Resistance gene analogues are clustered on chromosome 3 of sugar beet and cosegregate with QTL for rhizomania resistance
Bibliographic record
Abstract
Worldwide, rhizomania is the most important disease of sugar beet. The only way to control this disease is to use resistant varieties. Four full-length resistance gene analogues (RGAs) from sugar beet (cZR-1, cZR-3, cZR-7, and cZR-9) were used in this study. Their predicted polypeptides carry typical nucleotide-binding sites (NBSs) and leucin-rich repeat (LRR) regions, and share high homology to various plant virus resistance genes. Their corresponding alleles were cloned and sequenced from a rhizomania resistant genotype. The 4 RGAs were mapped as molecular markers, using sequence-specific primers to determine their linkage to the rhizomania resistance locus Rz1 in a population segregating for rhizomania resistance. One cZR-3 allele, named Rz-C, together with 5 other molecular markers, mapped to the Rz1 locus on chromosome 3 and cosegregated with quantitative trait loci for rhizomania resistance. After screening a bacterial artificial chromosome (BAC) library, 25 cZR-3-positive BACs were identified. Of these, 15 mapped within an interval of approximately 14 cM on chromosome 3, in clusters close to the Rz1 locus. Rz-C differentiates between susceptible and resistant beet varieties, and its transcripts could be detected in all rhizomania resistant varieties investigated. The potential of this RGA marker for cloning of rhizomania resistance genes is discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".