<i>Agrobacterium tumefaciens</i>-mediated transformation of corn (<i>Zea mays</i>L.) multiple shoots
Bibliographic record
Abstract
An Agrobacterium tumefaciens-mediated corn transformation method based on multiple shoot tissue cultures was developed, which is effective with a variety of corn inbred lines and standard binary vectors. Six factors that affected the success of corn transformation were tested, including A. tumefaciens strain, corn genotype, tissue culture growth stage, medium composition, co-culture temperature and surfactant treatment. Agropine-type bacteria (EHA 101 and AGL 1) were eightfold more effective than octopine-type strain for corn multi-shoot tissues transformation. The average frequency of Glucuronidase (GUS)-positive explants obtained from 14 corn genotypes ranged from 36% to 76%. L-proline (0.7 g L−1) in the co-culture medium apparently improved the frequency of transformation. The newly initiated multi-shoot tissues were most responsive to Agrobacterium infection. A positive correlation was found between multi-shoot tissue susceptibility to Agrobacterium and the proportion of cells in G1 phase. Transformants were identified by reverse transcription Polymerase Chain Reaction (PCR) and by southern blot hybridization assays. The frequency of transformants was approximately 2% based on the number of multi-shoot explants co-cultivated with Agrobacterium.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".