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Record W2317611856 · doi:10.4238/2014.april.30.8

Optimization of a biolistic transformation system for transfer of antifreeze gene KN2 and the bar herbicide resistance gene in common wheat

2014· article· en· W2317611856 on OpenAlexaff
Li-Kai Cai, Dongfa Sun, Genlou Sun

Bibliographic record

VenueGenetics and Molecular Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsTransformation (genetics)CallusBiologyBotanyRegeneration (biology)Murashige and Skoog mediumGene transferGeneSomatic embryogenesisEmbryoTissue cultureCell biologyIn vitroEmbryogenesisGenetics

Abstract

fetched live from OpenAlex

We studied the effects of different media for callus induction and differentiation, and pre-culture period of immature wheat embryo culture on biolistic transformation efficiency for including antifreeze gene KN2 and bar conferring resistance to the herbicide bialaphos. The percentage of plantlets generated from induction and differentiation media without Cu2+ was lower than those cultured on differentiation media with Cu2+ (71.15%) or induction media with Cu2+ (68.45%) and both induction and differentiation media with Cu2+ (52.17%). The combinations of Nor medium for callus induction and Cu2+ medium for regeneration, and Cu2+ medium for induction and R medium for regeneration were superior for biolistic transformation. The calli induced on Cu2+ medium and pre-cultured for 4 d before biolistic transformation, and cultured on R medium after biolistic transformation produced the highest percentage (65%) of transgenic plantlets with the KN2 gene. Overall, about 50% plantlets regenerated from calli pre-cultured 4d before bombardment carried the KN2 gene; 44.7% of the plantlets carried the bar gene, which was higher than for any other treatment, followed by pre-culture 1d with 31.43% transformation rate for the KN2 gene and 20% transformation rate for the bar gene.

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)0.000
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 score0.101
Threshold uncertainty score0.105

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.268
Teacher spread0.244 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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