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Record W1492772959 · doi:10.1002/9780470048672.wecb283

Lipid Domains, Chemistry of

2008· other· en· W1492772959 on OpenAlexaff
Richard M. Epand

Bibliographic record

VenueWiley Encyclopedia of Chemical Biology · 2008
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsMcMaster UniversityHealth Sciences CentreMcMaster University Medical Centre
Fundersnot available
KeywordsMembraneBiological membraneCaveolaeLipid raftRaftChemistryDomain (mathematical analysis)BiophysicsCholesterolMembrane biophysicsBiochemistryBiologyPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Biological membranes are composed largely of proteins and lipids. A wide range of molecular structures exists among these molecules. It is therefore not surprising that biological membranes are not uniform but rather cluster certain molecules in specific regions or domains. This behavior can be mimicked in model systems comprising a limited number of components to study in more detail the molecular nature of this domain formation. Two types of domains exist that have been more extensively studied. One is a membrane domain enriched in polyanionic lipids, such as phosphatidylinositol diphosphate. Such domains are formed by the presence of proteins with segments containing several cationic amino acid residues. Such proteins have been termed “pipmodulins.” Another kind of domain is formed as a consequence of the nonuniform distribution of cholesterol in the membrane. Caveolae represent one type of cholesterol‐rich domain that is well characterized. Other cholesterol‐rich domains are termed “rafts.” Characterization of rafts in model membranes is well documented, but the nature of cholesterol‐rich domains in biological membranes remains a subject of controversy. Several imaging and fluorescence methods are being employed to further characterize the size and lifetime of small raft domains in biological membranes.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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.

Study designBench or experimental
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
Published2008
Admission routes1
Has abstractyes

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