Cold hardening induces transfer of fatty acids between polar and nonpolar lipid pools in the Arctic collembollan <i>Megaphorura arctica</i>
Bibliographic record
Abstract
Cold hardiness in the Arctic Collembola Megaphorura arctica (Tullberg), formerly Onychiurus arcticus , has been the subject of extensive studies over the last decade. This species employs an unusual strategy known as cryoprotective dehydration to survive winter temperatures as low as −25 °C. To expand knowledge of cryoprotective dehydration in M. arctica , the present study investigates how a reduction in ambient temperature affects the fatty acid composition of the total body lipid content along with polar (mainly membrane phospholipids) and nonpolar (mainly triacylglycerols) lipids. Most ectothermic animals compensate for changes in fluidity by regulating fatty acid composition, a process often described as homeoviscous adaptation. In M. arctica , changes in the fatty acid composition of total body lipid content during cold treatment are only moderate, with no clear pattern emerging. However, the levels of unsaturated fatty acids in the polar lipids increase with cold exposure, largely attributable to 16 : 1( n − 7), 18 : 1( n − 9), 18 : 3( n − 6) and 18 : 3( n − 3), whereas unsaturated fatty acid levels in the nonpolar lipids correspondingly decrease. These results suggest a reallocation of fatty acids between the two lipid pools as a response to a temperature reduction of 6 °C. Because of hypometabolism, a characteristic of cold adaptation, such a mechanism could be less energy demanding than de novo synthesis of fatty acids and may comprise part of an adaptive homeostatic response.
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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) | 0.001 | 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".