Application of a Simple Method to Study Single-Particle Bioaerosols Including Preferential Aerosolization
Bibliographic record
Abstract
Bacteria, viruses, fungus, and other biological components (toxins, membranes, spores) can spread in the air through various aerosolization processes (breathing, bubbling, explosion, evaporation) and travel until they reach a surface or a host. Nosocomial diseases are an example of illnesses caused by a human contact with such pathogen vectors in hospital settings. Very little is known about the aerosolization processes of viruses and bacteria and their potential to infect people after their passage in the airborne state and about the microbial burden carried by individual aerosol particles. Here we propose a novel approach to study the aerosolization mechanisms of bacteria in single particles using fluorescence spectroscopy and a homemade system allowing the control of the aerosolization and the impaction of bacteria on a black filter. We validated the concept using P. fluorescence and E. coli. The results show that independently of the amount of P. fluorescens and E. coli aerosolized the average distribution of cells impacted on a black filter is described by a Poisson fit with λ ∼ 0.6 ± 0.2. This means that using this aerosolization process, an aerosol will present no bacterium, but when it does, the number of bacteria per particle in the distributions will more probably be one. We also observed that the aerosolization processes of these two bacterial species allow P. fluorescens to be preferentially aerosolized against E. coli. These results demonstrate that fluorescence spectroscopy is a powerful tool to study bioaerosols in single particles. This technique can be used to study several phenomena like preferential aerosolization.Copyright 2015 American Association for Aerosol 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".