Acetyl‐CoA carboxylase overexpression in herbicide‐resistant large crabgrass (<scp><i>Digitaria sanguinalis</i></scp>)
Bibliographic record
Abstract
BACKGROUND: The occurrence of herbicide-resistant weed biotypes is increasing and this report of an acetyl-CoA carboxylase (ACCase) inhibitor-resistant Digitaria sanguinalis L. Scop. from southwestern Ontario is another example. The identified weed escaped control in an onion and carrot rotation in which graminicides were used for several consecutive years. Our goal was to characterize the level and mechanism of resistance of the biotype. RESULTS: The biotype was resistant to all five ACCase inhibitor herbicides tested. Gene-expression profiling was performed because none of the mutations known to confer resistance in the ACCase gene were detected. RNASeq and quantitative reverse-transcriptase PCR (qRT-PCR) results indicated that transcription of ACCase was 3.4-9.3 times higher in the resistant biotype than the susceptible biotype. ACCase gene copy number was determined by qPCR to be five to seven times higher in the resistant compared with the susceptible biotype. ACCase gene overexpression was directly related to the increase of the ACCase gene copy number. CONCLUSION: Our results are consistent with the hypothesis that overexpression of the herbicide target gene ACCase confers resistance to the herbicide. This is the first reported case of target gene duplication conferring resistance to a herbicide other than glyphosate. © 2017 Society of Chemical Industry See related Article.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".