Search Results (18,803)

The desert riparian forest oasis, dominated by Populus euphratica and Tamarix chinensis, is an important barrier to protect the economic production and habitat of the Tarim River Basin. However, there is still a lack of high-precision spatial distribution data of desert ri-parian forest species below 10 m. The recently launched PlanetScope CubeSat constella-tion, which provides daily earth observation imagery with a resolution of 3 m, offers a highly favorable dataset for mapping the high-resolution distribution of P. euphratica and T. chinensis and an unprecedented opportunity to explore the optimal phenology window to distinguish between them. In this study, time-series PlanetScope images were first used to extract phenological metrics of P. euphratica, dividing the annual life cycle into four phenology windows: duration of leaf expansion (DLE), duration of leaf maturity (DLM), duration of leaf fall (DLF), and duration of the dormancy period (DDP). The random forest model was used to obtain the classification accuracy of 16 phenological window combinations. Results indicate that after gap filling of vegetation index time series, the identification accuracy for P. euphratica and T. chinensis exceeded 0.90. Among individual phenology windows, the DLE window exhibited the highest classification accuracy (average F1-score 0.87). Among the two phenology window combinations, the DLE-DLF and DLE-DLM windows have the highest classification accuracy (average F1-score 0.90). Among the three phenology window combinations, DLE-DLM-DLF displayed the highest classification accuracy (average F1-score 0.91). Nevertheless, the inclusion of features within the DDP window led to a decrease in accuracy by 1–2% points, which was unfavorable for discriminating tree species. Additionally, features observed during the phenology asynchrony period were found to be more valuable for distinguishing between tree species. Our findings highlight the potential of PlanetScope constellation imagery in tree species classification, offering guidance for selecting optimal image acquisition timing and identifying the most valuable images within time series data for future large-scale tree mapping. Full article

Phosphorus (P) is essential for plant growth but is frequently limited in soils. Lespedeza species are well-known for their ecological and economic benefits, as well as their tolerance to nutrient-poor soils. This study investigated the P acquisition strategies and adaptive mechanisms of three Lespedeza species (L. davurica, L. bicolor, and L. cuneata), focusing on biomass allocation, P distribution, root exudation, and absorption kinetics under P deficiency. Under P deficiency, L. davurica and L. bicolor allocated more biomass to roots to enhance P acquisition, whereas L. cuneata increased specific root length and area. Moreover, all three species preferentially allocated P to roots, but L. bicolor showed higher P content in stems and leaves than the others. P absorption kinetics indicated that Michaelis constant (K_m) and equilibrium concentration (C_min) were significantly decreased in all three species under P deficiency, with L. bicolor exhibiting the strongest P affinity and acquisition capacity. Secretion analysis revealed that while L. davurica and L. cuneata secreted higher levels of organic acids under P deficiency, exudates from L. bicolor were significantly enriched in acid phosphatase activity. Overall, the three Lespedeza species developed distinct P acquisition and adaptive strategies to cope with P deficiency, with L. bicolor demonstrating the greatest low-P tolerance and most efficient adaptive mechanisms. Full article

Background/Objectives: Biofilms are key in spreading antibiotic resistance in various ecosystems. This study employed comparative genomics to examine the resistance and adaptability mechanisms of the Vibrio parahaemolyticus strain Vaw-5, isolated from a seafood market biofilm. Methods: A comparative examination of Vaw-5 and 32 publicly available V. parahaemolyticus genomes identified a distinct set of genetic resistance characteristics. Results: Unlike clinical strains, Vaw-5 lacks acquired antimicrobial resistance genes like the blaCARB and qnr variations. Instead, its resistance potential is based on chromosomal alterations, efflux pump systems (vmeAB, vcmD), and a unique repertoire of 16 strain-specific transposons, including Tn5501 and Tn5393, which are well-known vectors for antibiotic resistance gene (ARG) mobilization. Although not multidrug-resistant, Vaw-5 possesses unique genomic islands that share negligible homology with those of clinical strains, enriched with gene clusters for environmental adaptation, such as exopolysaccharide production and a fully functional Type VI Secretion System. Vaw-5 carries a distinctive plasmid with the resistance gene aac(2${}^{\prime }$)-Ia. Conclusions: Biofilm adaptation promotes structural integrity, inherent processes, and resistance above standard ARG acquisition. This study focuses on how biofilm communities in the food chain can operate as covert incubators for mobilizable resistance determinants, emphasizing the significance of ecological monitoring within a One Health paradigm to reduce possible public health hazards. Full article

The induction of robust CD8 T cell immunity after intramuscular (i.m.) mRNA vaccination has remained a challenge. Due to the limited presence of professional antigen-presenting cells (APCs) in muscle tissue, this route of administration tends to result in the transfection of muscle cells at the injection site with insufficient T cell activation capacity. The attraction of migratory APCs and related processes that lead to the acquisition of antigenic material from transfected non-APCs arises as a potential alternative to facilitate activation of CD8 T cells in the draining lymph nodes. This indirect pathway, known as antigen cross-presentation, has remained underappreciated for mRNA vaccines. This review provides a comprehensive analysis of this process. Due to the paucity of information available in this context, it also extrapolates from insights for antigen cross-presentation more generally and for traditional vaccines. Arguments are provided as to why this natural process in the context of pro-drugs, such as mRNA vaccines, may engender both specific and nonspecific responses and, in certain situations, evoke cross-tolerance rather than immunity. This widely unaccounted T cell activation process may, therefore, explain several key mysteries surrounding i.m. RNA vaccination, including its impact on heterologous infections. But it also raises numerous open questions that are clearly described. Full article

Total internal reflection fluorescence (TIRF) microscopy excites fluorophores within a few hundred nanometers of the sample–substrate interface, enabling high-contrast imaging near the cell membrane. When cultured cells differentiate, the membrane in contact with the coverslip generally acquires basal characteristics, while the opposite membrane develops apical features. Consequently, conventional TIRF microscopy is limited to imaging the basal surface. We developed an immersed-prism TIRF (IP-TIRF) microscope, in which a prism immersed in the culture medium generates TIR at the cell/medium–prism interface, illuminating the apical membrane and reducing cytosolic background. In proof-of-principle experiments, we imaged fluorescent beads and 3xmNeonGreen-tagged intraflagellar transport (IFT) particles in cilia, and compared the performance with confocal microscopy. In cellular regions where both methods can be applied (such as the IFT base pool), on average, IP-TIRF achieved approximately 1.8 times the contrast-to-noise ratio (CNR~31) compared to confocal microscopy. Furthermore, IFT-particle motion was detected in IP-TIRF image sequences and Kymographs of cilia, with adequate spatial resolution. Kymograph analysis revealed an average anterograde IFT velocity of 0.156 ± 0.071 µm/s and an average retrograde velocity of 0.020 ± 0.007 µm/s, approximately one-quarter and one-twentieth, respectively, of the values reported for mammalian primary cilia, which we attribute to acquisition at room temperature rather than physiological conditions. Therefore, these velocity measurements should be regarded as proof-of-principle demonstrations obtained at room temperature, not as validated physiological transport rates. Our IP-TIRF method provides a high-resolution, cost-effective, and broadly accessible approach for imaging the apical membrane in live cells. Full article

Optogenetics leverages light to control neural circuits, but traditional systems are often bulky and tethered, limiting their use. This work introduces OptoBrain, a novel, portable wireless system for optogenetics designed to overcome these challenges. The system integrates modules for multichannel data acquisition, smart neurostimulation, and continuous processing, with a focus on low-power and low-voltage operation. OptoBrain features up to eight neuronal acquisition channels with a low input-referred noise (e.g., 0.99 µVRMS at 250 sps with 1 V/V gain), and reliably streams data via a Bluetooth 5.0 link at a measured throughput of up to 400 kbps. Experimental results demonstrate robust performance, highlighting its potential as a simple, practical, and low-cost solution for emerging optogenetics research centers and enabling new avenues in neuroscience. Full article

In Georgia (Sakartvelo), a program promoting bilingual education in preschool institutions was formally adopted in 2020. It aligns with the objectives of the 2021–2030 State Strategy for Civic Equality and Integration Plan, which envisions a comprehensive reform of bilingual education across Georgia’s regions. Any reform requires research and evaluation to measure how effectively it is being implemented and whether the intended outcomes have been achieved. The bilingual education initiative pursues a dual objective: to preserve the native languages of minority communities while ensuring effective acquisition of the state language. This dual mandate is intrinsically linked to state language policy and constitutes a sensitive issue for local communities, parents, and preschool administrators, thereby necessitating a careful and nuanced approach. The present study analyzed the readiness of the social environment to support the implementation of bilingual education programs at the preschool level in the regions of Georgia in which ethnic minorities live side by side. Research was carried out in two ethnically diverse regions—Kvemo Kartli and Samtskhe–Javakheti. The author conducted individual and group interviews, and the elicited data were analyzed with the help of content and thematic analyses. This study examines key attributes of the ongoing preschool reform to identify factors that facilitate the effective implementation of early bilingual education initiatives. The findings highlight both commonalities and regional variations in parental attitudes toward the bilingual education reform. Full article

In urban intelligent transportation systems, the real-time acquisition of network-wide traffic states is constrained by limited sensor density and high deployment costs. To address this challenge, this paper proposes a learnable Detection Point Selection Module (DPSM), which adaptively determines the most informative observation points through an end-to-end attention mechanism to support full-map traffic state estimation. Distinct from conventional fixed deployment strategies, DPSM provides an adaptive detector configuration that, under the same number of loop sensors, achieves significantly higher estimation accuracy by intelligently optimizing their placement. Specifically, the module takes normalized spatial and temporal information as input and generates an attention-based distribution to identify critical traffic flow readings, which are subsequently fed into various backbone prediction models, including fully connected networks, convolutional neural networks, and long short-term memory networks. Experiments on the real-world NGSIM-US101 dataset demonstrate that three variants—DPSM-NN, DPSM-CNN, and DPSM-LSTM—consistently outperform their corresponding baselines, with notable robustness under sparse observation scenarios. These results highlight the advantage of adaptive detector placement in maximizing the utility of limited sensors, effectively mitigating information loss from sparse deployments and offering a cost-efficient, scalable solution for urban traffic monitoring and control. Full article

Mycotoxin contamination represents a major risk to both human and animal health. Antioxidants can mitigate some of these effects through free radical scavenging, reduction in oxidative stress, and anti-inflammatory and immunomodulatory actions. This work investigated the potential of antioxidants derived from grapeseed and sea buckthorn to mitigate the adverse effects of aflatoxin B1 (AFB1) and ochratoxin A (OTA) in weaned piglets. An unbiased Data-Independent Acquisition (DIA) proteomic approach was used to analyse the impact of OTA- and AFB1-contaminated diets on liver and kidney cytoplasmic metabolism, particularly focusing on the conjugation phase. Our results indicate that several toxic effects of these mycotoxins were partially alleviated by dietary antioxidant supplementation. Additionally, in kidneys, some of the effects are synergistically amplified, such as proteins involved in fatty acid degradation, peroxisome, PPAR signalling, translation, the TCA cycle, and excretion pathways. Inclusion of antioxidants in the animal diet can have beneficial effects. Nevertheless, caution is advised; synergistic effects can occur with potentially more serious consequences than the effects of mycotoxins alone. Full article

Accurate vehicle state estimation in a lane coordinate system is essential for safe and reliable operation of Advanced Driver Assistance Systems (ADASs) and autonomous driving. However, achieving robust lane-based state estimation using only low-cost sensors, such as a camera, an IMU, and a steering angle sensor, remains challenging due to the complexity of vehicle dynamics and the inherent signal delays in vision systems. This paper presents a lane-coordinate-based vehicle state estimator that addresses these challenges by combining a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF) and a signal delay compensation algorithm. The estimator performs real-time estimation of lateral position, lateral velocity, and heading angle, including the unmeasurable lateral velocity about the lane, by predicting the vehicle’s state evolution during camera processing delays. A computationally efficient camera processing pipeline, incorporating lane segmentation via a pre-trained network and lane-based state extraction, is implemented to support practical applications. Validation using real vehicle driving data on straight and curved roads demonstrates that the proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states. Compared to conventional camera-only methods and estimators without delay compensation, the proposed approach significantly reduces estimation errors and phase lag, enabling the reliable and real-time acquisition of vehicle-state information critical for ADAS and autonomous driving applications. Full article

Information announcement is the process of propagating and synchronizing the information of Computing Resource Nodes (CRNs) within the system of the Computing Networks. Accurate and timely acquisition of information is crucial to ensuring the efficiency and quality of subsequent task scheduling. However, existing announcement mechanisms primarily focus on reducing communication overhead, often neglecting the direct impact of information freshness on scheduling accuracy and service quality. To address this issue, this paper proposes a hierarchical and clustering-based announcement mechanism for the wide-area Computing Networks. The mechanism first categorizes the Computing Network Nodes (CNNs) into different layers based on the type of CRNs they interconnect to, and a top-down cross-layer announcement strategy is introduced during this process; within each layer, CNNs are further divided into several domains according to the round-trip time (RTT) to each other; and in each domain, inspired by the “Six Degrees of Separation” concept from social propagation, a RTT-aware fast clustering algorithm canopy is employed to partition CNNs into multiple overlap clusters. Intra-cluster announcements are modeled as a Traveling Salesman Problem (TSP) and optimized to accelerate updates, while inter-cluster propagation leverages overlapping nodes for global dissemination. Experimental results demonstrate that, by exploiting shortest path optimization within clusters and overlapping-node-based inter-cluster transmission, the mechanism is significantly superior to the comparison scheme in key indicators such as convergence time, Age of Information (AoI), and communication data volume per hop. The mechanism exhibits strong scalability and adaptability in large-scale network environments, providing robust support for efficient and rapid information synchronization in the Computing Networks. Full article

To address the limitations inherent in traditional simulation control schemes for dual-engine parallel operation systems in diesel engines—such as protracted development cycles, suboptimal interface compatibility, insufficient real-time performance, and inadequate support for dynamic condition simulation in applications like marine power systems—this paper proposes an embedded real-time controller based on model-based design. This methodology facilitates efficient development and high-precision real-time control of parallel operation systems. A multi-domain coupled simulation model integrating diesel power and parallel control algorithms is built in MATLAB/Simulink, with optimized C code auto-generated via Embedded Coder. Hardware centers on STM32F407VE, enabling 4–20 mA speed acquisition, CAN communication, and Ethernet transmission. Experimental results indicate that the architecture shortens development cycles from 8 to 3 weeks, with 895 microseconds of simulation steps meeting 1-millisecond real-time requirements. Vessel tests achieve ±1.8 r/min synchronization error and ±1.2% load distribution error at low cost. It adapts to varied diesel power via modular substitution and supports RS485/CAN-FD. In conclusion, the controller effectively handles real-time simulated diesel engine parallel systems and excels in efficiency, compatibility, and cost, offering a viable technical pathway for modernizing parallel power systems in applications such as marine vessels and power generation. Full article

Lepidium meyenii Walp. (black maca, BM) is a traditional Andean crop increasingly studied for its bioactive potential. This work characterized the phytochemical profile and evaluated the antioxidant, antinociceptive, and neuroprotective properties of a lyophilized aqueous extract of BM hypocotyls. UHPLC-ESI-QTOF-MS/MS identified twelve major compounds, including macamides, imidazole alkaloids, sterols, and fatty acid amides. BM showed a moderate total phenolic content but strong electron transfer-based antioxidant activity in CUPRAC and FRAP assays, together with moderate radical scavenging capacity in ABTS and DPPH systems. In ovariectomized rats, BM significantly reduced brain malondialdehyde levels, mitigated oxidative stress, and improved spatial learning during acquisition in the Morris water maze, confirming its neuroprotective effect. Antinociceptive assays (hot plate, cold plate, and tail immersion) further revealed a rapid but transient increase in nociceptive thresholds. This study provides experimental evidence supporting the analgesic effect of black maca. Molecular docking highlighted lepidiline B and campesterol as key metabolites with strong interactions with redox enzymes, the μ-opioid receptor, and the FAAH enzyme, supporting their role in the observed bioactivities. ADMET predictions indicated favorable oral bioavailability, CNS penetration, systemic clearance, and acceptable safety profiles. These results substantiate the role of black maca as a neuroprotective nutraceutical and highlight its promise as a novel source of rapidly acting natural analgesic compounds. Full article

Exploring the factors and mechanisms influencing farmers’ participation behavior in formal financial markets is of great significance for improving the rural financial service system and comprehensively promoting the development of rural inclusive finance. Based on the data from the 2020 China Rural Revitalization Survey (CRRS), this paper explores the impact and mechanism of digital skills on farmers’ participation in formal financial markets through the theories of feasibility information, social capital, and technology acceptance. The research results show that digital skills significantly promote farmers’ likelihood and extent of participation in formal financial markets, including stocks, bonds, and wealth management products. This is because digital skills enhance farmers’ information acquisition and online transaction capabilities and broaden their social networks. Heterogeneity analysis reveals that digital skills exert a greater influence on both the likelihood and extent of participation of farmers with lower educational level, farmers in the middle-aged and elderly cohorts, and farmers with low income in formal financial markets. Further research reveals that participating in formal financial markets can significantly increase farmers’ annual income. Therefore, training should be strengthened to enhance farmers’ digital skills. Open information platforms should be established to broaden channels, gradually enabling farmers to freely acquire information, reducing the cost of maintaining social networks for farmers, and improving the convenience of farmers’ online transactions. In addition, efforts should be made to promote the development of inclusive finance, focus on the differentiation issues of farmers in information and wealth, and thereby more widely enhance farmers’ participation in formal financial markets. Full article

Coating degradation monitoring is increasingly important in offshore industries, where protective layers ensure corrosion prevention and structural integrity. In this context, coating thickness estimation provides critical information. The ultrasound pulse-echo technique is widely used for non-destructive testing (NDT), but closely spaced acoustic interfaces often produce overlapping echoes, which complicates detection and accurate isolation of each layer’s thickness. In this study, analysis of the pulse-echo signal from a coated sample has shown that the front-coating reflection affects each main backwall echo differently; by comparing two consecutive backwall echoes, we can cancel the acquisition system’s impulse response and isolate the propagation path-related information between the echoes. This work introduces an ultrasound echo-based methodology for estimating coating thickness by first obtaining the impulse response of the test medium (reflectivity sequence) through a deconvolution model, developed using two consecutive backwall echoes. This is followed by an enhanced detection of coating layer thickness in the reflectivity function using a 1D convolutional neural network (1D-CNN) trained with synthetic signals obtained from finite-difference time-domain (FDTD) simulations with k-Wave MATLAB toolbox (v1.4.0). The proposed approach estimates the front-side coating thickness in steel samples coated on both sides, with coating layers ranging from $60\mathsf{\mu }$m to $740\mathsf{\mu }$m applied over 5 mm substrates and under varying coating and steel properties. The minimum detectable thickness corresponds to approximately $\mathit{\lambda }/5$ for an 8 MHz ultrasonic transducer. On synthetic signals, where the true coating thickness and speed of sound are known, the model achieves an accuracy of approximately $8\mathsf{\mu }$m. These findings highlight the strong potential of the model for reliably monitoring relative thickness changes across a wide range of coatings in real samples. Full article