Photodynamic Inactivation reduces the diversity and changes the composition of bacterial and fungal communities associated with leaf surfaces
Bibliographic record
Abstract
Abstract Plant surfaces are colonized by a myriad of microorganisms including mutualistic strains and pathogens. Particularly in agricultural systems applications are required that protect the plants against pathogens without negative side effects on the environment and humans. Photodynamic Inactivation (PDI) has been demonstrated to be a promising approach to efficiently fight plant pathogens. Based on its mechanism of action, the light-induced and photosensitizer-mediated overproduction of reactive oxygen species in target cells, PDI is likely to generally inactivates microorganisms on plants irrespective of their pathogenicity. In order to prove this hypothesis we used next-generation 16S rRNA gene amplicon sequencing to characterize the bacterial and fungal communities associated with leaf surfaces of Arabidopsis thaliana before and after the photodynamic treatment using the chlorine e6 derivative B17-0024 as photoactive compound and showed that this treatment reduced the microbial richness and altered the microbial community composition. These findings may help to develop effective pathogen-control strategies and may also stimulate research on plant-microbe interactions exploiting the potential of PDI.
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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".