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Imaging of DNA and RNA in Living Eukaryotic Cells to Reveal Spatiotemporal Dynamics of Gene Expression

2020· review· en· W3011264939 on OpenAlexaff
Hanae Sato, Sulagna Das, Robert H. Singer, María Vera

Bibliographic record

VenueAnnual Review of Biochemistry · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsMcGill University
FundersNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Institute of General Medical SciencesNational Institute on Drug AbuseNational Institute on Aging
KeywordsRNADNAComputational biologyBiologyLive cell imagingFluorescence microscopeCell biologyGene expressionTranscription (linguistics)GeneCellGeneticsFluorescencePhysics

Abstract

fetched live from OpenAlex

This review focuses on imaging DNA and single RNA molecules in living cells to define eukaryotic functional organization and dynamic processes. The latest advances in technologies to visualize individual DNA loci and RNAs in real time are discussed. Single-molecule fluorescence microscopy provides the spatial and temporal resolution to reveal mechanisms regulating fundamental cell functions. Novel insights into the regulation of nuclear architecture, transcription, posttranscriptional RNA processing, and RNA localization provided by multicolor fluorescence microscopy are reviewed. A perspective on the future use of live imaging technologies and overcoming their current limitations is provided.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.307
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations67
Published2020
Admission routes1
Has abstractyes

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