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Record W2038683963 · doi:10.1109/jphot.2014.2304554

A Novel Lensless Miniature Contact Imaging System for Monitoring Calcium Changes in Live Neurons

2014· article· en· W2038683963 on OpenAlexafffund
Anil Kumar Mudraboyina, Lior Blockstein, Collin Luk, Naweed I. Syed, Orly Yadid-Pecht

Bibliographic record

VenueIEEE photonics journal · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Heritage Foundation for Medical ResearchCanadian Institutes of Health ResearchAsylum, Migration and Integration FundAlberta Innovates - Technology Futures
KeywordsMaterials scienceCalcium imagingOptoelectronicsFluorescenceImage sensorLymnaea stagnalisFabricationMicroscopyCMOSOpticsCalciumPhysics

Abstract

fetched live from OpenAlex

Here, we report on the design, fabrication, and verification of a novel CMOS-imager-based contact imaging system. We acquired fluorescent images from live neurons by monitoring calcium changes with Fura-2 dye. Our current device consists of a removable absorption filter interfaced with a CMOS imaging sensor and an external DG-4 lamp for excitation. Fura-2 loaded Lymnaea stagnalis neurons were stimulated with dual excitation wavelengths of 340 and 380 nm; our image sensor detected 510-nm emission. We show that our system is capable of detecting intracellular calcium changes in Fura-2 loaded neurons. Further, this sensor also enabled viewing of multiple neurons over a large surface area simultaneously, an option that is not readily available in conventional light microscopy.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.291
Teacher spread0.241 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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