The curious case of cyanobacteria: a tale of light and darkness
Bibliographic record
Abstract
ABSTRACT Toxic algal bloom-forming cyanobacteria are a persistent problem globally for many aquatic environments. Their occurrence is attributed to eutrophication and rising temperatures due to climate change. The result of these blooms is often loss in biodiversity, economic impacts on tourism and fisheries, and risks to human and animal health. Of particular concern is the poorly understood interplay between viruses and toxic species that form blooms because viruses may exacerbate their harmful effects. Concurrently, cyanobacteria are also a source of bioactive compounds other than toxins, which makes them good candidates for drug discovery. We show that virus infection of the cyanobacterium Microcystis aeruginosa , results in as high as a 40-fold increase in the toxin microcystin two days post virus infection (dpi), and predict that microcystin levels may remain high in a body of water up to 7 dpi, long after water discoloration and cell lysis. This implicates viruses as major contributors to toxin release from cyanobacteria and emphasizes the importance of taking them into account in predictive models and in the assessment of water safety. We also show that bioactive compounds of M. aeruginosa inhibit and delay infection of single stranded DNA and single stranded RNA viruses. This highlights the potential of cyanobacteria as an excellent source for the discovery of novel antiviral compounds, and the ease with which screening for cyanobacterial antivirals can be achieved.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".