Differential response to acute low dose radiation in primary and immortalized endothelial cells

Purpose : The low dose radiation response of primary human umbilical vein endothelial cells (HUVEC) and its immortalized derivative, the EA.hy926 cell line, was evaluated and compared. Material and methods: DNA damage and repair, cell cycle progression, apoptosis and cellular morphology in HUVEC and EA.hy926 were evaluated after exposure to low (0.05-0.5 Gy) and high doses (2 and 5 Gy) of acute X-rays. Results : Subtle, but significant increases in DNA double-strand breaks (DSB) were observed in HUVEC and EA.hy926 30 min after low dose irradiation (0.05 Gy). Compared to high dose irradiation (2 Gy), relatively more DSB/Gy were formed after low dose irradiation. Also, we observed a dose-dependent increase in apoptotic cells, down to 0.5 Gy in HUVEC and 0.1 Gy in EA.hy926 cells. Furthermore, radiation induced significantly more apoptosis in EA.hy926 compared to HUVEC. Conclusions : We demonstrated for the first time that acute low doses of X-rays induce DNA damage and apoptosis in endothelial cells. Our results point to a non-linear dose-response relationship for DSB formation in endothelial cells. Furthermore, the observed difference in radiation-induced apoptosis points to a higher radiosensitivity of EA.hy926 compared to HUVEC, which should be taken into account when using these cells as models for studying the endothelium radiation response

Weighted sparse graph based dimensionality reduction for hyperspectral images

Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. In this letter, we propose a weighted sparse graph based DR (WSGDR) method for HSIs. Instead of only exploring the locality structure (as in neighborhood preserving embedding) or the linearity structure (as in SGE) of the HSI data, the proposed method couples the locality and linearity properties of HSI data together in a unified framework for the DR of HSIs. The proposed method was tested on two widely used HSI data sets, and the results suggest that the locality and linearity are complementary properties for HSIs. In addition, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods

Non-convex regularization in remote sensing

In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of regularization used and its parametrization. We consider regularization via traditional squared (2) and sparsity-promoting (1) norms, as well as more unconventional nonconvex regularizers (p and Log Sum Penalty). We compare their properties and advantages on several classification and linear unmixing tasks and provide advices on the choice of the best regularizer for the problem at hand. Finally, we also provide a fully functional toolbox for the community.Comment: 11 pages, 11 figure