Mechanical and photo-fragmentation processes for nanonization of melanin to improve its efficacy in protecting cells from reactive oxygen species stress

2015 ◽  
Vol 117 (6) ◽  
pp. 064701 ◽  
Author(s):  
Yi-Cheng Liu ◽  
Sih-Min Chen ◽  
Jhong-Han Liu ◽  
Hsiang-Wei Hsu ◽  
Hoang-Yan Lin ◽  
...  
2016 ◽  
Vol 240 (4) ◽  
pp. 484-494 ◽  
Author(s):  
Hsuan-Shun Huang ◽  
Che-Fang Hsu ◽  
Sung-Chao Chu ◽  
Pao-Chu Chen ◽  
Dah-Ching Ding ◽  
...  

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Hok Khim Fam ◽  
Kunho Choi ◽  
Lauren Fougner ◽  
Chinten James Lim ◽  
Cornelius F. Boerkoel

2019 ◽  
Vol 10 (15) ◽  
pp. 3427-3434 ◽  
Author(s):  
Zheng Zhou ◽  
Ji Xu ◽  
Ximing Bao ◽  
Jiali Shi ◽  
Bin Liu ◽  
...  

2020 ◽  
Author(s):  
Inge De Clercq ◽  
Jan Van de Velde ◽  
Xiaopeng Luo ◽  
Li Liu ◽  
Veronique Storme ◽  
...  

ABSTRACTGene regulation is a dynamic process in which transcription factors (TFs) play an important role to control spatiotemporal gene expression. While gene regulatory networks describe the interactions between TFs and their target genes, our global knowledge about the complexity of TF control for different genes and biological processes is incomplete. To enhance our understanding of the global regulatory lexicon in Arabidopsis thaliana, different regulatory input networks capturing complementary information about DNA motifs, open chromatin, TF binding and expression-based regulatory interactions, were combined using a supervised learning approach, resulting in an integrated gene regulatory network (iGRN) covering 1,491 TFs and 31,393 target genes (1.7 million interactions). This iGRN outperforms the different input networks to predict known regulatory interactions and has a similar performance to recover functional interactions compared to state-of-the-art experimental methods like yeast one-hybrid and ChIP-seq. The iGRN correctly inferred known functions for 681 TFs and predicted new gene functions for hundreds of unknown TFs. For regulators predicted to be involved in reactive oxygen species stress regulation, we confirmed in total 75% of TFs with a function in ROS and/or physiological stress responses. This includes 13 novel ROS regulators, previously not connected to any ROS or stress function, that were experimentally validated in our ROS-specific phenotypic assays of loss- or gain-of-function lines. In conclusion, the presented iGRN offers a high-quality starting point integrating different experimental data types at the network level to enhance our understanding of gene regulation in plants.


2009 ◽  
pp. c3 ◽  
Author(s):  
Helena M. Cochemé ◽  
Michael P. Murphy

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