Transcriptome analysis identified the mechanism of synergy between sethoxydim herbicide and a mycoherbicide on green foxtail
Bibliographic record
Abstract
Certain synthetic herbicides can act synergistically with specific bioherbicides. In this study, a sethoxydim herbicide at 0.1× label rate improved biocontrol of herbicide-sensitive green foxtail (Setaria viridis, GFT) by Pyricularia setariae (a fungal bioherbicide agent), but did not change the efficacy on a herbicide-resistant GFT biotype. Reference transcriptomes were constructed for both GFT biotypes via de novo assembly of RNA-seq data. GFT plants treated with herbicide alone, fungus alone and herbicide + fungus were compared for weed-control efficacy and differences in transcriptomes. On herbicide-sensitive GFT, sethoxydim at the reduced rate induced ABA-activated signaling pathways and a bZIP transcription factor 60 (TF bZIP60), while improved the efficacy of biocontrol. The herbicide treatment did not increase these activities or improve biocontrol efficacy on herbicide-resistant plants. An exogenous application of ABA to herbicide-sensitive plants also enhanced bZIP60 expression and improved biocontrol efficacy, which supported the results of transcriptome analysis that identified the involvement of ABA and bZIP60 in impaired plant defense against P. setariae. It is novel to use transcriptome analysis to decipher the molecular basis for synergy between a synthetic herbicide and a bioherbicide agent. A better understanding of the mechanism underlining the synergy may facilitate the development of weed biocontrol.
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 | 0.000 | 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".