Identification of molecular markers linked to sugarbeet cyst nematode resistance gene(s)
Bibliographic record
Abstract
Beet cyst nematode (Heterodera schachtii Schmidt). Spreads extensively in different regions of sugar beet cultivation, and therefore many efforts are accomplished to prevent decrease of sugar and root yield caused by the disease. The most appropriate control method of this disease is improvement of resistant varieties. Wild species of procumbentes in Beta are very important because they contain resistance genes against different diseases including beet cyst nematode. Ten-mer primers and specific sequences were selected to identify DNA molecular markers linked to beet cyst nematode resistance genes. For this purpose, at first, segregating populations for resistance genes were selected and after seed sowing in pots in greenhouse, seedlings were inoculated with 1000 nematode larva several times. Resistant plants (with lower than 10 cysts) and susceptible plants (with less than 10 cysts) were identified and divided to two groups. In the next step, RAPD-PCR, finger printing of the samples and comparison the DNA banding pattern of the two groups was done by using selected primers such as: OP-X-02, OP-X-15, OP-G-02, OP-D-13, OP-B-11, OP-Y-10 and Sat-121 specific primers and the oligonncleotides TGAACACCTTTCAAAT (forward) and CGTAAGAGACTATGA (Reverse) and one hundred primers from the operon kits. After calculating of correlation between resistance plants and the presence of special DNA band, OP-D-13 and Sat-121 molecular markers were identified to be linked to resistant genes against beet cyst nematode. In conclusion, these two molecular markers can be used for screening of resistant plants in laboratory conditions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".