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Record W2969268508 · doi:10.1021/acs.langmuir.9b01932

Fiber Formation from Liquid Crystalline Collagen Vesicles Isolated from Mussels

2019· article· en· W2969268508 on OpenAlexafffund
Max Renner‐Rao, Madelyn Clark, Matthew J. Harrington

Bibliographic record

VenueLangmuir · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsVesicleMytilusMaterials scienceNanotechnologyChemistryShearing (physics)MembraneBiophysicsComposite materialBiologyBiochemistry

Abstract

fetched live from OpenAlex

) fabricate byssal threads, high-performance biopolymeric fibers, which exhibit exceptional toughness and self-healing capacity. These properties are associated with collagenous proteins in the fibrous thread core known as preCols that self-organize into a hierarchical semicrystalline structure. Threads assemble individually in a bottom-up process lasting just minutes via secretion of membrane bound vesicles filled with preCols. However, very little is understood about the details and dynamics of this assembly process. Here, we explore the hypothesis that preCols are stored within the vesicles in a liquid crystalline phase, which contributes to fiber assembly by preordering molecules. To achieve this, a protocol was developed for extracting and isolating intact preCol secretory vesicles in high yield and purity. Vesicles were characterized and were manipulated in vitro, clearly indicating the dynamic liquid crystalline nature of the proteins within. Moreover, mechanical shearing of vesicles led to formation of highly birefringent preCol fibers. These findings have relevance for efforts toward sustainable production of advanced polymeric materials, and possibly for engineering biomedical scaffolds based on collagenous proteins.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.061
Threshold uncertainty score0.991

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)0.0100.012

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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2019
Admission routes2
Has abstractyes

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