It is generally recognized that the root uptake capacity of grafted plants strongly depends on the rootstocks’ well-developed root system. However, we found that grafted plants showed different nitrate uptake capacities when different varieties of oriental melon scion were grafted onto the same squash rootstock, suggesting that the scion regulated the nitrate uptake capacity of the rootstock root. In this study, we estimated the nitrate uptake capacity of grafted plants with the different oriental melon varieties’ seedlings grafted onto the same squash rootstocks. The results indicated a significant difference in the nitrate uptake rate and activity of two heterologous grafting plants. We also showed a significant difference in CmoNRT2.1 expression in the roots of two grafting combinations and verified the positive regulation of nitrate uptake by CmoNRT2.1 expression. In addition, the two varieties of oriental melon scion had highly significant differences in CmHY5 expression, which was transported to the rootstock and positively induced CmoHY5-1 and CmoHY5-2 expression in the rootstock roots. Meanwhile, CmHY5 could positively regulate CmoNRT2.1 expression in the rootstock roots. Furthermore, CmoHY5-1 and CmoHY5-2 also positively regulated CmoNRT2.1 expression, respectively, and CmoHY5-1 dominated the positive regulation of CmoNRT2.1, while CmHY5 could interact with CmoHY5-1 and CmoHY5-2, respectively, to jointly regulate CmoNRT2.1 expression. The oriental melon scion regulated the nitrate uptake capacity of the melon/squash grafting plant roots, and the higher expression of CmHY5 in the oriental melon scion leaves, the more substantial the nitrate uptake capacity of squash rootstock roots.
At present, the core of lidar data registration algorithms depends on search correspondence, which has become the core factor limiting the performance of this kind of algorithm. For point-based algorithms, the data coincidence rate is too low, and for line-based algorithms, the method of searching the correspondence is too complex and unstable. In this paper, a laser radar data registration algorithm based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering is proposed, which avoids the search and establishment of the corresponding relationship. Firstly, a ring band filter is designed to process the outliers with noise in a point cloud. Then, the adaptive threshold is used to extract the line segment features in the laser radar point cloud. For the point cloud to be registered, a DBSCAN density clustering algorithm is used to obtain the key clusters of the rotation angle and translation matrix. In order to evaluate the similarity of the two frames of the point cloud in the key clusters after data registration, a kernel density estimation method is proposed to describe the registered point cloud, and K-L divergence is used to find the optimal value in the key clusters. The experimental results show that the proposed algorithm avoids the direct search of the correspondence between points or lines in complex scenes with many outliers in laser point clouds, which can effectively improve the robustness of the algorithm and suppress the influence of outliers on the algorithm. The relative error between the registration result and the actual value is within 10%, and the accuracy is better than the ICP algorithm.
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