Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the fitting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.

Purpose Recent clinical trials with agents targeting immune checkpoint pathway have emerged as an important therapeutic approach for a broad range of cancer types. Resveratrol has been shown to possess cancer preventive and therapeutic effects and has potential to be chemotherapeutic agent/adjuvant. Here, we assessed the effect of resveratrol on immune checkpoint pathways. Methods The expression patterns of Wnt components and PD-L1 were examined by Western blot, Chromatin immunoprecipitation (ChIP) was used for analysis of DNA–protein interaction, the promoter activity was determined by luciferase reporter assay, apoptosis was analyzed by flow cytometry and the ability of the resveratrol to modulate T cell function was assessed in a co-culture system. Results Although the dose-, and cell-type dependent effects of resveratrol on PD-L1 expression have been reported, we show here that resveratrol dose-dependently upregulates PD-L1 expression at the range of pharmacologic-achievable concentrations in lung cancer cells and that is essential for suppression of T-cell-mediated immune response. We also found that Wnt pathway is critical for mediating resveratrol-induced PD-L1 upregulation. Mechanistically, resveratrol activates SirT1 deacetylase to deacetylate and stabilize transcriptional factor Snail. Snail in turn inhibits transcription of Axin2, which leads in disassembly of destruction complex and enhanced binding of β-catenin/TCF to PD-L1 promoter. Conclusion We conclude that resveratrol is capable to suppress anti-tumor immunity by controlling mainly PD-L1 expression. This finding will extend the understanding of resveratrol in regulation of tumor immunity and is relevant to the debate on resveratrol supplements for lung cancer patients.

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