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Record W2048856254 · doi:10.1002/mrd.21030

The dynamics of gene products fluctuation during bovine pre‐hatching development

2009· article· en· W2048856254 on OpenAlexafffund
Isabelle Gilbert, Sara Scantland, Eve‐Lyne Sylvestre, Catherine Gravel, Isabelle Laflamme, Marc‐André Sirard, Claude Robert

Bibliographic record

VenueMolecular Reproduction and Development · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyHatchingDynamics (music)GeneGeneticsEvolutionary biologyComputational biologyCell biologyEcology

Abstract

fetched live from OpenAlex

Early embryonic development, spanning fertilization to blastocyst hatching, is a very dynamic developmental window that is characterized, especially in large mammals, by a period of transcriptional incompetence that ends during the maternal to embryonic transition (MET). Prior to the MET, the first cell cycles are supported by stored RNA and proteins pools accumulated during oogenesis. Therefore, RNA and protein content are different between developmental stages. It is also known that the stability of the stored mRNA and the mechanisms for translation recruitment are partly controlled by the length of the poly(A) tail. To date, little is known about RNA and protein content fluctuations during the pre-hatching period. In this report we present measurements of total RNA, mRNA, poly(A) bearing mRNA and protein contents, as well as estimations of the proportions of both mRNA fractions to total RNA contents within these developmental stages. We found that while the ontogenic profiles of the different transcript contents were expected, their amounts were considerably lower than the reported values. Additionally, low 28S rRNA abundance and a tendency for diminishing protein content prior to the MET, suggest a limited potential for ribosomal turnover and translation. We consider the overall fluctuations in RNA and protein contents to be reference points that are essential for downstream interpretation of gene expression data across stages whether it be through candidates or high throughput approaches.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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 designObservational
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

Citations37
Published2009
Admission routes2
Has abstractyes

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