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Stroustrup, S.

Publications and source records attributed to Stroustrup, S..

3 recordsLinked to original sources

Principal Components Analysis fails to recover phylogenetic structure in hominins

ObjectivesPaleoanthropologists often utilize geometric morphometrics and principal components analysis (PCA) to interpret shape variation within the hominin fossil record. It is common practice to interpret proximity in principal components (PC) space among taxa as indicative of not just morphological, but also phylogenetic affinity. This interpretation, however, has not been directly evaluated for hominins. Materials and MethodsFirst, we inferred the posterior distribution of hominin phylogenetic trees and subsampled trees from this distribution. On these phylogenies, we simulated 2D and 3D geometric morphometric datasets and traditional morphological datasets, containing traits analogous to measurements of size or length, with varying numbers of landmarks or traits and evolutionary rates. On each dataset, we conducted a PCA and used neighbor-joining to infer evolutionary relationships from the PC scores of each taxon. We measure the difference between the PCA tree and sampled tree with subtree pruning and regrafting distance and Robinson-Foulds distance. ResultsPCA trees inferred from traditional morphometric data were identical to the sampled tree in 0.11% of datasets when we only considered PC axes 1 and 2, and in 2.9% of datasets when we considered all axes. No PCA tree inferred from any of the 2,400,000 shape datasets was identical to the sampled tree, regardless of the number of axes. DiscussionPhylogenetic interpretations of the hominin fossil record based on proximity in PC space are inherently flawed and likely to be erroneous. Arguments in the hominin systematics literature based on PCA should therefore be reevaluated using phylogenetically-informed alternatives.

evolutionary biology↗

Stochastic Phylogenetic Models of Shape

Phylogenetic modeling of morphological shape in two or three dimensions is one of the most challenging statistical problems in evolutionary biology. As shape data are inherently correlated and non-linear, most naive methods for phylogenetic analysis of morphological shape fail to capture the biological realities of evolving shapes. In this study we propose a novel framework for evolutionary analysis of morphological shape which facilitates stochastic character mapping on landmark shapes. Our framework is based on recent advances in mathematical shape analysis and models the evolution of shape as a diffusion process that accounts for the evolutionary correlation between nearby landmarks. The diffusion process we consider is parametrized in terms of meaningful parameters describing the evolutionary rate and the degree of spatial autocorrelation among landmarks. The framework we propose assumes that the phylogenetic tree is fixed and uses a Metropolis-Hastings Markov Chain Monte Carlo sampling scheme for inferring ancestral shapes and parameters of the model. We evaluate the new inference algorithm using simulations and show that the method leads to improved estimates of the shape at the root and well-calibrated credible sets of shapes at internal nodes. In addition, we also compare the diffusion parameter describing the degree of spatial autocorrelation to an existing metric of integration and find that they quantify integration in a shape in a similar way. To illustrate the method, we also apply it to a previously published data set of butterfly wing images.

evolutionary biology↗

A Hierarchical Process Model Links Behavioral Aging and Lifespan in C. elegans

Individuals who remain vigorous longer tend to live longer, supporting the design of predictive behavioral biomarkers of aging. In C. elegans, the timing of age-associated vigorous movement cessation (VMC) and lifespan correlate strongly between individuals. However, many genetic and pharmaceutical interventions that alter aging produce disproportional effects on VMC and lifespan, appearing to "uncouple" the rate of behavioral aging and lifespan. To study the causal structure underlying such uncoupling, we developed a high-throughput, automated imaging platform to quantify behavioral aging and lifespan at an unprecedented scale. Our method reveals an inverse correlation between each individuals vigorous movement span and their remaining lifespan. Robust across many lifespan-altering interventions including a new RNA-polymerase II auxin-inducible degron system, our data shows that individual C. elegans experience at least two distinct but coupled physical declines--one governing VMC and the other governing lifespan. Through simulations and modeling, we clarify the causal relationship between these two "biological ages" and highlight a crucial but often untested assumption in conventional aging biomarker research: predictive biomarkers may not always report on the same biological age as that which determines long-term health outcomes.

systems biology↗