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Record W2894906880 · doi:10.1002/bmb.21158

Chemical cross‐linking of a variety of green fluorescent proteins as Förster resonance energy transfer donors for Yukon orange fluorescent protein: A project‐based undergraduate laboratory experience

2018· article· en· W2894906880 on OpenAlexaboutno aff
Mira K. Marchioretto, Jacqueline T. Horton, Calvin A. Berstler, Joseph B. Humphries, Isaac J. Koloditch, Samuel D. Voss, Kimberly de La Harpe, Barry W. Hicks, Latisha R. Jefferies

Bibliographic record

VenueBiochemistry and Molecular Biology Education · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFörster resonance energy transferFluorescenceOrange (colour)Energy transferChemistryGreen fluorescent proteinBiophysicsBiologyBiochemistryPhysicsChemical physicsOpticsFood science

Abstract

fetched live from OpenAlex

Förster resonance energy transfer (FRET) is the basis for many techniques used in biomedical research. Due to its wide use in molecular sensing, FRET is commonly introduced in many biology, chemistry, and physics courses. While FRET is of great importance in the biophysical sciences, the complexity and difficulty of constructing FRET experiments has resulted in limited usage in undergraduate laboratory settings. Here, we present a practical undergraduate laboratory experiment for teaching FRET using a diverse set of green-emitting fluorescent proteins (FPs) as donors for a cross-linked Yukon orange FP. This laboratory experiment enables students to make the connection of basic lab procedures to real world applications and can be applied to molecular biology, biochemistry, physical chemistry, and biophysical laboratory courses. Published 2018. This article is a U.S. Government work and is in the public domain in the USA., 46(5):516-522, 2018.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.310
Teacher spread0.303 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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