Specific flavonoids induced nod gene expression and pre‐activated nod genes of Rhizobium leguminosarum increased pea (Pisum sativum L.) and lentil (Lens culinaris L.) nodulation in controlled growth chamber environments
Bibliographic record
Abstract
The gram-negative soil bacteria Rhizobium spp. infect and establish a nitrogen-fixing symbiosis with legume crops which involves the mutual exchange of diffusable signal molecules. In this study, Rhizobium leguminosarum containing a nod-lacZ gene fusion was used to screen the most effective plant-to-bacteria signal molecules for pea and lentil and the induction conditions. Out of a number of signal compounds including apigenin, daidzein, genistein, hesperetin, kaempferol, luteolin, naringenin, and rutin, hesperetin and naringenin were found to be the most effective plant-to-bacteria signal molecules. The induction of nod genes was temperature-dependent, where nod gene induction was decreased with dropping incubation temperature. The combination of hesperetin at 7 microM and naringenin at 3 microM resulted in better induction of nod gene activities compared to either hesperetin or naringenin alone. Nodulation and plant dry matter accumulation of pea and lentil plants receiving preinduced R. leguminosarum were higher than those of plants receiving uninduced R. leguminosarum cells in controlled environment growth chamber conditions. Preinduced Rhizobium with hesperetin at a concentration of 10 microM increased nodule number on average by 60.5% and dry matter accumulation by 14% in field pea at 17 degrees C, while it was 32% and 9% at 24 degrees C, respectively. Similarly, averaged over two rhizobial strains, a 59% and 6% increase in nodule number and biomass production at 17 degrees C, and a 39% and 27% at 24 degrees C, were obtained from lentil inoculated with hesperetin-induced R. leguminosarum, respectively.
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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) | 0.001 | 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".