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Record W2918884514 · doi:10.1096/fj.201802268r

<i>In vivo</i> and <i>in vitro</i> analysis of starmaker activity in zebrafish otolith biomineralization

2019· article· en· W2918884514 on OpenAlexaff
Marta Kalka, Natalia Markiewicz, Maciej Ptak, Eli D. Sone, Andrzej Ożyhar, Piotr Dobryszycki, Magdalena Wojtas

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiomineralizationZebrafishAragoniteOtolithIn vivoChemistryIn vitroVateriteBiologyBiophysicsCell biologyBiochemistryCalcium carbonateFish <Actinopterygii>GeneGeneticsPaleontologyFishery

Abstract

fetched live from OpenAlex

ABSTRACT Otoliths are one of the biominerals whose formation is highly controlled by proteins. The first protein discovered to be involved in otolith biomineralization in zebrafish was starmaker (Stm). Previously, Stm was shown to be responsible for the preferential formation of aragonite, a polymorph of calcium carbonate, in otoliths. In this work, proteomic analysis of adult zebrafish otoliths was performed. Stm is the only highly phosphorylated protein found in our studies. Besides previously studied otolith proteins, we discovered several dozens of unknown proteins that reveal the likely mechanism of biomineralization. A comparison of aragonite and vaterite otoliths showed similarities in protein composition. We observed the presence of Stm in both types of otoliths. In vitro studies of 2 characteristic Stm fragments indicated that the DS‐rich region has a special biomineralization activity, especially after phosphorylation.—Kalka, M., Markiewicz, N., Ptak, M., Sone, E. D., Ożyhar, A., Dobryszycki, P., Wojtas, M. In vivo and in vitro analysis of starmaker activity in zebrafish otolith biomineralization. FASEB J. 33, 6877–6886 (2019). www.fasebj.org

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.428
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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.

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

Citations25
Published2019
Admission routes1
Has abstractyes

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