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  Deep learning enables fast, gentle STED microscopy

Ebrahimi, V., Stephan, T., Kim, J., Carravilla, P., Eggeling, C., Jakobs, S., et al. (2023). Deep learning enables fast, gentle STED microscopy. Communications Biology, 6(1): 674. doi:10.1038/s42003-023-05054-z.

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 Creators:
Ebrahimi, Vahid, Author
Stephan, Till1, Author           
Kim, Jiah, Author
Carravilla, Pablo, Author
Eggeling, Christian, Author
Jakobs, Stefan1, Author                 
Han, Kyu Young, Author
Affiliations:
1Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350048              

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 Abstract: STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of magnitude. Our method allows for efficient and robust restoration of noisy 2D and 3D STED images with multiple targets and facilitates long-term imaging of mitochondrial dynamics.

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Language(s): eng - English
 Dates: 2023-06-27
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s42003-023-05054-z
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Project name : This work was based in part on data recorded using an instrument whose acquisition was supported by the US National Science Foundation (1725984). This work was supported by the US National Institutes of Health (R35GM138039 and U01DK127422 to K.Y.H.), the European Research Council Advanced Grant (ERC AdG No. 835102), and the DFG-funded CRC 1286 (project A05). C.E. acknowledges funding by the Deutsche Forschungsgemeinschaft (German Research Foundation; under Germany’s Excellence Strategy – EXC 2051 – Project-ID 390713860; project number 316213987 – SFB 1278; Instrument funding MINFLUX Jena INST 275_405_1), the State of Thuringia (TMWWDG), and the Free State of Thuringia (TAB; AdvancedSTED/FGZ: 2018 FGI 0022; Advanced Flu-Spec/2020 FGZ: FGI 0031), and support by the integration into the Leibniz Center for Photonics in Infection Research (LPI, part of the BMBF national roadmap for research infrastructures). P. Carravilla received funding from the European Commission Horizon 2020 Marie Skłodowska Curie programme (H2020-MSCA-IF-2019-ST project 892232 FILM-HIV).
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Funding program : -
Funding organization : -
Project name : -
Grant ID : 835102
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : FILM-HIV
Grant ID : 892232
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Communications Biology
  Abbreviation : Commun. Biol.
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: - Volume / Issue: 6 (1) Sequence Number: 674 Start / End Page: - Identifier: ISSN: 2399-3642
CoNE: https://pure.mpg.de/cone/journals/resource/2399-3642