SimpliPyTEM: An open-source Python library and app to simplify transmission electron microscopy and in situ-TEM image analysis
Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and larg...
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| Vydané v: | PloS one Ročník 18; číslo 10; s. e0285691 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Public Library of Science
05.10.2023
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on
in situ
TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package. |
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| AbstractList | Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on in situ TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package. Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on in situ TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package.Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on in situ TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package. Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on in situ TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package. |
| Audience | Academic |
| Author | Battaglia, Guiseppe Stewart, Andrew Ing, Gabriel Ruiz-Perez, Lorena |
| AuthorAffiliation | 1 Institute of Structural and Molecular Biology, Department of Chemistry, University College London, London, United Kingdom 2 Department of Chemistry, University College London, London, United Kingdom University of A Coruña, SPAIN 3 Institute for Bioengineering of Catalonia, The Barcelona Institute of Science and Technology, Barcelona, Spain 4 Catalan Institution of Research and Advanced Studies, Barcelona, Spain |
| AuthorAffiliation_xml | – name: 1 Institute of Structural and Molecular Biology, Department of Chemistry, University College London, London, United Kingdom – name: 4 Catalan Institution of Research and Advanced Studies, Barcelona, Spain – name: 3 Institute for Bioengineering of Catalonia, The Barcelona Institute of Science and Technology, Barcelona, Spain – name: University of A Coruña, SPAIN – name: 2 Department of Chemistry, University College London, London, United Kingdom |
| Author_xml | – sequence: 1 givenname: Gabriel orcidid: 0000-0001-9572-0147 surname: Ing fullname: Ing, Gabriel – sequence: 2 givenname: Andrew surname: Stewart fullname: Stewart, Andrew – sequence: 3 givenname: Guiseppe surname: Battaglia fullname: Battaglia, Guiseppe – sequence: 4 givenname: Lorena orcidid: 0000-0003-0512-5606 surname: Ruiz-Perez fullname: Ruiz-Perez, Lorena |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37796914$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1038/s41467-020-18952-1 10.1107/S2059798317007859 10.1111/jmi.12889 10.1038/nmeth.4193 10.1109/ACCT.2012.41 10.1002/anie.201001493 10.1007/978-3-030-00069-1_3 10.1038/s41586-020-2649-2 10.1016/j.micron.2019.01.005 10.1038/nmeth.2019 10.1038/s41592-019-0686-2 10.7717/peerj.453 10.1109/CVPR.2019.00223 |
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| Copyright | Copyright: © 2023 Ing et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2023 Public Library of Science 2023 Ing et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 Ing et al 2023 Ing et al 2023 Ing et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Automation Computer and Information Sciences Data processing Deep learning Diagnostic imaging Electron microscopy Electrons Engineering and Technology Evaluation Image acquisition Image analysis Image contrast Image processing Image Processing, Computer-Assisted - methods Image quality Libraries Metadata Microscopy Microscopy, Electron, Transmission Mobile Applications Portable computers Python Research and Analysis Methods Software Transmission electron microscopy Workflow |
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| Title | SimpliPyTEM: An open-source Python library and app to simplify transmission electron microscopy and in situ-TEM image analysis |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/37796914 https://www.proquest.com/docview/2873244418 https://www.proquest.com/docview/2874262698 https://pubmed.ncbi.nlm.nih.gov/PMC10553249 https://doaj.org/article/0ffc7af36b8d4512b86ca0917c0f98ad http://dx.doi.org/10.1371/journal.pone.0285691 |
| Volume | 18 |
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