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Record W2912017367 · doi:10.1038/s41598-018-37065-w

Dinoflagellate nucleus contains an extensive endomembrane network, the nuclear net

2019· article· en· W2912017367 on OpenAlexaff
Gregory S. Gavelis, María Herranz, Kevin C. Wakeman, Christina Ripken, Satoshi Mitarai, Gillian H. Gile, Patrick J. Keeling, Brian S. Leander

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEndomembrane systemDinoflagellateComputer scienceNucleusBiologyNeuroscienceBotanyCellGenetics

Abstract

fetched live from OpenAlex

Dinoflagellates are some of the most common eukaryotic cells in the ocean, but have very unusual nuclei. Many exhibit a form of closed mitosis (dinomitosis) wherein the nuclear envelope (NE) invaginates to form one or more trans-nuclear tunnels. Rather than contact spindles directly, the chromatids then bind to membrane-based kinetochores on the NE. To better understand these unique mitotic features, we reconstructed the nuclear architecture of Polykrikos kofoidii in 3D using focused ion beam scanning electron microscopy (FIB-SEM) in conjunction with high-pressure freezing, freeze-substitution, TEM, and confocal microscopy. We found that P. kofoidii possessed six nuclear tunnels, which were continuous with a reticulating network of membranes that has thus far gone unnoticed. These membranous extensions interconnect the six tunnels while ramifying throughout the nucleus to form a "nuclear net." To our knowledge, the nuclear net is the most elaborate endomembrane structure described within a nucleus. Our findings demonstrate the utility of tomographic approaches for detecting 3D membrane networks and show that nuclear complexity has been underestimated in Polykrikos kofoidii and, potentially, in other dinoflagellates.

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 score0.003
Threshold uncertainty score0.006

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 studies0.0010.001
Scholarly communication0.0010.001
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 head0.209
Teacher spread0.201 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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