Mechanoresponsive lipid-protein nanoglobules facilitate reversible fibre formation in velvet worm slime
Bibliographic record
Abstract
Velvet worms eject a fluid capture slime that can be mechanically drawn into stiff biopolymeric fibres. Remarkably, these fibres can be dissolved by extended exposure to water, and new regenerated fibres can be drawn from the dissolved fibre solution-indicating a fully recyclable process. Here, we perform a multiscale structural and compositional investigation of this reversible fabrication process with the velvet worm Euperipatoides rowelli, revealing that biopolymeric fibre assembly is facilitated via mono-disperse lipid-protein nanoglobules. Shear forces cause nanoglobules to self-assemble into nano- and microfibrils, which can be drawn into macroscopic fibres with a protein-enriched core and lipid-rich coating. Fibre dissolution in water leads to re-formation of nanoglobules, suggesting that this dynamic supramolecular assembly of mechanoresponsive protein-building blocks is mediated by reversible non-covalent interactions. These findings offer important mechanistic insights into the role of mechanochemical processes in bio-fibre formation, providing potential avenues for sustainable material fabrication processes.Velvet worms expel a fluid slime that, under shear force, forms stiff fibres that can be dissolved and then regenerated. Here, the authors reveal that the recyclability of these biopolymers relies on mechanoresponsive lipid-protein nanoglobules in the slime that reversibly self-assemble into fibrils.
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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) | 0.000 | 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".