Anatomical phenotyping in the brain and skull of a mutant mouse by magnetic resonance imaging and computed tomography
Why this work is in the frame
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Bibliographic record
Abstract
Since genetically modified mice have become more common in biomedical research as models of human disease, a need has also grown for efficient and quantitative methods to assess mouse phenotype. One powerful means of phenotyping is characterization of anatomy in mutant vs. normal populations. Anatomical phenotyping requires visualization of structures in situ, quantification of complex shape differences between mouse populations, and detection of subtle or diffuse abnormalities during high-throughput survey work. These aims can be achieved with imaging techniques adapted from clinical radiology, such as magnetic resonance imaging and computed tomography. These imaging technologies provide an excellent nondestructive method for visualization of anatomy in live individuals or specimens. The computer-based analysis of these images then allows thorough anatomical characterizations. We present an automated method for analyzing multiple-image data sets. This method uses image registration to identify corresponding anatomy between control and mutant groups. Within- and between-group shape differences are used to map regions of significantly differing anatomy. These regions are highlighted and represented quantitatively by displacements and volume changes. This methodology is demonstrated for a partially characterized mouse mutation generated by N-ethyl-N-nitrosourea mutagenesis that is a putative model of the human syndrome oculodentodigital dysplasia, caused by point mutations in the gene encoding connexin 43.
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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 it