Dark stress for improved lipid quantity and quality in bioprospected acid-tolerant green microalgae
Bibliographic record
Abstract
The cost of microalgae cultivation is one of the largest limitations to achieving sustainable, large-scale microalgae production of commercially desirable lipids. Utilizing CO2 as a 'free' carbon source from waste industrial flue gas emissions can offer wide-ranging cost savings. However, these gas streams typically create acidic environments, in which most microalgae cannot survive due to the concentration of CO2 and the presence of other acidic gasses such as NO2 and SO2. To address this situation, we investigated growth of a mixed acid-tolerant green microalgal culture (91% dominated by a single Coccomyxa sp. taxon) bioprospected at pH 2.8 from an acid mine drainage impacted water body. The culture was grown at pH 2.5 and fed with a simulated flue gas containing 6% CO2 and 94% N2. On reaching the end of the exponential growth phase, the culture was exposed to either continued light-dark cycle conditions or continual dark conditions. After three days in the dark, the biomass consisted of 28% of lipids, which was 42% higher than at the end of the exponential phase and 55% higher than the maximum lipid content achieved under light/dark conditions. The stress caused by being continually in the dark also favoured the production of omega-3 and omega-6 polyunsaturated fatty acids (PUFAs; 19.47% and 21.04%, respectively, after 7 days) compared to 7-days of light-dark treatment (1.94% and 9.53%, respectively) and showed an increase in nitrogen content (C:N ratio of 6.4) compared to light-dark treatment (C:N ratio of 11.9). The results of the research indicate that use of acid tolerant microalgae overcomes issues using flue gasses that will create an acidic environment and that applying dark stress is a low-cost stressor stimulates production of desirable dietary lipids.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".