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Record W2893579492 · doi:10.1109/icton.2018.8473971

Label-Free Super-Resolution Microscopy with Coherent Nonlinear Structured-Illumination

2018· article· en· W2893579492 on OpenAlexaff
Mikko J. Huttunen, Aazad Abbas, Jeremy Upham, Robert W. Boyd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOpticsNonlinear systemMicroscopySecond-harmonic generationNonlinear opticsResolution (logic)High harmonic generationBeam (structure)Computer scienceMaterials sciencePhysicsArtificial intelligenceLaser

Abstract

fetched live from OpenAlex

Conventional structured-illumination microscopy provides up to a two-fold improvement in the achievable lateral resolution by spatially modulating the intensity profile of the illumination beam. However, the conventional structured-illumination scheme cannot be directly combined with label-free nonlinear coherent imaging modalities. Here we propose a way to overcome this limitation by generalizing the concept of structured-illumination microscopy to coherent nonlinear wide-field modalities, where the phase of the illumination beam is spatially modulated while interferometrically measuring the complex-valued scattered field. We demonstrate numerically how by using nonlinear processes of second-harmonic generation and third- harmonic generation up to four- and six-fold increases in the lateral resolution are possible, respectively. Since coherent nonlinear imaging modalities do not require use of labels, the demonstrated approach provides possibilities for biomedical applications benefitting from minimal sample preparation.

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 score0.286
Threshold uncertainty score0.599

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 head0.007
GPT teacher head0.279
Teacher spread0.272 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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