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Record W4319597578 · doi:10.1038/s41467-023-36059-1

Spatially non-uniform condensates emerge from dynamically arrested phase separation

2023· article· en· W4319597578 on OpenAlexafffund
Nadia A. Erkamp, Tomas Šneideris, Hannes Ausserwӧger, Daoyuan Qian, Seema Qamar, Jonathon Nixon‐Abell, Peter St George‐Hyslop, Jeremy D. Schmit, David A. Weitz, Tuomas P. J. Knowles

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity Health Network
FundersNational Institute of General Medical SciencesNovo NordiskEuropean CommissionWellcome TrustConsortium canadien en neurodégénérescence associée au vieillissementCanadian Institutes of Health ResearchAlzheimer Society
KeywordsSeparation (statistics)Phase (matter)Computer sciencePhysicsMaterials scienceBiophysicsBiologyQuantum mechanics

Abstract

fetched live from OpenAlex

The formation of biomolecular condensates through phase separation from proteins and nucleic acids is emerging as a spatial organisational principle used broadly by living cells. Many such biomolecular condensates are not, however, homogeneous fluids, but possess an internal structure consisting of distinct sub-compartments with different compositions. Notably, condensates can contain compartments that are depleted in the biopolymers that make up the condensate. Here, we show that such double-emulsion condensates emerge via dynamically arrested phase transitions. The combination of a change in composition coupled with a slow response to this change can lead to the nucleation of biopolymer-poor droplets within the polymer-rich condensate phase. Our findings demonstrate that condensates with a complex internal architecture can arise from kinetic, rather than purely thermodynamic driving forces, and provide more generally an avenue to understand and control the internal structure of condensates in vitro and in vivo.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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 designObservational
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

Citations89
Published2023
Admission routes2
Has abstractyes

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