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Record W3036581603 · doi:10.1038/s41598-020-66021-w

Functional shell matrix proteins tentatively identified by asymmetric snail shell morphology

2020· article· en· W3036581603 on OpenAlexfundno aff
Akito Ishikawa, Keisuke Shimizu, Yukinobu Isowa, Takeshi Takeuchi, Ran Zhao, Keiji Kito, Manabu Fujie, Noriyuki Satoh, Kazuyoshi Endo

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
FundersInstitute of GeneticsJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology
KeywordsLymnaea stagnalisSnailTranscriptomeBiomineralizationBiologyCell biologyGeneShell (structure)Gene expressionGeneticsEcologyMaterials sciencePaleontology

Abstract

fetched live from OpenAlex

Molluscan shell matrix proteins (SMPs) are essential in biomineralization. Here, we identify potentially important SMPs by exploiting the asymmetric shell growth in snail, Lymnaea stagnalis. Asymmetric shells require bilaterally asymmetric expression of SMP genes. We examined expression levels of 35,951 transcripts expressed in the left and right sides of mantle tissue of the pond snail, Lymnaea stagnalis. This transcriptome dataset was used to identify 207 SMPs by LC-MS/MS. 32 of the 207 SMP genes show asymmetric expression patterns, which were further verified for 4 of the 32 SMPs using quantitative PCR analysis. Among asymmetrically expressed SMPs in dextral snails, those that are more highly expressed on the left side than the right side are 3 times more abundant than those that are more highly expressed on the right than the left, suggesting potentially inhibitory roles of SMPs in shell formation. The 32 SMPs thus identified have distinctive features, such as conserved domains and low complexity regions, which may be essential in biomineralization.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicCalcium Carbonate Crystallization and InhibitionFrench-language works237,207