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Record W2967898800 · doi:10.1364/prj.7.001042

Simultaneous dual-contrast three-dimensional imaging in live cells via optical diffraction tomography and fluorescence

2019· article· en· W2967898800 on OpenAlexaff
Chen Liu, Michael Malek, Ivan K. H. Poon, Lanzhou Jiang, Arif M. Siddiquee, Colin J. R. Sheppard, Ann Roberts, Harry M. Quiney, Douguo Zhang, Xiaocong Yuan, Jiao Lin, Christian Depeursinge, Pierre Marquet, Shan Shan Kou

Bibliographic record

VenuePhotonics Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsUniversité Laval
FundersAustralian Research CouncilGovernment of Guangdong ProvinceScience, Technology and Innovation Commission of Shenzhen MunicipalityNational Natural Science Foundation of ChinaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsContrast (vision)OpticsFluorescenceMonte Carlo methodMaterials scienceScatteringSIGNAL (programming language)Fluorescence-lifetime imaging microscopyTomographyPhysicsComputer scienceMathematics

Abstract

fetched live from OpenAlex

We report a dual-contrast method of simultaneously measuring and visualizing the volumetric structural information in live biological samples in three-dimensional (3D) space. By introducing a direct way of deriving the 3D scattering potential of the object from the synthesized angular spectra, we obtain the quantitative subcellular morphology in refractive indices (RIs) side-by-side with its fluorescence signals. The additional contrast in RI complements the fluorescent signal, providing additional information of the targeted zones. The simultaneous dual-contrast 3D mechanism unveiled interesting information inaccessible with previous methods, as we demonstrated in the human immune cell (T cell) experiment. Further validation has been demonstrated using a Monte Carlo model. (C) 2019 Chinese Laser Press.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.309
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2019
Admission routes1
Has abstractyes

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