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Record W4312018546 · doi:10.1002/anie.202217028

Super‐Resolution Tension PAINT Imaging with a Molecular Beacon

2022· article· en· W4312018546 on OpenAlexafffund
Seong Ho Kim, Isaac T. S. Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMichael Smith Health Research BCCanada Foundation for Innovation
KeywordsMolecular beaconSuperresolutionTension (geology)Remote sensingOpticsGeologyMaterials sciencePhysicsChemistryComputer scienceComposite materialArtificial intelligenceImage (mathematics)DNA

Abstract

fetched live from OpenAlex

DNA-PAINT enabled super-resolution imaging through the transient binding of fluorescently-labelled single-stranded DNA (ssDNA) imagers to target ssDNA. However, its performance is constrained by imager background fluorescence, resulting in relatively long image acquisition and potential artifacts. We designed a molecular beacon (MB) as the PAINT imager. Unbound MB in solution reduces the background fluorescence due to its natively quenched state. They are fluorogenic upon binding to target DNA to create individual fluorescence events. We demonstrate that MB-PAINT provides localization precision similar to traditional linear imager DNA-PAINT. We also show that MB-PAINT is ideally suited for fast super-resolution imaging of molecular tension probes in living cells, eliminating the potential of artifacts from free-diffusing imagers in traditional DNA-PAINT at the cell-substrate interface.

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: none
Teacher disagreement score0.681
Threshold uncertainty score0.554

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 head0.005
GPT teacher head0.245
Teacher spread0.240 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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