Selection for longer limbs in mice increases bone stiffness and brittleness, but does not alter bending strength
Bibliographic record
Abstract
The ability of a bone to withstand loads depends on its structural and material properties. These tend to differ among species with different modes of locomotion, reflecting their unique loading patterns. The evolution of derived limb morphologies, such as the long limbs associated with jumping, may compromise overall bone strength. We evaluated bone mechanical properties in the Longshanks mouse, which was selectively bred for increased tibia length relative to body mass. We combined analyses of 3D shape and cross-sectional geometry of the tibia, with mechanical testing and bone composition assays, to compare bone strength, elastic properties and mineral composition in Longshanks mice and randomly bred controls. Our data show that, despite being more slender, cortical geometry and predicted bending strength of the Longshanks tibia were similar to controls. In whole bone bending tests, measures of bone bending strength were similar across groups; however, Longshanks tibiae were significantly more rigid, more brittle, and required less than half the energy to fracture. Tissue-level elastic properties were also altered in Longshanks mice, but the bones did not differ from the control in water content, ash content or density. These results indicate that while Longshanks bones are as strong as control tibiae, selection for increased tibia length has altered its elastic properties, possibly through changes in organic bony matrix composition. We conclude that selection for certain limb morphologies, and/or selection for rapid skeletal growth, can lead to tissue-level changes that can increase the risk of skeletal fracture, which in turn may favor the correlated evolution of compensatory mechanisms to mitigate increased fracture risk, such as delayed skeletal maturity.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".