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Bone Mineral Density Reduction Explains Buoyancy Adaptations in Notothenioids

2019· article· en· W3037295694 on OpenAlexaff
Henrik Lauridsen, Thomas Desvignes, Christian Damsgaard, Jesper Skovhus Thomsen, T S Stenum, Steffen Ringgaard, Kasper Hansen, Anette MD Funder, Thomas Levin Andersen, Lene WT Boel, Lars Rejnmark, John H. Postlethwait, Peter Rask Møller, HW Detrich

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReduction (mathematics)Bone mineralBuoyancyMineralChemistryEnvironmental scienceBiologyMechanicsMathematicsEndocrinologyOsteoporosisPhysics

Abstract

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In aquatic vertebrates, dense skeletons and buoyant fat constitute important components for buoyancy regulation in addition to their roles in structural support and energy storage. Some fishes can fine‐tune buoyancy using their swim bladder, whereas others rely on neutral buoyancy or constant motion to regulate vertical position. The Notothenioidei provides a model system to study the phenotypic implications of differential use of the water column over a large radiation of closely related species. It has been suggested that to expand from ancestral benthic to pelagic habitats, some notothenioids, all of which lack the swim bladder, have reduced skeletal mass and display enhanced lipid deposition. This, apparently, adaptive osteopenia has interesting medical implications in understanding the balance between osteopenic bone and structural integrity of the skeleton. While relative buoyancy in seawater (%B) and dry skeletal mass have previously been studied in some notothenioids, little is known about the specific anatomical changes resulting in osteopenia; hypotheses include reductions of bone mineralization, reductions in bone size, and/or modifications of bone architecture. Here we used a high‐throughput procedure relying on quantitative X‐ray computed tomography (qCT) imaging on a collection of 436 notothenioid specimens (7 families, 24 genera, 53 species) to measure overall volumetric bone mineral density (vBMD), body size‐adjusted mineral content of the entire skeleton (BMC total ), vertebrae (BMC vertebrae ), and skull bones (BMC skull ), and body size adjusted lipid content (LC). Dual‐energy X‐ray absorptiometry and magnetic resonance imaging on a subsample of 50 specimens was used for BMC total and LC validation. For 33 species in the collection, %B was available from the literature and we performed phylogenetic generalized least‐squares analysis with seven models to explain buoyancy (%B ~ BMC total , %B ~ BMC skull , %B ~ BMC vertebrae , %B ~ BMC total + BMC skull , %B ~ BMC total + BMC vertebrae , %B ~ BMC total + BMC skull + BMC vertebrae , %B ~ LC). This phylogenetically informed multivariate data analysis showed that the model %B ~ BMC total + BMC vertebrae best described the data, thus evolutionary reductions in %B are best explained by reductions in both BMC total and BMC vertebrae . In a series of studies, Eastman et al. established the link between buoyancy, skeletal mass, and LC in notothenioids, most recently in a comprehensive report spanning 54 specimens of 20 species (Eastman et al. J Morphol. 2014, 275:841–61). The present result confirms on a much broader scale the correlation between bone mineral content and buoyancy, and shows that vertebrae are the most important bone type for overall reductions in BMC. Based on qCT, micro‐CT, histology, and mechanical testing, we seek to answer to which extend the reduction in BMC compromises the mechanical integrity of the bone. Support or Funding Information US National Science Foundation grant PLR‐1444167 This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.001
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.042
GPT teacher head0.277
Teacher spread0.236 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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