Identification of regulatory genes to improve nitrogen use efficiency
Bibliographic record
Abstract
Guevara, D. R., Bi, Y.-M. and Rothstein, S. J. 2014. Identification of regulatory genes to improve nitrogen use efficiency. Can. J. Plant Sci. 94: 1009–1012. Crop production on soils containing sub-optimal levels of nitrogen (N) severely compromises yield potential. The development of crop varieties displaying high N use efficiency (NUE) is necessary in order to optimize N fertilizer use, and reduce the environmental damage caused by the current excessive application of N in agricultural areas. Genome-wide microarray analysis of rice plants grown under N-limiting environments was performed to identify NUE candidate genes. An early nodulin gene, OsENOD93-1, was strongly up-regulated during plant growth under low N. A constitutive Ubiquitin promoter was used to drive the expression of the OsENOD93-1 gene in transgenic plants to determine the importance of OsENOD93-1 for rice NUE. Transgenic rice plants over-expressing the OsENOD93-1 gene achieved ∼23% and 16% more yield and biomass, respectively, compared with wild-type plants when grown under N-limitation conditions. OsENOD93-1-OX transgenic plants accumulated a higher amount of total amino acids in the roots and xylem sap under N stress, suggesting that OsENOD93-1 plays a role in the transportation of amino acids. Taken together, we demonstrate that an effective way to identify NUE gene candidates involves both transcriptional profiling coupled with a transgenic validation approach to improve complex traits such as NUE in important crops.
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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 | 0.001 |
| 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".