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Record W3018950129 · doi:10.1071/ch19571

‘Live and Large’: Super-Resolution Optical Fluctuation Imaging (SOFI) and Expansion Microscopy (ExM) of Microtubule Remodelling by Rabies Virus P Protein

2020· article· en· W3018950129 on OpenAlex
Ashley M. Rozario, Fabian U. Zwettler, Sam Duwé, Riley B. Hargreaves, Aaron M. Brice, Peter Dedecker, Markus Sauer, Gregory W. Moseley, Donna R. Whelan, Toby D. M. Bell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian Journal of Chemistry · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsRabies virusMicroscopyRabiesResolution (logic)MicrotubuleVirusChemistryVirologyOpticsCell biologyNanotechnologyBiologyPhysicsMaterials scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The field of super-resolution microscopy continues to progress rapidly, both in terms of evolving techniques and methodologies as well as in the development of new multi-disciplinary applications. Two current drivers of innovation are increasing the possible resolution gain and application in live samples. Super-resolution optical fluctuation imaging (SOFI) is well suited to live samples while expansion microscopy (ExM) enables obtainment of sub-diffraction information via conventional imaging. In this Highlight we provide a brief outline of these methods and report results from application of SOFI and ExM in our on-going study into microtubule remodelling by rabies virus P proteins. We show that MT bundles in live cells transfected with rabies virus P3 protein can be visualised using SOFI in a time-lapse fashion for up to half an hour and can be expanded using current Pro-ExM protocols and imaged using conventional microscopy.

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.

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 score0.002
Threshold uncertainty score0.696

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 head0.009
GPT teacher head0.260
Teacher spread0.252 · 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