Altered counts and mitochondrial mass of peripheral blood leucocytes in patients with chronic hepatitis B virus infection.

Hepatitis B virus (HBV) damages liver cells through abnormal immune responses. Mitochondrial metabolism is necessary for effector functions of white blood cells (WBCs). The aim was to investigate the altered counts and mitochondrial mass (MM) of WBCs by two novel indicators of mitochondrial mass, MM and percentage of low mitochondrial membrane potential, MMPlow%, due to chronic HBV infection. The counts of lymphocytes, neutrophils and monocytes in the HBV infection group were in decline, especially for lymphocyte (p = 0.034) and monocyte counts (p = 0.003). The degraded MM (p = 0.003) and MMPlow% (p = 0.002) of lymphocytes and MM (p = 0.005) of monocytes suggested mitochondrial dysfunction of WBCs. HBV DNA within WBCs showed an extensive effect on mitochondria metabolic potential of lymphocytes, neutrophils and monocytes indicated by MM; hepatitis B e antigen was associated with instant mitochondrial energy supply indicated by MMPlow% of neutrophils; hepatitis B surface antigen, antiviral therapy by nucleos(t)ide analogues and prolonged infection were also vital factors contributing to WBC alterations. Moreover, degraded neutrophils and monocytes could be used to monitor immune responses reflecting chronic liver fibrosis and inflammatory damage. In conclusion, MM combined with cell counts of WBCs could profoundly reflect WBC alterations for monitoring chronic HBV infection. Moreover, HBV DNA within WBCs may be a vital factor in injuring mitochondria metabolic potential.

A multitask model for realtime fish detection and segmentation based on YOLOv5.

The accuracy of fish farming and real-time monitoring are essential to the development of "intelligent" fish farming. Although the existing instance segmentation networks (such as Maskrcnn) can detect and segment the fish, most of them are not effective in real-time monitoring. In order to improve the accuracy of fish image segmentation and promote the accurate and intelligent development of fish farming industry, this article uses YOLOv5 as the backbone network and object detection branch, combined with semantic segmentation head for real-time fish detection and segmentation. The experiments show that the object detection precision can reach 95.4% and the semantic segmentation accuracy can reach 98.5% with the algorithm structure proposed in this article, based on the golden crucian carp dataset, and 116.6 FPS can be achieved on RTX3060. On the publicly available dataset PASCAL VOC 2007, the object detection precision is 73.8%, the semantic segmentation accuracy is 84.3%, and the speed is up to 120 FPS on RTX3060.

Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours.

With the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precision animal husbandry and to avoid human influence on breeding, real-time automated monitoring methods have been used in this area. To be specific, on the basis of instance segmentation, the activities of individual geese are accurately detected, counted, and analyzed, which is effective for achieving traceability of the condition of the flock and reducing breeding costs. We trained QueryPNet, an advanced model, which could effectively perform segmentation and extraction of geese flock. Meanwhile, we proposed a novel neck module that improved the feature pyramid structure, making feature fusion more effective for both target detection and instance individual segmentation. At the same time, the number of model parameters was reduced by a rational design. This solution was tested on 639 datasets collected and labeled on specially created free-range goose farms. With the occlusion of vegetation and litters, the accuracies of the target detection and instance segmentation reached 0.963 (mAP@0.5) and 0.963 (mAP@0.5), respectively.