Impact of temperature on <i>Legionella pneumophila</i> , its protozoan host cells, and the microbial diversity of the biofilm community of a pilot cooling tower
Bibliographic record
Abstract
ABSTRACT Legionella pneumophila (Lp) is a waterborne bacterium known for causing Legionnaires’ Disease, a severe pneumonia. Cooling towers are a major source of outbreaks, since they provide ideal conditions for Lp growth and produce aerosols. In such systems, Lp typically grow inside protozoan hosts. Several abiotic factors such as water temperature, pipe material and disinfection regime affect the colonization of cooling towers by Lp. The local physical and biological factors promoting the growth of Lp in water systems and its spatial distribution are not well understood. Therefore, we built a lab-scale cooling tower to study the dynamics of Lp colonization in relationship to the resident microbiota and spatial distribution. The pilot was filled with water from an operating cooling tower harboring low levels of Lp . It was seeded with Vermamoeba vermiformis , a natural host of Lp , and then inoculated with Lp. After 92 days of operation, the pilot was disassembled, the water was collected, and biofilm was extracted from the pipes. The microbiome was studied using 16S rRNA and 18S rRNA genes amplicon sequencing. The communities of the water and of the biofilm were highly dissimilar. The relative abundance of Legionella in water samples reached up to 11% whereas abundance in the biofilm was extremely low (≤0.5 %). In contrast, the host cells were mainly present in the biofilm. This suggest that Lp grows in host cells associated with biofilm and is then released back into the water following host cell lysis. In addition, water temperature shaped the bacterial and eukaryotic community of the biofilm, indicating that different parts of the systems may have different effects on Legionella growth.
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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 | 0.000 |
| 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".