Recombinant production of SAG1 fused with xylanase in Pichia pastoris induced higher protective immunity against Eimeria tenella infection in chicken.

Chicken coccidiosis is an intestinal disease caused by the parasite Eimeria, which severely damages the growth of chickens and causes significant economic losses in the poultry industry. Improvement of the immune protective effect of antigens to develop high efficiency subunit vaccines is one of the hotspots in coccidiosis research. Sporozoite-specific surface antigen 1 (SAG1) of Eimeria tenella (E. tenella) is a well-known protective antigen and is one of the main target antigens for the development of subunit, DNA and vector vaccines. However, the production and immunoprotective effects of SAG1 need to be further improved. Here, we report that both SAG1 from E. tenella and its fusion protein with the xylanase XynCDBFV-SAG1 are recombinant expressed and produced in Pichia pastoris (P. pastoris). The substantial expression quantity of fusion protein XynCDBFV-SAG1 is achieved through fermentation in a 15-L bioreactor, reaching up to about 2 g/L. Moreover, chickens immunized with the fusion protein induced higher protective immunity as evidenced by a significant reduction in the shedding of oocysts after E. tenella challenge infection compared with immunized with recombinant SAG1. Our results indicate that the xylanase enhances the immunogenicity of subunit antigens and has the potential for developing novel molecular adjuvants. The high expression level of fusion protein XynCDBFV-SAG1 in P. pastoris holds promise for the development of effective recombinant anti-coccidial subunit vaccine.

SLAM and 3D Semantic Reconstruction Based on the Fusion of Lidar and Monocular Vision.

Monocular camera and Lidar are the two most commonly used sensors in unmanned vehicles. Combining the advantages of the two is the current research focus of SLAM and semantic analysis. In this paper, we propose an improved SLAM and semantic reconstruction method based on the fusion of Lidar and monocular vision. We fuse the semantic image with the low-resolution 3D Lidar point clouds and generate dense semantic depth maps. Through visual odometry, ORB feature points with depth information are selected to improve positioning accuracy. Our method uses parallel threads to aggregate 3D semantic point clouds while positioning the unmanned vehicle. Experiments are conducted on the public CityScapes and KITTI Visual Odometry datasets, and the results show that compared with the ORB-SLAM2 and DynaSLAM, our positioning error is approximately reduced by 87%; compared with the DEMO and DVL-SLAM, our positioning accuracy improves in most sequences. Our 3D reconstruction quality is better than DynSLAM and contains semantic information. The proposed method has engineering application value in the unmanned vehicles field.