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Record W2022623664 · doi:10.1364/boe.5.000407

Phase-gradient contrast in thick tissue with a scanning microscope

2014· article· en· W2022623664 on OpenAlexaff
Jérôme Mertz, Alicja Gąsecka, Amy Daradich, Ian G. Davison, Daniel Côté

Bibliographic record

VenueBiomedical Optics Express · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversité Laval
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsMicroscopeOpticsMicroscopyMaterials scienceOptical microscopeInterference microscopyPhysicsScanning electron microscope

Abstract

fetched live from OpenAlex

It is well known that the principle of reciprocity is valid for light traveling even through scattering or absorptive media. This principle has been used to establish an equivalence between conventional widefield microscopes and scanning microscopes. We make use of this principle to introduce a scanning version of oblique back-illumination microscopy, or sOBM. This technique provides sub-surface phase-gradient and amplitude images from unlabeled tissue, in an epi-detection geometry. That is, it may be applied to arbitrarily thick tissue. sOBM may be implemented as a simple, cost-effective add-on with any scanning microscope, requiring only the availability of an extra input channel in the microscope electronics. We demonstrate here its implementation in combination with two-photon excited fluorescence (TPEF) microscopy and with coherent anti-Stokes Raman scattering (CARS) microscopy, applied to brain or spinal cord tissue imaging. In both cases, sOBM provides information on tissue morphology complementary to TPEF or CARS contrast. This information is obtained simultaneously and is automatically co-registered. Finally, we show that sOBM can be operated at video rate.

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 score0.256
Threshold uncertainty score0.675

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 studies0.0000.001
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 head0.009
GPT teacher head0.320
Teacher spread0.311 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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