Optimization of zoospore production and inoculum concentration of <i>Aphanomyces euteiches</i> for resistance screening of pea and lentil
Bibliographic record
Abstract
Aphanomyces euteiches is an important root pathogen of several legume and forage crops worldwide. Disease management is challenging due to resilient oospores that can persist in the soil for long periods of time. Several research groups are working towards incorporating disease resistance into crop cultivars involving large-scale disease resistance screening and, for some crops, the use of molecular markers associated with quantitative trait loci for resistance. The refinement of laboratory protocols for efficient pathogen isolation, reliable and simple zoospore production, and a repeatable indoor screening assay are important tools needed to further research in this area. The standardization of these protocols among research groups would provide an efficient platform for research collaboration. The effect of different media and sporulation induction methods on zoospore production was assessed. The most effective and repeatable method of A. euteiches zoospore production involved culturing and incubation for 4 days on autoclaved wheat leaf segments placed on corn meal-yeast extract-phosphate buffer agar. Colonized wheat leaves were transferred into 100 mL of distilled water in 250 mL flasks and incubated at 100 rpm for 18 h at 24ºC to induce zoospore production. Five zoospore concentrations ranging from 100 to 10 000 zoospores mL−1 were evaluated using susceptible and partially resistant pea and lentil genotypes to optimize zoospore concentration for germplasm screening. The best differentiation of levels of resistance among lentil and pea genotypes was achieved with 1000 zoospores mL−1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".