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Record W2890276911 · doi:10.1186/s13104-018-3749-z

Regulatory processes that control haploid expression of salmon sperm mRNAs

2018· article· en· W2890276911 on OpenAlexafffund
Kristian R. von Schalburg, Éric Rondeau, Jong S. Leong, William S. Davidson, Ben F. Koop

Bibliographic record

VenueBMC Research Notes · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsUniversity of VictoriaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPloidySpermBiologyAndrologyGene expressionCell biologyComputational biologyGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Various stages of mRNA processing are necessary for functionally important genes required during late-stage sperm differentiation. Protein-RNA complexes form that edit, stabilize, store, deliver, localize and regulate translation of sperm mRNAs. These regulatory processes are often directed by recognition sequence elements and the particular composition of the proteins associated with the mRNAs. Previous work has shown that the cAMP response element modulator (CREM), estrogen receptor-alpha (ERα) and forkhead box L2A (FOXL2A) proteins are present in late-stage salmon sperm. Here we investigate whether these and other regulatory proteins might control processing of mRNAs not expressed until the haploid stage of development. We also examine regulatory processes that prepare and present mRNAs that generate unique products essential for differentiating sperm (i.e. for flagellar assembly and function). RESULTS: We provide evidence for potential sperm-specific recognition elements in 5'-untranslated regions (utrs) that may bind CREM, ERα, FOXL2A, Y-box and other proteins. We show that changes within the 5'-utrs and open reading frames of some sperm genes lead to distinct protein termini that may provide specific interfaces necessary for localization and function within the paternal gamete.

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 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)0.001
metaresearch head score (Gemma)0.006
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 score0.009
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.121
GPT teacher head0.385
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2018
Admission routes2
Has abstractyes

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