Preferential aerosolization of bacteria in the air of different composting plants
Bibliographic record
Abstract
B aerosols are generally characterized as biological particles suspended in the air. Mechanical, Physical and seasonal factors affect the concentration of bio aerosols released from composting plants and thus, the exposure of workers. The morphological and biochemical differences between different microorganisms may affect their potential to be aerosolized, although this phenomenon has not been well described. From this perspective, differential aerosolization of certain pathogenic microbes found in compost deserves to be studied in order to better understand the factors involved in occupational exposure. The goal of this study was to explore, using next generation sequencing, the preferential aerosolization of microorganisms in different composting plants and see if the compost microbial population and diversity differs from that of bio aerosols. Results suggest that Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria constituted the major phyla of bacteria found in bio aerosols released from compost. There is an obvious link between microorganisms found in the air and in compost samples. However, some phyla seem enriched in the air when compared to their proportion in compost whereas; others are preferentially kept in the compost, suggesting anon-random aerosolization process. Using MiSeq Illumina sequencing technology, this study suggests the preferential aerosolization of Actinobacteria and more specifically genera like Saccharopolyspora, Mycobacterium and for some Proteobacteria such as Legionella. Some phyla are also under represented in aerosols and do not seem to be easily aerosolized such as Pseudomonas sp. Exposure to preferentially aerosolized pathogenic bio aerosols may be a potential source of occupational risk and evaluation of the sources microbial content (compost) is not a good proxy of workers’ exposure to bio aerosols.
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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".