A role for manganese in superoxide dismutases and growth of iron‐deficient diatoms
Bibliographic record
Abstract
We have discovered that coastal and oceanic diatoms require more manganese (Mn) to grow in iron (Fe) ‐deficient than in Fe‐sufficient seawater. At low inorganic concentrations, like those of the open sea, Fe and Mn can thus colimit Thalassiosira pseudonana and T. oceanica so that maximum rates of cell division are achieved only when both resources are added simultaneously to cultures. Colimited diatoms amended with either Fe or Mn alone show unique physiological responses, which implies that the observed interaction between Fe and Mn is not caused by a substitution of one metal for the other. Iron deficiency increases the Mn quota of T. pseudonana by three times compared with controls and enhances the production of reactive oxygen species by 1.7 times in T. weissflogii. Both diatoms respond to this oxidative stress by increasing the activities of the antioxidant enzyme superoxide dismutase (SOD). The Mn content of the SODs increases by 1.8 to 2.8 times when Fe is limiting, which suggests that the SODs contain Mn and may account for part of the observed increase in the Mn quota. Such an increased biochemical requirement may elevate the Mn content of low Fe diatoms, and possibly other phytoplankton, resulting in high Mn: Fe ratios in surficial particulate matter in Fe‐limited regions of the sea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".