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Record W1541734047 · doi:10.1017/cbo9781139022217.004

Soft materials and fluids

2012· book-chapter· en· W1541734047 on OpenAlexaff
David H. Boal

Bibliographic record

VenueCambridge University Press eBooks · 2012
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMesoscopic physicsThermal fluctuationsSection (typography)MechanicsLength scaleLipid bilayerPhysicsVesicleDiffusionStatistical physicsClassical mechanicsMembraneChemistryThermodynamicsCondensed matter physics

Abstract

fetched live from OpenAlex

The softness of a material implies that it deforms easily when subjected to a stress. For a cell, an applied stress could arise from the cell’s environment, such as the action of a wave on water-borne cells or the pressure from a crowded region in a multicellular organism. The exchange of energy with the cell’s environment due to thermal fluctuations can also lead to deformations, although these may be stronger at the molecular level than the mesoscopic length scale of the cell proper. For example, Fig. 1.12 shows the fluctuations in shape of a synthetic vesicle whose membrane is a pure lipid bilayer that has low resistance to out-of-plane undulations because it is so thin. At fixed temperature, flexible systems may sample a variety of shapes, none of which need have the same energy because fixed temperature does not imply fixed energy. In this chapter, the kind of fluctuating ensembles of interest to cell mechanics are introduced in Section 2.1, followed up with a review of viscous fluids and their role in cell dynamics in Section 2.2. Many of the statistical concepts needed for describing fluctuating ensembles are then presented, using as illustrations random walks in Section 2.3 and diffusion in Section 2.4. Lastly, the subject of correlations is presented in Section 2.5, focusing on correlations within the shapes of long, sinuous filaments . Fluctuations at the cellular scale Among the common morphologies found among cyanobacteria (which trace their lineage back billions of years) are filamentous cells, two examples of which are displayed in Fig. 2.1. The images are shown at the same magnification, as indicated by the scale bars. The upper panel is the thin filament Geitlerinema PCC 7407, with a diameter of 1.5 ± 0.2 μm, while the lower panel is the much thicker filament Oscillatoria PCC 8973, with a diameter of 6.5 ± 0.7 μm (Boal and Ng, 2010). The filaments have been cultured in solution, then mildly stirred before imaging; clearly, the thinner filament has a more sinuous appearance than the thicker filament when seen at the same magnification. This is as expected: the resistance to bending possessed by a uniform solid cylinder grows like the fourth power of its diameter, so the thinner filament should have much less resistance to bending and hence appear more sinuous than the thicker one.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.043
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0430.021

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 head0.198
Teacher spread0.186 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2012
Admission routes1
Has abstractyes

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