Riverine mycobiome dynamics: From South African tributaries to laboratory bioreactors
Bibliographic record
Abstract
Riverine fungi have the capacity for both pathogenicity, pertinent for countries with elevated immunosuppressed individuals, and bioremediation potential.The purpose was (i) to screen for the presence of clinically relevant riverine fungi and associations with anthropogenic influence, and (ii) the acclimatisation of environmental communities toward potential bioremediation application.Communities were harvested from polluted rivers in Stellenbosch, South Africa, and mycobiomes characterised by high-throughput amplicon sequencing.The remainder of the biomass was inoculated into continuous bioreactors with filtered river water or sterile minimal medium.Seven weeks later, the mycobiomes were re-sequenced.At least nine clinically relevant species were detected, including agents of mycoses belonging to the genus Candida.The occurrence of genera that harbour opportunisticstrains was significantly higher (P = 0.04) at more polluted sites.Moreover, positive correlations occured between some genera and pollution indices, demonstrating the potential of fungi for addition to water quality indicators.Despite biomass increase, almost all pathogens were undetectable after seven weeks, demonstrating less resilience in conditions mimicking rivers.Thus, when screening riverine biomes for bioremediation potential, ambient reactors select against human pathogens.This indicates a transient introduction of allochthonous opportunistic species into rivers due to insufficient sanitation, and the potential of bioremediation strategies that selects for environmental rather than pathogenic traits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".