Search Results (897)

LuTan-1(LT-1) is China’s first L-band differential interferometric synthetic aperture radar system, comprising two multi-polarization SAR satellites, LT-1A and LT-1B. The satellite uses differential deformation measurement and interferometric altimetry technology to realize surface deformation monitoring and topographic mapping in designated areas. It has the characteristics of all-weather, all-time, and multi-polarization and can be applied to military and civilian fields. In order to further improve the accuracy of image geometric positioning, this paper analyzes the error sources of geometric positioning for the differential deformation measurement mode (strip 1) of the satellite service. The in-orbit data of three years since the launch (2022–2024) are selected to analyze the positioning accuracy and stability of the uncontrolled plane based on the corner reflector and active calibrator deployed in the calibration field. The experimental results show that the positioning accuracy of the satellite strip 1 image without a control plane meets the requirements of the in-orbit index and remains relatively stable. The geometric precision correction positioning accuracy after error source compensation is better than 3.0 m, providing a favorable support for the subsequent application. Full article

Multi-view Synthetic Aperture Radar (SAR) provides rich information for target recognition. However, fusing features from unaligned multi-view images presents challenges for existing methods. Conventional early fusion methods often rely on image registration, a process that is computationally intensive and can introduce feature distortions. More recent registration-free approaches based on the Transformer architecture are constrained by standard position encodings, which were not designed to represent the rotational relationships among multi-view SAR data and thus can cause spatial ambiguity. To address this specific limitation of position encodings, we propose a registration-free fusion framework based on a spatially aware Transformer. The framework includes two key components: (1) a multi-view polar coordinate position encoding that models the geometric relationships of patches both within and across views in a unified coordinate system; and (2) a spatially aware self-attention mechanism that injects this geometric information as a learnable inductive bias. Experiments were conducted on our self-developed FAST-Vehicle dataset, which provides full 360° azimuthal coverage. The results show that our method outperforms both registration-based strategies and Transformer baselines that use conventional position encodings. This work indicates that for multi-view SAR fusion, explicitly modeling the underlying geometric relationships with a suitable position encoding is an effective alternative to physical image registration or the use of generic, single-image position encodings. Full article

In the semantic segmentation tasks of polarimetric SAR images, deep learning has become an important end-to-end method that uses convolutional neural networks (CNNs) and other advanced network architectures to extract features and classify the target region pixel by pixel. However, applying original networks used to optical images for PolSAR image segmentation directly will result in the loss of rich phase information in PolSAR data, which leads to unsatisfactory classification results. In order to make full use of polarization information, the complex-valued BiSeNetV2 with a bilateral-segmentation structure is studied and expanded in this work. Then, considering further improving the ability to extract semantic features in the complex domain and alleviating the imbalance of polarization channel response, the complex-valued BiSeNetV2 with a lightweight attention module (LAM-CV-BiSeNetV2) is proposed for the semantic segmentation of PolSAR images. LAM-CV-BiSeNetV2 supports complex-valued operations, and a lightweight attention module (LAM) is designed and introduced at the end of the Semantic Branch to enhance the extraction of detailed features. Compared with the original BiSeNetV2, the LAM-CV-BiSeNetV2 can not only more fully extract the phase information from polarimetric SAR data, but also has stronger semantic feature extraction capabilities. The experimental results on the Flevoland and San Francisco datasets demonstrate that the proposed LAM has better and more stable performance than other commonly used attention modules, and the proposed network can always obtain better classification results than BiSeNetV2 and other known real-valued networks. Full article

The SARS-CoV-2 pandemic has driven the emergence of many viral variants carrying multiple mutations, particularly in the spike glycoprotein, which enhance viral adaptability and may alter the structure and functionality of the protein. Here, we present, to the best of our knowledge, the first systematic and comparative structural analysis of monomeric spike protein subunit 1 from three distinct SARS-CoV-2 variants at physiological pH (7.4). A multimodal approach was employed, integrating experimental techniques, including Attenuated Total Reflection Infrared and circular dichroism spectroscopies, with computational methods such as molecular dynamics simulations and surface polarity analyses. This combined approach allowed us to characterize the secondary structure composition, three-dimensional conformational organization, and solvent interaction profiles of each variant. Our findings reveal how the structural and functional properties of the spike protein subunit 1 are influenced by specific amino acid mutations. Indeed, the observed conformational changes and variations in solvent interactions have significant implications for viral infectivity and immune evasion. These findings contribute to the broader understanding of the evolution of SARS-CoV-2 variants and offer valuable insights for drug development, targeted prevention strategies, and biosensor design. Full article

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping. Full article

Coastal wetlands are a critical buffer between land and water that are threatened by land use and climate change, necessitating improved monitoring for management and resilience planning. The recently launched NASA-ISRO L- and S-band SAR satellite (NISAR) will provide regular collections of fully polarimetric SAR imagery over the Great Lakes, allowing for unprecedented remote monitoring of the large expanses of coastal wetlands in the region. Prior research with polarimetric C-band SAR showed inconsistencies with common polarimetric analysis techniques, including the erroneous misattribution of double-bounce scattering in three-component scattering models. To prepare for NISAR and determine whether SAR-based coastal wetland analysis methods established with the C-band are applicable to the L- and S-bands, the NASA-ISRO airborne system (ASAR) collected imagery over western Lake Erie and Lake St. Clair coincident with a field data collection campaign. ASAR data were analyzed to identify common Great Lakes coastal wetland vegetation species, assess the extent of inundation, and derive biomass retrieval algorithms. Co-polarized phase difference histograms were also analyzed to assess the validity of three-component scattering decompositions. The L- and S-bands allowed for the production of wetland type maps with high accuracies (92%), comparable to those produced using a fusion of optical and SAR data. Both frequencies could assess the extent of flooded vegetation, with the S-band correctly identifying inundated vegetation at a slightly higher rate than the L-band (83% to 78%). Marsh vegetation biomass retrieval algorithms derived from L-band data had the best correlation with field data (R² = 0.71). Three component scattering models were found to misattribute double-bounce scattering at incidence angles shallower than 35°. The L- and S-band results were compared with satellite RADARSAT-2 imagery collected close to the ASAR acquisitions. This study provides an advanced understanding of polarimetric SAR for monitoring wetlands and provides a framework for utilizing forthcoming NISAR data for effective monitoring. Full article

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research. Full article

The range-dependence on polarization distortion of spaceborne polarimetric synthetic aperture radar (SAR) affects the accuracy of wide-swath polarization applications, such as environmental monitoring, sea ice classification and ocean wave inversion. Traditional calibration methods, assessing the distortion mainly based on ground experiments, suffer from tedious active calibrator deployment work, which are time-consuming and cost-intensive. This paper proposes a novel polarimetric assessment and calibration method for the quad-polarized wide-swath ScanSAR imaging mode. Firstly, by using distributed target data that satisfy the system reciprocity requirement, we assess the polarization distortion matrices for a single beam in the mode. Secondly, we transfer the matrix results from one beam to another by analyzing data from the overlapping region between beams. Thirdly, we calibrate the quad-polarized data and achieve an overall assessment and calibration results. Compared to traditional calibration methods, the presented method focuses on using cross-beam (overlapping area) data to reduce the dependence on active calibrators and avoid conducting calibration work beam-by-beam. The assessment and calibration experiment is conducted on Gaofen-3 quad-polarized ScanSAR experiment mode data. The calibrated images and polarization decomposition results are compared with those from well-calibrated quad-polarized Stripmap mode data located in the same region. The results of the comparison revealed the effectiveness and accuracy of the proposed method. Full article

Respiratory viral infections are a major cause of morbidity and mortality worldwide. The COVID-19 pandemic has evidenced the need for broad-spectrum antivirals and improved preclinical models that more accurately recapitulate human respiratory disease. These new strategies should also involve the search for drug targets in the infected cell that hamper the development of resistance and of potential efficacy against diverse viruses. Since many viruses reprogram cellular metabolism to support viral replication, we performed a comparative analysis of inhibitors targeting the PI3K/AKT/mTOR pathway, central to virus-induced metabolic adaptations, using MRC5 lung fibroblasts and Huh7 hepatoma cells. HCoV-229E infection in MRC5 cells caused the expected shift in the energy metabolism but the inhibitors had markedly different effects on the metabolic profile and antiviral activity in these two cell lines. Dichloroacetate (DCA), a clinically approved inhibitor of aerobic glycolysis, showed antiviral activity against HCoV-229E in MRC5 cells, but not in Huh7 cells, underscoring that the screening model is more critical than previously assumed. We further tested DCA in polarized human small airway epithelial cells cultured in air–liquid interface, a 3D model that mimics the human respiratory tract. DCA reduced the viral progeny of HCoV-229E, SARS-CoV-2, and respiratory syncytial virus by 2–3 orders of magnitude, even when administered after infection was established. Our work reinforces the need for advanced human preclinical screening models to identify antivirals that target host metabolic pathways frequently hijacked by respiratory viruses, and establishes DCA as a proof-of-concept candidate. Full article

Background: Due to the wide host range of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), vaccination has been recommended for susceptible species in zoological collections, particularly to protect endangered species. The Zoetis^® Experimental Mink Coronavirus Vaccine (Subunit) was temporarily authorized in 2021–2024 for emergency use in North America for this purpose. However, there are limited data regarding its safety or efficacy in non-domestic mammals. The present study was conducted to assess the ability of this vaccine to elicit serum neutralizing titers against SARS-CoV-2 in selected animals from the Toronto Zoo (TZ) vaccinated during 2022. Methods: Serum samples were collected from 24 individuals across four families (Cervidae, Felidae, Ursidae, and Hyaenidae) and tested using a surrogate virus neutralization test (sVNT) and a plaque-reduction neutralization test (PRNT). Results: The results showed that all species developed some neutralizing titers after at least one vaccine dose, except for polar bears, which showed no seroconversion. Felids and hyenas had the highest neutralizing titers, which peaked at 3 and declined between 4 and 6 months after boost. These differences may stem from species-specific immune responses or lack of vaccination protocols tailored to individual species. Conclusions: While natural infection with SARS-CoV-2 could not be ruled out in the cohort of this study, insights from our results have the potential to inform future vaccine recommendations for non-domestic species. Furthermore, our study highlighted the value of competitive assays in assessing serological responses across a broad range of exotic species, for which reagents, such as anti-isotype antibodies, are often unavailable. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

As the pediatric COVID-19 landscape evolves, it is essential to evaluate whether SARS-CoV-2 infection predisposes children to allergic disorders. This narrative review synthesizes current epidemiological and immunological evidence linking pediatric COVID-19 with new-onset atopy. Epidemiological data remain heterogeneous: large Korean and multinational cohorts report increased risks of asthma and allergic rhinitis following COVID-19, whereas U.S. cohorts show neutral or protective associations, highlighting geographic and methodological variability. Mechanistic insights provide biological plausibility: epithelial injury and the release of alarmin cytokines (IL-33, IL-25, TSLP) promote Th2 polarization and ILC2 expansion, while epigenetic “scars” (e.g., LMAN2 methylation changes) and hematopoietic stem cell reprogramming may sustain long-term Th2 bias. Cytokine memory involving IL-7 and IL-15 contributes to altered T- and B-cell homeostasis, whereas disrupted regulatory T-cell function may reduce tolerance thresholds. Paradoxical trade-offs exist, such as ACE2 downregulation in allergic airways, which may lower viral entry but simultaneously amplify type-2 inflammation. Together, these processes suggest that SARS-CoV-2 infection could foster a pro-allergic milieu in susceptible children. Although current evidence is inconclusive, integrating epidemiological surveillance with mechanistic studies is crucial for predicting and alleviating post-COVID allergic outcomes. Longitudinal pediatric cohorts and interventions targeting epithelial alarmins or microbiome restoration may hold promise for prevention. Full article

This paper presents the design, manufacturing, testing, and validation of the MASKARA (Multiple Apertures for high-resolution SAR based on Ka-band Reflectarray) Breadboard Model (BBM), a large Ka-band reflectarray antenna developed for Synthetic Aperture Radar (SAR) applications. The BBM features a dual-offset antenna configuration intended for a high-resolution, wide-swath SAR instrument. At the core of the system is a 1.5 m × 0.55 m reflectarray operating between 35.5–36.0 GHz in the Ka-band. To our knowledge, this is the first demonstration of a reflectarray antenna designed to support two distinct modes of operation, exploiting the inherent advantages of reflectarrays—such as reduced cost and compact stowage—over traditional solutions. The antenna provides a high-resolution mode requiring a higher-gain beam in one polarization and a low-resolution mode covering a larger swath with broader beam coverage in the orthogonal polarization. The design process follows a holistic, multidisciplinary approach, integrating RF and thermomechanical considerations through iterative and concurrent design reviews. The BBM has been successfully manufactured and experimentally tested, and the measurement results show good agreement with simulations, confirming the validity of the proposed concept and demonstrating its potential for future high-performance SAR missions. Full article

Timely and reliable surface deformation monitoring is critical for hazard prevention and resource management in mining areas. However, traditional Time-Series Interferometric (TSI) Synthetic Aperture Radar techniques often suffer from low coherent point density in mining environments, limiting their effectiveness. To overcome this limitation, we propose an adaptive Polarimetric TSI (PolTSI) method that exploits dual-polarization Sentinel-1 data to achieve more reliable deformation monitoring in complex mining terrains. The method employs a dual-strategy optimization: amplitude dispersion–based optimization for Permanent Scatterer (PS) pixels and minimum mean square error (MMSE)-based polarimetric filtering followed by coherence maximization for Distributed Scatterer (DS) pixels. Experimental results from an open-pit mining area demonstrate that the proposed approach significantly improves phase quality and spatial coverage. In particular, the number of coherent monitoring points increased from 31,183 with conventional TSI to 465,328 using the proposed approach, corresponding to a 1392% improvement. This substantial enhancement confirms the method’s robustness in extracting deformation signals from low-coherence, heterogeneous mining surfaces. As one of the few studies to apply Polarimetric InSAR (Pol-InSAR) in active mining regions, our work demonstrates the underexplored potential of dual-pol SAR data for improving both the spatial density and reliability of time-series deformation mapping. The results provide a solid technical foundation for large-scale, high-precision surface monitoring in complex mining environments. Full article

Background: Ephedra przewalskii Stapf stems are a traditional Mongolian medicine commonly used to treat infectious diseases. Previous in vitro experiments have shown that the extract powder derived from its stems possesses antiviral activity. However, the active compounds responsible for this activity in E. przewalskii Stapf have not yet been identified or evaluated. This study aimed to identify the active components in E. przewalskii that exhibit antiviral effects against SARS-CoV-2 in vitro and validate their antiviral activity. Methods: E. przewalskii stem extracts were subjected to high-performance liquid chromatography with varying methanol ratios in the mobile phase to obtain fractions with different polarities. Antiviral activity was assessed by infecting VeroE6/TMPRSS2 cells with the SARS-CoV-2 Delta strain and treating them with the obtained fractions. Infectious titers were measured using the 50% tissue culture infective dose (TCID₅₀) method, and half-maximal inhibitory concentration (IC₅₀) values were calculated for each fraction. The active components in the two fractions with the highest antiviral activity were identified and structurally characterized by nuclear magnetic resonance analysis. The antiviral activity of these compounds was confirmed by adding them to SARS-CoV-2-infected cells and measuring their infectious titers using the TCID₅₀ method. The IC₅₀ values were also calculated. Viral-particle inactivation assays were conducted by mixing the extracts with SARS-CoV-2 and measuring infectious titers. Results: (−)-Catechin, (+)-epigallocatechin-(2α→O→7,4α→8)-(−)-epicatechin, and ent-epicatechin-(4α→8;2α→O→7)-catechin were isolated from E. przewalskii. These compounds exhibited significant antiviral activity against SARS-CoV-2 but demonstrated minimal direct virucidal effects. Conclusion: (−)-Catechin, (+)-epigallocatechin-(2α→O→7,4α→8)-(−)-epicatechin, and ent-epicatechin-(4α→8;2α→O→7)-catechin exhibit antiviral activity against SARS-CoV-2 in infected cells. Full article