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Record W4389245104 · doi:10.1021/acsnano.3c07706

Cyclophospholipids Enable a Protocellular Life Cycle

2023· article· en· W4389245104 on OpenAlexafffund
Ö. Duhan Toparlak, Lorenzo Sebastianelli, Veronica Egas Ortuno, Megha Karki, Yanfeng Xing, Jack W. Szostak, Ramanarayanan Krishnamurthy, Sheref S. Mansy

Bibliographic record

VenueACS Nano · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsUniversity of Alberta
FundersUniversità degli Studi di TrentoNatural Sciences and Engineering Research Council of CanadaEuropean CommissionGordon and Betty Moore FoundationSimons FoundationHorizon 2020 Framework ProgrammeAlfred P. Sloan FoundationHoward Hughes Medical Institute
KeywordsProtocellBiologyAbiogenesisNucleic acidArtificial cellNucleotideBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide There is currently no plausible path for the emergence of a self-replicating protocell, because prevalent formulations of model protocells are built with fatty acid vesicles that cannot withstand the concentrations of Mg 2+ needed for the function and replication of nucleic acids. Although prebiotic chelates increase the survivability of fatty acid vesicles, the resulting model protocells are incapable of growth and division. Here, we show that protocells made of mixtures of cyclophospholipids and fatty acids can grow and divide in the presence of Mg 2+ -citrate. Importantly, these protocells retain encapsulated nucleic acids during growth and division, can acquire nucleotides from their surroundings, and are compatible with the nonenzymatic extension of an RNA oligonucleotide, chemistry needed for the replication of a primitive genome. Our work shows that prebiotically plausible mixtures of lipids form protocells that are active under the conditions necessary for the emergence of Darwinian evolution.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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 designBench or experimental
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

Citations26
Published2023
Admission routes2
Has abstractyes

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