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Record W2432795554 · doi:10.1107/s1600577516008006

Development of a single-cell X-ray fluorescence flow cytometer

2016· article· en· W2432795554 on OpenAlexfundno aff
Andrew M. Crawford, Patrick Kurecka, Tsz Kwan Yim, Claire E. Kozemchak, Aniruddha Deb, Lubomír Dostál, Cheng-Jun Sun, Dale Brewe, Raúl A. Barrea, James E. Penner‐Hahn

Bibliographic record

VenueJournal of Synchrotron Radiation · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsnot available
FundersArgonne National LaboratoryBasic Energy SciencesNational Institute of General Medical SciencesOffice of ScienceCanadian Light SourceUniversity of WashingtonU.S. Department of EnergyNational Institutes of HealthNational Science Foundation
KeywordsFluorescenceSingle-cell analysisYeastSaccharomyces cerevisiaePopulationCellMetalChemistryBiophysicsAnalytical Chemistry (journal)Materials scienceCapillary actionChromatographyOpticsBiologyBiochemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

An X-ray fluorescence flow cytometer that can determine the total metal content of single cells has been developed. Capillary action or pressure was used to load cells into hydrophilic or hydrophobic capillaries, respectively. Once loaded, the cells were transported at a fixed vertical velocity past a focused X-ray beam. X-ray fluorescence was then used to determine the mass of metal in each cell. By making single-cell measurements, the population heterogeneity for metals in the µM to mM concentration range on fL sample volumes can be directly measured, a measurement that is difficult using most analytical methods. This approach has been used to determine the metal composition of 936 individual bovine red blood cells (bRBC), 31 individual 3T3 mouse fibroblasts (NIH3T3) and 18 Saccharomyces cerevisiae (yeast) cells with an average measurement frequency of ∼4 cells min(-1). These data show evidence for surprisingly broad metal distributions. Details of the device design, data analysis and opportunities for further sensitivity improvement are described.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.227
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2016
Admission routes1
Has abstractyes

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