Radiation Response Matrix of Array Charge Couple Device

2014 ◽  
Vol 51 (8) ◽  
pp. 080402
Author(s):  
叶钊 Ye Zhao ◽  
王超 Wang Chao ◽  
董小静 Dong Xiaojing ◽  
朱军 Zhu Jun
Keyword(s):  
Radiation Response ◽  
Charge Couple Device ◽  
Response Matrix

scholarly journals Study on multiple thermal radiation response of adjacent tanks based on numerical simulation

2021 ◽  
Vol 1827 (1) ◽  
pp. 012107
Author(s):  
Yawen Zuo ◽  
Mingguang Zhang ◽  
Feng Zheng ◽  
Wenjie Pan ◽  
Sifan Mo
Keyword(s):  
Numerical Simulation ◽  
Thermal Radiation ◽  
Radiation Response

Block-diagonalization of the variational nodal response matrix using the symmetry group theory

2017 ◽  
Vol 351 ◽  
pp. 230-253 ◽  
Author(s):  
Zhipeng Li ◽  
Hongchun Wu ◽  
Yunzhao Li ◽  
Liangzhi Cao
Keyword(s):  
Group Theory ◽  
Symmetry Group ◽  
Response Matrix ◽  
Block Diagonalization

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Radiation response characteristics of organic–inorganic perovskite-type compounds with a chlorophenethylamine

2021 ◽  
pp. 111409
Author(s):  
Naoki Kawano ◽  
Atsushi Horimoto ◽  
Hiromi Kimura ◽  
Daisuke Nakauchi ◽  
Masaki Akatsuka ◽  
...  
Keyword(s):  
Radiation Response ◽  
Inorganic Perovskite ◽  
Response Characteristics ◽  
Perovskite Type

scholarly journals Deconvoluting the Effect of Chromium and Aluminum on the Radiation Response of Wrought FeCrAl Alloys After Low-Dose Neutron Irradiation

2021 ◽  
Vol 549 ◽  
pp. 152804
Author(s):  
Caleb P. Massey ◽  
Dalong Zhang ◽  
Samuel A. Briggs ◽  
Philip D. Edmondson ◽  
Yukinori Yamamoto ◽  
...  
Keyword(s):  
Neutron Irradiation ◽  
Low Dose ◽  
Radiation Response ◽  
Fecral Alloys
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