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Record W2271193049 · doi:10.1364/boe.7.000001

Wavefront sensorless adaptive optics fluorescence biomicroscope for in vivo retinal imaging in mice

2015· article· en· W2271193049 on OpenAlexafffund
Daniel J. Wahl, Yifan Jian, Stefano Bonora, Robert J. Zawadzki, Marinko V. Šarunic

Bibliographic record

VenueBiomedical Optics Express · 2015
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser University
FundersNational Eye InstituteAlzheimer Society of B.C.Natural Sciences and Engineering Research Council of CanadaAlzheimer Society Research ProgramUniversity of California, DavisFondation pour la Recherche sur AlzheimerCanadian Institutes of Health ResearchAlzheimer SocietyMichael Smith Health Research BCNational Science Foundation
KeywordsAdaptive opticsWavefrontOpticsRetinalDeformable mirrorOptical coherence tomographyPreclinical imagingFluorescenceFluorescence-lifetime imaging microscopyIn vivoComputer sciencePhysicsOphthalmologyMedicineBiology

Abstract

fetched live from OpenAlex

Cellular-resolution in vivo fluorescence imaging is a valuable tool for longitudinal studies of retinal function in vision research. Wavefront sensorless adaptive optics (WSAO) is a developing technology that enables high-resolution imaging of the mouse retina. In place of the conventional method of using a Shack-Hartmann wavefront sensor to measure the aberrations directly, WSAO uses an image quality metric and a search algorithm to drive the shape of the adaptive element (i.e. deformable mirror). WSAO is a robust approach to AO and it is compatible with a compact, low-cost lens-based system. In this report, we demonstrated a hill-climbing algorithm for WSAO with a variable focus lens and deformable mirror for non-invasive in vivo imaging of EGFP (enhanced green fluorescent protein) labelled ganglion cells and microglia cells in the mouse retina.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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
GenreMethods

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

Citations66
Published2015
Admission routes2
Has abstractyes

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