Production of Transgenic Soybean Plants with Two Anti-Fungal Protein Genes Via Agrobacterium and Particle Bombardment
Bibliographic record
Abstract
Utilizing either Agrobacterium-mediated transformation or particle bombardment we obtained transgenic soybean [Glycine max (L.) Merr.] plants expressing the chitinase gene (chi) and the barley ribosome-inactivating protein gene (rip). Six regenerated plants were grown to maturity and set seed. The identification of transgenic soybean plants that co-integrated the two anti-fungal protein genes was determined by polymerase chain reaction (PCR) and Southern blot analysis. Protein detection from the soybean leaves demonstrated the expression of the chitinase (CHI) and the ribosome-inactivating protein (RIP) in the six R0 transformants. Soybean cotyledonary nodes were transformed using the bivalent plant expression vector pBRC containing chi and rip both driven by the CaMV 35S double promoter. Following vacuum (0.06 MPa) infiltration treatment of the tissue for 5 min, Agrobacterium was co-cultivated with the cotyledonary nodes for 3 d on MSB medium (MS salts and B5 vitamins) (pH 5.2), the transformation frequency reached a maximum of 1.33 %. The chi and rip genes were present in all the transgenic plants. Co-bombardment of immature cotyledons with plasmids pBchE (encoding chi) and pARIP (encoding rip) resulted in a maximum transformation frequency of 0.52 % with a 50 % co-integration rate. Our results demonstrate efficient co-transformation of multiple genes in soybean.
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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 | 0.001 |
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".