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Record W2952223359 · doi:10.1002/jez.b.22789

RNase reverses segment sequence in the anterior of a beetle egg ( <i>Callosobruchus maculatus</i> , Coleoptera)

2018· article· en· W2952223359 on OpenAlexaff
Jitse M. van der Meer

Bibliographic record

VenueJournal of Experimental Zoology Part B Molecular and Developmental Evolution · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsRedeemer University
FundersNational Science Foundation
KeywordsCallosobruchus maculatusBiologyInsectRNase PEmbryoAnatomyEmbryogenesisGeneticsCell biologyBotanyRNAGenePEST analysis

Abstract

fetched live from OpenAlex

The genetic regulation of anterior-posterior segment pattern development has been elucidated in detail for Drosophila, but it is not canonical for insects. A surprising diversity of regulatory mechanisms is being uncovered not only between insect orders, but also within the order of the Diptera. The question is whether the same diversity of regulatory mechanisms exists within other insect orders. I show that anterior puncture of the egg of the pea beetle Callosobruchus maculatus submerged in RNase can induce double abdomen development suggesting a role for maternal mRNA. In a double abdomen, anterior segments are replaced by posterior segments oriented in mirror image symmetry to the original posterior segments. This effect is specific for RNase activity, for treatment of the anterior egg pole and for cytoplasmic RNA. Yield depends on developmental stage, enzyme concentration, and temperature. A maximum of 30% of treated eggs reversed segment sequence after submersion and puncture in 10 μg/mL RNase S reconstituted from S-protein and S-peptide at 30°C. This result sets the stage for an analysis of the genetic regulation of segment pattern formation in the long germ embryo of the coleopteran Callosobruchus and for comparison with the short germ embryo of the coleopteran Tribolium.

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)0.000
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 score0.009
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

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