A Dual-Stream-Modulated Learning Framework for Illuminating and Super-Resolving Ultra-Dark Images.

Enhancement of image resolution for scenes captured under extremely dim conditions represents a practical yet challenging problem that has received little attention. In such low-light scenarios, the limited lighting and minimal signal clarity tend to intensify issues such as diminished detail visibility and altered color accuracy, which are often more severe during the image enhancement process than in scenarios with adequate lighting. Consequently, standard methods for enhancing low-light images or improving their resolution, whether implemented independently or through a combined approach, generally face challenges in effectively restoring luminance, preserving color integrity, and detailing intricate features. To conquer these issues, this article introduces an innovative dual-stream (DS) modulated learning framework designed to tackle the real-world coupled degradation issues in super-resolution (SR) under low-light conditions. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint to specifically target uneven illumination. We develop illumination-semantic dual modulator (ISDM), a refinement middleware embedded in the decoding stage to bridge illumination and semantic features concurrently, aimed at safeguarding the integrity of lighting and color details at the feature level. Our approach replaces simple upsampling methods with the resolution-sensitive merging upsampler (RSMU) module, which integrates diverse sampling techniques to effectively reduce artifacts and halo effects. Comprehensive experiments on three benchmarks showcase the applicability and generalizability of our approach to diverse and challenging ultra-poorly lit settings, outperforming state-of-the-art methods with a notable improvement. The code and benchmark are publicly available at https://github.com/moriyaya/UltraIS.

Preparation and Epitope Identification of Monoclonal Antibodies against the NS6 Protein of Porcine Deltacoronavirus (PDCoV).

Porcine deltacoronavirus (PDCoV) is an emerging enteric pathogen that causes substantial economic losses in the swine industry worldwide. The PDCoV NS6 protein is an accessory protein that plays a pivotal role in the viral life cycle and immune evasion. However, the functions of NS6 and its role in PDCoV pathogenesis remain largely unknown. In this study, we prepared a monoclonal antibody (mAb) 5-A11 that specifically recognizes the PDCoV NS6 protein. The mAb 5-A11 exhibited high specificity for PDCoV, with no cross-reactivity with several major porcine pathogenic viruses. Furthermore, the epitope recognized by mAb 5-A11 was precisely mapped to residues 70EYGSIYGKDFI80 of the NS6 protein using Western blot analysis. Notably, this epitope is highly conserved among different PDCoV isolates. Substantial variations were observed when comparing this epitope with the corresponding regions in the NS6 proteins of other δ coronaviruses, suggesting potential differences in the structure, function, and antigenicity of their NS6 proteins. Our findings provide valuable tools and insights for further elucidating the functions of the NS6 protein and its role in PDCoV pathogenesis, as well as for developing diagnostic and therapeutic strategies against PDCoV infection.

Porcine deltacoronavirus NS7a antagonizes JAK/STAT pathway by inhibiting the interferon-stimulated gene factor 3 (ISGF3) formation.

The accessory proteins of coronaviruses play a crucial role in facilitating virus-host interactions and modulating host immune responses. Previous study demonstrated that the NS7a protein of porcine deltacoronavirus (PDCoV) partially hindered the host immune response by impeding the induction of IFN-α/ß. However, the potential additional functions of NS7a protein in evading innate immunity have yet to be elucidated. This study aimed to investigate the mechanism of PDCoV NS7a protein regulating the JAK/STAT signaling pathway. We presented evidence that NS7a effectively inhibited ISRE promoter activity and ISGs transcription. NS7a hindered STAT1 phosphorylation, interacted with STAT2 and IRF9, and further impeded the formation and nuclear accumulation of ISGF3. Furthermore, comparative analysis of NS7a across different PDCoV strains revealed that the mutation of Leu4 to Pro4 led to an increase in the molecular weights of NS7a and disrupted its inhibition on the JAK/STAT signaling pathway. This finding implied that NS7a with key amino acids may be an indicator of virulence for PDCoV strains. Taken together, this study revealed a novel role of NS7a in antagonizing the IFN-I signaling pathway.

Coding-Complete Genome Sequences of Two Atypical Porcine Pestivirus Strains from Anhui Province, China.

In 2021, two atypical porcine pestivirus (APPV) strains, AH06/2021 and AH22/2021, were identified from suckling piglets showing congenital tremor in Anhui Province, China. Genome sequence analysis indicated that the two strains shared 81.19% to 93.98% nucleotide identities with other APPV strains.