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Record W2899278889 · doi:10.1128/mbio.02020-18

Epiplasts: Membrane Skeletons and Epiplastin Proteins in Euglenids, Glaucophytes, Cryptophytes, Ciliates, Dinoflagellates, and Apicomplexans

2018· article· en· W2899278889 on OpenAlexafffund
Ursula Goodenough, Robyn Roth, Thamali Kariyawasam, Amelia He, Jae‐Hyeok Lee

Bibliographic record

VenuemBio · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaKorea Carbon Capture and Sequestration R and D Center
KeywordsBiologyPlasmodium (life cycle)Cell biologyComputational biologyEvolutionary biologyParasite hostingComputer science

Abstract

fetched live from OpenAlex

Membrane skeletons associate with the inner surface of the plasma membrane to provide support for the fragile lipid bilayer and an elastic framework for the cell itself. Several radiations, including animals, organize such skeletons using actin/spectrin proteins, but four major radiations of eukaryotic unicellular organisms, including disease-causing parasites such asPlasmodium, have been known to construct an alternative and essential skeleton (the epiplast) using a class of proteins that we term epiplastins. We have identified epiplastins in two additional radiations and present images of their epiplasts using electron microscopy. We analyze the sequences and secondary structure of 219 epiplastins and present an in-depth overview and analysis of their known and posited roles in cellular organization and parasite infection. An understanding of epiplast assembly may suggest therapeutic approaches to combat infectious agents such asPlasmodiumas well as approaches to the engineering of useful viscoelastic biofilms.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 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

Citations26
Published2018
Admission routes2
Has abstractyes

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