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Record W2805054592 · doi:10.1002/adts.201800034

Curvature‐Induced Sorting of Lipids in Plasma Membrane Tethers

2018· article· en· W2805054592 on OpenAlexafffund
Svetlana Baoukina, Helgi I. Ingólfsson, ‪Siewert J. Marrink, D. Peter Tieleman

Bibliographic record

VenueAdvanced Theory and Simulations · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
FundersEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlberta InnovatesAlberta Innovates - Technology FuturesWestern Canada Research GridCompute Canada
KeywordsMembraneLipid bilayerCurvatureMembrane curvatureBilayerBiophysicsElasticity of cell membranesLipid bilayer phase behaviorChemistryLipid bilayer mechanicsModel lipid bilayerSortingChemical physicsSofteningMaterials scienceBiologyGeometryBiochemistryMathematics

Abstract

fetched live from OpenAlex

Abstract Membrane curvature controls the spatial organization and activity of cells. Lipid sorting in cell membranes can be explained by matching lipid molecular shape to regions of different curvatures. A molecular view of curvature‐induced lipid sorting is obtained using coarse‐grained molecular dynamics. A model membrane consisting of an asymmetric bilayer of multiple lipid species is simulated. Curvature is induced by pulling a tether, that is, a bilayer nanotube, from a flat membrane. Pulling is performed both from the inner and outer leaflets, corresponding to directions in and out of the cell. Redistribution of different lipid types between the tether and the bilayer is observed, leading to spatial variations in the composition of both leaflets, and, in turn, softening of the tether. Depending on the direction of pulling, the lipid distributions and the tether properties differ. Formation of a tether from the planar membrane thus induces lipid sorting without phase separation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.286
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations66
Published2018
Admission routes2
Has abstractyes

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