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Record W2914306076 · doi:10.1039/c8nr07809b

Live cell super resolution imaging by radial fluctuations using fluorogen binding tags

2019· article· en· W2914306076 on OpenAlexfundno aff
Muthukumaran Venkatachalapathy, Vivek Belapurkar, Mini Jose, Arnaud Gautier, Deepak Nair

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsnot available
FundersH2020 European Research CouncilScience and Engineering Research BoardMcGill UniversityDepartment of Biotechnology, Ministry of Science and Technology, IndiaTata TrustsUniversity Grants CommissionIndian Institute of Science
KeywordsResolution (logic)SuperresolutionBiological systemCellHigh resolutionComputer scienceBiophysicsChemistryImage (mathematics)Artificial intelligenceBiologyBiochemistryRemote sensingGeology

Abstract

fetched live from OpenAlex

Fluorescence-Activating and absorption-Shifting Tag (FAST) is a novel genetically encoded optical highlighter probe. Since the fluorescence of FAST originates from the stochastic and reversible diffusive association of a fluorogenic ligand, we investigate the application of FAST using Super-Resolution Radial Fluctuations (SRRF) to achieve routine imaging below the diffraction limit in a widefield epifluorescence microscope. We show that intensity fluctuation analysis like SRRF allows the imaging of FAST-tagged proteins with sub - 100 nm resolution in live cells. FAST co-labeled with conventional fluorophores enables real time multicolour 2D and 3D super-resolution imaging, indicating that FAST can be used for the observation of sub-diffraction limited structures in both living and fixed samples.

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)0.000
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 score0.012
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.251
Teacher spread0.246 · 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

Citations34
Published2019
Admission routes1
Has abstractyes

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