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Record W1968320713 · doi:10.1021/cn400012b

Improved Orange and Red Ca<sup>2+</sup> Indicators and Photophysical Considerations for Optogenetic Applications

2013· article· en· W1968320713 on OpenAlexafffund
Jiahui Wu, Lin Liu, Tomoki Matsuda, Yongxin Zhao, Aleksander Rebane, Mikhail Drobizhev, Yu-Fen Chang, Satoko Araki, Yoshiyuki Arai, Kelsey March, Thomas E. Hughes, Ken Sagou, Takaki Miyata, Takeharu Nagai, Wen-Hong Li, Robert E. Campbell

Bibliographic record

VenueACS Chemical Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsOptogeneticsFluorescenceOrange (colour)BiophysicsChemistryNeuroscienceBiologyOpticsPhysics

Abstract

fetched live from OpenAlex

We have used protein engineering to expand the palette of genetically encoded calcium ion (Ca(2+)) indicators to include orange and improved red fluorescent variants, and validated the latter for combined use with optogenetic activation by channelrhodopsin-2 (ChR2). These indicators feature intensiometric signal changes that are 1.7- to 9.7-fold improved relatively to the progenitor Ca(2+) indicator, R-GECO1. In the course of this work, we discovered a photoactivation phenomenon in red fluorescent Ca(2+) indicators that, if not appreciated and accounted for, can cause false-positive artifacts in Ca(2+) imaging traces during optogenetic activation with ChR2. We demonstrate, in both a beta cell line and slice culture of developing mouse neocortex, that these artifacts can be avoided by using an appropriately low intensity of blue light for ChR2 activation.

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.034
GPT teacher head0.295
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

Citations258
Published2013
Admission routes2
Has abstractyes

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