Characterization of Biofilm Formation by <i>Xylella fastidiosa</i> In Vitro
Bibliographic record
Abstract
Xylella fastidiosa colonizes the xylem of various host plants, causing economically important diseases such as Pierce's disease in grapevine and citrus variegated chlorosis (CVC) in sweet oranges. The aggregative nature of this bacterium has been extensively documented in the plant xylem and the insect's foregut. Structured communities of microbial aggregates enclosed in a self-produced polymeric matrix and attached to a surface are defined as biofilms. In this study, we characterized biofilm formation by X. fastidiosa through the use of a novel in vitro assay for studying biofilm growth in a potential mimic system of what might occur in planta. We used wood, a xylem rich material, as a surface for bacterial attachment and biofilm formation, under shear force. We demonstrated that X. fastidiosa strains isolated from various hosts formed biofilm on wood in this in vitro assay. Different biofilm morphology was detected, which seems to vary according to the strain tested and microenvironmental conditions analyzed. We observed that strains from different hosts could be grouped according to three parameters: biofilm morphology, the ability to form clumps in liquid culture, and the ability to attach to glass surfaces. We hypothesize that biofilm formation is likely a major virulence factor in diseases related to X. fastidiosa, bringing a new perspective for disease treatment.
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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) | 0.000 | 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".