Redox imbalance is a primary cause for endothelial dysfunction (ED). Under oxidant stress, many critical proteins regulating endothelial function undergo oxidative modifications that lead to ED. Cellular levels of GSH, the primary reducing source in cells, can significantly regulate cell function via reversible protein thiol modification. N-Acetyl cysteine (NAC), a precursor for GSH biosynthesis, is beneficial for many vascular diseases; however, the detailed mechanism of these benefits is still not clear. From HPLC analysis, NAC significantly increases both cellular GSH and BH4 levels. Immunoblotting of eNOS and DUSP4, a dual-specificity phosphatase with a cysteine as its active residue, revealed that both enzymes are up-regulated by NAC. EPR spin-trapping further demonstrated that NAC enhances NO generation from cells. Long-term exposure to Cd2+ contributes to DUSP4 degradation and the uncontrolled activation of p38 and ERK1/2, leading to apoptosis. Treatment with NAC prevents DUSP4 degradation and protects cells against Cd2+-induced apoptosis. Moreover, the increased DUSP4 expression can redox regulate p38 and ERK1/2 pathways from hyper-activation, providing a survival mechanism against the toxicity of Cd2+. DUSP4 gene knockdown further supports the hypothesis that DUSP4 is an antioxidant gene, critical in the modulation of eNOS translation, and thus protects against Cd2+-induced stress. Depletion of intracellular GSH by BSO makes cells more susceptible to Cd2+-induced apoptosis. Pre-treatment with NAC prevents p38 over-activation and thus protects the endothelium from this oxidative stress. Therefore, the identification of DUSP4 activation by NAC provides a novel target for future drug design.
The classification and segmentation of large-scale, sparse, LiDAR point cloud with deep learning are widely used in engineering survey and geoscience. The loose structure and the non-uniform point density are the two major constraints to utilize the sparse point cloud. This paper proposes a lightweight auxiliary network, called the rotated density-based network (RD-Net), and a novel point cloud preprocessing method, Grid Trajectory Box (GT-Box), to solve these problems. The combination of RD-Net and PointNet was used to achieve high-precision 3D classification and segmentation of the sparse point cloud. It emphasizes the importance of the density feature of LiDAR points for 3D object recognition of sparse point cloud. Furthermore, RD-Net plus PointCNN, PointNet, PointCNN, and RD-Net were introduced as comparisons. Public datasets were used to evaluate the performance of the proposed method. The results showed that the RD-Net could significantly improve the performance of sparse point cloud recognition for the coordinate-based network and could improve the classification accuracy to 94% and the segmentation per-accuracy to 70%. Additionally, the results concluded that point-density information has an independent spatial–local correlation and plays an essential role in the process of sparse point cloud recognition.
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