The Microstructure, Proteomics and Crystallization of the Limpet Teeth
Bibliographic record
Abstract
Limpets are marine mollusks that use mineralized teeth, one of the hardest and strongest biomaterials, to feed on algae on intertidal rocks. However, most of studies only focus on the ultrastructure and chemical composition of the teeth while the molecular information is largely unknown, limiting our understanding of this unique and fundamental biomineralization process. The study investigates the microstructure, proteomics, and crystallization in the teeth of limpet Cellana toreuma. It is found that the limpets formed alternatively tricuspid teeth and unicuspid teeth. Small nanoneedles are densely packed at the tips or leading regions of the cusps. In contrast, big nanoneedles resembling chemically synthesized goethite are loosely packed in the trailing regions of the cusps. Proteins extracted from the whole radula, such as ferritin, peroxiredoxin, arginine kinase, GTPase-Rabs, and clathrin, are identified by proteomics. A goethite-binding experiment coupled with proteomics and RNA-seq highlights six chitin-binding proteins (CtCBPs). Furthermore, the extracted proteins from the cusps of radula or the framework chitin induce packing of crystals and possibly affect crystal polymorphs in vitro. This study provides insight into the unique biomineralization process in the limpet teeth at the molecular levels, which may guide biomimetic strategies aimed at designing hard materials at room temperature.
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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".