Agrobacterium tumefaciens-Mediated Genetic Transformation of Cereals Using Immature Embryos
Bibliographic record
Abstract
A critical step in the development of a robust Agrobacterium tumefaciens-mediated transformation -system for cereal crop plants is the establishment of optimal conditions for efficient T-DNA delivery into target tissue, from which plants can be regenerated. Although, Agrobacterium-mediated transformation of cereals is an important method that has been widely used by many laboratories around the world, routine protocols have been established only in specific cultivars within a species and with specific tissues of high regeneration potential. Cocultivation of highly embryogenic callus tissue or healthy immature embryos with A. tumefaciens is considered one of the critical factors in successful genetic transformation of crop plants. Immature embryos collected only from vigorously growing healthy and green plants grown in the field or in the well-conditioned greenhouse are the ideal target for genetic transformation of recalcitrant crop species. Here, we describe an Agrobacterium-mediated transformation method that uses immature embryos as the starting material for inoculation with Agrobacterium. The aim of this chapter is to provide the key steps/components involved in Agrobacterium-mediated transformation of cereal crops. However, these steps or components often vary between protocols and from laboratory to laboratory, and can be optimized or modified based on the requirement of a specific cultivar or species.
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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) | 0.000 | 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 | 0.002 |
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".