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Record W2101578943 · doi:10.1105/tpc.113.116517

<i>TED</i>, an Autonomous and Rare Maize Transposon of the<i>Mutator</i>Superfamily with a High Gametophytic Excision Frequency

2013· article· en· W2101578943 on OpenAlexaff
Yubin Li, Linda J. Harris, Hugo K. Dooner

Bibliographic record

VenueThe Plant Cell · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Science Foundation
KeywordsBiologyTransposition (logic)Transposable elementGeneticsMeiosisP elementMitosisInbreedingChromatidChromosomeGenomeGene

Abstract

fetched live from OpenAlex

Mutator (Mu) elements, one of the most diverse superfamilies of DNA transposons, are found in all eukaryotic kingdoms, but are particularly numerous in plants. Most of the present knowledge on the transposition behavior of this superfamily comes from studies of the maize (Zea mays) Mu elements, whose transposition is mediated by the autonomous Mutator-Don Robertson (MuDR) element. Here, we describe the maize element TED (for Transposon Ellen Dempsey), an autonomous cousin that differs significantly from MuDR. Element excision and reinsertion appear to require both proteins encoded by MuDR, but only the single protein encoded by TED. Germinal excisions, rare with MuDR, are common with TED, but arise in one of the mitotic divisions of the gametophyte, rather than at meiosis. Instead, transposition-deficient elements arise at meiosis, suggesting that the double-strand breaks produced by element excision are repaired differently in mitosis and meiosis. Unlike MuDR, TED is a very low-copy transposon whose number and activity do not undergo dramatic changes upon inbreeding or outcrossing. Like MuDR, TED transposes mostly to unlinked sites and can form circular transposition products. Sequences closer to TED than to MuDR were detected only in the grasses, suggesting a rather recent evolutionary split from a common ancestor.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.270

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 head0.009
GPT teacher head0.153
Teacher spread0.145 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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