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Record W3015860439 · doi:10.1101/2020.04.08.032433

Improved genetically encoded near-infrared fluorescent calcium ion indicators for <i>in vivo</i> imaging

2020· preprint· en· W3015860439 on OpenAlexafffund
Yong Qian, Danielle M. Cosio, Kiryl D. Piatkevich, Sarah Aufmkolk, Wan-Chi Su, Orhan T. Celiker, Anne Schohl, Mitchell H. Murdock, Abhi Aggarwal, Yu‐Fen Chang, Paul W. Wiseman, Edward S. Ruthazer, Edward S. Boyden, Robert E. Campbell

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of Alberta
FundersCanadian Institutes of Health ResearchWestlake UniversityNatural Sciences and Engineering Research Council of CanadaFondation Brain CanadaMcGill UniversityUniversity of AlbertaNational Institutes of HealthNational Science Foundation
KeywordsFluorescencePhototoxicityOptogeneticsChemistryIn vivoNear-infrared spectroscopyXenopusBiophysicsPhotostimulationBiochemistryIn vitroBiologyOpticsNeuroscienceGene

Abstract

fetched live from OpenAlex

Abstract Near-infrared (NIR) genetically-encoded calcium ion (Ca 2+ ) indicators (GECIs) can provide advantages over visible wavelength fluorescent GECIs in terms of reduced phototoxicity, minimal spectral cross-talk with visible-light excitable optogenetic tools and fluorescent probes, and decreased scattering and absorption in mammalian tissues. Our previously reported NIR GECI, NIR-GECO1, has these advantages but also has several disadvantages including lower brightness and limited fluorescence response compared to state-of-the-art visible wavelength GECIs, when used for imaging of neuronal activity. Here, we report two improved NIR GECI variants, designated NIR-GECO2 and NIR-GECO2G, derived from NIR-GECO1. We characterized the performance of the new NIR GECIs in cultured cells, acute mouse brain slices, and C. elegans and Xenopus laevis in vivo . Our results demonstrate that NIR-GECO2 and NIR-GECO2G provide substantial improvements over NIR-GECO1 for imaging of neuronal Ca 2+ dynamics.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.035
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.028
GPT teacher head0.273
Teacher spread0.245 · 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.

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

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

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