Issue 12, 2023

Deep learning-assisted analysis of single molecule dynamics from liquid-phase electron microscopy

Abstract

We apply U-Net and UNet++ to analyze single-molecule movies obtained from liquid-phase electron microscopy. Neural networks allow full automation, and high throughput analysis of these low signal-to-noise ratio images, while achieving higher segmentation accuracy, and avoiding subjective errors as compared to the conventional threshold methods. The analysis enables the quantification of transient dynamics in chemical systems and the capture of rare intermediate states by resolving local conformational changes within a single molecule.

Graphical abstract: Deep learning-assisted analysis of single molecule dynamics from liquid-phase electron microscopy

Supplementary files

Article information

Article type
Communication
Submitted
30 ستمبر 2022
Accepted
17 جنؤری 2023
First published
17 جنؤری 2023

Chem. Commun., 2023,59, 1701-1704

Deep learning-assisted analysis of single molecule dynamics from liquid-phase electron microscopy

B. Cheng, E. Ye, H. Sun and H. Wang, Chem. Commun., 2023, 59, 1701 DOI: 10.1039/D2CC05354C

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements