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Record W4214881070 · doi:10.1117/12.2609008

Endoscopic MPM objective designed for depth scanning

2022· article· en· W4214881070 on OpenAlexaff
Christoph Brandt, Wentao Wu, Qihao Liu, Shuo Tang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

One major advantage of multiphoton microscopy (MPM) is that it can image below tissue surface and produce a stack of images showing sample structure at various depths. A miniature objective with depth scanning capability is needed for MPM endoscopy. Spherical aberration may be induced when changing the focusing depth during multiphoton microscope depth scanning, thus limiting the range over which images may be acquired. A specially designed miniature objective that minimizes spherical aberration across large range of focusing depths is presented. Simulations show that the 0.53 numerical aperture design can achieve on-axis diffraction limited focusing in water for depths from 0 μm to just over 1400 μm and a diffraction limited field of view of up to 290 μm for a 790 nm laser. In experiment, our multiphoton microscope demonstrates a field of view of 64 μm by 100 μm and a depth scanning range of 440 μm, limited by the scanning hardware. Depth scanning capability is confirmed by imaging 0.1 μm diameter fluorescent beads across the 440 μm range. Biological samples to a depth of 150 μm are imaged using the custom objective; the imaging depth is mainly limited by the absorption and scattering of the sample.

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: Methods · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score0.457

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 head0.012
GPT teacher head0.294
Teacher spread0.282 · 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
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

Citations5
Published2022
Admission routes1
Has abstractyes

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