Transcriptomic Screening of Alternaria oxytropis Isolated from Locoweed Plants for Genes Involved in Mycotoxin Swaisonine Production.

Locoweed is a collective name for a variety of plants, such as Oxytropis and Astragalus L. When these plants are infected by some fungi or endophytes, they will produce an alkaloid (swainsonine) that is harmful to livestock. Chronic toxicity characterized by neurological disorders occurs in livestock overfed on locoweed, and swainsonine (SW) is considered a major toxic component. The mechanism of the SW synthesis of endophytic fungi from locoweed remains unknown. In order to further discover the possible synthetic pathway of SW, in this study, a mycotoxin (SW) producer, Alternaria oxytropis isolate, UA003, isolated from Locoweed plants, and its mutant were subjected to transcriptomic analyses to ascertain the genes involved in the synthesis of this toxin. Mutant strain A. oxytropis E02 was obtained by ethyl methanesulfonate (EMS) mutagenesis treatment, and the strains were sequenced with different culture times for transcriptomic analysis and screening of differentially expressed genes. The results show a highly significant (p < 0.01) increase in SW yield in the A. oxytropis E02 strain obtained by EMS mutagenesis treatment compared to A. oxytropis UA003. A total of 637 differentially expressed genes were screened by transcriptome sequencing analysis, including 11 genes potentially associated with SW biosynthesis. These genes were screened using GO and KEGG data annotation and analysis. Among the differential genes, evm.TU.Contig4.409, evm.TU.Contig19.10, and evm.TU.Contig50.48 were associated with L-lysine biosynthesis, the L-pipecolic acid pathway, and the α-aminoadipic acid synthesis pathway. This study provides new insights to elucidate the mechanism of SW synthesis of endophytic fungi in locoweed and provides data support for further exploration of A. oxytropis genomics studies.

Single-Camera Multi-View 6DoF pose estimation for robotic grasping.

Accurately estimating the 6DoF pose of objects during robot grasping is a common problem in robotics. However, the accuracy of the estimated pose can be compromised during or after grasping the object when the gripper collides with other parts or occludes the view. Many approaches to improving pose estimation involve using multi-view methods that capture RGB images from multiple cameras and fuse the data. While effective, these methods can be complex and costly to implement. In this paper, we present a Single-Camera Multi-View (SCMV) method that utilizes just one fixed monocular camera and the initiative motion of robotic manipulator to capture multi-view RGB image sequences. Our method achieves more accurate 6DoF pose estimation results. We further create a new T-LESS-GRASP-MV dataset specifically for validating the robustness of our approach. Experiments show that the proposed approach outperforms many other public algorithms by a large margin. Quantitative experiments on a real robot manipulator demonstrate the high pose estimation accuracy of our method. Finally, the robustness of the proposed approach is demonstrated by successfully completing an assembly task on a real robot platform, achieving an assembly success rate of 80%.