Impact of Bayesian Priors on the Characterization of Binary Black Hole Coalescences.

PubMed

Vitale, Salvatore; Gerosa, Davide; Haster, Carl-Johan; Chatziioannou, Katerina; Zimmerman, Aaron

2017-12-22

In a regime where data are only mildly informative, prior choices can play a significant role in Bayesian statistical inference, potentially affecting the inferred physics. We show this is indeed the case for some of the parameters inferred from current gravitational-wave measurements of binary black hole coalescences. We reanalyze the first detections performed by the twin LIGO interferometers using alternative (and astrophysically motivated) prior assumptions. We find different prior distributions can introduce deviations in the resulting posteriors that impact the physical interpretation of these systems. For instance, (i) limits on the 90% credible interval on the effective black hole spin χ_{eff} are subject to variations of ∼10% if a prior with black hole spins mostly aligned to the binary's angular momentum is considered instead of the standard choice of isotropic spin directions, and (ii) under priors motivated by the initial stellar mass function, we infer tighter constraints on the black hole masses, and in particular, we find no support for any of the inferred masses within the putative mass gap M≲5  M_{⊙}.

Challenges in Species Tree Estimation Under the Multispecies Coalescent Model

PubMed Central

Xu, Bo; Yang, Ziheng

2016-01-01

The multispecies coalescent (MSC) model has emerged as a powerful framework for inferring species phylogenies while accounting for ancestral polymorphism and gene tree-species tree conflict. A number of methods have been developed in the past few years to estimate the species tree under the MSC. The full likelihood methods (including maximum likelihood and Bayesian inference) average over the unknown gene trees and accommodate their uncertainties properly but involve intensive computation. The approximate or summary coalescent methods are computationally fast and are applicable to genomic datasets with thousands of loci, but do not make an efficient use of information in the multilocus data. Most of them take the two-step approach of reconstructing the gene trees for multiple loci by phylogenetic methods and then treating the estimated gene trees as observed data, without accounting for their uncertainties appropriately. In this article we review the statistical nature of the species tree estimation problem under the MSC, and explore the conceptual issues and challenges of species tree estimation by focusing mainly on simple cases of three or four closely related species. We use mathematical analysis and computer simulation to demonstrate that large differences in statistical performance may exist between the two classes of methods. We illustrate that several counterintuitive behaviors may occur with the summary methods but they are due to inefficient use of information in the data by summary methods and vanish when the data are analyzed using full-likelihood methods. These include (i) unidentifiability of parameters in the model, (ii) inconsistency in the so-called anomaly zone, (iii) singularity on the likelihood surface, and (iv) deterioration of performance upon addition of more data. We discuss the challenges and strategies of species tree inference for distantly related species when the molecular clock is violated, and highlight the need for improving the computational efficiency and model realism of the likelihood methods as well as the statistical efficiency of the summary methods. PMID:27927902

DOE Office of Scientific and Technical Information (OSTI.GOV)

Del Pozzo, Walter; Nikhef National Institute for Subatomic Physics, Science Park 105, 1098 XG Amsterdam; Veitch, John

Second-generation interferometric gravitational-wave detectors, such as Advanced LIGO and Advanced Virgo, are expected to begin operation by 2015. Such instruments plan to reach sensitivities that will offer the unique possibility to test general relativity in the dynamical, strong-field regime and investigate departures from its predictions, in particular, using the signal from coalescing binary systems. We introduce a statistical framework based on Bayesian model selection in which the Bayes factor between two competing hypotheses measures which theory is favored by the data. Probability density functions of the model parameters are then used to quantify the inference on individual parameters. We alsomore » develop a method to combine the information coming from multiple independent observations of gravitational waves, and show how much stronger inference could be. As an introduction and illustration of this framework-and a practical numerical implementation through the Monte Carlo integration technique of nested sampling-we apply it to gravitational waves from the inspiral phase of coalescing binary systems as predicted by general relativity and a very simple alternative theory in which the graviton has a nonzero mass. This method can (and should) be extended to more realistic and physically motivated theories.« less

Impact of tree priors in species delimitation and phylogenetics of the genus Oligoryzomys (Rodentia: Cricetidae).

PubMed

da Cruz, Marcos de O R; Weksler, Marcelo

2018-02-01

The use of genetic data and tree-based algorithms to delimit evolutionary lineages is becoming an important practice in taxonomic identification, especially in morphologically cryptic groups. The effects of different phylogenetic and/or coalescent models in the analyses of species delimitation, however, are not clear. In this paper, we assess the impact of different evolutionary priors in phylogenetic estimation, species delimitation, and molecular dating of the genus Oligoryzomys (Mammalia: Rodentia), a group with complex taxonomy and morphological cryptic species. Phylogenetic and coalescent analyses included 20 of the 24 recognized species of the genus, comprising of 416 Cytochrome b sequences, 26 Cytochrome c oxidase I sequences, and 27 Beta-Fibrinogen Intron 7 sequences. For species delimitation, we employed the General Mixed Yule Coalescent (GMYC) and Bayesian Poisson tree processes (bPTP) analyses, and contrasted 4 genealogical and phylogenetic models: Pure-birth (Yule), Constant Population Size Coalescent, Multiple Species Coalescent, and a mixed Yule-Coalescent model. GMYC analyses of trees from different genealogical models resulted in similar species delimitation and phylogenetic relationships, with incongruence restricted to areas of poor nodal support. bPTP results, however, significantly differed from GMYC for 5 taxa. Oligoryzomys early diversification was estimated to have occurred in the Early Pleistocene, between 0.7 and 2.6 MYA. The mixed Yule-Coalescent model, however, recovered younger dating estimates for Oligoryzomys diversification, and for the threshold for the speciation-coalescent horizon in GMYC. Eight of the 20 included Oligoryzomys species were identified as having two or more independent evolutionary units, indicating that current taxonomy of Oligoryzomys is still unsettled. Copyright © 2017 Elsevier Inc. All rights reserved.

Population dynamics and in vitro antibody pressure of porcine parvovirus indicate a decrease in variability.

PubMed

Streck, André Felipe; Homeier, Timo; Foerster, Tessa; Truyen, Uwe

2013-09-01

To estimate the impact of porcine parvovirus (PPV) vaccines on the emergence of new phenotypes, the population dynamic history of the virus was calculated using the Bayesian Markov chain Monte Carlo method with a Bayesian skyline coalescent model. Additionally, an in vitro model was performed with consecutive passages of the 'Challenge' strain (a virulent field strain) and NADL2 strain (a vaccine strain) in a PK-15 cell line supplemented with polyclonal antibodies raised against the vaccine strain. A decrease in genetic diversity was observed in the presence of antibodies in vitro or after vaccination (as estimated by the in silico model). We hypothesized that the antibodies induced a selective pressure that may reduce the incidence of neutral selection, which should play a major role in the emergence of new mutations. In this scenario, vaccine failures and non-vaccinated populations (e.g. wild boars) may have an important impact in the emergence of new phenotypes.

A history estimate and evolutionary analysis of rabies virus variants in China.

PubMed

Ming, Pinggang; Yan, Jiaxin; Rayner, Simon; Meng, Shengli; Xu, Gelin; Tang, Qing; Wu, Jie; Luo, Jing; Yang, Xiaoming

2010-03-01

To investigate the evolutionary dynamics of rabies virus (RABV) in China, we collected and sequenced 55 isolates sampled from 14 Chinese provinces over the last 40 years and performed a coalescent-based analysis of the G gene. This revealed that the RABV currently circulating in China is composed of three main groups. Bayesian coalescent analysis estimated the date of the most recent common ancestor for the current RABV Chinese strains to be 1412 (with a 95 % confidence interval of 1006-1736). The estimated mean substitution rate for the G gene sequences (3.961x10(-4) substitutions per site per year) was in accordance with previous reports for RABV.

A Possible Geographic Origin of Endemic Hepatitis C Virus 6a in Hong Kong: Evidences for the Association with Vietnamese Immigration

PubMed Central

Zhou, Xiaoming; Chan, Paul K. S.; Tam, John S.; Tang, Julian W.

2011-01-01

Background Hepatitis C virus (HCV) 6a accounts for 23.6% of all HCV infections of the general population and 58.5% of intravenous drug users in Hong Kong. However, the geographical origin of this highly predominant HCV subgenotype is largely unknown. This study explores a hypothesis for one possible transmission route of HCV 6a to Hong Kong. Methods NS5A sequences derived from 26 HCV 6a samples were chosen from a five year period (1999–2004) from epidemiologically unrelated patients from Hong Kong. Partial-NS5A sequences (513-bp from nt 6728 to 7240) were adopted for Bayesian coalescent analysis to reconstruct the evolutionary history of HCV infections in Hong Kong using the BEAST v1.3 program. A rooted phylogenetic tree was drawn for these sequences by alignment with reference Vietnamese sequences. Demographic data were accessed from “The Statistic Yearbooks of Hong Kong”. Results Bayesian coalescent analysis showed that the rapid increase in 6a infections, which had increased more than 90-fold in Hong Kong from 1986 to 1994 correlated to two peaks of Vietnamese immigration to Hong Kong from 1978 to 1997. The second peak, which occurred from 1987 through 1997, overlapped with the rapid increase of HCV 6a occurrence in Hong Kong. Phylogenetic analyses have further revealed that HCV 6a strains from Vietnam may be ancestral to Hong Kong counterparts. Conclusions The high predominance of HCV 6a infections in Hong Kong was possibly associated with Vietnamese immigration during 1987–1997. PMID:21931867

Parameter Estimation for Compact Binaries with Ground-Based Gravitational-Wave Observations Using the LALInference

NASA Technical Reports Server (NTRS)

Veitch, J.; Raymond, V.; Farr, B.; Farr, W.; Graff, P.; Vitale, S.; Aylott, B.; Blackburn, K.; Christensen, N.; Coughlin, M.

2015-01-01

The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star (BNS), a neutron star - black hole binary (NSBH) and a binary black hole (BBH), where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence (CBC) parameter space.

Species trees from consensus single nucleotide polymorphism (SNP) data: Testing phylogenetic approaches with simulated and empirical data.

PubMed

Schmidt-Lebuhn, Alexander N; Aitken, Nicola C; Chuah, Aaron

2017-11-01

Datasets of hundreds or thousands of SNPs (Single Nucleotide Polymorphisms) from multiple individuals per species are increasingly used to study population structure, species delimitation and shallow phylogenetics. The principal software tool to infer species or population trees from SNP data is currently the BEAST template SNAPP which uses a Bayesian coalescent analysis. However, it is computationally extremely demanding and tolerates only small amounts of missing data. We used simulated and empirical SNPs from plants (Australian Craspedia, Asteraceae, and Pelargonium, Geraniaceae) to compare species trees produced (1) by SNAPP, (2) using SVD quartets, and (3) using Bayesian and parsimony analysis with several different approaches to summarising data from multiple samples into one set of traits per species. Our aims were to explore the impact of tree topology and missing data on the results, and to test which data summarising and analyses approaches would best approximate the results obtained from SNAPP for empirical data. SVD quartets retrieved the correct topology from simulated data, as did SNAPP except in the case of a very unbalanced phylogeny. Both methods failed to retrieve the correct topology when large amounts of data were missing. Bayesian analysis of species level summary data scoring the two alleles of each SNP as independent characters and parsimony analysis of data scoring each SNP as one character produced trees with branch length distributions closest to the true trees on which SNPs were simulated. For empirical data, Bayesian inference and Dollo parsimony analysis of data scored allele-wise produced phylogenies most congruent with the results of SNAPP. In the case of study groups divergent enough for missing data to be phylogenetically informative (because of additional mutations preventing amplification of genomic fragments or bioinformatic establishment of homology), scoring of SNP data as a presence/absence matrix irrespective of allele content might be an additional option. As this depends on sampling across species being reasonably even and a random distribution of non-informative instances of missing data, however, further exploration of this approach is needed. Properly chosen data summary approaches to inferring species trees from SNP data may represent a potential alternative to currently available individual-level coalescent analyses especially for quick data exploration and when dealing with computationally demanding or patchy datasets. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Molecular epidemiology of hepatitis B virus in Misiones, Argentina.

PubMed

Mojsiejczuk, Laura Noelia; Torres, Carolina; Sevic, Ina; Badano, Inés; Malan, Richard; Flichman, Diego Martin; Liotta, Domingo Javier; Campos, Rodolfo Hector

2016-10-01

Hepatitis B virus (HBV) infection is a major public health problem worldwide. The aims of this study were to describe the molecular epidemiology of HBV in the Province of Misiones, Argentina and estimate the phylodynamic of the main groups in a Bayesian coalescent framework. To this end, partial or complete genome sequences were obtained from 52 blood donor candidates. The phylogenetic analysis based on partial sequences of S/P region showed a predominance of genotype D (65.4%), followed by genotype F (30.8%) and genotype A as a minority (3.8%). At subgenotype level, the circulation of subgenotypes D3 (42.3%), D2 (13.5%), F1b (11.5%) and F4 (9.6%) was mainly identified. The Bayesian coalescent analysis of 29 complete genome sequences for the main groups revealed that the subgenotypes D2 and D3 had several introductions to the region, with ancestors dating back from 1921 to 1969 and diversification events until the late '70s. The genotype F in Misiones has a more recent history; subgenotype F4 isolates were intermixed with sequences from Argentina and neighboring countries and only one significant cluster dated back in 1994 was observed. Subgenotype F1b isolates exhibited low genetic distance and formed a closely related monophyletic cluster, suggesting a very recent introduction. In conclusion, the phylogenetic and coalescent analyses showed that the European genotype D has a higher circulation, a longer history of diversification and may be responsible for the largest proportion of chronic HBV infections in the Province of Misiones. Genotype F, especially subgenotype F1b, had a more recent introduction and its diversification in the last 20years might be related to its involvement in new transmission events. Copyright © 2016 Elsevier B.V. All rights reserved.

Bayesian Population Genomic Inference of Crossing Over and Gene Conversion

PubMed Central

Padhukasahasram, Badri; Rannala, Bruce

2011-01-01

Meiotic recombination is a fundamental cellular mechanism in sexually reproducing organisms and its different forms, crossing over and gene conversion both play an important role in shaping genetic variation in populations. Here, we describe a coalescent-based full-likelihood Markov chain Monte Carlo (MCMC) method for jointly estimating the crossing-over, gene-conversion, and mean tract length parameters from population genomic data under a Bayesian framework. Although computationally more expensive than methods that use approximate likelihoods, the relative efficiency of our method is expected to be optimal in theory. Furthermore, it is also possible to obtain a posterior sample of genealogies for the data using this method. We first check the performance of the new method on simulated data and verify its correctness. We also extend the method for inference under models with variable gene-conversion and crossing-over rates and demonstrate its ability to identify recombination hotspots. Then, we apply the method to two empirical data sets that were sequenced in the telomeric regions of the X chromosome of Drosophila melanogaster. Our results indicate that gene conversion occurs more frequently than crossing over in the su-w and su-s gene sequences while the local rates of crossing over as inferred by our program are not low. The mean tract lengths for gene-conversion events are estimated to be ∼70 bp and 430 bp, respectively, for these data sets. Finally, we discuss ideas and optimizations for reducing the execution time of our algorithm. PMID:21840857

TIGER: A data analysis pipeline for testing the strong-field dynamics of general relativity with gravitational wave signals from coalescing compact binaries

NASA Astrophysics Data System (ADS)

Agathos, M.; Del Pozzo, W.; Li, T. G. F.; Van Den Broeck, C.; Veitch, J.; Vitale, S.

2014-04-01

The direct detection of gravitational waves with upcoming second-generation gravitational wave observatories such as Advanced LIGO and Advanced Virgo will allow us to probe the genuinely strong-field dynamics of general relativity (GR) for the first time. We have developed a data analysis pipeline called TIGER (test infrastructure for general relativity), which uses signals from compact binary coalescences to perform a model-independent test of GR. In this paper we focus on signals from coalescing binary neutron stars, for which sufficiently accurate waveform models are already available which can be generated fast enough on a computer that they can be used in Bayesian inference. By performing numerical experiments in stationary, Gaussian noise, we show that for such systems, TIGER is robust against a number of unmodeled fundamental, astrophysical, and instrumental effects, such as differences between waveform approximants, a limited number of post-Newtonian phase contributions being known, the effects of neutron star tidal deformability on the orbital motion, neutron star spins, and instrumental calibration errors.

Bayesian Divergence-Time Estimation with Genome-Wide SNP Data of Sea Catfishes (Ariidae) Supports Miocene Closure of the Panamanian Isthmus.

PubMed

Stange, Madlen; Sánchez-Villagra, Marcelo R; Salzburger, Walter; Matschiner, Michael

2018-01-27

The closure of the Isthmus of Panama has long been considered to be one of the best defined biogeographic calibration points for molecular divergence-time estimation. However, geological and biological evidence has recently cast doubt on the presumed timing of the initial isthmus closure around 3 Ma but has instead suggested the existence of temporary land bridges as early as the Middle or Late Miocene. The biological evidence supporting these earlier land bridges was based either on only few molecular markers or on concatenation of genome-wide sequence data, an approach that is known to result in potentially misleading branch lengths and divergence times, which could compromise the reliability of this evidence. To allow divergence-time estimation with genomic data using the more appropriate multi-species coalescent model, we here develop a new method combining the SNP-based Bayesian species-tree inference of the software SNAPP with a molecular clock model that can be calibrated with fossil or biogeographic constraints. We validate our approach with simulations and use our method to reanalyze genomic data of Neotropical army ants (Dorylinae) that previously supported divergence times of Central and South American populations before the isthmus closure around 3 Ma. Our reanalysis with the multi-species coalescent model shifts all of these divergence times to ages younger than 3 Ma, suggesting that the older estimates supporting the earlier existence of temporary land bridges were artifacts resulting at least partially from the use of concatenation. We then apply our method to a new RAD-sequencing data set of Neotropical sea catfishes (Ariidae) and calibrate their species tree with extensive information from the fossil record. We identify a series of divergences between groups of Caribbean and Pacific sea catfishes around 10 Ma, indicating that processes related to the emergence of the isthmus led to vicariant speciation already in the Late Miocene, millions of years before the final isthmus closure. © The Author(s) 2018. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Phylogenetic analysis at deep timescales: unreliable gene trees, bypassed hidden support, and the coalescence/concatalescence conundrum.

PubMed

Gatesy, John; Springer, Mark S

2014-11-01

Large datasets are required to solve difficult phylogenetic problems that are deep in the Tree of Life. Currently, two divergent systematic methods are commonly applied to such datasets: the traditional supermatrix approach (= concatenation) and "shortcut" coalescence (= coalescence methods wherein gene trees and the species tree are not co-estimated). When applied to ancient clades, these contrasting frameworks often produce congruent results, but in recent phylogenetic analyses of Placentalia (placental mammals), this is not the case. A recent series of papers has alternatively disputed and defended the utility of shortcut coalescence methods at deep phylogenetic scales. Here, we examine this exchange in the context of published phylogenomic data from Mammalia; in particular we explore two critical issues - the delimitation of data partitions ("genes") in coalescence analysis and hidden support that emerges with the combination of such partitions in phylogenetic studies. Hidden support - increased support for a clade in combined analysis of all data partitions relative to the support evident in separate analyses of the various data partitions, is a hallmark of the supermatrix approach and a primary rationale for concatenating all characters into a single matrix. In the most extreme cases of hidden support, relationships that are contradicted by all gene trees are supported when all of the genes are analyzed together. A valid fear is that shortcut coalescence methods might bypass or distort character support that is hidden in individual loci because small gene fragments are analyzed in isolation. Given the extensive systematic database for Mammalia, the assumptions and applicability of shortcut coalescence methods can be assessed with rigor to complement a small but growing body of simulation work that has directly compared these methods to concatenation. We document several remarkable cases of hidden support in both supermatrix and coalescence paradigms and argue that in most instances, the emergent support in the shortcut coalescence analyses is an artifact. By referencing rigorous molecular clock studies of Mammalia, we suggest that inaccurate gene trees that imply unrealistically deep coalescences debilitate shortcut coalescence analyses of the placental dataset. We document contradictory coalescence results for Placentalia, and outline a critical conundrum that challenges the general utility of shortcut coalescence methods at deep phylogenetic scales. In particular, the basic unit of analysis in coalescence analysis, the coalescence-gene, is expected to shrink in size as more taxa are analyzed, but as the amount of data for reconstruction of a gene tree ratchets downward, the number of nodes in the gene tree that need to be resolved ratchets upward. Some advocates of shortcut coalescence methods have attempted to address problems with inaccurate gene trees by concatenating multiple coalescence-genes to yield "gene trees" that better match the species tree. However, this hybrid concatenation/coalescence approach, "concatalescence," contradicts the most basic biological rationale for performing a coalescence analysis in the first place. We discuss this reality in the context of recent simulation work that also suggests inaccurate reconstruction of gene trees is more problematic for shortcut coalescence methods than deep coalescence of independently segregating loci is for concatenation methods. Copyright © 2014 Elsevier Inc. All rights reserved.

High endemism at cave entrances: a case study of spiders of the genus Uthina

PubMed Central

Yao, Zhiyuan; Dong, Tingting; Zheng, Guo; Fu, Jinzhong; Li, Shuqiang

2016-01-01

Endemism, which is typically high on islands and in caves, has rarely been studied in the cave entrance ecotone. We investigated the endemism of the spider genus Uthina at cave entrances. Totally 212 spiders were sampled from 46 localities, from Seychelles across Southeast Asia to Fiji. They mostly occur at cave entrances but occasionally appear at various epigean environments. Phylogenetic analysis of DNA sequence data from COI and 28S genes suggested that Uthina was grouped into 13 well-supported clades. We used three methods, the Bayesian Poisson Tree Processes (bPTP) model, the Bayesian Phylogenetics and Phylogeography (BPP) method, and the general mixed Yule coalescent (GMYC) model, to investigate species boundaries. Both bPTP and BPP identified the 13 clades as 13 separate species, while GMYC identified 19 species. Furthermore, our results revealed high endemism at cave entrances. Of the 13 provisional species, twelve (one known and eleven new) are endemic to one or a cluster of caves, and all of them occurred only at cave entrances except for one population of one species. The only widely distributed species, U. luzonica, mostly occurred in epigean environments while three populations were found at cave entrances. Additionally, eleven new species of the genus are described. PMID:27775081

Influence of gene flow on divergence dating - implications for the speciation history of Takydromus grass lizards.

PubMed

Tseng, Shu-Ping; Li, Shou-Hsien; Hsieh, Chia-Hung; Wang, Hurng-Yi; Lin, Si-Min

2014-10-01

Dating the time of divergence and understanding speciation processes are central to the study of the evolutionary history of organisms but are notoriously difficult. The difficulty is largely rooted in variations in the ancestral population size or in the genealogy variation across loci. To depict the speciation processes and divergence histories of three monophyletic Takydromus species endemic to Taiwan, we sequenced 20 nuclear loci and combined with one mitochondrial locus published in GenBank. They were analysed by a multispecies coalescent approach within a Bayesian framework. Divergence dating based on the gene tree approach showed high variation among loci, and the divergence was estimated at an earlier date than when derived by the species-tree approach. To test whether variations in the ancestral population size accounted for the majority of this variation, we conducted computer inferences using isolation-with-migration (IM) and approximate Bayesian computation (ABC) frameworks. The results revealed that gene flow during the early stage of speciation was strongly favoured over the isolation model, and the initiation of the speciation process was far earlier than the dates estimated by gene- and species-based divergence dating. Due to their limited dispersal ability, it is suggested that geographical isolation may have played a major role in the divergence of these Takydromus species. Nevertheless, this study reveals a more complex situation and demonstrates that gene flow during the speciation process cannot be overlooked and may have a great impact on divergence dating. By using multilocus data and incorporating Bayesian coalescence approaches, we provide a more biologically realistic framework for delineating the divergence history of Takydromus. © 2014 John Wiley & Sons Ltd.

Sky island diversification meets the multispecies coalescent - divergence in the spruce-fir moss spider (Microhexura montivaga, Araneae, Mygalomorphae) on the highest peaks of southern Appalachia.

PubMed

Hedin, Marshal; Carlson, Dave; Coyle, Fred

2015-07-01

Microhexura montivaga is a miniature tarantula-like spider endemic to the highest peaks of the southern Appalachian mountains and is known only from six allopatric, highly disjunct montane populations. Because of severe declines in spruce-fir forest in the late 20th century, M. montivaga was formally listed as a US federally endangered species in 1995. Using DNA sequence data from one mitochondrial and seven nuclear genes, patterns of multigenic genetic divergence were assessed for six montane populations. Independent mitochondrial and nuclear discovery analyses reveal obvious genetic fragmentation both within and among montane populations, with five to seven primary genetic lineages recovered. Multispecies coalescent validation analyses [guide tree and unguided Bayesian Phylogenetics and Phylogeography (BPP), Bayes factor delimitation (BFD)] using nuclear-only data congruently recover six or seven distinct lineages; BFD analyses using combined nuclear plus mitochondrial data favour seven or eight lineages. In stark contrast to this clear genetic fragmentation, a survey of secondary sexual features for available males indicates morphological conservatism across montane populations. While it is certainly possible that morphologically cryptic speciation has occurred in this taxon, this system may alternatively represent a case where extreme population genetic structuring (but not speciation) leads to an oversplitting of lineage diversity by multispecies coalescent methods. Our results have clear conservation implications for this federally endangered taxon and illustrate a methodological issue expected to become more common as genomic-scale data sets are gathered for taxa found in naturally fragmented habitats. © 2015 John Wiley & Sons Ltd.

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Ancient mitochondrial DNA provides high-resolution time scale of the peopling of the Americas.

PubMed

Llamas, Bastien; Fehren-Schmitz, Lars; Valverde, Guido; Soubrier, Julien; Mallick, Swapan; Rohland, Nadin; Nordenfelt, Susanne; Valdiosera, Cristina; Richards, Stephen M; Rohrlach, Adam; Romero, Maria Inés Barreto; Espinoza, Isabel Flores; Cagigao, Elsa Tomasto; Jiménez, Lucía Watson; Makowski, Krzysztof; Reyna, Ilán Santiago Leboreiro; Lory, Josefina Mansilla; Torrez, Julio Alejandro Ballivián; Rivera, Mario A; Burger, Richard L; Ceruti, Maria Constanza; Reinhard, Johan; Wells, R Spencer; Politis, Gustavo; Santoro, Calogero M; Standen, Vivien G; Smith, Colin; Reich, David; Ho, Simon Y W; Cooper, Alan; Haak, Wolfgang

2016-04-01

The exact timing, route, and process of the initial peopling of the Americas remains uncertain despite much research. Archaeological evidence indicates the presence of humans as far as southern Chile by 14.6 thousand years ago (ka), shortly after the Pleistocene ice sheets blocking access from eastern Beringia began to retreat. Genetic estimates of the timing and route of entry have been constrained by the lack of suitable calibration points and low genetic diversity of Native Americans. We sequenced 92 whole mitochondrial genomes from pre-Columbian South American skeletons dating from 8.6 to 0.5 ka, allowing a detailed, temporally calibrated reconstruction of the peopling of the Americas in a Bayesian coalescent analysis. The data suggest that a small population entered the Americas via a coastal route around 16.0 ka, following previous isolation in eastern Beringia for ~2.4 to 9 thousand years after separation from eastern Siberian populations. Following a rapid movement throughout the Americas, limited gene flow in South America resulted in a marked phylogeographic structure of populations, which persisted through time. All of the ancient mitochondrial lineages detected in this study were absent from modern data sets, suggesting a high extinction rate. To investigate this further, we applied a novel principal components multiple logistic regression test to Bayesian serial coalescent simulations. The analysis supported a scenario in which European colonization caused a substantial loss of pre-Columbian lineages.

Speciation at the Mogollon Rim in the Arizona Mountain Kingsnake (Lampropeltis pyromelana).

PubMed

Burbrink, Frank T; Yao, Helen; Ingrasci, Matthew; Bryson, Robert W; Guiher, Timothy J; Ruane, Sara

2011-09-01

Studies of speciation and taxon delimitation are usually decoupled. Combining these methods provides a stronger theoretical ground for recognizing new taxa and understanding processes of speciation. Using coalescent methods, we examine speciation, post-speciation population demographics, and taxon delimitation in the Arizona Mountain Kingsnake (Lampropeltis pyromelana), a species restricted to high elevations in southwestern United States and northern Mexico (SW). These methods provide a solid foundation for understanding how biogeographic barriers operate at the regional scale in the SW. Bayesian species delimitation methods, using three loci from samples of L. pyromelana taken throughout their range, show strong support for the existence of two species that are separated by low elevation habitats found between the Colorado Plateau/ Mogollon Rim and the Sierra Madre Occidental. Our results suggest an allopatric mode of speciation given the near absence of gene flow over time, which resulted in two lineages of unequal population sizes. Speciation likely occurred prior to the Pleistocene, during the aridification of the SW and/or the uplift of the Colorado Plateau, and while these species occupy similar high-elevation niches, they are isolated by xeric conditions found in the intervening low deserts. Furthermore, post-speciation demographics suggest that populations of both lineages were not negatively impacted by climate change throughout the Pleistocene. Finally, our results suggest that at least for this group, where divergence is old and gene flow is low, Bayesian species delimitation performs well. Copyright © 2011 Elsevier Inc. All rights reserved.

Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty

PubMed Central

Baele, Guy; Lemey, Philippe; Suchard, Marc A.

2016-01-01

Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian phylogenetic inference. Approaches to estimate marginal likelihoods have garnered increased attention over the past decade. In particular, the introduction of path sampling (PS) and stepping-stone sampling (SS) into Bayesian phylogenetics has tremendously improved the accuracy of model selection. These sampling techniques are now used to evaluate complex evolutionary and population genetic models on empirical data sets, but considerable computational demands hamper their widespread adoption. Further, when very diffuse, but proper priors are specified for model parameters, numerical issues complicate the exploration of the priors, a necessary step in marginal likelihood estimation using PS or SS. To avoid such instabilities, generalized SS (GSS) has recently been proposed, introducing the concept of “working distributions” to facilitate—or shorten—the integration process that underlies marginal likelihood estimation. However, the need to fix the tree topology currently limits GSS in a coalescent-based framework. Here, we extend GSS by relaxing the fixed underlying tree topology assumption. To this purpose, we introduce a “working” distribution on the space of genealogies, which enables estimating marginal likelihoods while accommodating phylogenetic uncertainty. We propose two different “working” distributions that help GSS to outperform PS and SS in terms of accuracy when comparing demographic and evolutionary models applied to synthetic data and real-world examples. Further, we show that the use of very diffuse priors can lead to a considerable overestimation in marginal likelihood when using PS and SS, while still retrieving the correct marginal likelihood using both GSS approaches. The methods used in this article are available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses. PMID:26526428

Indigenous Wildlife Rabies in Taiwan: Ferret Badgers, a Long Term Terrestrial Reservoir.

PubMed

Lan, Yu-Ching; Wen, Tzai-Hung; Chang, Chao-Chin; Liu, Hsin-Fu; Lee, Pei-Fen; Huang, Chung-Yuan; Chomel, Bruno B; Chen, Yi-Ming A

2017-01-01

The emerging disease of rabies was confirmed in Taiwan ferret badgers (FBs) and reported to the World Organization for Animal Health (OIE) on July 17, 2013. The spread of wildlife rabies can be related to neighborhood countries in Asia. The phylogenetic analysis was conducted by maximum likelihood (ML) methods and the Bayesian coalescent approach based on the glycoprotein (G) and nucleoprotein (N) genes. The phylogeographic and spatial temporal dynamics of viral transmission were determined by using SPREAD, QGIS. Therefore, the origin and the change with time of the viruses can be identified. Results showed the rabies virus of FB strains in Taiwan is a unique clade among other strains in Asia. According to the phylogeographic coalescent tree, three major genotypes of the FB rabies virus have circulated in three different geographical areas in Taiwan. Two genotypes have distributed into central and southern Taiwan between two ecological river barriers. The third genotype has been limited in southeastern Taiwan by the natural mountain barrier. The diversity of FB rabies viruses indicates that the biological profile of FBs could vary in different geographical areas in Taiwan. An enhanced surveillance system needs to be established near the currently identified natural barriers for early warnings of the rabies virus outbreak in Taiwan.

Indigenous Wildlife Rabies in Taiwan: Ferret Badgers, a Long Term Terrestrial Reservoir

PubMed Central

Wen, Tzai-Hung; Liu, Hsin-Fu; Lee, Pei-Fen; Chomel, Bruno B.

2017-01-01

The emerging disease of rabies was confirmed in Taiwan ferret badgers (FBs) and reported to the World Organization for Animal Health (OIE) on July 17, 2013. The spread of wildlife rabies can be related to neighborhood countries in Asia. The phylogenetic analysis was conducted by maximum likelihood (ML) methods and the Bayesian coalescent approach based on the glycoprotein (G) and nucleoprotein (N) genes. The phylogeographic and spatial temporal dynamics of viral transmission were determined by using SPREAD, QGIS. Therefore, the origin and the change with time of the viruses can be identified. Results showed the rabies virus of FB strains in Taiwan is a unique clade among other strains in Asia. According to the phylogeographic coalescent tree, three major genotypes of the FB rabies virus have circulated in three different geographical areas in Taiwan. Two genotypes have distributed into central and southern Taiwan between two ecological river barriers. The third genotype has been limited in southeastern Taiwan by the natural mountain barrier. The diversity of FB rabies viruses indicates that the biological profile of FBs could vary in different geographical areas in Taiwan. An enhanced surveillance system needs to be established near the currently identified natural barriers for early warnings of the rabies virus outbreak in Taiwan. PMID:28497055

Wright-Fisher diffusion bridges.

PubMed

Griffiths, Robert C; Jenkins, Paul A; Spanò, Dario

2017-10-06

The trajectory of the frequency of an allele which begins at x at time 0 and is known to have frequency z at time T can be modelled by the bridge process of the Wright-Fisher diffusion. Bridges when x=z=0 are particularly interesting because they model the trajectory of the frequency of an allele which appears at a time, then is lost by random drift or mutation after a time T. The coalescent genealogy back in time of a population in a neutral Wright-Fisher diffusion process is well understood. In this paper we obtain a new interpretation of the coalescent genealogy of the population in a bridge from a time t∈(0,T). In a bridge with allele frequencies of 0 at times 0 and T the coalescence structure is that the population coalesces in two directions from t to 0 and t to T such that there is just one lineage of the allele under consideration at times 0 and T. The genealogy in Wright-Fisher diffusion bridges with selection is more complex than in the neutral model, but still with the property of the population branching and coalescing in two directions from time t∈(0,T). The density of the frequency of an allele at time t is expressed in a way that shows coalescence in the two directions. A new algorithm for exact simulation of a neutral Wright-Fisher bridge is derived. This follows from knowing the density of the frequency in a bridge and exact simulation from the Wright-Fisher diffusion. The genealogy of the neutral Wright-Fisher bridge is also modelled by branching Pólya urns, extending a representation in a Wright-Fisher diffusion. This is a new very interesting representation that relates Wright-Fisher bridges to classical urn models in a Bayesian setting. Copyright © 2017 Elsevier Inc. All rights reserved.

Cross-Border Sexual Transmission of the Newly Emerging HIV-1 Clade CRF51_01B

PubMed Central

Cheong, Hui Ting; Ng, Kim Tien; Ong, Lai Yee; Chook, Jack Bee; Chan, Kok Gan; Takebe, Yutaka; Kamarulzaman, Adeeba; Tee, Kok Keng

2014-01-01

A novel HIV-1 recombinant clade (CRF51_01B) was recently identified among men who have sex with men (MSM) in Singapore. As cases of sexually transmitted HIV-1 infection increase concurrently in two socioeconomically intimate countries such as Malaysia and Singapore, cross transmission of HIV-1 between said countries is highly probable. In order to investigate the timeline for the emergence of HIV-1 CRF51_01B in Singapore and its possible introduction into Malaysia, 595 HIV-positive subjects recruited in Kuala Lumpur from 2008 to 2012 were screened. Phylogenetic relationship of 485 amplified polymerase gene sequences was determined through neighbour-joining method. Next, near-full length sequences were amplified for genomic sequences inferred to be CRF51_01B and subjected to further analysis implemented through Bayesian Markov chain Monte Carlo (MCMC) sampling and maximum likelihood methods. Based on the near full length genomes, two isolates formed a phylogenetic cluster with CRF51_01B sequences of Singapore origin, sharing identical recombination structure. Spatial and temporal information from Bayesian MCMC coalescent and maximum likelihood analysis of the protease, gp120 and gp41 genes suggest that Singapore is probably the country of origin of CRF51_01B (as early as in the mid-1990s) and featured a Malaysian who acquired the infection through heterosexual contact as host for its ancestral lineages. CRF51_01B then spread rapidly among the MSM in Singapore and Malaysia. Although the importation of CRF51_01B from Singapore to Malaysia is supported by coalescence analysis, the narrow timeframe of the transmission event indicates a closely linked epidemic. Discrepancies in the estimated divergence times suggest that CRF51_01B may have arisen through multiple recombination events from more than one parental lineage. We report the cross transmission of a novel CRF51_01B lineage between countries that involved different sexual risk groups. Understanding the cross-border transmission of HIV-1 involving sexual networks is crucial for effective intervention strategies in the region. PMID:25340817

Cross-border sexual transmission of the newly emerging HIV-1 clade CRF51_01B.

PubMed

Cheong, Hui Ting; Ng, Kim Tien; Ong, Lai Yee; Chook, Jack Bee; Chan, Kok Gan; Takebe, Yutaka; Kamarulzaman, Adeeba; Tee, Kok Keng

2014-01-01

A novel HIV-1 recombinant clade (CRF51_01B) was recently identified among men who have sex with men (MSM) in Singapore. As cases of sexually transmitted HIV-1 infection increase concurrently in two socioeconomically intimate countries such as Malaysia and Singapore, cross transmission of HIV-1 between said countries is highly probable. In order to investigate the timeline for the emergence of HIV-1 CRF51_01B in Singapore and its possible introduction into Malaysia, 595 HIV-positive subjects recruited in Kuala Lumpur from 2008 to 2012 were screened. Phylogenetic relationship of 485 amplified polymerase gene sequences was determined through neighbour-joining method. Next, near-full length sequences were amplified for genomic sequences inferred to be CRF51_01B and subjected to further analysis implemented through Bayesian Markov chain Monte Carlo (MCMC) sampling and maximum likelihood methods. Based on the near full length genomes, two isolates formed a phylogenetic cluster with CRF51_01B sequences of Singapore origin, sharing identical recombination structure. Spatial and temporal information from Bayesian MCMC coalescent and maximum likelihood analysis of the protease, gp120 and gp41 genes suggest that Singapore is probably the country of origin of CRF51_01B (as early as in the mid-1990s) and featured a Malaysian who acquired the infection through heterosexual contact as host for its ancestral lineages. CRF51_01B then spread rapidly among the MSM in Singapore and Malaysia. Although the importation of CRF51_01B from Singapore to Malaysia is supported by coalescence analysis, the narrow timeframe of the transmission event indicates a closely linked epidemic. Discrepancies in the estimated divergence times suggest that CRF51_01B may have arisen through multiple recombination events from more than one parental lineage. We report the cross transmission of a novel CRF51_01B lineage between countries that involved different sexual risk groups. Understanding the cross-border transmission of HIV-1 involving sexual networks is crucial for effective intervention strategies in the region.

Gene trees, species trees, and morphology converge on a similar phylogeny of living gars (Actinopterygii: Holostei: Lepisosteidae), an ancient clade of ray-finned fishes.

PubMed

Wright, Jeremy J; David, Solomon R; Near, Thomas J

2012-06-01

Extant gars represent the remaining members of a formerly diverse assemblage of ancient ray-finned fishes and have been the subject of multiple phylogenetic analyses using morphological data. Here, we present the first hypothesis of phylogenetic relationships among living gar species based on molecular data, through the examination of gene tree heterogeneity and coalescent species tree analyses of a portion of one mitochondrial (COI) and seven nuclear (ENC1, myh6, plagl2, S7 ribosomal protein intron 1, sreb2, tbr1, and zic1) genes. Individual gene trees displayed varying degrees of resolution with regards to species-level relationships, and the gene trees inferred from COI and the S7 intron were the only two that were completely resolved. Coalescent species tree analyses of nuclear genes resulted in a well-resolved and strongly supported phylogenetic tree of living gar species, for which Bayesian posterior node support was further improved by the inclusion of the mitochondrial gene. Species-level relationships among gars inferred from our molecular data set were highly congruent with previously published morphological phylogenies, with the exception of the placement of two species, Lepisosteus osseus and L. platostomus. Re-examination of the character coding used by previous authors provided partial resolution of this topological discordance, resulting in broad concordance in the phylogenies inferred from individual genes, the coalescent species tree analysis, and morphology. The completely resolved phylogeny inferred from the molecular data set with strong Bayesian posterior support at all nodes provided insights into the potential for introgressive hybridization and patterns of allopatric speciation in the evolutionary history of living gars, as well as a solid foundation for future examinations of functional diversification and evolutionary stasis in a "living fossil" lineage. Copyright © 2012 Elsevier Inc. All rights reserved.

An analysis of shock coalescence including three-dimensional effects with application to sonic boom extrapolation. Ph.D. Thesis - George Washington Univ.

NASA Technical Reports Server (NTRS)

Darden, C. M.

1984-01-01

A method for analyzing shock coalescence which includes three dimensional effects was developed. The method is based on an extension of the axisymmetric solution, with asymmetric effects introduced through an additional set of governing equations, derived by taking the second circumferential derivative of the standard shock equations in the plane of symmetry. The coalescence method is consistent with and has been combined with a nonlinear sonic boom extrapolation program which is based on the method of characteristics. The extrapolation program, is able to extrapolate pressure signatures which include embedded shocks from an initial data line in the plane of symmetry at approximately one body length from the axis of the aircraft to the ground. The axisymmetric shock coalescence solution, the asymmetric shock coalescence solution, the method of incorporating these solutions into the extrapolation program, and the methods used to determine spatial derivatives needed in the coalescence solution are described. Results of the method are shown for a body of revolution at a small, positive angle of attack.

Assessment of partial coalescence in whippable oil-in-water food emulsions.

PubMed

Petrut, Raul Flaviu; Danthine, Sabine; Blecker, Christophe

2016-03-01

Partial coalescence influences to a great extent the properties of final food products such as ice cream and whipped toppings. In return, the partial coalescence occurrence and development are conditioned, in such systems, by the emulsion's intrinsic properties (e.g. solid fat content, fat crystal shape and size), formulation (e.g. protein content, surfactants presence) and extrinsic factors (e.g. cooling rate, shearing). A set of methods is available for partial coalescence investigation and quantification. These methods are critically reviewed in this paper, balancing the weaknesses of the methods in terms of structure alteration (for turbidity, dye dilution, etc.) and assumptions made for mathematical models (for particle size determination) with their advantages (good repeatability, high sensitivity, etc.). With the methods proposed in literature, the partial coalescence investigations can be conducted quantitatively and/or qualitatively. Good correlation were observed between some of the quantitative methods such as dye dilution, calorimetry, fat particle size; while a poor correlation was found in the case of solvent extraction method with other quantitative methods. The most suitable way for partial coalescence quantification was implied to be the fat particle size method, which would give results with a high degree of confidence if used in combination with a microscopic technique for the confirmation of partial coalescence as the main destabilization mechanism. Copyright © 2015 Elsevier B.V. All rights reserved.

Ancient mitochondrial DNA provides high-resolution time scale of the peopling of the Americas

PubMed Central

Llamas, Bastien; Fehren-Schmitz, Lars; Valverde, Guido; Soubrier, Julien; Mallick, Swapan; Rohland, Nadin; Nordenfelt, Susanne; Valdiosera, Cristina; Richards, Stephen M.; Rohrlach, Adam; Romero, Maria Inés Barreto; Espinoza, Isabel Flores; Cagigao, Elsa Tomasto; Jiménez, Lucía Watson; Makowski, Krzysztof; Reyna, Ilán Santiago Leboreiro; Lory, Josefina Mansilla; Torrez, Julio Alejandro Ballivián; Rivera, Mario A.; Burger, Richard L.; Ceruti, Maria Constanza; Reinhard, Johan; Wells, R. Spencer; Politis, Gustavo; Santoro, Calogero M.; Standen, Vivien G.; Smith, Colin; Reich, David; Ho, Simon Y. W.; Cooper, Alan; Haak, Wolfgang

2016-01-01

The exact timing, route, and process of the initial peopling of the Americas remains uncertain despite much research. Archaeological evidence indicates the presence of humans as far as southern Chile by 14.6 thousand years ago (ka), shortly after the Pleistocene ice sheets blocking access from eastern Beringia began to retreat. Genetic estimates of the timing and route of entry have been constrained by the lack of suitable calibration points and low genetic diversity of Native Americans. We sequenced 92 whole mitochondrial genomes from pre-Columbian South American skeletons dating from 8.6 to 0.5 ka, allowing a detailed, temporally calibrated reconstruction of the peopling of the Americas in a Bayesian coalescent analysis. The data suggest that a small population entered the Americas via a coastal route around 16.0 ka, following previous isolation in eastern Beringia for ~2.4 to 9 thousand years after separation from eastern Siberian populations. Following a rapid movement throughout the Americas, limited gene flow in South America resulted in a marked phylogeographic structure of populations, which persisted through time. All of the ancient mitochondrial lineages detected in this study were absent from modern data sets, suggesting a high extinction rate. To investigate this further, we applied a novel principal components multiple logistic regression test to Bayesian serial coalescent simulations. The analysis supported a scenario in which European colonization caused a substantial loss of pre-Columbian lineages. PMID:27051878

Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets.

PubMed

Springer, Mark S; Gatesy, John

2018-02-26

coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset-the 'recombination ratchet'-is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d'etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful enough to infer the correct species tree for difficult phylogenetic problems in the anomaly zone, where concatenation is expected to fail because of ILS, then there should be a decreasing probability of inferring the correct species tree using longer loci with many intralocus recombination breakpoints (i.e., increased levels of concatenation).

Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets

PubMed Central

Springer, Mark S.; Gatesy, John

2018-01-01

Summary coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset—the ‘recombination ratchet’—is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d’etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful enough to infer the correct species tree for difficult phylogenetic problems in the anomaly zone, where concatenation is expected to fail because of ILS, then there should be a decreasing probability of inferring the correct species tree using longer loci with many intralocus recombination breakpoints (i.e., increased levels of concatenation). PMID:29495400

Species trees for the tree swallows (Genus Tachycineta): an alternative phylogenetic hypothesis to the mitochondrial gene tree.

PubMed

Dor, Roi; Carling, Matthew D; Lovette, Irby J; Sheldon, Frederick H; Winkler, David W

2012-10-01

The New World swallow genus Tachycineta comprises nine species that collectively have a wide geographic distribution and remarkable variation both within- and among-species in ecologically important traits. Existing phylogenetic hypotheses for Tachycineta are based on mitochondrial DNA sequences, thus they provide estimates of a single gene tree. In this study we sequenced multiple individuals from each species at 16 nuclear intron loci. We used gene concatenated approaches (Bayesian and maximum likelihood) as well as coalescent-based species tree inference to reconstruct phylogenetic relationships of the genus. We examined the concordance and conflict between the nuclear and mitochondrial trees and between concatenated and coalescent-based inferences. Our results provide an alternative phylogenetic hypothesis to the existing mitochondrial DNA estimate of phylogeny. This new hypothesis provides a more accurate framework in which to explore trait evolution and examine the evolution of the mitochondrial genome in this group. Copyright © 2012 Elsevier Inc. All rights reserved.

Application of parametric equations of motion to study the resonance coalescence in H2(+).

PubMed

Kalita, Dhruba J; Gupta, Ashish K

2012-12-07

Recently, occurrence of coalescence point was reported in H(2)(+) undergoing multiphoton dissociation in strong laser field. We have applied parametric equations of motion and smooth exterior scaling method to study the coalescence phenomenon of H(2)(+). The advantage of this method is that one can easily trace the different states that are changing as the field parameters change. It was reported earlier that in the parameter space, only two bound states coalesce [R. Lefebvre, O. Atabek, M. Sindelka, and N. Moiseyev, Phys. Rev. Lett. 103, 123003 (2009)]. However, it is found that increasing the accuracy of the calculation leads to the coalescence between resonance states originating from the bound and the continuum states. We have also reported many other coalescence points.

"Contrasting patterns of selection at Pinus pinaster Ait. Drought stress candidate genes as revealed by genetic differentiation analyses".

PubMed

Eveno, Emmanuelle; Collada, Carmen; Guevara, M Angeles; Léger, Valérie; Soto, Alvaro; Díaz, Luis; Léger, Patrick; González-Martínez, Santiago C; Cervera, M Teresa; Plomion, Christophe; Garnier-Géré, Pauline H

2008-02-01

The importance of natural selection for shaping adaptive trait differentiation among natural populations of allogamous tree species has long been recognized. Determining the molecular basis of local adaptation remains largely unresolved, and the respective roles of selection and demography in shaping population structure are actively debated. Using a multilocus scan that aims to detect outliers from simulated neutral expectations, we analyzed patterns of nucleotide diversity and genetic differentiation at 11 polymorphic candidate genes for drought stress tolerance in phenotypically contrasted Pinus pinaster Ait. populations across its geographical range. We compared 3 coalescent-based methods: 2 frequentist-like, including 1 approach specifically developed for biallelic single nucleotide polymorphisms (SNPs) here and 1 Bayesian. Five genes showed outlier patterns that were robust across methods at the haplotype level for 2 of them. Two genes presented higher F(ST) values than expected (PR-AGP4 and erd3), suggesting that they could have been affected by the action of diversifying selection among populations. In contrast, 3 genes presented lower F(ST) values than expected (dhn-1, dhn2, and lp3-1), which could represent signatures of homogenizing selection among populations. A smaller proportion of outliers were detected at the SNP level suggesting the potential functional significance of particular combinations of sites in drought-response candidate genes. The Bayesian method appeared robust to low sample sizes, flexible to assumptions regarding migration rates, and powerful for detecting selection at the haplotype level, but the frequentist-like method adapted to SNPs was more efficient for the identification of outlier SNPs showing low differentiation. Population-specific effects estimated in the Bayesian method also revealed populations with lower immigration rates, which could have led to favorable situations for local adaptation. Outlier patterns are discussed in relation to the different genes' putative involvement in drought tolerance responses, from published results in transcriptomics and association mapping in P. pinaster and other related species. These genes clearly constitute relevant candidates for future association studies in P. pinaster.

Gravitational waves: search results, data analysis and parameter estimation: Amaldi 10 Parallel session C2.

PubMed

Astone, Pia; Weinstein, Alan; Agathos, Michalis; Bejger, Michał; Christensen, Nelson; Dent, Thomas; Graff, Philip; Klimenko, Sergey; Mazzolo, Giulio; Nishizawa, Atsushi; Robinet, Florent; Schmidt, Patricia; Smith, Rory; Veitch, John; Wade, Madeline; Aoudia, Sofiane; Bose, Sukanta; Calderon Bustillo, Juan; Canizares, Priscilla; Capano, Colin; Clark, James; Colla, Alberto; Cuoco, Elena; Da Silva Costa, Carlos; Dal Canton, Tito; Evangelista, Edgar; Goetz, Evan; Gupta, Anuradha; Hannam, Mark; Keitel, David; Lackey, Benjamin; Logue, Joshua; Mohapatra, Satyanarayan; Piergiovanni, Francesco; Privitera, Stephen; Prix, Reinhard; Pürrer, Michael; Re, Virginia; Serafinelli, Roberto; Wade, Leslie; Wen, Linqing; Wette, Karl; Whelan, John; Palomba, C; Prodi, G

The Amaldi 10 Parallel Session C2 on gravitational wave (GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity.

Gravitational Waves: Search Results, Data Analysis and Parameter Estimation. Amaldi 10 Parallel Session C2

NASA Technical Reports Server (NTRS)

Astone, Pia; Weinstein, Alan; Agathos, Michalis; Bejger, Michal; Christensen, Nelson; Dent, Thomas; Graff, Philip; Klimenko, Sergey; Mazzolo, Giulio; Nishizawa, Atsushi

2015-01-01

The Amaldi 10 Parallel Session C2 on gravitational wave(GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity.

A possible geographic origin of endemic hepatitis C virus 6a in Hong Kong: evidences for the association with Vietnamese immigration.

PubMed

Zhou, Xiaoming; Chan, Paul K S; Tam, John S; Tang, Julian W

2011-01-01

Hepatitis C virus (HCV) 6a accounts for 23.6% of all HCV infections of the general population and 58.5% of intravenous drug users in Hong Kong. However, the geographical origin of this highly predominant HCV subgenotype is largely unknown. This study explores a hypothesis for one possible transmission route of HCV 6a to Hong Kong. NS5A sequences derived from 26 HCV 6a samples were chosen from a five year period (1999-2004) from epidemiologically unrelated patients from Hong Kong. Partial-NS5A sequences (513-bp from nt 6728 to 7240) were adopted for Bayesian coalescent analysis to reconstruct the evolutionary history of HCV infections in Hong Kong using the BEAST v1.3 program. A rooted phylogenetic tree was drawn for these sequences by alignment with reference Vietnamese sequences. Demographic data were accessed from "The Statistic Yearbooks of Hong Kong". Bayesian coalescent analysis showed that the rapid increase in 6a infections, which had increased more than 90-fold in Hong Kong from 1986 to 1994 correlated to two peaks of Vietnamese immigration to Hong Kong from 1978 to 1997. The second peak, which occurred from 1987 through 1997, overlapped with the rapid increase of HCV 6a occurrence in Hong Kong. Phylogenetic analyses have further revealed that HCV 6a strains from Vietnam may be ancestral to Hong Kong counterparts. The high predominance of HCV 6a infections in Hong Kong was possibly associated with Vietnamese immigration during 1987-1997.

On incomplete sampling under birth-death models and connections to the sampling-based coalescent.

PubMed

Stadler, Tanja

2009-11-07

The constant rate birth-death process is used as a stochastic model for many biological systems, for example phylogenies or disease transmission. As the biological data are usually not fully available, it is crucial to understand the effect of incomplete sampling. In this paper, we analyze the constant rate birth-death process with incomplete sampling. We derive the density of the bifurcation events for trees on n leaves which evolved under this birth-death-sampling process. This density is used for calculating prior distributions in Bayesian inference programs and for efficiently simulating trees. We show that the birth-death-sampling process can be interpreted as a birth-death process with reduced rates and complete sampling. This shows that joint inference of birth rate, death rate and sampling probability is not possible. The birth-death-sampling process is compared to the sampling-based population genetics model, the coalescent. It is shown that despite many similarities between these two models, the distribution of bifurcation times remains different even in the case of very large population sizes. We illustrate these findings on an Hepatitis C virus dataset from Egypt. We show that the transmission times estimates are significantly different-the widely used Gamma statistic even changes its sign from negative to positive when switching from the coalescent to the birth-death process.

Genes with minimal phylogenetic information are problematic for coalescent analyses when gene tree estimation is biased.

PubMed

Xi, Zhenxiang; Liu, Liang; Davis, Charles C

2015-11-01

The development and application of coalescent methods are undergoing rapid changes. One little explored area that bears on the application of gene-tree-based coalescent methods to species tree estimation is gene informativeness. Here, we investigate the accuracy of these coalescent methods when genes have minimal phylogenetic information, including the implementation of the multilocus bootstrap approach. Using simulated DNA sequences, we demonstrate that genes with minimal phylogenetic information can produce unreliable gene trees (i.e., high error in gene tree estimation), which may in turn reduce the accuracy of species tree estimation using gene-tree-based coalescent methods. We demonstrate that this problem can be alleviated by sampling more genes, as is commonly done in large-scale phylogenomic analyses. This applies even when these genes are minimally informative. If gene tree estimation is biased, however, gene-tree-based coalescent analyses will produce inconsistent results, which cannot be remedied by increasing the number of genes. In this case, it is not the gene-tree-based coalescent methods that are flawed, but rather the input data (i.e., estimated gene trees). Along these lines, the commonly used program PhyML has a tendency to infer one particular bifurcating topology even though it is best represented as a polytomy. We additionally corroborate these findings by analyzing the 183-locus mammal data set assembled by McCormack et al. (2012) using ultra-conserved elements (UCEs) and flanking DNA. Lastly, we demonstrate that when employing the multilocus bootstrap approach on this 183-locus data set, there is no strong conflict between species trees estimated from concatenation and gene-tree-based coalescent analyses, as has been previously suggested by Gatesy and Springer (2014). Copyright © 2015 Elsevier Inc. All rights reserved.

Inference of Epidemiological Dynamics Based on Simulated Phylogenies Using Birth-Death and Coalescent Models

PubMed Central

Boskova, Veronika; Bonhoeffer, Sebastian; Stadler, Tanja

2014-01-01

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2–13% vs. 31–75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population. PMID:25375100

Tracing the spatio-temporal dynamics of endangered fin whales (Balaenoptera physalus) within baleen whale (Mysticeti) lineages: a mitogenomic perspective.

PubMed

Yu, Jihyun; Nam, Bo-Hye; Yoon, Joon; Kim, Eun Bae; Park, Jung Youn; Kim, Heebal; Yoon, Sook Hee

2017-12-01

To explore the spatio-temporal dynamics of endangered fin whales (Balaenoptera physalus) within the baleen whale (Mysticeti) lineages, we analyzed 148 published mitochondrial genome sequences of baleen whales. We used a Bayesian coalescent approach as well as Bayesian inferences and maximum likelihood methods. The results showed that the fin whales had a single maternal origin, and that there is a significant correlation between geographic location and evolution of global fin whales. The most recent common female ancestor of this species lived approximately 9.88 million years ago (Mya). Here, North Pacific fin whales first appeared about 7.48 Mya, followed by a subsequent divergence in Southern Hemisphere approximately 6.63 Mya and North Atlantic about 4.42 Mya. Relatively recently, approximately 1.76 and 1.42 Mya, there were two additional occurrences of North Pacific populations; one originated from the Southern Hemisphere and the other from an uncertain location. The evolutionary rate of this species was 1.002 × 10 -3 substitutions/site/My. Our Bayesian skyline plot illustrates that the fin whale population has the rapid expansion event since ~ 2.5 Mya, during the Quaternary glaciation stage. Additionally, this study indicates that the fin whale has a sister group relationship with humpback whale (Meganoptera novaeangliae) within the baleen whale lineages. Of the 16 genomic regions, NADH5 showed the most powerful signal for baleen whale phylogenetics. Interestingly, fin whales have 16 species-specific amino acid residues in eight mitochondrial genes: NADH2, COX2, COX3, ATPase6, ATPase8, NADH4, NADH5, and Cytb.

Inferences about binary stellar populations using gravitational wave observations

NASA Astrophysics Data System (ADS)

Wysocki, Daniel; Gerosa, Davide; O'Shaughnessy, Richard; Belczynski, Krzysztof; Gladysz, Wojciech; Berti, Emanuele; Kesden, Michael; Holz, Daniel

2018-01-01

With the dawn of gravitational wave astronomy, enabled by the LIGO and Virgo interferometers, we now have a new window into the Universe. In the short time these detectors have been in use, multiple confirmed detections of gravitational waves from compact binary coalescences have been made. Stellar binary systems are one of the likely progenitors of the observed compact binary sources. If this is indeed the case, then we can use measured properties of these binary systems to learn about their progenitors. We will discuss the Bayesian framework in which we make these inferences, and results which include mass and spin distributions.

Effective population size dynamics reveal impacts of historic climatic events and recent anthropogenic pressure in African elephants.

PubMed

Okello, J B A; Wittemyer, G; Rasmussen, H B; Arctander, P; Nyakaana, S; Douglas-Hamilton, I; Siegismund, H R

2008-09-01

Two hundred years of elephant hunting for ivory, peaking in 1970-1980s, caused local extirpations and massive population declines across Africa. The resulting genetic impacts on surviving populations have not been studied, despite the importance of understanding the evolutionary repercussions of such human-mediated events on this keystone species. Using Bayesian coalescent-based genetic methods to evaluate time-specific changes in effective population size, we analysed genetic variation in 20 highly polymorphic microsatellite loci from 400 elephants inhabiting the greater Samburu-Laikipia region of northern Kenya. This area experienced a decline of between 80% and 90% in the last few decades when ivory harvesting was rampant. The most significant change in effective population size, however, occurred approximately 2500 years ago during a mid-Holocene period of climatic drying in tropical Africa. Contrary to expectations, detailed analyses of four contemporary age-based cohorts showed that the peak poaching epidemic in the 1970s caused detectable temporary genetic impacts, with genetic diversity rebounding as juveniles surviving the poaching era became reproductively mature. This study demonstrates the importance of climatic history in shaping the distribution and genetic history of a keystone species and highlights the utility of coalescent-based demographic approaches in unravelling ancestral demographic events despite a lack of ancient samples. Unique insights into the genetic signature of mid-Holocene climatic change in Africa and effects of recent poaching pressure on elephants are discussed.

Viscosity Measurement Using Drop Coalescence in Microgravity

NASA Technical Reports Server (NTRS)

Antar, Basil N.; Ethridge, Edwin C.; Maxwell, Daniel; Curreri, Peter A. (Technical Monitor)

2002-01-01

We present in here validation studies of a new method for application in microgravity environment which measures the viscosity of highly viscous undercooled liquids using drop coalescence. The method has the advantage of avoiding heterogeneous nucleation at container walls caused by crystallization of undercooled liquids during processing. Homogeneous nucleation can also be avoided due to the rapidity of the measurement using this method. The technique relies on measurements from experiments conducted in near zero gravity environment as well as highly accurate analytical formulation for the coalescence process. The viscosity of the liquid is determined by allowing the computed free surface shape relaxation time to be adjusted in response to the measured free surface velocity for two coalescing drops. Results are presented from two sets of validation experiments for the method which were conducted on board aircraft flying parabolic trajectories. In these tests the viscosity of a highly viscous liquid, namely glycerin, was determined at different temperatures using the drop coalescence method described in here. The experiments measured the free surface velocity of two glycerin drops coalescing under the action of surface tension alone in low gravity environment using high speed photography. The liquid viscosity was determined by adjusting the computed free surface velocity values to the measured experimental data. The results of these experiments were found to agree reasonably well with the known viscosity for the test liquid used.

Mechanism and simulation of droplet coalescence in molten steel

NASA Astrophysics Data System (ADS)

Ni, Bing; Zhang, Tao; Ni, Hai-qi; Luo, Zhi-guo

2017-11-01

Droplet coalescence in liquid steel was carefully investigated through observations of the distribution pattern of inclusions in solidified steel samples. The process of droplet coalescence was slow, and the critical Weber number ( We) was used to evaluate the coalescence or separation of droplets. The relationship between the collision parameter and the critical We indicated whether slow coalescence or bouncing of droplets occurred. The critical We was 5.5, which means that the droplets gradually coalesce when We ≤ 5.5, whereas they bounce when We > 5.5. For the carbonate wire feeding into liquid steel, a mathematical model implementing a combined computational fluid dynamics (CFD)-discrete element method (DEM) approach was developed to simulate the movement and coalescence of variably sized droplets in a bottom-argon-blowing ladle. In the CFD model, the flow field was solved on the premise that the fluid was a continuous medium. Meanwhile, the droplets were dispersed in the DEM model, and the coalescence criterion of the particles was added to simulate the collision- coalescence process of the particles. The numerical simulation results and observations of inclusion coalescence in steel samples are consistent.

Gene tree rooting methods give distributions that mimic the coalescent process.

PubMed

Tian, Yuan; Kubatko, Laura S

2014-01-01

Multi-locus phylogenetic inference is commonly carried out via models that incorporate the coalescent process to model the possibility that incomplete lineage sorting leads to incongruence between gene trees and the species tree. An interesting question that arises in this context is whether data "fit" the coalescent model. Previous work (Rosenfeld et al., 2012) has suggested that rooting of gene trees may account for variation in empirical data that has been previously attributed to the coalescent process. We examine this possibility using simulated data. We show that, in the case of four taxa, the distribution of gene trees observed from rooting estimated gene trees with either the molecular clock or with outgroup rooting can be closely matched by the distribution predicted by the coalescent model with specific choices of species tree branch lengths. We apply commonly-used coalescent-based methods of species tree inference to assess their performance in these situations. Copyright © 2013 Elsevier Inc. All rights reserved.

Minimal-assumption inference from population-genomic data

NASA Astrophysics Data System (ADS)

Weissman, Daniel; Hallatschek, Oskar

Samples of multiple complete genome sequences contain vast amounts of information about the evolutionary history of populations, much of it in the associations among polymorphisms at different loci. Current methods that take advantage of this linkage information rely on models of recombination and coalescence, limiting the sample sizes and populations that they can analyze. We introduce a method, Minimal-Assumption Genomic Inference of Coalescence (MAGIC), that reconstructs key features of the evolutionary history, including the distribution of coalescence times, by integrating information across genomic length scales without using an explicit model of recombination, demography or selection. Using simulated data, we show that MAGIC's performance is comparable to PSMC' on single diploid samples generated with standard coalescent and recombination models. More importantly, MAGIC can also analyze arbitrarily large samples and is robust to changes in the coalescent and recombination processes. Using MAGIC, we show that the inferred coalescence time histories of samples of multiple human genomes exhibit inconsistencies with a description in terms of an effective population size based on single-genome data.

Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data.

PubMed Central

Drummond, Alexei J; Nicholls, Geoff K; Rodrigo, Allen G; Solomon, Wiremu

2002-01-01

Molecular sequences obtained at different sampling times from populations of rapidly evolving pathogens and from ancient subfossil and fossil sources are increasingly available with modern sequencing technology. Here, we present a Bayesian statistical inference approach to the joint estimation of mutation rate and population size that incorporates the uncertainty in the genealogy of such temporally spaced sequences by using Markov chain Monte Carlo (MCMC) integration. The Kingman coalescent model is used to describe the time structure of the ancestral tree. We recover information about the unknown true ancestral coalescent tree, population size, and the overall mutation rate from temporally spaced data, that is, from nucleotide sequences gathered at different times, from different individuals, in an evolving haploid population. We briefly discuss the methodological implications and show what can be inferred, in various practically relevant states of prior knowledge. We develop extensions for exponentially growing population size and joint estimation of substitution model parameters. We illustrate some of the important features of this approach on a genealogy of HIV-1 envelope (env) partial sequences. PMID:12136032

Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data.

PubMed

Drummond, Alexei J; Nicholls, Geoff K; Rodrigo, Allen G; Solomon, Wiremu

2002-07-01

Molecular sequences obtained at different sampling times from populations of rapidly evolving pathogens and from ancient subfossil and fossil sources are increasingly available with modern sequencing technology. Here, we present a Bayesian statistical inference approach to the joint estimation of mutation rate and population size that incorporates the uncertainty in the genealogy of such temporally spaced sequences by using Markov chain Monte Carlo (MCMC) integration. The Kingman coalescent model is used to describe the time structure of the ancestral tree. We recover information about the unknown true ancestral coalescent tree, population size, and the overall mutation rate from temporally spaced data, that is, from nucleotide sequences gathered at different times, from different individuals, in an evolving haploid population. We briefly discuss the methodological implications and show what can be inferred, in various practically relevant states of prior knowledge. We develop extensions for exponentially growing population size and joint estimation of substitution model parameters. We illustrate some of the important features of this approach on a genealogy of HIV-1 envelope (env) partial sequences.

Prevention of nanoparticle coalescence under high-temperature annealing.

PubMed

Mizuno, Mikihisa; Sasaki, Yuichi; Yu, Andrew C C; Inoue, Makoto

2004-12-21

An effective method of employing 3-aminopropyldimethylethoxysilane linker molecules to stabilize 4.4 nm FePt nanoparticle monolayer films on a SiO2 substrate as well as to prevent coalescence of the particles under 800 degrees C annealing is reported. As-deposited FePt nanoparticle films in chemically disordered face-centered-cubic phase transform to mostly chemically ordered L1 0 structure after annealing, while the nanoparticles are free from serious coalescence. The method may fulfill the pressing need to prevent nanoparticle coalescence under high-temperature annealing for the development of FePt nanoparticle based products, such as ultrahigh-density magnetic recording media and novel memory devices.

A lattice Boltzmann simulation of coalescence-induced droplet jumping on superhydrophobic surfaces with randomly distributed structures

NASA Astrophysics Data System (ADS)

Zhang, Li-Zhi; Yuan, Wu-Zhi

2018-04-01

The motion of coalescence-induced condensate droplets on superhydrophobic surface (SHS) has attracted increasing attention in energy-related applications. Previous researches were focused on regularly rough surfaces. Here a new approach, a mesoscale lattice Boltzmann method (LBM), is proposed and used to model the dynamic behavior of coalescence-induced droplet jumping on SHS with randomly distributed rough structures. A Fast Fourier Transformation (FFT) method is used to generate non-Gaussian randomly distributed rough surfaces with the skewness (Sk), kurtosis (K) and root mean square (Rq) obtained from real surfaces. Three typical spreading states of coalesced droplets are observed through LBM modeling on various rough surfaces, which are found to significantly influence the jumping ability of coalesced droplet. The coalesced droplets spreading in Cassie state or in composite state will jump off the rough surfaces, while the ones spreading in Wenzel state would eventually remain on the rough surfaces. It is demonstrated that the rough surfaces with smaller Sks, larger Rqs and a K at 3.0 are beneficial to coalescence-induced droplet jumping. The new approach gives more detailed insights into the design of SHS.

Viscosity Measurement of Highly Viscous Liquids Using Drop Coalescence in Low Gravity

NASA Technical Reports Server (NTRS)

Antar, Basil N.; Ethridge, Edwin; Maxwell, Daniel

1999-01-01

The method of drop coalescence is being investigated for use as a method for determining the viscosity of highly viscous undercooled liquids. Low gravity environment is necessary in this case to minimize the undesirable effects of body forces and liquid motion in levitated drops. Also, the low gravity environment will allow for investigating large liquid volumes which can lead to much higher accuracy for the viscosity calculations than possible under 1 - g conditions. The drop coalescence method is preferred over the drop oscillation technique since the latter method can only be applied for liquids with vanishingly small viscosities. The technique developed relies on both the highly accurate solution of the Navier-Stokes equations as well as on data from experiments conducted in near zero gravity environment. In the analytical aspect of the method two liquid volumes are brought into contact which will coalesce under the action of surface tension alone. The free surface geometry development as well as its velocity during coalescence which are obtained from numerical computations are compared with an analogous experimental model. The viscosity in the numerical computations is then adjusted to bring into agreement of the experimental results with the calculations. The true liquid viscosity is the one which brings the experiment closest to the calculations. Results are presented for method validation experiments performed recently on board the NASA/KC-135 aircraft. The numerical solution for this validation case was produced using the Boundary Element Method. In these tests the viscosity of a highly viscous liquid, in this case glycerine at room temperature, was determined to high degree of accuracy using the liquid coalescence method. These experiments gave very encouraging results which will be discussed together with plans for implementing the method in a shuttle flight experiment.

Demographic history and gene flow during silkworm domestication

PubMed Central

2014-01-01

Background Gene flow plays an important role in domestication history of domesticated species. However, little is known about the demographic history of domesticated silkworm involving gene flow with its wild relative. Results In this study, four model-based evolutionary scenarios to describe the demographic history of B. mori were hypothesized. Using Approximate Bayesian Computation method and DNA sequence data from 29 nuclear loci, we found that the gene flow at bottleneck model is the most likely scenario for silkworm domestication. The starting time of silkworm domestication was estimated to be approximate 7,500 years ago; the time of domestication termination was 3,984 years ago. Using coalescent simulation analysis, we also found that bi-directional gene flow occurred during silkworm domestication. Conclusions Estimates of silkworm domestication time are nearly consistent with the archeological evidence and our previous results. Importantly, we found that the bi-directional gene flow might occur during silkworm domestication. Our findings add a dimension to highlight the important role of gene flow in domestication of crops and animals. PMID:25123546

Simulation on Thermocapillary-Driven Drop Coalescence by Hybrid Lattice Boltzmann Method

NASA Astrophysics Data System (ADS)

Xie, Haiqiong; Zeng, Zhong; Zhang, Liangqi; Yokota, Yuui; Kawazoe, Yoshiyuki; Yoshikawa, Akira

2016-04-01

A hybrid two-phase model, incorporating lattice Boltzmann method (LBM) and finite difference method (FDM), was developed to investigate the coalescence of two drops during their thermocapillary migration. The lattice Boltzmann method with a multi-relaxation-time (MRT) collision model was applied to solve the flow field for incompressible binary fluids, and the method was implemented in an axisymmetric form. The deformation of the drop interface was captured with the phase-field theory, and the continuum surface force model (CSF) was adopted to introduce the surface tension, which depends on the temperature. Both phase-field equation and the energy equation were solved with the finite difference method. The effects of Marangoni number and Capillary numbers on the drop's motion and coalescence were investigated.

EggLib: processing, analysis and simulation tools for population genetics and genomics

PubMed Central

2012-01-01

Background With the considerable growth of available nucleotide sequence data over the last decade, integrated and flexible analytical tools have become a necessity. In particular, in the field of population genetics, there is a strong need for automated and reliable procedures to conduct repeatable and rapid polymorphism analyses, coalescent simulations, data manipulation and estimation of demographic parameters under a variety of scenarios. Results In this context, we present EggLib (Evolutionary Genetics and Genomics Library), a flexible and powerful C++/Python software package providing efficient and easy to use computational tools for sequence data management and extensive population genetic analyses on nucleotide sequence data. EggLib is a multifaceted project involving several integrated modules: an underlying computationally efficient C++ library (which can be used independently in pure C++ applications); two C++ programs; a Python package providing, among other features, a high level Python interface to the C++ library; and the egglib script which provides direct access to pre-programmed Python applications. Conclusions EggLib has been designed aiming to be both efficient and easy to use. A wide array of methods are implemented, including file format conversion, sequence alignment edition, coalescent simulations, neutrality tests and estimation of demographic parameters by Approximate Bayesian Computation (ABC). Classes implementing different demographic scenarios for ABC analyses can easily be developed by the user and included to the package. EggLib source code is distributed freely under the GNU General Public License (GPL) from its website http://egglib.sourceforge.net/ where a full documentation and a manual can also be found and downloaded. PMID:22494792

A three-stage colonization model for the peopling of the Americas.

PubMed

Kitchen, Andrew; Miyamoto, Michael M; Mulligan, Connie J

2008-02-13

We evaluate the process by which the Americas were originally colonized and propose a three-stage model that integrates current genetic, archaeological, geological, and paleoecological data. Specifically, we analyze mitochondrial and nuclear genetic data by using complementary coalescent models of demographic history and incorporating non-genetic data to enhance the anthropological relevance of the analysis. Bayesian skyline plots, which provide dynamic representations of population size changes over time, indicate that Amerinds went through two stages of growth approximately 40,000 and approximately 15,000 years ago separated by a long period of population stability. Isolation-with-migration coalescent analyses, which utilize data from sister populations to estimate a divergence date and founder population sizes, suggest an Amerind population expansion starting approximately 15,000 years ago. These results support a model for the peopling of the New World in which Amerind ancestors diverged from the Asian gene pool prior to 40,000 years ago and experienced a gradual population expansion as they moved into Beringia. After a long period of little change in population size in greater Beringia, Amerinds rapidly expanded into the Americas approximately 15,000 years ago either through an interior ice-free corridor or along the coast. This rapid colonization of the New World was achieved by a founder group with an effective population size of approximately 1,000-5,400 individuals. Our model presents a detailed scenario for the timing and scale of the initial migration to the Americas, substantially refines the estimate of New World founders, and provides a unified theory for testing with future datasets and analytic methods.

EggLib: processing, analysis and simulation tools for population genetics and genomics.

PubMed

De Mita, Stéphane; Siol, Mathieu

2012-04-11

With the considerable growth of available nucleotide sequence data over the last decade, integrated and flexible analytical tools have become a necessity. In particular, in the field of population genetics, there is a strong need for automated and reliable procedures to conduct repeatable and rapid polymorphism analyses, coalescent simulations, data manipulation and estimation of demographic parameters under a variety of scenarios. In this context, we present EggLib (Evolutionary Genetics and Genomics Library), a flexible and powerful C++/Python software package providing efficient and easy to use computational tools for sequence data management and extensive population genetic analyses on nucleotide sequence data. EggLib is a multifaceted project involving several integrated modules: an underlying computationally efficient C++ library (which can be used independently in pure C++ applications); two C++ programs; a Python package providing, among other features, a high level Python interface to the C++ library; and the egglib script which provides direct access to pre-programmed Python applications. EggLib has been designed aiming to be both efficient and easy to use. A wide array of methods are implemented, including file format conversion, sequence alignment edition, coalescent simulations, neutrality tests and estimation of demographic parameters by Approximate Bayesian Computation (ABC). Classes implementing different demographic scenarios for ABC analyses can easily be developed by the user and included to the package. EggLib source code is distributed freely under the GNU General Public License (GPL) from its website http://egglib.sourceforge.net/ where a full documentation and a manual can also be found and downloaded.

Evolution of the human immunodeficiency virus envelope gene is dominated by purifying selection.

PubMed

Edwards, C T T; Holmes, E C; Pybus, O G; Wilson, D J; Viscidi, R P; Abrams, E J; Phillips, R E; Drummond, A J

2006-11-01

The evolution of the human immunodeficiency virus (HIV-1) during chronic infection involves the rapid, continuous turnover of genetic diversity. However, the role of natural selection, relative to random genetic drift, in governing this process is unclear. We tested a stochastic model of genetic drift using partial envelope sequences sampled longitudinally in 28 infected children. In each case the Bayesian posterior (empirical) distribution of coalescent genealogies was estimated using Markov chain Monte Carlo methods. Posterior predictive simulation was then used to generate a null distribution of genealogies assuming neutrality, with the null and empirical distributions compared using four genealogy-based summary statistics sensitive to nonneutral evolution. Because both null and empirical distributions were generated within a coalescent framework, we were able to explicitly account for the confounding influence of demography. From the distribution of corrected P-values across patients, we conclude that empirical genealogies are more asymmetric than expected if evolution is driven by mutation and genetic drift only, with an excess of low-frequency polymorphisms in the population. This indicates that although drift may still play an important role, natural selection has a strong influence on the evolution of HIV-1 envelope. A negative relationship between effective population size and substitution rate indicates that as the efficacy of selection increases, a smaller proportion of mutations approach fixation in the population. This suggests the presence of deleterious mutations. We therefore conclude that intrahost HIV-1 evolution in envelope is dominated by purifying selection against low-frequency deleterious mutations that do not reach fixation.

A Continuous Method for Gene Flow

PubMed Central

Palczewski, Michal; Beerli, Peter

2013-01-01

Most modern population genetics inference methods are based on the coalescence framework. Methods that allow estimating parameters of structured populations commonly insert migration events into the genealogies. For these methods the calculation of the coalescence probability density of a genealogy requires a product over all time periods between events. Data sets that contain populations with high rates of gene flow among them require an enormous number of calculations. A new method, transition probability-structured coalescence (TPSC), replaces the discrete migration events with probability statements. Because the speed of calculation is independent of the amount of gene flow, this method allows calculating the coalescence densities efficiently. The current implementation of TPSC uses an approximation simplifying the interaction among lineages. Simulations and coverage comparisons of TPSC vs. MIGRATE show that TPSC allows estimation of high migration rates more precisely, but because of the approximation the estimation of low migration rates is biased. The implementation of TPSC into programs that calculate quantities on phylogenetic tree structures is straightforward, so the TPSC approach will facilitate more general inferences in many computer programs. PMID:23666937

Origin and Evolution of the Unique Hepatitis C Virus Circulating Recombinant Form 2k/1b

PubMed Central

Thomas, Xiomara V.; Koekkoek, Sylvie M.; Schinkel, Janke; Molenkamp, Richard; van de Laar, Thijs J.; Takebe, Yutaka; Tanaka, Yasuhito; Mizokami, Masashi; Rambaut, Andrew

2012-01-01

Since its initial identification in St. Petersburg, Russia, the recombinant hepatitis C virus (HCV) 2k/1b has been isolated from several countries throughout Eurasia. The 2k/1b strain is the only recombinant HCV to have spread widely, raising questions about the epidemiological background in which it first appeared. In order to further understand the circumstances by which HCV recombinants might be formed and spread, we estimated the date of the recombination event that generated the 2k/1b strain using a Bayesian phylogenetic approach. Our study incorporates newly isolated 2k/1b strains from Amsterdam, The Netherlands, and has employed a hierarchical Bayesian framework to combine information from different genomic regions. We estimate that 2k/1b originated sometime between 1923 and 1956, substantially before the first detection of the strain in 1999. The timescale and the geographic spread of 2k/1b suggest that it originated in the former Soviet Union at about the time that the world's first centralized national blood transfusion and storage service was being established. We also reconstructed the epidemic history of 2k/1b using coalescent theory-based methods, matching patterns previously reported for other epidemic HCV subtypes. This study demonstrates the practicality of jointly estimating dates of recombination from flanking regions of the breakpoint and further illustrates that rare genetic-exchange events can be particularly informative about the underlying epidemiological processes. PMID:22114341

Viscosity Measurement using Drop Coalescence in Microgravity

NASA Technical Reports Server (NTRS)

Antar, Basil N.; Ethridge, Edwin; Maxwell, Daniel

1999-01-01

We present in here details of a new method, using drop coalescence, for application in microgravity environment for determining the viscosity of highly viscous undercooled liquids. The method has the advantage of eliminating heterogeneous nucleation at container walls caused by crystallization of undercooled liquids during processing. Also, due to the rapidity of the measurement, homogeneous nucleation would be avoided. The technique relies on both a highly accurate solution to the Navier-Stokes equations as well as on data gathered from experiments conducted in near zero gravity environment. The liquid viscosity is determined by allowing the computed free surface shape relaxation time to be adjusted in response to the measured free surface velocity of two coalescing drops. Results are presented from two validation experiments of the method which were conducted recently on board the NASA KC-135 aircraft. In these tests the viscosity of a highly viscous liquid, such as glycerine at different temperatures, was determined to reasonable accuracy using the liquid coalescence method. The experiments measured the free surface velocity of two glycerine drops coalescing under the action of surface tension alone in low gravity environment using high speed photography. The free surface velocity was then compared with the computed values obtained from different viscosity values. The results of these experiments were found to agree reasonably well with the calculated values.

The effects of Medieval dams on genetic divergence and demographic history in brown trout populations

PubMed Central

2014-01-01

Background Habitat fragmentation has accelerated within the last century, but may have been ongoing over longer time scales. We analyzed the timing and genetic consequences of fragmentation in two isolated lake-dwelling brown trout populations. They are from the same river system (the Gudenå River, Denmark) and have been isolated from downstream anadromous trout by dams established ca. 600–800 years ago. For reference, we included ten other anadromous populations and two hatchery strains. Based on analysis of 44 microsatellite loci we investigated if the lake populations have been naturally genetically differentiated from anadromous trout for thousands of years, or have diverged recently due to the establishment of dams. Results Divergence time estimates were based on 1) Approximate Bayesian Computation and 2) a coalescent-based isolation-with-gene-flow model. Both methods suggested divergence times ca. 600–800 years bp, providing strong evidence for establishment of dams in the Medieval as the factor causing divergence. Bayesian cluster analysis showed influence of stocked trout in several reference populations, but not in the focal lake and anadromous populations. Estimates of effective population size using a linkage disequilibrium method ranged from 244 to > 1,000 in all but one anadromous population, but were lower (153 and 252) in the lake populations. Conclusions We show that genetic divergence of lake-dwelling trout in two Danish lakes reflects establishment of water mills and impassable dams ca. 600–800 years ago rather than a natural genetic population structure. Although effective population sizes of the two lake populations are not critically low they may ultimately limit response to selection and thereby future adaptation. Our results demonstrate that populations may have been affected by anthropogenic disturbance over longer time scales than normally assumed. PMID:24903056

The effects of Medieval dams on genetic divergence and demographic history in brown trout populations.

PubMed

Hansen, Michael M; Limborg, Morten T; Ferchaud, Anne-Laure; Pujolar, José-Martin

2014-06-05

Habitat fragmentation has accelerated within the last century, but may have been ongoing over longer time scales. We analyzed the timing and genetic consequences of fragmentation in two isolated lake-dwelling brown trout populations. They are from the same river system (the Gudenå River, Denmark) and have been isolated from downstream anadromous trout by dams established ca. 600-800 years ago. For reference, we included ten other anadromous populations and two hatchery strains. Based on analysis of 44 microsatellite loci we investigated if the lake populations have been naturally genetically differentiated from anadromous trout for thousands of years, or have diverged recently due to the establishment of dams. Divergence time estimates were based on 1) Approximate Bayesian Computation and 2) a coalescent-based isolation-with-gene-flow model. Both methods suggested divergence times ca. 600-800 years bp, providing strong evidence for establishment of dams in the Medieval as the factor causing divergence. Bayesian cluster analysis showed influence of stocked trout in several reference populations, but not in the focal lake and anadromous populations. Estimates of effective population size using a linkage disequilibrium method ranged from 244 to > 1,000 in all but one anadromous population, but were lower (153 and 252) in the lake populations. We show that genetic divergence of lake-dwelling trout in two Danish lakes reflects establishment of water mills and impassable dams ca. 600-800 years ago rather than a natural genetic population structure. Although effective population sizes of the two lake populations are not critically low they may ultimately limit response to selection and thereby future adaptation. Our results demonstrate that populations may have been affected by anthropogenic disturbance over longer time scales than normally assumed.

The conquering of North America: dated phylogenetic and biogeographic inference of migratory behavior in bee hummingbirds.

PubMed

Licona-Vera, Yuyini; Ornelas, Juan Francisco

2017-06-05

Geographical and temporal patterns of diversification in bee hummingbirds (Mellisugini) were assessed with respect to the evolution of migration, critical for colonization of North America. We generated a dated multilocus phylogeny of the Mellisugini based on a dense sampling using Bayesian inference, maximum-likelihood and maximum parsimony methods, and reconstructed the ancestral states of distributional areas in a Bayesian framework and migratory behavior using maximum parsimony, maximum-likelihood and re-rooting methods. All phylogenetic analyses confirmed monophyly of the Mellisugini and the inclusion of Atthis, Calothorax, Doricha, Eulidia, Mellisuga, Microstilbon, Myrmia, Tilmatura, and Thaumastura. Mellisugini consists of two clades: (1) South American species (including Tilmatura dupontii), and (2) species distributed in North and Central America and the Caribbean islands. The second clade consists of four subclades: Mexican (Calothorax, Doricha) and Caribbean (Archilochus, Calliphlox, Mellisuga) sheartails, Calypte, and Selasphorus (incl. Atthis). Coalescent-based dating places the origin of the Mellisugini in the mid-to-late Miocene, with crown ages of most subclades in the early Pliocene, and subsequent species splits in the Pleistocene. Bee hummingbirds reached western North America by the end of the Miocene and the ancestral mellisuginid (bee hummingbirds) was reconstructed as sedentary, with four independent gains of migratory behavior during the evolution of the Mellisugini. Early colonization of North America and subsequent evolution of migration best explained biogeographic and diversification patterns within the Mellisugini. The repeated evolution of long-distance migration by different lineages was critical for the colonization of North America, contributing to the radiation of bee hummingbirds. Comparative phylogeography is needed to test whether the repeated evolution of migration resulted from northward expansion of southern sedentary populations.

Applying species-tree analyses to deep phylogenetic histories: challenges and potential suggested from a survey of empirical phylogenetic studies.

PubMed

Lanier, Hayley C; Knowles, L Lacey

2015-02-01

Coalescent-based methods for species-tree estimation are becoming a dominant approach for reconstructing species histories from multi-locus data, with most of the studies examining these methodologies focused on recently diverged species. However, deeper phylogenies, such as the datasets that comprise many Tree of Life (ToL) studies, also exhibit gene-tree discordance. This discord may also arise from the stochastic sorting of gene lineages during the speciation process (i.e., reflecting the random coalescence of gene lineages in ancestral populations). It remains unknown whether guidelines regarding methodologies and numbers of loci established by simulation studies at shallow tree depths translate into accurate species relationships for deeper phylogenetic histories. We address this knowledge gap and specifically identify the challenges and limitations of species-tree methods that account for coalescent variance for deeper phylogenies. Using simulated data with characteristics informed by empirical studies, we evaluate both the accuracy of estimated species trees and the characteristics associated with recalcitrant nodes, with a specific focus on whether coalescent variance is generally responsible for the lack of resolution. By determining the proportion of coalescent genealogies that support a particular node, we demonstrate that (1) species-tree methods account for coalescent variance at deep nodes and (2) mutational variance - not gene-tree discord arising from the coalescent - posed the primary challenge for accurate reconstruction across the tree. For example, many nodes were accurately resolved despite predicted discord from the random coalescence of gene lineages and nodes with poor support were distributed across a range of depths (i.e., they were not restricted to a particular recent divergences). Given their broad taxonomic scope and large sampling of taxa, deep level phylogenies pose several potential methodological complications including difficulties with MCMC convergence and estimation of requisite population genetic parameters for coalescent-based approaches. Despite these difficulties, the findings generally support the utility of species-tree analyses for the estimation of species relationships throughout the ToL. We discuss strategies for successful application of species-tree approaches to deep phylogenies. Copyright © 2014 Elsevier Inc. All rights reserved.

Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction.

PubMed

Sayyari, Erfan; Mirarab, Siavash

2016-11-11

Inferring species trees from gene trees using the coalescent-based summary methods has been the subject of much attention, yet new scalable and accurate methods are needed. We introduce DISTIQUE, a new statistically consistent summary method for inferring species trees from gene trees under the coalescent model. We generalize our results to arbitrary phylogenetic inference problems; we show that two arbitrarily chosen leaves, called anchors, can be used to estimate relative distances between all other pairs of leaves by inferring relevant quartet trees. This results in a family of distance-based tree inference methods, with running times ranging between quadratic to quartic in the number of leaves. We show in simulated studies that DISTIQUE has comparable accuracy to leading coalescent-based summary methods and reduced running times.

Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses

PubMed Central

Bayzid, Md Shamsuzzoha; Mirarab, Siavash; Boussau, Bastien; Warnow, Tandy

2015-01-01

Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees), they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014) presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistically consistent under the multi-species coalescent model. New data used in this study are available at DOI: http://dx.doi.org/10.6084/m9.figshare.1411146, and the software is available at https://github.com/smirarab/binning. PMID:26086579

Estimating diversifying selection and functional constraint in the presence of recombination.

PubMed

Wilson, Daniel J; McVean, Gilean

2006-03-01

Models of molecular evolution that incorporate the ratio of nonsynonymous to synonymous polymorphism (dN/dS ratio) as a parameter can be used to identify sites that are under diversifying selection or functional constraint in a sample of gene sequences. However, when there has been recombination in the evolutionary history of the sequences, reconstructing a single phylogenetic tree is not appropriate, and inference based on a single tree can give misleading results. In the presence of high levels of recombination, the identification of sites experiencing diversifying selection can suffer from a false-positive rate as high as 90%. We present a model that uses a population genetics approximation to the coalescent with recombination and use reversible-jump MCMC to perform Bayesian inference on both the dN/dS ratio and the recombination rate, allowing each to vary along the sequence. We demonstrate that the method has the power to detect variation in the dN/dS ratio and the recombination rate and does not suffer from a high false-positive rate. We use the method to analyze the porB gene of Neisseria meningitidis and verify the inferences using prior sensitivity analysis and model criticism techniques.

Estimating Diversifying Selection and Functional Constraint in the Presence of Recombination

PubMed Central

Wilson, Daniel J.; McVean, Gilean

2006-01-01

Models of molecular evolution that incorporate the ratio of nonsynonymous to synonymous polymorphism (dN/dS ratio) as a parameter can be used to identify sites that are under diversifying selection or functional constraint in a sample of gene sequences. However, when there has been recombination in the evolutionary history of the sequences, reconstructing a single phylogenetic tree is not appropriate, and inference based on a single tree can give misleading results. In the presence of high levels of recombination, the identification of sites experiencing diversifying selection can suffer from a false-positive rate as high as 90%. We present a model that uses a population genetics approximation to the coalescent with recombination and use reversible-jump MCMC to perform Bayesian inference on both the dN/dS ratio and the recombination rate, allowing each to vary along the sequence. We demonstrate that the method has the power to detect variation in the dN/dS ratio and the recombination rate and does not suffer from a high false-positive rate. We use the method to analyze the porB gene of Neisseria meningitidis and verify the inferences using prior sensitivity analysis and model criticism techniques. PMID:16387887

Reconstruction of a windborne insect invasion using a particle dispersal model, historical wind data, and Bayesian analysis of genetic data

PubMed Central

Lander, Tonya A; Klein, Etienne K; Oddou-Muratorio, Sylvie; Candau, Jean-Noël; Gidoin, Cindy; Chalon, Alain; Roig, Anne; Fallour, Delphine; Auger-Rozenberg, Marie-Anne; Boivin, Thomas

2014-01-01

Understanding how invasive species establish and spread is vital for developing effective management strategies for invaded areas and identifying new areas where the risk of invasion is highest. We investigated the explanatory power of dispersal histories reconstructed based on local-scale wind data and a regional-scale wind-dispersed particle trajectory model for the invasive seed chalcid wasp Megastigmus schimitscheki (Hymenoptera: Torymidae) in France. The explanatory power was tested by: (1) survival analysis of empirical data on M. schimitscheki presence, absence and year of arrival at 52 stands of the wasp's obligate hosts, Cedrus (true cedar trees); and (2) Approximate Bayesian analysis of M. schimitscheki genetic data using a coalescence model. The Bayesian demographic modeling and traditional population genetic analysis suggested that initial invasion across the range was the result of long-distance dispersal from the longest established sites. The survival analyses of the windborne expansion patterns derived from a particle dispersal model indicated that there was an informative correlation between the M. schimitscheki presence/absence data from the annual surveys and the scenarios based on regional-scale wind data. These three very different analyses produced highly congruent results supporting our proposal that wind is the most probable vector for passive long-distance dispersal of this invasive seed wasp. This result confirms that long-distance dispersal from introduction areas is a likely driver of secondary expansion of alien invasive species. Based on our results, management programs for this and other windborne invasive species may consider (1) focusing effort at the longest established sites and (2) monitoring outlying populations remains critically important due to their influence on rates of spread. We also suggest that there is a distinct need for new analysis methods that have the capacity to combine empirical spatiotemporal field data, genetic data, and environmental data to investigate dispersal and invasion. PMID:25558356

Delimiting cryptic pathogen species causing apple Valsa canker with multilocus data

PubMed Central

Wang, Xuli; Zang, Rui; Yin, Zhiyuan; Kang, Zhensheng; Huang, Lili

2014-01-01

Fungal diseases are posing tremendous threats to global economy and food safety. Among them, Valsa canker, caused by fungi of Valsa and their Cytospora anamorphs, has been a serious threat to fruit and forest trees and is one of the most destructive diseases of apple in East Asia, particularly. Accurate and robust delimitation of pathogen species is not only essential for the development of effective disease control programs, but also will advance our understanding of the emergence of plant diseases. However, species delimitation is especially difficult in Valsa because of the high variability of morphological traits and in many cases the lack of the teleomorph. In this study, we delimitated species boundary for pathogens causing apple Valsa canker with a multifaceted approach. Based on three independent loci, the internal transcribed spacer (ITS), β-tubulin (Btu), and translation elongation factor-1 alpha (EF1α), we inferred gene trees with both maximum likelihood and Bayesian methods, estimated species tree with Bayesian multispecies coalescent approaches, and validated species tree with Bayesian species delimitation. Through divergence time estimation and ancestral host reconstruction, we tested the possible underlying mechanisms for fungal speciation and host-range change. Our results proved that two varieties of the former morphological species V. mali represented two distinct species, V. mali and V. pyri, which diverged about 5 million years ago, much later than the divergence of their preferred hosts, excluding a scenario of fungi–host co-speciation. The marked different thermal preferences and contrasting pathogenicity in cross-inoculation suggest ecological divergences between the two species. Apple was the most likely ancestral host for both V. mali and V. pyri. Host-range expansion led to the occurrence of V. pyri on both pear and apple. Our results also represent an example in which ITS data might underestimate species diversity. PMID:24834333

An investigation of bubble coalescence and post-rupture oscillation in non-ionic surfactant solutions using high-speed cinematography.

PubMed

Bournival, G; Ata, S; Karakashev, S I; Jameson, G J

2014-01-15

Most processes involving bubbling in a liquid require small bubbles to maximise mass/energy transfer. A common method to prevent bubbles from coalescing is by the addition of surfactants. In order to get an insight into the coalescence process, capillary bubbles were observed using a high speed cinematography. Experiments were performed in solutions of 1-pentanol, 4-methyl-2-pentanol, tri(propylene glycol) methyl ether, and poly(propylene glycol) for which information such as the coalescence time and the deformation of the resultant bubble upon coalescence was extracted. It is shown in this study that the coalescence time increases with surfactant concentration until the appearance of a plateau. The increase in coalescence time with surfactant concentration could not be attributed only to surface elasticity. The oscillation of the resultant bubble was characterised by the damping of the oscillation. The results suggested that a minimum elasticity is required to achieve an increased damping and considerable diffusion has a detrimental effect on the dynamic response of the bubble, thereby reducing the damping. Copyright © 2013 Elsevier Inc. All rights reserved.

Dynamics of gas cell coalescence during baking expansion of leavened dough.

PubMed

Miś, Antoni; Nawrocka, Agnieszka; Lamorski, Krzysztof; Dziki, Dariusz

2018-01-01

The investigation of the dynamics of gas cell coalescence, i.e. a phenomenon that deteriorates the homogeneity of the cellular structure of bread crumb, was carried out performing simultaneously measurements of the dough volume, pressure, and viscosity. It was demonstrated that, during the baking expansion of chemically leavened wheat flour dough, the maximum growth rate of the gas cell radius determined from the ratio of pressure exerted by the expanded dough to its viscosity was on average four-fold lower than that calculated from volume changes in the gas phase of the dough. Such a high discrepancy was interpreted as a result of the course of coalescence, and a formula for determination of its rate was developed. The coalescence rate in the initial baking expansion phase had negative values, indicating nucleation of newly formed gas cells, which increased the number of gas cells even by 8%. In the next baking expansion phase, the coalescence rate started to exhibit positive values, reflecting dominance of the coalescence phenomenon over nucleation. The maximum coalescence rates indicate that, during the period of the most intensive dough expansion, the number of gas cells decreased by 2-3% within one second. At the end of the formation of bread crumb, the number of the gas cells declined by 55-67% in comparison with the initial value. The correctness of the results was positively verified using X-ray micro-computed tomography. The developed method can be a useful tool for more profound exploration of the coalescence phenomenon at various stages of evolution of the cellular structure and its determinants, which may contribute to future development of more effective methods for improving the texture and sensory quality of bread crumb. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid radiation in spiny lobsters (Palinurus spp) as revealed by classic and ABC methods using mtDNA and microsatellite data.

PubMed

Palero, Ferran; Lopes, Joao; Abelló, Pere; Macpherson, Enrique; Pascual, Marta; Beaumont, Mark A

2009-11-09

Molecular tools may help to uncover closely related and still diverging species from a wide variety of taxa and provide insight into the mechanisms, pace and geography of marine speciation. There is a certain controversy on the phylogeography and speciation modes of species-groups with an Eastern Atlantic-Western Indian Ocean distribution, with previous studies suggesting that older events (Miocene) and/or more recent (Pleistocene) oceanographic processes could have influenced the phylogeny of marine taxa. The spiny lobster genus Palinurus allows for testing among speciation hypotheses, since it has a particular distribution with two groups of three species each in the Northeastern Atlantic (P. elephas, P. mauritanicus and P. charlestoni) and Southeastern Atlantic and Southwestern Indian Oceans (P. gilchristi, P. delagoae and P. barbarae). In the present study, we obtain a more complete understanding of the phylogenetic relationships among these species through a combined dataset with both nuclear and mitochondrial markers, by testing alternative hypotheses on both the mutation rate and tree topology under the recently developed approximate Bayesian computation (ABC) methods. Our analyses support a North-to-South speciation pattern in Palinurus with all the South-African species forming a monophyletic clade nested within the Northern Hemisphere species. Coalescent-based ABC methods allowed us to reject the previously proposed hypothesis of a Middle Miocene speciation event related with the closure of the Tethyan Seaway. Instead, divergence times obtained for Palinurus species using the combined mtDNA-microsatellite dataset and standard mutation rates for mtDNA agree with known glaciation-related processes occurring during the last 2 my. The Palinurus speciation pattern is a typical example of a series of rapid speciation events occurring within a group, with very short branches separating different species. Our results support the hypothesis that recent climate change-related oceanographic processes have influenced the phylogeny of marine taxa, with most Palinurus species originating during the last two million years. The present study highlights the value of new coalescent-based statistical methods such as ABC for testing different speciation hypotheses using molecular data.

Molecular and morphological data reveal non-monophyly and speciation in imperiled freshwater mussels (Anodontoides and Strophitus)

USGS Publications Warehouse

Smith, Chase; Johnson, Nathan A.; Pfeiffer, John M.; Gangloff, Michael M.

2018-01-01

Accurate taxonomic placement is vital to conservation efforts considering many intrinsic biological characteristics of understudied species are inferred from closely related taxa. The rayed creekshell, Anodontoides radiatus (Conrad, 1834), exists in the Gulf of Mexico drainages from western Florida to Louisiana and has been petitioned for listing under the Endangered Species Act. We set out to resolve the evolutionary history of A. radiatus, primarily generic placement and species boundaries, using phylogenetic, morphometric, and geographic information. Our molecular matrix contained 3 loci: cytochrome c oxidase subunit I, NADH dehydrogenase subunit I, and the nuclear-encoded ribosomal internal transcribed spacer I. We employed maximum likelihood and Bayesian inference to estimate a phylogeny and test the monophyly of Anodontoides and Strophitus. We implemented two coalescent-based species delimitation models to test seven species models and evaluate species boundaries within A. radiatus. Concomitant to molecular data, we also employed linear morphometrics and geographic information to further evaluate species boundaries. Molecular and morphological evidence supports the inclusion of A. radiatus in the genus Strophitus, and we resurrect the binomial Strophitus radiatus to reflect their shared common ancestry. We also found strong support for polyphyly in Strophitus and advocate the resurrection of the genus Pseudodontoideus to represent ‘Strophitus’ connasaugaensis and ‘Strophitus’ subvexus. Strophitus radiatus exists in six well-supported clades that were distinguished as evolutionary independent lineages using Bayesian inference, maximum likelihood, and coalescent-based species delimitation models. Our integrative approach found evidence for as many as 4 evolutionary divergent clades within S. radiatus. Therefore, we formally describe two new species from the S. radiatus species complex (Strophitus williamsi and Strophitus pascagoulaensis) and recognize the potential for a third putative species (Strophitus sp. cf. pascagoulaensis). Our findings aid stakeholders in establishing conservation and management strategies for the members of Anodontoides, Strophitus, and Pseudodontoideus.

Utilization of Low Gravity Environment for Measuring Liquid Viscosity

NASA Technical Reports Server (NTRS)

Antar, Basil N.; Ethridge, Edwin

1998-01-01

The method of drop coalescence is used for determining the viscosity of highly viscous undercooled liquids. Low gravity environment is necessary in order to allow for examining large volumes affording much higher accuracy for the viscosity calculations than possible for smaller volumes available under 1 - g conditions. The drop coalescence method is preferred over the drop oscillation technique since the latter method can only be applied for liquids with vanishingly small viscosities. The technique developed relies on both the highly accurate solution of the Navier-Stokes equations as well as on data from experiments conducted in near zero gravity environment. Results are presented for method validation experiments recently performed on board the NASA/KC-135 aircraft. While the numerical solution was produced using the Boundary Element Method. In these tests the viscosity of a highly viscous liquid, glycerine at room temperature, was determined using the liquid coalescence method. The results from these experiments will be discussed.

Molecular characterization and epidemic history of hepatitis C virus using core sequences of isolates from Central Province, Saudi Arabia.

PubMed

Shier, Medhat K; Iles, James C; El-Wetidy, Mohammad S; Ali, Hebatallah H; Al Qattan, Mohammad M

2017-01-01

The source of HCV transmission in Saudi Arabia is unknown. This study aimed to determine HCV genotypes in a representative sample of chronically infected patients in Saudi Arabia. All HCV isolates were genotyped and subtyped by sequencing of the HCV core region and 54 new HCV isolates were identified. Three sets of primers targeting the core region were used for both amplification and sequencing of all isolates resulting in a 326 bp fragment. Most HCV isolates were genotype 4 (85%), whereas only a few isolates were recognized as genotype 1 (15%). With the assistance of Genbank database and BLAST, subtyping results showed that most of genotype 4 isolates were 4d whereas most of genotype 1 isolates were 1b. Nucleotide conservation and variation rates of HCV core sequences showed that 4a and 1b have the highest levels of variation. Phylogenetic analysis of sequences by Maximum Likelihood and Bayesian Coalescent methods was used to explore the source of HCV transmission by investigating the relationship between Saudi Arabia and other countries in the Middle East and Africa. Coalescent analysis showed that transmissions of HCV from Egypt to Saudi Arabia are estimated to have occurred in three major clusters: 4d was introduced into the country before 1900, the major 4a clade's MRCA was introduced between 1900 and 1920, and the remaining lineages were introduced between 1940 and 1960 from Egypt and Middle Africa. Results showed that no lineages seem to have crossed from Egypt to Saudi Arabia in the last 15 years. Finally, sequencing and characterization of new HCV isolates from Saudi Arabia will enrich the HCV database and help further studies related to treatment and management of the virus.

Molecular characterization and epidemic history of hepatitis C virus using core sequences of isolates from Central Province, Saudi Arabia

PubMed Central

Iles, James C.; El-Wetidy, Mohammad S.; Ali, Hebatallah H.; Al Qattan, Mohammad M.

2017-01-01

The source of HCV transmission in Saudi Arabia is unknown. This study aimed to determine HCV genotypes in a representative sample of chronically infected patients in Saudi Arabia. All HCV isolates were genotyped and subtyped by sequencing of the HCV core region and 54 new HCV isolates were identified. Three sets of primers targeting the core region were used for both amplification and sequencing of all isolates resulting in a 326 bp fragment. Most HCV isolates were genotype 4 (85%), whereas only a few isolates were recognized as genotype 1 (15%). With the assistance of Genbank database and BLAST, subtyping results showed that most of genotype 4 isolates were 4d whereas most of genotype 1 isolates were 1b. Nucleotide conservation and variation rates of HCV core sequences showed that 4a and 1b have the highest levels of variation. Phylogenetic analysis of sequences by Maximum Likelihood and Bayesian Coalescent methods was used to explore the source of HCV transmission by investigating the relationship between Saudi Arabia and other countries in the Middle East and Africa. Coalescent analysis showed that transmissions of HCV from Egypt to Saudi Arabia are estimated to have occurred in three major clusters: 4d was introduced into the country before 1900, the major 4a clade’s MRCA was introduced between 1900 and 1920, and the remaining lineages were introduced between 1940 and 1960 from Egypt and Middle Africa. Results showed that no lineages seem to have crossed from Egypt to Saudi Arabia in the last 15 years. Finally, sequencing and characterization of new HCV isolates from Saudi Arabia will enrich the HCV database and help further studies related to treatment and management of the virus. PMID:28863156

A Three-Stage Colonization Model for the Peopling of the Americas

PubMed Central

Kitchen, Andrew; Miyamoto, Michael M.; Mulligan, Connie J.

2008-01-01

Background We evaluate the process by which the Americas were originally colonized and propose a three-stage model that integrates current genetic, archaeological, geological, and paleoecological data. Specifically, we analyze mitochondrial and nuclear genetic data by using complementary coalescent models of demographic history and incorporating non-genetic data to enhance the anthropological relevance of the analysis. Methodology/Findings Bayesian skyline plots, which provide dynamic representations of population size changes over time, indicate that Amerinds went through two stages of growth ≈40,000 and ≈15,000 years ago separated by a long period of population stability. Isolation-with-migration coalescent analyses, which utilize data from sister populations to estimate a divergence date and founder population sizes, suggest an Amerind population expansion starting ≈15,000 years ago. Conclusions/Significance These results support a model for the peopling of the New World in which Amerind ancestors diverged from the Asian gene pool prior to 40,000 years ago and experienced a gradual population expansion as they moved into Beringia. After a long period of little change in population size in greater Beringia, Amerinds rapidly expanded into the Americas ≈15,000 years ago either through an interior ice-free corridor or along the coast. This rapid colonization of the New World was achieved by a founder group with an effective population size of ≈1,000–5,400 individuals. Our model presents a detailed scenario for the timing and scale of the initial migration to the Americas, substantially refines the estimate of New World founders, and provides a unified theory for testing with future datasets and analytic methods. PMID:18270583

Generalized Boltzmann-Type Equations for Aggregation in Gases

NASA Astrophysics Data System (ADS)

Adzhiev, S. Z.; Vedenyapin, V. V.; Volkov, Yu. A.; Melikhov, I. V.

2017-12-01

The coalescence and fragmentation of particles in a dispersion system are investigated by applying kinetic theory methods, namely, by generalizing the Boltzmann kinetic equation to coalescence and fragmentation processes. Dynamic equations for the particle concentrations in the system are derived using the kinetic equations of motion. For particle coalescence and fragmentation, equations for the particle momentum, coordinate, and mass distribution functions are obtained and the coalescence and fragmentation coefficients are calculated. The equilibrium mass and velocity distribution functions of the particles in the dispersion system are found in the approximation of an active terminal group (Becker-Döring-type equation). The transition to a continuum description is performed.

On the Adequacy of Bayesian Evaluations of Categorization Models: Reply to Vanpaemel and Lee (2012)

ERIC Educational Resources Information Center

Wills, Andy J.; Pothos, Emmanuel M.

2012-01-01

Vanpaemel and Lee (2012) argued, and we agree, that the comparison of formal models can be facilitated by Bayesian methods. However, Bayesian methods neither precede nor supplant our proposals (Wills & Pothos, 2012), as Bayesian methods can be applied both to our proposals and to their polar opposites. Furthermore, the use of Bayesian methods to…

Bubble coalescence in a Newtonian fluid

NASA Astrophysics Data System (ADS)

Garg, Vishrut; Basaran, Osman

2017-11-01

Bubble coalescence plays a central role in the hydrodynamics of gas-liquid systems such as bubble column reactors, spargers, and foams. Two bubbles approaching each other at velocity V coalesce when the thin film between them ruptures, which is often the rate-limiting step. Experimental studies of this system are difficult, and recent works provide conflicting results on the effect of V on coalescence times. We simulate the head-on approach of two bubbles of equal radii R in an incompressible Newtonian fluid (density ρ, viscosity μ, and surface tension σ) by solving numerically the free boundary problem comprised of the Navier Stokes and continuity equations. Simulations are made challenging by the existence of highly disparate lengthscales, i.e. film thickness and drop radii, which are resolved by using the method of elliptic mesh generation. For a given liquid, the bubbles are shown to coalesce for all velocities below a critical value. The effects of Ohnesorge number Oh = μ /√{ ρσR } on coalescence time and critical velocity are also investigated.

Micro- and macro-geographic scale effect on the molecular imprint of selection and adaptation in Norway spruce.

PubMed

Scalfi, Marta; Mosca, Elena; Di Pierro, Erica Adele; Troggio, Michela; Vendramin, Giovanni Giuseppe; Sperisen, Christoph; La Porta, Nicola; Neale, David B

2014-01-01

Forest tree species of temperate and boreal regions have undergone a long history of demographic changes and evolutionary adaptations. The main objective of this study was to detect signals of selection in Norway spruce (Picea abies [L.] Karst), at different sampling-scales and to investigate, accounting for population structure, the effect of environment on species genetic diversity. A total of 384 single nucleotide polymorphisms (SNPs) representing 290 genes were genotyped at two geographic scales: across 12 populations distributed along two altitudinal-transects in the Alps (micro-geographic scale), and across 27 populations belonging to the range of Norway spruce in central and south-east Europe (macro-geographic scale). At the macrogeographic scale, principal component analysis combined with Bayesian clustering revealed three major clusters, corresponding to the main areas of southern spruce occurrence, i.e. the Alps, Carpathians, and Hercynia. The populations along the altitudinal transects were not differentiated. To assess the role of selection in structuring genetic variation, we applied a Bayesian and coalescent-based F(ST)-outlier method and tested for correlations between allele frequencies and climatic variables using regression analyses. At the macro-geographic scale, the F(ST)-outlier methods detected together 11 F(ST)-outliers. Six outliers were detected when the same analyses were carried out taking into account the genetic structure. Regression analyses with population structure correction resulted in the identification of two (micro-geographic scale) and 38 SNPs (macro-geographic scale) significantly correlated with temperature and/or precipitation. Six of these loci overlapped with F(ST)-outliers, among them two loci encoding an enzyme involved in riboflavin biosynthesis and a sucrose synthase. The results of this study indicate a strong relationship between genetic and environmental variation at both geographic scales. It also suggests that an integrative approach combining different outlier detection methods and population sampling at different geographic scales is useful to identify loci potentially involved in adaptation.

Micro- and Macro-Geographic Scale Effect on the Molecular Imprint of Selection and Adaptation in Norway Spruce

PubMed Central

Scalfi, Marta; Mosca, Elena; Di Pierro, Erica Adele; Troggio, Michela; Vendramin, Giovanni Giuseppe; Sperisen, Christoph; La Porta, Nicola; Neale, David B.

2014-01-01

Forest tree species of temperate and boreal regions have undergone a long history of demographic changes and evolutionary adaptations. The main objective of this study was to detect signals of selection in Norway spruce (Picea abies [L.] Karst), at different sampling-scales and to investigate, accounting for population structure, the effect of environment on species genetic diversity. A total of 384 single nucleotide polymorphisms (SNPs) representing 290 genes were genotyped at two geographic scales: across 12 populations distributed along two altitudinal-transects in the Alps (micro-geographic scale), and across 27 populations belonging to the range of Norway spruce in central and south-east Europe (macro-geographic scale). At the macrogeographic scale, principal component analysis combined with Bayesian clustering revealed three major clusters, corresponding to the main areas of southern spruce occurrence, i.e. the Alps, Carpathians, and Hercynia. The populations along the altitudinal transects were not differentiated. To assess the role of selection in structuring genetic variation, we applied a Bayesian and coalescent-based F ST-outlier method and tested for correlations between allele frequencies and climatic variables using regression analyses. At the macro-geographic scale, the F ST-outlier methods detected together 11 F ST-outliers. Six outliers were detected when the same analyses were carried out taking into account the genetic structure. Regression analyses with population structure correction resulted in the identification of two (micro-geographic scale) and 38 SNPs (macro-geographic scale) significantly correlated with temperature and/or precipitation. Six of these loci overlapped with F ST-outliers, among them two loci encoding an enzyme involved in riboflavin biosynthesis and a sucrose synthase. The results of this study indicate a strong relationship between genetic and environmental variation at both geographic scales. It also suggests that an integrative approach combining different outlier detection methods and population sampling at different geographic scales is useful to identify loci potentially involved in adaptation. PMID:25551624

Bayesian data analysis in population ecology: motivations, methods, and benefits

USGS Publications Warehouse

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Multi-locus phylogeny and divergence time estimates of Enallagma damselflies (Odonata: Coenagrionidae).

PubMed

Callahan, Melissa S; McPeek, Mark A

2016-01-01

Reconstructing evolutionary patterns of species and populations provides a framework for asking questions about the impacts of climate change. Here we use a multilocus dataset to estimate gene trees under maximum likelihood and Bayesian models to obtain a robust estimate of relationships for a genus of North American damselflies, Enallagma. Using a relaxed molecular clock, we estimate the divergence times for this group. Furthermore, to account for the fact that gene tree analyses can overestimate ages of population divergences, we use a multi-population coalescent model to gain a more accurate estimate of divergence times. We also infer diversification rates using a method that allows for variation in diversification rate through time and among lineages. Our results reveal a complex evolutionary history of Enallagma, in which divergence events both predate and occur during Pleistocene climate fluctuations. There is also evidence of diversification rate heterogeneity across the tree. These divergence time estimates provide a foundation for addressing the relative significance of historical climatic events in the diversification of this genus. Copyright © 2015 Elsevier Inc. All rights reserved.

Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations

NASA Astrophysics Data System (ADS)

Del Pozzo, W.; Berry, C. P. L.; Ghosh, A.; Haines, T. S. F.; Singer, L. P.; Vecchio, A.

2018-06-01

We reconstruct posterior distributions for the position (sky area and distance) of a simulated set of binary neutron-star gravitational-waves signals observed with Advanced LIGO and Advanced Virgo. We use a Dirichlet Process Gaussian-mixture model, a fully Bayesian non-parametric method that can be used to estimate probability density functions with a flexible set of assumptions. The ability to reliably reconstruct the source position is important for multimessenger astronomy, as recently demonstrated with GW170817. We show that for detector networks comparable to the early operation of Advanced LIGO and Advanced Virgo, typical localization volumes are ˜104-105 Mpc3 corresponding to ˜102-103 potential host galaxies. The localization volume is a strong function of the network signal-to-noise ratio, scaling roughly ∝ϱnet-6. Fractional localizations improve with the addition of further detectors to the network. Our Dirichlet Process Gaussian-mixture model can be adopted for localizing events detected during future gravitational-wave observing runs, and used to facilitate prompt multimessenger follow-up.

Near Full-Length Characterization and Population Dynamics of the Human Immunodeficiency Virus Type I Circulating Recombinant Form 42 (CRF42_BF) in Luxembourg.

PubMed

Struck, Daniel; Roman, François; De Landtsheer, Sébastien; Servais, Jean-Yves; Lambert, Christine; Masquelier, Cécile; Venard, Véronique; Ruelle, Jean; Nijhuis, Monique; Schmit, Jean-Claude; Seguin-Devaux, Carole

2015-05-01

A new recombinant form representing a mosaic of HIV-1 subtype B and F1 and designated as CRF42_BF was identified in Luxembourg. We confirmed the inedited nature of CRF42_BF by near full-length genome characterization and retrieved a possible ancestor originating from Brazil. The demographic history of CRF42_BF in Luxembourg using Bayesian coalescent-based methods was investigated. The exponential phase of the logistic growth happened in a very short time period of approximately 5 months associated with a high mean rate of population growth of 15.02 new infections per year. However, CRF42_BF was not characterized by either a higher ex vivo replication capacity in peripheral blood mononuclear cells (PBMCs) or a higher ex vivo transmission efficiency from monocyte-derived dendritic cells to PBMCs as compared to B and F1 viruses. These data do not support a high pathogenic potential of CFR42_BF but rather an initial bursting spread of the recombinant probably due to a more favorable transmission route.

Reconstructing Past Admixture Processes from Local Genomic Ancestry Using Wavelet Transformation

PubMed Central

Sanderson, Jean; Sudoyo, Herawati; Karafet, Tatiana M.; Hammer, Michael F.; Cox, Murray P.

2015-01-01

Admixture between long-separated populations is a defining feature of the genomes of many species. The mosaic block structure of admixed genomes can provide information about past contact events, including the time and extent of admixture. Here, we describe an improved wavelet-based technique that better characterizes ancestry block structure from observed genomic patterns. principal components analysis is first applied to genomic data to identify the primary population structure, followed by wavelet decomposition to develop a new characterization of local ancestry information along the chromosomes. For testing purposes, this method is applied to human genome-wide genotype data from Indonesia, as well as virtual genetic data generated using genome-scale sequential coalescent simulations under a wide range of admixture scenarios. Time of admixture is inferred using an approximate Bayesian computation framework, providing robust estimates of both admixture times and their associated levels of uncertainty. Crucially, we demonstrate that this revised wavelet approach, which we have released as the R package adwave, provides improved statistical power over existing wavelet-based techniques and can be used to address a broad range of admixture questions. PMID:25852078

Population dynamics of American horseshoe crabs-historic climatic events and recent anthropogenic pressures

USGS Publications Warehouse

Faurby, S.; King, T.L.; Obst, M.; Hallerman, E.M.; Pertoldi, C.; Funch, P.

2010-01-01

Populations of the American horseshoe crab, Limulus polyphemus, have declined, but neither the causes nor the magnitude are fully understood. In order to evaluate historic demography, variation at 12 microsatellite DNA loci surveyed in 1218 L. polyphemus sampled from 28 localities was analysed with Bayesian coalescent-based methods. The analysis showed strong declines in population sizes throughout the species' distribution except in the geographically isolated southern-most population in Mexico, where a strong increase in population size was inferred. Analyses suggested that demographic changes in the core of the distribution occurred in association with the recolonization after the Ice Age and also by anthropogenic effects, such as the past overharvest of the species for fertilizer or the current use of the animals as bait for American eel (Anguilla rostrata) and whelk (Busycon spp.) fisheries. This study highlights the importance of considering both climatic changes and anthropogenic effects in efforts to understand population dynamics-a topic which is highly relevant in the ongoing assessments of the effects of climate change and overharvest. ?? 2010 Blackwell Publishing Ltd.

Population dynamics of American horseshoe crabs--historic climatic events and recent anthropogenic pressures.

PubMed

Faurby, Søren; King, Tim L; Obst, Matthias; Hallerman, Eric M; Pertoldi, Cino; Funch, Peter

2010-08-01

Populations of the American horseshoe crab, Limulus polyphemus, have declined, but neither the causes nor the magnitude are fully understood. In order to evaluate historic demography, variation at 12 microsatellite DNA loci surveyed in 1218 L. polyphemus sampled from 28 localities was analysed with Bayesian coalescent-based methods. The analysis showed strong declines in population sizes throughout the species' distribution except in the geographically isolated southern-most population in Mexico, where a strong increase in population size was inferred. Analyses suggested that demographic changes in the core of the distribution occurred in association with the recolonization after the Ice Age and also by anthropogenic effects, such as the past overharvest of the species for fertilizer or the current use of the animals as bait for American eel (Anguilla rostrata) and whelk (Busycon spp.) fisheries. This study highlights the importance of considering both climatic changes and anthropogenic effects in efforts to understand population dynamics--a topic which is highly relevant in the ongoing assessments of the effects of climate change and overharvest.

Bayesian demography 250 years after Bayes

PubMed Central

Bijak, Jakub; Bryant, John

2016-01-01

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889

Theoretical Coalescence: A Method to Develop Qualitative Theory

PubMed Central

Morse, Janice M.

2018-01-01

Background Qualitative research is frequently context bound, lacks generalizability, and is limited in scope. Objectives The purpose of this article was to describe a method, theoretical coalescence, that provides a strategy for analyzing complex, high-level concepts and for developing generalizable theory. Theoretical coalescence is a method of theoretical expansion, inductive inquiry, of theory development, that uses data (rather than themes, categories, and published extracts of data) as the primary source for analysis. Here, using the development of the lay concept of enduring as an example, I explore the scientific development of the concept in multiple settings over many projects and link it within the Praxis Theory of Suffering. Methods As comprehension emerges when conducting theoretical coalescence, it is essential that raw data from various different situations be available for reinterpretation/reanalysis and comparison to identify the essential features of the concept. The concept is then reconstructed, with additional inquiry that builds description, and evidence is conducted and conceptualized to create a more expansive concept and theory. Results By utilizing apparently diverse data sets from different contexts that are linked by certain characteristics, the essential features of the concept emerge. Such inquiry is divergent and less bound by context yet purposeful, logical, and with significant pragmatic implications for practice in nursing and beyond our discipline. Conclusion Theoretical coalescence is a means by which qualitative inquiry is broadened to make an impact, to accommodate new theoretical shifts and concepts, and to make qualitative research applied and accessible in new ways. PMID:29360688

Species limits in the Morelet's Alligator lizard (Anguidae: Gerrhonotinae).

PubMed

Solano-Zavaleta, Israel; Nieto-Montes de Oca, Adrián

2018-03-01

The widely distributed, Central American anguid lizard Mesaspis moreletii is currently recognized as a polytypic species with five subspecies (M. m. fulvus, M. m. moreletii, M. m. rafaeli, M. m. salvadorensis, and M. m. temporalis). We reevaluated the species limits within Mesaspis moreletii using DNA sequences of one mitochondrial and three nuclear genes. The multi-locus data set included samples of all of the subspecies of M. moreletii, the other species of Mesaspis in Central America (M. cuchumatanus and M. monticola), and some populations assignable to M. moreletii but of uncertain subspecific identity from Honduras and Nicaragua. We first used a tree-based method for delimiting species based on mtDNA data to identify potential evolutionary independent lineages, and then analized the multilocus dataset with two species delimitation methods that use the multispecies coalescent model to evaluate different competing species delimitation models: the Bayes factors species delimitation method (BFD) implemented in ∗ BEAST, and the Bayesian Phylogenetics and Phylogeography (BP&P) method. Our results suggest that M. m. moreletii, M. m. rafaeli, M. m. salvadorensis, and M. m. temporalis represent distinct evolutionary independent lineages, and that the populations of uncertain status from Honduras and Nicaragua may represent additional undescribed species. Our results also suggest that M. m. fulvus is a synonym of M. m. moreletii. The biogeography of the Central American lineages of Mesaspis is discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Filling phylogenetic gaps and the biogeographic relationships of the Octodontidae (Mammalia: Hystricognathi).

PubMed

Suárez-Villota, Elkin Y; González-Wevar, Claudio A; Gallardo, Milton H; Vásquez, Rodrigo A; Poulin, Elie

2016-12-01

Endemic to South America, octodontid rodents are remarkable by being the only mammal taxa where allotetraploidy has been documented. The taxon's extensive morpho-physiological radiation associated to niche shifts has allowed testing phylogeographic hypotheses. Using maximum likelihood and Bayesian inference analyses, applied to all nominal species of octodontids, phylogenetic reconstructions based on sequences of 12S rRNA and growth hormone receptor gene are presented. Species boundaries were determined by coalescent analyses and divergence times among taxa were estimated based on mutation rates. Two main clades associated to the Andean orogenesis were recognized. The essentially western clade comprises genera Aconaemys, Octodon, Spalacopus, and Octodontomys whereas the eastern one included genera Octomys, Pipanacoctomys, Salinoctomys, and Tympanoctomys. Genetic relationships, coalescent analyses, and genetic distance supported the specific status given to Octodon pacificus and that given to Pipanacoctomys aureus as a species of Tympanoctomys. However, these analyses failed to recognize Salinoctomys loschalchalerosorum as a valid taxon considering its position within the diversity of Tympanoctomys barrerae. Although the origin of genome duplication remains contentious, the coincidence of the basal clade split with distinctive modes of karyotypic evolution across the Andes emphasizes the role of physiographic barriers and westerlies in shaping different edaphological conditions, selective grounds, and concomitantly distinct adaptations within the octodontids. Copyright © 2016 Elsevier Inc. All rights reserved.

Are Antarctic minke whales unusually abundant because of 20th century whaling?

PubMed

Ruegg, Kristen C; Anderson, Eric C; Scott Baker, C; Vant, Murdoch; Jackson, Jennifer A; Palumbi, Stephen R

2010-01-01

Severe declines in megafauna worldwide illuminate the role of top predators in ecosystem structure. In the Antarctic, the Krill Surplus Hypothesis posits that the killing of more than 2 million large whales led to competitive release for smaller krill-eating species like the Antarctic minke whale. If true, the current size of the Antarctic minke whale population may be unusually high as an indirect result of whaling. Here, we estimate the long-term population size of the Antarctic minke whale prior to whaling by sequencing 11 nuclear genetic markers from 52 modern samples purchased in Japanese meat markets. We use coalescent simulations to explore the potential influence of population substructure and find that even though our samples are drawn from a limited geographic area, our estimate reflects ocean-wide genetic diversity. Using Bayesian estimates of the mutation rate and coalescent-based analyses of genetic diversity across loci, we calculate the long-term population size of the Antarctic minke whale to be 670,000 individuals (95% confidence interval: 374,000-1,150,000). Our estimate of long-term abundance is similar to, or greater than, contemporary abundance estimates, suggesting that managing Antarctic ecosystems under the assumption that Antarctic minke whales are unusually abundant is not warranted.

Fluorescence correlation spectroscopy directly monitors coalescence during nanoparticle preparation.

PubMed

Schaeffel, David; Staff, Roland Hinrich; Butt, Hans-Juergen; Landfester, Katharina; Crespy, Daniel; Koynov, Kaloian

2012-11-14

Dual color fluorescence cross-correlation spectroscopy (DC FCCS) experiments were conducted to study the coalescence and aggregation during the formation of nanoparticles. To assess the generality of the method, three completely different processes were selected to prepare the nanoparticles. Polymeric nanoparticles were formed either by solvent evaporation from emulsion nanodroplets of polymer solutions or by miniemulsion polymerization. Inorganic nanocapsules were formed by polycondensation of alkoxysilanes at the interface of nanodroplets. In all cases, DC FCCS provided fast and unambiguous information about the occurrence of coalescence and thus a deeper insight into the mechanism of nanoparticle formation. In particular, it was found that coalescence played a minor role for the emulsion-solvent evaporation process and the miniemulsion polymerization, whereas substantial coalescence was detected during the formation of the inorganic nanocapsules. These findings demonstrate that DC FCCS is a powerful tool for monitoring nanoparticles genesis.

Pre-Whaling Genetic Diversity and Population Ecology in Eastern Pacific Gray Whales: Insights from Ancient DNA and Stable Isotopes

PubMed Central

Alter, S. Elizabeth; Newsome, Seth D.; Palumbi, Stephen R.

2012-01-01

Commercial whaling decimated many whale populations, including the eastern Pacific gray whale, but little is known about how population dynamics or ecology differed prior to these removals. Of particular interest is the possibility of a large population decline prior to whaling, as such a decline could explain the ∼5-fold difference between genetic estimates of prior abundance and estimates based on historical records. We analyzed genetic (mitochondrial control region) and isotopic information from modern and prehistoric gray whales using serial coalescent simulations and Bayesian skyline analyses to test for a pre-whaling decline and to examine prehistoric genetic diversity, population dynamics and ecology. Simulations demonstrate that significant genetic differences observed between ancient and modern samples could be caused by a large, recent population bottleneck, roughly concurrent with commercial whaling. Stable isotopes show minimal differences between modern and ancient gray whale foraging ecology. Using rejection-based Approximate Bayesian Computation, we estimate the size of the population bottleneck at its minimum abundance and the pre-bottleneck abundance. Our results agree with previous genetic studies suggesting the historical size of the eastern gray whale population was roughly three to five times its current size. PMID:22590499

Rheological properties, shape oscillations, and coalescence of liquid drops with surfactants

NASA Technical Reports Server (NTRS)

Apfel, R. E.; Holt, R. G.

1990-01-01

A method was developed to deduce dynamic interfacial properties of liquid drops. The method involves measuring the frequency and damping of free quadrupole oscillations of an acoustically levitated drop. Experimental results from pure liquid-liquid systems agree well with theoretical predictions. Additionally, the effects of surfactants is considered. Extension of these results to a proposed microgravity experiment on the drop physics module (DPM) in USML-1 are discussed. Efforts are also underway to model the time history of the thickness of the fluid layer between two pre-coalescence drops, and to measure the film thickness experimentally. Preliminary results will be reported, along with plans for coalescence experiments proposed for USML-1.

Marangoni-flow-induced partial coalescence of a droplet on a liquid/air interface

NASA Astrophysics Data System (ADS)

Sun, Kai; Zhang, Peng; Che, Zhizhao; Wang, Tianyou

2018-02-01

The coalescence of a droplet and a liquid/air interface of lower surface tension was numerically studied by using the lattice Boltzmann phase-field method. The experimental phenomenon of droplet ejection observed by Blanchette et al. [Phys. Fluids 21, 072107 (2009), 10.1063/1.3177339] at sufficiently large surface tension differences was successfully reproduced for the first time. Furthermore, the emergence, disappearance, and re-emergence of "partial coalescence" with increasing surface tension difference was observed and explained. The re-emergence of partial coalescence under large surface tension differences is caused by the remarkable lifting motion of the Marangoni flow, which significantly retards the vertical collapse. Two different modes of partial coalescence were identified by the simulation, namely peak injection occurs at lower Ohnesorge numbers and bottom pinch-off at higher Ohnesorge numbers. By comparing the characteristic timescales of the upward Marangoni flow with that of the downward flow driven by capillary pressure, a criterion for the transition from partial to total coalescence was derived based on scaling analysis and numerically validated.

Reducing the number of templates for aligned-spin compact binary coalescence gravitational wave searches using metric-agnostic template nudging

NASA Astrophysics Data System (ADS)

Indik, Nathaniel; Fehrmann, Henning; Harke, Franz; Krishnan, Badri; Nielsen, Alex B.

2018-06-01

Efficient multidimensional template placement is crucial in computationally intensive matched-filtering searches for gravitational waves (GWs). Here, we implement the neighboring cell algorithm (NCA) to improve the detection volume of an existing compact binary coalescence (CBC) template bank. This algorithm has already been successfully applied for a binary millisecond pulsar search in data from the Fermi satellite. It repositions templates from overdense regions to underdense regions and reduces the number of templates that would have been required by a stochastic method to achieve the same detection volume. Our method is readily generalizable to other CBC parameter spaces. Here we apply this method to the aligned-single-spin neutron star-black hole binary coalescence inspiral-merger-ringdown gravitational wave parameter space. We show that the template nudging algorithm can attain the equivalent effectualness of the stochastic method with 12% fewer templates.

Prior approval: the growth of Bayesian methods in psychology.

PubMed

Andrews, Mark; Baguley, Thom

2013-02-01

Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.

An introduction to using Bayesian linear regression with clinical data.

PubMed

Baldwin, Scott A; Larson, Michael J

2017-11-01

Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

NASA Astrophysics Data System (ADS)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

PubMed Central

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B; Neyer, Franz J; van Aken, Marcel AG

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided. PMID:24116396

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

ERIC Educational Resources Information Center

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A. G.

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are…

When do species-tree and concatenated estimates disagree? An empirical analysis with higher-level scincid lizard phylogeny.

PubMed

Lambert, Shea M; Reeder, Tod W; Wiens, John J

2015-01-01

Simulation studies suggest that coalescent-based species-tree methods are generally more accurate than concatenated analyses. However, these species-tree methods remain impractical for many large datasets. Thus, a critical but unresolved issue is when and why concatenated and coalescent species-tree estimates will differ. We predict such differences for branches in concatenated trees that are short, weakly supported, and have conflicting gene trees. We test these predictions in Scincidae, the largest lizard family, with data from 10 nuclear genes for 17 ingroup taxa and 44 genes for 12 taxa. We support our initial predictions, andsuggest that simply considering uncertainty in concatenated trees may sometimes encompass the differences between these methods. We also found that relaxed-clock concatenated trees can be surprisingly similar to the species-tree estimate. Remarkably, the coalescent species-tree estimates had slightly lower support values when based on many more genes (44 vs. 10) and a small (∼30%) reduction in taxon sampling. Thus, taxon sampling may be more important than gene sampling when applying species-tree methods to deep phylogenetic questions. Finally, our coalescent species-tree estimates tentatively support division of Scincidae into three monophyletic subfamilies, a result otherwise found only in concatenated analyses with extensive species sampling. Copyright © 2014 Elsevier Inc. All rights reserved.

Theoretical Coalescence: A Method to Develop Qualitative Theory: The Example of Enduring.

PubMed

Morse, Janice M

Qualitative research is frequently context bound, lacks generalizability, and is limited in scope. The purpose of this article was to describe a method, theoretical coalescence, that provides a strategy for analyzing complex, high-level concepts and for developing generalizable theory. Theoretical coalescence is a method of theoretical expansion, inductive inquiry, of theory development, that uses data (rather than themes, categories, and published extracts of data) as the primary source for analysis. Here, using the development of the lay concept of enduring as an example, I explore the scientific development of the concept in multiple settings over many projects and link it within the Praxis Theory of Suffering. As comprehension emerges when conducting theoretical coalescence, it is essential that raw data from various different situations be available for reinterpretation/reanalysis and comparison to identify the essential features of the concept. The concept is then reconstructed, with additional inquiry that builds description, and evidence is conducted and conceptualized to create a more expansive concept and theory. By utilizing apparently diverse data sets from different contexts that are linked by certain characteristics, the essential features of the concept emerge. Such inquiry is divergent and less bound by context yet purposeful, logical, and with significant pragmatic implications for practice in nursing and beyond our discipline. Theoretical coalescence is a means by which qualitative inquiry is broadened to make an impact, to accommodate new theoretical shifts and concepts, and to make qualitative research applied and accessible in new ways.

A study of finite mixture model: Bayesian approach on financial time series data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-07-01

Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.

Teaching Bayesian Statistics in a Health Research Methodology Program

ERIC Educational Resources Information Center

Pullenayegum, Eleanor M.; Thabane, Lehana

2009-01-01

Despite the appeal of Bayesian methods in health research, they are not widely used. This is partly due to a lack of courses in Bayesian methods at an appropriate level for non-statisticians in health research. Teaching such a course can be challenging because most statisticians have been taught Bayesian methods using a mathematical approach, and…

Nuclear genomic sequences reveal that polar bears are an old and distinct bear lineage.

PubMed

Hailer, Frank; Kutschera, Verena E; Hallström, Björn M; Klassert, Denise; Fain, Steven R; Leonard, Jennifer A; Arnason, Ulfur; Janke, Axel

2012-04-20

Recent studies have shown that the polar bear matriline (mitochondrial DNA) evolved from a brown bear lineage since the late Pleistocene, potentially indicating rapid speciation and adaption to arctic conditions. Here, we present a high-resolution data set from multiple independent loci across the nuclear genomes of a broad sample of polar, brown, and black bears. Bayesian coalescent analyses place polar bears outside the brown bear clade and date the divergence much earlier, in the middle Pleistocene, about 600 (338 to 934) thousand years ago. This provides more time for polar bear evolution and confirms previous suggestions that polar bears carry introgressed brown bear mitochondrial DNA due to past hybridization. Our results highlight that multilocus genomic analyses are crucial for an accurate understanding of evolutionary history.

Rapid molecular evolution of human bocavirus revealed by Bayesian coalescent inference.

PubMed

Zehender, Gianguglielmo; De Maddalena, Chiara; Canuti, Marta; Zappa, Alessandra; Amendola, Antonella; Lai, Alessia; Galli, Massimo; Tanzi, Elisabetta

2010-03-01

Human bocavirus (HBoV) is a linear single-stranded DNA virus belonging to the Parvoviridae family that has recently been isolated from the upper respiratory tract of children with acute respiratory infection. All of the strains observed so far segregate into two genotypes (1 and 2) with a low level of polymorphism. Given the recent description of the infection and the lack of epidemiological and molecular data, we estimated the virus's rates of molecular evolution and population dynamics. A dataset of forty-nine dated VP2 sequences, including also eight new isolates obtained from pharyngeal swabs of Italian patients with acute respiratory tract infections, was submitted to phylogenetic analysis. The model parameters, evolutionary rates and population dynamics were co-estimated using a Bayesian Markov Chain Monte Carlo approach, and site-specific positive and negative selection was also investigated. Recombination was investigated by seven different methods and one suspected recombinant strain was excluded from further analysis. The estimated mean evolutionary rate of HBoV was 8.6x10(-4)subs/site/year, and that of the 1st+2nd codon positions was more than 15 times less than that of the 3rd codon position. Viral population dynamics analysis revealed that the two known genotypes diverged recently (mean tMRCA: 24 years), and that the epidemic due to HBoV genotype 2 grew exponentially at a rate of 1.01year(-1). Selection analysis of the partial VP2 showed that 8.5% of sites were under significant negative pressure and the absence of positive selection. Our results show that, like other parvoviruses, HBoV is characterised by a rapid evolution. The low level of polymorphism is probably due to a relatively recent divergence between the circulating genotypes and strong purifying selection acting on viral antigens.

Enhancements to the Bayesian Infrasound Source Location Method

DTIC Science & Technology

2012-09-01

ENHANCEMENTS TO THE BAYESIAN INFRASOUND SOURCE LOCATION METHOD Omar E. Marcillo, Stephen J. Arrowsmith, Rod W. Whitaker, and Dale N. Anderson Los...ABSTRACT We report on R&D that is enabling enhancements to the Bayesian Infrasound Source Location (BISL) method for infrasound event location...the Bayesian Infrasound Source Location Method 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER

The problem of estimating recent genetic connectivity in a changing world.

PubMed

Samarasin, Pasan; Shuter, Brian J; Wright, Stephen I; Rodd, F Helen

2017-02-01

Accurate understanding of population connectivity is important to conservation because dispersal can play an important role in population dynamics, microevolution, and assessments of extirpation risk and population rescue. Genetic methods are increasingly used to infer population connectivity because advances in technology have made them more advantageous (e.g., cost effective) relative to ecological methods. Given the reductions in wildlife population connectivity since the Industrial Revolution and more recent drastic reductions from habitat loss, it is important to know the accuracy of and biases in genetic connectivity estimators when connectivity has declined recently. Using simulated data, we investigated the accuracy and bias of 2 common estimators of migration (movement of individuals among populations) rate. We focused on the timing of the connectivity change and the magnitude of that change on the estimates of migration by using a coalescent-based method (Migrate-n) and a disequilibrium-based method (BayesAss). Contrary to expectations, when historically high connectivity had declined recently: (i) both methods over-estimated recent migration rates; (ii) the coalescent-based method (Migrate-n) provided better estimates of recent migration rate than the disequilibrium-based method (BayesAss); (iii) the coalescent-based method did not accurately reflect long-term genetic connectivity. Overall, our results highlight the problems with comparing coalescent and disequilibrium estimates to make inferences about the effects of recent landscape change on genetic connectivity among populations. We found that contrasting these 2 estimates to make inferences about genetic-connectivity changes over time could lead to inaccurate conclusions. © 2016 Society for Conservation Biology.

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

NASA Astrophysics Data System (ADS)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Shear driven droplet shedding and coalescence on a superhydrophobic surface

NASA Astrophysics Data System (ADS)

Moghtadernejad, S.; Tembely, M.; Jadidi, M.; Esmail, N.; Dolatabadi, A.

2015-03-01

The interest on shedding and coalescence of sessile droplets arises from the importance of these phenomena in various scientific problems and industrial applications such as ice formation on wind turbine blades, power lines, nacelles, and aircraft wings. It is shown recently that one of the ways to reduce the probability of ice accretion on industrial components is using superhydrophobic coatings due to their low adhesion to water droplets. In this study, a combined experimental and numerical approach is used to investigate droplet shedding and coalescence phenomena under the influence of air shear flow on a superhydrophobic surface. Droplets with a size of 2 mm are subjected to various air speeds ranging from 5 to 90 m/s. A numerical simulation based on the Volume of Fluid method coupled with the Large Eddy Simulation turbulent model is carried out in conjunction with the validating experiments to shed more light on the coalescence of droplets and detachment phenomena through a detailed analysis of the aerodynamics forces and velocity vectors on the droplet and the streamlines around it. The results indicate a contrast in the mechanism of two-droplet coalescence and subsequent detachment with those related to the case of a single droplet shedding. At lower speeds, the two droplets coalesce by attracting each other with successive rebounds of the merged droplet on the substrate, while at higher speeds, the detachment occurs almost instantly after coalescence, with a detachment time decreasing exponentially with the air speed. It is shown that coalescence phenomenon assists droplet detachment from the superhydrophobic substrate at lower air speeds.

Parameter estimation method that directly compares gravitational wave observations to numerical relativity

NASA Astrophysics Data System (ADS)

Lange, J.; O'Shaughnessy, R.; Boyle, M.; Calderón Bustillo, J.; Campanelli, M.; Chu, T.; Clark, J. A.; Demos, N.; Fong, H.; Healy, J.; Hemberger, D. A.; Hinder, I.; Jani, K.; Khamesra, B.; Kidder, L. E.; Kumar, P.; Laguna, P.; Lousto, C. O.; Lovelace, G.; Ossokine, S.; Pfeiffer, H.; Scheel, M. A.; Shoemaker, D. M.; Szilagyi, B.; Teukolsky, S.; Zlochower, Y.

2017-11-01

We present and assess a Bayesian method to interpret gravitational wave signals from binary black holes. Our method directly compares gravitational wave data to numerical relativity (NR) simulations. In this study, we present a detailed investigation of the systematic and statistical parameter estimation errors of this method. This procedure bypasses approximations used in semianalytical models for compact binary coalescence. In this work, we use the full posterior parameter distribution for only generic nonprecessing binaries, drawing inferences away from the set of NR simulations used, via interpolation of a single scalar quantity (the marginalized log likelihood, ln L ) evaluated by comparing data to nonprecessing binary black hole simulations. We also compare the data to generic simulations, and discuss the effectiveness of this procedure for generic sources. We specifically assess the impact of higher order modes, repeating our interpretation with both l ≤2 as well as l ≤3 harmonic modes. Using the l ≤3 higher modes, we gain more information from the signal and can better constrain the parameters of the gravitational wave signal. We assess and quantify several sources of systematic error that our procedure could introduce, including simulation resolution and duration; most are negligible. We show through examples that our method can recover the parameters for equal mass, zero spin, GW150914-like, and unequal mass, precessing spin sources. Our study of this new parameter estimation method demonstrates that we can quantify and understand the systematic and statistical error. This method allows us to use higher order modes from numerical relativity simulations to better constrain the black hole binary parameters.

Coalescence induced dislocation reduction in selectively grown lattice-mismatched heteroepitaxy: Theoretical prediction and experimental verification

NASA Astrophysics Data System (ADS)

Yako, Motoki; Ishikawa, Yasuhiko; Wada, Kazumi

2018-05-01

A method for reduction of threading dislocation density (TDD) in lattice-mismatched heteroepitaxy is proposed, and the reduction is experimentally verified for Ge on Si. Flat-top epitaxial layers are formed through coalescences of non-planar selectively grown epitaxial layers, and enable the TDD reduction in terms of image force. Numerical calculations and experiments for Ge on Si verify the TDD reduction by this method. The method should be applicable to not only Ge on Si but also other lattice-mismatched heteroepitaxy such as III-V on Si.

Phylogenetic Analysis and Epidemic History of Hepatitis C Virus Genotype 2 in Tunisia, North Africa

PubMed Central

Rajhi, Mouna; Ghedira, Kais; Chouikha, Anissa; Djebbi, Ahlem; Cheikh, Imed; Ben Yahia, Ahlem; Sadraoui, Amel; Hammami, Walid; Azouz, Msaddek; Ben Mami, Nabil; Triki, Henda

2016-01-01

HCV genotype 2 (HCV-2) has a worldwide distribution with prevalence rates that vary from country to country. High genetic diversity and long-term endemicity were suggested in West African countries. A global dispersal of HCV-2 would have occurred during the 20th century, especially in European countries. In Tunisia, genotype 2 was the second prevalent genotype after genotype 1 and most isolates belong to subtypes 2c and 2k. In this study, phylogenetic analyses based on the NS5B genomic sequences of 113 Tunisian HCV isolates from subtypes 2c and 2k were carried out. A Bayesian coalescent-based framework was used to estimate the origin and the spread of these subtypes circulating in Tunisia. Phylogenetic analyses of HCV-2c sequences suggest the absence of country-specific or time-specific variants. In contrast, the phylogenetic grouping of HCV-2k sequences shows the existence of two major genetic clusters that may represent two distinct circulating variants. Coalescent analysis indicated a most recent common ancestor (tMRCA) of Tunisian HCV-2c around 1886 (1869–1902) before the introduction of HCV-2k in 1901 (1867–1931). Our findings suggest that the introduction of HCV-2c in Tunisia is possibly a result of population movements between Tunisia and European population following the French colonization. PMID:27100294

Phylogenetic Analysis and Epidemic History of Hepatitis C Virus Genotype 2 in Tunisia, North Africa.

PubMed

Rajhi, Mouna; Ghedira, Kais; Chouikha, Anissa; Djebbi, Ahlem; Cheikh, Imed; Ben Yahia, Ahlem; Sadraoui, Amel; Hammami, Walid; Azouz, Msaddek; Ben Mami, Nabil; Triki, Henda

2016-01-01

HCV genotype 2 (HCV-2) has a worldwide distribution with prevalence rates that vary from country to country. High genetic diversity and long-term endemicity were suggested in West African countries. A global dispersal of HCV-2 would have occurred during the 20th century, especially in European countries. In Tunisia, genotype 2 was the second prevalent genotype after genotype 1 and most isolates belong to subtypes 2c and 2k. In this study, phylogenetic analyses based on the NS5B genomic sequences of 113 Tunisian HCV isolates from subtypes 2c and 2k were carried out. A Bayesian coalescent-based framework was used to estimate the origin and the spread of these subtypes circulating in Tunisia. Phylogenetic analyses of HCV-2c sequences suggest the absence of country-specific or time-specific variants. In contrast, the phylogenetic grouping of HCV-2k sequences shows the existence of two major genetic clusters that may represent two distinct circulating variants. Coalescent analysis indicated a most recent common ancestor (tMRCA) of Tunisian HCV-2c around 1886 (1869-1902) before the introduction of HCV-2k in 1901 (1867-1931). Our findings suggest that the introduction of HCV-2c in Tunisia is possibly a result of population movements between Tunisia and European population following the French colonization.

The space of ultrametric phylogenetic trees.

PubMed

Gavryushkin, Alex; Drummond, Alexei J

2016-08-21

The reliability of a phylogenetic inference method from genomic sequence data is ensured by its statistical consistency. Bayesian inference methods produce a sample of phylogenetic trees from the posterior distribution given sequence data. Hence the question of statistical consistency of such methods is equivalent to the consistency of the summary of the sample. More generally, statistical consistency is ensured by the tree space used to analyse the sample. In this paper, we consider two standard parameterisations of phylogenetic time-trees used in evolutionary models: inter-coalescent interval lengths and absolute times of divergence events. For each of these parameterisations we introduce a natural metric space on ultrametric phylogenetic trees. We compare the introduced spaces with existing models of tree space and formulate several formal requirements that a metric space on phylogenetic trees must possess in order to be a satisfactory space for statistical analysis, and justify them. We show that only a few known constructions of the space of phylogenetic trees satisfy these requirements. However, our results suggest that these basic requirements are not enough to distinguish between the two metric spaces we introduce and that the choice between metric spaces requires additional properties to be considered. Particularly, that the summary tree minimising the square distance to the trees from the sample might be different for different parameterisations. This suggests that further fundamental insight is needed into the problem of statistical consistency of phylogenetic inference methods. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Measuring neutron star tidal deformability with Advanced LIGO: A Bayesian analysis of neutron star-black hole binary observations

NASA Astrophysics Data System (ADS)

Kumar, Prayush; Pürrer, Michael; Pfeiffer, Harald P.

2017-02-01

The pioneering discovery of gravitational waves (GWs) by Advanced LIGO has ushered us into an era of observational GW astrophysics. Compact binaries remain the primary target sources for GW observation, of which neutron star-black hole (NSBH) binaries form an important subset. GWs from NSBH sources carry signatures of (a) the tidal distortion of the neutron star by its companion black hole during inspiral, and (b) its potential tidal disruption near merger. In this paper, we present a Bayesian study of the measurability of neutron star tidal deformability ΛNS∝(R /M )NS5 using observation(s) of inspiral-merger GW signals from disruptive NSBH coalescences, taking into account the crucial effect of black hole spins. First, we find that if nontidal templates are used to estimate source parameters for an NSBH signal, the bias introduced in the estimation of nontidal physical parameters will only be significant for loud signals with signal-to-noise ratios greater than ≃30 . For similarly loud signals, we also find that we can begin to put interesting constraints on ΛNS (factor of 1-2) with individual observations. Next, we study how a population of realistic NSBH detections will improve our measurement of neutron star tidal deformability. For an astrophysically likely population of disruptive NSBH coalescences, we find that 20-35 events are sufficient to constrain ΛNS within ±25 %- 50 % , depending on the neutron star equation of state. For these calculations we assume that LIGO will detect black holes with masses within the astrophysical mass gap. In case the mass gap remains preserved in NSBHs detected by LIGO, we estimate that approximately 25% additional detections will furnish comparable ΛNS measurement accuracy. In both cases, we find that it is the loudest 5-10 events that provide most of the tidal information, and not the combination of tens of low-SNR events, thereby facilitating targeted numerical-GR follow-ups of NSBHs. We find these results encouraging, and recommend that an effort to measure ΛNS be planned for upcoming NSBH observations with the LIGO-Virgo instruments.

Integrated Taxonomy and DNA Barcoding of Alpine Midges (Diptera: Chironomidae)

PubMed Central

Montagna, Matteo; Mereghetti, Valeria; Lencioni, Valeria; Rossaro, Bruno

2016-01-01

Rapid and efficient DNA-based tools are recommended for the evaluation of the insect biodiversity of high-altitude streams. In the present study, focused principally on larvae of the genus Diamesa Meigen 1835 (Diptera: Chironomidae), the congruence between morphological/molecular delimitation of species as well as performances in taxonomic assignments were evaluated. A fragment of the mitochondrial cox1 gene was obtained from 112 larvae, pupae and adults (Diamesinae, Orthocladiinae and Tanypodinae) that were collected in different mountain regions of the Alps and Apennines. On the basis of morphological characters 102 specimens were attributed to 16 species, and the remaining ten specimens were identified to the genus level. Molecular species delimitation was performed using: i) distance-based Automatic Barcode Gap Discovery (ABGD), with no a priori assumptions on species identification; and ii) coalescent tree-based approaches as the Generalized Mixed Yule Coalescent model, its Bayesian implementation and Bayesian Poisson Tree Processes. The ABGD analysis, estimating an optimal intra/interspecific nucleotide distance threshold of 0.7%-1.4%, identified 23 putative species; the tree-based approaches, identified between 25–26 entities, provided nearly identical results. All species belonging to zernyi, steinboecki, latitarsis, bertrami, dampfi and incallida groups, as well as outgroup species, are recovered as separate entities, perfectly matching the identified morphospecies. In contrast, within the cinerella group, cases of discrepancy arose: i) the two morphologically separate species D. cinerella and D. tonsa are neither monophyletic nor diagnosable exhibiting low values of between-taxa nucleotide mean divergence (0.94%); ii) few cases of larvae morphological misidentification were observed. Head capsule color is confirmed to be a valid character able to discriminate larvae of D. zernyi, D. tonsa and D. cinerella, but it is here better defined as a color gradient between the setae submenti and genal setae. DNA barcodes performances were high: average accuracy was ~89% and precision of ~99%. On the basis of the present data, we can thus conclude that molecular identification represents a promising tool that could be effectively adopted in evaluating biodiversity of high-altitude streams. PMID:26938660

Integrated Taxonomy and DNA Barcoding of Alpine Midges (Diptera: Chironomidae).

PubMed

Montagna, Matteo; Mereghetti, Valeria; Lencioni, Valeria; Rossaro, Bruno

2016-01-01

Rapid and efficient DNA-based tools are recommended for the evaluation of the insect biodiversity of high-altitude streams. In the present study, focused principally on larvae of the genus Diamesa Meigen 1835 (Diptera: Chironomidae), the congruence between morphological/molecular delimitation of species as well as performances in taxonomic assignments were evaluated. A fragment of the mitochondrial cox1 gene was obtained from 112 larvae, pupae and adults (Diamesinae, Orthocladiinae and Tanypodinae) that were collected in different mountain regions of the Alps and Apennines. On the basis of morphological characters 102 specimens were attributed to 16 species, and the remaining ten specimens were identified to the genus level. Molecular species delimitation was performed using: i) distance-based Automatic Barcode Gap Discovery (ABGD), with no a priori assumptions on species identification; and ii) coalescent tree-based approaches as the Generalized Mixed Yule Coalescent model, its Bayesian implementation and Bayesian Poisson Tree Processes. The ABGD analysis, estimating an optimal intra/interspecific nucleotide distance threshold of 0.7%-1.4%, identified 23 putative species; the tree-based approaches, identified between 25-26 entities, provided nearly identical results. All species belonging to zernyi, steinboecki, latitarsis, bertrami, dampfi and incallida groups, as well as outgroup species, are recovered as separate entities, perfectly matching the identified morphospecies. In contrast, within the cinerella group, cases of discrepancy arose: i) the two morphologically separate species D. cinerella and D. tonsa are neither monophyletic nor diagnosable exhibiting low values of between-taxa nucleotide mean divergence (0.94%); ii) few cases of larvae morphological misidentification were observed. Head capsule color is confirmed to be a valid character able to discriminate larvae of D. zernyi, D. tonsa and D. cinerella, but it is here better defined as a color gradient between the setae submenti and genal setae. DNA barcodes performances were high: average accuracy was ~89% and precision of ~99%. On the basis of the present data, we can thus conclude that molecular identification represents a promising tool that could be effectively adopted in evaluating biodiversity of high-altitude streams.

Estimating Tree Height-Diameter Models with the Bayesian Method

PubMed Central

Duan, Aiguo; Zhang, Jianguo; Xiang, Congwei

2014-01-01

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the “best” model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2. PMID:24711733

Estimating tree height-diameter models with the Bayesian method.

PubMed

Zhang, Xiongqing; Duan, Aiguo; Zhang, Jianguo; Xiang, Congwei

2014-01-01

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the "best" model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.

A SAS Interface for Bayesian Analysis with WinBUGS

ERIC Educational Resources Information Center

Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki

2008-01-01

Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…

A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

DOE PAGES

Groth, Katrina M.; Smith, Curtis L.; Swiler, Laura P.

2014-04-05

In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existingmore » HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.« less

Estimating trace-suspect match probabilities for singleton Y-STR haplotypes using coalescent theory.

PubMed

Andersen, Mikkel Meyer; Caliebe, Amke; Jochens, Arne; Willuweit, Sascha; Krawczak, Michael

2013-02-01

Estimation of match probabilities for singleton haplotypes of lineage markers, i.e. for haplotypes observed only once in a reference database augmented by a suspect profile, is an important problem in forensic genetics. We compared the performance of four estimators of singleton match probabilities for Y-STRs, namely the count estimate, both with and without Brenner's so-called 'kappa correction', the surveying estimate, and a previously proposed, but rarely used, coalescent-based approach implemented in the BATWING software. Extensive simulation with BATWING of the underlying population history, haplotype evolution and subsequent database sampling revealed that the coalescent-based approach is characterized by lower bias and lower mean squared error than the uncorrected count estimator and the surveying estimator. Moreover, in contrast to the two count estimators, both the surveying and the coalescent-based approach exhibited a good correlation between the estimated and true match probabilities. However, although its overall performance is thus better than that of any other recognized method, the coalescent-based estimator is still computation-intense on the verge of general impracticability. Its application in forensic practice therefore will have to be limited to small reference databases, or to isolated cases of particular interest, until more powerful algorithms for coalescent simulation have become available. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Coalescent Processes with Skewed Offspring Distributions and Nonequilibrium Demography.

PubMed

Matuszewski, Sebastian; Hildebrandt, Marcel E; Achaz, Guillaume; Jensen, Jeffrey D

2018-01-01

Nonequilibrium demography impacts coalescent genealogies leaving detectable, well-studied signatures of variation. However, similar genomic footprints are also expected under models of large reproductive skew, posing a serious problem when trying to make inference. Furthermore, current approaches consider only one of the two processes at a time, neglecting any genomic signal that could arise from their simultaneous effects, preventing the possibility of jointly inferring parameters relating to both offspring distribution and population history. Here, we develop an extended Moran model with exponential population growth, and demonstrate that the underlying ancestral process converges to a time-inhomogeneous psi-coalescent. However, by applying a nonlinear change of time scale-analogous to the Kingman coalescent-we find that the ancestral process can be rescaled to its time-homogeneous analog, allowing the process to be simulated quickly and efficiently. Furthermore, we derive analytical expressions for the expected site-frequency spectrum under the time-inhomogeneous psi-coalescent, and develop an approximate-likelihood framework for the joint estimation of the coalescent and growth parameters. By means of extensive simulation, we demonstrate that both can be estimated accurately from whole-genome data. In addition, not accounting for demography can lead to serious biases in the inferred coalescent model, with broad implications for genomic studies ranging from ecology to conservation biology. Finally, we use our method to analyze sequence data from Japanese sardine populations, and find evidence of high variation in individual reproductive success, but few signs of a recent demographic expansion. Copyright © 2018 by the Genetics Society of America.

Coalescent Times and Patterns of Genetic Diversity in Species with Facultative Sex: Effects of Gene Conversion, Population Structure, and Heterogeneity

PubMed Central

Hartfield, Matthew; Wright, Stephen I.; Agrawal, Aneil F.

2016-01-01

Many diploid organisms undergo facultative sexual reproduction. However, little is currently known concerning the distribution of neutral genetic variation among facultative sexual organisms except in very simple cases. Understanding this distribution is important when making inferences about rates of sexual reproduction, effective population size, and demographic history. Here we extend coalescent theory in diploids with facultative sex to consider gene conversion, selfing, population subdivision, and temporal and spatial heterogeneity in rates of sex. In addition to analytical results for two-sample coalescent times, we outline a coalescent algorithm that accommodates the complexities arising from partial sex; this algorithm can be used to generate multisample coalescent distributions. A key result is that when sex is rare, gene conversion becomes a significant force in reducing diversity within individuals. This can reduce genomic signatures of infrequent sex (i.e., elevated within-individual allelic sequence divergence) or entirely reverse the predicted patterns. These models offer improved methods for assessing null patterns of molecular variation in facultative sexual organisms. PMID:26584902

Coalescent Times and Patterns of Genetic Diversity in Species with Facultative Sex: Effects of Gene Conversion, Population Structure, and Heterogeneity.

PubMed

Hartfield, Matthew; Wright, Stephen I; Agrawal, Aneil F

2016-01-01

Many diploid organisms undergo facultative sexual reproduction. However, little is currently known concerning the distribution of neutral genetic variation among facultative sexual organisms except in very simple cases. Understanding this distribution is important when making inferences about rates of sexual reproduction, effective population size, and demographic history. Here we extend coalescent theory in diploids with facultative sex to consider gene conversion, selfing, population subdivision, and temporal and spatial heterogeneity in rates of sex. In addition to analytical results for two-sample coalescent times, we outline a coalescent algorithm that accommodates the complexities arising from partial sex; this algorithm can be used to generate multisample coalescent distributions. A key result is that when sex is rare, gene conversion becomes a significant force in reducing diversity within individuals. This can reduce genomic signatures of infrequent sex (i.e., elevated within-individual allelic sequence divergence) or entirely reverse the predicted patterns. These models offer improved methods for assessing null patterns of molecular variation in facultative sexual organisms. Copyright © 2016 by the Genetics Society of America.

Hepatitis C Virus Diversification in Argentina: Comparative Analysis between the Large City of Buenos Aires and the Small Rural Town of O'Brien

PubMed Central

Golemba, Marcelo D.; Culasso, Andrés C. A.; Villamil, Federico G.; Bare, Patricia; Gadano, Adrián; Ridruejo, Ezequiel; Martinez, Alfredo; Di Lello, Federico A.; Campos, Rodolfo H.

2013-01-01

Background The estimated prevalence of HCV infection in Argentina is around 2%. However, higher rates of infection have been described in population studies of small urban and rural communities. The aim of this work was to compare the origin and diversification of HCV-1b in samples from two different epidemiological scenarios: Buenos Aires, a large cosmopolitan city, and O'Brien, a small rural town with a high prevalence of HCV infection. Patients and Methods The E1/E2 and NS5B regions of the viral genome from 83 patients infected with HCV-1b were sequenced. Phylogenetic analysis and Bayesian Coalescent methods were used to study the origin and diversification of HCV-1b in both patient populations. Results Samples from Buenos Aires showed a polyphyletic behavior with a tMRCA around 1887–1900 and a time of spread of infection approximately 60 years ago. In contrast, samples from ÓBrien showed a monophyletic behavior with a tMRCA around 1950–1960 and a time of spread of infection more recent than in Buenos Aires, around 20–30 years ago. Conclusion Phylogenetic and coalescence analysis revealed a different behavior in the epidemiological histories of Buenos Aires and ÓBrien. HCV infection in Buenos Aires shows a polyphyletic behavior and an exponential growth in two phases, whereas that in O'Brien shows a monophyletic cluster and an exponential growth in one single step with a more recent tMRCA. The polyphyletic origin and the probability of encountering susceptible individuals in a large cosmopolitan city like Buenos Aires are in agreement with a longer period of expansion. In contrast, in less populated areas such as O'Brien, the chances of HCV transmission are strongly restricted. Furthermore, the monophyletic character and the most recent time of emergence suggest that different HCV-1b ancestors (variants) that were in expansion in Buenos Aires had the opportunity to colonize and expand in O’Brien. PMID:24386322

A Stochastic Mixing Model for Predicting Emissions in a Direct Injection Diesel Engine.

DTIC Science & Technology

1986-09-01

of chemical reactors. The fundamental concept of these models is coalescence/dis- persion micromixing . C1] Details of this method are provided in Appen...Togby,A.H., "Monte Carlo Methods of Simulating Micromixing in Chemical Reactors", Chemical Engineering Science, Vol.27, p.1 4 97, 1972. 46. Kattan,A...on a molecular level. 2. Micromixing or stream mixing refers to the mixing of particles on a molecular level. Until the coalescence and dispersion

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Demographic Divergence History of Pied Flycatcher and Collared Flycatcher Inferred from Whole-Genome Re-sequencing Data

PubMed Central

Nadachowska-Brzyska, Krystyna; Burri, Reto; Olason, Pall I.; Kawakami, Takeshi; Smeds, Linnéa; Ellegren, Hans

2013-01-01

Profound knowledge of demographic history is a prerequisite for the understanding and inference of processes involved in the evolution of population differentiation and speciation. Together with new coalescent-based methods, the recent availability of genome-wide data enables investigation of differentiation and divergence processes at unprecedented depth. We combined two powerful approaches, full Approximate Bayesian Computation analysis (ABC) and pairwise sequentially Markovian coalescent modeling (PSMC), to reconstruct the demographic history of the split between two avian speciation model species, the pied flycatcher and collared flycatcher. Using whole-genome re-sequencing data from 20 individuals, we investigated 15 demographic models including different levels and patterns of gene flow, and changes in effective population size over time. ABC provided high support for recent (mode 0.3 my, range <0.7 my) species divergence, declines in effective population size of both species since their initial divergence, and unidirectional recent gene flow from pied flycatcher into collared flycatcher. The estimated divergence time and population size changes, supported by PSMC results, suggest that the ancestral species persisted through one of the glacial periods of middle Pleistocene and then split into two large populations that first increased in size before going through severe bottlenecks and expanding into their current ranges. Secondary contact appears to have been established after the last glacial maximum. The severity of the bottlenecks at the last glacial maximum is indicated by the discrepancy between current effective population sizes (20,000–80,000) and census sizes (5–50 million birds) of the two species. The recent divergence time challenges the supposition that avian speciation is a relatively slow process with extended times for intrinsic postzygotic reproductive barriers to evolve. Our study emphasizes the importance of using genome-wide data to unravel tangled demographic histories. Moreover, it constitutes one of the first examples of the inference of divergence history from genome-wide data in non-model species. PMID:24244198

Demographic divergence history of pied flycatcher and collared flycatcher inferred from whole-genome re-sequencing data.

PubMed

Nadachowska-Brzyska, Krystyna; Burri, Reto; Olason, Pall I; Kawakami, Takeshi; Smeds, Linnéa; Ellegren, Hans

2013-11-01

Profound knowledge of demographic history is a prerequisite for the understanding and inference of processes involved in the evolution of population differentiation and speciation. Together with new coalescent-based methods, the recent availability of genome-wide data enables investigation of differentiation and divergence processes at unprecedented depth. We combined two powerful approaches, full Approximate Bayesian Computation analysis (ABC) and pairwise sequentially Markovian coalescent modeling (PSMC), to reconstruct the demographic history of the split between two avian speciation model species, the pied flycatcher and collared flycatcher. Using whole-genome re-sequencing data from 20 individuals, we investigated 15 demographic models including different levels and patterns of gene flow, and changes in effective population size over time. ABC provided high support for recent (mode 0.3 my, range <0.7 my) species divergence, declines in effective population size of both species since their initial divergence, and unidirectional recent gene flow from pied flycatcher into collared flycatcher. The estimated divergence time and population size changes, supported by PSMC results, suggest that the ancestral species persisted through one of the glacial periods of middle Pleistocene and then split into two large populations that first increased in size before going through severe bottlenecks and expanding into their current ranges. Secondary contact appears to have been established after the last glacial maximum. The severity of the bottlenecks at the last glacial maximum is indicated by the discrepancy between current effective population sizes (20,000-80,000) and census sizes (5-50 million birds) of the two species. The recent divergence time challenges the supposition that avian speciation is a relatively slow process with extended times for intrinsic postzygotic reproductive barriers to evolve. Our study emphasizes the importance of using genome-wide data to unravel tangled demographic histories. Moreover, it constitutes one of the first examples of the inference of divergence history from genome-wide data in non-model species.

Hepatitis C virus diversification in Argentina: comparative analysis between the large city of Buenos Aires and the small rural town of O'Brien.

PubMed

Golemba, Marcelo D; Culasso, Andrés C A; Villamil, Federico G; Bare, Patricia; Gadano, Adrián; Ridruejo, Ezequiel; Martinez, Alfredo; Di Lello, Federico A; Campos, Rodolfo H

2013-01-01

The estimated prevalence of HCV infection in Argentina is around 2%. However, higher rates of infection have been described in population studies of small urban and rural communities. The aim of this work was to compare the origin and diversification of HCV-1b in samples from two different epidemiological scenarios: Buenos Aires, a large cosmopolitan city, and O'Brien, a small rural town with a high prevalence of HCV infection. The E1/E2 and NS5B regions of the viral genome from 83 patients infected with HCV-1b were sequenced. Phylogenetic analysis and Bayesian Coalescent methods were used to study the origin and diversification of HCV-1b in both patient populations. Samples from Buenos Aires showed a polyphyletic behavior with a tMRCA around 1887-1900 and a time of spread of infection approximately 60 years ago. In contrast, samples from ÓBrien showed a monophyletic behavior with a tMRCA around 1950-1960 and a time of spread of infection more recent than in Buenos Aires, around 20-30 years ago. Phylogenetic and coalescence analysis revealed a different behavior in the epidemiological histories of Buenos Aires and ÓBrien. HCV infection in Buenos Aires shows a polyphyletic behavior and an exponential growth in two phases, whereas that in O'Brien shows a monophyletic cluster and an exponential growth in one single step with a more recent tMRCA. The polyphyletic origin and the probability of encountering susceptible individuals in a large cosmopolitan city like Buenos Aires are in agreement with a longer period of expansion. In contrast, in less populated areas such as O'Brien, the chances of HCV transmission are strongly restricted. Furthermore, the monophyletic character and the most recent time of emergence suggest that different HCV-1b ancestors (variants) that were in expansion in Buenos Aires had the opportunity to colonize and expand in O'Brien.

mtDNA variation predicts population size in humans and reveals a major Southern Asian chapter in human prehistory.

PubMed

Atkinson, Quentin D; Gray, Russell D; Drummond, Alexei J

2008-02-01

The relative timing and size of regional human population growth following our expansion from Africa remain unknown. Human mitochondrial DNA (mtDNA) diversity carries a legacy of our population history. Given a set of sequences, we can use coalescent theory to estimate past population size through time and draw inferences about human population history. However, recent work has challenged the validity of using mtDNA diversity to infer species population sizes. Here we use Bayesian coalescent inference methods, together with a global data set of 357 human mtDNA coding-region sequences, to infer human population sizes through time across 8 major geographic regions. Our estimates of relative population sizes show remarkable concordance with the contemporary regional distribution of humans across Africa, Eurasia, and the Americas, indicating that mtDNA diversity is a good predictor of population size in humans. Plots of population size through time show slow growth in sub-Saharan Africa beginning 143-193 kya, followed by a rapid expansion into Eurasia after the emergence of the first non-African mtDNA lineages 50-70 kya. Outside Africa, the earliest and fastest growth is inferred in Southern Asia approximately 52 kya, followed by a succession of growth phases in Northern and Central Asia (approximately 49 kya), Australia (approximately 48 kya), Europe (approximately 42 kya), the Middle East and North Africa (approximately 40 kya), New Guinea (approximately 39 kya), the Americas (approximately 18 kya), and a second expansion in Europe (approximately 10-15 kya). Comparisons of relative regional population sizes through time suggest that between approximately 45 and 20 kya most of humanity lived in Southern Asia. These findings not only support the use of mtDNA data for estimating human population size but also provide a unique picture of human prehistory and demonstrate the importance of Southern Asia to our recent evolutionary past.

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood

ERIC Educational Resources Information Center

Karabatsos, George

2017-01-01

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon…

Power in Bayesian Mediation Analysis for Small Sample Research

PubMed Central

Miočević, Milica; MacKinnon, David P.; Levy, Roy

2018-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results. PMID:29662296

Power in Bayesian Mediation Analysis for Small Sample Research.

PubMed

Miočević, Milica; MacKinnon, David P; Levy, Roy

2017-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.

A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data

PubMed Central

Huynh, Tran; Quick, Harrison; Ramachandran, Gurumurthy; Banerjee, Sudipto; Stenzel, Mark; Sandler, Dale P.; Engel, Lawrence S.; Kwok, Richard K.; Blair, Aaron; Stewart, Patricia A.

2016-01-01

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method’s performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications. PMID:26209598

Population Genetic Structure of the Tropical Two-Wing Flyingfish (Exocoetus volitans)

PubMed Central

Lewallen, Eric A.; Bohonak, Andrew J.; Bonin, Carolina A.; van Wijnen, Andre J.; Pitman, Robert L.; Lovejoy, Nathan R.

2016-01-01

Delineating populations of pantropical marine fish is a difficult process, due to widespread geographic ranges and complex life history traits in most species. Exocoetus volitans, a species of two-winged flyingfish, is a good model for understanding large-scale patterns of epipelagic fish population structure because it has a circumtropical geographic range and completes its entire life cycle in the epipelagic zone. Buoyant pelagic eggs should dictate high local dispersal capacity in this species, although a brief larval phase, small body size, and short lifespan may limit the dispersal of individuals over large spatial scales. Based on these biological features, we hypothesized that E. volitans would exhibit statistically and biologically significant population structure defined by recognized oceanographic barriers. We tested this hypothesis by analyzing cytochrome b mtDNA sequence data (1106 bps) from specimens collected in the Pacific, Atlantic and Indian oceans (n = 266). AMOVA, Bayesian, and coalescent analytical approaches were used to assess and interpret population-level genetic variability. A parsimony-based haplotype network did not reveal population subdivision among ocean basins, but AMOVA revealed limited, statistically significant population structure between the Pacific and Atlantic Oceans (ΦST = 0.035, p<0.001). A spatially-unbiased Bayesian approach identified two circumtropical population clusters north and south of the Equator (ΦST = 0.026, p<0.001), a previously unknown dispersal barrier for an epipelagic fish. Bayesian demographic modeling suggested the effective population size of this species increased by at least an order of magnitude ~150,000 years ago, to more than 1 billion individuals currently. Thus, high levels of genetic similarity observed in E. volitans can be explained by high rates of gene flow, a dramatic and recent population expansion, as well as extensive and consistent dispersal throughout the geographic range of the species. PMID:27736863

Emerging Concepts of Data Integration in Pathogen Phylodynamics.

PubMed

Baele, Guy; Suchard, Marc A; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.

Population Genetic Structure of the Tropical Two-Wing Flyingfish (Exocoetus volitans).

PubMed

Lewallen, Eric A; Bohonak, Andrew J; Bonin, Carolina A; van Wijnen, Andre J; Pitman, Robert L; Lovejoy, Nathan R

2016-01-01

Delineating populations of pantropical marine fish is a difficult process, due to widespread geographic ranges and complex life history traits in most species. Exocoetus volitans, a species of two-winged flyingfish, is a good model for understanding large-scale patterns of epipelagic fish population structure because it has a circumtropical geographic range and completes its entire life cycle in the epipelagic zone. Buoyant pelagic eggs should dictate high local dispersal capacity in this species, although a brief larval phase, small body size, and short lifespan may limit the dispersal of individuals over large spatial scales. Based on these biological features, we hypothesized that E. volitans would exhibit statistically and biologically significant population structure defined by recognized oceanographic barriers. We tested this hypothesis by analyzing cytochrome b mtDNA sequence data (1106 bps) from specimens collected in the Pacific, Atlantic and Indian oceans (n = 266). AMOVA, Bayesian, and coalescent analytical approaches were used to assess and interpret population-level genetic variability. A parsimony-based haplotype network did not reveal population subdivision among ocean basins, but AMOVA revealed limited, statistically significant population structure between the Pacific and Atlantic Oceans (ΦST = 0.035, p<0.001). A spatially-unbiased Bayesian approach identified two circumtropical population clusters north and south of the Equator (ΦST = 0.026, p<0.001), a previously unknown dispersal barrier for an epipelagic fish. Bayesian demographic modeling suggested the effective population size of this species increased by at least an order of magnitude ~150,000 years ago, to more than 1 billion individuals currently. Thus, high levels of genetic similarity observed in E. volitans can be explained by high rates of gene flow, a dramatic and recent population expansion, as well as extensive and consistent dispersal throughout the geographic range of the species.

Emerging Concepts of Data Integration in Pathogen Phylodynamics

PubMed Central

Baele, Guy; Suchard, Marc A.; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics. PMID:28173504

Approximate Bayesian estimation of extinction rate in the Finnish Daphnia magna metapopulation.

PubMed

Robinson, John D; Hall, David W; Wares, John P

2013-05-01

Approximate Bayesian computation (ABC) is useful for parameterizing complex models in population genetics. In this study, ABC was applied to simultaneously estimate parameter values for a model of metapopulation coalescence and test two alternatives to a strict metapopulation model in the well-studied network of Daphnia magna populations in Finland. The models shared four free parameters: the subpopulation genetic diversity (θS), the rate of gene flow among patches (4Nm), the founding population size (N0) and the metapopulation extinction rate (e) but differed in the distribution of extinction rates across habitat patches in the system. The three models had either a constant extinction rate in all populations (strict metapopulation), one population that was protected from local extinction (i.e. a persistent source), or habitat-specific extinction rates drawn from a distribution with specified mean and variance. Our model selection analysis favoured the model including a persistent source population over the two alternative models. Of the closest 750,000 data sets in Euclidean space, 78% were simulated under the persistent source model (estimated posterior probability = 0.769). This fraction increased to more than 85% when only the closest 150,000 data sets were considered (estimated posterior probability = 0.774). Approximate Bayesian computation was then used to estimate parameter values that might produce the observed set of summary statistics. Our analysis provided posterior distributions for e that included the point estimate obtained from previous data from the Finnish D. magna metapopulation. Our results support the use of ABC and population genetic data for testing the strict metapopulation model and parameterizing complex models of demography. © 2013 Blackwell Publishing Ltd.

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

A Single Early Introduction of HIV-1 Subtype B into Central America Accounts for Most Current Cases

PubMed Central

Murillo, Wendy; Veras, Nazle; Prosperi, Mattia; de Rivera, Ivette Lorenzana; Paz-Bailey, Gabriela; Morales-Miranda, Sonia; Juarez, Sandra I.; Yang, Chunfu; DeVos, Joshua; Marín, José Pablo; Mild, Mattias; Albert, Jan

2013-01-01

Human immunodeficiency virus type 1 (HIV-1) variants show considerable geographical separation across the world, but there is limited information from Central America. We provide the first detailed investigation of the genetic diversity and molecular epidemiology of HIV-1 in six Central American countries. Phylogenetic analysis was performed on 625 HIV-1 pol gene sequences collected between 2002 and 2010 in Honduras, El Salvador, Nicaragua, Costa Rica, Panama, and Belize. Published sequences from neighboring countries (n = 57) and the rest of the world (n = 740) were included as controls. Maximum likelihood methods were used to explore phylogenetic relationships. Bayesian coalescence-based methods were used to time HIV-1 introductions. Nearly all (98.9%) Central American sequences were of subtype B. Phylogenetic analysis revealed that 437 (70%) sequences clustered within five significantly supported monophyletic clades formed essentially by Central American sequences. One clade contained 386 (62%) sequences from all six countries; the other four clades were smaller and more country specific, suggesting discrete subepidemics. The existence of one large well-supported Central American clade provides evidence that a single introduction of HIV-1 subtype B in Central America accounts for most current cases. An introduction during the early phase of the HIV-1 pandemic may explain its epidemiological success. Moreover, the smaller clades suggest a subsequent regional spread related to specific transmission networks within each country. PMID:23616665

Electrohydrodynamic coalescence of droplets using an embedded potential flow model

NASA Astrophysics Data System (ADS)

Garzon, M.; Gray, L. J.; Sethian, J. A.

2018-03-01

The coalescence, and subsequent satellite formation, of two inviscid droplets is studied numerically. The initial drops are taken to be of equal and different sizes, and simulations have been carried out with and without the presence of an electrical field. The main computational challenge is the tracking of a free surface that changes topology. Coupling level set and boundary integral methods with an embedded potential flow model, we seamlessly compute through these singular events. As a consequence, the various coalescence modes that appear depending upon the relative ratio of the parent droplets can be studied. Computations of first stage pinch-off, second stage pinch-off, and complete engulfment are analyzed and compared to recent numerical studies and laboratory experiments. Specifically, we study the evolution of bridge radii and the related scaling laws, the minimum drop radii evolution from coalescence to satellite pinch-off, satellite sizes, and the upward stretching of the near cylindrical protrusion at the droplet top. Clear evidence of partial coalescence self-similarity is presented for parent droplet ratios between 1.66 and 4. This has been possible due to the fact that computational initial conditions only depend upon the mother droplet size, in contrast with laboratory experiments where the difficulty in establishing the same initial physical configuration is well known. The presence of electric forces changes the coalescence patterns, and it is possible to control the satellite droplet size by tuning the electrical field intensity. All of the numerical results are in very good agreement with recent laboratory experiments for water droplet coalescence.

Basics of Bayesian methods.

PubMed

Ghosh, Sujit K

2010-01-01

Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

A sequential coalescent algorithm for chromosomal inversions

PubMed Central

Peischl, S; Koch, E; Guerrero, R F; Kirkpatrick, M

2013-01-01

Chromosomal inversions are common in natural populations and are believed to be involved in many important evolutionary phenomena, including speciation, the evolution of sex chromosomes and local adaptation. While recent advances in sequencing and genotyping methods are leading to rapidly increasing amounts of genome-wide sequence data that reveal interesting patterns of genetic variation within inverted regions, efficient simulation methods to study these patterns are largely missing. In this work, we extend the sequential Markovian coalescent, an approximation to the coalescent with recombination, to include the effects of polymorphic inversions on patterns of recombination. Results show that our algorithm is fast, memory-efficient and accurate, making it feasible to simulate large inversions in large populations for the first time. The SMC algorithm enables studies of patterns of genetic variation (for example, linkage disequilibria) and tests of hypotheses (using simulation-based approaches) that were previously intractable. PMID:23632894

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group.

PubMed

Natanegara, Fanni; Neuenschwander, Beat; Seaman, John W; Kinnersley, Nelson; Heilmann, Cory R; Ohlssen, David; Rochester, George

2014-01-01

Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability. Copyright © 2013 John Wiley & Sons, Ltd.

Model Diagnostics for Bayesian Networks

ERIC Educational Resources Information Center

Sinharay, Sandip

2006-01-01

Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…

A guide to Bayesian model selection for ecologists

USGS Publications Warehouse

Hooten, Mevin B.; Hobbs, N.T.

2015-01-01

The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.

Finding the best resolution for the Kingman-Tajima coalescent: theory and applications.

PubMed

Sainudiin, Raazesh; Stadler, Tanja; Véber, Amandine

2015-05-01

Many summary statistics currently used in population genetics and in phylogenetics depend only on a rather coarse resolution of the underlying tree (the number of extant lineages, for example). Hence, for computational purposes, working directly on these resolutions appears to be much more efficient. However, this approach seems to have been overlooked in the past. In this paper, we describe six different resolutions of the Kingman-Tajima coalescent together with the corresponding Markov chains, which are essential for inference methods. Two of the resolutions are the well-known n-coalescent and the lineage death process due to Kingman. Two other resolutions were mentioned by Kingman and Tajima, but never explicitly formalized. Another two resolutions are novel, and complete the picture of a multi-resolution coalescent. For all of them, we provide the forward and backward transition probabilities, the probability of visiting a given state as well as the probability of a given realization of the full Markov chain. We also provide a description of the state-space that highlights the computational gain obtained by working with lower-resolution objects. Finally, we give several examples of summary statistics that depend on a coarser resolution of Kingman's coalescent, on which simulations are usually based.

A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data.

PubMed

Huynh, Tran; Quick, Harrison; Ramachandran, Gurumurthy; Banerjee, Sudipto; Stenzel, Mark; Sandler, Dale P; Engel, Lawrence S; Kwok, Richard K; Blair, Aaron; Stewart, Patricia A

2016-01-01

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method's performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications. © The Author 2015. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Analyzing contentious relationships and outlier genes in phylogenomics.

PubMed

Walker, Joseph F; Brown, Joseph W; Smith, Stephen A

2018-06-08

Recent studies have demonstrated that conflict is common among gene trees in phylogenomic studies, and that less than one percent of genes may ultimately drive species tree inference in supermatrix analyses. Here, we examined two datasets where supermatrix and coalescent-based species trees conflict. We identified two highly influential "outlier" genes in each dataset. When removed from each dataset, the inferred supermatrix trees matched the topologies obtained from coalescent analyses. We also demonstrate that, while the outlier genes in the vertebrate dataset have been shown in a previous study to be the result of errors in orthology detection, the outlier genes from a plant dataset did not exhibit any obvious systematic error and therefore may be the result of some biological process yet to be determined. While topological comparisons among a small set of alternate topologies can be helpful in discovering outlier genes, they can be limited in several ways, such as assuming all genes share the same topology. Coalescent species tree methods relax this assumption but do not explicitly facilitate the examination of specific edges. Coalescent methods often also assume that conflict is the result of incomplete lineage sorting (ILS). Here we explored a framework that allows for quickly examining alternative edges and support for large phylogenomic datasets that does not assume a single topology for all genes. For both datasets, these analyses provided detailed results confirming the support for coalescent-based topologies. This framework suggests that we can improve our understanding of the underlying signal in phylogenomic datasets by asking more targeted edge-based questions.

Evidence reasoning method for constructing conditional probability tables in a Bayesian network of multimorbidity.

PubMed

Du, Yuanwei; Guo, Yubin

2015-01-01

The intrinsic mechanism of multimorbidity is difficult to recognize and prediction and diagnosis are difficult to carry out accordingly. Bayesian networks can help to diagnose multimorbidity in health care, but it is difficult to obtain the conditional probability table (CPT) because of the lack of clinically statistical data. Today, expert knowledge and experience are increasingly used in training Bayesian networks in order to help predict or diagnose diseases, but the CPT in Bayesian networks is usually irrational or ineffective for ignoring realistic constraints especially in multimorbidity. In order to solve these problems, an evidence reasoning (ER) approach is employed to extract and fuse inference data from experts using a belief distribution and recursive ER algorithm, based on which evidence reasoning method for constructing conditional probability tables in Bayesian network of multimorbidity is presented step by step. A multimorbidity numerical example is used to demonstrate the method and prove its feasibility and application. Bayesian network can be determined as long as the inference assessment is inferred by each expert according to his/her knowledge or experience. Our method is more effective than existing methods for extracting expert inference data accurately and is fused effectively for constructing CPTs in a Bayesian network of multimorbidity.

Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

PubMed

Molitor, John

2012-03-01

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

A Bayesian approach to meta-analysis of plant pathology studies.

PubMed

Mila, A L; Ngugi, H K

2011-01-01

Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework. Bayesian meta-analysis can readily include information not easily incorporated in classical methods, and allow for a full evaluation of competing models. Given the power and flexibility of Bayesian methods, we expect them to become widely adopted for meta-analysis of plant pathology studies.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

On the use of higher order wave forms in the search for gravitational waves emitted by compact binary coalescences

NASA Astrophysics Data System (ADS)

McKechan, David J. A.

2010-11-01

This thesis concerns the use, in gravitational wave data analysis, of higher order wave form models of the gravitational radiation emitted by compact binary coalescences. We begin with an introductory chapter that includes an overview of the theory of general relativity, gravitational radiation and ground-based interferometric gravitational wave detectors. We then discuss, in Chapter 2, the gravitational waves emitted by compact binary coalescences, with an explanation of higher order waveforms and how they differ from leading order waveforms we also introduce the post-Newtonian formalism. In Chapter 3 the method and results of a gravitational wave search for low mass compact binary coalescences using a subset of LIGO's 5th science run data are presented and in the subsequent chapter we examine how one could use higher order waveforms in such analyses. We follow the development of a new search algorithm that incorporates higher order waveforms with promising results for detection efficiency and parameter estimation. In Chapter 5, a new method of windowing time-domain waveforms that offers benefit to gravitational wave searches is presented. The final chapter covers the development of a game designed as an outreach project to raise public awareness and understanding of the search for gravitational waves.

Results of the Fluid Merging Viscosity Measurement International Space Station Experiment

NASA Technical Reports Server (NTRS)

Ethridge, Edwin C.; Kaukler, William; Antar, Basil

2009-01-01

The purpose of FMVM is to measure the rate of coalescence of two highly viscous liquid drops and correlate the results with the liquid viscosity and surface tension. The experiment takes advantage of the low gravitational free floating conditions in space to permit the unconstrained coalescence of two nearly spherical drops. The merging of the drops is accomplished by deploying them from a syringe and suspending them on Nomex threads followed by the astronaut s manipulation of one of the drops toward a stationary droplet till contact is achieved. Coalescence and merging occurs due to shape relaxation and reduction of surface energy, being resisted by the viscous drag within the liquid. Experiments were conducted onboard the International Space Station in July of 2004 and subsequently in May of 2005. The coalescence was recorded on video and down-linked near real-time. When the coefficient of surface tension for the liquid is known, the increase in contact radius can be used to determine the coefficient of viscosity for that liquid. The viscosity is determined by fitting the experimental speed to theoretically calculated contact radius speed for the same experimental parameters. Recent fluid dynamical numerical simulations of the coalescence process will be presented. The results are important for a better understanding of the coalescence process. The experiment is also relevant to liquid phase sintering, free form in-situ fabrication, and as a potential new method for measuring the viscosity of viscous glass formers at low shear rates.

Climate change underlies global demographic, genetic, and cultural transitions in pre-Columbian southern Peru.

PubMed

Fehren-Schmitz, Lars; Haak, Wolfgang; Mächtle, Bertil; Masch, Florian; Llamas, Bastien; Cagigao, Elsa Tomasto; Sossna, Volker; Schittek, Karsten; Isla Cuadrado, Johny; Eitel, Bernhard; Reindel, Markus

2014-07-01

Several archaeological studies in the Central Andes have pointed at the temporal coincidence of climatic fluctuations (both long- and short-term) and episodes of cultural transition and changes of socioeconomic structures throughout the pre-Columbian period. Although most scholars explain the connection between environmental and cultural changes by the impact of climatic alterations on the capacities of the ecosystems inhabited by pre-Columbian cultures, direct evidence for assumed demographic consequences is missing so far. In this study, we address directly the impact of climatic changes on the spatial population dynamics of the Central Andes. We use a large dataset of pre-Columbian mitochondrial DNA sequences from the northern Rio Grande de Nasca drainage (RGND) in southern Peru, dating from ∼840 BC to 1450 AD. Alternative demographic scenarios are tested using Bayesian serial coalescent simulations in an approximate Bayesian computational framework. Our results indicate migrations from the lower coastal valleys of southern Peru into the Andean highlands coincident with increasing climate variability at the end of the Nasca culture at ∼640 AD. We also find support for a back-migration from the highlands to the coast coincident with droughts in the southeastern Andean highlands and improvement of climatic conditions on the coast after the decline of the Wari and Tiwanaku empires (∼1200 AD), leading to a genetic homogenization in the RGND and probably southern Peru as a whole.

Climate change underlies global demographic, genetic, and cultural transitions in pre-Columbian southern Peru

PubMed Central

Fehren-Schmitz, Lars; Haak, Wolfgang; Mächtle, Bertil; Masch, Florian; Llamas, Bastien; Tomasto Cagigao, Elsa; Sossna, Volker; Schittek, Karsten; Isla Cuadrado, Johny; Eitel, Bernhard; Reindel, Markus

2014-01-01

Several archaeological studies in the Central Andes have pointed at the temporal coincidence of climatic fluctuations (both long- and short-term) and episodes of cultural transition and changes of socioeconomic structures throughout the pre-Columbian period. Although most scholars explain the connection between environmental and cultural changes by the impact of climatic alterations on the capacities of the ecosystems inhabited by pre-Columbian cultures, direct evidence for assumed demographic consequences is missing so far. In this study, we address directly the impact of climatic changes on the spatial population dynamics of the Central Andes. We use a large dataset of pre-Columbian mitochondrial DNA sequences from the northern Rio Grande de Nasca drainage (RGND) in southern Peru, dating from ∼840 BC to 1450 AD. Alternative demographic scenarios are tested using Bayesian serial coalescent simulations in an approximate Bayesian computational framework. Our results indicate migrations from the lower coastal valleys of southern Peru into the Andean highlands coincident with increasing climate variability at the end of the Nasca culture at ∼640 AD. We also find support for a back-migration from the highlands to the coast coincident with droughts in the southeastern Andean highlands and improvement of climatic conditions on the coast after the decline of the Wari and Tiwanaku empires (∼1200 AD), leading to a genetic homogenization in the RGND and probably southern Peru as a whole. PMID:24979787

Cryptic genetic diversity, population structure, and gene flow in the Mojave rattlesnake (Crotalus scutulatus).

PubMed

Schield, Drew R; Adams, Richard H; Card, Daren C; Corbin, Andrew B; Jezkova, Tereza; Hales, Nicole R; Meik, Jesse M; Perry, Blair W; Spencer, Carol L; Smith, Lydia L; García, Gustavo Campillo; Bouzid, Nassima M; Strickland, Jason L; Parkinson, Christopher L; Borja, Miguel; Castañeda-Gaytán, Gamaliel; Bryson, Robert W; Flores-Villela, Oscar A; Mackessy, Stephen P; Castoe, Todd A

2018-06-15

The Mojave rattlesnake (Crotalus scutulatus) inhabits deserts and arid grasslands of the western United States and Mexico. Despite considerable interest in its highly toxic venom and the recognition of two subspecies, no molecular studies have characterized range-wide genetic diversity and population structure or tested species limits within C. scutulatus. We used mitochondrial DNA and thousands of nuclear loci from double-digest restriction site associated DNA sequencing to infer population genetic structure throughout the range of C. scutulatus, and to evaluate divergence times and gene flow between populations. We find strong support for several divergent mitochondrial and nuclear clades of C. scutulatus, including splits coincident with two major phylogeographic barriers: the Continental Divide and the elevational increase associated with the Central Mexican Plateau. We apply Bayesian clustering, phylogenetic inference, and coalescent-based species delimitation to our nuclear genetic data to test hypotheses of population structure. We also performed demographic analyses to test hypotheses relating to population divergence and gene flow. Collectively, our results support the existence of four distinct lineages within C. scutulatus, and genetically defined populations do not correspond with currently recognized subspecies ranges. Finally, we use approximate Bayesian computation to test hypotheses of divergence among multiple rattlesnake species groups distributed across the Continental Divide, and find evidence for co-divergence at this boundary during the mid-Pleistocene. Copyright © 2018 Elsevier Inc. All rights reserved.

Is black-hole ringdown a memory of its progenitor?

PubMed

Kamaretsos, Ioannis; Hannam, Mark; Sathyaprakash, B S

2012-10-05

We perform an extensive numerical study of coalescing black-hole binaries to understand the gravitational-wave spectrum of quasinormal modes excited in the merged black hole. Remarkably, we find that the masses and spins of the progenitor are clearly encoded in the mode spectrum of the ringdown signal. Some of the mode amplitudes carry the signature of the binary's mass ratio, while others depend critically on the spins. Simulations of precessing binaries suggest that our results carry over to generic systems. Using Bayesian inference, we demonstrate that it is possible to accurately measure the mass ratio and a proper combination of spins even when the binary is itself invisible to a detector. Using a mapping of the binary masses and spins to the final black-hole spin allows us to further extract the spin components of the progenitor. Our results could have tremendous implications for gravitational astronomy by facilitating novel tests of general relativity using merging black holes.

Origin of Clothing Lice Indicates Early Clothing Use by Anatomically Modern Humans in Africa

PubMed Central

Toups, Melissa A.; Kitchen, Andrew; Light, Jessica E.; Reed, David L.

2011-01-01

Clothing use is an important modern behavior that contributed to the successful expansion of humans into higher latitudes and cold climates. Previous research suggests that clothing use originated anywhere between 40,000 and 3 Ma, though there is little direct archaeological, fossil, or genetic evidence to support more specific estimates. Since clothing lice evolved from head louse ancestors once humans adopted clothing, dating the emergence of clothing lice may provide more specific estimates of the origin of clothing use. Here, we use a Bayesian coalescent modeling approach to estimate that clothing lice diverged from head louse ancestors at least by 83,000 and possibly as early as 170,000 years ago. Our analysis suggests that the use of clothing likely originated with anatomically modern humans in Africa and reinforces a broad trend of modern human developments in Africa during the Middle to Late Pleistocene. PMID:20823373

Application of Bayesian Approach in Cancer Clinical Trial

PubMed Central

Bhattacharjee, Atanu

2014-01-01

The application of Bayesian approach in clinical trials becomes more useful over classical method. It is beneficial from design to analysis phase. The straight forward statement is possible to obtain through Bayesian about the drug treatment effect. Complex computational problems are simple to handle with Bayesian techniques. The technique is only feasible to performing presence of prior information of the data. The inference is possible to establish through posterior estimates. However, some limitations are present in this method. The objective of this work was to explore the several merits and demerits of Bayesian approach in cancer research. The review of the technique will be helpful for the clinical researcher involved in the oncology to explore the limitation and power of Bayesian techniques. PMID:29147387

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

A new prior for bayesian anomaly detection: application to biosurveillance.

PubMed

Shen, Y; Cooper, G F

2010-01-01

Bayesian anomaly detection computes posterior probabilities of anomalous events by combining prior beliefs and evidence from data. However, the specification of prior probabilities can be challenging. This paper describes a Bayesian prior in the context of disease outbreak detection. The goal is to provide a meaningful, easy-to-use prior that yields a posterior probability of an outbreak that performs at least as well as a standard frequentist approach. If this goal is achieved, the resulting posterior could be usefully incorporated into a decision analysis about how to act in light of a possible disease outbreak. This paper describes a Bayesian method for anomaly detection that combines learning from data with a semi-informative prior probability over patterns of anomalous events. A univariate version of the algorithm is presented here for ease of illustration of the essential ideas. The paper describes the algorithm in the context of disease-outbreak detection, but it is general and can be used in other anomaly detection applications. For this application, the semi-informative prior specifies that an increased count over baseline is expected for the variable being monitored, such as the number of respiratory chief complaints per day at a given emergency department. The semi-informative prior is derived based on the baseline prior, which is estimated from using historical data. The evaluation reported here used semi-synthetic data to evaluate the detection performance of the proposed Bayesian method and a control chart method, which is a standard frequentist algorithm that is closest to the Bayesian method in terms of the type of data it uses. The disease-outbreak detection performance of the Bayesian method was statistically significantly better than that of the control chart method when proper baseline periods were used to estimate the baseline behavior to avoid seasonal effects. When using longer baseline periods, the Bayesian method performed as well as the control chart method. The time complexity of the Bayesian algorithm is linear in the number of the observed events being monitored, due to a novel, closed-form derivation that is introduced in the paper. This paper introduces a novel prior probability for Bayesian outbreak detection that is expressive, easy-to-apply, computationally efficient, and performs as well or better than a standard frequentist method.

The origin and phylogeography of dog rabies virus

PubMed Central

Bourhy, Hervé; Reynes, Jean-Marc; Dunham, Eleca J.; Dacheux, Laurent; Larrous, Florence; Huong, Vu Thi Que; Xu, Gelin; Yan, Jiaxin; Miranda, Mary Elizabeth G.; Holmes, Edward C.

2012-01-01

Rabies is a progressively fatal and incurable viral encephalitis caused by a lyssavirus infection. Almost all of the 55 000 annual rabies deaths in humans result from infection with dog rabies viruses (RABV). Despite the importance of rabies for human health, little is known about the spread of RABV in dog populations, and patterns of biodiversity have only been studied in limited geographical space. To address these questions on a global scale, we sequenced 62 new isolates and performed an extensive comparative analysis of RABV gene sequence data, representing 192 isolates sampled from 55 countries. From this, we identified six clades of RABV in non-flying mammals, each of which has a distinct geographical distribution, most likely reflecting major physical barriers to gene flow. Indeed, a detailed analysis of phylogeographic structure revealed only limited viral movement among geographical localities. Using Bayesian coalescent methods we also reveal that the sampled lineages of canid RABV derive from a common ancestor that originated within the past 1500 years. Additionally, we found no evidence for either positive selection or widespread population bottlenecks during the global expansion of canid RABV. Overall, our study reveals that the stochastic processes of genetic drift and population subdivision are the most important factors shaping the global phylogeography of canid RABV. PMID:18931062

Phylodynamic Analysis of Clinical and Environmental Vibrio cholerae Isolates from Haiti Reveals Diversification Driven by Positive Selection

PubMed Central

Azarian, Taj; Ali, Afsar; Johnson, Judith A.; Mohr, David; Prosperi, Mattia; Veras, Nazle M.; Jubair, Mohammed; Strickland, Samantha L.; Rashid, Mohammad H.; Alam, Meer T.; Weppelmann, Thomas A.; Katz, Lee S.; Tarr, Cheryl L.; Colwell, Rita R.

2014-01-01

ABSTRACT Phylodynamic analysis of genome-wide single-nucleotide polymorphism (SNP) data is a powerful tool to investigate underlying evolutionary processes of bacterial epidemics. The method was applied to investigate a collection of 65 clinical and environmental isolates of Vibrio cholerae from Haiti collected between 2010 and 2012. Characterization of isolates recovered from environmental samples identified a total of four toxigenic V. cholerae O1 isolates, four non-O1/O139 isolates, and a novel nontoxigenic V. cholerae O1 isolate with the classical tcpA gene. Phylogenies of strains were inferred from genome-wide SNPs using coalescent-based demographic models within a Bayesian framework. A close phylogenetic relationship between clinical and environmental toxigenic V. cholerae O1 strains was observed. As cholera spread throughout Haiti between October 2010 and August 2012, the population size initially increased and then fluctuated over time. Selection analysis along internal branches of the phylogeny showed a steady accumulation of synonymous substitutions and a progressive increase of nonsynonymous substitutions over time, suggesting diversification likely was driven by positive selection. Short-term accumulation of nonsynonymous substitutions driven by selection may have significant implications for virulence, transmission dynamics, and even vaccine efficacy. PMID:25538191

Nuclear and plastid markers reveal the persistence of genetic identity: a new perspective on the evolutionary history of Petunia exserta.

PubMed

Segatto, Ana Lúcia Anversa; Cazé, Ana Luíza Ramos; Turchetto, Caroline; Klahre, Ulrich; Kuhlemeier, Cris; Bonatto, Sandro Luis; Freitas, Loreta Brandão

2014-01-01

Recently divergent species that can hybridize are ideal models for investigating the genetic exchanges that can occur while preserving the species boundaries. Petunia exserta is an endemic species from a very limited and specific area that grows exclusively in rocky shelters. These shaded spots are an inhospitable habitat for all other Petunia species, including the closely related and widely distributed species P. axillaris. Individuals with intermediate morphologic characteristics have been found near the rocky shelters and were believed to be putative hybrids between P. exserta and P. axillaris, suggesting a situation where Petunia exserta is losing its genetic identity. In the current study, we analyzed the plastid intergenic spacers trnS/trnG and trnH/psbA and six nuclear CAPS markers in a large sampling design of both species to understand the evolutionary process occurring in this biological system. Bayesian clustering methods, cpDNA haplotype networks, genetic diversity statistics, and coalescence-based analyses support a scenario where hybridization occurs while two genetic clusters corresponding to two species are maintained. Our results reinforce the importance of coupling differentially inherited markers with an extensive geographic sample to assess the evolutionary dynamics of recently diverged species that can hybridize. Copyright © 2013 Elsevier Inc. All rights reserved.

Extensive cryptic species diversity and fine-scale endemism in the marine red alga Portieria in the Philippines

PubMed Central

Payo, Dioli Ann; Leliaert, Frederik; Verbruggen, Heroen; D'hondt, Sofie; Calumpong, Hilconida P.; De Clerck, Olivier

2013-01-01

We investigated species diversity and distribution patterns of the marine red alga Portieria in the Philippine archipelago. Species boundaries were tested based on mitochondrial, plastid and nuclear encoded loci, using a general mixed Yule-coalescent (GMYC) model-based approach and a Bayesian multilocus species delimitation method. The outcome of the GMYC analysis of the mitochondrial encoded cox2-3 dataset was highly congruent with the multilocus analysis. In stark contrast with the current morphology-based assumption that the genus includes a single, widely distributed species in the Indo-West Pacific (Portieria hornemannii), DNA-based species delimitation resulted in the recognition of 21 species within the Philippines. Species distributions were found to be highly structured with most species restricted to island groups within the archipelago. These extremely narrow species ranges and high levels of intra-archipelagic endemism contrast with the wide-held belief that marine organisms generally have large geographical ranges and that endemism is at most restricted to the archipelagic level. Our results indicate that speciation in the marine environment may occur at spatial scales smaller than 100 km, comparable with some terrestrial systems. Our finding of fine-scale endemism has important consequences for marine conservation and management. PMID:23269854

Molecular phylogenetics reveals convergent evolution in lower Congo River spiny eels.

PubMed

Alter, S Elizabeth; Brown, Bianca; Stiassny, Melanie L J

2015-10-15

The lower Congo River (LCR) is a region of exceptional species diversity and endemism in the Congo basin, including numerous species of spiny eels (genus Mastacembelus). Four of these exhibit distinctive phenotypes characterized by greatly reduced optic globes deeply embedded into the head (cryptophthalmia) and reduced (or absent) melanin pigmentation, among other characteristics. A strikingly similar cryptophthalmic phenotype is also found in members of a number of unrelated fish families, strongly suggesting the possibility of convergent evolution. However, little is known about the evolutionary processes that shaped diversification in LCR Mastacembelus, their biogeographic origins, or when colonization of the LCR occurred. We sequenced mitochondrial and nuclear genes from Mastacembelus species collected in the lower Congo River, and compared them with other African species and Asian representatives as outgroups. We analyzed the sequence data using Maximum Likelihood and Bayesian phylogenetic inference. Bayesian and Maximum Likelihood phylogenetic analyses, and Bayesian coalescent methods for species tree reconstruction, reveal that endemic LCR spiny eels derive from two independent origins, clearly demonstrating convergent evolution of the cryptophthalmic phenotype. Mastacembelus crassus, M. aviceps, and M. simbi form a clade, allied to species found in southern, eastern and central Africa. Unexpectedly, M. brichardi and brachyrhinus fall within a clade otherwise endemic to Lake Tanganikya (LT) ca. 1500 km east of the LCR. Divergence dating suggests the ages of these two clades of LCR endemics differ markedly. The age of the crassus group is estimated at ~4 Myr while colonization of the LCR by the brichardi-brachyrhinus progenitor was considerably more recent, dated at ~0.5 Myr. The phylogenetic framework of spiny eels presented here, the first to include LCR species, demonstrates that cryptophthalmia and associated traits evolved at least twice in Mastacembelus: once in M. brichardi and at least once in the M. crassus clade. Timing of diversification is broadly consistent with the onset of modern high-energy flow conditions in the LCR and with previous studies of endemic cichlids. The close genetic relationship between M. brichardi and M. brachyrhinus is particularly notable given the extreme difference in phenotype between these species, and additional work is needed to better understand the evolutionary history of diversification in this clade. The findings presented here demonstrate strong, multi-trait convergence in LCR spiny eels, suggesting that extreme selective pressures have shaped numerous phenotypic attributes of the endemic species of this region.

Bayesian data augmentation methods for the synthesis of qualitative and quantitative research findings

PubMed Central

Crandell, Jamie L.; Voils, Corrine I.; Chang, YunKyung; Sandelowski, Margarete

2010-01-01

The possible utility of Bayesian methods for the synthesis of qualitative and quantitative research has been repeatedly suggested but insufficiently investigated. In this project, we developed and used a Bayesian method for synthesis, with the goal of identifying factors that influence adherence to HIV medication regimens. We investigated the effect of 10 factors on adherence. Recognizing that not all factors were examined in all studies, we considered standard methods for dealing with missing data and chose a Bayesian data augmentation method. We were able to summarize, rank, and compare the effects of each of the 10 factors on medication adherence. This is a promising methodological development in the synthesis of qualitative and quantitative research. PMID:21572970

Practical Bayesian tomography

NASA Astrophysics Data System (ADS)

Granade, Christopher; Combes, Joshua; Cory, D. G.

2016-03-01

In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of-the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we address all three problems. First, we use modern statistical methods, as pioneered by Huszár and Houlsby (2012 Phys. Rev. A 85 052120) and by Ferrie (2014 New J. Phys. 16 093035), to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first priors on quantum states and channels that allow for including useful experimental insight. Finally, we develop a method that allows tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.

Bayesian data analysis in observational comparative effectiveness research: rationale and examples.

PubMed

Olson, William H; Crivera, Concetta; Ma, Yi-Wen; Panish, Jessica; Mao, Lian; Lynch, Scott M

2013-11-01

Many comparative effectiveness research and patient-centered outcomes research studies will need to be observational for one or both of two reasons: first, randomized trials are expensive and time-consuming; and second, only observational studies can answer some research questions. It is generally recognized that there is a need to increase the scientific validity and efficiency of observational studies. Bayesian methods for the design and analysis of observational studies are scientifically valid and offer many advantages over frequentist methods, including, importantly, the ability to conduct comparative effectiveness research/patient-centered outcomes research more efficiently. Bayesian data analysis is being introduced into outcomes studies that we are conducting. Our purpose here is to describe our view of some of the advantages of Bayesian methods for observational studies and to illustrate both realized and potential advantages by describing studies we are conducting in which various Bayesian methods have been or could be implemented.

Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

NASA Astrophysics Data System (ADS)

Sharma, Sanjib

2017-08-01

Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlo based methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (available at https://github.com/sanjibs/bmcmc/ ) that implements some of the algorithms and examples discussed here.

Experimental study on the coalescence process of SiO2 supported colloidal Au nanoparticles

NASA Astrophysics Data System (ADS)

Ruffino, F.; Torrisi, V.; Grimaldi, M. G.

2015-11-01

We report on an experimental study of the coalescence-driven grow process of colloidal Au nanoparticles on SiO2 surface. Nanoparticles with 30, 50, 80, 100 nm nominal diameters on a SiO2 substrate were deposited, from solutions, by the drop-casting method. Then, annealing processes, in the 573-1173 K temperature range and 900-3600 s time range, were performed. Using scanning electron microscopy analyses, the temporal evolution of the nanoparticles sizes has been studied. In particular, for all classes of nanoparticles, the experimental-obtained diameters distributions evidenced double-peak shapes (i. e. bimodal distributions): a first peak centered (and unchanged changing the annealing temperature and/or time) at the nominal diameter of the as-deposited nanoparticles, , and a second peak shifting at higher mean diameters, , increasing the annealing temperature and/or time. This observation suggested us a coalescence-driven growth process of a nanoparticles sub-population. As a consequence, the temporal evolution of (for each class of nanoparticles and each annealing temperature), within the well-established particles coalescence theoretical framework, has been analyzed. In particular, by the analyses of the experimental data using relations as prescribed by the theoretical model, a characteristic size-dependent activation energy for the Au nanoparticles coalescence process has been evaluated.

Wavelength-Dependent Plasmon-Mediated Coalescence of Two Gold Nanorods

NASA Astrophysics Data System (ADS)

Liaw, Jiunn-Woei; Lin, Wu-Chun; Kuo, Mao-Kuen

2017-04-01

Plasmon-mediated coalescence of two nearby gold nanorods (NRs) suspended in water induced by the illumination of a linearly polarized (LP) light was studied theoretically. We analyzed the coupled optical forces and torques in terms of Maxwell’s stress tensor upon two identical NRs irradiated by a LP plane wave using the multiple multipole method to estimate the optomechanical outcome. Numerical results show that the light-matter interaction can perform attraction or repulsion, depending on their initial configurations. For the attraction, the end-to-end or side-by-side coalescence of the two gold NRs could be caused by the LP light, depending on the wavelength. For example, the side-by-side coalescence of two adjacent NRs of r = 15 nm and L = 120 nm is most likely induced by 800-nm LP laser beam, whereas the end-to-end coalescence by 1064-nm or 1700-nm LP laser. These distinct phenomena are attributed to the perpendicular or parallel alignment of NR to the polarization of LP light in different wavelength ranges. The magnitude of optical force, proportional to the light’s fluence, could be stronger than van der Waals force. The estimation based on quasi-static model without considering the fluid dynamics may provide an insight to optical manipulation on the self-assembly of gold colloid.

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Bayesian Analysis of Longitudinal Data Using Growth Curve Models

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamagami, Fumiaki; Wang, Lijuan Lijuan; Nesselroade, John R.; Grimm, Kevin J.

2007-01-01

Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data…

Species delimitation in northern European water scavenger beetles of the genus Hydrobius (Coleoptera, Hydrophilidae)

PubMed Central

Fossen, Erlend I.; Ekrem, Torbjørn; Nilsson, Anders N.; Bergsten, Johannes

2016-01-01

Abstract The chiefly Holarctic Hydrobius species complex (Coleoptera, Hydrophilidae) currently consists of Hydrobius arcticus Kuwert, 1890, and three morphological variants of Hydrobius fuscipes (Linnaeus, 1758): var. fuscipes, var. rottenbergii and var. subrotundus in northern Europe. Here molecular and morphological data are used to test the species boundaries in this species complex. Three gene segments (COI, H3 and ITS2) were sequenced and analyzed with Bayesian methods to infer phylogenetic relationships. The Generalized Mixed Yule Coalescent (GMYC) model and two versions of the Bayesian species delimitation method BPP, with or without an a priori defined guide tree (v2.2 & v3.0), were used to evaluate species limits. External and male genital characters of primarily Fennoscandian specimens were measured and statistically analyzed to test for significant differences in quantitative morphological characters. The four morphotypes formed separate genetic clusters on gene trees and were delimited as separate species by GMYC and by both versions of BPP, despite specimens of Hydrobius fuscipes var. fuscipes and Hydrobius fuscipes var. subrotundus being sympatric. Hydrobius arcticus and Hydrobius fuscipes var. rottenbergii could only be separated genetically with ITS2, and were delimited statistically with GMYC on ITS2 and with BPP on the combined data. In addition, six or seven potentially cryptic species of the Hydrobius fuscipes complex from regions outside northern Europe were delimited genetically. Although some overlap was found, the mean values of six male genital characters were significantly different between the morphotypes (p < 0.001). Morphological characters previously presumed to be diagnostic were less reliable to separate Hydrobius fuscipes var. fuscipes from Hydrobius fuscipes var. subrotundus, but characters in the literature for Hydrobius arcticus and Hydrobius fuscipes var. rottenbergii were diagnostic. Overall, morphological and molecular evidence strongly suggest that Hydrobius arcticus and the three morphological variants of Hydrobius fuscipes are separate species and Hydrobius rottenbergii Gerhardt, 1872, stat. n. and Hydrobius subrotundus Stephens, 1829, stat. n. are elevated to valid species. An identification key to northern European species of Hydrobius is provided. PMID:27081333

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

PubMed

Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon

2017-12-01

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.

Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision-makers.

PubMed

Steingroever, Helen; Pachur, Thorsten; Šmíra, Martin; Lee, Michael D

2018-06-01

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

Characterization of solids deposited on the modular caustic-side solvent extraction unit (MCU) coalescer media removed in October 2014

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fondeur, F.

2016-03-01

In February 2015, Savannah River National Laboratory (SRNL) received a Strip Effluent (SE) coalescer (FLT-304) from MCU. That coalescer was first installed at MCU in July 2014 and removed in October 2014. While processing approximately 31,400 gallons of strip solution, the pressure drop steadily increased from 1 psi to beyond the administrative limit of 20 psi. The physical and chemical analysis was conducted on this coalescer to determine the mechanism that led to the plugging of this coalescer. Characterization of this coalescer revealed the adsorption of organic containing amines as well as MCU modifier. The amines are probably from themore » decomposition of the suppressor (TiDG) as well as from bacteria. This adsorption may have changed the surface energetics (characteristics) of the coalescer fibers and therefore, their wetting behavior. A very small amount of inorganic solids were found to have deposited on this coalescer (possibly an artifact of cleaning the coalescer with Boric acid. However, we believe that inorganic precipitation, as has been seen in the past, did not play a role in the high pressure drop rise of this coalescer. With regards to the current practice of reducing the radioactive content of the SE coalescer, it is recommended that future SE coalescer should be flushed with 10 mM boric acid which is currently used at MCU. Plugging of the SE coalescer was most likely due to the formation and accumulation of a water-in-oil emulsion that reduced the overall porosity of the coalescer. There is also evidence that a bimodal oil particle distribution may have entered and deposited in the coalescer and caused the initial increase in pressure drop.« less

Critical Void Volume Fraction fc at Void Coalescence for S235JR Steel at Low Initial Stress Triaxiality

NASA Astrophysics Data System (ADS)

Grzegorz Kossakowski, Paweł; Wciślik, Wiktor

2017-10-01

The paper is concerned with the nucleation, growth and coalescence of microdefects in the form of voids in S235JR steel. The material is known to be one of the basic steel grades commonly used in the construction industry. The theory and methods of damage mechanics were applied to determine and describe the failure mechanisms that occur when the material undergoes deformation. Until now, engineers have generally employed the Gurson-Tvergaard- Needleman model. This material model based on damage mechanics is well suited to define and analyze failure processes taking place in the microstructure of S235JR steel. It is particularly important to determine the critical void volume fraction fc , which is one of the basic parameters of the Gurson-Tvergaard-Needleman material model. As the critical void volume fraction fc refers to the failure stage, it is determined from the data collected for the void coalescence phase. A case of multi-axial stresses is considered taking into account the effects of spatial stress state. In this study, the parameter of stress triaxiality η was used to describe the failure phenomena. Cylindrical tensile specimens with a circumferential notch were analysed to obtain low values of initial stress triaxiality (η = 0.556 of the range) in order to determine the critical void volume fraction fc . It is essential to emphasize how unique the method applied is and how different it is from the other more common methods involving parameter calibration, i.e. curve-fitting methods. The critical void volume fraction fc at void coalescence was established through digital image analysis of surfaces of S235JR steel, which involved studying real, physical results obtained directly from the material tested.

Phylodynamic Analysis Reveals CRF01_AE Dissemination between Japan and Neighboring Asian Countries and the Role of Intravenous Drug Use in Transmission

PubMed Central

Shiino, Teiichiro; Hattori, Junko; Yokomaku, Yoshiyuki; Iwatani, Yasumasa; Sugiura, Wataru

2014-01-01

Background One major circulating HIV-1 subtype in Southeast Asian countries is CRF01_AE, but little is known about its epidemiology in Japan. We conducted a molecular phylodynamic study of patients newly diagnosed with CRF01_AE from 2003 to 2010. Methods Plasma samples from patients registered in Japanese Drug Resistance HIV-1 Surveillance Network were analyzed for protease-reverse transcriptase sequences; all sequences undergo subtyping and phylogenetic analysis using distance-matrix-based, maximum likelihood and Bayesian coalescent Markov Chain Monte Carlo (MCMC) phylogenetic inferences. Transmission clusters were identified using interior branch test and depth-first searches for sub-tree partitions. Times of most recent common ancestor (tMRCAs) of significant clusters were estimated using Bayesian MCMC analysis. Results Among 3618 patient registered in our network, 243 were infected with CRF01_AE. The majority of individuals with CRF01_AE were Japanese, predominantly male, and reported heterosexual contact as their risk factor. We found 5 large clusters with ≥5 members and 25 small clusters consisting of pairs of individuals with highly related CRF01_AE strains. The earliest cluster showed a tMRCA of 1996, and consisted of individuals with their known risk as heterosexual contacts. The other four large clusters showed later tMRCAs between 2000 and 2002 with members including intravenous drug users (IVDU) and non-Japanese, but not men who have sex with men (MSM). In contrast, small clusters included a high frequency of individuals reporting MSM risk factors. Phylogenetic analysis also showed that some individuals infected with HIV strains spread in East and South-eastern Asian countries. Conclusions Introduction of CRF01_AE viruses into Japan is estimated to have occurred in the 1990s. CFR01_AE spread via heterosexual behavior, then among persons connected with non-Japanese, IVDU, and MSM. Phylogenetic analysis demonstrated that some viral variants are largely restricted to Japan, while others have a broad geographic distribution. PMID:25025900

Sequential Probability Ratio Test for Collision Avoidance Maneuver Decisions

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Markley, F. Landis

2010-01-01

When facing a conjunction between space objects, decision makers must chose whether to maneuver for collision avoidance or not. We apply a well-known decision procedure, the sequential probability ratio test, to this problem. We propose two approaches to the problem solution, one based on a frequentist method, and the other on a Bayesian method. The frequentist method does not require any prior knowledge concerning the conjunction, while the Bayesian method assumes knowledge of prior probability densities. Our results show that both methods achieve desired missed detection rates, but the frequentist method's false alarm performance is inferior to the Bayesian method's

Informative priors on fetal fraction increase power of the noninvasive prenatal screen.

PubMed

Xu, Hanli; Wang, Shaowei; Ma, Lin-Lin; Huang, Shuai; Liang, Lin; Liu, Qian; Liu, Yang-Yang; Liu, Ke-Di; Tan, Ze-Min; Ban, Hao; Guan, Yongtao; Lu, Zuhong

2017-11-09

PurposeNoninvasive prenatal screening (NIPS) sequences a mixture of the maternal and fetal cell-free DNA. Fetal trisomy can be detected by examining chromosomal dosages estimated from sequencing reads. The traditional method uses the Z-test, which compares a subject against a set of euploid controls, where the information of fetal fraction is not fully utilized. Here we present a Bayesian method that leverages informative priors on the fetal fraction.MethodOur Bayesian method combines the Z-test likelihood and informative priors of the fetal fraction, which are learned from the sex chromosomes, to compute Bayes factors. Bayesian framework can account for nongenetic risk factors through the prior odds, and our method can report individual positive/negative predictive values.ResultsOur Bayesian method has more power than the Z-test method. We analyzed 3,405 NIPS samples and spotted at least 9 (of 51) possible Z-test false positives.ConclusionBayesian NIPS is more powerful than the Z-test method, is able to account for nongenetic risk factors through prior odds, and can report individual positive/negative predictive values.Genetics in Medicine advance online publication, 9 November 2017; doi:10.1038/gim.2017.186.

Frequency-independent approach to calculate physical optics radiations with the quadratic concave phase variations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Yu Mao, E-mail: yumaowu@fudan.edu.cn; Teng, Si Jia, E-mail: sjteng12@fudan.edu.cn

In this work, we develop the numerical steepest descent path (NSDP) method to calculate the physical optics (PO) radiations with the quadratic concave phase variations. With the surface integral equation method, the physical optics (PO) scattered fields are formulated and further reduced to the surface integrals. The high frequency physical critical points contributions, including the stationary phase points, the boundary resonance points and the vertex points are comprehensively studied via the proposed NSDP method. The key contributions of this work are twofold. One is that together with the PO integrals taking the quadratic parabolic and hyperbolic phase terms, this workmore » makes the NSDP theory be complete for treating the PO integrals with quadratic phase variations. Another is that, in order to illustrate the transition effect of the high frequency physical critical points, in this work, we consider and further extend the NSDP method to calculate the PO integrals with the coalescence of the high frequency critical points. Numerical results for the highly oscillatory PO integral with the coalescence of the critical points are given to verify the efficiency of the proposed NSDP method. The NSDP method could achieve the frequency independent computational workload and error controllable accuracy in all the numerical experiments, especially for the case of the coalescence of the high frequency critical points.« less

Determination of confidence limits for experiments with low numbers of counts. [Poisson-distributed photon counts from astrophysical sources

NASA Technical Reports Server (NTRS)

Kraft, Ralph P.; Burrows, David N.; Nousek, John A.

1991-01-01

Two different methods, classical and Bayesian, for determining confidence intervals involving Poisson-distributed data are compared. Particular consideration is given to cases where the number of counts observed is small and is comparable to the mean number of background counts. Reasons for preferring the Bayesian over the classical method are given. Tables of confidence limits calculated by the Bayesian method are provided for quick reference.

Comparison between the basic least squares and the Bayesian approach for elastic constants identification

NASA Astrophysics Data System (ADS)

Gogu, C.; Haftka, R.; LeRiche, R.; Molimard, J.; Vautrin, A.; Sankar, B.

2008-11-01

The basic formulation of the least squares method, based on the L2 norm of the misfit, is still widely used today for identifying elastic material properties from experimental data. An alternative statistical approach is the Bayesian method. We seek here situations with significant difference between the material properties found by the two methods. For a simple three bar truss example we illustrate three such situations in which the Bayesian approach leads to more accurate results: different magnitude of the measurements, different uncertainty in the measurements and correlation among measurements. When all three effects add up, the Bayesian approach can have a large advantage. We then compared the two methods for identification of elastic constants from plate vibration natural frequencies.

Bayesian methods in reliability

NASA Astrophysics Data System (ADS)

Sander, P.; Badoux, R.

1991-11-01

The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.

An introduction to Bayesian statistics in health psychology.

PubMed

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

Molecular dynamics simulation of the coalescence and melting process of Au and Cu nano-clusters

NASA Astrophysics Data System (ADS)

Chen, Gang; Wang, Chuan Jie; Zhang, Peng

2018-03-01

Molecular dynamic (MD) method is used to study the coalescence and fusing process of Au and Cu nanoclusters. The results show that shear deformation, surface and interface diffusion play important role in different stages of all simulation procedure. In most cases, shear deformation produces the twin boundary or/and stacking fault in particles by particle rotation and slide. The angle between the {111} of Au and Cu particles decrease with increasing temperature, which promotes the formation of the stable interface. Furthermore, the coalescence point and melting temperature increase as cluster diameter increases. For the other cases, there are no particle rotation and slide phenomenon in the elevating temperature process because the stable interface can be formed by forming twin boundaries once two particles contact.

Reconstructing population histories from single nucleotide polymorphism data.

PubMed

Sirén, Jukka; Marttinen, Pekka; Corander, Jukka

2011-01-01

Population genetics encompasses a strong theoretical and applied research tradition on the multiple demographic processes that shape genetic variation present within a species. When several distinct populations exist in the current generation, it is often natural to consider the pattern of their divergence from a single ancestral population in terms of a binary tree structure. Inference about such population histories based on molecular data has been an intensive research topic in the recent years. The most common approach uses coalescent theory to model genealogies of individuals sampled from the current populations. Such methods are able to compare several different evolutionary scenarios and to estimate demographic parameters. However, their major limitation is the enormous computational complexity associated with the indirect modeling of the demographies, which limits the application to small data sets. Here, we propose a novel Bayesian method for inferring population histories from unlinked single nucleotide polymorphisms, which is applicable also to data sets harboring large numbers of individuals from distinct populations. We use an approximation to the neutral Wright-Fisher diffusion to model random fluctuations in allele frequencies. The population histories are modeled as binary rooted trees that represent the historical order of divergence of the different populations. A combination of analytical, numerical, and Monte Carlo integration techniques are utilized for the inferences. A particularly important feature of our approach is that it provides intuitive measures of statistical uncertainty related with the estimates computed, which may be entirely lacking for the alternative methods in this context. The potential of our approach is illustrated by analyses of both simulated and real data sets.

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.

PubMed

Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L

2016-02-10

Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure. Copyright © 2015 John Wiley & Sons, Ltd.

Phylogenetic Relationships and Species Delimitation in Pinus Section Trifoliae Inferrred from Plastid DNA

PubMed Central

Hernández-León, Sergio; Gernandt, David S.; Pérez de la Rosa, Jorge A.; Jardón-Barbolla, Lev

2013-01-01

Recent diversification followed by secondary contact and hybridization may explain complex patterns of intra- and interspecific morphological and genetic variation in the North American hard pines (Pinus section Trifoliae), a group of approximately 49 tree species distributed in North and Central America and the Caribbean islands. We concatenated five plastid DNA markers for an average of 3.9 individuals per putative species and assessed the suitability of the five regions as DNA bar codes for species identification, species delimitation, and phylogenetic reconstruction. The ycf1 gene accounted for the greatest proportion of the alignment (46.9%), the greatest proportion of variable sites (74.9%), and the most unique sequences (75 haplotypes). Phylogenetic analysis recovered clades corresponding to subsections Australes, Contortae, and Ponderosae. Sequences for 23 of the 49 species were monophyletic and sequences for another 9 species were paraphyletic. Morphologically similar species within subsections usually grouped together, but there were exceptions consistent with incomplete lineage sorting or introgression. Bayesian relaxed molecular clock analyses indicated that all three subsections diversified relatively recently during the Miocene. The general mixed Yule-coalescent method gave a mixed model estimate of only 22 or 23 evolutionary entities for the plastid sequences, which corresponds to less than half the 49 species recognized based on morphological species assignments. Including more unique haplotypes per species may result in higher estimates, but low mutation rates, recent diversification, and large effective population sizes may limit the effectiveness of this method to detect evolutionary entities. PMID:23936218

Identifying predictors of time-inhomogeneous viral evolutionary processes.

PubMed

Bielejec, Filip; Baele, Guy; Rodrigo, Allen G; Suchard, Marc A; Lemey, Philippe

2016-07-01

Various factors determine the rate at which mutations are generated and fixed in viral genomes. Viral evolutionary rates may vary over the course of a single persistent infection and can reflect changes in replication rates and selective dynamics. Dedicated statistical inference approaches are required to understand how the complex interplay of these processes shapes the genetic diversity and divergence in viral populations. Although evolutionary models accommodating a high degree of complexity can now be formalized, adequately informing these models by potentially sparse data, and assessing the association of the resulting estimates with external predictors, remains a major challenge. In this article, we present a novel Bayesian evolutionary inference method, which integrates multiple potential predictors and tests their association with variation in the absolute rates of synonymous and non-synonymous substitutions along the evolutionary history. We consider clinical and virological measures as predictors, but also changes in population size trajectories that are simultaneously inferred using coalescent modelling. We demonstrate the potential of our method in an application to within-host HIV-1 sequence data sampled throughout the infection of multiple patients. While analyses of individual patient populations lack statistical power, we detect significant evidence for an abrupt drop in non-synonymous rates in late stage infection and a more gradual increase in synonymous rates over the course of infection in a joint analysis across all patients. The former is predicted by the immune relaxation hypothesis while the latter may be in line with increasing replicative fitness during the asymptomatic stage.

Sorting through the chaff, nDNA gene trees for phylogenetic inference and hybrid identification of annual sunflowers (Helianthus sect. Helianthus).

PubMed

Moody, Michael L; Rieseberg, Loren H

2012-07-01

The annual sunflowers (Helianthus sect. Helianthus) present a formidable challenge for phylogenetic inference because of ancient hybrid speciation, recent introgression, and suspected issues with deep coalescence. Here we analyze sequence data from 11 nuclear DNA (nDNA) genes for multiple genotypes of species within the section to (1) reconstruct the phylogeny of this group, (2) explore the utility of nDNA gene trees for detecting hybrid speciation and introgression; and (3) test an empirical method of hybrid identification based on the phylogenetic congruence of nDNA gene trees from tightly linked genes. We uncovered considerable topological heterogeneity among gene trees with or without three previously identified hybrid species included in the analyses, as well as a general lack of reciprocal monophyly of species. Nonetheless, partitioned Bayesian analyses provided strong support for the reciprocal monophyly of all species except H. annuus (0.89 PP), the most widespread and abundant annual sunflower. Previous hypotheses of relationships among taxa were generally strongly supported (1.0 PP), except among taxa typically associated with H. annuus, apparently due to the paraphyly of the latter in all gene trees. While the individual nDNA gene trees provided a useful means for detecting recent hybridization, identification of ancient hybridization was problematic for all ancient hybrid species, even when linkage was considered. We discuss biological factors that affect the efficacy of phylogenetic methods for hybrid identification.

Meiotic gene-conversion rate and tract length variation in the human genome.

PubMed

Padhukasahasram, Badri; Rannala, Bruce

2013-02-27

Meiotic recombination occurs in the form of two different mechanisms called crossing-over and gene-conversion and both processes have an important role in shaping genetic variation in populations. Although variation in crossing-over rates has been studied extensively using sperm-typing experiments, pedigree studies and population genetic approaches, our knowledge of variation in gene-conversion parameters (ie, rates and mean tract lengths) remains far from complete. To explore variability in population gene-conversion rates and its relationship to crossing-over rate variation patterns, we have developed and validated using coalescent simulations a comprehensive Bayesian full-likelihood method that can jointly infer crossing-over and gene-conversion rates as well as tract lengths from population genomic data under general variable rate models with recombination hotspots. Here, we apply this new method to SNP data from multiple human populations and attempt to characterize for the first time the fine-scale variation in gene-conversion parameters along the human genome. We find that the estimated ratio of gene-conversion to crossing-over rates varies considerably across genomic regions as well as between populations. However, there is a great degree of uncertainty associated with such estimates. We also find substantial evidence for variation in the mean conversion tract length. The estimated tract lengths did not show any negative relationship with the local heterozygosity levels in our analysis.European Journal of Human Genetics advance online publication, 27 February 2013; doi:10.1038/ejhg.2013.30.

Phylogenetic relationships and species delimitation in pinus section trifoliae inferrred from plastid DNA.

PubMed

Hernández-León, Sergio; Gernandt, David S; Pérez de la Rosa, Jorge A; Jardón-Barbolla, Lev

2013-01-01

Recent diversification followed by secondary contact and hybridization may explain complex patterns of intra- and interspecific morphological and genetic variation in the North American hard pines (Pinus section Trifoliae), a group of approximately 49 tree species distributed in North and Central America and the Caribbean islands. We concatenated five plastid DNA markers for an average of 3.9 individuals per putative species and assessed the suitability of the five regions as DNA bar codes for species identification, species delimitation, and phylogenetic reconstruction. The ycf1 gene accounted for the greatest proportion of the alignment (46.9%), the greatest proportion of variable sites (74.9%), and the most unique sequences (75 haplotypes). Phylogenetic analysis recovered clades corresponding to subsections Australes, Contortae, and Ponderosae. Sequences for 23 of the 49 species were monophyletic and sequences for another 9 species were paraphyletic. Morphologically similar species within subsections usually grouped together, but there were exceptions consistent with incomplete lineage sorting or introgression. Bayesian relaxed molecular clock analyses indicated that all three subsections diversified relatively recently during the Miocene. The general mixed Yule-coalescent method gave a mixed model estimate of only 22 or 23 evolutionary entities for the plastid sequences, which corresponds to less than half the 49 species recognized based on morphological species assignments. Including more unique haplotypes per species may result in higher estimates, but low mutation rates, recent diversification, and large effective population sizes may limit the effectiveness of this method to detect evolutionary entities.

Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees.

PubMed

Yang, Ziheng; Zhu, Tianqi

2018-02-20

The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.

Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.

PubMed

Yalch, Matthew M

2016-03-01

Several contemporary researchers have noted the virtues of Bayesian methods of data analysis. Although debates continue about whether conventional or Bayesian statistics is the "better" approach for researchers in general, there are reasons why Bayesian methods may be well suited to the study of psychological trauma in particular. This article describes how Bayesian statistics offers practical solutions to the problems of data non-normality, small sample size, and missing data common in research on psychological trauma. After a discussion of these problems and the effects they have on trauma research, this article explains the basic philosophical and statistical foundations of Bayesian statistics and how it provides solutions to these problems using an applied example. Results of the literature review and the accompanying example indicates the utility of Bayesian statistics in addressing problems common in trauma research. Bayesian statistics provides a set of methodological tools and a broader philosophical framework that is useful for trauma researchers. Methodological resources are also provided so that interested readers can learn more. (c) 2016 APA, all rights reserved).

Bayesian data analysis for newcomers.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

Bayes in biological anthropology.

PubMed

Konigsberg, Lyle W; Frankenberg, Susan R

2013-12-01

In this article, we both contend and illustrate that biological anthropologists, particularly in the Americas, often think like Bayesians but act like frequentists when it comes to analyzing a wide variety of data. In other words, while our research goals and perspectives are rooted in probabilistic thinking and rest on prior knowledge, we often proceed to use statistical hypothesis tests and confidence interval methods unrelated (or tenuously related) to the research questions of interest. We advocate for applying Bayesian analyses to a number of different bioanthropological questions, especially since many of the programming and computational challenges to doing so have been overcome in the past two decades. To facilitate such applications, this article explains Bayesian principles and concepts, and provides concrete examples of Bayesian computer simulations and statistics that address questions relevant to biological anthropology, focusing particularly on bioarchaeology and forensic anthropology. It also simultaneously reviews the use of Bayesian methods and inference within the discipline to date. This article is intended to act as primer to Bayesian methods and inference in biological anthropology, explaining the relationships of various methods to likelihoods or probabilities and to classical statistical models. Our contention is not that traditional frequentist statistics should be rejected outright, but that there are many situations where biological anthropology is better served by taking a Bayesian approach. To this end it is hoped that the examples provided in this article will assist researchers in choosing from among the broad array of statistical methods currently available. Copyright © 2013 Wiley Periodicals, Inc.

Unravelling the genetic differentiation among varieties of the Neotropical savanna tree Hancornia speciosa Gomes.

PubMed

Collevatti, Rosane G; Rodrigues, Eduardo E; Vitorino, Luciana C; Lima-Ribeiro, Matheus S; Chaves, Lázaro J; Telles, Mariana P C

2018-04-20

Spatial distribution of species genetic diversity is often driven by geographical distance (isolation by distance) or environmental conditions (isolation by environment), especially under climate change scenarios such as Quaternary glaciations. Here, we used coalescent analyses coupled with ecological niche modelling (ENM), spatially explicit quantile regression analyses and the multiple matrix regression with randomization (MMRR) approach to unravel the patterns of genetic differentiation in the widely distributed Neotropical savanna tree, Hancornia speciosa (Apocynaceae). Due to its high morphological differentiation, the species was originally classified into six botanical varieties by Monachino, and has recently been recognized as only two varieties by Flora do Brasil 2020. Thus, H. speciosa is a good biological model for learning about evolution of phenotypic plasticity under genetic and ecological effects, and predicting their responses to changing environmental conditions. We sampled 28 populations (777 individuals) of Monachino's four varieties of H. speciosa and used seven microsatellite loci to genotype them. Bayesian clustering showed five distinct genetic groups (K = 5) with high admixture among Monachino's varieties, mainly among populations in the central area of the species geographical range. Genetic differentiation among Monachino's varieties was lower than the genetic differentiation among populations within varieties, with higher within-population inbreeding. A high historical connectivity among populations of the central Cerrado shown by coalescent analyses may explain the high admixture among varieties. In addition, areas of higher climatic suitability also presented higher genetic diversity in such a way that the wide historical refugium across central Brazil might have promoted the long-term connectivity among populations. Yet, FST was significantly related to geographic distances, but not to environmental distances, and coalescent analyses and ENM predicted a demographical scenario of quasi-stability through time. Our findings show that demographical history and isolation by distance, but not isolation by environment, drove genetic differentiation of populations. Finally, the genetic clusters do not support the two recently recognized botanical varieties of H. speciosa, but partially support Monachino's classification at least for the four sampled varieties, similar to morphological variation.

Rigorous Approach in Investigation of Seismic Structure and Source Characteristicsin Northeast Asia: Hierarchical and Trans-dimensional Bayesian Inversion

NASA Astrophysics Data System (ADS)

Mustac, M.; Kim, S.; Tkalcic, H.; Rhie, J.; Chen, Y.; Ford, S. R.; Sebastian, N.

2015-12-01

Conventional approaches to inverse problems suffer from non-linearity and non-uniqueness in estimations of seismic structures and source properties. Estimated results and associated uncertainties are often biased by applied regularizations and additional constraints, which are commonly introduced to solve such problems. Bayesian methods, however, provide statistically meaningful estimations of models and their uncertainties constrained by data information. In addition, hierarchical and trans-dimensional (trans-D) techniques are inherently implemented in the Bayesian framework to account for involved error statistics and model parameterizations, and, in turn, allow more rigorous estimations of the same. Here, we apply Bayesian methods throughout the entire inference process to estimate seismic structures and source properties in Northeast Asia including east China, the Korean peninsula, and the Japanese islands. Ambient noise analysis is first performed to obtain a base three-dimensional (3-D) heterogeneity model using continuous broadband waveforms from more than 300 stations. As for the tomography of surface wave group and phase velocities in the 5-70 s band, we adopt a hierarchical and trans-D Bayesian inversion method using Voronoi partition. The 3-D heterogeneity model is further improved by joint inversions of teleseismic receiver functions and dispersion data using a newly developed high-efficiency Bayesian technique. The obtained model is subsequently used to prepare 3-D structural Green's functions for the source characterization. A hierarchical Bayesian method for point source inversion using regional complete waveform data is applied to selected events from the region. The seismic structure and source characteristics with rigorously estimated uncertainties from the novel Bayesian methods provide enhanced monitoring and discrimination of seismic events in northeast Asia.

Crack Coalescence in Molded Gypsum and Carrara Marble

NASA Astrophysics Data System (ADS)

Wong, N.; Einstein, H. H.

2007-12-01

This research investigates the fracturing and coalescence behavior in prismatic laboratory-molded gypsum and Carrara marble specimens, which consist of either one or two pre-existing open flaws, under uniaxial compression. The tests are monitored by a high speed video system with a frame rate up to 24,000 frames/second. It allows one to precisely observe the cracking mechanisms, in particular if shear or tensile fracturing takes place. Seven crack types and nine crack coalescence categories are identified. The flaw inclination angle, the ligament length and the bridging angle between two flaws have different extents of influence on the coalescence patterns. For coplanar flaws, as the flaw inclination angle increases, there is a general trend of variation from shear coalescence to tensile coalescence. For stepped flaws, as the bridging angle changes from negative to small positive, and further up to large positive values, the coalescence generally progresses from categories of no coalescence, indirect coalescence to direct coalescence. For direct coalescence, it generally progresses from shear, mixed shear-tensile to tensile as the bridging angle increases. Some differences in fracturing and coalescence processes are observed in gypsum and marble, particularly the crack initiation in marble is preceded by the development of macroscopic white patches, but not in gypsum. Scanning Electron Microprobe (SEM) study reveals that the white patches consist of zones of microcracks (process zones).

Computations of Drop Collision and Coalescence

NASA Technical Reports Server (NTRS)

Tryggvason, Gretar; Juric, Damir; Nas, Selman; Mortazavi, Saeed

1996-01-01

Computations of drops collisions, coalescence, and other problems involving drops are presented. The computations are made possible by a finite difference/front tracking technique that allows direct solutions of the Navier-Stokes equations for a multi-fluid system with complex, unsteady internal boundaries. This method has been used to examine the various collision modes for binary collisions of drops of equal size, mixing of two drops of unequal size, behavior of a suspension of drops in linear and parabolic shear flows, and the thermal migration of several drops. The key results from these simulations are reviewed. Extensions of the method to phase change problems and preliminary results for boiling are also shown.

Careful with Those Priors: A Note on Bayesian Estimation in Two-Parameter Logistic Item Response Theory Models

ERIC Educational Resources Information Center

Marcoulides, Katerina M.

2018-01-01

This study examined the use of Bayesian analysis methods for the estimation of item parameters in a two-parameter logistic item response theory model. Using simulated data under various design conditions with both informative and non-informative priors, the parameter recovery of Bayesian analysis methods were examined. Overall results showed that…

Tree Biomass Estimation of Chinese fir (Cunninghamia lanceolata) Based on Bayesian Method

PubMed Central

Zhang, Jianguo

2013-01-01

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is the most important conifer species for timber production with huge distribution area in southern China. Accurate estimation of biomass is required for accounting and monitoring Chinese forest carbon stocking. In the study, allometric equation was used to analyze tree biomass of Chinese fir. The common methods for estimating allometric model have taken the classical approach based on the frequency interpretation of probability. However, many different biotic and abiotic factors introduce variability in Chinese fir biomass model, suggesting that parameters of biomass model are better represented by probability distributions rather than fixed values as classical method. To deal with the problem, Bayesian method was used for estimating Chinese fir biomass model. In the Bayesian framework, two priors were introduced: non-informative priors and informative priors. For informative priors, 32 biomass equations of Chinese fir were collected from published literature in the paper. The parameter distributions from published literature were regarded as prior distributions in Bayesian model for estimating Chinese fir biomass. Therefore, the Bayesian method with informative priors was better than non-informative priors and classical method, which provides a reasonable method for estimating Chinese fir biomass. PMID:24278198

Tree biomass estimation of Chinese fir (Cunninghamia lanceolata) based on Bayesian method.

PubMed

Zhang, Xiongqing; Duan, Aiguo; Zhang, Jianguo

2013-01-01

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is the most important conifer species for timber production with huge distribution area in southern China. Accurate estimation of biomass is required for accounting and monitoring Chinese forest carbon stocking. In the study, allometric equation W = a(D2H)b was used to analyze tree biomass of Chinese fir. The common methods for estimating allometric model have taken the classical approach based on the frequency interpretation of probability. However, many different biotic and abiotic factors introduce variability in Chinese fir biomass model, suggesting that parameters of biomass model are better represented by probability distributions rather than fixed values as classical method. To deal with the problem, Bayesian method was used for estimating Chinese fir biomass model. In the Bayesian framework, two priors were introduced: non-informative priors and informative priors. For informative priors, 32 biomass equations of Chinese fir were collected from published literature in the paper. The parameter distributions from published literature were regarded as prior distributions in Bayesian model for estimating Chinese fir biomass. Therefore, the Bayesian method with informative priors was better than non-informative priors and classical method, which provides a reasonable method for estimating Chinese fir biomass.

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

NASA Technical Reports Server (NTRS)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Droplets coalescence at microchannel intersection chambers with different shapes

NASA Astrophysics Data System (ADS)

Liu, Zhaomiao; Wang, Xiang; Cao, Rentuo; Pang, Yan

2016-11-01

The influence of microchannel intersection chamber shape on droplets coalescence process is investigated in this study. Three kinds of chamber shapes (half-round, triangle and camber) are designed to realize head-on droplets coalescence. The coalescence processes are visualized with high-speed camera system and the internal flow patterns are resolved with micro-PIV system. Experimental analyses on droplets coalescence position, coalescence time and the critical conditions are discussed. Both direct coalescence and late coalescence can be observed in the camber junction while only the late coalescence is present for the half-round and the triangle junction. The critical capillary number Ca* varies for different working systems or intersection shapes. Ca* in the camber junction is larger than that in the other two junctions for each working system and it decreases with the increase of the viscosity ratios for each intersection shape. Moreover, the characteristics of the velocity fields for different coalescence cases are analyzed for in-depth understanding of the process. The authors do appreciate the financial support of No.11572013 of National Nature Scicence Funding of China.

Age estimation by assessment of pulp chamber volume: a Bayesian network for the evaluation of dental evidence.

PubMed

Sironi, Emanuele; Taroni, Franco; Baldinotti, Claudio; Nardi, Cosimo; Norelli, Gian-Aristide; Gallidabino, Matteo; Pinchi, Vilma

2017-11-14

The present study aimed to investigate the performance of a Bayesian method in the evaluation of dental age-related evidence collected by means of a geometrical approximation procedure of the pulp chamber volume. Measurement of this volume was based on three-dimensional cone beam computed tomography images. The Bayesian method was applied by means of a probabilistic graphical model, namely a Bayesian network. Performance of that method was investigated in terms of accuracy and bias of the decisional outcomes. Influence of an informed elicitation of the prior belief of chronological age was also studied by means of a sensitivity analysis. Outcomes in terms of accuracy were adequate with standard requirements for forensic adult age estimation. Findings also indicated that the Bayesian method does not show a particular tendency towards under- or overestimation of the age variable. Outcomes of the sensitivity analysis showed that results on estimation are improved with a ration elicitation of the prior probabilities of age.

[Bayesian approach for the cost-effectiveness evaluation of healthcare technologies].

PubMed

Berchialla, Paola; Gregori, Dario; Brunello, Franco; Veltri, Andrea; Petrinco, Michele; Pagano, Eva

2009-01-01

The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed.

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Wang, Hongrui; Wang, Cheng; Wang, Ying; Gao, Xiong; Yu, Chen

2017-06-01

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.

Light-driven self-assembly of hetero-shaped gold nanorods

NASA Astrophysics Data System (ADS)

Liaw, Jiunn-Woei; Chao, Hsueh-Yu; Huang, Cheng-Wei; Kuo, Mao-Kuen

2018-01-01

Light-driven self-assembly and coalescence of two nearby hetero-shaped gold nanorods (GNRs) with different lengths are studied theoretically. The optical forces and torques, in terms of Maxwell's stress tensor, upon these GNRs provided by a linearly polarized (LP) plane wave are analyzed using the multiple multipole (MMP) method. Numerical results show that the optical torque dominates their alignments and the optical force their attraction. The most likely outcome of the plasmon-mediated light-matter interaction is wavelength dependent. Three different coalescences of the two GNRs could be induced by a LP light in three different wavelength regimes, respectively. For example, the side-by-side coalescence of two GNRs with radius of 15 nm and different lengths (120 and 240 nm) is induced in water as irradiated by a LP light at 633 nm, the T-shaped one at 1064 nm, and the end-to-end one at 1700 nm. The plasmonic attractive force and heating power densities inside GNRs with different gaps are also studied; the smaller the gap, the larger the attractive force and heating power. The results imply that the plasmonic coalescence and heating of two discrete GNRs may cause the local fusion at the junction of the assembly and the subsequent annealing (even recrystallization). Because the heating makes the two discrete GNRs fused to become a new nanostructure, the plasmonic coalescence of optical manipulation is irreversible.

The Psychology of Bayesian Reasoning

DTIC Science & Technology

2014-10-21

The psychology of Bayesian reasoning David R. Mandel* Socio-Cognitive Systems Section, Defence Research and Development Canada and Department...belief revision, subjective probability, human judgment, psychological methods. Most psychological research on Bayesian reasoning since the 1970s has...attention to some important problems with the conventional approach to studying Bayesian reasoning in psychology that has been dominant since the

Bayesian survival analysis in clinical trials: What methods are used in practice?

PubMed

Brard, Caroline; Le Teuff, Gwénaël; Le Deley, Marie-Cécile; Hampson, Lisa V

2017-02-01

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

Rooting phylogenetic trees under the coalescent model using site pattern probabilities.

PubMed

Tian, Yuan; Kubatko, Laura

2017-12-19

Phylogenetic tree inference is a fundamental tool to estimate ancestor-descendant relationships among different species. In phylogenetic studies, identification of the root - the most recent common ancestor of all sampled organisms - is essential for complete understanding of the evolutionary relationships. Rooted trees benefit most downstream application of phylogenies such as species classification or study of adaptation. Often, trees can be rooted by using outgroups, which are species that are known to be more distantly related to the sampled organisms than any other species in the phylogeny. However, outgroups are not always available in evolutionary research. In this study, we develop a new method for rooting species tree under the coalescent model, by developing a series of hypothesis tests for rooting quartet phylogenies using site pattern probabilities. The power of this method is examined by simulation studies and by application to an empirical North American rattlesnake data set. The method shows high accuracy across the simulation conditions considered, and performs well for the rattlesnake data. Thus, it provides a computationally efficient way to accurately root species-level phylogenies that incorporates the coalescent process. The method is robust to variation in substitution model, but is sensitive to the assumption of a molecular clock. Our study establishes a computationally practical method for rooting species trees that is more efficient than traditional methods. The method will benefit numerous evolutionary studies that require rooting a phylogenetic tree without having to specify outgroups.

Reconstructing the Sky Location of Gravitational-Wave Detected Compact Binary Systems: Methodology for Testing and Comparison

NASA Technical Reports Server (NTRS)

Sidney, T.; Aylott, B.; Christensen, N.; Farr, B.; Farr, W.; Feroz, F.; Gair, J.; Grover, K.; Graff, P.; Hanna, C.;

Reconstructing the sky location of gravitational-wave detected compact binary systems: Methodology for testing and comparison

NASA Astrophysics Data System (ADS)

Sidery, T.; Aylott, B.; Christensen, N.; Farr, B.; Farr, W.; Feroz, F.; Gair, J.; Grover, K.; Graff, P.; Hanna, C.; Kalogera, V.; Mandel, I.; O'Shaughnessy, R.; Pitkin, M.; Price, L.; Raymond, V.; Röver, C.; Singer, L.; van der Sluys, M.; Smith, R. J. E.; Vecchio, A.; Veitch, J.; Vitale, S.

2014-04-01

The problem of reconstructing the sky position of compact binary coalescences detected via gravitational waves is a central one for future observations with the ground-based network of gravitational-wave laser interferometers, such as Advanced LIGO and Advanced Virgo. Different techniques for sky localization have been independently developed. They can be divided in two broad categories: fully coherent Bayesian techniques, which are high latency and aimed at in-depth studies of all the parameters of a source, including sky position, and "triangulation-based" techniques, which exploit the data products from the search stage of the analysis to provide an almost real-time approximation of the posterior probability density function of the sky location of a detection candidate. These techniques have previously been applied to data collected during the last science runs of gravitational-wave detectors operating in the so-called initial configuration. Here, we develop and analyze methods for assessing the self consistency of parameter estimation methods and carrying out fair comparisons between different algorithms, addressing issues of efficiency and optimality. These methods are general, and can be applied to parameter estimation problems other than sky localization. We apply these methods to two existing sky localization techniques representing the two above-mentioned categories, using a set of simulated inspiral-only signals from compact binary systems with a total mass of ≤20M⊙ and nonspinning components. We compare the relative advantages and costs of the two techniques and show that sky location uncertainties are on average a factor ≈20 smaller for fully coherent techniques than for the specific variant of the triangulation-based technique used during the last science runs, at the expense of a factor ≈1000 longer processing time.

Is probabilistic bias analysis approximately Bayesian?

PubMed Central

MacLehose, Richard F.; Gustafson, Paul

2011-01-01

Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. While the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well. PMID:22157311

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

DOE PAGES

Wang, Hongrui; Wang, Cheng; Wang, Ying; ...

2017-04-05

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLEmore » confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.« less

An experimental study on the coalescence process of binary droplets in oil under ultrasonic standing waves.

PubMed

Luo, Xiaoming; Cao, Juhang; He, Limin; Wang, Hongping; Yan, Haipeng; Qin, Yahua

2017-01-01

The coalescence process of binary droplets in oil under ultrasonic standing waves was investigated with high-speed photography. Three motion models of binary droplets in coalescence process were illustrated: (1) slight translational oscillation; (2) sinusoidal translational oscillation; (3) migration along with acoustic streaming. To reveal the droplets coalescence mechanisms, the influence of main factors (such as acoustic intensity, droplet size, viscosity and interfacial tension, etc) on the motion and coalescence of binary droplets was studied under ultrasonic standing waves. Results indicate that the shortest coalescence time is achieved when binary droplets show sinusoidal translational oscillation. The corresponding acoustic intensity in this case is the optimum acoustic intensity. Under the optimum acoustic intensity, drop size decrease will bring about coalescence time decrease by enhancing the binary droplets oscillation. Moreover, there is an optimum interfacial tension to achieve the shortest coalescence time. Copyright © 2016 Elsevier B.V. All rights reserved.

Bayesian methods for characterizing unknown parameters of material models

DOE PAGES

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

2016-02-04

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

Bayesian methods for characterizing unknown parameters of material models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

Experimental and numerical study of the effects of a wall on the coalescence and collapse of bubble pairs

NASA Astrophysics Data System (ADS)

Han, Rui; Zhang, A.-Man; Li, Shuai; Zong, Zhi

2018-04-01

Two-bubble interaction is the most fundamental problem in multi-bubbles dynamics, which is crucial in many practical applications involving air-gun arrays and underwater explosions. In this paper, we experimentally and numerically investigate coalescence, collapse, and rebound of non-buoyant bubble pairs below a rigid wall. Two oscillating vapor bubbles with similar size are generated simultaneously near a rigid wall in axisymmetric configuration using the underwater electric discharge method, and the physical process is captured by a high-speed camera. Numerical simulations are conducted based on potential flow theory coupled with the boundary integral method. Our numerical results show excellent agreement with the experimental data until the splashing of the jet impact sets in. With different ranges of γbw (the dimensionless distance between the rigid wall and the nearest bubble center), the interaction between the coalesced bubble and the rigid wall is divided into three types, i.e., "weak," "intermediate," and "strong." As γbw decreases, the contact point of the two axial jets migrates toward the wall. In "strong interaction" cases, only an upward jet towards the upper rigid wall forms and a secondary jet with a larger width appears at the base of the first jet. The collapsing coalesced bubble in a toroidal form splits into many smaller bubbles due to the instabilities and presents as bubble clouds during the rebounding phase, which may lead to a weakened pressure wave because the focusing energy associated with the collapsing bubble is disintegrated.

Bayesian randomized clinical trials: From fixed to adaptive design.

PubMed

Yin, Guosheng; Lam, Chi Kin; Shi, Haolun

2017-08-01

Randomized controlled studies are the gold standard for phase III clinical trials. Using α-spending functions to control the overall type I error rate, group sequential methods are well established and have been dominating phase III studies. Bayesian randomized design, on the other hand, can be viewed as a complement instead of competitive approach to the frequentist methods. For the fixed Bayesian design, the hypothesis testing can be cast in the posterior probability or Bayes factor framework, which has a direct link to the frequentist type I error rate. Bayesian group sequential design relies upon Bayesian decision-theoretic approaches based on backward induction, which is often computationally intensive. Compared with the frequentist approaches, Bayesian methods have several advantages. The posterior predictive probability serves as a useful and convenient tool for trial monitoring, and can be updated at any time as the data accrue during the trial. The Bayesian decision-theoretic framework possesses a direct link to the decision making in the practical setting, and can be modeled more realistically to reflect the actual cost-benefit analysis during the drug development process. Other merits include the possibility of hierarchical modeling and the use of informative priors, which would lead to a more comprehensive utilization of information from both historical and longitudinal data. From fixed to adaptive design, we focus on Bayesian randomized controlled clinical trials and make extensive comparisons with frequentist counterparts through numerical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Itô-SDE MCMC method for Bayesian characterization of errors associated with data limitations in stochastic expansion methods for uncertainty quantification

NASA Astrophysics Data System (ADS)

Arnst, M.; Abello Álvarez, B.; Ponthot, J.-P.; Boman, R.

2017-11-01

This paper is concerned with the characterization and the propagation of errors associated with data limitations in polynomial-chaos-based stochastic methods for uncertainty quantification. Such an issue can arise in uncertainty quantification when only a limited amount of data is available. When the available information does not suffice to accurately determine the probability distributions that must be assigned to the uncertain variables, the Bayesian method for assigning these probability distributions becomes attractive because it allows the stochastic model to account explicitly for insufficiency of the available information. In previous work, such applications of the Bayesian method had already been implemented by using the Metropolis-Hastings and Gibbs Markov Chain Monte Carlo (MCMC) methods. In this paper, we present an alternative implementation, which uses an alternative MCMC method built around an Itô stochastic differential equation (SDE) that is ergodic for the Bayesian posterior. We draw together from the mathematics literature a number of formal properties of this Itô SDE that lend support to its use in the implementation of the Bayesian method, and we describe its discretization, including the choice of the free parameters, by using the implicit Euler method. We demonstrate the proposed methodology on a problem of uncertainty quantification in a complex nonlinear engineering application relevant to metal forming.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iliadis, C.; Anderson, K. S.; Coc, A.

The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We presentmore » astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.« less

Coalescence computations for large samples drawn from populations of time-varying sizes

PubMed Central

Polanski, Andrzej; Szczesna, Agnieszka; Garbulowski, Mateusz; Kimmel, Marek

2017-01-01

We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset. PMID:28170404

Bayesian analysis of rare events

NASA Astrophysics Data System (ADS)

Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

DEMOGRAPHIC CONSEQUENCES OF COALESCENCE IN SPORELING POPULATIONS OF MAZZAELLA LAMINARIOIDES (GIGARTINALES, RHODOPHYTA)(1).

PubMed

Santelices, B; Alvarado, J L

2008-06-01

Coalescing macroalgae are ecologically important members of intertidal and shallow subtidal communities. However, we still lack quantitative information on the demographic consequences of coalescence. Using demographic models developed for modular invertebrates, we studied the demography of settlement and early recruitment in the coalescing macroalga Mazzaella laminarioides (Bory) Fredericq. Permanently marked microscopic fields on laboratory-incubated and field-incubated plates were monitored regularly (at 15, 30, 45, and 60 d) using image analysis techniques to evaluate the relative importance of settler abundance, mortality, coalescence (fusion), and fission on the changes in size and numbers of recruits. On the plates, spores settled individually or in groups. Over time, spores in close proximity may coalesce, resulting in a mixture of unisporic and multisporic crusts. When new spores arrive, they may or may not coalesce with previously settled crusts. Coalescence and mortality reduce the number of sporelings, but coalescence increases the size of the sporelings, thereby reducing further probability of sporeling mortality. Crust fissions are negligible in frequency, while the frequency of coalescence increases from ∼25% after only 3 d, to ∼75% after 60 d. Thus, as a result of variable settlement, mortality, and coalescence, any area colonized by M. laminarioides would contain a mixture of crusts of different sizes, ages, and genetic constitution. The interactions between the above three processes create a more complex survivorship curve than the ones known for unitary organisms. © 2008 Phycological Society of America.

Stochastic Background from Coalescences of Neutron Star-Neutron Star Binaries

NASA Astrophysics Data System (ADS)

Regimbau, T.; de Freitas Pacheco, J. A.

2006-05-01

In this work, numerical simulations were used to investigate the gravitational stochastic background produced by coalescences of double neutron star systems occurring up to z~5. The cosmic coalescence rate was derived from Monte Carlo methods using the probability distributions for massive binaries to form and for a coalescence to occur in a given redshift. A truly continuous background is produced by events located only beyond the critical redshift z*=0.23. Events occurring in the redshift interval 0.027

Improved inference in Bayesian segmentation using Monte Carlo sampling: application to hippocampal subfield volumetry.

PubMed

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen

2013-10-01

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the technique also allows one to compute informative "error bars" on the volume estimates of individual structures. Copyright © 2013 Elsevier B.V. All rights reserved.

Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry

PubMed Central

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Leemput, Koen Van

2013-01-01

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the technique also allows one to compute informative “error bars” on the volume estimates of individual structures. PMID:23773521

Numerical modelling of microdroplet self-propelled jumping on micro-textured surface

NASA Astrophysics Data System (ADS)

Attarzadeh, S. M. Reza; Dolatabadi, Ali; Chun Kim, Kyung

2015-11-01

Understanding various stages of single and multiple droplet impact on a super-hydrophobic surface is of interest for many industrial applications such as aerospace industry. In this study, the phenomenon of coalescence induced droplets self-propelled jumping on a micro-textured super-hydrophobic surface is numerically simulated using Volume of Fluid (VOF) method. This model mimics the scenario of coalescing cloud-sized particles over the surface structure of an aircraft. The VOF coupled with a dynamic contact angle model is used to simulate the coalescence of two equal size droplets, that are initially placed very closed to each other with their interface overlapping with each other's which triggers the incipience of their coalescence. The textured surface is modeled as a series of equally spaced squared pillars, with 111° as the intrinsic contact angle all over the solid contact area. It is shown that the radial velocity of coalescing liquid bridge is reverted to upward direction due to the counter action of the surface to the basal area of droplet in contact. The presence of air beneath the droplet inside micro grooves which aimed at repelling water droplet is also captured in this model. The simulated results are found in good agreement with experimental observations. The authors gratefully acknowledge the financial support from Natural Sciences and Engineering Research Council of Canada (NSERC), Consortium de Recherche et d'innovation en Aerospatiale au Quebec (CRIAQ), Bombardier Aerospace, Pratt Whitney Canada.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data.

PubMed

Liu, Kai; Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

PubMed Central

Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods. PMID:27362654

Application of Bayesian Maximum Entropy Filter in parameter calibration of groundwater flow model in PingTung Plain

NASA Astrophysics Data System (ADS)

Cheung, Shao-Yong; Lee, Chieh-Han; Yu, Hwa-Lung

2017-04-01

Due to the limited hydrogeological observation data and high levels of uncertainty within, parameter estimation of the groundwater model has been an important issue. There are many methods of parameter estimation, for example, Kalman filter provides a real-time calibration of parameters through measurement of groundwater monitoring wells, related methods such as Extended Kalman Filter and Ensemble Kalman Filter are widely applied in groundwater research. However, Kalman Filter method is limited to linearity. This study propose a novel method, Bayesian Maximum Entropy Filtering, which provides a method that can considers the uncertainty of data in parameter estimation. With this two methods, we can estimate parameter by given hard data (certain) and soft data (uncertain) in the same time. In this study, we use Python and QGIS in groundwater model (MODFLOW) and development of Extended Kalman Filter and Bayesian Maximum Entropy Filtering in Python in parameter estimation. This method may provide a conventional filtering method and also consider the uncertainty of data. This study was conducted through numerical model experiment to explore, combine Bayesian maximum entropy filter and a hypothesis for the architecture of MODFLOW groundwater model numerical estimation. Through the virtual observation wells to simulate and observe the groundwater model periodically. The result showed that considering the uncertainty of data, the Bayesian maximum entropy filter will provide an ideal result of real-time parameters estimation.

DIM SUM: demography and individual migration simulated using a Markov chain.

PubMed

Brown, Jeremy M; Savidge, Kevin; McTavish, Emily Jane B

2011-03-01

An increasing number of studies seek to infer demographic history, often jointly with genetic relationships. Despite numerous analytical methods for such data, few simulations have investigated the methods' power and robustness, especially when underlying assumptions have been violated. DIM SUM (Demography and Individual Migration Simulated Using a Markov chain) is a stand-alone Java program for the simulation of population demography and individual migration while recording ancestor-descendant relationships. It does not employ coalescent assumptions or discrete population boundaries. It is extremely flexible, allowing the user to specify border positions, reactions of organisms to borders, local and global carrying capacities, individual dispersal kernels, rates of reproduction and strategies for sampling individuals. Spatial variables may be specified using image files (e.g., as exported from gis software) and may vary through time. In combination with software for genetic marker simulation, DIM SUM will be useful for testing phylogeographic (e.g., nested clade phylogeographic analysis, coalescent-based tests and continuous-landscape frameworks) and landscape-genetic methods, specifically regarding violations of coalescent assumptions. It can also be used to explore the qualitative features of proposed demographic scenarios (e.g. regarding biological invasions) and as a pedagogical tool. DIM SUM (with user's manual) can be downloaded from http://code.google.com/p/bio-dimsum. © 2010 Blackwell Publishing Ltd.

A zero-gravity demonstration of the collision and coalescence of water droplets

NASA Technical Reports Server (NTRS)

Hung, R. J.; Vaughan, O. H.; Smith, R. E.

1974-01-01

The mechanics of the collision and coalescence of liquid droplets is one of the main research areas in the fields of nuclear physics, astrophysics, meteorology and fluid mechanics. The crew members on the Skylab 3 and 4 missions were requested to perform demonstrations of the collision and coalescence of water droplets under the low gravity environment at orbital altitude. In Skylab 4 two water droplets with equal volumes, 30 cu cm each, were used. A dark colored droplet (contaminated with grape drink) moving with a velocity of 3.14 cm/sec collided with a stationary pink colored droplet (contaminated with strawberry drink) and coalescence occurred. Theoretical models are proposed to study the various stages of the collision-coalescence processes. Special considerations are concentrated in the investigation of the bounce-coalescence and coalescence-instability processes. The surface tension of the coalesced droplets was calculated to be 52 dynes/cm in perfect agreement with laboratory measurements made after the flight using a reproduction of the liquids.

Coalescence-Induced Jumping of Multiple Condensate Droplets on Hierarchical Superhydrophobic Surfaces

PubMed Central

Chen, Xuemei; Patel, Ravi S.; Weibel, Justin A.; Garimella, Suresh V.

2016-01-01

Coalescence-induced jumping of condensate droplets from a superhydrophobic surface with hierarchical micro/nanoscale roughness is quantitatively characterized. Experimental observations show that the condensate droplet jumping is induced by coalescence of multiple droplets of different sizes, and that the coalesced droplet trajectories typically deviate from the surface normal. A depth-from-defocus image processing technique is developed to track the out-of-plane displacement of the jumping droplets, so as to accurately measure the droplet size and velocity. The results demonstrate that the highest jumping velocity is achieved when two droplets coalesce. The jumping velocity decreases gradually with an increase in the number of coalescing droplets, despite the greater potential surface energy released upon coalescence. A general theoretical model that accounts for viscous dissipation, surface adhesion, line tension, the initial droplet wetting states, and the number and sizes of the coalescing droplets is developed to explain the trends of droplet jumping velocity observed in the experiments. PMID:26725512

Bayesian calibration for forensic age estimation.

PubMed

Ferrante, Luigi; Skrami, Edlira; Gesuita, Rosaria; Cameriere, Roberto

2015-05-10

Forensic medicine is increasingly called upon to assess the age of individuals. Forensic age estimation is mostly required in relation to illegal immigration and identification of bodies or skeletal remains. A variety of age estimation methods are based on dental samples and use of regression models, where the age of an individual is predicted by morphological tooth changes that take place over time. From the medico-legal point of view, regression models, with age as the dependent random variable entail that age tends to be overestimated in the young and underestimated in the old. To overcome this bias, we describe a new full Bayesian calibration method (asymmetric Laplace Bayesian calibration) for forensic age estimation that uses asymmetric Laplace distribution as the probability model. The method was compared with three existing approaches (two Bayesian and a classical method) using simulated data. Although its accuracy was comparable with that of the other methods, the asymmetric Laplace Bayesian calibration appears to be significantly more reliable and robust in case of misspecification of the probability model. The proposed method was also applied to a real dataset of values of the pulp chamber of the right lower premolar measured on x-ray scans of individuals of known age. Copyright © 2015 John Wiley & Sons, Ltd.

Assessment of parametric uncertainty for groundwater reactive transport modeling,

USGS Publications Warehouse

Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun

2014-01-01

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Bayesian Statistics for Biological Data: Pedigree Analysis

ERIC Educational Resources Information Center

Stanfield, William D.; Carlton, Matthew A.

2004-01-01

The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.

Win-Stay, Lose-Sample: a simple sequential algorithm for approximating Bayesian inference.

PubMed

Bonawitz, Elizabeth; Denison, Stephanie; Gopnik, Alison; Griffiths, Thomas L

2014-11-01

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a "mini-microgenetic method", investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments. Copyright © 2014 Elsevier Inc. All rights reserved.

Bayes and the Law

PubMed Central

Fenton, Norman; Neil, Martin; Berger, Daniel

2016-01-01

Although the last forty years has seen considerable growth in the use of statistics in legal proceedings, it is primarily classical statistical methods rather than Bayesian methods that have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This paper reviews the potential and actual use of Bayes in the law and explains the main reasons for its lack of impact on legal practice. These include misconceptions by the legal community about Bayes’ theorem, over-reliance on the use of the likelihood ratio and the lack of adoption of modern computational methods. We argue that Bayesian Networks (BNs), which automatically produce the necessary Bayesian calculations, provide an opportunity to address most concerns about using Bayes in the law. PMID:27398389

Bayes and the Law.

PubMed

Fenton, Norman; Neil, Martin; Berger, Daniel

2016-06-01

Although the last forty years has seen considerable growth in the use of statistics in legal proceedings, it is primarily classical statistical methods rather than Bayesian methods that have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This paper reviews the potential and actual use of Bayes in the law and explains the main reasons for its lack of impact on legal practice. These include misconceptions by the legal community about Bayes' theorem, over-reliance on the use of the likelihood ratio and the lack of adoption of modern computational methods. We argue that Bayesian Networks (BNs), which automatically produce the necessary Bayesian calculations, provide an opportunity to address most concerns about using Bayes in the law.

Congruence between distribution modelling and phylogeographical analyses reveals Quaternary survival of a toadflax species (Linaria elegans) in oceanic climate areas of a mountain ring range.

PubMed

Fernández-Mazuecos, Mario; Vargas, Pablo

2013-06-01

· The role of Quaternary climatic shifts in shaping the distribution of Linaria elegans, an Iberian annual plant, was investigated using species distribution modelling and molecular phylogeographical analyses. Three hypotheses are proposed to explain the Quaternary history of its mountain ring range. · The distribution of L. elegans was modelled using the maximum entropy method and projected to the last interglacial and to the last glacial maximum (LGM) using two different paleoclimatic models: the Community Climate System Model (CCSM) and the Model for Interdisciplinary Research on Climate (MIROC). Two nuclear and three plastid DNA regions were sequenced for 24 populations (119 individuals sampled). Bayesian phylogenetic, phylogeographical, dating and coalescent-based population genetic analyses were conducted. · Molecular analyses indicated the existence of northern and southern glacial refugia and supported two routes of post-glacial recolonization. These results were consistent with the LGM distribution as inferred under the CCSM paleoclimatic model (but not under the MIROC model). Isolation between two major refugia was dated back to the Riss or Mindel glaciations, > 100 kyr before present (bp). · The Atlantic distribution of inferred refugia suggests that the oceanic (buffered)-continental (harsh) gradient may have played a key and previously unrecognized role in determining Quaternary distribution shifts of Mediterranean plants. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Hiding deep in the trees: discovery of divergent mitochondrial lineages in Malagasy chameleons of the Calumma nasutum group

PubMed Central

Gehring, Philip-Sebastian; Tolley, Krystal A; Eckhardt, Falk Sebastian; Townsend, Ted M; Ziegler, Thomas; Ratsoavina, Fanomezana; Glaw, Frank; Vences, Miguel

2012-01-01

We conducted a comprehensive molecular phylogenetic study for a group of chameleons from Madagascar (Chamaeleonidae: Calumma nasutum group, comprising seven nominal species) to examine the genetic and species diversity in this widespread genus. Based on DNA sequences of the mitochondrial gene (ND2) from 215 specimens, we reconstructed the phylogeny using a Bayesian approach. Our results show deep divergences among several unnamed mitochondrial lineages that are difficult to identify morphologically. We evaluated lineage diversification using a number of statistical phylogenetic methods (general mixed Yule-coalescent model; SpeciesIdentifier; net p-distances) to objectively delimit lineages that we here consider as operational taxonomic units (OTUs), and for which the taxonomic status remains largely unknown. In addition, we compared molecular and morphological differentiation in detail for one particularly diverse clade (the C. boettgeri complex) from northern Madagascar. To assess the species boundaries within this group we used an integrative taxonomic approach, combining evidence from two independent molecular markers (ND2 and CMOS), together with genital and other external morphological characters, and conclude that some of the newly discovered OTUs are separate species (confirmed candidate species, CCS), while others should best be considered as deep conspecific lineages (DCLs). Our analysis supports a total of 33 OTUs, of which seven correspond to described species, suggesting that the taxonomy of the C. nasutum group is in need of revision. PMID:22957155

Influence of electron beam irradiation on the microrheology of incompatible polymer blends: Thread break-up and coalescence

DOE Office of Scientific and Technical Information (OSTI.GOV)

van Gisbergen, J.G.M.; Meijer, H.E.H.

1991-01-01

The microrheology of polymer blends as influenced by crosslinks induced in the dispersed phase via electron beam irradiation, is systematically investigated for the model system polystyrene/low density polyethylene (PS/LDPE). Both break-up of threads and coalescence of particles are delayed to a large extent, but are not inhibited completely and occur faster than would be expected for a nonirradiated material with a comparable viscosity. Small amplitude, dynamic rheological measurements indicated that in the irradiated materials a yield stress could exist. In contrast, direct microrheological measurements showed that this yield stress, which would prevent both break-up and coalescence, could not be realizedmore » by EB irradiation. Apparently, the direct study of the microrheology of a blend system is important for the prediction of the development of its morphology and it is not possible to rely only on rheological data obtained via other methods.« less

The Flow Induced by the Coalescence of Two Initially Stationary Drops

NASA Technical Reports Server (NTRS)

Nobari, M. R.; Tryggvason, G.

1994-01-01

The coalescence of two initially stationary drops of different size is investigated by solving the unsteady, axisymmetric Navier-Stokes equations numerically, using a Front-Tracking/Finite Difference method. Initially, the drops are put next to each other and the film between them ruptured. Due to surface tension forces, the drops coalesce rapidly and the fluid from the small drop is injected into the larger one. For low nondimensional viscosity, or Ohnesorge number, little mixing takes place and the small drop fluid forms a blob near the point where the drops touched initially. For low Ohnesorge number, on the other hand, the small drop forms a jet that penetrates far into the large drop. The penetration depth also depends on the size of the drops and shows that for a given fluid of sufficiently low viscosity, there is a maximum penetration depth for intermediate size ratios.

Divergence and codon usage bias of Betanodavirus, a neurotropic pathogen in fish.

PubMed

He, Mei; Teng, Chun-Bo

2015-02-01

Betanodavirus is a small bipartite RNA virus of global economical significance that can cause severe neurological disorders to an increasing number of marine fish species. Herein, to further the understanding of the evolution of betanodavirus, Bayesian coalescent analyses were conducted to the time-stamped entire coding sequences of their RNA polymerase and coat protein genes. Similar moderate nucleotide substitution rates were then estimated for the two genes. According to age calculations, the divergence of the two genes into the four genotypes initiated nearly simultaneously at ∼700 years ago, despite the different scenarios, whereas the seven analyzed chimeric isolates might be the outcomes of a single genetic reassortment event taking place in the early 1980s in Southern Europe. Furthermore, codon usage bias analyses indicated that each gene had influences in addition to mutational bias and codon choice of betanodavirus was not completely complied with that of fish host. Copyright © 2014 Elsevier Inc. All rights reserved.

Solving the relativistic inverse stellar problem through gravitational waves observation of binary neutron stars

NASA Astrophysics Data System (ADS)

Abdelsalhin, Tiziano; Maselli, Andrea; Ferrari, Valeria

2018-04-01

The LIGO/Virgo Collaboration has recently announced the direct detection of gravitational waves emitted in the coalescence of a neutron star binary. This discovery allows, for the first time, to set new constraints on the behavior of matter at supranuclear density, complementary with those coming from astrophysical observations in the electromagnetic band. In this paper we demonstrate the feasibility of using gravitational signals to solve the relativistic inverse stellar problem, i.e., to reconstruct the parameters of the equation of state (EoS) from measurements of the stellar mass and tidal Love number. We perform Bayesian inference of mock data, based on different models of the star internal composition, modeled through piecewise polytropes. Our analysis shows that the detection of a small number of sources by a network of advanced interferometers would allow to put accurate bounds on the EoS parameters, and to perform a model selection among the realistic equations of state proposed in the literature.

Holarctic genetic structure and range dynamics in the woolly mammoth

PubMed Central

Palkopoulou, Eleftheria; Dalén, Love; Lister, Adrian M.; Vartanyan, Sergey; Sablin, Mikhail; Sher, Andrei; Edmark, Veronica Nyström; Brandström, Mikael D.; Germonpré, Mietje; Barnes, Ian; Thomas, Jessica A.

2013-01-01

Ancient DNA analyses have provided enhanced resolution of population histories in many Pleistocene taxa. However, most studies are spatially restricted, making inference of species-level biogeographic histories difficult. Here, we analyse mitochondrial DNA (mtDNA) variation in the woolly mammoth from across its Holarctic range to reconstruct its history over the last 200 thousand years (kyr). We identify a previously undocumented major mtDNA lineage in Europe, which was replaced by another major mtDNA lineage 32–34 kyr before present (BP). Coalescent simulations provide support for demographic expansions at approximately 121 kyr BP, suggesting that the previous interglacial was an important driver for demography and intraspecific genetic divergence. Furthermore, our results suggest an expansion into Eurasia from America around 66 kyr BP, coinciding with the first exposure of the Bering Land Bridge during the Late Pleistocene. Bayesian inference indicates Late Pleistocene demographic stability until 20–15 kyr BP, when a severe population size decline occurred. PMID:24026825

Origin, Spread and Demography of the Mycobacterium tuberculosis Complex

PubMed Central

Wirth, Thierry; Hildebrand, Falk; Allix-Béguec, Caroline; Wölbeling, Florian; Kubica, Tanja; Kremer, Kristin; van Soolingen, Dick; Rüsch-Gerdes, Sabine; Locht, Camille; Brisse, Sylvain; Meyer, Axel

2008-01-01

The evolutionary timing and spread of the Mycobacterium tuberculosis complex (MTBC), one of the most successful groups of bacterial pathogens, remains largely unknown. Here, using mycobacterial tandem repeat sequences as genetic markers, we show that the MTBC consists of two independent clades, one composed exclusively of M. tuberculosis lineages from humans and the other composed of both animal and human isolates. The latter also likely derived from a human pathogenic lineage, supporting the hypothesis of an original human host. Using Bayesian statistics and experimental data on the variability of the mycobacterial markers in infected patients, we estimated the age of the MTBC at 40,000 years, coinciding with the expansion of “modern” human populations out of Africa. Furthermore, coalescence analysis revealed a strong and recent demographic expansion in almost all M. tuberculosis lineages, which coincides with the human population explosion over the last two centuries. These findings thus unveil the dynamic dimension of the association between human host and pathogen populations. PMID:18802459

Evolutionary history and dynamics of dog rabies virus in western and central Africa.

PubMed

Talbi, Chiraz; Holmes, Edward C; de Benedictis, Paola; Faye, Ousmane; Nakouné, Emmanuel; Gamatié, Djibo; Diarra, Abass; Elmamy, Bezeid Ould; Sow, Adama; Adjogoua, Edgard Valery; Sangare, Oumou; Dundon, William G; Capua, Ilaria; Sall, Amadou A; Bourhy, Hervé

2009-04-01

The burden of rabies in Africa is estimated at 24,000 human deaths year(-1), almost all of which result from infection with dog rabies viruses (RABV). To investigate the evolutionary dynamics of RABV in western and central Africa, 92 isolates sampled from 27 African countries over 29 years were collected and sequenced. This revealed that RABV currently circulating in dogs in this region fell into a single lineage designated 'Africa 2'. A detailed analysis of the phylogeographical structure of this Africa 2 lineage revealed strong population subdivision at the country level, with only limited movement of virus among localities, including a possible east-to-west spread across Africa. In addition, Bayesian coalescent analysis suggested that the Africa 2 lineage was introduced into this region of Africa only recently (probably <200 years ago), in accordance with the timescale of expanding European colonial influence and urbanization, and then spread relatively slowly, perhaps occupying the entire region in a 100 year period.

Bayesian inference based on dual generalized order statistics from the exponentiated Weibull model

NASA Astrophysics Data System (ADS)

Al Sobhi, Mashail M.

2015-02-01

Bayesian estimation for the two parameters and the reliability function of the exponentiated Weibull model are obtained based on dual generalized order statistics (DGOS). Also, Bayesian prediction bounds for future DGOS from exponentiated Weibull model are obtained. The symmetric and asymmetric loss functions are considered for Bayesian computations. The Markov chain Monte Carlo (MCMC) methods are used for computing the Bayes estimates and prediction bounds. The results have been specialized to the lower record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

Bayesian ensemble refinement by replica simulations and reweighting.

PubMed

Hummer, Gerhard; Köfinger, Jürgen

2015-12-28

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Bayesian ensemble refinement by replica simulations and reweighting

NASA Astrophysics Data System (ADS)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Characterization of Solids Deposited on the Modular Caustic-Side Solvent Extraction Unit (MCU) Strip Effluent (SE) Coalescer Media Removed in April 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fondeur, F. F.

On June 2015, Savannah River National Laboratory (SRNL) received a Strip Effluent (SE) coalescer (FLT-304) from MCU. That coalescer was first installed at MCU in late October 2014 and removed in April 2015. While processing approximately 48,700 gallons of strip solution, the pressure drop steadily increased linearly from 1 psi to near 16 psi (the administrative limit is 17 psi) with the total filtrate volume (2.1E-4 psi/gal of filtrate). The linear behavior is due to the combined effect of a constant deposition of material that starts from the closed-end to the mid-section of the coalescer reducing the available surface areamore » of the coalescer for fluid passage (linearly with filtrate volume) and the formation of a secondary emulsion (water in NG-CSSX) on the fibers of the coalescer media. Both effects reduced the coalescer porosity by at least 13% (after processing 48,700 gallons). Before the coalescer was removed, it was flushed with a 10 mM boric acid solution to reduce the dose level. To determine the nature of the deposited material, a physical and chemical analysis of the coalescer was conducted. Characterization of this coalescer revealed the adsorption of organic containing amines (secondary amides and primary amines), TiDG, degraded modifier (with no hydroxyl group), MaxCalix, and oxidized hydrocarbon (possibly from Isopar™L or from lubricant used at MCU) onto the coalescer media. The amide and amines are possibly from the decomposition of the suppressor (TiDG). The modifier and MaxCalix were the largest components of the deposited organic material, as determined from leaching the coalescer with dichloromethane. Both the Fourier-Transformed Infrared (FTIR) and Fourier-Transformed Hydrogen Nuclear Magnetic Resonance (FT-HNMR) results indicated that some of the modifier was degraded (missing their OH groups). The modifier was observed everywhere in the examined coalescer pieces (FTIR), while the TiDG and its decomposition products were observed at the entrance discs of the coalescer. A solvent trim (a cocktail of solvent components with a high concentration of modifier) was added to the solvent on 2/22/2015. It is believed that the trim did not mix completely with the solvent and that it was subsequently spread around the MCU components including the coalescers, where it may have deposited. Chronologically, the modifier, the TiDG’s decomposition products and silicates deposited on the entrance discs first and after the pressure drop increased significantly, parts of the coalescer media detached itself from the central porous steel mandrel and a significant amount of steel debris, mercury, titanium, and additional aluminum and silicates deposited on the coalescer.« less

Coalescence preference in densely packed microbubbles

DOE PAGES

Kim, Yeseul; Lim, Su Jin; Gim, Bopil; ...

2015-01-13

A bubble merged from two parent bubbles with different size tends to be placed closer to the larger parent. This phenomenon is known as the coalescence preference. Here we demonstrate that the coalescence preference can be blocked inside a densely packed cluster of bubbles. We utilized high-speed high-resolution X-ray microscopy to clearly visualize individual coalescence events inside densely packed microbubbles with a local packing fraction of ~40%. Thus, the surface energy release theory predicts an exponent of 5 in a relation between the relative coalescence position and the parent size ratio, whereas our observation for coalescence in densely packed microbubblesmore » shows a different exponent of 2. We believe that this result would be important to understand the reality of coalescence dynamics in a variety of packing situations of soft matter.« less

Coalescence preference in densely packed microbubbles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Yeseul; Lim, Su Jin; Gim, Bopil

A bubble merged from two parent bubbles with different size tends to be placed closer to the larger parent. This phenomenon is known as the coalescence preference. Here we demonstrate that the coalescence preference can be blocked inside a densely packed cluster of bubbles. We utilized high-speed high-resolution X-ray microscopy to clearly visualize individual coalescence events inside densely packed microbubbles with a local packing fraction of ~40%. Thus, the surface energy release theory predicts an exponent of 5 in a relation between the relative coalescence position and the parent size ratio, whereas our observation for coalescence in densely packed microbubblesmore » shows a different exponent of 2. We believe that this result would be important to understand the reality of coalescence dynamics in a variety of packing situations of soft matter.« less

Studying the kinematic asymmetries of disks and post-coalescence mergers using a new "kinemetry" criterion

NASA Astrophysics Data System (ADS)

Bellocchi, E.; Arribas, S.; Colina, L.

2012-06-01

Context. Ultra luminous and luminous infrared galaxies [(U)LIRGs] are important galaxy populations for studying galaxy evolution, and are likely to have been responsible for a significant fraction of the star formation that occurred prior to z ~ 1. Local (U)LIRGs can be used to study criteria that are suitable for characterizing similar high redshift populations. We are particularly interested in identifying reliable kinematic-based methods capable of distinguishing disks and mergers, as their relative fraction is a key observational input to constrain different evolutionary scenarios. Aims: Our goal is to analyze in detail the kinematics of the ionized gas of a small sample of LIRGs and study criteria that permit us to characterize the evolutionary status of these systems. Methods: We obtained Very Large Telescope VIMOS optical integral field spectroscopy (IFS) data of four LIRGs selected at similar distances (~70 Mpc) to avoid relative resolution effects. Two of these systems had been previously classified as regular isolated disks galaxies and the other two as post-coalescence mergers based on their morphology. The kinemetry method (developed by Krajnović and coworkers) is used to characterize the kinematic properties of these galaxies and discuss new criteria for distinguishing their status. Results: We present and discuss new kinematic maps (i.e., velocity field and velocity dispersion) for these four galaxies. These kinematic data suggest that nuclear outflows exist in all these galaxies, and are particularly intense for the post-coalescence merger systems. The vc/σc parameter has values between those that are typical of local spiral galaxies (i.e., vc/σc = 5-15) and those obtained for Lyman break analogs at z ~ 0.2 (i.e., vc/σc = 0.4-1.8). Our use of one-dimensional parameters, such as vc/σc or vshear/Σ, does not allow us to distinguish between the two groups (i.e., disks, post-coalescence systems). However, when the full two-dimensional kinematic information of the IFS data is analyzed by means of kinemetry, their morphological and kinematic classifications are consistent, with disks having lower kinematic asymmetries than post-coalescence mergers. We propose and discuss a new kinematic criterion to differentiate between these two groups. In particular, we introduce a weighting that favors the outer parts of the kinematic maps when computing the total asymmetries. This step is taken because post-coalescence mergers display relatively small kinematic asymmetries in their inner parts as a consequence of the rapid relaxation of gas into a rotating disk, whereas the outer parts continue to be out of equilibrium (i.e., to have larger asymmetries). We find that, in addition to distinguishing post-coalescence mergers from rotating disks, this new criterion has the advantage of being less sensitive to angular resolution effects. According to previous kinemetry-based analyses designed to distinguish disks and mergers at high-z, the present post-coalescence systems would have been classified as disks. This indicates that the separation of disks from mergers depends on the definition of a merger. It also suggests that previous estimates of the merger/disk ratio might have been underestimated, but larger samples are necessary to establish a firmer conclusion.

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.

PubMed

Karabatsos, George

2018-06-01

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

PubMed

Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D

2016-10-01

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. © The Author 2016. Published by Oxford University Press.

Bayesian analysis of rare events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into themore » probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.« less

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T

2016-12-20

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs.

PubMed

Schmidt, Amand F; Klugkist, Irene; Klungel, Olaf H; Nielen, Mirjam; de Boer, Anthonius; Hoes, Arno W; Groenwold, Rolf H H

2015-04-01

Findings from nonrandomized studies on safety or efficacy of treatment in patient subgroups may trigger postlaunch randomized clinical trials (RCTs). In the analysis of such RCTs, results from nonrandomized studies are typically ignored. This study explores the trade-off between bias and power of Bayesian RCT analysis incorporating information from nonrandomized studies. A simulation study was conducted to compare frequentist with Bayesian analyses using noninformative and informative priors in their ability to detect interaction effects. In simulated subgroups, the effect of a hypothetical treatment differed between subgroups (odds ratio 1.00 vs. 2.33). Simulations varied in sample size, proportions of the subgroups, and specification of the priors. As expected, the results for the informative Bayesian analyses were more biased than those from the noninformative Bayesian analysis or frequentist analysis. However, because of a reduction in posterior variance, informative Bayesian analyses were generally more powerful to detect an effect. In scenarios where the informative priors were in the opposite direction of the RCT data, type 1 error rates could be 100% and power 0%. Bayesian methods incorporating data from nonrandomized studies can meaningfully increase power of interaction tests in postlaunch RCTs. Copyright © 2015 Elsevier Inc. All rights reserved.

Using Bayesian belief networks in adaptive management.

Treesearch

J.B. Nyberg; B.G. Marcot; R. Sulyma

2006-01-01

Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...

The Application of Bayesian Analysis to Issues in Developmental Research

ERIC Educational Resources Information Center

Walker, Lawrence J.; Gustafson, Paul; Frimer, Jeremy A.

2007-01-01

This article reviews the concepts and methods of Bayesian statistical analysis, which can offer innovative and powerful solutions to some challenging analytical problems that characterize developmental research. In this article, we demonstrate the utility of Bayesian analysis, explain its unique adeptness in some circumstances, address some…

Coalescence of viscous drops translating through a capillary tube

NASA Astrophysics Data System (ADS)

AlMatroushi, Eisa; Borhan, Ali

2014-03-01

An experimental study of the interaction and coalescence of viscous drops moving through a cylindrical capillary tube under low Reynolds number conditions is presented. The combined pressure- and buoyancy-driven motion of drops in a Newtonian continuous phase is examined. The interaction between two drops is quantified using image analysis, and measurements of the coalescence time are reported for various drop size ratios, Bond numbers, and viscosity ratios. The time scale for coalescence in the non-axisymmetric configuration is found to be substantially larger than that for coalescence in the axisymmetric configuration. Measurements of the radius of the liquid film formed between the two drops at the instant of apparent contact are used in conjunction with a planar film drainage model to predict the dependence of the coalescence time on drop size ratio for coalescence of low viscosity-ratio drops in the axisymmetric configuration.

Semisupervised learning using Bayesian interpretation: application to LS-SVM.

PubMed

Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

2011-04-01

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

Phylotranscriptomic analysis of the origin and early diversification of land plants

PubMed Central

Wickett, Norman J.; Mirarab, Siavash; Nguyen, Nam; Warnow, Tandy; Carpenter, Eric; Matasci, Naim; Ayyampalayam, Saravanaraj; Barker, Michael S.; Burleigh, J. Gordon; Gitzendanner, Matthew A.; Ruhfel, Brad R.; Wafula, Eric; Graham, Sean W.; Mathews, Sarah; Melkonian, Michael; Soltis, Douglas E.; Soltis, Pamela S.; Miles, Nicholas W.; Rothfels, Carl J.; Pokorny, Lisa; Shaw, A. Jonathan; DeGironimo, Lisa; Stevenson, Dennis W.; Surek, Barbara; Villarreal, Juan Carlos; Roure, Béatrice; Philippe, Hervé; dePamphilis, Claude W.; Chen, Tao; Deyholos, Michael K.; Baucom, Regina S.; Kutchan, Toni M.; Augustin, Megan M.; Wang, Jun; Zhang, Yong; Tian, Zhijian; Yan, Zhixiang; Wu, Xiaolei; Sun, Xiao; Wong, Gane Ka-Shu; Leebens-Mack, James

2014-01-01

Reconstructing the origin and evolution of land plants and their algal relatives is a fundamental problem in plant phylogenetics, and is essential for understanding how critical adaptations arose, including the embryo, vascular tissue, seeds, and flowers. Despite advances in molecular systematics, some hypotheses of relationships remain weakly resolved. Inferring deep phylogenies with bouts of rapid diversification can be problematic; however, genome-scale data should significantly increase the number of informative characters for analyses. Recent phylogenomic reconstructions focused on the major divergences of plants have resulted in promising but inconsistent results. One limitation is sparse taxon sampling, likely resulting from the difficulty and cost of data generation. To address this limitation, transcriptome data for 92 streptophyte taxa were generated and analyzed along with 11 published plant genome sequences. Phylogenetic reconstructions were conducted using up to 852 nuclear genes and 1,701,170 aligned sites. Sixty-nine analyses were performed to test the robustness of phylogenetic inferences to permutations of the data matrix or to phylogenetic method, including supermatrix, supertree, and coalescent-based approaches, maximum-likelihood and Bayesian methods, partitioned and unpartitioned analyses, and amino acid versus DNA alignments. Among other results, we find robust support for a sister-group relationship between land plants and one group of streptophyte green algae, the Zygnematophyceae. Strong and robust support for a clade comprising liverworts and mosses is inconsistent with a widely accepted view of early land plant evolution, and suggests that phylogenetic hypotheses used to understand the evolution of fundamental plant traits should be reevaluated. PMID:25355905

A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test.

PubMed

Yu, Qingzhao; Zhu, Lin; Zhu, Han

2017-11-01

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size. Copyright © 2017 John Wiley & Sons, Ltd.

Genetic divergence and phylogeographic history of two closely related species (Leucomeris decora and Nouelia insignis) across the 'Tanaka Line' in Southwest China.

PubMed

Zhao, Yu-Juan; Gong, Xun

2015-07-08

Leucomeris decora and Nouelia insignis (Asteraceae) are narrowly and allopatrically distributed species, separated by the important biogeographic boundary Tanaka Line in Southwest China. Previous morphological, cytogenetic and molecular studies suggested that L. decora is sister to N. insignis. However, it is less clear how the two species diverged, whether in full isolation or occurring gene flow across the Tanaka Line. Here, we performed a molecular study at the population level to characterize genetic differentiation and decipher phylogeographic history in two closely related species based on variation examined in plastid and nuclear DNAs using a coalescent-based approach. These morphologically distinct species share plastid DNA (cpDNA) haplotypes. In contrast, Bayesian analysis of nuclear DNA (nDNA) uncovered two distinct clusters corresponding to L. decora and N. insignis. Based on the IMa analysis, no strong indication of migration was detected based on both cpDNA and nDNA sequences. The molecular data pointed to a major west-east split in nuclear DNA between the two species corresponding with the Tanaka Line. The coalescent time estimate for all cpDNA haplotypes dated to the Mid-Late Pleistocene. The estimated demographic parameters showed that the population size of L. decora was similar to that of N. insignis and both experienced limited demographic fluctuations recently. The study revealed comprehensive species divergence and phylogeographic histories of N. insignis and L. decora divided by the Tanaka Line. The phylogeographic pattern inferred from cpDNA reflected ancestrally shared polymorphisms without post-divergence gene flow between species. The marked genealogical lineage divergence in nDNA provided some indication of Tanaka Line for its role as a barrier to plant dispersal, and lent support to its importance in promoting strong population structure and allopatric divergence.

Toward a DNA Taxonomy of Alpine Rhithrogena (Ephemeroptera: Heptageniidae) Using a Mixed Yule-Coalescent Analysis of Mitochondrial and Nuclear DNA

PubMed Central

Vuataz, Laurent; Sartori, Michel; Wagner, André; Monaghan, Michael T.

2011-01-01

Aquatic larvae of many Rhithrogena mayflies (Ephemeroptera) inhabit sensitive Alpine environments. A number of species are on the IUCN Red List and many recognized species have restricted distributions and are of conservation interest. Despite their ecological and conservation importance, ambiguous morphological differences among closely related species suggest that the current taxonomy may not accurately reflect the evolutionary diversity of the group. Here we examined the species status of nearly 50% of European Rhithrogena diversity using a widespread sampling scheme of Alpine species that included 22 type localities, general mixed Yule-coalescent (GMYC) model analysis of one standard mtDNA marker and one newly developed nDNA marker, and morphological identification where possible. Using sequences from 533 individuals from 144 sampling localities, we observed significant clustering of the mitochondrial (cox1) marker into 31 GMYC species. Twenty-one of these could be identified based on the presence of topotypes (expertly identified specimens from the species' type locality) or unambiguous morphology. These results strongly suggest the presence of both cryptic diversity and taxonomic oversplitting in Rhithrogena. Significant clustering was not detected with protein-coding nuclear PEPCK, although nine GMYC species were congruent with well supported terminal clusters of nDNA. Lack of greater congruence in the two data sets may be the result of incomplete sorting of ancestral polymorphism. Bayesian phylogenetic analyses of both gene regions recovered four of the six recognized Rhithrogena species groups in our samples as monophyletic. Future development of more nuclear markers would facilitate multi-locus analysis of unresolved, closely related species pairs. The DNA taxonomy developed here lays the groundwork for a future revision of the important but cryptic Rhithrogena genus in Europe. PMID:21611178

Jumping Mechanism of Self-Propelled Droplet

NASA Astrophysics Data System (ADS)

Lian, Yongsheng; Chen, Yan

2017-11-01

The self-propelled behavior of coalesced droplets can be utilized to enhance heat transfer performance of dropwise condensation. It has been recognized that the droplet self-propelling is the combined result of the conversion of surface energy to kinetic energy and the unsymmetrical boundary conditions imposed on the droplets. However, the roles of boundary conditions, which largely determine the conversion ratio of surface energy to the effective jumping kinetic energy, are not well understood. In this paper we use a numerical approach to investigate the boundary condition effect on the self-propelling behavior. A Navier-Stokes equation solver for multiphase flows is used to describe the flow field. The moment of fluid interface reconstruction technique is applied to resolute the interfaces. A direction splitting method is applied to advect the interface. And an approximate projection method is used to decouple the calculation of velocity and pressure. Comparisons are made with experimental results and show the simulation can accurately capture self-propelling behavior. Our simulation show the vertical flow velocity inside the coalesced droplet can increase the normalized jumping velocity but the contact area between droplets and substrate can decrease jumping velocity. High viscous dissipation is observed at the beginning of the coalescence which reduces jumping velocity.

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Impact of censoring on learning Bayesian networks in survival modelling.

PubMed

Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from data can be used to learn from censored survival data in the presence of light censoring (up to 20%) by treating censored cases as event-free. Given intermediate or heavy censoring, the learnt models become tuned to the majority class and would thus require a different approach.

A Gibbs sampler for Bayesian analysis of site-occupancy data

USGS Publications Warehouse

Dorazio, Robert M.; Rodriguez, Daniel Taylor

2012-01-01

1. A Bayesian analysis of site-occupancy data containing covariates of species occurrence and species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of species occurrence and species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of species occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.

A Comparison of Imputation Methods for Bayesian Factor Analysis Models

ERIC Educational Resources Information Center

Merkle, Edgar C.

2011-01-01

Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…

Using Bayesian neural networks to classify forest scenes

NASA Astrophysics Data System (ADS)

Vehtari, Aki; Heikkonen, Jukka; Lampinen, Jouko; Juujarvi, Jouni

1998-10-01

We present results that compare the performance of Bayesian learning methods for neural networks on the task of classifying forest scenes into trees and background. Classification task is demanding due to the texture richness of the trees, occlusions of the forest scene objects and diverse lighting conditions under operation. This makes it difficult to determine which are optimal image features for the classification. A natural way to proceed is to extract many different types of potentially suitable features, and to evaluate their usefulness in later processing stages. One approach to cope with large number of features is to use Bayesian methods to control the model complexity. Bayesian learning uses a prior on model parameters, combines this with evidence from a training data, and the integrates over the resulting posterior to make predictions. With this method, we can use large networks and many features without fear of overfitting. For this classification task we compare two Bayesian learning methods for multi-layer perceptron (MLP) neural networks: (1) The evidence framework of MacKay uses a Gaussian approximation to the posterior weight distribution and maximizes with respect to hyperparameters. (2) In a Markov Chain Monte Carlo (MCMC) method due to Neal, the posterior distribution of the network parameters is numerically integrated using the MCMC method. As baseline classifiers for comparison we use (3) MLP early stop committee, (4) K-nearest-neighbor and (5) Classification And Regression Tree.

Bayesian-MCMC-based parameter estimation of stealth aircraft RCS models

NASA Astrophysics Data System (ADS)

Xia, Wei; Dai, Xiao-Xia; Feng, Yuan

2015-12-01

When modeling a stealth aircraft with low RCS (Radar Cross Section), conventional parameter estimation methods may cause a deviation from the actual distribution, owing to the fact that the characteristic parameters are estimated via directly calculating the statistics of RCS. The Bayesian-Markov Chain Monte Carlo (Bayesian-MCMC) method is introduced herein to estimate the parameters so as to improve the fitting accuracies of fluctuation models. The parameter estimations of the lognormal and the Legendre polynomial models are reformulated in the Bayesian framework. The MCMC algorithm is then adopted to calculate the parameter estimates. Numerical results show that the distribution curves obtained by the proposed method exhibit improved consistence with the actual ones, compared with those fitted by the conventional method. The fitting accuracy could be improved by no less than 25% for both fluctuation models, which implies that the Bayesian-MCMC method might be a good candidate among the optimal parameter estimation methods for stealth aircraft RCS models. Project supported by the National Natural Science Foundation of China (Grant No. 61101173), the National Basic Research Program of China (Grant No. 613206), the National High Technology Research and Development Program of China (Grant No. 2012AA01A308), the State Scholarship Fund by the China Scholarship Council (CSC), and the Oversea Academic Training Funds, and University of Electronic Science and Technology of China (UESTC).

Development of dynamic Bayesian models for web application test management

NASA Astrophysics Data System (ADS)

Azarnova, T. V.; Polukhin, P. V.; Bondarenko, Yu V.; Kashirina, I. L.

2018-03-01

The mathematical apparatus of dynamic Bayesian networks is an effective and technically proven tool that can be used to model complex stochastic dynamic processes. According to the results of the research, mathematical models and methods of dynamic Bayesian networks provide a high coverage of stochastic tasks associated with error testing in multiuser software products operated in a dynamically changing environment. Formalized representation of the discrete test process as a dynamic Bayesian model allows us to organize the logical connection between individual test assets for multiple time slices. This approach gives an opportunity to present testing as a discrete process with set structural components responsible for the generation of test assets. Dynamic Bayesian network-based models allow us to combine in one management area individual units and testing components with different functionalities and a direct influence on each other in the process of comprehensive testing of various groups of computer bugs. The application of the proposed models provides an opportunity to use a consistent approach to formalize test principles and procedures, methods used to treat situational error signs, and methods used to produce analytical conclusions based on test results.

Trans-dimensional and hierarchical Bayesian approaches toward rigorous estimation of seismic sources and structures in the Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, Seongryong; Tkalčić, Hrvoje; Mustać, Marija; Rhie, Junkee; Ford, Sean

2016-04-01

A framework is presented within which we provide rigorous estimations for seismic sources and structures in the Northeast Asia. We use Bayesian inversion methods, which enable statistical estimations of models and their uncertainties based on data information. Ambiguities in error statistics and model parameterizations are addressed by hierarchical and trans-dimensional (trans-D) techniques, which can be inherently implemented in the Bayesian inversions. Hence reliable estimation of model parameters and their uncertainties is possible, thus avoiding arbitrary regularizations and parameterizations. Hierarchical and trans-D inversions are performed to develop a three-dimensional velocity model using ambient noise data. To further improve the model, we perform joint inversions with receiver function data using a newly developed Bayesian method. For the source estimation, a novel moment tensor inversion method is presented and applied to regional waveform data of the North Korean nuclear explosion tests. By the combination of new Bayesian techniques and the structural model, coupled with meaningful uncertainties related to each of the processes, more quantitative monitoring and discrimination of seismic events is possible.

CytoBayesJ: software tools for Bayesian analysis of cytogenetic radiation dosimetry data.

PubMed

Ainsbury, Elizabeth A; Vinnikov, Volodymyr; Puig, Pedro; Maznyk, Nataliya; Rothkamm, Kai; Lloyd, David C

2013-08-30

A number of authors have suggested that a Bayesian approach may be most appropriate for analysis of cytogenetic radiation dosimetry data. In the Bayesian framework, probability of an event is described in terms of previous expectations and uncertainty. Previously existing, or prior, information is used in combination with experimental results to infer probabilities or the likelihood that a hypothesis is true. It has been shown that the Bayesian approach increases both the accuracy and quality assurance of radiation dose estimates. New software entitled CytoBayesJ has been developed with the aim of bringing Bayesian analysis to cytogenetic biodosimetry laboratory practice. CytoBayesJ takes a number of Bayesian or 'Bayesian like' methods that have been proposed in the literature and presents them to the user in the form of simple user-friendly tools, including testing for the most appropriate model for distribution of chromosome aberrations and calculations of posterior probability distributions. The individual tools are described in detail and relevant examples of the use of the methods and the corresponding CytoBayesJ software tools are given. In this way, the suitability of the Bayesian approach to biological radiation dosimetry is highlighted and its wider application encouraged by providing a user-friendly software interface and manual in English and Russian. Copyright © 2013 Elsevier B.V. All rights reserved.

Bayesian molecular dating: opening up the black box.

PubMed

Bromham, Lindell; Duchêne, Sebastián; Hua, Xia; Ritchie, Andrew M; Duchêne, David A; Ho, Simon Y W

2018-05-01

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales. © 2017 Cambridge Philosophical Society.

On the uncertainty in single molecule fluorescent lifetime and energy emission measurements

NASA Technical Reports Server (NTRS)

Brown, Emery N.; Zhang, Zhenhua; Mccollom, Alex D.

1995-01-01

Time-correlated single photon counting has recently been combined with mode-locked picosecond pulsed excitation to measure the fluorescent lifetimes and energy emissions of single molecules in a flow stream. Maximum likelihood (ML) and least square methods agree and are optimal when the number of detected photons is large however, in single molecule fluorescence experiments the number of detected photons can be less than 20, 67% of those can be noise and the detection time is restricted to 10 nanoseconds. Under the assumption that the photon signal and background noise are two independent inhomogeneous poisson processes, we derive the exact joint arrival time probably density of the photons collected in a single counting experiment performed in the presence of background noise. The model obviates the need to bin experimental data for analysis, and makes it possible to analyze formally the effect of background noise on the photon detection experiment using both ML or Bayesian methods. For both methods we derive the joint and marginal probability densities of the fluorescent lifetime and fluorescent emission. the ML and Bayesian methods are compared in an analysis of simulated single molecule fluorescence experiments of Rhodamine 110 using different combinations of expected background nose and expected fluorescence emission. While both the ML or Bayesian procedures perform well for analyzing fluorescence emissions, the Bayesian methods provide more realistic measures of uncertainty in the fluorescent lifetimes. The Bayesian methods would be especially useful for measuring uncertainty in fluorescent lifetime estimates in current single molecule flow stream experiments where the expected fluorescence emission is low. Both the ML and Bayesian algorithms can be automated for applications in molecular biology.

On the Uncertainty in Single Molecule Fluorescent Lifetime and Energy Emission Measurements

NASA Technical Reports Server (NTRS)

Brown, Emery N.; Zhang, Zhenhua; McCollom, Alex D.

1996-01-01

Time-correlated single photon counting has recently been combined with mode-locked picosecond pulsed excitation to measure the fluorescent lifetimes and energy emissions of single molecules in a flow stream. Maximum likelihood (ML) and least squares methods agree and are optimal when the number of detected photons is large, however, in single molecule fluorescence experiments the number of detected photons can be less than 20, 67 percent of those can be noise, and the detection time is restricted to 10 nanoseconds. Under the assumption that the photon signal and background noise are two independent inhomogeneous Poisson processes, we derive the exact joint arrival time probability density of the photons collected in a single counting experiment performed in the presence of background noise. The model obviates the need to bin experimental data for analysis, and makes it possible to analyze formally the effect of background noise on the photon detection experiment using both ML or Bayesian methods. For both methods we derive the joint and marginal probability densities of the fluorescent lifetime and fluorescent emission. The ML and Bayesian methods are compared in an analysis of simulated single molecule fluorescence experiments of Rhodamine 110 using different combinations of expected background noise and expected fluorescence emission. While both the ML or Bayesian procedures perform well for analyzing fluorescence emissions, the Bayesian methods provide more realistic measures of uncertainty in the fluorescent lifetimes. The Bayesian methods would be especially useful for measuring uncertainty in fluorescent lifetime estimates in current single molecule flow stream experiments where the expected fluorescence emission is low. Both the ML and Bayesian algorithms can be automated for applications in molecular biology.

Introduction to Bayesian statistical approaches to compositional analyses of transgenic crops 1. Model validation and setting the stage.

PubMed

Harrison, Jay M; Breeze, Matthew L; Harrigan, George G

2011-08-01

Statistical comparisons of compositional data generated on genetically modified (GM) crops and their near-isogenic conventional (non-GM) counterparts typically rely on classical significance testing. This manuscript presents an introduction to Bayesian methods for compositional analysis along with recommendations for model validation. The approach is illustrated using protein and fat data from two herbicide tolerant GM soybeans (MON87708 and MON87708×MON89788) and a conventional comparator grown in the US in 2008 and 2009. Guidelines recommended by the US Food and Drug Administration (FDA) in conducting Bayesian analyses of clinical studies on medical devices were followed. This study is the first Bayesian approach to GM and non-GM compositional comparisons. The evaluation presented here supports a conclusion that a Bayesian approach to analyzing compositional data can provide meaningful and interpretable results. We further describe the importance of method validation and approaches to model checking if Bayesian approaches to compositional data analysis are to be considered viable by scientists involved in GM research and regulation. Copyright © 2011 Elsevier Inc. All rights reserved.

Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis

PubMed Central

Fancher, Chris M.; Han, Zhen; Levin, Igor; Page, Katharine; Reich, Brian J.; Smith, Ralph C.; Wilson, Alyson G.; Jones, Jacob L.

2016-01-01

A Bayesian inference method for refining crystallographic structures is presented. The distribution of model parameters is stochastically sampled using Markov chain Monte Carlo. Posterior probability distributions are constructed for all model parameters to properly quantify uncertainty by appropriately modeling the heteroskedasticity and correlation of the error structure. The proposed method is demonstrated by analyzing a National Institute of Standards and Technology silicon standard reference material. The results obtained by Bayesian inference are compared with those determined by Rietveld refinement. Posterior probability distributions of model parameters provide both estimates and uncertainties. The new method better estimates the true uncertainties in the model as compared to the Rietveld method. PMID:27550221

A sub-space greedy search method for efficient Bayesian Network inference.

PubMed

Zhang, Qing; Cao, Yong; Li, Yong; Zhu, Yanming; Sun, Samuel S M; Guo, Dianjing

2011-09-01

Bayesian network (BN) has been successfully used to infer the regulatory relationships of genes from microarray dataset. However, one major limitation of BN approach is the computational cost because the calculation time grows more than exponentially with the dimension of the dataset. In this paper, we propose a sub-space greedy search method for efficient Bayesian Network inference. Particularly, this method limits the greedy search space by only selecting gene pairs with higher partial correlation coefficients. Using both synthetic and real data, we demonstrate that the proposed method achieved comparable results with standard greedy search method yet saved ∼50% of the computational time. We believe that sub-space search method can be widely used for efficient BN inference in systems biology. Copyright © 2011 Elsevier Ltd. All rights reserved.

Quantifying Uncertainty in Near Surface Electromagnetic Imaging Using Bayesian Methods

NASA Astrophysics Data System (ADS)

Blatter, D. B.; Ray, A.; Key, K.

2017-12-01

Geoscientists commonly use electromagnetic methods to image the Earth's near surface. Field measurements of EM fields are made (often with the aid an artificial EM source) and then used to infer near surface electrical conductivity via a process known as inversion. In geophysics, the standard inversion tool kit is robust and can provide an estimate of the Earth's near surface conductivity that is both geologically reasonable and compatible with the measured field data. However, standard inverse methods struggle to provide a sense of the uncertainty in the estimate they provide. This is because the task of finding an Earth model that explains the data to within measurement error is non-unique - that is, there are many, many such models; but the standard methods provide only one "answer." An alternative method, known as Bayesian inversion, seeks to explore the full range of Earth model parameters that can adequately explain the measured data, rather than attempting to find a single, "ideal" model. Bayesian inverse methods can therefore provide a quantitative assessment of the uncertainty inherent in trying to infer near surface conductivity from noisy, measured field data. This study applies a Bayesian inverse method (called trans-dimensional Markov chain Monte Carlo) to transient airborne EM data previously collected over Taylor Valley - one of the McMurdo Dry Valleys in Antarctica. Our results confirm the reasonableness of previous estimates (made using standard methods) of near surface conductivity beneath Taylor Valley. In addition, we demonstrate quantitatively the uncertainty associated with those estimates. We demonstrate that Bayesian inverse methods can provide quantitative uncertainty to estimates of near surface conductivity.

A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Jin; Yu, Yaming; Van Dyk, David A.

2014-10-20

Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use amore » principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.« less

Bayesian-information-gap decision theory with an application to CO 2 sequestration

DOE PAGES

O'Malley, D.; Vesselinov, V. V.

2015-09-04

Decisions related to subsurface engineering problems such as groundwater management, fossil fuel production, and geologic carbon sequestration are frequently challenging because of an overabundance of uncertainties (related to conceptualizations, parameters, observations, etc.). Because of the importance of these problems to agriculture, energy, and the climate (respectively), good decisions that are scientifically defensible must be made despite the uncertainties. We describe a general approach to making decisions for challenging problems such as these in the presence of severe uncertainties that combines probabilistic and non-probabilistic methods. The approach uses Bayesian sampling to assess parametric uncertainty and Information-Gap Decision Theory (IGDT) to addressmore » model inadequacy. The combined approach also resolves an issue that frequently arises when applying Bayesian methods to real-world engineering problems related to the enumeration of possible outcomes. In the case of zero non-probabilistic uncertainty, the method reduces to a Bayesian method. Lastly, to illustrate the approach, we apply it to a site-selection decision for geologic CO 2 sequestration.« less

Solution of the modified Helmholtz equation in a triangular domain and an application to diffusion-limited coalescence.

PubMed

ben-Avraham, D; Fokas, A S

2001-07-01

A new transform method for solving boundary value problems for linear and integrable nonlinear partial differential equations recently introduced in the literature is used here to obtain the solution of the modified Helmholtz equation q(xx)(x,y)+q(yy)(x,y)-4 beta(2)q(x,y)=0 in the triangular domain 0< or =x< or =L-y< or =L, with mixed boundary conditions. This solution is applied to the problem of diffusion-limited coalescence, A+A<==>A, in the segment (-L/2,L/2), with traps at the edges.

Divergent evolutionary processes associated with colonization of offshore islands.

PubMed

Martínková, Natália; Barnett, Ross; Cucchi, Thomas; Struchen, Rahel; Pascal, Marine; Pascal, Michel; Fischer, Martin C; Higham, Thomas; Brace, Selina; Ho, Simon Y W; Quéré, Jean-Pierre; O'Higgins, Paul; Excoffier, Laurent; Heckel, Gerald; Hoelzel, A Rus; Dobney, Keith M; Searle, Jeremy B

2013-10-01

Oceanic islands have been a test ground for evolutionary theory, but here, we focus on the possibilities for evolutionary study created by offshore islands. These can be colonized through various means and by a wide range of species, including those with low dispersal capabilities. We use morphology, modern and ancient sequences of cytochrome b (cytb) and microsatellite genotypes to examine colonization history and evolutionary change associated with occupation of the Orkney archipelago by the common vole (Microtus arvalis), a species found in continental Europe but not in Britain. Among possible colonization scenarios, our results are most consistent with human introduction at least 5100 bp (confirmed by radiocarbon dating). We used approximate Bayesian computation of population history to infer the coast of Belgium as the possible source and estimated the evolutionary timescale using a Bayesian coalescent approach. We showed substantial morphological divergence of the island populations, including a size increase presumably driven by selection and reduced microsatellite variation likely reflecting founder events and genetic drift. More surprisingly, our results suggest that a recent and widespread cytb replacement event in the continental source area purged cytb variation there, whereas the ancestral diversity is largely retained in the colonized islands as a genetic 'ark'. The replacement event in the continental M. arvalis was probably triggered by anthropogenic causes (land-use change). Our studies illustrate that small offshore islands can act as field laboratories for studying various evolutionary processes over relatively short timescales, informing about the mainland source area as well as the island. © 2013 John Wiley & Sons Ltd.

Coalescence-induced jumping of nanoscale droplets on super-hydrophobic surfaces

NASA Astrophysics Data System (ADS)

Liang, Zhi; Keblinski, Pawel; Nanoscale Science; Engineering Center Team

The coalescence-induced jumping of tens of microns size droplets on super-hydrophobic surfaces has been observed in both experiments and simulations. However, whether the coalescence-induced jumping would occur for smaller, particularly nanoscale droplets, is an open question. Using molecular dynamics simulations, we demonstrate that in spite of the large internal viscous dissipation, coalescence of two nanoscale droplets on a super-hydrophobic surface can result in a jumping of the coalesced droplet from the surface with a speed of a few m/s. Similar to the coalescence-induced jumping of microscale droplets, we observe that the bridge between the coalescing nano-droplets expands and impacts the solid surface, which leads to an acceleration of the coalesced droplet by the pressure force from the solid surface. We observe that the jumping velocity decreases with the droplet size and its ratio to the inertial-capillary velocity is a constant of about 0.126, which is close to the minimum value of 0.111 predicted by continuum-level modeling of Enright et al. [R. Enright, N. Miljkovic, J. Sprittles, K. Nolan, R. Mitchell, and E. N. Wang, ACS Nano 8, 10352 (2014)].

Effect of electrolytes on bubble coalescence in columns observed with visualization techniques.

PubMed

Aguilera, María Eugenia; Ojeda, Antonieta; Rondón, Carolina; López De Ramos, Aura

2002-10-01

Bubble coalescence and the effect of electrolytes on this phenomenon have been previously studied. This interfacial phenomenon has attracted attention for reactor design/operation and enhanced oil recovery. Predicting bubble coalescence may help prevent low yields in reactors and predict crude oil recovery. Because of the importance of bubble coalescence, the objectives of this work were to improve the accuracy of measuring the percentage of coalescing bubbles and to observe the interfacial gas-liquid behavior. An experimental setup was designed and constructed. Bubble interactions were monitored with a visualization setup. The percentage of air bubble coalescence was 100% in distilled water, about 50% in 0.1 M sodium chloride (NaCl) aqueous solution, and 0% in 0.145 M NaCl aqueous solution. A reduction of the contact gas-liquid area was observed in distillate water. The volume of the resulting bubble was the sum of the original bubble volumes. Repulsion of bubbles was observed in NaCl solutions exceeding 0.07 M. The percentage of bubble coalescence diminishes as the concentration of NaCl chloride increases. High-speed video recording is an accurate technique to measure the percentage of bubble coalescence, and represents an important advance in gas-liquid interfacial studies.

Coalescence driven self-organization of growing nanodroplets around a microcap

NASA Astrophysics Data System (ADS)

Dyett, Brendan; Hao, Hao; Lohse, Detlef; Zhang, Xuehua

The coalescence between growing droplets is important for the surface coverage and spatial arrangements of droplets on surfaces. In this work, total internal reflection fluorescence (TIRF) microscopy is utilized to in-situ investigate the formation of nanodroplets around the rim of a polymer microcap, with sub-micron spatial and millisecond temporal resolution. We observe that the coalescence among droplets occurs frequently during their growth by solvent exchange. Our experimental results show that the position of the droplet from two merged droplets is related to the size of the parent droplets. The position of the coalesced droplet and the ratio of parent droplet sizes obey a scaling law, reflecting a coalescence preference based on the size inequality. As a result of droplet coalescence, the angles between the centroids of two neighbouring droplets increase with time, obeying a nearly symmetrical arrangement of droplets at various time intervals. The evolution of the position and number from coalescence of growing droplets is modelled. The mechanism for coalescence driven self-organization of growing droplets is general, applicable to microcaps of different sizes and droplets of different liquids. The understanding from this work may be valuable for positioning nanodroplets by nucleation and growth without using templates.

Coalescence driven self-organization of growing nanodroplets around a microcap.

PubMed

Dyett, Brendan; Hao, Hao; Lohse, Detlef; Zhang, Xuehua

2018-04-04

The coalescence between growing droplets is important for the surface coverage and spatial arrangements of droplets on surfaces. In this work, total internal reflection fluorescence (TIRF) microscopy is utilized to in situ investigate the formation of nanodroplets around the rim of a polymer microcap, with sub-micron spatial and millisecond temporal resolution. We observe that the coalescence among droplets occurs frequently during their growth by solvent exchange. Our experimental results show that the position of the droplet from two merged droplets is related to the size of the parent droplets. The position of the coalesced droplet and the ratio of parent droplet sizes obey a scaling law, reflecting a coalescence preference based on the size inequality. As a result of droplet coalescence, the angles between the centroids of two neighbouring droplets increase with time, obeying a nearly symmetrical arrangement of droplets at various time intervals. The evolution of the position and number from coalescence of growing droplets is modelled. The mechanism for coalescence driven self-organization of growing droplets is general, applicable to microcaps of different sizes and droplets of different liquids. The understanding from this work may be valuable for positioning nanodroplets by nucleation and growth without using templates.

Optofluidic droplet coalescence on a microfluidic chip

NASA Astrophysics Data System (ADS)

Jung, Jin Ho; Lee, Kyung Heon; Lee, Kang Soo; Cho, Hyunjun; Ha, Byung Hang; Destgeer, Ghulam; Sung, Hyung Jin

2013-11-01

Coalescence is the procedure that two or more droplets fuse during contact to form a larger droplet. Optofluidic droplet coalescence on a microfluidic chip was demonstrated with theoretical and experimental approaches. Droplets were produced in a T-junction geometry and their velocities and sizes were adjusted by flow rate. In order to bring them in a direct contact of coalescence, optical gradient force was used to trap the droplets. A theoretical modeling of the coalescence was derived by combining the optical force and drag force on the droplet. The analytical expression of the optical force on a sphere droplet was employed to estimate the trapping efficiency in the ray optics regime. The drag force acting on the droplet was calculated in terms of the fluid velocity, viscosity and the geometrical parameters of a microfluidic channel. The droplet coalescence was conducted in a microfluidic setup equipped with a 1064 CW laser, focusing optics, a syringe pump, a custom-made stage and a sCMOS camera. The droplets were successfully coalesced using the optical gradient force. The experimental data of coalescence were in good agreement with the prediction. This work was supported by the Creative Research Initiatives program (No.2013-003364) of the National Research Foundation of Korea (MSIP).

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

A Bayesian approach to tracking patients having changing pharmacokinetic parameters

NASA Technical Reports Server (NTRS)

Bayard, David S.; Jelliffe, Roger W.

2004-01-01

This paper considers the updating of Bayesian posterior densities for pharmacokinetic models associated with patients having changing parameter values. For estimation purposes it is proposed to use the Interacting Multiple Model (IMM) estimation algorithm, which is currently a popular algorithm in the aerospace community for tracking maneuvering targets. The IMM algorithm is described, and compared to the multiple model (MM) and Maximum A-Posteriori (MAP) Bayesian estimation methods, which are presently used for posterior updating when pharmacokinetic parameters do not change. Both the MM and MAP Bayesian estimation methods are used in their sequential forms, to facilitate tracking of changing parameters. Results indicate that the IMM algorithm is well suited for tracking time-varying pharmacokinetic parameters in acutely ill and unstable patients, incurring only about half of the integrated error compared to the sequential MM and MAP methods on the same example.

Multi-body coalescence in Pickering emulsions

NASA Astrophysics Data System (ADS)

Wu, Tong; Wang, Haitao; Jing, Benxin; Liu, Fang; Burns, Peter C.; Na, Chongzheng

2015-01-01

Particle-stabilized Pickering emulsions have shown unusual behaviours such as the formation of non-spherical droplets and the sudden halt of coalescence between individual droplets. Here we report another unusual behaviour of Pickering emulsions—the simultaneous coalescence of multiple droplets in a single event. Using latex particles, silica particles and carbon nanotubes as model stabilizers, we show that multi-body coalescence can occur in both water-in-oil and oil-in-water emulsions. The number of droplets involved in the nth coalscence event equals four times the corresponding number of the tetrahedral sequence in close packing. Furthermore, coalescence is promoted by repulsive latex and silica particles but inhibited by attractive carbon nanotubes. The revelation of multi-body coalescence is expected to help better understand Pickering emulsions in natural systems and improve their designs in engineering applications.

New applications of maximum likelihood and Bayesian statistics in macromolecular crystallography.

PubMed

McCoy, Airlie J

2002-10-01

Maximum likelihood methods are well known to macromolecular crystallographers as the methods of choice for isomorphous phasing and structure refinement. Recently, the use of maximum likelihood and Bayesian statistics has extended to the areas of molecular replacement and density modification, placing these methods on a stronger statistical foundation and making them more accurate and effective.

Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection

PubMed Central

Dialdestoro, Kevin; Sibbesen, Jonas Andreas; Maretty, Lasse; Raghwani, Jayna; Gall, Astrid; Kellam, Paul; Pybus, Oliver G.; Hein, Jotun; Jenkins, Paul A.

2016-01-01

Human immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput “deep” sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different time points during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intrahost viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this article we develop a new method for inference using HIV deep sequencing data, using an approach based on importance sampling of ancestral recombination graphs under a multilocus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions, a quantity of key interest when approximating coalescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different time points and missing data without extra computational difficulty. We apply our method to a data set of HIV-1, in which several hundred sequences were obtained from an infected individual at seven time points over 2 years. We find mutation rate and effective population size estimates to be comparable to those produced by the software BEAST. Additionally, our method is able to produce local recombination rate estimates. The software underlying our method, Coalescenator, is freely available. PMID:26857628

A Bayesian estimate of the concordance correlation coefficient with skewed data.

PubMed

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2015-01-01

Concordance correlation coefficient (CCC) is one of the most popular scaled indices used to evaluate agreement. Most commonly, it is used under the assumption that data is normally distributed. This assumption, however, does not apply to skewed data sets. While methods for the estimation of the CCC of skewed data sets have been introduced and studied, the Bayesian approach and its comparison with the previous methods has been lacking. In this study, we propose a Bayesian method for the estimation of the CCC of skewed data sets and compare it with the best method previously investigated. The proposed method has certain advantages. It tends to outperform the best method studied before when the variation of the data is mainly from the random subject effect instead of error. Furthermore, it allows for greater flexibility in application by enabling incorporation of missing data, confounding covariates, and replications, which was not considered previously. The superiority of this new approach is demonstrated using simulation as well as real-life biomarker data sets used in an electroencephalography clinical study. The implementation of the Bayesian method is accessible through the Comprehensive R Archive Network. Copyright © 2015 John Wiley & Sons, Ltd.

Characterization of background concentrations of contaminants using a mixture of normal distributions.

PubMed

Qian, Song S; Lyons, Regan E

2006-10-01

We present a Bayesian approach for characterizing background contaminant concentration distributions using data from sites that may have been contaminated. Our method, focused on estimation, resolves several technical problems of the existing methods sanctioned by the U.S. Environmental Protection Agency (USEPA) (a hypothesis testing based method), resulting in a simple and quick procedure for estimating background contaminant concentrations. The proposed Bayesian method is applied to two data sets from a federal facility regulated under the Resource Conservation and Restoration Act. The results are compared to background distributions identified using existing methods recommended by the USEPA. The two data sets represent low and moderate levels of censorship in the data. Although an unbiased estimator is elusive, we show that the proposed Bayesian estimation method will have a smaller bias than the EPA recommended method.

Coalescence preference and droplet size inequality during fluid phase segregation

NASA Astrophysics Data System (ADS)

Roy, Sutapa

2018-02-01

Using molecular dynamics simulations and scaling arguments, we investigate the coalescence preference dynamics of liquid droplets in a phase-segregating off-critical, single-component fluid. It is observed that the preferential distance of the product drop from its larger parent, during a coalescence event, gets smaller for large parent size inequality. The relative coalescence position exhibits a power-law dependence on the parent size ratio with an exponent q ≃ 3.1 . This value of q is in strong contrast with earlier reports 2.1 and 5.1 in the literature. The dissimilarity is explained by considering the underlying coalescence mechanisms.

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

PubMed Central

Ferragina, A.; de los Campos, G.; Vazquez, A. I.; Cecchinato, A.; Bittante, G.

2017-01-01

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict “difficult-to-predict” dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm−1 were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R2 value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R2 (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R2 of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations. PMID:26387015

A Bayesian approach to the statistical analysis of device preference studies.

PubMed

Fu, Haoda; Qu, Yongming; Zhu, Baojin; Huster, William

2012-01-01

Drug delivery devices are required to have excellent technical specifications to deliver drugs accurately, and in addition, the devices should provide a satisfactory experience to patients because this can have a direct effect on drug compliance. To compare patients' experience with two devices, cross-over studies with patient-reported outcomes (PRO) as response variables are often used. Because of the strength of cross-over designs, each subject can directly compare the two devices by using the PRO variables, and variables indicating preference (preferring A, preferring B, or no preference) can be easily derived. Traditionally, methods based on frequentist statistics can be used to analyze such preference data, but there are some limitations for the frequentist methods. Recently, Bayesian methods are considered an acceptable method by the US Food and Drug Administration to design and analyze device studies. In this paper, we propose a Bayesian statistical method to analyze the data from preference trials. We demonstrate that the new Bayesian estimator enjoys some optimal properties versus the frequentist estimator. Copyright © 2012 John Wiley & Sons, Ltd.

Moving beyond qualitative evaluations of Bayesian models of cognition.

PubMed

Hemmer, Pernille; Tauber, Sean; Steyvers, Mark

2015-06-01

Bayesian models of cognition provide a powerful way to understand the behavior and goals of individuals from a computational point of view. Much of the focus in the Bayesian cognitive modeling approach has been on qualitative model evaluations, where predictions from the models are compared to data that is often averaged over individuals. In many cognitive tasks, however, there are pervasive individual differences. We introduce an approach to directly infer individual differences related to subjective mental representations within the framework of Bayesian models of cognition. In this approach, Bayesian data analysis methods are used to estimate cognitive parameters and motivate the inference process within a Bayesian cognitive model. We illustrate this integrative Bayesian approach on a model of memory. We apply the model to behavioral data from a memory experiment involving the recall of heights of people. A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. In addition, the model with inferred priors at the individual subject level led to the best overall generalization performance, suggesting that individual differences are important to consider in Bayesian models of cognition.

On-Chip generation of polymer microcapsules through droplet coalescence

NASA Astrophysics Data System (ADS)

Eqbal, Md Danish; Gundabala, Venkat; Gundabala lab Team

Alginate microbeads and microcapsules have numerous applications in drug delivery, tissue engineering and other biomedical areas due to their unique properties. Microcapsules with liquid core are of particular interest in the area of cell encapsulation. Various methods such as coacervation, emulsification, micro-nozzle, etc. exist for the generation of microbeads and microcapsules. However, these methods have several drawbacks like coagulation, non-uniformity, and polydispersity. In this work we present a method for complete on chip generation of alginate microcapsules (single core as well as double core) through the use of droplet merging technique. For this purpose, a combined Coflow and T-junction configuration is implemented in a hybrid glass-PDMS (Polydimethylsiloxane) microfluidic device. Efficient generation is achieved through precise matching of the generation rates of the coalescing drops. Through this approach, microcapsules with intact single and double (liquid) cores surrounded by alginate shell have been successfully generated and characterized.

Inferring human population size and separation history from multiple genome sequences.

PubMed

Schiffels, Stephan; Durbin, Richard

2014-08-01

The availability of complete human genome sequences from populations across the world has given rise to new population genetic inference methods that explicitly model ancestral relationships under recombination and mutation. So far, application of these methods to evolutionary history more recent than 20,000-30,000 years ago and to population separations has been limited. Here we present a new method that overcomes these shortcomings. The multiple sequentially Markovian coalescent (MSMC) analyzes the observed pattern of mutations in multiple individuals, focusing on the first coalescence between any two individuals. Results from applying MSMC to genome sequences from nine populations across the world suggest that the genetic separation of non-African ancestors from African Yoruban ancestors started long before 50,000 years ago and give information about human population history as recent as 2,000 years ago, including the bottleneck in the peopling of the Americas and separations within Africa, East Asia and Europe.

Asymptotic Distributions of Coalescence Times and Ancestral Lineage Numbers for Populations with Temporally Varying Size

PubMed Central

Chen, Hua; Chen, Kun

2013-01-01

The distributions of coalescence times and ancestral lineage numbers play an essential role in coalescent modeling and ancestral inference. Both exact distributions of coalescence times and ancestral lineage numbers are expressed as the sum of alternating series, and the terms in the series become numerically intractable for large samples. More computationally attractive are their asymptotic distributions, which were derived in Griffiths (1984) for populations with constant size. In this article, we derive the asymptotic distributions of coalescence times and ancestral lineage numbers for populations with temporally varying size. For a sample of size n, denote by Tm the mth coalescent time, when m + 1 lineages coalesce into m lineages, and An(t) the number of ancestral lineages at time t back from the current generation. Similar to the results in Griffiths (1984), the number of ancestral lineages, An(t), and the coalescence times, Tm, are asymptotically normal, with the mean and variance of these distributions depending on the population size function, N(t). At the very early stage of the coalescent, when t → 0, the number of coalesced lineages n − An(t) follows a Poisson distribution, and as m → n, n(n−1)Tm/2N(0) follows a gamma distribution. We demonstrate the accuracy of the asymptotic approximations by comparing to both exact distributions and coalescent simulations. Several applications of the theoretical results are also shown: deriving statistics related to the properties of gene genealogies, such as the time to the most recent common ancestor (TMRCA) and the total branch length (TBL) of the genealogy, and deriving the allele frequency spectrum for large genealogies. With the advent of genomic-level sequencing data for large samples, the asymptotic distributions are expected to have wide applications in theoretical and methodological development for population genetic inference. PMID:23666939

Asymptotic distributions of coalescence times and ancestral lineage numbers for populations with temporally varying size.

PubMed

Chen, Hua; Chen, Kun

2013-07-01

The distributions of coalescence times and ancestral lineage numbers play an essential role in coalescent modeling and ancestral inference. Both exact distributions of coalescence times and ancestral lineage numbers are expressed as the sum of alternating series, and the terms in the series become numerically intractable for large samples. More computationally attractive are their asymptotic distributions, which were derived in Griffiths (1984) for populations with constant size. In this article, we derive the asymptotic distributions of coalescence times and ancestral lineage numbers for populations with temporally varying size. For a sample of size n, denote by Tm the mth coalescent time, when m + 1 lineages coalesce into m lineages, and An(t) the number of ancestral lineages at time t back from the current generation. Similar to the results in Griffiths (1984), the number of ancestral lineages, An(t), and the coalescence times, Tm, are asymptotically normal, with the mean and variance of these distributions depending on the population size function, N(t). At the very early stage of the coalescent, when t → 0, the number of coalesced lineages n - An(t) follows a Poisson distribution, and as m → n, $$n\\left(n-1\\right){T}_{m}/2N\\left(0\\right)$$ follows a gamma distribution. We demonstrate the accuracy of the asymptotic approximations by comparing to both exact distributions and coalescent simulations. Several applications of the theoretical results are also shown: deriving statistics related to the properties of gene genealogies, such as the time to the most recent common ancestor (TMRCA) and the total branch length (TBL) of the genealogy, and deriving the allele frequency spectrum for large genealogies. With the advent of genomic-level sequencing data for large samples, the asymptotic distributions are expected to have wide applications in theoretical and methodological development for population genetic inference.

Reconstructing gravitational wave source parameters via direct comparisons to numerical relativity I: Method

NASA Astrophysics Data System (ADS)

Lange, Jacob; O'Shaughnessy, Richard; Healy, James; Lousto, Carlos; Shoemaker, Deirdre; Lovelace, Geoffrey; Scheel, Mark; Ossokine, Serguei

2016-03-01

In this talk, we describe a procedure to reconstruct the parameters of sufficiently massive coalescing compact binaries via direct comparison with numerical relativity simulations. For sufficiently massive sources, existing numerical relativity simulations are long enough to cover the observationally accessible part of the signal. Due to the signal's brevity, the posterior parameter distribution it implies is broad, simple, and easily reconstructed from information gained by comparing to only the sparse sample of existing numerical relativity simulations. We describe how followup simulations can corroborate and improve our understanding of a detected source. Since our method can include all physics provided by full numerical relativity simulations of coalescing binaries, it provides a valuable complement to alternative techniques which employ approximations to reconstruct source parameters. Supported by NSF Grant PHY-1505629.

Strategies for improving approximate Bayesian computation tests for synchronous diversification.

PubMed

Overcast, Isaac; Bagley, Justin C; Hickerson, Michael J

2017-08-24

Estimating the variability in isolation times across co-distributed taxon pairs that may have experienced the same allopatric isolating mechanism is a core goal of comparative phylogeography. The use of hierarchical Approximate Bayesian Computation (ABC) and coalescent models to infer temporal dynamics of lineage co-diversification has been a contentious topic in recent years. Key issues that remain unresolved include the choice of an appropriate prior on the number of co-divergence events (Ψ), as well as the optimal strategies for data summarization. Through simulation-based cross validation we explore the impact of the strategy for sorting summary statistics and the choice of prior on Ψ on the estimation of co-divergence variability. We also introduce a new setting (β) that can potentially improve estimation of Ψ by enforcing a minimal temporal difference between pulses of co-divergence. We apply this new method to three empirical datasets: one dataset each of co-distributed taxon pairs of Panamanian frogs and freshwater fishes, and a large set of Neotropical butterfly sister-taxon pairs. We demonstrate that the choice of prior on Ψ has little impact on inference, but that sorting summary statistics yields substantially more reliable estimates of co-divergence variability despite violations of assumptions about exchangeability. We find the implementation of β improves estimation of Ψ, with improvement being most dramatic given larger numbers of taxon pairs. We find equivocal support for synchronous co-divergence for both of the Panamanian groups, but we find considerable support for asynchronous divergence among the Neotropical butterflies. Our simulation experiments demonstrate that using sorted summary statistics results in improved estimates of the variability in divergence times, whereas the choice of hyperprior on Ψ has negligible effect. Additionally, we demonstrate that estimating the number of pulses of co-divergence across co-distributed taxon-pairs is improved by applying a flexible buffering regime over divergence times. This improves the correlation between Ψ and the true variability in isolation times and allows for more meaningful interpretation of this hyperparameter. This will allow for more accurate identification of the number of temporally distinct pulses of co-divergence that generated the diversification pattern of a given regional assemblage of sister-taxon-pairs.

Metastable nanobubbles at the solid-liquid interface due to contact angle hysteresis.

PubMed

Nishiyama, Takashi; Yamada, Yutaka; Ikuta, Tatsuya; Takahashi, Koji; Takata, Yasuyuki

2015-01-27

Nanobubbles exist at solid-liquid interfaces between pure water and hydrophobic surfaces with very high stability, lasting in certain cases up to several days. Not only semispherical but also other shapes, such as micropancakes, are known to exist at such interfaces. However, doubt has been raised as to whether or not the nanobubbles are gas-phase entities. In this study, surface nanobubbles at a pure water-highly ordered pyrolytic graphite (HOPG) interface were investigated by peak force quantitative nanomechanics (PF-QNM). Multiple isolated nanobubbles generated by the solvent-exchange method were present on the terraced areas, avoiding the steps of the HOPG surface. Adjacent nanobubbles coalesced and formed metastable nanobubbles. Coalescence was enhanced by the PF-QNM measurement. We determined that nanobubbles can exist for a long time because of nanoscale contact angle hysteresis at the water-HOPG interface. Moreover, the hydrophilic steps of HOPG were avoided during coalescence, providing evidence that the nanobubbles are truly gas phase.

Bayesian methodology for the design and interpretation of clinical trials in critical care medicine: a primer for clinicians.

PubMed

Kalil, Andre C; Sun, Junfeng

2014-10-01

To review Bayesian methodology and its utility to clinical decision making and research in the critical care field. Clinical, epidemiological, and biostatistical studies on Bayesian methods in PubMed and Embase from their inception to December 2013. Bayesian methods have been extensively used by a wide range of scientific fields, including astronomy, engineering, chemistry, genetics, physics, geology, paleontology, climatology, cryptography, linguistics, ecology, and computational sciences. The application of medical knowledge in clinical research is analogous to the application of medical knowledge in clinical practice. Bedside physicians have to make most diagnostic and treatment decisions on critically ill patients every day without clear-cut evidence-based medicine (more subjective than objective evidence). Similarly, clinical researchers have to make most decisions about trial design with limited available data. Bayesian methodology allows both subjective and objective aspects of knowledge to be formally measured and transparently incorporated into the design, execution, and interpretation of clinical trials. In addition, various degrees of knowledge and several hypotheses can be tested at the same time in a single clinical trial without the risk of multiplicity. Notably, the Bayesian technology is naturally suited for the interpretation of clinical trial findings for the individualized care of critically ill patients and for the optimization of public health policies. We propose that the application of the versatile Bayesian methodology in conjunction with the conventional statistical methods is not only ripe for actual use in critical care clinical research but it is also a necessary step to maximize the performance of clinical trials and its translation to the practice of critical care medicine.

Hepatitis disease detection using Bayesian theory

NASA Astrophysics Data System (ADS)

Maseleno, Andino; Hidayati, Rohmah Zahroh

2017-02-01

This paper presents hepatitis disease diagnosis using a Bayesian theory for better understanding of the theory. In this research, we used a Bayesian theory for detecting hepatitis disease and displaying the result of diagnosis process. Bayesian algorithm theory is rediscovered and perfected by Laplace, the basic idea is using of the known prior probability and conditional probability density parameter, based on Bayes theorem to calculate the corresponding posterior probability, and then obtained the posterior probability to infer and make decisions. Bayesian methods combine existing knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. The initial symptoms of hepatitis which include malaise, fever and headache. The probability of hepatitis given the presence of malaise, fever, and headache. The result revealed that a Bayesian theory has successfully identified the existence of hepatitis disease.

Fitting Residual Error Structures for Growth Models in SAS PROC MCMC

ERIC Educational Resources Information Center

McNeish, Daniel

2017-01-01

In behavioral sciences broadly, estimating growth models with Bayesian methods is becoming increasingly common, especially to combat small samples common with longitudinal data. Although Mplus is becoming an increasingly common program for applied research employing Bayesian methods, the limited selection of prior distributions for the elements of…

Bayesian and Frequentist Methods for Estimating Joint Uncertainty of Freundlich Adsorption Isotherm Fitting Parameters

EPA Science Inventory

In this paper, we present methods for estimating Freundlich isotherm fitting parameters (K and N) and their joint uncertainty, which have been implemented into the freeware software platforms R and WinBUGS. These estimates were determined by both Frequentist and Bayesian analyse...

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

An interface tracking model for droplet electrocoalescence.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erickson, Lindsay Crowl

This report describes an Early Career Laboratory Directed Research and Development (LDRD) project to develop an interface tracking model for droplet electrocoalescence. Many fluid-based technologies rely on electrical fields to control the motion of droplets, e.g. microfluidic devices for high-speed droplet sorting, solution separation for chemical detectors, and purification of biodiesel fuel. Precise control over droplets is crucial to these applications. However, electric fields can induce complex and unpredictable fluid dynamics. Recent experiments (Ristenpart et al. 2009) have demonstrated that oppositely charged droplets bounce rather than coalesce in the presence of strong electric fields. A transient aqueous bridge forms betweenmore » approaching drops prior to pinch-off. This observation applies to many types of fluids, but neither theory nor experiments have been able to offer a satisfactory explanation. Analytic hydrodynamic approximations for interfaces become invalid near coalescence, and therefore detailed numerical simulations are necessary. This is a computationally challenging problem that involves tracking a moving interface and solving complex multi-physics and multi-scale dynamics, which are beyond the capabilities of most state-of-the-art simulations. An interface-tracking model for electro-coalescence can provide a new perspective to a variety of applications in which interfacial physics are coupled with electrodynamics, including electro-osmosis, fabrication of microelectronics, fuel atomization, oil dehydration, nuclear waste reprocessing and solution separation for chemical detectors. We present a conformal decomposition finite element (CDFEM) interface-tracking method for the electrohydrodynamics of two-phase flow to demonstrate electro-coalescence. CDFEM is a sharp interface method that decomposes elements along fluid-fluid boundaries and uses a level set function to represent the interface.« less

Bayesian-based estimation of acoustic surface impedance: Finite difference frequency domain approach.

PubMed

Bockman, Alexander; Fackler, Cameron; Xiang, Ning

2015-04-01

Acoustic performance for an interior requires an accurate description of the boundary materials' surface acoustic impedance. Analytical methods may be applied to a small class of test geometries, but inverse numerical methods provide greater flexibility. The parameter estimation problem requires minimizing prediction vice observed acoustic field pressure. The Bayesian-network sampling approach presented here mitigates other methods' susceptibility to noise inherent to the experiment, model, and numerics. A geometry agnostic method is developed here and its parameter estimation performance is demonstrated for an air-backed micro-perforated panel in an impedance tube. Good agreement is found with predictions from the ISO standard two-microphone, impedance-tube method, and a theoretical model for the material. Data by-products exclusive to a Bayesian approach are analyzed to assess sensitivity of the method to nuisance parameters.

Coalescent histories for caterpillar-like families.

PubMed

Rosenberg, Noah A

2013-01-01

A coalescent history is an assignment of branches of a gene tree to branches of a species tree on which coalescences in the gene tree occur. The number of coalescent histories for a pair consisting of a labeled gene tree topology and a labeled species tree topology is important in gene tree probability computations, and more generally, in studying evolutionary possibilities for gene trees on species trees. Defining the Tr-caterpillar-like family as a sequence of n-taxon trees constructed by replacing the r-taxon subtree of n-taxon caterpillars by a specific r-taxon labeled topology Tr, we examine the number of coalescent histories for caterpillar-like families with matching gene tree and species tree labeled topologies. For each Tr with size r≤8, we compute the number of coalescent histories for n-taxon trees in the Tr-caterpillar-like family. Next, as n→∞, we find that the limiting ratio of the numbers of coalescent histories for the Tr family and caterpillars themselves is correlated with the number of labeled histories for Tr. The results support a view that large numbers of coalescent histories occur when a tree has both a relatively balanced subtree and a high tree depth, contributing to deeper understanding of the combinatorics of gene trees and species trees.

Recent results and persisting problems in modeling flow induced coalescence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fortelný, I., E-mail: fortelny@imc.cas.cz, E-mail: juza@imc.cas.cz; Jza, J., E-mail: fortelny@imc.cas.cz, E-mail: juza@imc.cas.cz

2014-05-15

The contribution summarizes recent results of description of the flow induced coalescence in immiscible polymer blends and addresses problems that call for which solving. The theory of coalescence based on the switch between equations for matrix drainage between spherical or deformed droplets provides a good agreement with more complicated modeling and available experimental data for probability, P{sub c}, that the collision of droplets will be followed by their fusion. A new equation for description of the matrix drainage between deformed droplets, applicable to the whole range of viscosity ratios, p, of the droplets and matrixes, is proposed. The theory facilitatesmore » to consider the effect of the matrix elasticity on coalescence. P{sub c} decreases with the matrix relaxation time but this decrease is not pronounced for relaxation times typical of most commercial polymers. Modeling of the flow induced coalescence in concentrated systems is needed for prediction of the dependence of coalescence rate on volume fraction of droplets. The effect of the droplet anisometry on P{sub c} should be studied for better understanding the coalescence in flow field with high and moderate deformation rates. A reliable description of coalescence in mixing and processing devices requires proper modeling of complex flow fields.« less

Application of Bayesian methods to habitat selection modeling of the northern spotted owl in California: new statistical methods for wildlife research

Treesearch

Howard B. Stauffer; Cynthia J. Zabel; Jeffrey R. Dunk

2005-01-01

We compared a set of competing logistic regression habitat selection models for Northern Spotted Owls (Strix occidentalis caurina) in California. The habitat selection models were estimated, compared, evaluated, and tested using multiple sample datasets collected on federal forestlands in northern California. We used Bayesian methods in interpreting...

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

USGS Publications Warehouse

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

Recursions for the exchangeable partition function of the seedbank coalescent.

PubMed

Kurt, Noemi; Rafler, Mathias

2017-04-01

For the seedbank coalescent with mutation under the infinite alleles assumption, which describes the gene genealogy of a population with a strong seedbank effect subject to mutations, we study the distribution of the final partition with mutation. This generalizes the coalescent with freeze by Dong et al. (2007) to coalescents where ancestral lineages are blocked from coalescing. We derive an implicit recursion which we show to have a unique solution and give an interpretation in terms of absorption problems of a random walk. Moreover, we derive recursions for the distribution of the number of blocks in the final partition. Copyright © 2017 Elsevier Inc. All rights reserved.

Phase segregation due to simultaneous migration and coalescence

NASA Technical Reports Server (NTRS)

Davis, Robert H.

1994-01-01

The primary objective of the research is to perform ground-based analysis and experiments on the interaction and coalescence of drops (or bubbles) leading to macroscopic phase separation. Migration of the drops occurs as a result of the individual and collective action of gravity and thermocapillary effects. Larger drops migrate faster than smaller ones, leading to the possibility of collisions and coalescence. Coalescence increases the rate of macroscopic phase separation, since the result is larger drops with higher migration rates. It is hoped that the understanding gained will lead to the design of microgravity experiments to further elucidate the mechanisms governing coalescence and phase separation.

Short time dynamics of water coalescence on a flat water pool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Su Jin; Gim, Bopil; Fezzaa, Kamel

2016-12-01

Coalescence is an important hydrodynamic event that frequently takes place in nature as well as in industry. Here we provide an experimental study on short time dynamics of water coalescence, particularly when a water droplet comes in contact with a flat water surface, by utilizing high-resolution high-penetration ultrafast X-ray microscopy. Our results demonstrate a possibility that an extreme curvature difference between a drop and a flat surface can significantly modify the hydrodynamics of water coalescence, which is unexpected in the existing theory. We suggest a plausible explanation for why coalescence can be modified by an extreme curvature difference.

Characteristic time scales of coalescence of silver nanocomposite and nanoparticle films induced by continuous wave laser irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paeng, Dongwoo; Grigoropoulos, Costas P., E-mail: cgrigoro@berkeley.edu; Lee, Daeho

2014-08-18

In-situ optical probing has been performed to analyze and compare the characteristic coalescence time scales of silver ion-doped polyvinylalcohol nanocomposite (Ag-PVA NC) and polyvinylpyrrolidone-capped silver nanoparticle (Ag-PVP NP) films subjected to continuous wave laser irradiation. The Ag-PVA NC yielded conductive metallic patterns by photothermal reduction of PVA, formation of nanoparticles from silver ions and their subsequent coalescence. On the other hand, Ag-PVP NP thin films produced conductive patterns through only coalescence of nanoparticles. Upon laser irradiation, Ag-PVA NC and Ag-PVP NP films exhibited different coalescence characteristics.

Traffic Video Image Segmentation Model Based on Bayesian and Spatio-Temporal Markov Random Field

NASA Astrophysics Data System (ADS)

Zhou, Jun; Bao, Xu; Li, Dawei; Yin, Yongwen

2017-10-01

Traffic video image is a kind of dynamic image and its background and foreground is changed at any time, which results in the occlusion. In this case, using the general method is more difficult to get accurate image segmentation. A segmentation algorithm based on Bayesian and Spatio-Temporal Markov Random Field is put forward, which respectively build the energy function model of observation field and label field to motion sequence image with Markov property, then according to Bayesian' rule, use the interaction of label field and observation field, that is the relationship of label field’s prior probability and observation field’s likelihood probability, get the maximum posterior probability of label field’s estimation parameter, use the ICM model to extract the motion object, consequently the process of segmentation is finished. Finally, the segmentation methods of ST - MRF and the Bayesian combined with ST - MRF were analyzed. Experimental results: the segmentation time in Bayesian combined with ST-MRF algorithm is shorter than in ST-MRF, and the computing workload is small, especially in the heavy traffic dynamic scenes the method also can achieve better segmentation effect.

A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Ye, M.; Liang, F.

2016-12-01

Due to simplification and/or misrepresentation of the real aquifer system, numerical groundwater flow and solute transport models are usually subject to model structural error. During model calibration, the hydrogeological parameters may be overly adjusted to compensate for unknown structural error. This may result in biased predictions when models are used to forecast aquifer response to new forcing. In this study, we extend a fully Bayesian method [Xu and Valocchi, 2015] to calibrate a real-world, regional groundwater flow model. The method uses a data-driven error model to describe model structural error and jointly infers model parameters and structural error. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models. The surrogate models are constructed using machine learning techniques to emulate the response simulated by the computationally expensive groundwater model. We demonstrate in the real-world case study that explicitly accounting for model structural error yields parameter posterior distributions that are substantially different from those derived by the classical Bayesian calibration that does not account for model structural error. In addition, the Bayesian with error model method gives significantly more accurate prediction along with reasonable credible intervals.

Effects of fullerene coalescence on the thermal conductivity of carbon nanopeapods

NASA Astrophysics Data System (ADS)

Li, Jiaqian; Shen, Haijun

2018-05-01

The heat conduction and its dependence on fullerene coalescence in carbon nanopeapods (CNPs) have been investigated by equilibrium molecular dynamics simulations. The effects of fullerene coalescence on the thermal conductivity of CNPs were discussed under different temperatures. It is shown that the thermal conductivity of the CNPs decreases with the coalescence of encapsulated fullerene molecules. The thermal transmission mechanism of the effect of fullerene coalescence was analysed by the mass transfer contribution, the relative contributions of phonon oscillation frequencies to total heat current and the phonon vibrational density of states (VDOS). The mass transfer in CNPs is mainly attributed to the motion of encapsulated fullerene molecule and it gets more restricted with the coalescence of the fullerene. It shows that the low-frequency phonon modes below 20 THz contribute mostly to thermal conductivity in CNPs. The analysis of VDOS demonstrates that the dominating contribution to heat transfer is from the inner fullerene chain. With the coalescence of fullerene, the interfacial heat transfer between the CNT and fullerene chain is strengthened; however, the heat conduction of the fullerene chain decreases more rapidly at the same time.

Model-based Bayesian inference for ROC data analysis

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Bae, K. Ty

2013-03-01

This paper presents a study of model-based Bayesian inference to Receiver Operating Characteristics (ROC) data. The model is a simple version of general non-linear regression model. Different from Dorfman model, it uses a probit link function with a covariate variable having zero-one two values to express binormal distributions in a single formula. Model also includes a scale parameter. Bayesian inference is implemented by Markov Chain Monte Carlo (MCMC) method carried out by Bayesian analysis Using Gibbs Sampling (BUGS). Contrast to the classical statistical theory, Bayesian approach considers model parameters as random variables characterized by prior distributions. With substantial amount of simulated samples generated by sampling algorithm, posterior distributions of parameters as well as parameters themselves can be accurately estimated. MCMC-based BUGS adopts Adaptive Rejection Sampling (ARS) protocol which requires the probability density function (pdf) which samples are drawing from be log concave with respect to the targeted parameters. Our study corrects a common misconception and proves that pdf of this regression model is log concave with respect to its scale parameter. Therefore, ARS's requirement is satisfied and a Gaussian prior which is conjugate and possesses many analytic and computational advantages is assigned to the scale parameter. A cohort of 20 simulated data sets and 20 simulations from each data set are used in our study. Output analysis and convergence diagnostics for MCMC method are assessed by CODA package. Models and methods by using continuous Gaussian prior and discrete categorical prior are compared. Intensive simulations and performance measures are given to illustrate our practice in the framework of model-based Bayesian inference using MCMC method.

A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?

PubMed Central

Pressman, Alice R.; Avins, Andrew L.; Hubbard, Alan; Satariano, William A.

2014-01-01

Background There is a paucity of literature comparing Bayesian analytic techniques with traditional approaches for analyzing clinical trials using real trial data. Methods We compared Bayesian and frequentist group sequential methods using data from two published clinical trials. We chose two widely accepted frequentist rules, O'Brien–Fleming and Lan–DeMets, and conjugate Bayesian priors. Using the nonparametric bootstrap, we estimated a sampling distribution of stopping times for each method. Because current practice dictates the preservation of an experiment-wise false positive rate (Type I error), we approximated these error rates for our Bayesian and frequentist analyses with the posterior probability of detecting an effect in a simulated null sample. Thus for the data-generated distribution represented by these trials, we were able to compare the relative performance of these techniques. Results No final outcomes differed from those of the original trials. However, the timing of trial termination differed substantially by method and varied by trial. For one trial, group sequential designs of either type dictated early stopping of the study. In the other, stopping times were dependent upon the choice of spending function and prior distribution. Conclusions Results indicate that trialists ought to consider Bayesian methods in addition to traditional approaches for analysis of clinical trials. Though findings from this small sample did not demonstrate either method to consistently outperform the other, they did suggest the need to replicate these comparisons using data from varied clinical trials in order to determine the conditions under which the different methods would be most efficient. PMID:21453792

Next Steps in Bayesian Structural Equation Models: Comments on, Variations of, and Extensions to Muthen and Asparouhov (2012)

ERIC Educational Resources Information Center

Rindskopf, David

2012-01-01

Muthen and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By…

Modeling of Academic Achievement of Primary School Students in Ethiopia Using Bayesian Multilevel Approach

ERIC Educational Resources Information Center

Sebro, Negusse Yohannes; Goshu, Ayele Taye

2017-01-01

This study aims to explore Bayesian multilevel modeling to investigate variations of average academic achievement of grade eight school students. A sample of 636 students is randomly selected from 26 private and government schools by a two-stage stratified sampling design. Bayesian method is used to estimate the fixed and random effects. Input and…

A Simulation Study Comparison of Bayesian Estimation with Conventional Methods for Estimating Unknown Change Points

ERIC Educational Resources Information Center

Wang, Lijuan; McArdle, John J.

2008-01-01

The main purpose of this research is to evaluate the performance of a Bayesian approach for estimating unknown change points using Monte Carlo simulations. The univariate and bivariate unknown change point mixed models were presented and the basic idea of the Bayesian approach for estimating the models was discussed. The performance of Bayesian…

Classifying emotion in Twitter using Bayesian network

NASA Astrophysics Data System (ADS)

Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Estimation of parameter uncertainty for an activated sludge model using Bayesian inference: a comparison with the frequentist method.

PubMed

Zonta, Zivko J; Flotats, Xavier; Magrí, Albert

2014-08-01

The procedure commonly used for the assessment of the parameters included in activated sludge models (ASMs) relies on the estimation of their optimal value within a confidence region (i.e. frequentist inference). Once optimal values are estimated, parameter uncertainty is computed through the covariance matrix. However, alternative approaches based on the consideration of the model parameters as probability distributions (i.e. Bayesian inference), may be of interest. The aim of this work is to apply (and compare) both Bayesian and frequentist inference methods when assessing uncertainty for an ASM-type model, which considers intracellular storage and biomass growth, simultaneously. Practical identifiability was addressed exclusively considering respirometric profiles based on the oxygen uptake rate and with the aid of probabilistic global sensitivity analysis. Parameter uncertainty was thus estimated according to both the Bayesian and frequentist inferential procedures. Results were compared in order to evidence the strengths and weaknesses of both approaches. Since it was demonstrated that Bayesian inference could be reduced to a frequentist approach under particular hypotheses, the former can be considered as a more generalist methodology. Hence, the use of Bayesian inference is encouraged for tackling inferential issues in ASM environments.

Nuclear introns outperform mitochondrial DNA in inter-specific phylogenetic reconstruction: Lessons from horseshoe bats (Rhinolophidae: Chiroptera).

PubMed

Dool, Serena E; Puechmaille, Sebastien J; Foley, Nicole M; Allegrini, Benjamin; Bastian, Anna; Mutumi, Gregory L; Maluleke, Tinyiko G; Odendaal, Lizelle J; Teeling, Emma C; Jacobs, David S

2016-04-01

Despite many studies illustrating the perils of utilising mitochondrial DNA in phylogenetic studies, it remains one of the most widely used genetic markers for this purpose. Over the last decade, nuclear introns have been proposed as alternative markers for phylogenetic reconstruction. However, the resolution capabilities of mtDNA and nuclear introns have rarely been quantified and compared. In the current study we generated a novel ∼5kb dataset comprising six nuclear introns and a mtDNA fragment. We assessed the relative resolution capabilities of the six intronic fragments with respect to each other, when used in various combinations together, and when compared to the traditionally used mtDNA. We focused on a major clade in the horseshoe bat family (Afro-Palaearctic clade; Rhinolophidae) as our case study. This old, widely distributed and speciose group contains a high level of conserved morphology. This morphological stasis renders the reconstruction of the phylogeny of this group with traditional morphological characters complex. We sampled multiple individuals per species to represent their geographic distributions as best as possible (122 individuals, 24 species, 68 localities). We reconstructed the species phylogeny using several complementary methods (partitioned Maximum Likelihood and Bayesian and Bayesian multispecies-coalescent) and made inferences based on consensus across these methods. We computed pairwise comparisons based on Robinson-Foulds tree distance metric between all Bayesian topologies generated (27,000) for every gene(s) and visualised the tree space using multidimensional scaling (MDS) plots. Using our supported species phylogeny we estimated the ancestral state of key traits of interest within this group, e.g. echolocation peak frequency which has been implicated in speciation. Our results revealed many potential cryptic species within this group, even in taxa where this was not suspected a priori and also found evidence for mtDNA introgression. We demonstrated that by using just two introns one can recover a better supported species tree than when using the mtDNA alone, despite the shorter overall length of the combined introns. Additionally, when combining any single intron with mtDNA, we showed that the result is highly similar to the mtDNA gene tree and far from the true species tree and therefore this approach should be avoided. We caution against the indiscriminate use of mtDNA in phylogenetic studies and advocate for pilot studies to select nuclear introns. The selection of marker type and number is a crucial step that is best based on critical examination of preliminary or previously published data. Based on our findings and previous publications, we recommend the following markers to recover phylogenetic relationships between recently diverged taxa (<20 My) in bats and other mammals: ACOX2, COPS7A, BGN, ROGDI and STAT5A. Copyright © 2016 Elsevier Inc. All rights reserved.

Review of Reliability-Based Design Optimization Approach and Its Integration with Bayesian Method

NASA Astrophysics Data System (ADS)

Zhang, Xiangnan

2018-03-01

A lot of uncertain factors lie in practical engineering, such as external load environment, material property, geometrical shape, initial condition, boundary condition, etc. Reliability method measures the structural safety condition and determine the optimal design parameter combination based on the probabilistic theory. Reliability-based design optimization (RBDO) is the most commonly used approach to minimize the structural cost or other performance under uncertainty variables which combines the reliability theory and optimization. However, it cannot handle the various incomplete information. The Bayesian approach is utilized to incorporate this kind of incomplete information in its uncertainty quantification. In this paper, the RBDO approach and its integration with Bayesian method are introduced.

Detection and Classification of Transformer Winding Mechanical Faults Using UWB Sensors and Bayesian Classifier

NASA Astrophysics Data System (ADS)

Alehosseini, Ali; A. Hejazi, Maryam; Mokhtari, Ghassem; B. Gharehpetian, Gevork; Mohammadi, Mohammad

2015-06-01

In this paper, the Bayesian classifier is used to detect and classify the radial deformation and axial displacement of transformer windings. The proposed method is tested on a model of transformer for different volumes of radial deformation and axial displacement. In this method, ultra-wideband (UWB) signal is sent to the simplified model of the transformer winding. The received signal from the winding model is recorded and used for training and testing of Bayesian classifier in different axial displacement and radial deformation states of the winding. It is shown that the proposed method has a good accuracy to detect and classify the axial displacement and radial deformation of the winding.

Bayesian multimodel inference for dose-response studies

USGS Publications Warehouse

Link, W.A.; Albers, P.H.

2007-01-01

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Coalescence-Induced Jumping of Nanodroplets on Textured Surfaces.

PubMed

Gao, Shan; Liao, Quanwen; Liu, Wei; Liu, Zhichun

2018-01-04

Conducting experimental studies on nanoscale droplet coalescence using traditional microscopes is a challenging research topic, and views differ as to whether the spontaneous removal can occur in the coalescing nanodroplets. Here, a molecular dynamics simulation is carried out to investigate the coalescence process of two equally sized nanodroplets. On the basis of atomic coordinates, we compute the liquid bridge radii for various cases, which is described by a power law of spreading time, and these nanodroplets undergo coalescence in the inertially limited-viscous regime. Moreover, coalescence-induced jumping is also possible for the nanodroplets, and the attraction force between surface and water molecules plays a crucial role in this process, where the merged nanodroplets prefer to jump away from those surfaces with lower attraction force. When the solid-liquid interaction intensity and surface structure parameters are varied, the attraction force is shown to decrease with decreasing surface wettability intensity and solid fraction.

Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees.

PubMed

Zhu, Sha; Degnan, James H; Goldstien, Sharyn J; Eldon, Bjarki

2015-09-15

There has been increasing interest in coalescent models which admit multiple mergers of ancestral lineages; and to model hybridization and coalescence simultaneously. Hybrid-Lambda is a software package that simulates gene genealogies under multiple merger and Kingman's coalescent processes within species networks or species trees. Hybrid-Lambda allows different coalescent processes to be specified for different populations, and allows for time to be converted between generations and coalescent units, by specifying a population size for each population. In addition, Hybrid-Lambda can generate simulated datasets, assuming the infinitely many sites mutation model, and compute the F ST statistic. As an illustration, we apply Hybrid-Lambda to infer the time of subdivision of certain marine invertebrates under different coalescent processes. Hybrid-Lambda makes it possible to investigate biogeographic concordance among high fecundity species exhibiting skewed offspring distribution.

Surfactant effect on drop coalescence and film drainage hydrodynamics

NASA Astrophysics Data System (ADS)

Weheliye, Weheliye; Chinaud, Maxime; Voulgaropoulos, Victor; Angeli, Panagiota

2015-11-01

Coalescence of a drop on an aqueous-organic interface is studied in two test geometries A rectangular acrylic vessel and a Hele-Shaw cell (two parallel plates placed 2mm apart) are investigated for the experiments. Time resolved Particle Image Velocimetry (PIV) measurements provide information on the hydrodynamics during the bouncing stage of the droplet and on the vortices generated at the bulk fluid after the droplet has coalesced. The velocity field inside the droplet during its coalescence is presented. By localizing the rupture point of the coalescence in the quasi two dimensional cell, the film drainage dynamics are discussed by acquiring its flow velocity by PIV measurements with a straddling camera. The effect of surface tension forces in the coalescence of the droplet is investigated by introducing surface active agents at various concentrations extending on both sides of the critical micelle concentration.

Coalescence of Fluid-Driven Fractures

NASA Astrophysics Data System (ADS)

O'Keeffe, Niall; Zheng, Zhong; Huppert, Herbert; Linden, Paul

2017-11-01

We present an experimental study on the coalescence of two in-plane fluid-driven penny-shaped fractures in a brittle elastic medium. Initially, two fluid-driven fractures propagate independently of each other in the same plane. Then when the radial extent of each fracture reaches a certain distance the fractures begin to interact and coalesce. This coalescence forms a bridge between the fractures and then, in an intermediate period following the contact of the two fractures, most growth is observed to focus along this bridge, perpendicular to the line connecting the injection sources. We analyse the growth and shape of this bridge at various stages after coalescence and the transitions between different stages of growth. We also investigate the influence of the injection rate, the distance between two injection points, the viscosity of the fluid and the Young's modulus of the elastic medium on the coalescence of the fractures.

The detachment of particles from coalescing bubble pairs.

PubMed

Ata, Seher

2009-10-15

This paper is concerned with the detachment of particles from coalescing bubble pairs. Two bubbles were generated at adjacent capillaries and coated with hydrophobic glass particles of mean diameter 66 microm. The bubbles were then positioned next to each other until the thin liquid film between them ruptured. The particles that dropped from the bubble surface during the coalescence process were collected and measured. The coalescence process was very vigorous and observations showed that particles detached from the bubble surfaces as a result of the oscillations caused by coalescence. The attached particles themselves and, to some extent the presence of the surfactant had a damping affect on the bubble oscillation, which played a decisive role on the particle detachment phenomena. The behaviour of particles on the surfaces of the bubbles during coalescence was described, and implications of results for the flotation process were discussed.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

PubMed

Houseman, E Andres; Virji, M Abbas

2017-08-01

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates were significant in some frequentist models, but in the Bayesian model their credible intervals contained zero; such discrepancies were observed in multiple datasets. Variance components from the Bayesian model reflected substantial autocorrelation, consistent with the frequentist models, except for the auto-regressive moving average model. Plots of means from the Bayesian model showed good fit to the observed data. The proposed Bayesian model provides an approach for modeling non-stationary autocorrelation in a hierarchical modeling framework to estimate task means, standard deviations, quantiles, and parameter estimates for covariates that are less biased and have better performance characteristics than some of the contemporary methods. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.

Bayesian B-spline mapping for dynamic quantitative traits.

PubMed

Xing, Jun; Li, Jiahan; Yang, Runqing; Zhou, Xiaojing; Xu, Shizhong

2012-04-01

Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.

Explaining Inference on a Population of Independent Agents Using Bayesian Networks

ERIC Educational Resources Information Center

Sutovsky, Peter

2013-01-01

The main goal of this research is to design, implement, and evaluate a novel explanation method, the hierarchical explanation method (HEM), for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is modeled as a subnetwork. For example, consider disease-outbreak…

Application of Bayesian Methods for Detecting Fraudulent Behavior on Tests

ERIC Educational Resources Information Center

Sinharay, Sandip

2018-01-01

Producers and consumers of test scores are increasingly concerned about fraudulent behavior before and during the test. There exist several statistical or psychometric methods for detecting fraudulent behavior on tests. This paper provides a review of the Bayesian approaches among them. Four hitherto-unpublished real data examples are provided to…

Evidence of major genes affecting stress response in rainbow trout using Bayesian methods of complex segregation analysis

USDA-ARS?s Scientific Manuscript database

As a first step towards the genetic mapping of quantitative trait loci (QTL) affecting stress response variation in rainbow trout, we performed complex segregation analyses (CSA) fitting mixed inheritance models of plasma cortisol using Bayesian methods in large full-sib families of rainbow trout. ...

Bayesian Probability Theory

NASA Astrophysics Data System (ADS)

von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

2014-06-01

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

A hierarchical Bayesian method for vibration-based time domain force reconstruction problems

NASA Astrophysics Data System (ADS)

Li, Qiaofeng; Lu, Qiuhai

2018-05-01

Traditional force reconstruction techniques require prior knowledge on the force nature to determine the regularization term. When such information is unavailable, the inappropriate term is easily chosen and the reconstruction result becomes unsatisfactory. In this paper, we propose a novel method to automatically determine the appropriate q as in ℓq regularization and reconstruct the force history. The method incorporates all to-be-determined variables such as the force history, precision parameters and q into a hierarchical Bayesian formulation. The posterior distributions of variables are evaluated by a Metropolis-within-Gibbs sampler. The point estimates of variables and their uncertainties are given. Simulations of a cantilever beam and a space truss under various loading conditions validate the proposed method in providing adaptive determination of q and better reconstruction performance than existing Bayesian methods.

Travelling to the south: Phylogeographic spatial diffusion model in Monttea aphylla (Plantaginaceae), an endemic plant of the Monte Desert

PubMed Central

Cosacov, Andrea; Ferreiro, Gabriela; Johnson, Leigh A.; Sérsic, Alicia N.

2017-01-01

Effects of Pleistocene climatic oscillations on plant phylogeographic patterns are relatively well studied in forest, savanna and grassland biomes, but such impacts remain less explored on desert regions of the world, especially in South America. Here, we performed a phylogeographical study of Monttea aphylla, an endemic species of the Monte Desert, to understand the evolutionary history of vegetation communities inhabiting the South American Arid Diagonal. We obtained sequences of three chloroplast (trnS–trnfM, trnH–psbA and trnQ–rps16) and one nuclear (ITS) intergenic spacers from 272 individuals of 34 localities throughout the range of the species. Population genetic and Bayesian coalescent analyses were performed to infer genealogical relationships among haplotypes, population genetic structure, and demographic history of the study species. Timing of demographic events was inferred using Bayesian Skyline Plot and the spatio-temporal patterns of lineage diversification was reconstructed using Bayesian relaxed diffusion models. Palaeo-distribution models (PDM) were performed through three different timescales to validate phylogeographical patterns. Twenty-five and 22 haplotypes were identified in the cpDNA and nDNA data, respectively. that clustered into two main genealogical lineages following a latitudinal pattern, the northern and the southern Monte (south of 35° S). The northern Monte showed two lineages of high genetic structure, and more relative stable demography than the southern Monte that retrieved three groups with little phylogenetic structure and a strong signal of demographic expansion that would have started during the Last Interglacial period (ca. 120 Ka). The PDM and diffusion models analyses agreed in the southeast direction of the range expansion. Differential effect of climatic oscillations across the Monte phytogeographic province was observed in Monttea aphylla lineages. In northern Monte, greater genetic structure and more relative stable demography resulted from a more stable climate than in the southern Monte. Pleistocene glaciations drastically decreased the species area in the southern Monte, which expanded in a southeastern direction to the new available areas during the interglacial periods. PMID:28582433

Insights into the phylogeny of Northern Hemisphere Armillaria: Neighbor-net and Bayesian analyses of translation elongation factor 1-α gene sequences.

PubMed

Klopfenstein, Ned B; Stewart, Jane E; Ota, Yuko; Hanna, John W; Richardson, Bryce A; Ross-Davis, Amy L; Elías-Román, Rubén D; Korhonen, Kari; Keča, Nenad; Iturritxa, Eugenia; Alvarado-Rosales, Dionicio; Solheim, Halvor; Brazee, Nicholas J; Łakomy, Piotr; Cleary, Michelle R; Hasegawa, Eri; Kikuchi, Taisei; Garza-Ocañas, Fortunato; Tsopelas, Panaghiotis; Rigling, Daniel; Prospero, Simone; Tsykun, Tetyana; Bérubé, Jean A; Stefani, Franck O P; Jafarpour, Saeideh; Antonín, Vladimír; Tomšovský, Michal; McDonald, Geral I; Woodward, Stephen; Kim, Mee-Sook

2017-01-01

Armillaria possesses several intriguing characteristics that have inspired wide interest in understanding phylogenetic relationships within and among species of this genus. Nuclear ribosomal DNA sequence-based analyses of Armillaria provide only limited information for phylogenetic studies among widely divergent taxa. More recent studies have shown that translation elongation factor 1-α (tef1) sequences are highly informative for phylogenetic analysis of Armillaria species within diverse global regions. This study used Neighbor-net and coalescence-based Bayesian analyses to examine phylogenetic relationships of newly determined and existing tef1 sequences derived from diverse Armillaria species from across the Northern Hemisphere, with Southern Hemisphere Armillaria species included for reference. Based on the Bayesian analysis of tef1 sequences, Armillaria species from the Northern Hemisphere are generally contained within the following four superclades, which are named according to the specific epithet of the most frequently cited species within the superclade: (i) Socialis/Tabescens (exannulate) superclade including Eurasian A. ectypa, North American A. socialis (A. tabescens), and Eurasian A. socialis (A. tabescens) clades; (ii) Mellea superclade including undescribed annulate North American Armillaria sp. (Mexico) and four separate clades of A. mellea (Europe and Iran, eastern Asia, and two groups from North America); (iii) Gallica superclade including Armillaria Nag E (Japan), multiple clades of A. gallica (Asia and Europe), A. calvescens (eastern North America), A. cepistipes (North America), A. altimontana (western USA), A. nabsnona (North America and Japan), and at least two A. gallica clades (North America); and (iv) Solidipes/Ostoyae superclade including two A. solidipes/ostoyae clades (North America), A. gemina (eastern USA), A. solidipes/ostoyae (Eurasia), A. cepistipes (Europe and Japan), A. sinapina (North America and Japan), and A. borealis (Eurasia) clade 2. Of note is that A. borealis (Eurasia) clade 1 appears basal to the Solidipes/Ostoyae and Gallica superclades. The Neighbor-net analysis showed similar phylogenetic relationships. This study further demonstrates the utility of tef1 for global phylogenetic studies of Armillaria species and provides critical insights into multiple taxonomic issues that warrant further study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szymański, Tomasz, E-mail: tomasz.szymanski@pwr.edu.pl; Wośko, Mateusz; Paszkiewicz, Bartłomiej

Herein, silicon substrates in alternative orientations from the commonly used Si(111) were used to enable the growth of polar and semipolar GaN-based structures by the metalorganic vapor phase epitaxy method. Specifically, Si(112) and Si(115) substrates were used for the epitaxial growth of nitride multilayer structures, while the same layer schemes were also deposited on Si(111) for comparison purposes. Multiple approaches were studied to examine the influence of the seed layers and the growth process conditions upon the final properties of the GaN/Si(11x) templates. Scanning electron microscope images were acquired to examine the topography of the deposited samples. It was observedmore » that the substrate orientation and the process conditions allow control to produce an isolated GaN block growth or a coalesced layer growth, resulting in inclined c-axis GaN structures under various forms. The angles of the GaN c-axis inclination were determined by x-ray diffraction measurements and compared with the results obtained from the analysis of the atomic force microscope (AFM) images. The AFM image analysis method to determine the structure tilt was found to be a viable method to estimate the c-axis inclination angles of the isolated blocks and the not-fully coalesced layers. The quality of the grown samples was characterized by the photoluminescence method conducted at a wide range of temperatures from 77 to 297 K, and was correlated with the sample degree of coalescence. Using the free-excitation peak positions plotted as a function of temperature, analytical Bose-Einstein model parameters were fitted to obtain further information about the grown structures.« less

The rare peat moss Sphagnum wulfianum (Sphagnaceae) did not survive the last glacial period in northern European refugia.

PubMed

Kyrkjeeide, Magni Olsen; Hassel, Kristian; Flatberg, Kjell I; Stenøien, Hans K

2012-04-01

Organisms may survive unfavorable conditions either by moving to more favorable areas by means of dispersal or by adapting to stressful environments. Pleistocene glacial periods represent extremely unfavorable conditions for the majority of life forms, especially sessile organisms. Many studies have revealed placements of refugial areas and postglacial colonization patterns of seed plants, but little is still known about areas of long-term survival and historical migration routes of bryophytes. Given overall differences in stress tolerance between seed plants and bryophytes, it is of interest to know whether bryophytes have survived periods of extreme climatic conditions better then seed plants in northern areas. The haploid and rarely spore-producing peat moss Sphagnum wulfianum is mostly found in areas that were covered by ice during the last glacial maximum. Twelve microsatellite markers were amplified from 43 populations (367 shoots) of this species, and data were analyzed using population genetic diversity statistics, Bayesian clustering methods, and coalescence-based inference tools to estimate historical and demographic parameters. Genetic diversity within populations was low, but populations were highly differentiated, with two main genetic clusters being recognized. The two main genetic groups have diverged quite recently in the Holocene, and the pattern of genetic variability and structuring gives no support for survival in Scandinavian refugia during the last glacial period in this species. The dispersal ability of this plant thus seems surprisingly high despite its infrequent spore production.

Balancing a Cline by Influx of Migrants: A Genetic Transition in Water Frogs of Eastern Greece

PubMed Central

Beerli, Peter

2013-01-01

Variation patterns of allozymes and of ND3 haplotypes of mitochondrial DNA reveal a zone of genetic transition among western Palearctic water frogs extending across northeastern Greece and European Turkey. At the western end of the zone, allozymes characteristic of Central European frogs known as Pelophylax ridibundus predominate, whereas at the eastern end, alleles characteristic of western Anatolian water frogs (P. cf. bedriagae) prevail. The ND3 haplotypes reveal 2 major clades, 1 characteristic of Anatolian frogs, the other of European; the European clade itself has distinct eastern and western subclades. Both the 2 major clades and the 2 subclades overlap within the transition zone. Using Bayesian model selection methods, allozyme data suggest considerable immigration into the Nestos River area from eastern and western populations. In contrast, the ND3 data suggest that migration rates are so high among all locations that they form a single panmictic unit; the best model for allozymes is second best for mitochondrial DNA (mtDNA). Nuclear markers (allozymes), which have roughly 4 times as deep a coalescent history as mtDNA data and thus may reflect patterns over a longer time, indicate that eastern and western refugial populations have expanded since deglaciation (in the last 10 000 years) and have met near the Nestos River, whereas the mtDNA with its smaller effective population size has already lost the signal of partitioning into refugia. PMID:23125403

Approximate string matching algorithms for limited-vocabulary OCR output correction

NASA Astrophysics Data System (ADS)

Lasko, Thomas A.; Hauser, Susan E.

2000-12-01

Five methods for matching words mistranslated by optical character recognition to their most likely match in a reference dictionary were tested on data from the archives of the National Library of Medicine. The methods, including an adaptation of the cross correlation algorithm, the generic edit distance algorithm, the edit distance algorithm with a probabilistic substitution matrix, Bayesian analysis, and Bayesian analysis on an actively thinned reference dictionary were implemented and their accuracy rates compared. Of the five, the Bayesian algorithm produced the most correct matches (87%), and had the advantage of producing scores that have a useful and practical interpretation.

Quantum state estimation when qubits are lost: a no-data-left-behind approach

DOE PAGES

Williams, Brian P.; Lougovski, Pavel

2017-04-06

We present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean and maximum likelihood estimates for the ideal single qubit. Due to computational constraints, we utilize numerical sampling to determine the Bayesian mean estimate for a photonic two-qubit experiment in which our novel analysis reduces burdens associated with experimental asymmetries and inefficiencies. This method can be applied to quantum states of any dimension and experimental complexity.

Wetting and Coalescence of Drops of Self-Healing Agents on Electrospun Nanofiber Mats.

PubMed

An, Seongpil; Kim, Yong Il; Lee, Min Wook; Yarin, Alexander L; Yoon, Sam S

2017-10-10

Here we study experimentally the behavior of liquid healing agents released in vascular core-shell nanofiber mats used in self-healing engineered materials. It is shown that wettability-driven spreading of liquid drops is accompanied by the imbibition into the nanofiber matrix, and its laws deviate from those known for spreading on an intact surface. We also explore coalescence of the released drops on nanofiber mats, in particular, coalescence of drops of resin monomer and cure important for self-healing. The coalescence process is also affected by the imbibition into the pores of an underlying nanofiber mat. A theoretical model is developed to account for the imbibition effect on drop coalescence.

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.

PubMed

Ferragina, A; de los Campos, G; Vazquez, A I; Cecchinato, A; Bittante, G

2015-11-01

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Peng, E-mail: peng@ices.utexas.edu; Schwab, Christoph, E-mail: christoph.schwab@sam.math.ethz.ch

2016-07-01

We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by themore » so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online data assimilation and for Bayesian estimation. They also open a perspective for optimal experimental design.« less

Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

PubMed Central

Shen, Li; Qi, Yuan; Kim, Sungeun; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Saykin, Andrew J.

2010-01-01

We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer’s disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse models that is easy to interpret. PARD selects the model with the best estimate of the predictive performance instead of choosing the one with the largest marginal model likelihood. Comparative study with support vector machine (SVM) shows that ARD/PARD in general outperform SVM in terms of prediction accuracy. Additional comparison with surface-based general linear model (GLM) analysis shows that regions with strongest signals are identified by both GLM and ARD/PARD. While GLM P-map returns significant regions all over the cortex, ARD/PARD provide a small number of relevant and meaningful imaging markers with predictive power, including both cortical and subcortical measures. PMID:20879451

The impact of using informative priors in a Bayesian cost-effectiveness analysis: an application of endovascular versus open surgical repair for abdominal aortic aneurysms in high-risk patients.

PubMed

McCarron, C Elizabeth; Pullenayegum, Eleanor M; Thabane, Lehana; Goeree, Ron; Tarride, Jean-Eric

2013-04-01

Bayesian methods have been proposed as a way of synthesizing all available evidence to inform decision making. However, few practical applications of the use of Bayesian methods for combining patient-level data (i.e., trial) with additional evidence (e.g., literature) exist in the cost-effectiveness literature. The objective of this study was to compare a Bayesian cost-effectiveness analysis using informative priors to a standard non-Bayesian nonparametric method to assess the impact of incorporating additional information into a cost-effectiveness analysis. Patient-level data from a previously published nonrandomized study were analyzed using traditional nonparametric bootstrap techniques and bivariate normal Bayesian models with vague and informative priors. Two different types of informative priors were considered to reflect different valuations of the additional evidence relative to the patient-level data (i.e., "face value" and "skeptical"). The impact of using different distributions and valuations was assessed in a sensitivity analysis. Models were compared in terms of incremental net monetary benefit (INMB) and cost-effectiveness acceptability frontiers (CEAFs). The bootstrapping and Bayesian analyses using vague priors provided similar results. The most pronounced impact of incorporating the informative priors was the increase in estimated life years in the control arm relative to what was observed in the patient-level data alone. Consequently, the incremental difference in life years originally observed in the patient-level data was reduced, and the INMB and CEAF changed accordingly. The results of this study demonstrate the potential impact and importance of incorporating additional information into an analysis of patient-level data, suggesting this could alter decisions as to whether a treatment should be adopted and whether more information should be acquired.

Identification of transmissivity fields using a Bayesian strategy and perturbative approach

NASA Astrophysics Data System (ADS)

Zanini, Andrea; Tanda, Maria Giovanna; Woodbury, Allan D.

2017-10-01

The paper deals with the crucial problem of the groundwater parameter estimation that is the basis for efficient modeling and reclamation activities. A hierarchical Bayesian approach is developed: it uses the Akaike's Bayesian Information Criteria in order to estimate the hyperparameters (related to the covariance model chosen) and to quantify the unknown noise variance. The transmissivity identification proceeds in two steps: the first, called empirical Bayesian interpolation, uses Y* (Y = lnT) observations to interpolate Y values on a specified grid; the second, called empirical Bayesian update, improve the previous Y estimate through the addition of hydraulic head observations. The relationship between the head and the lnT has been linearized through a perturbative solution of the flow equation. In order to test the proposed approach, synthetic aquifers from literature have been considered. The aquifers in question contain a variety of boundary conditions (both Dirichelet and Neuman type) and scales of heterogeneities (σY2 = 1.0 and σY2 = 5.3). The estimated transmissivity fields were compared to the true one. The joint use of Y* and head measurements improves the estimation of Y considering both degrees of heterogeneity. Even if the variance of the strong transmissivity field can be considered high for the application of the perturbative approach, the results show the same order of approximation of the non-linear methods proposed in literature. The procedure allows to compute the posterior probability distribution of the target quantities and to quantify the uncertainty in the model prediction. Bayesian updating has advantages related both to the Monte-Carlo (MC) and non-MC approaches. In fact, as the MC methods, Bayesian updating allows computing the direct posterior probability distribution of the target quantities and as non-MC methods it has computational times in the order of seconds.

A Preliminary Bayesian Analysis of Incomplete Longitudinal Data from a Small Sample: Methodological Advances in an International Comparative Study of Educational Inequality

ERIC Educational Resources Information Center

Hsieh, Chueh-An; Maier, Kimberly S.

2009-01-01

The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection.…

Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.

PubMed

Wang, Tianli; Baron, Kyle; Zhong, Wei; Brundage, Richard; Elmquist, William

2014-03-01

The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation. In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.

Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

PubMed Central

Antal, Péter; Kiszel, Petra Sz.; Gézsi, András; Hadadi, Éva; Virág, Viktor; Hajós, Gergely; Millinghoffer, András; Nagy, Adrienne; Kiss, András; Semsei, Ágnes F.; Temesi, Gergely; Melegh, Béla; Kisfali, Péter; Széll, Márta; Bikov, András; Gálffy, Gabriella; Tamási, Lilla; Falus, András; Szalai, Csaba

2012-01-01

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance. PMID:22432035

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

PubMed

Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E

2017-01-01

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.

Using Bayesian analysis in repeated preclinical in vivo studies for a more effective use of animals.

PubMed

Walley, Rosalind; Sherington, John; Rastrick, Joe; Detrait, Eric; Hanon, Etienne; Watt, Gillian

2016-05-01

Whilst innovative Bayesian approaches are increasingly used in clinical studies, in the preclinical area Bayesian methods appear to be rarely used in the reporting of pharmacology data. This is particularly surprising in the context of regularly repeated in vivo studies where there is a considerable amount of data from historical control groups, which has potential value. This paper describes our experience with introducing Bayesian analysis for such studies using a Bayesian meta-analytic predictive approach. This leads naturally either to an informative prior for a control group as part of a full Bayesian analysis of the next study or using a predictive distribution to replace a control group entirely. We use quality control charts to illustrate study-to-study variation to the scientists and describe informative priors in terms of their approximate effective numbers of animals. We describe two case studies of animal models: the lipopolysaccharide-induced cytokine release model used in inflammation and the novel object recognition model used to screen cognitive enhancers, both of which show the advantage of a Bayesian approach over the standard frequentist analysis. We conclude that using Bayesian methods in stable repeated in vivo studies can result in a more effective use of animals, either by reducing the total number of animals used or by increasing the precision of key treatment differences. This will lead to clearer results and supports the "3Rs initiative" to Refine, Reduce and Replace animals in research. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction

PubMed Central

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R.; Buenrostro-Mariscal, Raymundo

2017-01-01

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. PMID:28391241

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R; Buenrostro-Mariscal, Raymundo

2017-06-07

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. Copyright © 2017 Montesinos-López et al.

A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?

PubMed

Pressman, Alice R; Avins, Andrew L; Hubbard, Alan; Satariano, William A

2011-07-01

There is a paucity of literature comparing Bayesian analytic techniques with traditional approaches for analyzing clinical trials using real trial data. We compared Bayesian and frequentist group sequential methods using data from two published clinical trials. We chose two widely accepted frequentist rules, O'Brien-Fleming and Lan-DeMets, and conjugate Bayesian priors. Using the nonparametric bootstrap, we estimated a sampling distribution of stopping times for each method. Because current practice dictates the preservation of an experiment-wise false positive rate (Type I error), we approximated these error rates for our Bayesian and frequentist analyses with the posterior probability of detecting an effect in a simulated null sample. Thus for the data-generated distribution represented by these trials, we were able to compare the relative performance of these techniques. No final outcomes differed from those of the original trials. However, the timing of trial termination differed substantially by method and varied by trial. For one trial, group sequential designs of either type dictated early stopping of the study. In the other, stopping times were dependent upon the choice of spending function and prior distribution. Results indicate that trialists ought to consider Bayesian methods in addition to traditional approaches for analysis of clinical trials. Though findings from this small sample did not demonstrate either method to consistently outperform the other, they did suggest the need to replicate these comparisons using data from varied clinical trials in order to determine the conditions under which the different methods would be most efficient. Copyright © 2011 Elsevier Inc. All rights reserved.

Construction of monitoring model and algorithm design on passenger security during shipping based on improved Bayesian network.

PubMed

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

PubMed Central

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping. PMID:25254227

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

On spatial coalescents with multiple mergers in two dimensions.

PubMed

Heuer, Benjamin; Sturm, Anja

2013-08-01

We consider the genealogy of a sample of individuals taken from a spatially structured population when the variance of the offspring distribution is relatively large. The space is structured into discrete sites of a graph G. If the population size at each site is large, spatial coalescents with multiple mergers, so called spatial Λ-coalescents, for which ancestral lines migrate in space and coalesce according to some Λ-coalescent mechanism, are shown to be appropriate approximations to the genealogy of a sample of individuals. We then consider as the graph G the two dimensional torus with side length 2L+1 and show that as L tends to infinity, and time is rescaled appropriately, the partition structure of spatial Λ-coalescents of individuals sampled far enough apart converges to the partition structure of a non-spatial Kingman coalescent. From a biological point of view this means that in certain circumstances both the spatial structure as well as larger variances of the underlying offspring distribution are harder to detect from the sample. However, supplemental simulations show that for moderately large L the different structure is still evident. Copyright © 2012 Elsevier Inc. All rights reserved.

Coalescence preference in dense packing of bubbles

NASA Astrophysics Data System (ADS)

Kim, Yeseul; Gim, Bopil; Gim, Bopil; Weon, Byung Mook

2015-11-01

Coalescence preference is the tendency that a merged bubble from the contact of two original bubbles (parent) tends to be near to the bigger parent. Here, we show that the coalescence preference can be blocked by densely packing of neighbor bubbles. We use high-speed high-resolution X-ray microscopy to clearly visualize individual coalescence phenomenon which occurs in micro scale seconds and inside dense packing of microbubbles with a local packing fraction of ~40%. Previous theory and experimental evidence predict a power of -5 between the relative coalescence position and the parent size. However, our new observation for coalescence preference in densely packed microbubbles shows a different power of -2. We believe that this result may be important to understand coalescence dynamics in dense packing of soft matter. This work (NRF-2013R1A22A04008115) was supported by Mid-career Researcher Program through NRF grant funded by the MEST and also was supported by Ministry of Science, ICT and Future Planning (2009-0082580) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry and Education, Science and Technology (NRF-2012R1A6A3A04039257).

On the energetics of tensile and shear void coalescences

NASA Astrophysics Data System (ADS)

Wong, W. H.; Guo, T. F.

2015-09-01

This paper investigates the mechanisms of tensile and shear void coalescences in ductile materials from energetics perspective. By examining and comparing the elastic and plastic energies of a voided cell throughout its deformation history, the onset of and final coalescence can be distinctly established. This energy-based approach offers a single unified method and criterion for determining the occurrences of both mechanisms. This paper also reports a novel micromechanics model that considers general stress states described by three macroscopic normal stresses and one macroscopic shear stress. Detailed formulation of the model is presented that includes the homogenization-based derivation and implementation of a 4×4 orthogonal transformation matrix, which relates the macroscopic deformation rate of the cell to displacement rates of non-physical degrees-of-freedom (DOFs), and the polar decomposition of the macroscopic deformation gradient tensor which admits the explicit determination of the logarithmic strain measures and rotation angle. In terms of stress ratios, ρ1 (=Σ11 /Σ22) ,ρ2 (=Σ12 /Σ22) ,ρ3 (=Σ33 /Σ22), it is analytically shown that multiple macroscopic stress states {ρ1 ,ρ2 ,ρ3 } can exist that result in the same stress triaxiality T and Lode parameter L. Specifically, it is shown that for a prescribed pair of T and L and in the absence of shear stress, at most six stress states {ρ1 , 0 ,ρ3 } are possible. On the other extreme in the presence of shear stress, an infinite number of stress states is possible, due to the existence of Mohr's circle for this stress state. This model, together with the proposed energy-based criteria, is used to examine void coalescence under multiple stress-state conditions for any given T and L. Numerical results have shown that the presence of shear stress has a significant effect of reducing the effective strains for the onset of and final void coalescences. In addition, a relationship has also been established between shear angle and effective strain at the onset of shear void coalescence.

Hydrodynamical processes in coalescing binary stars

NASA Astrophysics Data System (ADS)

Lai, Dong

1994-01-01

Coalescing neutron star binaries are considered to be the most promising sources of gravitational waves that could be detected by the planned laser-interferometer LIGO/VIRGO detectors. Extracting gravity wave signals from noisy data requires accurate theoretical waveforms in the frequency range 10-1000 Hz end detailed understanding of the dynamics of the binary orbits. We investigate the quasi-equilibrium and dynamical tidal interactions in coalescing binary stars, with particular focus on binary neutron stars. We develop a new formalism to study the equilibrium and dynamics of fluid stars in binary systems. The stars are modeled as compressible ellipsoids, and satisfy polytropic equation of state. The hydrodynamic equations are reduced to a set of ordinary differential equations for the evolution of the principal axes and other global quantities. The equilibrium binary structure is determined by a set of algebraic equations. We consider both synchronized and nonsynchronized systems, obtaining the generalizations to compressible fluid of the classical results for the ellipsoidal binary configurations. Our method can be applied to a wide variety of astrophysical binary systems containing neutron stars, white dwarfs, main-sequence stars and planets. We find that both secular and dynamical instabilities can develop in close binaries. The quasi-static (secular) orbital evolution, as well as the dynamical evolution of binaries driven by viscous dissipation and gravitational radiation reaction are studied. The development of the dynamical instability accelerates the binary coalescence at small separation, leading to appreciable radial infall velocity near contact. We also study resonant excitations of g-mode oscillations in coalescing binary neutron stars. A resonance occurs when the frequency of the tidal driving force equals one of the intrinsic g-mode frequencies. Using realistic microscopic nuclear equations of state, we determine the g-modes in a cold neutron atar. Resonant excitations of these g-modes during the last few minutes of the binary coalescence result in energy transfer and angular momentum transfer from the binary orbit to the neutron star. Because of the weak coupling between the g-modes and the tidal potential, the induced orbital phase errors due to resonances are small. However, resonant excitations of the g-modes play an important role in the tidal heating of binary neutron stars.

Statistical inference on genetic data reveals the complex demographic history of human populations in central Asia.

PubMed

Palstra, Friso P; Heyer, Evelyne; Austerlitz, Frédéric

2015-06-01

The demographic history of modern humans constitutes a combination of expansions, colonizations, contractions, and remigrations. The advent of large scale genetic data combined with statistically refined methods facilitates inference of this complex history. Here we study the demographic history of two genetically admixed ethnic groups in Central Asia, an area characterized by high levels of genetic diversity and a history of recurrent immigration. Using Approximate Bayesian Computation, we infer that the timing of admixture markedly differs between the two groups. Admixture in the traditionally agricultural Tajiks could be dated back to the onset of the Neolithic transition in the region, whereas admixture in Kyrgyz is more recent, and may have involved the westward movement of Turkic peoples. These results are confirmed by a coalescent method that fits an isolation-with-migration model to the genetic data, with both Central Asian groups having received gene flow from the extremities of Eurasia. Interestingly, our analyses also uncover signatures of gene flow from Eastern to Western Eurasia during Paleolithic times. In conclusion, the high genetic diversity currently observed in these two Central Asian peoples most likely reflects the effects of recurrent immigration that likely started before historical times. Conversely, conquests during historical times may have had a relatively limited genetic impact. These results emphasize the need for a better understanding of the genetic consequences of transmission of culture and technological innovations, as well as those of invasions and conquests. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Evolution of the viral hemorrhagic septicemia virus: divergence, selection and origin.

PubMed

He, Mei; Yan, Xue-Chun; Liang, Yang; Sun, Xiao-Wen; Teng, Chun-Bo

2014-08-01

Viral hemorrhagic septicemia virus (VHSV) is an economically significant rhabdovirus that affects an increasing number of freshwater and marine fish species. Extensive studies have been conducted on the molecular epizootiology, genetic diversity, and phylogeny of VHSV. However, there are discrepancies between the reported estimates of the nucleotide substitution rate for the G gene and the divergence times for the genotypes. Herein, Bayesian coalescent analyses were conducted to the time-stamped entire coding sequences of the six VHSV genes. Rate estimates based on the G gene indicated that the marine genotypes/subtypes might not all evolve slower than their major European freshwater counterpart. Age calculations on the six genes revealed that the first bifurcation event of the analyzed isolates might have taken place within the last 300 years, which was much younger than previously thought. Selection analyses suggested that two codons of the G gene might be positively selected. Surveys of codon usage bias showed that the P, M and NV genes exhibited genotype-specific variations. Furthermore, we proposed that VHSV originated from the Pacific Northwest of North America. Copyright © 2014 Elsevier Inc. All rights reserved.

The tale of a modern animal plague: Tracing the evolutionary history and determining the time-scale for foot and mouth disease virus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tully, Damien C.; Fares, Mario A.

2008-12-20

Despite significant advances made in the understanding of its epidemiology, foot and mouth disease virus (FMDV) is among the most unexpected agricultural devastating plagues. While the disease manifests itself as seven immunologically distinct strains their origin, population dynamics, migration patterns and divergence times remain unknown. Herein we have assembled a comprehensive data set of gene sequences representing the global diversity of the disease and inferred the time-scale and evolutionary history for FMDV. Serotype-specific rates of evolution and divergence times were estimated using a Bayesian coalescent framework. We report that an ancient precursor FMDV gave rise to two major diversification eventsmore » spanning a relatively short interval of time. This radiation event is estimated to have taken place towards the end of the 17th and the beginning of the 18th century giving us the present circulating Euro-Asiatic and South African viral strains. Furthermore our results hint that Europe acted as a possible hub for the disease from where it successfully dispersed elsewhere via exploration and trading routes.« less

Comparison of nonwoven fiberglass and stainless steel microfiber media in aerosol coalescence filtration

NASA Astrophysics Data System (ADS)

Manzo, Gabriel

Coalescing filters are used to remove small liquid droplets from air streams. They have numerous industrial applications including dehumidification, cabin air filtration, compressed air filtration, metal working, CCV, and agriculture. In compressed air systems, oils used for lubrication of compressor parts can aerosolize into the main air stream causing potential contamination concerns for downstream applications. In many systems, humid air can present problems to sensitive equipment and sensors. As the humid air cools, small water drops condense and can disrupt components that need to be kept dry. Fibrous nonwoven filter media are commonly used to coalesce small drops into larger drops for easier removal. The coalescing performance of a medium is dependent upon several parameters including permeability, porosity, and wettability. In many coalescing filters, glass fibers are used. In this work, the properties of steel fiber media are measured to see how these properties compare to glass fiber media. Steel fiber media has different permeability, porosity and wettability to oil and water than fiber glass media. These differences can impact coalescence performance. The impact of these differences in properties on coalescence filtration performance was evaluated in a coalescence test apparatus. The overall coalescence performance of the steel and glass nonwoven fiber media are compared using a filtration efficiency and filtration index. In many cases, the stainless steel media performed comparably to fiber glass media with efficiencies near 90%. Since stainless steel media had lower pressure drops than fiber glass media, its filtration index values were significantly higher. Broader impact of this work is the use of stainless steel fiber media as an alternative to fiber glass media in applications where aerosol filtration is needed to protect the environment or sensitive equipment and sensors.

A BAYESIAN METHOD OF ESTIMATING KINETIC PARAMETERS FOR THE INACTIVATION OF CRYPTOSPORIDIUM PARVUM OOCYSTS WITH CHLORINE DIOXIDE AND OZONE

EPA Science Inventory

The main objective of this paper is to use Bayesian methods to estimate the kinetic parameters for the inactivation kinetics of Cryptosporidium parvum oocysts with chlorine dioxide or ozone which are characterized by the delayed Chick-Watson model, i.e., a lag phase or shoulder f...

A General and Flexible Approach to Estimating the Social Relations Model Using Bayesian Methods

ERIC Educational Resources Information Center

Ludtke, Oliver; Robitzsch, Alexander; Kenny, David A.; Trautwein, Ulrich

2013-01-01

The social relations model (SRM) is a conceptual, methodological, and analytical approach that is widely used to examine dyadic behaviors and interpersonal perception within groups. This article introduces a general and flexible approach to estimating the parameters of the SRM that is based on Bayesian methods using Markov chain Monte Carlo…

Rejoinder to MacCallum, Edwards, and Cai (2012) and Rindskopf (2012): Mastering a New Method

ERIC Educational Resources Information Center

Muthen, Bengt; Asparouhov, Tihomir

2012-01-01

This rejoinder discusses the general comments on how to use Bayesian structural equation modeling (BSEM) wisely and how to get more people better trained in using Bayesian methods. Responses to specific comments cover how to handle sign switching, nonconvergence and nonidentification, and prior choices in latent variable models. Two new…

Bayesian learning

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

In 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.

A Bayes linear Bayes method for estimation of correlated event rates.

PubMed

Quigley, John; Wilson, Kevin J; Walls, Lesley; Bedford, Tim

2013-12-01

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates. © 2013 Society for Risk Analysis.

Determination of the coalescence temperature of latexes by environmental scanning electron microscopy.

PubMed

Gonzalez, Edurne; Tollan, Christopher; Chuvilin, Andrey; Barandiaran, Maria J; Paulis, Maria

2012-08-01

A new methodology for quantitative characterization of the coalescence process of waterborne polymer dispersion (latex) particles by environmental scanning electron microscopy (ESEM) is proposed. The experimental setup has been developed to provide reproducible latex monolayer depositions, optimized contrast of the latex particles, and a reliable readout of the sample temperature. Quantification of the coalescence process under dry conditions has been performed by image processing based on evaluation of the image autocorrelation function. As a proof of concept the coalescence of two latexes with known and differing glass transition temperatures has been measured. It has been shown that a reproducibility of better than 1.5 °C can be obtained for the measurement of the coalescence temperature.

A default Bayesian hypothesis test for mediation.

PubMed

Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

2015-03-01

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.

PubMed

Liao, Stephen Shaoyi; Wang, Huai Qing; Li, Qiu Dan; Liu, Wei Yi

2006-06-01

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010.

PubMed

Vaughan, Adam S; Kramer, Michael R; Waller, Lance A; Schieb, Linda J; Greer, Sophia; Casper, Michele

2015-05-01

To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010. We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty. The average local percent change in heart disease mortality from each method was -4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0-0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods. The similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates. Copyright © 2015 Elsevier Inc. All rights reserved.

Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics

PubMed Central

Chen, Wenan; Larrabee, Beth R.; Ovsyannikova, Inna G.; Kennedy, Richard B.; Haralambieva, Iana H.; Poland, Gregory A.; Schaid, Daniel J.

2015-01-01

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf. PMID:25948564

[Bayesian statistics in medicine -- part II: main applications and inference].

PubMed

Montomoli, C; Nichelatti, M

2008-01-01

Bayesian statistics is not only used when one is dealing with 2-way tables, but it can be used for inferential purposes. Using the basic concepts presented in the first part, this paper aims to give a simple overview of Bayesian methods by introducing its foundation (Bayes' theorem) and then applying this rule to a very simple practical example; whenever possible, the elementary processes at the basis of analysis are compared to those of frequentist (classical) statistical analysis. The Bayesian reasoning is naturally connected to medical activity, since it appears to be quite similar to a diagnostic process.

The Bayesian approach to reporting GSR analysis results: some first-hand experiences

NASA Astrophysics Data System (ADS)

Charles, Sebastien; Nys, Bart

2010-06-01

The use of Bayesian principles in the reporting of forensic findings has been a matter of interest for some years. Recently, also the GSR community is gradually exploring the advantages of this method, or rather approach, for writing reports. Since last year, our GSR group is adapting reporting procedures to the use of Bayesian principles. The police and magistrates find the reports more directly accessible and useful in their part of the criminal investigation. In the lab we find that, through applying the Bayesian principles, unnecessary analyses can be eliminated and thus time can be freed on the instruments.

Bayesian propensity scores for high-dimensional causal inference: A comparison of drug-eluting to bare-metal coronary stents.

PubMed

Spertus, Jacob V; Normand, Sharon-Lise T

2018-04-23

High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high-dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high-dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Upper limits for gravitational radiation from supermassive coalescing binaries

NASA Technical Reports Server (NTRS)

Anderson, J. D.; Armstrong, J. W.; Lau, E. L.

1993-01-01

We report a search for waves from supermassive coalescing binaries using a 10.5 day Pioneer 10 data set taken in 1988. Depending on the time to coalescence, the initial frequency of the wave, and the length of the observing interval, a coalescing binary waveform appears in the tracking record either as a sinusoid, a 'chirp', or as a more complicated signal. We searched our data for coalescing binary waveforms in all three regimes. We successfully detected a (fortuitous) 'chirp' signal caused by the varying spin rate of the spacecraft; this nicely served as a calibration of the data quality and as a test of our analysis procedures on real data. We did not detect any signals of astronomical origin in the millihertz band to an upper limit of about 7 x 10 exp -15 (rms amplitude). This is the first time spacecraft Doppler data have been analyzed for coalescing binary waveforms, and the upper limits reported here are the best to date for any waveform in the millihertz band.

Pareto genealogies arising from a Poisson branching evolution model with selection.

PubMed

Huillet, Thierry E

2014-02-01

We study a class of coalescents derived from a sampling procedure out of N i.i.d. Pareto(α) random variables, normalized by their sum, including β-size-biasing on total length effects (β < α). Depending on the range of α we derive the large N limit coalescents structure, leading either to a discrete-time Poisson-Dirichlet (α, -β) Ξ-coalescent (α ε[0, 1)), or to a family of continuous-time Beta (2 - α, α - β)Λ-coalescents (α ε[1, 2)), or to the Kingman coalescent (α ≥ 2). We indicate that this class of coalescent processes (and their scaling limits) may be viewed as the genealogical processes of some forward in time evolving branching population models including selection effects. In such constant-size population models, the reproduction step, which is based on a fitness-dependent Poisson Point Process with scaling power-law(α) intensity, is coupled to a selection step consisting of sorting out the N fittest individuals issued from the reproduction step.

Liquid Marble Coalescence and Triggered Microreaction Driven by Acoustic Levitation.

PubMed

Chen, Zhen; Zang, Duyang; Zhao, Liang; Qu, Mengfei; Li, Xu; Li, Xiaoguang; Li, Lixin; Geng, Xingguo

2017-06-27

Liquid marbles show promising potential for application in the microreactor field. Control of the coalescence between two or among multiple liquid marbles is critical; however, the successful merging of two isolated marbles is difficult because of their mechanically robust particle shells. In this work, the coalescence of multiple liquid marbles was achieved via acoustic levitation. The dynamic behaviors of the liquid marbles were monitored by a high-speed camera. Driven by the sound field, the liquid marbles moved toward each other, collided, and eventually coalesced into a larger single marble. The underlying mechanisms of this process were probed via sound field simulation and acoustic radiation pressure calculation. The results indicated that the pressure gradient on the liquid marble surface favors the formation of a liquid bridge between the liquid marbles, resulting in their coalescence. A preliminary indicator reaction was induced by the coalescence of dual liquid marbles, which suggests that expected chemical reactions can be successfully triggered with multiple reagents contained in isolated liquid marbles via acoustic levitation.

Bayesian Inference for Time Trends in Parameter Values using Weighted Evidence Sets

DOE Office of Scientific and Technical Information (OSTI.GOV)

D. L. Kelly; A. Malkhasyan

2010-09-01

There is a nearly ubiquitous assumption in PSA that parameter values are at least piecewise-constant in time. As a result, Bayesian inference tends to incorporate many years of plant operation, over which there have been significant changes in plant operational and maintenance practices, plant management, etc. These changes can cause significant changes in parameter values over time; however, failure to perform Bayesian inference in the proper time-dependent framework can mask these changes. Failure to question the assumption of constant parameter values, and failure to perform Bayesian inference in the proper time-dependent framework were noted as important issues in NUREG/CR-6813, performedmore » for the U. S. Nuclear Regulatory Commission’s Advisory Committee on Reactor Safeguards in 2003. That report noted that “in-dustry lacks tools to perform time-trend analysis with Bayesian updating.” This paper describes an applica-tion of time-dependent Bayesian inference methods developed for the European Commission Ageing PSA Network. These methods utilize open-source software, implementing Markov chain Monte Carlo sampling. The paper also illustrates an approach to incorporating multiple sources of data via applicability weighting factors that address differences in key influences, such as vendor, component boundaries, conditions of the operating environment, etc.« less

Bayesian Inference for Time Trends in Parameter Values: Case Study for the Ageing PSA Network of the European Commission

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dana L. Kelly; Albert Malkhasyan

2010-06-01

There is a nearly ubiquitous assumption in PSA that parameter values are at least piecewise-constant in time. As a result, Bayesian inference tends to incorporate many years of plant operation, over which there have been significant changes in plant operational and maintenance practices, plant management, etc. These changes can cause significant changes in parameter values over time; however, failure to perform Bayesian inference in the proper time-dependent framework can mask these changes. Failure to question the assumption of constant parameter values, and failure to perform Bayesian inference in the proper time-dependent framework were noted as important issues in NUREG/CR-6813, performedmore » for the U. S. Nuclear Regulatory Commission’s Advisory Committee on Reactor Safeguards in 2003. That report noted that “industry lacks tools to perform time-trend analysis with Bayesian updating.” This paper describes an application of time-dependent Bayesian inference methods developed for the European Commission Ageing PSA Network. These methods utilize open-source software, implementing Markov chain Monte Carlo sampling. The paper also illustrates the development of a generic prior distribution, which incorporates multiple sources of generic data via weighting factors that address differences in key influences, such as vendor, component boundaries, conditions of the operating environment, etc.« less

Estimating size and scope economies in the Portuguese water sector using the Bayesian stochastic frontier analysis.

PubMed

Carvalho, Pedro; Marques, Rui Cunha

2016-02-15

This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. Copyright © 2015 Elsevier B.V. All rights reserved.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

PubMed

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Numerical study on the sequential Bayesian approach for radioactive materials detection

NASA Astrophysics Data System (ADS)

Qingpei, Xiang; Dongfeng, Tian; Jianyu, Zhu; Fanhua, Hao; Ge, Ding; Jun, Zeng

2013-01-01

A new detection method, based on the sequential Bayesian approach proposed by Candy et al., offers new horizons for the research of radioactive detection. Compared with the commonly adopted detection methods incorporated with statistical theory, the sequential Bayesian approach offers the advantages of shorter verification time during the analysis of spectra that contain low total counts, especially in complex radionuclide components. In this paper, a simulation experiment platform implanted with the methodology of sequential Bayesian approach was developed. Events sequences of γ-rays associating with the true parameters of a LaBr3(Ce) detector were obtained based on an events sequence generator using Monte Carlo sampling theory to study the performance of the sequential Bayesian approach. The numerical experimental results are in accordance with those of Candy. Moreover, the relationship between the detection model and the event generator, respectively represented by the expected detection rate (Am) and the tested detection rate (Gm) parameters, is investigated. To achieve an optimal performance for this processor, the interval of the tested detection rate as a function of the expected detection rate is also presented.

Effects of waveform model systematics on the interpretation of GW150914

NASA Astrophysics Data System (ADS)

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J.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dasgupta, A.; Da Silva Costa, C. F.; Dattilo, V.; Dave, I.; Davier, M.; Davies, G. S.; Davis, D.; Daw, E. J.; Day, B.; Day, R.; De, S.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Devenson, J.; Devine, R. C.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Doctor, Z.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Douglas, R.; Dovale Álvarez, M.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; E Dwyer, S.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Eisenstein, R. A.; Essick, R. C.; Etienne, Z.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Farinon, S.; Farr, B.; Farr, W. M.; Fauchon-Jones, E. J.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Fernández Galiana, A.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fong, H.; Forsyth, S. S.; Fournier, J.-D.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fries, E. M.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H.; Gadre, B. U.; Gaebel, S. M.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gaur, G.; Gayathri, V.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gorodetsky, M. L.; E Gossan, S.; Gosselin, M.; Gouaty, R.; Grado, A.; Graef, C.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; E Gushwa, K.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Henry, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hofman, D.; Holt, K.; E Holz, D.; Hopkins, P.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Junker, J.; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karki, S.; Karvinen, K. S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kéfélian, F.; Keitel, D.; Kelley, D. B.; Kennedy, R.; Key, J. S.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, Chunglee; Kim, J. C.; Kim, Whansun; Kim, W.; Kim, Y.-M.; Kimbrell, S. J.; King, E. J.; King, P. J.; Kirchhoff, R.; Kissel, J. S.; Klein, B.; Kleybolte, L.; Klimenko, S.; Koch, P.; Koehlenbeck, S. M.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Krämer, C.; Kringel, V.; Krishnan, B.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lang, R. N.; Lange, J.; Lantz, B.; Lanza, R. K.; Lartaux-Vollard, A.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lehmann, J.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Liu, J.; Lockerbie, N. A.; Lombardi, A. L.; London, L. T.; E Lord, J.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lovelace, G.; Lück, H.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macfoy, S.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martynov, D. V.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; McCarthy, R.; E McClelland, D.; McCormick, S.; McGrath, C.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McRae, T.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E. L.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Metzdorff, R.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; E Mikhailov, E.; Milano, L.; Miller, A. L.; Miller, A.; Miller, B. B.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mours, B.; Mow-Lowry, C. M.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Muniz, E. A. M.; Murray, P. G.; Mytidis, A.; Napier, K.; Nardecchia, I.; Naticchioni, L.; Nelemans, G.; Nelson, T. J. N.; Neri, M.; Nery, M.; Neunzert, A.; Newport, J. M.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Noack, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Overmier, H.; Owen, B. J.; E Pace, A.; Page, J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perez, C. J.; Perreca, A.; Perri, L. M.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O. J.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poe, M.; Poggiani, R.; Popolizio, P.; Post, A.; Powell, J.; Prasad, J.; Pratt, J. W. W.; Predoi, V.; Prestegard, T.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Qiu, S.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Rhoades, E.; Ricci, F.; Riles, K.; Rizzo, M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sampson, L. M.; Sanchez, E. J.; Sandberg, V.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Scheuer, J.; Schmidt, E.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Schwalbe, S. G.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Setyawati, Y.; Shaddock, D. A.; Shaffer, T. J.; Shahriar, M. S.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, A. D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, B.; Smith, J. R.; E Smith, R. J.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Spencer, A. P.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson, S. P.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; E Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Taracchini, A.; Taylor, R.; Theeg, T.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thrane, E.; Tippens, T.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tomlinson, C.; Tonelli, M.; Tornasi, Z.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Trinastic, J.; Tringali, M. C.; Trozzo, L.; Tse, M.; Tso, R.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Varma, V.; Vass, S.; Vasúth, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Venugopalan, G.; Verkindt, D.; Vetrano, F.; Viceré, A.; Viets, A. D.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D. V.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; E Wade, L.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Watchi, J.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; Whiting, B. F.; Whittle, C.; Williams, D.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Worden, J.; Wright, J. L.; Wu, D. S.; Wu, G.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, Hang; Yu, Haocun; Yvert, M.; Zadrożny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, T.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, S. J.; Zhu, X. J.; E Zucker, M.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration; Boyle, M.; Chu, T.; Hemberger, D.; Hinder, I.; E Kidder, L.; Ossokine, S.; Scheel, M.; Szilagyi, B.; Teukolsky, S.; Vano Vinuales, A.

2017-05-01

Parameter estimates of GW150914 were obtained using Bayesian inference, based on three semi-analytic waveform models for binary black hole coalescences. These waveform models differ from each other in their treatment of black hole spins, and all three models make some simplifying assumptions, notably to neglect sub-dominant waveform harmonic modes and orbital eccentricity. Furthermore, while the models are calibrated to agree with waveforms obtained by full numerical solutions of Einstein’s equations, any such calibration is accurate only to some non-zero tolerance and is limited by the accuracy of the underlying phenomenology, availability, quality, and parameter-space coverage of numerical simulations. This paper complements the original analyses of GW150914 with an investigation of the effects of possible systematic errors in the waveform models on estimates of its source parameters. To test for systematic errors we repeat the original Bayesian analysis on mock signals from numerical simulations of a series of binary configurations with parameters similar to those found for GW150914. Overall, we find no evidence for a systematic bias relative to the statistical error of the original parameter recovery of GW150914 due to modeling approximations or modeling inaccuracies. However, parameter biases are found to occur for some configurations disfavored by the data of GW150914: for binaries inclined edge-on to the detector over a small range of choices of polarization angles, and also for eccentricities greater than  ˜0.05. For signals with higher signal-to-noise ratio than GW150914, or in other regions of the binary parameter space (lower masses, larger mass ratios, or higher spins), we expect that systematic errors in current waveform models may impact gravitational-wave measurements, making more accurate models desirable for future observations.

The roles of history and ecology in chloroplast phylogeographic patterns of the bird-dispersed plant parasite Phoradendron californicum (Viscaceae) in the Sonoran Desert.

PubMed

Lira-Noriega, Andrés; Toro-Núñez, Oscar; Oaks, Jamie R; Mort, Mark E

2015-01-01

• A recurrent explanation for phylogeographic discontinuities in the Baja California Peninsula and the Sonoran Desert Region has been the association of vicariant events with Pliocene and Pleistocene seaway breaks. Nevertheless, despite its relevance for plant dispersal, other explanations such as ecological and paleoclimatic factors have received little attention. Here, we analyzed the role of several of these factors to describe the phylogeographic patterns of the desert mistletoe, Phoradendron californicum.• Using noncoding chloroplast regions, we assess the marginal probability of 19 a priori hypotheses related to geological and ecological factors to predict the cpDNA variation in P. californicum using a Bayesian coalescent framework. Complementarily, we used the macrofossil record and niche model projections on Last Glacial Maximum climatic conditions for hosts, mistletoe, and a bird specialist to interpret phylogeographic patterns.• Genealogical reconstructions revealed five clades, which suggest a combination of cryptic divergence, long-distance seed dispersal, and isolating postdivergence events. Bayesian hypothesis test favored a series of Pliocene and Pleistocene geological events related to the formation of the Baja California Peninsula and seaways across the peninsula as the most supported explanation for this genealogical pattern. However, age estimates, niche projections, and fossil records show dynamic host-mistletoe interactions and evidence of host races, indicating that ecological and geological factors have been interacting during the formation and structuring of phylogeographic divergence.• Variation in cpDNA across the species range results from the interplay of vicariant events, past climatic oscillations, and more dynamic factors related to ecological processes at finer temporal and spatial scales. © 2015 Botanical Society of America, Inc.

Unveiling an ancient biological invasion: molecular analysis of an old European alien, the crested porcupine (Hystrix cristata).

PubMed

Trucchi, Emiliano; Sbordoni, Valerio

2009-05-18

Biological invasions can be considered one of the main threats to biodiversity, and the recognition of common ecological and evolutionary features among invaders can help developing a predictive framework to control further invasions. In particular, the analysis of successful invasive species and of their autochthonous source populations by means of genetic, phylogeographic and demographic tools can provide novel insights into the study of biological invasion patterns. Today, long-term dynamics of biological invasions are still poorly understood and need further investigations. Moreover, distribution and molecular data on native populations could contribute to the recognition of common evolutionary features of successful aliens. We analyzed 2,195 mitochondrial base pairs, including Cytochrome b, Control Region and rRNA 12S, in 161 Italian and 27 African specimens and assessed the ancient invasive origin of Italian crested porcupine (Hystrix cristata) populations from Tunisia. Molecular coalescent-based Bayesian analyses proposed the Roman Age as a putative timeframe of introduction and suggested a retention of genetic diversity during the early phases of colonization. The characterization of the native African genetic background revealed the existence of two differentiated clades: a Mediterranean group and a Sub-Saharan one. Both standard population genetic and advanced molecular demography tools (Bayesian Skyline Plot) did not evidence a clear genetic signature of the expected increase in population size after introduction. Along with the genetic diversity retention during the bottlenecked steps of introduction, this finding could be better described by hypothesizing a multi-invasion event. Evidences of the ancient anthropogenic invasive origin of the Italian Hystrix cristata populations were clearly shown and the native African genetic background was preliminary described. A more complex pattern than a simple demographic exponential growth from a single propagule seems to have characterized this long-term invasion.

Epidemic history of hepatitis C virus infection in two remote communities in Nigeria, West Africa.

PubMed

Forbi, Joseph C; Purdy, Michael A; Campo, David S; Vaughan, Gilberto; Dimitrova, Zoya E; Ganova-Raeva, Lilia M; Xia, Guo-Liang; Khudyakov, Yury E

2012-07-01

We investigated the molecular epidemiology and population dynamics of HCV infection among indigenes of two semi-isolated communities in North-Central Nigeria. Despite remoteness and isolation, ~15% of the population had serological or molecular markers of hepatitis C virus (HCV) infection. Phylogenetic analysis of the NS5b sequences obtained from 60 HCV-infected residents showed that HCV variants belonged to genotype 1 (n=51; 85%) and genotype 2 (n=9; 15%). All sequences were unique and intermixed in the phylogenetic tree with HCV sequences from people infected from other West African countries. The high-throughput 454 pyrosequencing of the HCV hypervariable region 1 and an empirical threshold error correction algorithm were used to evaluate intra-host heterogeneity of HCV strains of genotype 1 (n=43) and genotype 2 (n=6) from residents of the communities. Analysis revealed a rare detectable intermixing of HCV intra-host variants among residents. Identification of genetically close HCV variants among all known groups of relatives suggests a common intra-familial HCV transmission in the communities. Applying Bayesian coalescent analysis to the NS5b sequences, the most recent common ancestors for genotype 1 and 2 variants were estimated to have existed 675 and 286 years ago, respectively. Bayesian skyline plots suggest that HCV lineages of both genotypes identified in the Nigerian communities experienced epidemic growth for 200-300 years until the mid-20th century. The data suggest a massive introduction of numerous HCV variants to the communities during the 20th century in the background of a dynamic evolutionary history of the hepatitis C epidemic in Nigeria over the past three centuries.

Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data.

PubMed

Kärkkäinen, Hanni P; Sillanpää, Mikko J

2013-09-04

Because of the increased availability of genome-wide sets of molecular markers along with reduced cost of genotyping large samples of individuals, genomic estimated breeding values have become an essential resource in plant and animal breeding. Bayesian methods for breeding value estimation have proven to be accurate and efficient; however, the ever-increasing data sets are placing heavy demands on the parameter estimation algorithms. Although a commendable number of fast estimation algorithms are available for Bayesian models of continuous Gaussian traits, there is a shortage for corresponding models of discrete or censored phenotypes. In this work, we consider a threshold approach of binary, ordinal, and censored Gaussian observations for Bayesian multilocus association models and Bayesian genomic best linear unbiased prediction and present a high-speed generalized expectation maximization algorithm for parameter estimation under these models. We demonstrate our method with simulated and real data. Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction. Furthermore, the example analyses indicate that the correct threshold model is more accurate than the directly used Gaussian model with a censored Gaussian data, while with a binary or an ordinal data the superiority of the threshold model could not be confirmed.

Fast Genomic Predictions via Bayesian G-BLUP and Multilocus Models of Threshold Traits Including Censored Gaussian Data

PubMed Central

Kärkkäinen, Hanni P.; Sillanpää, Mikko J.

2013-01-01

Because of the increased availability of genome-wide sets of molecular markers along with reduced cost of genotyping large samples of individuals, genomic estimated breeding values have become an essential resource in plant and animal breeding. Bayesian methods for breeding value estimation have proven to be accurate and efficient; however, the ever-increasing data sets are placing heavy demands on the parameter estimation algorithms. Although a commendable number of fast estimation algorithms are available for Bayesian models of continuous Gaussian traits, there is a shortage for corresponding models of discrete or censored phenotypes. In this work, we consider a threshold approach of binary, ordinal, and censored Gaussian observations for Bayesian multilocus association models and Bayesian genomic best linear unbiased prediction and present a high-speed generalized expectation maximization algorithm for parameter estimation under these models. We demonstrate our method with simulated and real data. Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction. Furthermore, the example analyses indicate that the correct threshold model is more accurate than the directly used Gaussian model with a censored Gaussian data, while with a binary or an ordinal data the superiority of the threshold model could not be confirmed. PMID:23821618

Dynamical Bayesian inference of time-evolving interactions: from a pair of coupled oscillators to networks of oscillators.

PubMed

Duggento, Andrea; Stankovski, Tomislav; McClintock, Peter V E; Stefanovska, Aneta

2012-12-01

Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. [Phys. Rev. Lett. 109, 024101 (2012)] introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time-evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically generated data, data from an analog electronic circuit, and cardiorespiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.

Bayesian analyses of time-interval data for environmental radiation monitoring.

PubMed

Luo, Peng; Sharp, Julia L; DeVol, Timothy A

2013-01-01

Time-interval (time difference between two consecutive pulses) analysis based on the principles of Bayesian inference was investigated for online radiation monitoring. Using experimental and simulated data, Bayesian analysis of time-interval data [Bayesian (ti)] was compared with Bayesian and a conventional frequentist analysis of counts in a fixed count time [Bayesian (cnt) and single interval test (SIT), respectively]. The performances of the three methods were compared in terms of average run length (ARL) and detection probability for several simulated detection scenarios. Experimental data were acquired with a DGF-4C system in list mode. Simulated data were obtained using Monte Carlo techniques to obtain a random sampling of the Poisson distribution. All statistical algorithms were developed using the R Project for statistical computing. Bayesian analysis of time-interval information provided a similar detection probability as Bayesian analysis of count information, but the authors were able to make a decision with fewer pulses at relatively higher radiation levels. In addition, for the cases with very short presence of the source (< count time), time-interval information is more sensitive to detect a change than count information since the source data is averaged by the background data over the entire count time. The relationships of the source time, change points, and modifications to the Bayesian approach for increasing detection probability are presented.

Bayesian inference for disease prevalence using negative binomial group testing

PubMed Central

Pritchard, Nicholas A.; Tebbs, Joshua M.

2011-01-01

Group testing, also known as pooled testing, and inverse sampling are both widely used methods of data collection when the goal is to estimate a small proportion. Taking a Bayesian approach, we consider the new problem of estimating disease prevalence from group testing when inverse (negative binomial) sampling is used. Using different distributions to incorporate prior knowledge of disease incidence and different loss functions, we derive closed form expressions for posterior distributions and resulting point and credible interval estimators. We then evaluate our new estimators, on Bayesian and classical grounds, and apply our methods to a West Nile Virus data set. PMID:21259308

BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.

PubMed

Khakabimamaghani, Sahand; Ester, Martin

2016-01-01

The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data.

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

Bayesian least squares deconvolution

NASA Astrophysics Data System (ADS)

Asensio Ramos, A.; Petit, P.

2015-11-01

Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

PubMed

Sampid, Marius Galabe; Hasim, Haslifah M; Dai, Hongsheng

2018-01-01

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

Approximate Genealogies Under Genetic Hitchhiking

PubMed Central

Pfaffelhuber, P.; Haubold, B.; Wakolbinger, A.

2006-01-01

The rapid fixation of an advantageous allele leads to a reduction in linked neutral variation around the target of selection. The genealogy at a neutral locus in such a selective sweep can be simulated by first generating a random path of the advantageous allele's frequency and then a structured coalescent in this background. Usually the frequency path is approximated by a logistic growth curve. We discuss an alternative method that approximates the genealogy by a random binary splitting tree, a so-called Yule tree that does not require first constructing a frequency path. Compared to the coalescent in a logistic background, this method gives a slightly better approximation for identity by descent during the selective phase and a much better approximation for the number of lineages that stem from the founder of the selective sweep. In applications such as the approximation of the distribution of Tajima's D, the two approximation methods perform equally well. For relevant parameter ranges, the Yule approximation is faster. PMID:17182733

Inferring the post-merger gravitational wave emission from binary neutron star coalescences

NASA Astrophysics Data System (ADS)

Chatziioannou, Katerina; Clark, James Alexander; Bauswein, Andreas; Millhouse, Margaret; Littenberg, Tyson B.; Cornish, Neil

2017-12-01

We present a robust method to characterize the gravitational wave emission from the remnant of a neutron star coalescence. Our approach makes only minimal assumptions about the morphology of the signal and provides a full posterior probability distribution of the underlying waveform. We apply our method on simulated data from a network of advanced ground-based detectors and demonstrate the gravitational wave signal reconstruction. We study the reconstruction quality for different binary configurations and equations of state for the colliding neutron stars. We show how our method can be used to constrain the yet-uncertain equation of state of neutron star matter. The constraints on the equation of state we derive are complementary to measurements of the tidal deformation of the colliding neutron stars during the late inspiral phase. In the case of nondetection of a post-merger signal following a binary neutron star inspiral, we show that we can place upper limits on the energy emitted.

Practical differences among probabilities, possibilities, and credibilities

NASA Astrophysics Data System (ADS)

Grandin, Jean-Francois; Moulin, Caroline

2002-03-01

This paper presents some important differences that exist between theories, which allow the uncertainty management in data fusion. The main comparative results illustrated in this paper are the followings: Incompatibility between decisions got from probabilities and credibilities is highlighted. In the dynamic frame, as remarked in [19] or [17], belief and plausibility of Dempster-Shafer model do not frame the Bayesian probability. This framing can however be obtained by the Modified Dempster-Shafer approach. It also can be obtained in the Bayesian framework either by simulation techniques, or with a studentization. The uncommitted in the Dempster-Shafer way, e.g. the mass accorded to the ignorance, gives a mechanism similar to the reliability in the Bayesian model. Uncommitted mass in Dempster-Shafer theory or reliability in Bayes theory act like a filter that weakens extracted information, and improves robustness to outliners. So, it is logical to observe on examples like the one presented particularly by D.M. Buede, a faster convergence of a Bayesian method that doesn't take into account the reliability, in front of Dempster-Shafer method which uses uncommitted mass. But, on Bayesian masses, if reliability is taken into account, at the same level that the uncommited, e.g. F=1-m, we observe an equivalent rate for convergence. When Dempster-Shafer and Bayes operator are informed by uncertainty, faster or lower convergence can be exhibited on non Bayesian masses. This is due to positive or negative synergy between information delivered by sensors. This effect is a direct consequence of non additivity when considering non Bayesian masses. Unknowledge of the prior in bayesian techniques can be quickly compensated by information accumulated as time goes on by a set of sensors. All these results are presented on simple examples, and developed when necessary.

Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis

PubMed Central

Turner, Rebecca M; Jackson, Dan; Wei, Yinghui; Thompson, Simon G; Higgins, Julian P T

2015-01-01

Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-study heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-study heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:25475839

Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration.

PubMed

Conner, Mary M; Saunders, W Carl; Bouwes, Nicolaas; Jordan, Chris

2015-10-01

Before-after-control-impact (BACI) designs are an effective method to evaluate natural and human-induced perturbations on ecological variables when treatment sites cannot be randomly chosen. While effect sizes of interest can be tested with frequentist methods, using Bayesian Markov chain Monte Carlo (MCMC) sampling methods, probabilities of effect sizes, such as a ≥20 % increase in density after restoration, can be directly estimated. Although BACI and Bayesian methods are used widely for assessing natural and human-induced impacts for field experiments, the application of hierarchal Bayesian modeling with MCMC sampling to BACI designs is less common. Here, we combine these approaches and extend the typical presentation of results with an easy to interpret ratio, which provides an answer to the main study question-"How much impact did a management action or natural perturbation have?" As an example of this approach, we evaluate the impact of a restoration project, which implemented beaver dam analogs, on survival and density of juvenile steelhead. Results indicated the probabilities of a ≥30 % increase were high for survival and density after the dams were installed, 0.88 and 0.99, respectively, while probabilities for a higher increase of ≥50 % were variable, 0.17 and 0.82, respectively. This approach demonstrates a useful extension of Bayesian methods that can easily be generalized to other study designs from simple (e.g., single factor ANOVA, paired t test) to more complicated block designs (e.g., crossover, split-plot). This approach is valuable for estimating the probabilities of restoration impacts or other management actions.

Conventional, Bayesian, and Modified Prony's methods for characterizing fast and slow waves in equine cancellous bone

PubMed Central

Groopman, Amber M.; Katz, Jonathan I.; Holland, Mark R.; Fujita, Fuminori; Matsukawa, Mami; Mizuno, Katsunori; Wear, Keith A.; Miller, James G.

2015-01-01

Conventional, Bayesian, and the modified least-squares Prony's plus curve-fitting (MLSP + CF) methods were applied to data acquired using 1 MHz center frequency, broadband transducers on a single equine cancellous bone specimen that was systematically shortened from 11.8 mm down to 0.5 mm for a total of 24 sample thicknesses. Due to overlapping fast and slow waves, conventional analysis methods were restricted to data from sample thicknesses ranging from 11.8 mm to 6.0 mm. In contrast, Bayesian and MLSP + CF methods successfully separated fast and slow waves and provided reliable estimates of the ultrasonic properties of fast and slow waves for sample thicknesses ranging from 11.8 mm down to 3.5 mm. Comparisons of the three methods were carried out for phase velocity at the center frequency and the slope of the attenuation coefficient for the fast and slow waves. Good agreement among the three methods was also observed for average signal loss at the center frequency. The Bayesian and MLSP + CF approaches were able to separate the fast and slow waves and provide good estimates of the fast and slow wave properties even when the two wave modes overlapped in both time and frequency domains making conventional analysis methods unreliable. PMID:26328678

Void growth and coalescence in irradiated copper under deformation

NASA Astrophysics Data System (ADS)

Barrioz, P. O.; Hure, J.; Tanguy, B.

2018-04-01

A decrease of fracture toughness of irradiated materials is usually observed, as reported for austenitic stainless steels in Light Water Reactors (LWRs) or copper alloys for fusion applications. For a wide range of applications (e.g. structural steels irradiated at low homologous temperature), void growth and coalescence fracture mechanism has been shown to be still predominant. As a consequence, a comprehensive study of the effects of irradiation-induced hardening mechanisms on void growth and coalescence in irradiated materials is required. The effects of irradiation on ductile fracture mechanisms - void growth to coalescence - are assessed in this study based on model experiments. Pure copper thin tensile samples have been irradiated with protons up to 0.01 dpa. Micron-scale holes drilled through the thickness of these samples subjected to uniaxial loading conditions allow a detailed description of void growth and coalescence. In this study, experimental data show that physical mechanisms of micron-scale void growth and coalescence are similar between the unirradiated and irradiated copper. However, an acceleration of void growth is observed in the later case, resulting in earlier coalescence, which is consistent with the decrease of fracture toughness reported in irradiated materials. These results are qualitatively reproduced with numerical simulations accounting for irradiation macroscopic hardening and decrease of strain-hardening capability.

Inhibition of bubble coalescence: effects of salt concentration and speed of approach.

PubMed

Del Castillo, Lorena A; Ohnishi, Satomi; Horn, Roger G

2011-04-01

Bubble coalescence experiments have been performed using a sliding bubble apparatus, in which mm-sized bubbles in an aqueous electrolyte solution without added surfactant rose toward an air meniscus at different speeds obtained by varying the inclination of a closed glass cylinder containing the liquid. The coalescence times of single bubbles contacting the meniscus were monitored using a high speed camera. Results clearly show that stability against coalescence of colliding air bubbles is influenced by both the salt concentration and the approach speed of the bubbles. Contrary to the widespread belief that bubbles in pure water are unstable, we demonstrate that bubbles formed in highly purified water and colliding with the meniscus at very slow approach speeds can survive for minutes or even hours. At higher speeds, bubbles in water only survive for a few seconds, and at still higher speeds they coalesce instantly. Addition of a simple electrolyte (KCl) removes the low-speed stability and shifts the transition between transient stability and instant coalescence to higher approach speeds. At high electrolyte concentration no bubbles were observed to coalesce instantly. These observations are consistent with recent results of Yaminsky et al. (Langmuir 26 (2010) 8061) and the transitions between different regions of behavior are in semi-quantitative agreement with Yaminsky's model. Copyright © 2010 Elsevier Inc. All rights reserved.

Bayesian methods for outliers detection in GNSS time series

NASA Astrophysics Data System (ADS)

Qianqian, Zhang; Qingming, Gui

2013-07-01

This article is concerned with the problem of detecting outliers in GNSS time series based on Bayesian statistical theory. Firstly, a new model is proposed to simultaneously detect different types of outliers based on the conception of introducing different types of classification variables corresponding to the different types of outliers; the problem of outlier detection is converted into the computation of the corresponding posterior probabilities, and the algorithm for computing the posterior probabilities based on standard Gibbs sampler is designed. Secondly, we analyze the reasons of masking and swamping about detecting patches of additive outliers intensively; an unmasking Bayesian method for detecting additive outlier patches is proposed based on an adaptive Gibbs sampler. Thirdly, the correctness of the theories and methods proposed above is illustrated by simulated data and then by analyzing real GNSS observations, such as cycle slips detection in carrier phase data. Examples illustrate that the Bayesian methods for outliers detection in GNSS time series proposed by this paper are not only capable of detecting isolated outliers but also capable of detecting additive outlier patches. Furthermore, it can be successfully used to process cycle slips in phase data, which solves the problem of small cycle slips.

A simulation study on Bayesian Ridge regression models for several collinearity levels

NASA Astrophysics Data System (ADS)

Efendi, Achmad; Effrihan

2017-12-01

When analyzing data with multiple regression model if there are collinearities, then one or several predictor variables are usually omitted from the model. However, there sometimes some reasons, for instance medical or economic reasons, the predictors are all important and should be included in the model. Ridge regression model is not uncommon in some researches to use to cope with collinearity. Through this modeling, weights for predictor variables are used for estimating parameters. The next estimation process could follow the concept of likelihood. Furthermore, for the estimation nowadays the Bayesian version could be an alternative. This estimation method does not match likelihood one in terms of popularity due to some difficulties; computation and so forth. Nevertheless, with the growing improvement of computational methodology recently, this caveat should not at the moment become a problem. This paper discusses about simulation process for evaluating the characteristic of Bayesian Ridge regression parameter estimates. There are several simulation settings based on variety of collinearity levels and sample sizes. The results show that Bayesian method gives better performance for relatively small sample sizes, and for other settings the method does perform relatively similar to the likelihood method.

Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

NASA Astrophysics Data System (ADS)

Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

2018-05-01

Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

DTIC Science & Technology

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model , is able to model the rate of occurrence of...which adds specificity to the model and can make nonlinear data more manageable. Early results show that the 1. REPORT DATE (DD-MM-YYYY) 4. TITLE

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

PubMed Central

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

PubMed

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Bayesian Mass Estimates of the Milky Way: Including Measurement Uncertainties with Hierarchical Bayes

NASA Astrophysics Data System (ADS)

Eadie, Gwendolyn M.; Springford, Aaron; Harris, William E.

2017-02-01

We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie et al. and Eadie and Harris and builds upon the preliminary reports by Eadie et al. The method uses a distribution function f({ E },L) to model the Galaxy and kinematic data from satellite objects, such as globular clusters (GCs), to trace the Galaxy’s gravitational potential. A major advantage of the method is that it not only includes complete and incomplete data simultaneously in the analysis, but also incorporates measurement uncertainties in a coherent and meaningful way. We first test the hierarchical Bayesian framework, which includes measurement uncertainties, using the same data and power-law model assumed in Eadie and Harris and find the results are similar but more strongly constrained. Next, we take advantage of the new statistical framework and incorporate all possible GC data, finding a cumulative mass profile with Bayesian credible regions. This profile implies a mass within 125 kpc of 4.8× {10}11{M}⊙ with a 95% Bayesian credible region of (4.0{--}5.8)× {10}11{M}⊙ . Our results also provide estimates of the true specific energies of all the GCs. By comparing these estimated energies to the measured energies of GCs with complete velocity measurements, we observe that (the few) remote tracers with complete measurements may play a large role in determining a total mass estimate of the Galaxy. Thus, our study stresses the need for more remote tracers with complete velocity measurements.

Molecular phylogenetics and species delimitation of leaf-toed geckos (Phyllodactylidae: Phyllodactylus) throughout the Mexican tropical dry forest.

PubMed

Blair, Christopher; Méndez de la Cruz, Fausto R; Law, Christopher; Murphy, Robert W

2015-03-01

Methods and approaches for accurate species delimitation continue to be a highly controversial subject in the systematics community. Inaccurate assessment of species' limits precludes accurate inference of historical evolutionary processes. Recent evidence suggests that multilocus coalescent methods show promise in delimiting species in cryptic clades. We combine multilocus sequence data with coalescence-based phylogenetics in a hypothesis-testing framework to assess species limits and elucidate the timing of diversification in leaf-toed geckos (Phyllodactylus) of Mexico's dry forests. Tropical deciduous forests (TDF) of the Neotropics are among the planet's most diverse ecosystems. However, in comparison to moist tropical forests, little is known about the mode and tempo of biotic evolution throughout this threatened biome. We find increased speciation and substantial, cryptic molecular diversity originating following the formation of Mexican TDF 30-20million years ago due to orogenesis of the Sierra Madre Occidental and Mexican Volcanic Belt. Phylogenetic results suggest that the Mexican Volcanic Belt, the Rio Fuerte, and Isthmus of Tehuantepec may be important biogeographic barriers. Single- and multilocus coalescent analyses suggest that nearly every sampling locality may be a distinct species. These results suggest unprecedented levels of diversity, a complex evolutionary history, and that the formation and expansion of TDF vegetation in the Miocene may have influenced subsequent cladogenesis of leaf-toed geckos throughout western Mexico. Copyright © 2015 Elsevier Inc. All rights reserved.

A weighted multiple-relaxation-time lattice Boltzmann method for multiphase flows and its application to partial coalescence cascades

NASA Astrophysics Data System (ADS)

Fakhari, Abbas; Bolster, Diogo; Luo, Li-Shi

2017-07-01

We present a lattice Boltzmann method (LBM) with a weighted multiple-relaxation-time (WMRT) collision model and an adaptive mesh refinement (AMR) algorithm for direct numerical simulation of two-phase flows in three dimensions. The proposed WMRT model enhances the numerical stability of the LBM for immiscible fluids at high density ratios, particularly on the D3Q27 lattice. The effectiveness and efficiency of the proposed WMRT-LBM-AMR is validated through simulations of (a) buoyancy-driven motion and deformation of a gas bubble rising in a viscous liquid; (b) the bag-breakup mechanism of a falling drop; (c) crown splashing of a droplet on a wet surface; and (d) the partial coalescence mechanism of a liquid drop at a liquid-liquid interface. The numerical simulations agree well with available experimental data and theoretical approximations where applicable.

A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks

PubMed Central

Zaikin, Alexey; Míguez, Joaquín

2017-01-01

We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introducing dynamical noise in the model and assuming that the partial observations of the system are contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we tackle the Bayesian estimation of a subset of the model parameters. Specifically, we compare three Monte Carlo based numerical methods for the approximation of the posterior probability density function of the unknown parameters given a set of partial and noisy observations of the system. The schemes we assess are the particle Metropolis-Hastings (PMH) algorithm, the nonlinear population Monte Carlo (NPMC) method and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) scheme. We present an extensive numerical simulation study, which shows that while the three techniques can effectively solve the problem there are significant differences both in estimation accuracy and computational efficiency. PMID:28797087

Bayesian coronal seismology

NASA Astrophysics Data System (ADS)

Arregui, Iñigo

2018-01-01

In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.

Results of Bayesian methods depend on details of implementation: An example of estimating salmon escapement goals

USGS Publications Warehouse

Adkison, Milo D.; Peterman, R.M.

1996-01-01

Bayesian methods have been proposed to estimate optimal escapement goals, using both knowledge about physical determinants of salmon productivity and stock-recruitment data. The Bayesian approach has several advantages over many traditional methods for estimating stock productivity: it allows integration of information from diverse sources and provides a framework for decision-making that takes into account uncertainty reflected in the data. However, results can be critically dependent on details of implementation of this approach. For instance, unintended and unwarranted confidence about stock-recruitment relationships can arise if the range of relationships examined is too narrow, if too few discrete alternatives are considered, or if data are contradictory. This unfounded confidence can result in a suboptimal choice of a spawning escapement goal.

Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

PubMed

Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

2018-03-01

Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

Bayesian analysis of the flutter margin method in aeroelasticity

DOE PAGES

Khalil, Mohammad; Poirel, Dominique; Sarkar, Abhijit

2016-08-27

A Bayesian statistical framework is presented for Zimmerman and Weissenburger flutter margin method which considers the uncertainties in aeroelastic modal parameters. The proposed methodology overcomes the limitations of the previously developed least-square based estimation technique which relies on the Gaussian approximation of the flutter margin probability density function (pdf). Using the measured free-decay responses at subcritical (preflutter) airspeeds, the joint non-Gaussain posterior pdf of the modal parameters is sampled using the Metropolis–Hastings (MH) Markov chain Monte Carlo (MCMC) algorithm. The posterior MCMC samples of the modal parameters are then used to obtain the flutter margin pdfs and finally the fluttermore » speed pdf. The usefulness of the Bayesian flutter margin method is demonstrated using synthetic data generated from a two-degree-of-freedom pitch-plunge aeroelastic model. The robustness of the statistical framework is demonstrated using different sets of measurement data. In conclusion, it will be shown that the probabilistic (Bayesian) approach reduces the number of test points required in providing a flutter speed estimate for a given accuracy and precision.« less

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

2014-01-01

We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892

Impacts of Non-Divergence-Free Flows on the Coalescence of Initially Distant Buoyant Scalars on a Turbulent Free Surface

NASA Astrophysics Data System (ADS)

Pratt, K.; Crimaldi, J. P.

2016-02-01

Lagrangian Coherent Structures (LCS) have been shown to play a predictive role in the coalescence of initially distant scalars in incompressible flows. Buoyant scalars on the free surface of a 3D incompressible turbulent fluid, however, are advected by a 2D compressible velocity field, resulting in scalar distributions that differ from those seen in a 2D incompressible flow. Our research uses both numerical and experimental approaches to investigate the coalescence of two initially distant reactive scalars to infer the impact of non-divergence-free behavior on buoyant scalar coalescence. Preliminary numerical results, utilizing incompressible and compressible chaotic 2D models, indicate that non-divergence-free behavior increases the likelihood of scalar coalescence and therefore enhances any interactions or reactions between the scalars. In addition, the shape and distribution of LCS is altered in compressible flows, which may explain the increased likelihood of scalar coalescence. Experimentally, we have constructed a 60 X 60 X 60 cm tank that generates three-dimensional turbulence via random pulsing of 36 jets on the tank bottom. Buoyant fluorescent red and green particles are used to quantify coalescence. Through the addition of a thin surfactant film on the free surface, results for incompressible flow cases are also obtained and directly compared to the compressible results. From these results, we hope to elucidate the role of free-surface flow on the coalescence of initially distant buoyant scalars, and extend these results to oceanic mixing problems, such as the transport of phytoplankton blooms and oil spills.

Estimation of Lithological Classification in Taipei Basin: A Bayesian Maximum Entropy Method

NASA Astrophysics Data System (ADS)

Wu, Meng-Ting; Lin, Yuan-Chien; Yu, Hwa-Lung

2015-04-01

In environmental or other scientific applications, we must have a certain understanding of geological lithological composition. Because of restrictions of real conditions, only limited amount of data can be acquired. To find out the lithological distribution in the study area, many spatial statistical methods used to estimate the lithological composition on unsampled points or grids. This study applied the Bayesian Maximum Entropy (BME method), which is an emerging method of the geological spatiotemporal statistics field. The BME method can identify the spatiotemporal correlation of the data, and combine not only the hard data but the soft data to improve estimation. The data of lithological classification is discrete categorical data. Therefore, this research applied Categorical BME to establish a complete three-dimensional Lithological estimation model. Apply the limited hard data from the cores and the soft data generated from the geological dating data and the virtual wells to estimate the three-dimensional lithological classification in Taipei Basin. Keywords: Categorical Bayesian Maximum Entropy method, Lithological Classification, Hydrogeological Setting

Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.

PubMed

Fan, Yue; Wang, Xiao; Peng, Qinke

2017-01-01

Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.

Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

NASA Astrophysics Data System (ADS)

Kozoderov, V. V.; Kondranin, T. V.; Dmitriev, E. V.

2017-12-01

The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedure of statistical decision making in machine-learning methods of pattern recognition. The principal component method is implemented to decompose the hyperspectral measurements on the basis of empirical orthogonal functions. Application examples are shown of various modifications of the Bayesian classifier and Support Vector Machine method. Examples are provided of comparing these classifiers and a metrical classifier that operates on finding the minimal Euclidean distance between different points and sets in the multidimensional feature space. A comparison is also carried out with the " K-weighted neighbors" method that is close to the nonparametric Bayesian classifier.

Hypothesis testing on the fractal structure of behavioral sequences: the Bayesian assessment of scaling methodology.

PubMed

Moscoso del Prado Martín, Fermín

2013-12-01

I introduce the Bayesian assessment of scaling (BAS), a simple but powerful Bayesian hypothesis contrast methodology that can be used to test hypotheses on the scaling regime exhibited by a sequence of behavioral data. Rather than comparing parametric models, as typically done in previous approaches, the BAS offers a direct, nonparametric way to test whether a time series exhibits fractal scaling. The BAS provides a simpler and faster test than do previous methods, and the code for making the required computations is provided. The method also enables testing of finely specified hypotheses on the scaling indices, something that was not possible with the previously available methods. I then present 4 simulation studies showing that the BAS methodology outperforms the other methods used in the psychological literature. I conclude with a discussion of methodological issues on fractal analyses in experimental psychology. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods

PubMed Central

Teng, Ming; Nathoo, Farouk S.; Johnson, Timothy D.

2017-01-01

The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data. PMID:29200537

Dynamical Bayesian inference of time-evolving interactions: From a pair of coupled oscillators to networks of oscillators

NASA Astrophysics Data System (ADS)

Duggento, Andrea; Stankovski, Tomislav; McClintock, Peter V. E.; Stefanovska, Aneta

2012-12-01

Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.109.024101 109, 024101 (2012)] introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time-evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically generated data, data from an analog electronic circuit, and cardiorespiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brewer, Brendon J.; Foreman-Mackey, Daniel; Hogg, David W., E-mail: bj.brewer@auckland.ac.nz

We present and implement a probabilistic (Bayesian) method for producing catalogs from images of stellar fields. The method is capable of inferring the number of sources N in the image and can also handle the challenges introduced by noise, overlapping sources, and an unknown point-spread function. The luminosity function of the stars can also be inferred, even when the precise luminosity of each star is uncertain, via the use of a hierarchical Bayesian model. The computational feasibility of the method is demonstrated on two simulated images with different numbers of stars. We find that our method successfully recovers the inputmore » parameter values along with principled uncertainties even when the field is crowded. We also compare our results with those obtained from the SExtractor software. While the two approaches largely agree about the fluxes of the bright stars, the Bayesian approach provides more accurate inferences about the faint stars and the number of stars, particularly in the crowded case.« less

Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics.

PubMed

Chen, Wenan; Larrabee, Beth R; Ovsyannikova, Inna G; Kennedy, Richard B; Haralambieva, Iana H; Poland, Gregory A; Schaid, Daniel J

2015-07-01

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf. Copyright © 2015 by the Genetics Society of America.

Electron acceleration via magnetic island coalescence

NASA Astrophysics Data System (ADS)

Shinohara, I.; Yumura, T.; Tanaka, K. G.; Fujimoto, M.

2009-06-01

Electron acceleration via fast magnetic island coalescence that happens as quick magnetic reconnection triggering (QMRT) proceeds has been studied. We have carried out a three-dimensional full kinetic simulation of the Harris current sheet with a large enough simulation run for two magnetic islands coalescence. Due to the strong inductive electric field associated with the non-linear evolution of the lower-hybrid-drift instability and the magnetic island coalescence process observed in the non-linear stage of the collisionless tearing mode, electrons are significantly accelerated at around the neutral sheet and the subsequent X-line. The accelerated meandering electrons generated by the non-linear evolution of the lower-hybrid-drift instability are resulted in QMRT, and QMRT leads to fast magnetic island coalescence. As a whole, the reconnection triggering and its transition to large-scale structure work as an effective electron accelerator.

Characterization of solids deposited on the modular caustic-side solvent extraction unit (MCU) coalescer media removed in May and October 2014

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fondeur, F. F.

During routine maintenance, the coalescers utilized in the Modular Caustic-Side Solvent Extraction Unit (MCU) processing of Salt Batch 6 and a portion of Salt Batch 7 were sampled and submitted to the Savannah River National Laboratory (SRNL) for characterization, for the purpose of identifying solid phase constituents that may be accumulating in these coalescers. Specifically, two samples were received and characterized: A decontaminated salt solution (DSS) coalescer sample and a strip effluent (SE) coalescer sample. Aliquots of the samples were analyzed by XRD, Fourier Transform Infrared (FTIR) Spectroscopy, SEM, and EDS. Other aliquots of the samples were leached in acidmore » solution, and the leachates were analyzed by ICP-AES. In addition, modeling was performed to provide a basis for comparison of the analytical results.« less