Bibliographic record
Abstract
The core component of a biological membrane is a fluid-lipid bilayer held together by interfacial-hydrophobic and van der Waals interactions, which are balanced for the most part by acyl chain entropy confinement. If biomembranes are subjected to persistent tensions, an unstable (nanoscale) hole will emerge at some time to cause rupture. Because of the large energy required to create a hole, thermal activation appears to be requisite for initiating a hole and the activation energy is expected to depend significantly on mechanical tension. Although models exist for the kinetic process of hole nucleation in tense membranes, studies of membrane survival have failed to cover the ranges of tension and lifetime needed to critically examine nucleation theory. Hence, rupturing giant (~20 μm) membrane vesicles ultra-slowly to ultra-quickly with slow to fast ramps of tension, we demonstrate a method to directly quantify kinetic rates at which unstable holes form in fluid membranes, at the same time providing a range of kinetic rates from < 0.01 s(-1) to > 100 s(-1). Measuring lifetimes of many hundreds of vesicles, each tensed by precision control of micropipet suction, we have determined the rates of failure for vesicles made from several synthetic phospholipids plus 1:1 mixtures of phospho- and sphingo-lipids with cholesterol, all of which represent prominent constituents of eukaryotic cell membranes. Plotted on a logarithmic scale, the failure rates for vesicles are found to rise dramatically with increase of tension. Converting the experimental profiles of kinetic rates into changes of activation energy versus tension, we show that the results closely match expressions for thermal activation derived from a combination of meso-scale theory and molecular-scale simulations of hole formation. Moreover, we demonstrate a generic approach to transform analytical fits of activation energies obtained from rupture experiments into energy landscapes characterizing the process hole nucleation along the reaction coordinate defined by hole size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".