Comparative Metabolite Profiling of Antarctic and Korean Mosses: Insights into Adaptation Mechanisms of Antarctic Moss Species
Bibliographic record
Abstract
This study investigates the relationship between secondary metabolites and stress tolerance in moss species, with a specific emphasis on comparing Antarctic and Korean mosses. Analyses of total phenolic content (TPC) and total flavonoid content (TFC) revealed that Antarctic mosses contain these compounds at significantly higher levels compared to the Korean mosses. These findings are consistent with greater antioxidant activities observed in Antarctic mosses through DPPH and ABTS•+ radical scavenging assays. In this study, a total of 620 metabolites were identified from the moss samples. The results showed that Antarctic mosses exhibited a high number and diversity of compounds including terpenoids, flavonoids, lipids, and other classes. Additionally, Antarctic mosses had fewer lipids with carbon chain lengths below 18 and a higher content of unsaturated lipids, indicating adaptations to maintain membrane fluidity under cold stress. The phylogenetic relationships suggested a correlation between metabolite profiles and genetic adaptations between these species. This research highlights the complex biochemical strategies that mosses, particularly those in Antarctic regions, employ to adapt the environmental stressors. The high abundance of secondary metabolites in Antarctic mosses not only serves as a defense mechanism against oxidative stress but also suggests their potential applications in various biotechnological aspects. This study reveals new avenues for exploring the ecological roles and potential uses of these resilient plant species.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".