The stimulatory effect of Thuricin 17, a PGPR-produced bacteriocin, on canola (Brassica, napus L.) germination and vegetative growth under stressful temperatures
Bibliographic record
Abstract
Exposure to unfavorable conditions is becoming more frequent for plants due to climate change, posing a threat to global food security. Stressful temperature, as a major environmental factor, adversely affects plant growth and development, and consequently agricultural production. Hence, development of sustainable approaches to assist plants in dealing with environmental challenges is of great importance. Compatible plant-microbe interactions and signal molecules produced within these interactions, such as bacteriocins, could be promising approaches to managing the impacts of abiotic stresses on crops. Although the use of bacteriocins in food preservation is widespread, only a small number of studies have examined their potential in agriculture. Therefore, we studied the effect of three concentrations of Thuricin17 (Th17), a plant growth-promoting rhizobacterial signal molecule produced by Bacillus thuringiensis , on germination and vegetative growth of canola ( Brassica napus L.) under stressful temperatures. Canola responded positively to treatment with the bacterial signal molecule under stressful temperatures. Treatment with 10 -9 M Th17 (Thu2) was found to significantly enhance germination rate, seed vigor index, radical and shoot length and seedling fresh weight under low temperature, and this treatment reduced germination time which would be an asset for higher latitude, short growing season climates. Likewise, Thu2 was able to alleviate the adverse effects of high temperature on germination and seed vigor. Regarding vegetative growth, interestingly, moderate high temperature with the assistance of the compound caused more growth and development than the control conditions. Conversely, low temperature negatively affected plant growth, and Th17 did not help overcome this effect. Specifically, the application of 10 -9 (Thu2) and 10 -11 M (Thu3) Th17 had a stimulatory effect on height, leaf area and biomass accumulation under above-optimal conditions, which could be attributed to modifications of below-ground structures, including root length, root surface, root volume and root diameter, as well as photosynthetic rate. However, no significant effects were observed under optimal conditions for almost all measured variables. Therefore, the signal compound tends to have a stimulatory impact at stressful temperatures but not under optimal conditions. Hence, supplementation with Th17 would have the potential as a plant growth promoter under stressed circumstances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".