An easy, simple inexpensive test for the specific detection of Pectobacterium carotovorum subsp. carotovorum based on sequence analysis of the pmrA gene
Bibliographic record
Abstract
BACKGROUND: The species Pectobacterium carotovorum includes a diverse subspecies of bacteria that cause disease on a wide variety of plants. In Morocco, approximately 95% of the P. carotovorum isolates from potato plants with tuber soft rot are P. carotovorum subsp. carotovorum. However, identification of this pathogen is not always related to visual disease symptoms. This is especially true when different pathogen cause similar diseases on potato, citing as an example, P. carotovorum, P. atrosepticum and P. wasabiae. Numerous conventional methods were used to characterize Pectobacterium spp., including biochemical assays, specific PCR-based tests, and construction of phylogenetic trees by using gene sequences. In this study, an alternative method is presented using a gene linked to pathogenicity, in order to allow accuracy at subspecies level. The pmrA gene (response regulator) has been used for identification and analysis of the relationships among twenty nine Pectobacterium carotovorum subsp. carotovorum and other Pectobacterium subspecies. RESULTS: Phylogenetic analyses of pmrA sequences compared to ERIC-PCR and 16S rDNA sequencing, demonstrated that there is considerable genetic diversity in P. carotovorum subsp. carotovorum strains, which can be divided into two distinct groups within the same clade. CONCLUSIONS: pmrA sequence analysis is likely to be a reliable tool to identify the subspecies Pectobacterium carotovorum subsp. carotovorum and estimate their genetic diversity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".