Search Results (643)

Background/Objectives: SARS-CoV-2 continues to generate antigenically divergent variants that reduce the breadth of existing vaccine-induced antibody responses. Fc-fusion subunit vaccines offer advantages in stability, antigen display, and Fc-mediated immune engagement. This study aimed to design and evaluate a tetravalent RBD–Fc fusion construct incorporating RBDs from Wuhan-Hu-1 and Omicron BA.4/BA.5 and to determine whether this configuration can induce broad antibody recognition across SARS-CoV-2 variants. The objective was to assess its feasibility, biochemical properties, and initial immunogenicity. Methods: Immune responses to the construct were first assessed using the C-ImmSim simulation platform. The full-length fusion was synthesized, subcloned into pcDNA3.1(+), expressed in HEK293 cells, and purified by Protein G affinity chromatography. Protein integrity was evaluated by reducing SDS–PAGE. BALB/c mice (female, 8 weeks) were immunized with a prime–boost–boost schedule, and sera were analyzed by ELISA, considering binding to Wuhan-Hu-1, Omicron BA.4/BA.5, and a panel of RBD variants. Results: In silico analysis predicted coordinated antigen clearance, class switching, memory B- and CD4⁺ T-cell formation, and transient cytokine induction. The recombinant protein was expressed efficiently, yielding a major ~56 kDa band and a ~23 kDa RBD fragment. Vaccinated mice generated strong IgG responses to Wuhan-Hu-1 and BA.4/BA.5 RBDs and showed broad binding to major variant RBDs. Conclusions: The tetravalent RBD–Fc fusion vaccine was successfully produced and elicited broad antibody binding across SARS-CoV-2 variants, supporting its potential as a versatile protein-based vaccine platform. Full article

Monitoring and identifying perennial cash crops is essential for optimizing agricultural resource allocation and supporting sustainable rural development. However, cross-regional recognition remains challenging due to cloud contamination, irregular mountainous topography, and climatic-driven phenological shifts. To address these issues, we propose a Region-Adaptive Multi-Head Phenology-Aware Network (RAM-PAMNet) that incorporates three key innovations. First, a Multi-source Temporal Attention Fusion (MTAF) module dynamically fuses Sentinel-1 SAR and Sentinel-2 optical time series to enhance temporal consistency and cloud robustness. Second, a Region-Aware Module (RAM) encodes topographic and climatic factors to adaptively adjust phenological windows across regions. Third, a Multi-Head Phenology-Aware Module (MHA-PAM) captures short-, mid-, and long-term phenological rhythms while integrating region-modulated attention for adaptive feature learning. The model was trained and validated in Changde, Hunan (694 patches; augmented to 2776; 70%/15%/15% split) and independently tested in Yaan, Sichuan (574 patches), two regions with contrasting elevation, terrain complexity, and hydrothermal regimes. RAM-PAMNet achieved an OA of 83.3%, mean F1 of 78.8%, and mIoU of 65.4% in Changde, and maintained strong generalization in Yaan with an mIoU of 59.2% and a DecayRate of 9.5, outperforming all baseline models. These results demonstrate that RAM-PAMNet effectively mitigates regional phenological mismatches and improves perennial crop mapping across heterogeneous environments. The proposed framework provides an interpretable and region-adaptive solution for large-scale monitoring of tea, citrus, and grape. Full article

Background: Cutaneous viral infections, defined as viral pathogens that either primarily affect the skin (e.g., herpesviruses, enteroviruses) or frequently produce dermatologic manifestations despite systemic tropism (e.g., HIV, SARS-CoV-2), can trigger systemic inflammatory and neurotropic responses that extend their impact to the nervous system. A growing body of evidence suggests that viruses with dermatologic manifestations may play a significant role in the pathogenesis of neurologic disorders. Summary: Although individual viruses have been studied in isolation, the skin–brain axis in viral infections remains incompletely characterized. This review synthesizes existing knowledge and highlights gaps in understanding the mechanisms linking cutaneous viral infections to neurologic disease. We explore the principal mechanisms linking viral skin infections to central and peripheral nervous system damage, including direct neuroinvasion, immune-mediated injury, and vascular or endothelial dysfunction. Particular attention is given to herpesviruses, retroviruses, enteroviruses, and respiratory viruses, which have been associated with conditions such as dementia, multiple sclerosis, myelopathies, Guillain-Barré syndrome, and the post-acute neurologic sequelae of COVID-19. Furthermore, we discuss the role of neuroinflammation in viral-associated neurodegeneration and highlight emerging evidence supporting the recombinant zoster vaccine (Shingrix) as a potential modulator of neuroinflammatory processes and a protective factor against dementia. Conclusions: Cutaneous viral infections extend beyond local skin pathology, contributing to a broad spectrum of neurologic complications through intertwined infectious and inflammatory mechanisms. A clearer understanding of how peripheral viral activity shapes central nervous system vulnerability remains a major unmet need. A multidisciplinary approach integrating dermatologic and neurologic perspectives is essential for early recognition and prevention. While observational studies suggest that zoster vaccination may reduce viral reactivation and modulate neuroinflammatory pathways, definitive evidence of neuroprotection is still lacking. Future studies should clarify causal relationships, test mechanistic hypotheses regarding skin–brain immune crosstalk, and explore vaccine-mediated neuroprotection as a novel therapeutic strategy. Full article

Background: Pediatric SARS-CoV-2 infection is usually mild, but in rare cases may lead to severe complications. Early recognition and comprehensive management are critical for favorable outcomes. Case Presentation: We present the case of a 2-year-old girl, previously healthy and unvaccinated against Streptococcus Pneumoniae (S. pneumoniae), who developed SARS-CoV-2 infection and acute otitis media. Initial laboratory evaluation revealed leukocytosis with neutrophilia and increased inflammatory markers. Antiviral and antibiotic treatment was initiated, but she remained febrile, polypneic, and tachycardic. The diagnosis of MIS-C was excluded; there was no involvement of two organs, and infection with S. pneumoniae serotype 19 F was identified. Given the unfavorable evolution, corticosteroid therapy and immunoglobulin were instituted, and subsequently, following the antibiogram result, antibiotic therapy was escalated to Meropenem and Linezolid. Clinical and laboratory parameters improved, but pericarditis with a small fluid slide and ECG changes were associated. The evolution was favorable with complete cardiac recovery at 30 days. Conclusion: This case highlights the importance of vigilant assessment for secondary bacterial infections and cardiac complications in pediatric COVID-19. Prompt recognition and targeted treatment are essential, and pneumococcal vaccination remains a fundamental preventive measure. Moreover, the scarcity of literature documenting SARS-CoV-2 infections complicated by pericarditis further underscores the uniqueness of this case and its relevance for specialists in the field. Full article

Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) methods exhibit a tendency to overfit specific operating conditions—such as radar parameters and background clutter—which frequently leads to high sensitivity against variations in these conditions. A novel electromagnetic reconstruction feature alignment (ERFA) method is proposed in this paper, which integrates electromagnetic reconstruction with feature alignment into a fully convolutional network, forming the ERFA-FVGGNet. The ERFA-FVGGNet comprises three modules: electromagnetic reconstruction using our proposed orthogonal matching pursuit with image-domain cropping-optimization (OMP-IC) algorithm for efficient, high-precision attributed scattering center (ASC) reconstruction and extraction; the designed FVGGNet combining transfer learning with a lightweight fully convolutional network to enhance feature extraction and generalization; and feature alignment employing a dual-loss to suppress background clutter while improving robustness and interpretability. Experimental results demonstrate that ERFA-FVGGNet boosts trustworthiness by enhancing robustness, generalization and interpretability. Full article

Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from overfitting under few-shot conditions, making it difficult to fully exploit the discriminative features contained in limited samples. Moreover, SAR targets frequently exhibit highly similar background scattering patterns, which further increase intra-class variations and reduce inter-class separability, thereby constraining the performance of few-shot recognition. To address these challenges, this paper proposes an adaptive contrastive metric (ACM) network with background suppression for few-shot SAR target recognition. Specifically, a spatial squeeze-and-excitation (SSE) attention module is introduced to adaptively highlight salient scattering structures of the target while effectively suppressing noise and irrelevant background interference, thus enhancing the robustness of feature representation. In addition, an ACM module is designed, where query samples are compared not only with their corresponding support class but also with the remaining classes. This enables explicit suppression of confusing background features and enlarges inter-class margins, thereby improving the discriminability of the learned feature space. The experimental results on publicly available SAR target recognition datasets demonstrate that the proposed method achieves significant improvements in background suppression and consistently outperforms several state-of-the-art metric-based few-shot learning approaches, validating the effectiveness and generalizability of the proposed framework. Full article

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and Spatial Transformation Network (MFE-STN), specifically designed for the task of discriminating between true targets and deceptive false targets created by SAR jamming, which can be seamlessly integrated with existing CNN backbones without architecture modification. MFE-STN integrates three complementary operations: (i) wavelet decomposition to extract the overall geometric features and scattering distribution of the target, (ii) a manifold transformation module for non-linear alignment of heterogeneous feature spaces, and (iii) a lightweight deformable spatial transformer that compensates for local geometric distortions introduced by deceptive jamming. By analyzing seven typical parameter-mismatch effects, we construct a simulated dataset containing six representative classes—four known classes and two unseen classes. Experimental results demonstrate that inserting MFE-STN boosts the average F1-score of known targets by 12.19% and significantly improves identification accuracy for unseen targets. This confirms the module’s capability to capture discriminative signatures to distinguish genuine targets from deceptive ones while exhibiting strong cross-domain generalization capabilities. Full article

Owing to the all-weather, day-and-night imaging capability of Synthetic Aperture Radar (SAR), SAR automatic target recognition (ATR) has long been a central focus in academia and industry. Since 2013, deep learning has become the dominant paradigm for SAR ATR owing to its end-to-end learning capability and robust feature-extraction capacity. To the best of our knowledge, this work provides the first systematic survey of SAR target recognition from dual closed-set and open-set perspectives and identifies four major performance bottlenecks: data scarcity, algorithmic limitations, hardware constraints, and application barriers. To address the first three bottlenecks, an in-depth analysis of closed-set solutions is presented, covering data augmentation, network optimization, and lightweight architectures. For the fourth challenge, a comprehensive analysis of open-set SAR recognition methods is provided. The intrinsic relationship and distinctions between closed-set and open-set recognition are further examined. To tackle the open-set challenge, an enhanced domain-adaptive algorithm for open-set recognition is proposed. Experiments on the OpenSAR and FUSAR datasets demonstrate at least a 3% improvement in open-set accuracy (OSA) over seven recent domain-adaptation algorithms. The rejection rate of unknown targets (RRU) reaches 80.30%, demonstrating a strong ability to distinguish unknown-class targets and offering practical insights for future research. Finally, potential directions for advancing SAR ATR are outlined, providing a comprehensive reference for the continued development of deep-learning-based SAR recognition. Full article

Synthetic aperture radar (SAR) features all-weather and all-day imaging capabilities, long-range detection, and high resolution, making it indispensable for battlefield reconnaissance, target detection, and guidance. In recent years, deep learning has emerged as a prominent approach for the classification of SAR image targets, owing to its hierarchical feature extraction, progressive refinement, and end-to-end learning capabilities. However, challenges such as the high cost of SAR data acquisition and the limited number of labeled samples often result in overfitting and poor model generalization. In addition, conventional layers typically operate with fixed receptive fields, making it difficult to simultaneously capture multiscale contextual information and dynamically focus on salient target features. To address these limitations, this paper proposes a novel architecture: the Multiscale Dilated Fusion Attention All-Convolution Network (MDFA-AconvNet). The model incorporates a multiscale dilated attention mechanism that significantly broadens the receptive field across varying target scales in SAR images without compromising spatial resolution, thereby enhancing multiscale feature extraction. Furthermore, by introducing both channel attention and spatial attention mechanisms, the model is able to selectively emphasize informative feature channels and spatial regions relevant to target recognition. These attention modules are seamlessly integrated into the All-Convolution Network (A-convNet) backbone, resulting in comprehensive performance improvements. Extensive experiments on the MSTAR dataset demonstrate that the proposed MDFA-AconvNet achieves a high classification accuracy of 99.38% in ten target classes, markedly outperforming the original A-convNet algorithm. These compelling results highlight the model’s robustness against target variations and its significant potential for practical deployment, paving the way for more efficient SAR image classification and recognition systems. Full article

Aberrant activation of innate immunity promotes the development of pulmonary arterial hypertension (PAH); however, the role of pattern recognition by Toll-like receptors (TLRs) within the pulmonary vasculature remains unclear. To consolidate knowledge (as of June 2025) about TLRs and their interactions with viruses in PAH and to identify therapeutic implications. A narrative review of experimental and clinical studies investigating ten TLRs in the context of the pulmonary vascular microenvironment and viral infections. Activation of TLR1/2, TLR4, TLR5/6, TLR7/8, and TLR9 converges on the MyD88–NF-κB/IL-6 axis, thereby enhancing endothelial-mesenchymal transition, smooth muscle proliferation, oxidative stress, thrombosis, and maladaptive inflammation, ultimately increasing pulmonary vascular resistance. Conversely, TLR3, through TRIF–IFN-I, preserves endothelial integrity and inhibits vascular remodeling; its downregulation correlates with PAH severity, and poly (I:C) restitution has been shown to improve hemodynamics and right ventricular function. HIV-1, EBV, HCV, endogenous retrovirus K, and SARS-CoV-2 infections modulate TLR circuits, either amplifying pro-remodeling cascades or attenuating protective pathways. The “TLR rheostat” is shaped by polymorphisms, ligand biochemistry, compartmentalization, and biomechanical forces. The balance between MyD88-dependent signaling and the TRIF–IFN-I axis determines the trajectory of PAH. Prospective therapeutic strategies may include TLR3 agonists, MyD88/NF-κB inhibitors, modulation of IL-6, and combination approaches integrating antiviral therapy with targeted immunomodulation in a precision approach. Full article

Background/Objectives: In this work, a series of six new indazole-benzimidazole hybrids (M1–M6) were designed, synthesized, and fully characterized. The design of these compounds was based on the combination of two pharmacophoric units, indazole and benzimidazole, both known for their broad spectrum of biological activities. Methods: The molecular hybridization strategy was planned to combine these scaffolds through an effective synthetic pathway, using 6-nitroindazole, two 2-mercaptobenzimidazoles, and 1,3- or 1,5-dihaloalkanes as key precursors, affording the desired hybrids in good yields and with enhanced biological activity. Quantum chemical calculations were performed to investigate the structural, electronic, and electrostatic properties of M1–M6 molecules using Density Functional Theory (DFT) at the B3LYP/6-311++G(d,p) level. The antimicrobial activity efficacy of these compounds was assessed in vitro against four Gram-positive bacteria (Staphylococcus aureus, Enterococcus faecalis, Bacillus cereus, and Lactobacillus plantarum), four Gram-negative bacteria (Salmonella enteritidis, Escherichia coli, Campylobacter coli, Campylobacter jejuni), and four fungal strains (Saccharomyces cerevisiae, Candida albicans, Candida tropicalis, and Candida glabrata) using ampicillin and tetracycline as reference standard drugs. Results: Among the series, compound M6 exhibited remarkable antimicrobial activity, with minimum inhibitory concentrations (MIC) of 1.95 µg/mL against S. cerevisiae and C. tropicalis, and 3.90 µg/mL against S. aureus, B. cereus, and S. enteritidis, while the standards Ampicillin (AmB) (MIC ≥ 15.62 µg/mL) and Tetracycline (TET) (MIC ≥ 7.81 µg/mL) exhibited higher MIC values. To gain molecular insights into the compounds, an in silico docking study was performed to determine the interactions of M1–M6 ligands against the antimicrobial target beta-ketoacyl-acyl carrier protein (ACP) synthase III complexed with malonyl-COA (PDB ID: 1HNJ). Molecular modeling data provided valuable information on the structure-activity relationship (SAR) and the binding modes influencing the candidate ligand-protein recognition. Amino acid residues, such as Arg249, located in the solvent-exposed region, were essential for hydrogen bonding with the nitro group of the 6-nitroindazole moiety. Furthermore, polar side chains such as Asn274, Asn247, and His244 participated in interactions mediated by hydrogen bonding with the 5-nitrobenzimidazole moiety of these compound series. Conclusions: The hybridization of indazole and benzimidazole scaffolds produced compounds with promising antimicrobial activity, particularly M6, which demonstrated superior potency compared to standard antibiotics. Computational and docking analyses provided insights into the structure–activity relationships, highlighting these hybrids as potential candidates for antimicrobial drug development. Full article

The efficacy of data-driven automatic target recognition (ATR) algorithms relies on the prior knowledge acquired from the target sample set. However, the lack of knowledge of high-value unknown target samples hinders the practical application of existing ATR models, as the acquisition of this SAR imagery is often challenging. In this paper, we propose an out-of-distribution knowledge inference-based approach for the implementation of open-set-recognition tasks in SAR imagery. The proposed method integrates two modules: out-of-distribution feature inference and a knowledge-sharing retrain mechanism. First, the proposed out-of-distribution feature inference module aims to provide the requisite prior knowledge for the ATR model to effectively recognize unknown target samples. Furthermore, the aforementioned module also employs a compact feature extraction scheme to mitigate the potential overlap between the constructed out-of-distribution feature distribution and the known sample feature distribution. Finally, the proposed method employs the novel knowledge-sharing retraining mechanism to learn prior knowledge of unknown SAR target samples. Several experimental results show the superiority of the proposed approach based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Some ablation experiments also demonstrate the role of each module of the proposed approach. Even when one category of target sample information is completely absent from the training set, the recognition accuracy of the proposed approach still achieves $90.31\%$. Full article

The SARS-CoV-2 pandemic has posed global medical challenges due to its ability to affect multiple organ systems. Among the post-COVID-19 complications, multisystem inflammatory syndrome has emerged as a severe condition affecting both children (MIS-C) and adults (MIS-A). This review aims to compile and analyze published data to investigate clinical characteristics, laboratory findings, and outcomes of MIS post-COVID-19. A comprehensive search of various databases was conducted to identify studies reporting MIS-related complications in pediatric and adult populations post-COVID-19 infection. Screening, data extraction, and cross-checking were performed by two independent reviewers. Only 64 studies met our inclusion criteria, and compiled results revealed that cardiac complications were the predominant manifestation followed by gastrointestinal, hematologic, neurological, and mucocutaneous involvement. Laboratory findings consistently demonstrated elevated inflammatory markers including CRP, ferritin, D-dimer, and IL-6. Most patients required hospitalization, and many needed intensive care; treatment typically involved IVIG, corticosteroids, and biologic therapies. While most patients recovered, a subset experienced persistent complications. These findings highlight the importance of early recognition, multidisciplinary management, and structured follow-up for MIS. Future research is warranted to clarify the underlying mechanisms, risk factors, and long-term outcomes associated with MIS in post-COVID-19 patients. Full article

Interferometric Synthetic Aperture Radar (InSAR) provides unique advantages for sea-crossing bridge monitoring through continuous, large-scale deformation detection. Dividing monitoring data into specific deformation patterns helps establish the connection between bridge deformation and its underlying mechanisms. However, the classification of complex and nonlinear bridge deformations often requires extensive manual labeling work. To achieve automatic classification of deformation patterns with minimal labeled data, this study introduces a transfer learning approach and proposes an InSAR-based method for deformation pattern recognition of cross-sea bridges. At first, deformation time series of the study area are acquired by PS-InSAR, with GNSS results confirming less than 10% error. Then, six types of deformation are identified, including stable, linear, step, piecewise linear, power law, and temperature-related types. Large amounts of simulated data with labels are generated based on these six types. Subsequently, four models—TCN, Transformer, TFT, and ROCKET—are trained using synthetic data and finely adjusted using few real data. Finally, the final classification results are weighted by the classification results of multiple models. Even though confidence and global consistency of each single model are also calculated, the final result is the combined result of a set of multi-type confidences. ROCKET achieved the highest accuracy on simulation data (96.27%) in these four representative models, while ensemble weighting improved robustness on real data. The methodology addresses supervised learning’s labeled data requirements through synthetic data generation and ensemble classification, producing probabilistic outputs that preserve uncertainty information rather than deterministic labels. The framework enables automatic classification of sea-crossing bridge deformation patterns with minimal labeled data, identifying patterns with distinct dominant factors and providing probabilistic information for engineering decision making. 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