Interactions between Metals and Eudistomins of Ascidian Origin: A Computational Study
Bibliographic record
Abstract
Ascidians are marine animals that adopt unusual techniques to deter predation. The three main methods are sequestration of unusual metals, high concentrations of sulfuric acid/sulfate ions in tunicate cells, and the presence of eudistomins. In this study, we hypothesize that ascidians sequester metals in their sulfate form, and the complexation of eudistomins with the metals could liberate the sulfate ion. Three representative metal aqua ions were chosen, viz., vanadyl, uranyl, and thorium ions, as well as four simple eudistomins which act as bidentate ligands, viz., eudistomin-W, debromoeudistomin-K, eudistomidin-C, and eudistomidin-B. By designing 7 model reactions, we tested our hypothesis using density functional theory (DFT) methods PBE-D3, BLYP, and B3LYP. The Δ G values of the model reactions provide strong support for our hypothesis. To verify the hypothesis further, we calculated the metal–eudistomin interactions with Be, Zn, and Pb. Based on our results, we suggest that ascidians may not prefer any particular metal. In addition, despite using different DFT functionals, we have observed similar Δ G values for each case. With our work, we have successfully used computational tools in our attempt to understand the unique behavior of ascidians.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".