 vertices, conducting a topographic survey, and recording bathymetric data along coastal transects extending 1 km offshore using an echo sounder and GNSS positioning. The data were processed in a GIS environment to generate a Coastal–Marine Digital Terrain Model (CM-DTM) with metric resolution. The results showed a total area of 171.451 ha, with elevation variations ranging from sea level to 71.617 m above sea level. Distinct geomorphological units were identified, such as coastal plains (0–5% slope), hills (15–35%), and cliffs (>45%), in addition to 16 rocky islets covering 1.537 ha. In the underwater environment, the model made it possible to identify submerged terraces, slopes, and local depressions down to a depth of −115 m, revealing a continuous transition between the land and sea topography; additionally, areas with a higher susceptibility to erosion and areas of high ecological importance were identified. This study’s contribution lies in the integration of GNSS geodetic data with topobathymetric surveys, which enabled the generation of a high-precision continuous model in an area with limited prior information, establishing a scientific baseline for coastal and marine management and conservation. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

Intracranial atherosclerosis (ICAS) is a distinct, inflammation-dominant vasculopathy and a leading cause of global stroke morbidity. Unlike extracranial atherosclerosis (ECAS), which often utilizes compensatory positive remodeling to maintain patency, ICAS is characterized by a unique architecture and a localized antioxidant gap that favor maladaptive negative remodeling. We critically analyze the molecular cascade initiated by the breakdown of the Piezo-type mechanosensitive ion channel component 1 (PIEZO1) and the Krüppel-like factor 2/4 (KLF2/4) mechanotransduction axis, which triggers endothelial nitric oxide synthase (eNOS) uncoupling and establishes a state of chronic inflammation. This environment facilitates the subendothelial lipid retention of oxidized low-density lipoprotein (oxLDL), a process exacerbated by the intracranial deficiency of Apolipoprotein A-I (ApoA-I) and impaired glymphatic clearance. Crucially, we evaluate how these metabolic and mechanical insults drive vascular smooth muscle cell (VSMC) phenotypic switching; the transdifferentiation of contractile VSMCs into macrophage-like foam cells accounts for up to 60% of the plaque’s lipid-laden pool and destabilizes the fibrous cap. This vascular failure directly compromises the neurovascular unit (NVU), leading to pericyte dropout and blood–brain barrier breakdown. Beyond environmental stressors, we highlight the ring finger protein 213 (RNF213) variant as a critical genetic determinant of this susceptibility. Shifting the clinical paradigm from simple luminal narrowing toward the identification of the vulnerable plaque, we discuss how High-Resolution Vessel Wall Imaging (HR-VWI) and microRNA biomarkers can identify unstable lesions. By integrating these molecular and imaging signatures, we propose a precision medicine framework centered on the NLR family pyrin domain containing 3 (NLRP3) inflammasome and the NVU to effectively mitigate the high residual recurrence risk that persists under conventional therapy. Full article

The forest understory litter fraction $\left({\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}\right)$ is a critical indicator for evaluating the effectiveness of “understory erosion” control in red soil regions; however, its high-precision, large-scale monitoring remains challenging due to canopy occlusion. This study proposes an ${\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}$ inversion framework that integrates high-spatial-resolution Sentinel-2 imagery with multi-angular prior knowledge from MODIS BRDF products. First, a linear mapping model between multi-band reflectances at 0° and 45° view angles was constructed using 500 m MODIS MCD43A1 products $(R^2>0.8)$. This model was subsequently employed as a physical prior for anisotropic characterization and transferred to 10 m Sentinel-2 imagery to generate a long-term, dual-angle reflectance dataset. Subsequently, the four-scale geometric-optical model was utilized to decouple canopy and understory background signals, followed by quantitative ${\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}$ inversion using a pixel-based dimidiate model. Validation results confirmed the reliability of the framework $(R^2=0.74, RMSE=0.1073)$. Spatiotemporal evolution analysis indicated a significant upward trend in ${\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}$ across Changting County from 2016 to 2025, with over 90% of the area showing improvement. The proportion of high-coverage areas $({\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}>0.75)$ increased from 10% to 38%, exhibiting a “high in the center, low in the periphery” spatial pattern that aligns closely with core ecological restoration zones. Stability and persistence analyses further revealed that 61.18% of the study area reached moderate-to-high stability, and 70% of pixels exhibited a “positive persistence-improvement” trend, highlighting a pronounced inertia-driven enhancement in ecological recovery. This study provides a refined technical pathway for assessing soil and water conservation benefits in red soil regions. Full article

Carrier-phase ambiguity validation is essential to ensure the reliability of integer ambiguity resolution in high-precision GNSS positioning. Although integer equivariant (IE) estimators provide optimal integer candidates within their class, noise and model limitations may lead to incorrect fixing. Validation procedures are therefore crucial for safeguarding the transition from float to fixed solutions, particularly in high-precision and safety-critical applications. In this contribution we introduce the concept of Fourier ambiguity validation and show how it is rooted in the principles of integer aperture (IA) estimation and its periodic representation. Unlike classical integer estimators that always return an integer solution, IA estimators introduce adjustable acceptance regions in the float ambiguity domain and fix ambiguities only when sufficient statistical evidence is present. As a result we present a general Fourier representation of IA estimators and provide an analytical description of the probabilistic properties of integer-aperture bootstrapping. We also present a hybrid description and show how the spatial and frequency representations can be mixed so as to do justice to the practical situation when carrier-phase ambiguities have a wide range of varying precision. Full article

Caves are fragile subterranean ecosystems whose conservation depends on accurate microclimatic zonation. Traditional fixed-distance sampling often overlooks non-linear thermodynamic transitions at geomorphological thresholds, hindering sustainable management of subterranean biodiversity. This study introduces the Adaptive Morpho-Statistical Protocol (AMSP), a novel, resource-efficient framework for functional cave profiling. The methodology integrates high-precision atmospheric monitoring with adaptive spatial positioning to identify three distinct sectors (S1–S3) based on thermodynamic homeostasis rather than linear distance. Validated across five diverse cave archetypes in the Vratsa Karst Region (Bulgaria), the AMSP demonstrated exceptional predictive power using second-order polynomial regressions (R² > 0.92). A key finding is the definition of a standardized reference threshold for deep-reach stability (Sector 3), consistently characterized by a Dew Point Standard Deviation (SD_DP < 0.40) and stabilized thermal coupling (∆T → 0). Furthermore, the adaptive strategy successfully captured extreme hygrometric jumps at morphological bottlenecks—critical inflection points for protecting sensitive biota. By providing a cost-effective and replicable standard, the AMSP bridges the gap between spatial resolution and logistical feasibility in challenging environments. These results confirm that morphological isolation is the primary driver of microclimatic inertia, offering a robust tool for sustainable subterranean heritage management and high-precision ecological monitoring in protected karst landscapes. Full article