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Record W2401899855 · doi:10.1007/978-1-61779-046-1_11

Live Cell Imaging of Argonaute Proteins in Mammalian Cells

2011· article· en· W2401899855 on OpenAlexafffund
Justin M. Pare, Joaquín López-Orozco, Tom C. Hobman

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsArgonauteCell biologyGene silencingRNA interferenceRNA-induced silencing complexBiologyPiwi-interacting RNAEffectormicroRNARNA silencingRNASmall interfering RNAGeneticsGene

Abstract

fetched live from OpenAlex

The central effector of mammalian RNA interference (RNAi) is the RNA-induced silencing complex (RISC). Proteins of the Argonaute family are the core components of RISC. Recent work from multiple laboratories has shown that Argonaute family members are associated with at least two types of cytoplasmic RNA granules: GW/Processing bodies and stress granules. These Argonaute-containing granules harbor proteins that function in mRNA degradation and translational repression in response to stress. The known role of Argonaute proteins in miRNA-mediated translational repression and siRNA-directed mRNA cleavage (i.e., Argonaute 2) has prompted speculation that the association of Argonautes with these granules may reflect the activity of RNAi in vivo. Accordingly, studying the dynamic association between Argonautes and RNA granules in living cells will undoubtedly provide insight into the regulatory mechanisms of RNA-based silencing. This chapter describes a method for imaging fluorescently tagged Argonaute proteins in living mammalian cells using spinning disk confocal 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.

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)0.000
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 score0.011

Distilled classifier scores by category (both heads)

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 communication0.0010.001
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 head0.023
GPT teacher head0.351
Teacher spread0.328 · 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

Citations3
Published2011
Admission routes2
Has abstractyes

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