Physiological analysis of nitrogen-efficient rice overexpressing alanine aminotransferase under different N regimes
Bibliographic record
Abstract
Cereal crop plants have low nitrogen (N) use efficiency, taking up only 30% to 50% of the applied N fertilizers, with the rest having the potential for loss into the environment as N pollution. One way to address this problem is to improve the nitrogen use efficiency of cereal crops using a transgenic approach. We developed alanine aminotransferase overexpressing rice, and we have previously determined that this modification provided an improved nitrogen-use phenotype to the engineered plants. In this study, the transgenic rice were grown in low, medium, and high nitrogen supply, and morphology, plant N levels, enzymatic activity, metabolite levels, and transcriptome response in the roots and shoots at active and maximum tillering at each N level were measured. The transcriptome response was analysed further using MapMan and PageMan to view multiple comparisons. The transgenic rice plants showed improved nitrogen use efficiency at medium and high N supply, but with few significant changes to the amino acid levels or to the transcriptome. The transgenic plants grown in high N showed up-regulation of transcripts associated with photosynthesis, non-melavonate pathway secondary metabolites, protein degradation, and many unknown function transcripts.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".