.png){kind=icon}
返回
Detecting structural heterogeneity in single-molecule localization microscopy data
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-021-24106-8.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes. Particle fusion can improve signal-to-noise ratio in single molecule localization microscopy, but is limited by structural heterogeneity. Here, the authors demonstrate an unsupervised classification method that differentiates structurally different DNA origami structures without prior knowledge.
Keyword:
NUCLEAR-PORE COMPLEX
SUPERRESOLUTION MICROSCOPY
PARTICLE RECONSTRUCTION
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.3W
被引数:
91.2W
机构
引用论文
VirusMapper: open-source nanoscale mapping of viral architecture through super-resolution microscopy.png)
SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏分享收藏
查看更多内容