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Record W2064612471 · doi:10.1251/bpo119

Practical three color live cell imaging by widefield microscopy

2006· article· en· W2064612471 on OpenAlexafffund
Jianrun Xia, Song Hon H. Kim, Susan V. MacMillan, Ray Truant

Bibliographic record

VenueBiological Procedures Online · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsFluorophoreAequorea victoriaFluorescenceFluorescence microscopeGreen fluorescent proteinMicroscopeMicroscopyLive cell imagingCytoplasmBiophysicsFusion proteinCell biologyProtein subcellular localization predictionChemistryBiologyCellOpticsBiochemistryPhysicsGene

Abstract

fetched live from OpenAlex

Live cell fluorescence microscopy using fluorescent protein tags derived from jellyfish and coral species has been a successful tool to image proteins and dynamics in many species. Multi-colored aequorea fluorescent protein (AFP) derivatives allow investigators to observe multiple proteins simultaneously, but overlapping spectral properties sometimes require the use of sophisticated and expensive microscopes. Here, we show that the aequorea coerulescens fluorescent protein derivative, PS-CFP2 has excellent practical properties as a blue fluorophore that are distinct from green or red fluorescent proteins and can be imaged with standard filter sets on a widefield microscope. We also find that by widefield illumination in live cells, that PS-CFP2 is very photostable. When fused to proteins that form concentrated puncta in either the cytoplasm or nucleus, PSCFP2 fusions do not artifactually interact with other AFP fusion proteins, even at very high levels of over-expression. PSCFP2 is therefore a good blue fluorophore for distinct three color imaging along with eGFP and mRFP using a relatively simple and inexpensive microscope.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.310
Teacher spread0.299 · 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
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

Citations11
Published2006
Admission routes2
Has abstractyes

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