Convex hull estimation of mammalian body segment parameters.
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ABSTRACT:
SUBMITTER: Coatham SJ
PROVIDER: S-EPMC8242930 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The external appearance of the dodo (Raphus cucullatus, Linnaeus, 1758) has been a source of considerable intrigue, as contemporaneous accounts or depictions are rare. The body mass of the dodo has been particularly contentious, with the flightless pigeon alternatively reconstructed as slim or fat depending upon the skeletal metric used as the basis for mass prediction. Resolving this dichotomy and obtaining a reliable estimate for mass is essential before future analyses regarding dodo life history, physiology or biomechanics can be conducted. Previous mass estimates of the dodo have relied upon predictive equations based upon hind limb dimensions of extant pigeons. Yet the hind limb proportions of dodo have been found to differ considerably from those of their modern relatives, particularly with regards to midshaft diameter. Therefore, application of predictive equations to unusually robust fossil skeletal elements may bias mass estimates. We present a whole-body computed tomography (CT) -based mass estimation technique for application to the dodo. We generate 3D volumetric renders of the articulated skeletons of 20 species of extant pigeons, and wrap minimum-fit 'convex hulls' around their bony extremities. Convex hull volume is subsequently regressed against mass to generate predictive models based upon whole skeletons. Our best-performing predictive model is characterized by high correlation coefficients and low mean squared error (a = - 2.31, b = 0.90, r (2) = 0.97, MSE = 0.0046). When applied to articulated composite skeletons of the dodo (National Museums Scotland, NMS.Z.1993.13; Natural History Museum, NHMUK A.9040 and S/1988.50.1), we estimate eviscerated body masses of 8-10.8 kg. When accounting for missing soft tissues, this may equate to live masses of 10.6-14.3 kg. Mass predictions presented here overlap at the lower end of those previously published, and support recent suggestions of a relatively slim dodo. CT-based reconstructions provide a means of objectively estimating mass and body segment properties of extinct species using whole articulated skeletons.
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Project description:The volumetric method of 'convex hulling' has recently been put forward as a mass prediction technique for fossil vertebrates. Convex hulling involves the calculation of minimum convex hull volumes (vol(CH)) from the complete mounted skeletons of modern museum specimens, which are subsequently regressed against body mass (Mb) to derive predictive equations for extinct species. The convex hulling technique has recently been applied to estimate body mass in giant sauropods and fossil ratites, however the biomechanical signal contained within vol(CH) has remained unclear. Specifically, when vol(CH) scaling departs from isometry in a group of vertebrates, how might this be interpreted? Here we derive predictive equations for primates, non-primate mammals and birds and compare the scaling behaviour of Mb to volCH between groups. We find predictive equations to be characterised by extremely high correlation coefficients (r(2) = 0.97-0.99) and low mean percentage prediction error (11-20%). Results suggest non-primate mammals scale body mass to volCH isometrically (b = 0.92, 95%CI = 0.85-1.00, p = 0.08). Birds scale body mass to volCH with negative allometry (b = 0.81, 95%CI = 0.70-0.91, p = 0.011) and apparent density (volCH/Mb) therefore decreases with mass (r(2)?= 0.36, p<0.05). In contrast, primates scale body mass to vol(CH) with positive allometry (b = 1.07, 95%CI = 1.01-1.12, p = 0.05) and apparent density therefore increases with size (r(2) = 0.46, p = 0.025). We interpret such departures from isometry in the context of the 'missing mass' of soft tissues that are excluded from the convex hulling process. We conclude that the convex hulling technique can be justifiably applied to the fossil record when a large proportion of the skeleton is preserved. However we emphasise the need for future studies to quantify interspecific variation in the distribution of soft tissues such as muscle, integument and body fat.
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Project description:Classification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routine clinical practice. They generally classify all patients according to the same criteria, under an implicit assumption of population homogeneity. The objective here is to allow for population heterogeneity, possibly unrecognized, in order to increase classification accuracy and further the goal of tailoring therapies on an individualized basis.A new selective-voting algorithm is developed in the context of a classifier ensemble of two-dimensional convex hulls of positive and negative training samples. Individual classifiers in the ensemble are allowed to vote on test samples only if those samples are located within or behind pruned convex hulls of training samples that define the classifiers.Validation of the new algorithm's increased accuracy is carried out using two publicly available datasets having cancer as the outcome variable and expression levels of thousands of genes as predictors. Selective voting leads to statistically significant increases in accuracy from 86.0% to 89.8% (p<0.001) and 63.2% to 67.8% (p<0.003) compared to the original algorithm.Selective voting by members of convex-hull classifier ensembles significantly increases classification accuracy compared to one-size-fits-all approaches.
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Project description:Body segment parameters are inputs for a range of applications. Participant-specific estimates of body segment parameters are desirable as this requires fewer prior assumptions and can reduce outcome measurement errors. Commonly used methods for estimating participant-specific body segment parameters are either expensive and out of reach (medical imaging), have many underlying assumptions (geometrical modelling) or are based on a specific subset of a population (regression models). Our objective was to develop a participant-specific 3D scanning and body segmentation method that estimates body segment parameters without any assumptions about the geometry of the body, ethnic background, and gender, is low-cost, fast, and can be readily available. Using a Microsoft Kinect Version 2 camera, we developed a 3D surface scanning protocol that enabled the estimation of participant-specific body segment parameters. To evaluate our system, we performed repeated 3D scans of 21 healthy participants (10 male, 11 female). We used open source tools to segment each body scan into 16 segments (head, torso, abdomen, pelvis, left and right hand, forearm, upper arm, foot, shank and thigh) and wrote custom software to estimate each segment's mass, mass moment of inertia in the three principal orthogonal axes relevant to the center of the segment, longitudinal length, and center of mass. We compared our body segment parameter estimates to those obtained using two comparison methods and found that our system was consistent in estimating total body volume between repeated scans (male p = 0.1194, female p = 0.2240), estimated total body mass without significant differences when compared to our comparison method and a medical scale (male p = 0.8529, female p = 0.6339), and generated consistent and comparable estimates across a range of the body segment parameters of interest. Our work here outlines and provides the code for an inexpensive 3D surface scanning method for estimating a range of participant-specific body segment parameters.
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