YOLO-SK: A lightweight multiscale object detection algorithm.

YOLOv5 is an excellent object-detection model. However, it fails to fully use multiscale information when detecting objects with significant scale variations. It might use irrelevant contextual information, leading to incorrect predictions, particularly for low-performance devices. In this study, we selected lightweight YOLOv5s as the baseline model and proposed an improved model called YOLO-SK to overcome this limitation. YOLO-SK introduced several key improvements, the most important being the collaborative work of the weighted dense feature fusion network and SK attention prediction head. The proposed weighted dense feature fusion network could dynamically fuse features at different scales using autonomous learning parameters and cross-layer fusion capabilities. This enabled a balanced feature fusion ability in the output feature maps of different scales, thereby enhancing the richness of the effective information in the fused feature maps. The prediction head equipped with the SK attention mechanism broadened the scope of the model's receptive field and sharpened the focus on the target characteristics. This made it possible to glean more information about the target from the feature map output by employing a weighted dense feature fusion network. In addition, in order to improve the model's performance in terms of both accuracy and volume, we implemented the SIoU loss function and the Ghost Conv. The use of the model allowed for a more precise and in-depth comprehension of the event, which was made possible by all of these various methods of improvement. Extensive testing done on the PASCAL VOC 2007 and 2012 datasets showed that YOLO-SK was able to achieve considerable gains in prediction accuracy when compared with the baseline model (YOLOv5s), all while keeping the same level of model complexity. To be more specific, mAP@.5 increased by 2.6 %, and mAP@.5:.95 increased by 4.8 %. The advancements that were made and detailed in this paper could serve as a springboard for additional research that aims to improve the precision of multiscale object identification models for low-performance devices.

Immune cell early activation, apoptotic kinetic, and T-cell functional impairment in domestic pigs after ASFV CADC_HN09 strain infection.

African swine fever (ASF) caused by the African swine fever virus (ASFV) is a fatal and highly contagious disease of domestic pigs characterized by rapid disease progression and death within 2 weeks. How the immune cells respond to acute ASFV infection and contribute to the immunopathogenesis of ASFV has not been completely understood. In this study, we examined the activation, apoptosis, and functional changes of distinct immune cells in domestic pigs following acute infection with the ASFV CADC_HN09 strain using multicolor flow cytometry. We found that ASFV infection induced broad apoptosis of DCs, monocytes, neutrophils, and lymphocytes in the peripheral blood of pigs over time. The expression of MHC class II molecule (SLA-DR/DQ) on monocytes and conventional DCs as well as CD21 expression on B cells were downregulated after ASFV infection, implying a potential impairment of antigen presentation and humoral response. Further examination of CD69 and ex vivo expression of IFN-γ on immune cells showed that T cells were transiently activated and expressed IFN-γ as early as 5 days post-infection. However, the capability of T cells to produce cytokines was significantly impaired in the infected pigs when stimulated with mitogen. These results suggest that the adaptive cellular immunity to ASFV might be initiated but later overridden by ASFV-induced immunosuppression. Our study clarified the cell types that were affected by ASFV infection and contributed to lymphopenia, improving our understanding of the immunopathogenesis of ASFV.