Data Mining Nitrogen-Responsive Gene Expression for Source–Sink Relations and Indicators of N Status in Potato
Bibliographic record
Abstract
Potato tuber yields depend on nitrogen (N) supply, which affects source–sink relations. Transcriptome sequencing of the foliar source using a single field trial identified gene expression responsive to 180 kg N ha−1. The expression of N-responsive genes was further analyzed in the next stage using a NanoString nCounter over an expanded number of foliar samples from seven field trials with varying N rates, sites, and cultivars. Least absolute shrinkage and selection operator (LASSO) regression models of gene expression predictive of yield, total plant N uptake, and tuber-specific gravity (proxy for dry matter) were built. Genes in the LASSO model for yield were associated with source–sink partitioning. A key gene regulating tuberization and senescence, StSP6A Flowering locus T, was found in the LASSO model predicting tuber yield, but not the other models. An aminotransferase involved in photorespiratory N assimilation and amino acid biosynthesis was found in all LASSO models. Other genes functioning in amino acid biosynthesis and integration of sulfur (S) and N metabolism were also found in the yield prediction model. The study provides insights on N responses in foliage of potato plants that affect source–sink partitioning. Additionally, N-responsive genes predictive of yield are candidate indicators of N status.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".