ROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy.

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 E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated diff

SUBMITTER: Chowdhury MAZ 

PROVIDER: S-EPMC9641565 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature