<i>Agrobacterium tumefaciens</i>‐mediated transformation for targeted disruption and over expression of genes in the poplar pathogen<i><scp>S</scp>phaerulina musiva</i>
Bibliographic record
Abstract
Summary Sphaerulina musivacauses both leaf spots and cankers on poplar. Leaf spots can lead to defoliation and cankers on branches and primary stems can lead to stem breakage and tree mortality. The recent availability of both theS. musivaandPopulus trichocarpagenomes offers a great opportunity to study host–pathogen interactions. To better understand the factors involved inS. musivapathology, we present a strategy for the transformation of this species usingAgrobacterium tumefaciens. Binary plasmids were generated with hygromycin B phosphotransferase (hph) flanked by upstream and downstream sequences of polyketide synthase‐like (PKS‐L1) gene to generate targeted gene disruptants by homologous recombination. Plasmids were also constructed for constitutive expression reporter geneseGFPandmCherry to help with histological characterization of the pathogen during infection. Gene knockouts were identified byPCRand confirmed by sequencing and Southern blotting. No significant differences were observed in melanin production betweenPKS‐L1 disruptants and wild type isolates. Colonies expressing reporter genes were identified by fluorescent stereomicroscopy. This method is a promising tool for the characterization of pathogen genes through reverse and forward genetics and for introducing markers for histopathological study.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".