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Single-Molecule Resolution Fluorescent In Situ Hybridization (smFISH) in the Yeast S. cerevisiae

2013· article· en· W77265774 on OpenAlex
Samir Rahman, Daniel Zenklusen

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMethods in molecular biology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsIn situ hybridizationGene expressionBiologyRNAYeastCell biologyIn situContext (archaeology)Molecular biologyDNASaccharomyces cerevisiaeFluorescence in situ hybridizationFluorescenceComputational biologyCellGeneSingle-cell analysisChemistryBiochemistryChromosome

Abstract

fetched live from OpenAlex

Regulating gene expression is a major task for all cellular systems. RNA production and degradation plays a critical role in this process and accurately measuring cellular mRNA levels is essential to understanding gene expression regulation. Classical biochemical assays that study gene expression rely on extracting RNAs from large populations of cells, taking them out of their native context and thereby losing spatial information as well as cell-to-cell variability. In this chapter, we describe a fluorescent in situ hybridization (FISH) technique that circumvents this problem by detecting single RNAs in single cells. The technique employs multiple single-stranded short DNA probes fluorescently labeled with organic dyes that hybridize to target RNAs in fixed cells, allowing quantification and localization of RNAs at the single-cell level and at single-molecule resolution. The protocol described here has been optimized for the yeast S. cerevisiae.

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)0.002
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 score0.200
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.316
Teacher spread0.300 · 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