Clouds influence the functioning of airborne microorganisms
Bibliographic record
Abstract
Abstract. Airborne microorganisms can remain at altitude for several days, exposed to multiple environmental constraints that prevent or limit microbial activity, the most important of which is probably the lack of available liquid water. Clouds, i.e., air masses containing liquid water, could offer more favorable conditions. In order to investigate the influence of clouds on the functioning of airborne microorganisms, we captured aerosols in a nucleic acid preservation buffer from a high-altitude mountain meteorological station under cloudy and clear-atmosphere conditions and examined the metatranscriptomes. The specificities of aeromicrobiome's functioning in clouds and a clear atmosphere were then decrypted using differential expression analysis (DEA). The data reveal a higher RNA : DNA content in clouds than in the clear atmosphere, suggesting higher metabolic activity, and the overrepresentation of microbial transcripts related to energy metabolism, the processing of carbon and nitrogen compounds, intracellular signaling, metabolic regulations, and transmembrane transports. Stress response in clouds tends towards responses to osmotic shocks and starvation rather than oxidants in a clear atmosphere. Autophagy processes in eukaryotes (macropexophagy, i.e., the recycling of peroxisomes) could help to alleviate the limited amounts of nutrients in the restricted microenvironments provided by cloud droplets. The whole phenomenon resembles the rapid resumption of microbial activity in dry soils after rewetting by rain, which is known as the Birch effect and is described here for the first time for the atmosphere. This work provides unprecedented information on the modulations of an aeromicrobiome's functioning in relation to atmospheric conditions. In addition to contributing to the processing and fate of chemical compounds in the atmosphere, cloud-induced modulations of biological processes could have ecological repercussions by shaping airborne microbial diversity and their capacity to invade surface environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".