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Inbred Background Effects On Craniofacial Shape Dysmorphology In Mice With Spry Deletions

2015· article· en· W761140573 on OpenAlexafffund
Christopher J. Percival, Vagan Mushegyan, Dong‐Kha Tran, Ophir D. Klein, Benedikt Hallgrímsson

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsCraniofacialPhenotypeHeterozygote advantageBiologyGeneticsGene deletionCraniofacial abnormalityInbred strainGeneGenotype

Abstract

fetched live from OpenAlex

A mutation's effect on craniofacial phenotype is modulated by genetic background, but associated developmental interactions are not well understood. Our objective was to quantify the influence of three inbred backgrounds (FVB, 129, BL6) on the expression of craniofacial dysmorphology associated with Sprouty (Spry) 1, 2, and 4 deletions in mice. We quantified adult morphology with landmarks placed on micro‐computed tomography derived surfaces of heterozygote and homozygote knockouts, and unaffected littermates of each background. Within a background strain, mice with two deletions differ strongly from controls, including a visually identifiable rounded cranium and shortened face in Spry 2 homozygotes. Although not as extreme, heterozygotes significantly differ in shape from controls in many cases. Deletion effects are significantly different between background strains after correcting for shape differences in background controls, including a larger reduction of the nasal aperture associated with Spry1 deletion on 129 background versus FVB. Spry 4 deletion on FVB results in a more projected upper face associated with diastema teeth, while the effect of it on C57 is minimal. There are significant background effects on expression of craniofacial dysmorphology associated with Spry deletions, patterns of which can help illuminate how modifier genes can influence the effects of major genetic perturbations. Grant Funding Source: NIH (1R01‐DE021708, DP2‐OD007191, RO1‐DE021420, F30‐DE022482); NSERC (238992‐12); CIHR fellowship through ACHRI to CJP

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.263
Teacher spread0.234 · 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 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

Citations0
Published2015
Admission routes2
Has abstractyes

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