The Effect of Temperature on the Hypersensitive Response (HR) in the Brassica napus–Leptosphaeria maculans Pathosystem
Bibliographic record
Abstract
Temperature is considered one of the crucial environmental elements in plant pathological interactions, and previous studies have indicated that there is a relationship between temperature change and host–pathogen interactions. The objective of this research is to investigate the link between temperature and the incompatible interactions of the host and pathogen. In this study, two Leptosphaeria maculans isolates (HCRT75 8-1 and HCRT77 7-2) and two Brassica napus genotypes (Surpass400 and 01-23-2-1) were selected. The selected B. napus genotypes displayed intermediate and resistant phenotypes. The inoculated seedlings were tested under three temperature conditions: 16 °C/10 °C, 22 °C/16 °C and 28 °C/22 °C (day/night: 16 h/8 h). Lesion measurements demonstrated that the necrotic lesions from the 28 °C/22 °C treatment were enlarged compared with the other two temperature treatments (i.e., 16 °C/10 °C and 22 °C/16 °C). The results of expression analysis indicated that the three temperature treatments displayed distinct differences in two marker genes (PATHOGENESIS–RELATED (PR) 1 and 2) for plant defense and one temperature-sensitive gene BONZAI 1 (BON1). Additionally, seven dpi at 22 °C/16 °C appeared to be the optimal pre-condition for the induction of PR1 and 2. These findings suggest that B. napus responds to temperature changes when infected with L. maculans.
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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) | 0.001 | 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 | 0.001 |
| 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".