Microbes at Surface-Air Interfaces: The Metabolic Harnessing of Relative Humidity, Surface Hygroscopicity, and Oligotrophy for Resilience
Bibliographic record
Abstract
The human environment is predominantly not aqueous, and microbes are ubiquitous at the surface-air interfaces with which we interact. Yet microbial studies at surface-air interfaces are largely survival-oriented, whilst microbial metabolism has overwhelmingly been investigated from the perspective of liquid saturation. This study explored microbial survival and metabolism under desiccation, particularly the influence of relative humidity (RH), surface hygroscopicity, and nutrient availability on the interchange between these two phenomena. The combination of a hygroscopic matrix (i.e., clay or 4,000 MW polyethylene glycol) and high RH resulted in persistent measurable microbial metabolism during desiccation. In contrast, no microbial metabolism was detected at (a) hygroscopic interfaces at low RH, and (b) less hygroscopic interfaces (i.e., sand and plastic/glass) at high or low RH. Cell survival was conversely inhibited at high RH and promoted at low RH, irrespective of surface hygroscopicity. Based on this demonstration of metabolic persistence and survival inhibition at high RH, it was proposed that biofilm metabolic rates might inversely influence whole-biofilm resilience, with ‘resilience’ defined in this study as a biofilm’s capacity to recover from desiccation. The concept of whole-biofilm resilience being promoted by oligotrophy was supported in desiccation-tolerant Arthrobacter spp. biofilms, but not in desiccation-sensitive Pseudomonas aeruginosa biofilms. The ability of microbes to interact with surfaces to harness water vapor during desiccation was demonstrated, and potentially to harness oligotrophy (the most ubiquitous natural condition facing microbes) for adaptation to desiccation.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".