Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling

The placenta plays a key contribution to successful pregnancy outcome. New MR imaging techniques are able to reveal intricate details about placental structure and function and measure placental blood flow and feto-placental oxygenation. Placental diffusion-weighted MRI is however challenging due to maternal breathing motion and poor signal-to-noise ratio making motion correction important for subsequent quantitative analysis. In this work, we (i) introduce an iterative model-based registration technique which incorporates a placenta-specific model into the motion correction process and (ii) describe a new technique making use of a Bayesian shrinkage prior to obtain robust estimates of individual and population trends in parameters. Our results suggest that the proposed registration method improves alignment of placental data and that the Bayesian fitting technique allows the estimation of voxel-level placenta flow parameters and the population trend in each parameter with gestational age (GA). We report gestational age dependent differences in vascular compartments and fetal oxygen saturation values observed across 9 normally grown pregnancies between 25–34 weeks gestational age and show qualitatively improved parameter mapping and more precise longitudinal fitting. Fetal oxygen saturation ( FO2 ) is observed to decrease at FO2=−3.6(GAweeks)+190.2(%) . This technique provides a robust framework for analysing longitudinal changes in both normal and pathological placental function

Intelligent Imaging of Perfusion Using Arterial Spin Labelling

Arterial spin labelling (ASL) is a powerful magnetic resonance imaging technique, which can be used to noninvasively measure perfusion in the brain and other organs of the body. Promising research results show how ASL might be used in stroke, tumours, dementia and paediatric medicine, in addition to many other areas. However, significant obstacles remain to prevent widespread use: ASL images have an inherently low signal to noise ratio, and are susceptible to corrupting artifacts from motion and other sources. The objective of the work in this thesis is to move towards an "intelligent imaging" paradigm: one in which the image acquisition, reconstruction and processing are mutually coupled, and tailored to the individual patient. This thesis explores how ASL images may be improved at several stages of the imaging pipeline. We review the relevant ASL literature, exploring details of ASL acquisitions, parameter inference and artifact post-processing. We subsequently present original work: we use the framework of Bayesian experimental design to generate optimised ASL acquisitions, we present original methods to improve parameter inference through anatomically-driven modelling of spatial correlation, and we describe a novel deep learning approach for simultaneous denoising and artifact filtering. Using a mixture of theoretical derivation, simulation results and imaging experiments, the work in this thesis presents several new approaches for ASL, and hopefully will shape future research and future ASL usage

Generalised Hierarchical Bayesian Microstructure Modelling for Diffusion MRI

Microstructure imaging combines tailored diffusion MRI acquisition protocols with a mathematical model to give insights into subvoxel tissue features. The model is typically fit voxel-by-voxel to the MRI image with least squares minimisation to give voxelwise maps of parameters relating to microstructural features, such as diffusivities and tissue compartment fractions. However, this fitting approach is susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Data-driven Bayesian hierarchical modelling defines prior distributions on parameters and learns them from the data, and can hence reduce such noise effects. Bayesian hierarchical modelling has been demonstrated for microstructure imaging with diffusion MRI, but only for a few, relatively simple, models. In this paper, we generalise hierarchical Bayesian modelling to a wide range of multi-compartment microstructural models, and fit the models with a Markov chain Monte Carlo (MCMC) algorithm. We implement our method by utilising Dmipy, a microstructure modelling software package for diffusion MRI data. Our code is available at github.com/PaddySlator/dmipy-bayesian

Shotgun Mitogenomics Provides a Reference Phylogenetic Framework and Timescale for Living Xenarthrans

Xenarthra (armadillos, sloths, and anteaters) constitutes one of the four major clades of placental mammals. Despite their phylogenetic distinctiveness in mammals, a reference phylogeny is still lacking for the 31 described species. Here we used Illumina shotgun sequencing to assemble 33 new complete mitochondrial genomes, establishing Xenarthra as the first major placental clade to be fully sequenced at the species level for mitogenomes. The resulting data set allowed the reconstruction of a robust phylogenetic framework and timescale that are consistent with previous studies conducted at the genus level using nuclear genes. Incorporating the full species diversity of extant xenarthrans points to a number of inconsistencies in xenarthran systematics and species definition. We propose to split armadillos in two distinct families Dasypodidae (dasypodines) and Chlamyphoridae (euphractines, chlamyphorines, and tolypeutines) to better reflect their ancient divergence, estimated around 42 million years ago. Species delimitation within long-nosed armadillos (genus Dasypus) appeared more complex than anticipated, with the discovery of a divergent lineage in French Guiana. Diversification analyses showed Xenarthra to be an ancient clade with a constant diversification rate through time with a species turnover driven by high but constant extinction. We also detected a significant negative correlation between speciation rate and past temperature fluctuations with an increase in speciation rate corresponding to the general cooling observed during the last 15 million years. Biogeographic reconstructions identified the tropical rainforest biome of Amazonia and the Guianan shield as the cradle of xenarthran evolutionary history with subsequent dispersions into more open and dry habitats.Fil: Gibb, Gillian C.. Universite de Montpellier; Francia. Massey Universit; Nueva ZelandaFil: Condamine, Fabien L.. University of Gothenburg; Suecia. Universite de Montpellier; Francia. University of Alberta; CanadáFil: Kuch, Melanie. McMaster University; CanadáFil: Enk, Jacob. McMaster University; CanadáFil: Moraes Barros, Nadia. Universidade Do Porto; Portugal. Universidade de Sao Paulo; BrasilFil: Superina, Mariella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Poinar, Hendrik N.. McMaster University; CanadáFil: Delsuc, Frederic. Universite de Montpellier; Franci

Bayesian molecular clock dating of species divergences in the genomics era

It has been five decades since the proposal of the molecular clock hypothesis, which states that the rate of evolution at the molecular level is constant through time and among species. This hypothesis has become a powerful tool in evolutionary biology, making it possible to use molecular sequences to estimate the geological ages of species divergence events. With recent advances in Bayesian clock dating methodology and the explosive accumulation of genetic sequence data, molecular clock dating has found widespread applications, from tracking virus pandemics, to studying the macroevolutionary process of speciation and extinction, to estimating a timescale for Life on Earth

Investigating Evolutionary History Using Phylogenomics

Reconstructing the Tree of Life is one of the principal aims of evolutionary biology. The development of molecular phylogenetics to elucidate evolutionary history has complemented palaeontology, biogeography, and archaeology in elucidating biological history. The development of molecular-clock analyses allowed evolutionary timescales to be estimated using nucleotide sequences and other products of the evolutionary process Until recently, the twin challenges of molecular dating were in obtaining sufficient data and developing robust methods. The former concern is now less important as high–throughput sequencing technology allows entire genomes to be sampled. Genome–scale data enhances statistical power, but accompanying this wealth of data is a new suite of analytical challenges. One of these key challenges is analysing these data in synthesis with the paleontological record without statistical overparameterisation. There are also aspects of the evolutionary process, such as among–lineage rate variation, that can affect the precision and accuracy of current methods. In this thesis, I first use the richest nucleotide sequence data set of insects available to estimate an authoritative insect evolutionary timescale that dates the origins and diversification of every major insect order. I then focus on molecular-clock methods by testing their performance in inferring evolutionary rates from time–structured data, common in the study of ancient DNA. I find that among–rate lineage variation and phylo–temporal clustering affect rate estimates. I also study data partitioning, a common technique used to optimise the analysis of multilocus data where independent parameters are applied across different subsets of the data. New data from the genomic revolution gifts biologists new opportunities to re-examine enduring questions about the evolutionary process. Here, I use phylogenetic tools to show that evolution leaves figurative fingerprints on genomes over millions of years

Generalised hierarchical bayesian microstructure modelling for diffusion MRI

Microstructure imaging combines tailored diffusion MRI acquisition protocols with a mathematical model to give insights into subvoxel tissue features. The model is typically fit voxel-by-voxel to the MRI image with least squares minimisation to give voxelwise maps of parameters relating to microstructural features, such as diffusivities and tissue compartment fractions. However, this fitting approach is susceptible to voxelwise noise, which can lead to erroneous values in parameter maps. Data-driven Bayesian hierarchical modelling defines prior distributions on parameters and learns them from the data, and can hence reduce such noise effects. Bayesian hierarchical modelling has been demonstrated for microstructure imaging with diffusion MRI, but only for a few, relatively simple, models. In this paper, we generalise hierarchical Bayesian modelling to a wide range of multi-compartment microstructural models, and fit the models with a Markov chain Monte Carlo (MCMC) algorithm. We implement our method by utilising Dmipy, a microstructure modelling software package for diffusion MRI data. Our code is available at github.com/PaddySlator/dmipy-bayesian

Testing the molecular clock using mechanistic models of both fossil preservation and molecular evolution

Molecular sequence data provide information about relative times only, and fossil-based age constraints are the ultimate source of information about absolute times in molecular clock dating analyses. Thus, fossil calibrations are critical to molecular clock dating, but competing methods are difficult to evaluate empirically because the true evolutionary time scale is never known. Here, we combine mechanistic models of fossil preservation and sequence evolution in simulations to evaluate different approaches to constructing fossil calibrations and their impact on Bayesian molecular clock dating, and the relative impact of fossil versus molecular sampling. We show that divergence time estimation is impacted by the model of fossil preservation, sampling intensity and tree shape. The addition of sequence data may improve molecular clock estimates, but accuracy and precision is dominated by the quality of the fossil calibrations. Posterior means and medians are poor representatives of true divergence times; posterior intervals provide a much more accurate estimate of divergence times, though they may be wide and often do not have high coverage probability. Our results highlight the importance of increased fossil sampling and improved statistical approaches to generating calibrations, which should incorporate the non-uniform nature of ecological and temporal fossil species distributions

Testing the molecular clock using mechanistic models of fossil preservation and molecular evolution

Molecular sequence data provide information about relative times only, and fossil-based age constraints are the ultimate source of information about absolute times in molecular clock dating analyses. Thus, fossil calibrations are critical to molecular clock dating, but competing methods are difficult to evaluate empirically because the true evolutionary time scale is never known. Here, we combine mechanistic models of fossil preservation and sequence evolution in simulations to evaluate different approaches to constructing fossil calibrations and their impact on Bayesian molecular clock dating, and the relative impact of fossil versus molecular sampling. We show that divergence time estimation is impacted by the model of fossil preservation, sampling intensity and tree shape. The addition of sequence data may improve molecular clock estimates, but accuracy and precision is dominated by the quality of the fossil calibrations. Posterior means and medians are poor representatives of true divergence times; posterior intervals provide a much more accurate estimate of divergence times, though they may be wide and often do not have high coverage probability. Our results highlight the importance of increased fossil sampling and improved statistical approaches to generating calibrations, which should incorporate the non-uniform nature of ecological and temporal fossil species distributions.ISSN:0962-8452ISSN:1471-295