biostudies-literatureChowdhury MAZArgonne National LaboratoryNational Institutes of HealthUS Department of Energy, Office of ScienceNIH HHS1495-1503https://www.ebi.ac.uk/biostudies/studies/S-EPMC9641565biostudies-literatureUnknown29(Pt 6)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 diffJournal of synchrotron radiationROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy.PMC9641565GM038784LDRD PRJ1008870DE-AC02-06CH1135ARRA S10 SP0007167LDRD PRJ1008073PRJ1009594P41GM181350Kettimuthu ROk KLiu ZChowdhury MAZO'Halloran TVLuo YTekawade AChen SfalseROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy.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 diff2022-01-01T00:00:00Z2022 Nov2025-04-18T19:06:56.11Z2025-04-07T06:46:21.827ZS-EPMC96415653634575710.1107/S1600577522008876