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Record W2335268857 · doi:10.1242/jeb.105841

Adhesive constrains hagfish thread skeins

2014· article· en· W2335268857 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2014
Typearticle
Languageen
FieldEngineering
TopicSlime Mold and Myxomycetes Research
Canadian institutionsnot available
Fundersnot available
KeywordsHagfishEnvironmental ethicsBiologyZoologyPolymer scienceChemistryPhilosophyVertebrate

Abstract

fetched live from OpenAlex

Hagfish are pretty repellent by most standards: reputed to consume prey from the inside out, the animals look ghastly and, worst of all, they release gallons of disgusting slime in less than 0.1 s when under attack. ‘The slime lodges in and clogs the gills of any fish that tries to eat a hagfish’, says Doug Fudge from the University of Guelph, Canada. But that hasn't deterred Fudge from investing 16 years of his life in studying the revolting gunge. He explains that irritated Atlantic hagfish release minute volumes of concentrated mucous and microscopic skeins of protein filaments that rapidly unravel when mixed with seawater and the mucous to engulf their victims. ‘If either of these [the mucous or the mixing] is missing, skein unravelling is compromised,’ says Fudge. And he had no reason to think that the same would not be true for Pacific hagfish until his undergraduate student, Isdin Oke, decided to test their skein unravelling, only to discover that these protein filaments unravelled spontaneously in seawater. Fudge had another hagfish mystery on his hands (p. 1263).Thinking about possible mechanisms that could rapidly liberate the tightly bound skeins, Fudge came up with two alternatives: that the threads swell in contact with water to burst the skeins open; or that the stiff protein fibres are coiled into skeins that are secured by an adhesive that dissolves in contact with water, releasing the skeins to spring open.Curious to find out which theory held water, Oke and Mark Bernards gently anaesthetised Pacific hagfish to collect minute samples of the unexploded mucous and carefully extracted the coiled skeins of slime fibre. Then they tested different strengths of simulated seawater to find out which stabilised the skeins and which burst them open. Monitoring the proportion of ruptured bundles as they increased the temperature and salt concentration, the team was reassured to see that the skeins rapidly unravelled in conditions that simulated the animal's natural aquatic environment but were reluctant to unravel in dilute conditions and at high temperatures. And when the team took a close look at the skeins before and after unravelling with a scanning electron microscope, they could clearly see fluffy clumps coating the tightly coiled skeins that vanished after the skeins burst apart. Could they be some sort of protein adhesive?Stabilising the skeins in dilute seawater, the team added a protein-digesting enzyme, trypsin, and waited to see whether the skeins sprung apart – which they did. And when the team scrutinised the surface of the trypsin-treated skeins, the enzyme had removed the clumpy coating that had restrained the coiled skeins.So, the Pacific hagfish slime fibres are coiled into tight skeins that are restrained by a water-soluble protein adhesive that dissolves on contact with seawater to release the strain energy stored in the stiff fibre coils. Having confirmed that the Pacific hagfish deploy slime fibres in a completely different way from their Atlantic cousins, Fudge is keen to learn more about the adhesive that keeps the skeins intact. He also hopes that hagfish slime fibres will eventually adorn the catwalks of the fashion world. ‘We are currently working on a project whose aim is to produce protein fibres that are as strong and tough as hagfish slime threads in the hope that we could one day replace petroleum-based polymers like Nylon with more eco-friendly protein-based materials,’ he says.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.293
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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