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Record W2296190451 · doi:10.1002/jbio.201500261

Motion‐free endoscopic system for brain imaging at variable focal depth using liquid crystal lenses

2016· article· en· W2296190451 on OpenAlexafffund
Arutyun Bagramyan, Tigran Galstian, Armen Saghatelyan

Bibliographic record

VenueJournal of Biophotonics · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsFocal lengthOpticsLiquid crystalPolarization (electrochemistry)Materials scienceVoltageFocal adhesionOptical imagingBiomedical engineeringOptoelectronicsPhysicsChemistryMedicine

Abstract

fetched live from OpenAlex

We present a motion-free system for microendoscopic imaging of biological tissues at variable focal depths. Fixed gradient index and electrically tunable liquid crystal lenses (TLCL) were used to build the imaging optical probe. The design of the TLCL enables polarization-independent and relatively low-voltage operation, significantly improving the energy efficiency of the system. A focal shift of approximately 74 ± 3 µm could be achieved by electrically controlling the TLCL using the driving frequency at a constant voltage. The potential of the system was tested by imaging neurons and spines in thick adult mouse brain sections and in vivo, in the adult mouse brain at different focal planes. Our results indicate that the developed system may enable depth-variable imaging of morpho-functional properties of neural circuitries in freely moving animals and can be used to investigate the functioning of these circuitries under normal and pathological conditions.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.309
Teacher spread0.261 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

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