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Record W2518539871 · doi:10.1021/acs.macromol.6b01295

Long Range Polymer Chain Dynamics Studied by Fluorescence Quenching

2016· article· en· W2518539871 on OpenAlexafffund
Shiva Farhangi, Jean Duhamel

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuenching (fluorescence)FluorescenceFolding (DSP implementation)ChemistryChain (unit)MacromoleculePolymerChemical physicsDispersityRange (aeronautics)Dynamics (music)Biological systemMaterials sciencePhysicsPolymer chemistryOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Over the years, fluorescence quenching experiments have provided a robust analytical means to retrieve information about the internal dynamics of macromolecules in general and the long range polymer chain dynamics (LRPCD) of linear chains in particular. This report reviews the results obtained to date with the two main fluorescence experiments based on collisional quenching that have been used over the years to describe LRPCD. These experiments involve the labeling of a chain with dyes and quenchers either at the ends of a monodisperse chain for fluorescence quenching end-to-end cyclization (fqEEC) experiments or randomly along a polydisperse chain for fluorescence decay analysis with the fluorescence blob model (FBM). The advantages and disadvantages of these two types of experiments are discussed as well as their range of applications and applicability to the field of protein folding. In particular, this Perspective illustrates how fqEEc experiments are being applied to probe loop formation in polypeptides and how FBM analysis of randomly labeled polypeptides could help determine the size of foldons which are expected to solve Levinthal’s long-standing paradox.

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 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 score0.080
Threshold uncertainty score0.669

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 studies0.0000.000
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 head0.004
GPT teacher head0.218
Teacher spread0.214 · 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.

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

Citations27
Published2016
Admission routes2
Has abstractyes

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