scholarly journals Novel non-parametric models to estimate evolutionary rates and divergence times from heterochronous sequence data

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Mathieu Fourment ◽  
Edward C Holmes
Keyword(s):  
Sequence Data ◽  
Evolutionary Rates ◽  
Parametric Models ◽  
Divergence Times ◽  
Non Parametric

scholarly journals Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance

2017 ◽  
Author(s):  
Erik M. Volz ◽  
Xavier Didelot
Keyword(s):  
Growth Rate ◽  
Population Size ◽  
Effective Population Size ◽  
Growth Rates ◽  
Sequence Data ◽  
Demographic History ◽  
Parametric Models ◽  
Effective Population ◽  
Epidemic Dynamics ◽  
Non Parametric

AbstractNon-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stationary stochastic processes which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of β-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://mrc-ide.github.io/skygrowth/.

scholarly journals Maximum likelihood inference of pathogen population size history from a phylogeny

2021 ◽  
Author(s):  
Xavier Didelot ◽  
Erik M Volz
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Demographic History ◽  
Genetic Data ◽  
Parametric Models ◽  
Process Models ◽  
Effective Population ◽  
Latent Process ◽  
Non Parametric

ABSTRACTInference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. Non-parametric models for population dynamics combined with molecular clock models which relate genetic data to time have enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The theory for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the models to genetic data from several pathogen data sets.

scholarly journals Molecular clocks and the early evolution of metazoan nervous systems

2015 ◽  
Vol 370 (1684) ◽  
pp. 20150046 ◽  
Author(s):  
Gregory A. Wray
Keyword(s):  
Sensory Processing ◽  
Fossil Record ◽  
Sequence Data ◽  
Divergence Times ◽  
Molecular Clocks ◽  
Sense Organs ◽  
Animal Evolution ◽  
Nervous Systems ◽  
Nervous System Evolution ◽  
Fossil Records

The timing of early animal evolution remains poorly resolved, yet remains critical for understanding nervous system evolution. Methods for estimating divergence times from sequence data have improved considerably, providing a more refined understanding of key divergences. The best molecular estimates point to the origin of metazoans and bilaterians tens to hundreds of millions of years earlier than their first appearances in the fossil record. Both the molecular and fossil records are compatible, however, with the possibility of tiny, unskeletonized, low energy budget animals during the Proterozoic that had planktonic, benthic, or meiofaunal lifestyles. Such animals would likely have had relatively simple nervous systems equipped primarily to detect food, avoid inhospitable environments and locate mates. The appearance of the first macropredators during the Cambrian would have changed the selective landscape dramatically, likely driving the evolution of complex sense organs, sophisticated sensory processing systems, and diverse effector systems involved in capturing prey and avoiding predation.

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

Bayesian non-parametric models for regional prevalence estimation

2008 ◽  
Vol 35 (5) ◽  
pp. 567-582 ◽  
Author(s):  
Adam J. Branscum ◽  
Timothy E. Hanson ◽  
Ian A. Gardner
Keyword(s):  
Parametric Models ◽  
Prevalence Estimation ◽  
Non Parametric

Non-parametric lactation curves

1989 ◽  
Vol 48 (2) ◽  
pp. 331-339 ◽  
Author(s):  
D. A. Elston ◽  
C. A. Glasbey ◽  
D. R. Neilson
Keyword(s):  
Parametric Method ◽  
Parametric Models ◽  
Summary Statistics ◽  
Parametric Curves ◽  
Biased Estimation ◽  
Derivatives Of ◽  
Non Parametric

ABSTRACTLactation curves are fitted to data as a preliminary to estimating summary statistics. Two widely quoted curves are atbe-ct (Wood, 1967) and a(1 - e-bt) - ct (Cobby and Le Du, 1978), each of which has three parameters. Restriction to either of these curves imposes limitations on the fit to the data and can result in biased estimation of summary statistics. Alternatively, lactation curves can be generated by the use of a non-parametric method which requires only weak assumptions about the signs of derivatives of the curves. Because the non-parametric curves are more flexible, estimates of summary statistics are less likely to be biased than those based on parametric models. Use of the non-parametric curves is particularly advantageous around the time of peak yield, where the curves of Wood and Cobby and Le Du are known to fit data poorly.

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