The Effect of Ultraviolet Radiation on Production of Antioxidant Compounds from Bioprospected Acid Tolerant Microalgae Used to Mitigate Industrial CO <sub>2</sub>
Bibliographic record
Abstract
Controlling light exposure (wavelength and intensity) has been shown to increase the cellular production of antioxidants in some species of microalgae. However, these light stress studies do not typically examine extremophiles, which have been shown to produce antioxidants as a defense mechanism. This study aims to examine the effect of ultraviolet radiation (UVR) on the production of natural antioxidant compounds (carotenoids, chlorophyll a, and chlorophyll b) in extremophiles, with the ultimate goal of improving the feasibility of industrial CO 2 mitigation. Three strains of microalgae were bioprospected from low pH (<4) mining impacted water bodies in Ontario, Canada. This study found that the bioprospected strains naturally produce higher levels of antioxidant compounds compared to culture collection strains. However, decreasing the pH, as would happen with industrial off-gas application, resulted in a decreased concentration of antioxidant compounds and activity, with the exception: strain M2 maintained high activities at both low and unregulated pH. Three UVR treatments were tested, consisting of 10 h of UVR exposure and different recovery periods. Treatments resulted in increased antioxidant concentrations in samples with initially low concentrations, with these concentrations continuing to increase for up to 48 h after UVR exposure. A concurrent increase in antioxidant activity was also determined based on ABTS radical scavenging activity and ferric reducing power. Furthermore, this study identifies a promising strain (M2) that could simultaneously produce health beneficial compounds and mitigate industrial CO 2 emissions.
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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 | 0.000 | 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 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".