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Record W2608051567 · doi:10.1038/s41598-017-01302-5

Variability in bacterial flagella re-growth patterns after breakage

2017· article· en· W2608051567 on OpenAlexaff
Guillaume Paradis, Fabienne F. V. Chevance, Willisa Liou, Thibaud T. Renault, Kelly T. Hughes, Simon Rainville, Marc Erhardt

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversité Laval
FundersNational Institute of General Medical SciencesNational Institutes of Health
KeywordsProtein filamentFlagellumShearing (physics)BiophysicsIntermediate filamentChemistryBiologyMaterials scienceCytoskeletonBacteriaCellComposite material

Abstract

fetched live from OpenAlex

Many bacteria swim through liquids or crawl on surfaces by rotating long appendages called flagella. Flagellar filaments are assembled from thousands of subunits that are exported through a narrow secretion channel and polymerize beneath a capping scaffold at the tip of the growing filament. The assembly of a flagellum uses a significant proportion of the biosynthetic capacities of the cell with each filament constituting ~1% of the total cell protein. Here, we addressed a significant question whether a flagellar filament can form a new cap and resume growth after breakage. Re-growth of broken filaments was visualized using sequential 3-color fluorescent labeling of filaments after mechanical shearing. Differential electron microscopy revealed the formation of new cap structures on broken filaments that re-grew. Flagellar filaments are therefore able to re-grow if broken by mechanical shearing forces, which are expected to occur frequently in nature. In contrast, no re-growth was observed on filaments that had been broken using ultrashort laser pulses, a technique allowing for very local damage to individual filaments. We thus conclude that assembly of a new cap at the tip of a broken filament depends on how the filament was broken.

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)0.002
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 score0.077
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.210
Teacher spread0.200 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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