Monitoring <i>in planta</i> bacterial infection at both cellular and whole‐plant levels using the green fluorescent protein variant GFPuv
Bibliographic record
Abstract
* Green fluorescent protein (GFP) labeling of bacteria has been used to study their infection of and localization in plants, but strong autofluorescence from leaves and the relatively weak green fluorescence of GFP-labeled bacteria restrict its broader application to investigations of plant-bacterial interactions. * A stable and broad-host-range plasmid vector (pDSK-GFPuv) that strongly expresses GFPuv protein was constructed not only for in vivo monitoring of bacterial infection, localization, activity, and movement at the cellular level under fluorescence microscopy, but also for monitoring bacterial disease development at the whole-plant level under long-wavelength ultraviolet (UV) light. * The presence of pDSK-GFPuv did not have significant impact on the in vitro or in planta growth and virulence of phytobacteria. A good correlation between bacterial cell number and fluorescence intensity was observed, which allowed us to rapidly estimate the bacterial population in plant leaf tissue. We demonstrated that GFPuv-expressing bacteria can be used to screen plants that are compromised for nonhost disease resistance and Agrobacterium attachment. * The use of GFPuv-labeled bacteria has a wide range of applications in host-bacterial interaction studies and bacterial ecology-related research.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".