scholarly journals Accounting for spatial sampling patterns in Bayesian phylogeography

2021 ◽  
Vol 118 (52) ◽  
pp. e2105273118
Author(s):  
Stéphane Guindon ◽  
Nicola De Maio
Keyword(s):  
Bayesian Modeling ◽  
Evolutionary Process ◽  
Simulated Data ◽  
Spatial Sampling ◽  
Statistical Computing ◽  
Sampling Strategies ◽  
Demographic Dynamics ◽  
Genetic Sequences ◽  
Statistical Phylogeography ◽  
Modeling Data

Statistical phylogeography provides useful tools to characterize and quantify the spread of organisms during the course of evolution. Analyzing georeferenced genetic data often relies on the assumption that samples are preferentially collected in densely populated areas of the habitat. Deviation from this assumption negatively impacts the inference of the spatial and demographic dynamics. This issue is pervasive in phylogeography. It affects analyses that approximate the habitat as a set of discrete demes as well as those that treat it as a continuum. The present study introduces a Bayesian modeling approach that explicitly accommodates for spatial sampling strategies. An original inference technique, based on recent advances in statistical computing, is then described that is most suited to modeling data where sequences are preferentially collected at certain locations, independently of the outcome of the evolutionary process. The analysis of georeferenced genetic sequences from the West Nile virus in North America along with simulated data shows how assumptions about spatial sampling may impact our understanding of the forces shaping biodiversity across time and space.

scholarly journals Photovoltaic models parameter estimation via an enhanced Rao-1 algorithm

2021 ◽  
Vol 19 (2) ◽  
pp. 1128-1153
Author(s):  
Junhua Ku ◽  
◽  
Shuijia Li ◽  
Wenyin Gong ◽  
Keyword(s):  
Parameter Estimation ◽  
Population Size ◽  
Evolution Operator ◽  
Evolutionary Process ◽  
Simulated Data ◽  
Local Optimum ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Arduous Task ◽  
Important Position

<abstract><p>The accuracy of unknown parameters determines the accuracy of photovoltaic (PV) models that occupy an important position in the PV power generation system. Due to the complexity of the equation equivalent of PV models, estimating the parameters of the PV model is still an arduous task. In order to accurately and reliably estimate the unknown parameters in PV models, in this paper, an enhanced Rao-1 algorithm is proposed. The main point of enhancement lies in i) a repaired evolution operator is presented; ii) to prevent the Rao-1 algorithm from falling into a local optimum, a new evolution operator is developed; iii) in order to enable population size to change adaptively with the evolutionary process, the population size linear reduction strategy is employed. To verify the validity of ERao-1 algorithm, we embark a study on parameter estimation of three different PV models. Experimental results show that the proposed ERao-1 algorithm performs better than existing parameter estimation algorithms in terms of the accuracy and reliability, especially for the double diode model with RMSE 9.8248E-04, three diode model with RMSE 9.8257E-04 for the R.T.C France silicon cell, and 2.4251E-03 for the three diode model of Photowatt- PWP201 cell. In addition, the fitting curve of the simulated data and the measured data also shows the accuracy of the estimated parameters.</p></abstract>

scholarly journals Inferring pandemic growth rates from sequence data

2012 ◽  
Vol 9 (73) ◽  
pp. 1797-1808 ◽  
Author(s):  
Eric de Silva ◽  
Neil M. Ferguson ◽  
Christophe Fraser
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Simulated Data ◽  
Parametric Methods ◽  
Sampling Strategies ◽  
Size Estimation ◽  
Effective Population ◽  
Population Size Estimates ◽  
Non Parametric

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

scholarly journals Detecting evolutionary patterns of cancers using consensus trees

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i684-i691
Author(s):  
Sarah Christensen ◽  
Juho Kim ◽  
Nicholas Chia ◽  
Oluwasanmi Koyejo ◽  
Mohammed El-Kebir
Keyword(s):  
Evolutionary Process ◽  
Simulated Data ◽  
Therapy Response ◽  
Supplementary Information ◽  
Driver Mutations ◽  
Sequencing Data ◽  
Large Solution ◽  
Evolutionary Patterns ◽  
Cancer Subtypes ◽  
Evolutionary Trajectories

Abstract Motivation While each cancer is the result of an isolated evolutionary process, there are repeated patterns in tumorigenesis defined by recurrent driver mutations and their temporal ordering. Such repeated evolutionary trajectories hold the potential to improve stratification of cancer patients into subtypes with distinct survival and therapy response profiles. However, current cancer phylogeny methods infer large solution spaces of plausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patterns. Results To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose to leverage common patterns of evolution found in patient cohorts. We first formulate the Multiple Choice Consensus Tree problem, which seeks to select a tumor tree for each patient and assign patients into clusters in such a way that maximizes consistency within each cluster of patient trees. We prove that this problem is NP-hard and develop a heuristic algorithm, Revealing Evolutionary Consensus Across Patients (RECAP), to solve this problem in practice. Finally, on simulated data, we show RECAP outperforms existing methods that do not account for patient subtypes. We then use RECAP to resolve ambiguities in patient trees and find repeated evolutionary trajectories in lung and breast cancer cohorts. Availability and implementation https://github.com/elkebir-group/RECAP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Impact of satellite viewing-swath width on global and regional aerosol optical thickness statistics and trends

2014 ◽  
Vol 7 (7) ◽  
pp. 2313-2335 ◽  
Author(s):  
P. R. Colarco ◽  
R. A. Kahn ◽  
L. A. Remer ◽  
R. C. Levy
Keyword(s):  
Optical Thickness ◽  
Radiative Forcing ◽  
Aerosol Optical Thickness ◽  
Spatial Sampling ◽  
Sampling Strategies ◽  
Data Set ◽  
Modis Data ◽  
Aerosol Loading ◽  
The Impact ◽  
Single Pixel

Abstract. We use the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite aerosol optical thickness (AOT) product to assess the impact of reduced swath width on global and regional AOT statistics and trends. Along-track and across-track sampling strategies are employed, in which the full MODIS data set is sub-sampled with various narrow-swath (~ 400–800 km) and single pixel width (~ 10 km) configurations. Although view-angle artifacts in the MODIS AOT retrieval confound direct comparisons between averages derived from different sub-samples, careful analysis shows that with many portions of the Earth essentially unobserved, spatial sampling introduces uncertainty in the derived seasonal–regional mean AOT. These AOT spatial sampling artifacts comprise up to 60% of the full-swath AOT value under moderate aerosol loading, and can be as large as 0.1 in some regions under high aerosol loading. Compared to full-swath observations, narrower swath and single pixel width sampling exhibits a reduced ability to detect AOT trends with statistical significance. On the other hand, estimates of the global, annual mean AOT do not vary significantly from the full-swath values as spatial sampling is reduced. Aggregation of the MODIS data at coarse grid scales (10°) shows consistency in the aerosol trends across sampling strategies, with increased statistical confidence, but quantitative errors in the derived trends are found even for the full-swath data when compared to high spatial resolution (0.5°) aggregations. Using results of a model-derived aerosol reanalysis, we find consistency in our conclusions about a seasonal–regional spatial sampling artifact in AOT. Furthermore, the model shows that reduced spatial sampling can amount to uncertainty in computed shortwave top-of-atmosphere aerosol radiative forcing of 2–3 W m−2. These artifacts are lower bounds, as possibly other unconsidered sampling strategies would perform less well. These results suggest that future aerosol satellite missions having significantly less than full-swath viewing are unlikely to sample the true AOT distribution well enough to obtain the statistics needed to reduce uncertainty in aerosol direct forcing of climate.

Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products

2014 ◽  
Vol 35 (20) ◽  
pp. 7230-7244 ◽  
Author(s):  
Yanling Ding ◽  
Yong Ge ◽  
Maogui Hu ◽  
Jinfeng Wang ◽  
Jianghao Wang ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Sampling Strategies ◽  
Modis Lai

scholarly journals Spatial Sampling Strategies for the Effect of Interpolation Accuracy

2015 ◽  
Vol 4 (4) ◽  
pp. 2742-2768 ◽  
Author(s):  
Hairong Zhang ◽  
Lijiang Lu ◽  
Yanhua Liu ◽  
Wei Liu
Keyword(s):  
Spatial Sampling ◽  
Sampling Strategies ◽  
Interpolation Accuracy
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