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XRF-ROI-Finder: Machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy
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  • M Arshad Zahangir Chowdhury ,
  • Kiwon Ok ,
  • Yanqi Luo ,
  • Zhengchun Liu ,
  • Si Chen ,
  • Thomas O'Halloran ,
  • Rajkumar Kettimuthu ,
  • Aniket Tekawade
M Arshad Zahangir Chowdhury
Argonne National Laboratory

Corresponding Author:[email protected]

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Yanqi Luo
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Zhengchun Liu
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Thomas O'Halloran
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Rajkumar Kettimuthu
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Aniket Tekawade
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Abstract

The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli (E. coli), also serve as label-free biological fingerprints to identify differently treated samples via fuzzy clustering. The key limitations of achieving good identification performance is the extraction of cells from raw XRF measurement via binary conversion, definition of features based on domain knowledge, dwell time and proportion of differently treated cells in the measurement.
01 Nov 2022Published in Journal of Synchrotron Radiation volume 29 issue 6 on pages 1495-1503. 10.1107/S1600577522008876