Role of soft tissue and bone interactions in the developmental integration and modularity of the skull in neural crest‐specific gap junction alpha‐1 knockout mice
Bibliographic record
Abstract
The vertebrate skull is composed of bones derived from neural crest cells and mesoderm. The evolutionary capacity of the skull has been linked, in part, to the emergence of neural crest cells; however, this increased capacity for evolutionary change requires that variation within neural crest- and mesoderm-derived bones remains partly autonomous. One way to assess whether tissue origin leads to discrete patterns of variation is through measures of morphological integration and modularity. In this study, we use a neural crest-specific gap junction alpha-1 (Gja1) knockout mouse model (Cx43cKO) to determine the effect of tissue origin on skull integration and modularity. Micro-computed tomography images obtained from embryonic, newborn, and 2-month Cx43cKO and wildtype (Cx43WT) mice were used to measure and compare skull shape, size, integration, and modularity between genotypes. To determine if the phenotypic differences observed between genotypes reflect Cx43 function, mRNA expression data for markers of bone differentiation were measured from the neural crest-derived frontal bones and mesoderm-derived parietal and occipital bones. We found that patterns of integration and modularity change over development and these changes correspond with differences in Cx43 expression throughout the lifespan. Most interestingly, the patterns of developmental integration and modularity we observed at birth were influenced most greatly by tissue interactions, rather than Cx43 expression in the bones. Ultimately, our findings highlight the power of experimental models for investigating integration and modularity and the importance of tissue interactions in skull development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".