<i>Pseudomonas mosselii</i> improves cold tolerance of Asian rice (<i>Oryza sativa</i> L.) in a genotype-dependent manner by increasing proline in <i>japonica</i> and reduced glutathione in <i>indica</i> varieties
Bibliographic record
Abstract
Cold stress is an important factor limiting rice production and distribution. Identifying factors that contribute to cold tolerance in rice is of primary importance. While some plant specific genetic factors involved in cold tolerance have been identified, the role of the rice microbiome remains unexplored. In this study, we evaluated the influence of plant growth promoting bacteria (PGPB) with the ability of phosphate solubilization on rice cold tolerance and survival. To reach this goal, inoculated and uninoculated 2-week-old seedlings were cold stressed and evaluated for survival and other phenotypes such as electrolyte leakage (EL) and necessary elements for cold tolerance. The results of this study showed that of the five bacteria, Pseudomonas mosselii, improved both indica and japonica varietal plants’ survival and decreased EL, indicating increased membrane integrity. We observed different possible cold tolerance mechanisms in japonica and indica plants such as increases in proline and reduced glutathione levels, respectively. This bacterium also improved the shoot growth of cold exposed indica plants during the recovery period. This study confirmed the host genotype dependent activity of P. mosselii and indicated that there is an interaction between specific plant genes and bacterial genes that causes different plant responses to cold stress.
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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".