Deployment of hagfish slime thread skeins requires the transmission of mixing forces<i>via</i>mucin strands
Bibliographic record
Abstract
Hagfishes are benthic marine protovertebrates that secrete copious quantities of slime when threatened. The slime originates as a two-component glandular exudate comprised of coiled bundles of cytoskeletal intermediate filaments (thread skeins) and mucin vesicles. Holocrine secretion of the slime into seawater results in the rapid deployment of both fibrous and mucin components, resulting in about a liter of dilute slime. Deployment of the thread skeins involves their unraveling in a fraction of a second from a 150 microm-long ellipsoid bundle to a thread that is 100x longer. We hypothesized that thread skein deployment requires both vigorous hydrodynamic mixing and the presence of mucin vesicles, both of which are required for whole slime deployment. Here we provide evidence that mixing and mucin vesicles are indeed crucial for skein unraveling. Specifically, we show that mucin vesicles mixed into seawater swell and elongate into high-aspect ratio mucin strands that attach to the thread skeins, transmit hydrodynamic forces to them and effect their unraveling by loading them in tension. Our discovery of mucin strands in hagfish slime not only provides a mechanism for the rapid deployment of thread skeins in vivo, it also helps explain how hagfish slime is able to trap such impressive volumes of seawater via viscous entrainment. We believe that the deployment of thread skeins via their interaction with shear-elongated mucins represents a unique mechanism in biology and may lead to novel technologies for transmitting hydrodynamic forces to microscale particles that would typically be immune to such forces.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".