Non-uniform salt distribution in the root zone alleviates salt damage in wheat
Bibliographic record
Abstract
Furrow sowing could significantly decrease salt damage to wheat; however, the molecular mechanism in wheat is not well known. In this study, a split-root system was used to simulate non-uniform root zone salinity. Our hydroponic experiments showed that wheat seedlings under non-uniform salt stress probably use a salt avoidance strategy to ensure growth. RNA sequencing analysis showed that 1648 and 3245 differentially expressed genes were identified in 0/150 and 75/75 salt treatments, respectively, with an intersection of 690 genes. Gene ontology terms representing normal growth were specifically enriched by upregulated genes in the 0/150 treatment and downregulated genes in the 75/75 treatment, and terms representing phytoremediation were specifically enriched by upregulated genes in the 75/75 treatment and downregulated genes in the 0/150 treatment. Differentially expressed genes that are probably associated with salt stress and transcription factors showed significantly higher expression in the 75/75 treatment than in the 0/150 treatment. These findings suggest that a uniform salt treatment causes wheat to initiate a more complex salt tolerance mechanism for salt stress. In addition, the expression of 11 genes annotated as peroxidase was higher in the 0/150 treatment than in the 75/75 treatment, and the enzyme activity showed the same trend, indicating that peroxidase probably played a role in the better performance of wheat plants under non-uniform salt stress. Pot culture experiments showed that wheat plants under non-uniform salt stress produced higher yields than those under uniform stress, further indicating that inducing unequal salt distribution in soil could significantly improve wheat cultivation.
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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".