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Record W2945360569 · doi:10.1093/icb/icz048

Fibers on the Fly: Multiscale Mechanisms of Fiber Formation in the Capture Slime of Velvet Worms

2019· article· en· W2945360569 on OpenAlexafffund
Alexander B. Baer, Stephan Schmidt, Georg Mayer, Matthew J. Harrington

Bibliographic record

VenueIntegrative and Comparative Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsVelvetFiberBiologyChemistry

Abstract

fetched live from OpenAlex

Many organisms have evolved a capacity to form biopolymeric fibers outside their bodies for functions such as defense, prey capture, attachment, and protection. In particular, the adhesive capture slime of onychophorans (velvet worms) is remarkable for its ability to rapidly form stiff fibers through mechanical drawing. Notably, fibers that are formed ex vivo from extracted slime can be dissolved in water and new fibers can be drawn from the solution, indicating that fiber formation is encoded in the biomolecules that comprise the slime. This review highlights recent findings on the biochemical and physicochemical principles guiding this circular process in the Australian onychophoran Euperipatoides rowelli. A multiscale cross-disciplinary approach utilizing techniques from biology, biochemistry, physical chemistry, and materials science has revealed that the slime is a concentrated emulsion of nanodroplets comprised primarily of proteins, stabilized via electrostatic interactions, possibly in a coacervate phase. Upon mechanical agitation, droplets coalesce, leading to spontaneous self-assembly and fibrillation of proteins-a completely reversible process. Recent investigations highlight the importance of subtle transitions in protein structure and charge balance. These findings have clear relevance for better understanding this adaptive prey capture behavior and providing inspiration toward sustainable polymer processing.

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 score0.027
Threshold uncertainty score0.211

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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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