Bayesian Factor Analysis When Only a Sample Covariance Matrix Is Available

ERIC Educational Resources Information Center

Hayashi, Kentaro; Arav, Marina

2006-01-01

In traditional factor analysis, the variance-covariance matrix or the correlation matrix has often been a form of inputting data. In contrast, in Bayesian factor analysis, the entire data set is typically required to compute the posterior estimates, such as Bayes factor loadings and Bayes unique variances. We propose a simple method for computing…

A fast Bayesian approach to discrete object detection in astronomical data sets - PowellSnakes I

NASA Astrophysics Data System (ADS)

Carvalho, Pedro; Rocha, Graça; Hobson, M. P.

2009-03-01

A new fast Bayesian approach is introduced for the detection of discrete objects immersed in a diffuse background. This new method, called PowellSnakes, speeds up traditional Bayesian techniques by (i) replacing the standard form of the likelihood for the parameters characterizing the discrete objects by an alternative exact form that is much quicker to evaluate; (ii) using a simultaneous multiple minimization code based on Powell's direction set algorithm to locate rapidly the local maxima in the posterior and (iii) deciding whether each located posterior peak corresponds to a real object by performing a Bayesian model selection using an approximate evidence value based on a local Gaussian approximation to the peak. The construction of this Gaussian approximation also provides the covariance matrix of the uncertainties in the derived parameter values for the object in question. This new approach provides a speed up in performance by a factor of `100' as compared to existing Bayesian source extraction methods that use Monte Carlo Markov chain to explore the parameter space, such as that presented by Hobson & McLachlan. The method can be implemented in either real or Fourier space. In the case of objects embedded in a homogeneous random field, working in Fourier space provides a further speed up that takes advantage of the fact that the correlation matrix of the background is circulant. We illustrate the capabilities of the method by applying to some simplified toy models. Furthermore, PowellSnakes has the advantage of consistently defining the threshold for acceptance/rejection based on priors which cannot be said of the frequentist methods. We present here the first implementation of this technique (version I). Further improvements to this implementation are currently under investigation and will be published shortly. The application of the method to realistic simulated Planck observations will be presented in a forthcoming publication.

Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning.

PubMed

Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Rao, Bhaskar D

2013-02-01

Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

Application of the R-matrix method to photoionization of molecules.

PubMed

Tashiro, Motomichi

2010-04-07

The R-matrix method has been used for theoretical calculation of electron collision with atoms and molecules for long years. The method was also formulated to treat photoionization process, however, its application has been mostly limited to photoionization of atoms. In this work, we implement the R-matrix method to treat molecular photoionization problem based on the UK R-matrix codes. This method can be used for diatomic as well as polyatomic molecules, with multiconfigurational description for electronic states of both target neutral molecule and product molecular ion. Test calculations were performed for valence electron photoionization of nitrogen (N(2)) as well as nitric oxide (NO) molecules. Calculated photoionization cross sections and asymmetry parameters agree reasonably well with the available experimental results, suggesting usefulness of the method for molecular photoionization.

Technical note: an R package for fitting sparse neural networks with application in animal breeding.

PubMed

Wang, Yangfan; Mi, Xue; Rosa, Guilherme J M; Chen, Zhihui; Lin, Ping; Wang, Shi; Bao, Zhenmin

2018-05-04

Neural networks (NNs) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypic outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that finds an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L1-norm of the parameters (weights and biases), therefore solving the problem of over-parameterization in NN. We have also tested some models fitted in the snnR package to demonstrate their feasibility and effectiveness to be used in several cases as examples. In comparison of snnR to the R package brnn (the Bayesian regularized single layer NNs), with both using the entries of a genotype matrix or a genomic relationship matrix as inputs, snnR has greatly improved the computational efficiency and the prediction ability for the GS in animal breeding because snnR implements a sparse NN with many hidden layers.

Uncertainty quantification in (α,n) neutron source calculations for an oxide matrix

DOE PAGES

Pigni, M. T.; Croft, S.; Gauld, I. C.

2016-04-25

Here we present a methodology to propagate nuclear data covariance information in neutron source calculations from (α,n) reactions. The approach is applied to estimate the uncertainty in the neutron generation rates for uranium oxide fuel types due to uncertainties on 1) 17,18O( α,n) reaction cross sections and 2) uranium and oxygen stopping power cross sections. The procedure to generate reaction cross section covariance information is based on the Bayesian fitting method implemented in the R-matrix SAMMY code. The evaluation methodology uses the Reich-Moore approximation to fit the 17,18O(α,n) reaction cross-sections in order to derive a set of resonance parameters andmore » a related covariance matrix that is then used to calculate the energydependent cross section covariance matrix. The stopping power cross sections and related covariance information for uranium and oxygen were obtained by the fit of stopping power data in the -energy range of 1 keV up to 12 MeV. Cross section perturbation factors based on the covariance information relative to the evaluated 17,18O( α,n) reaction cross sections, as well as uranium and oxygen stopping power cross sections, were used to generate a varied set of nuclear data libraries used in SOURCES4C and ORIGEN for inventory and source term calculations. The set of randomly perturbed output (α,n) source responses, provide the mean values and standard deviations of the calculated responses reflecting the uncertainties in nuclear data used in the calculations. Lastly, the results and related uncertainties are compared with experiment thick target (α,n) yields for uranium oxide.« less

Bayesian hierarchical model for large-scale covariance matrix estimation.

PubMed

Zhu, Dongxiao; Hero, Alfred O

2007-12-01

Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

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.

COMPUTATION OF GLOBAL PHOTOCHEMISTRY WITH SMVGEAR II (R823186)

EPA Science Inventory

A computer model was developed to simulate global gas-phase photochemistry. The model solves chemical equations with SMVGEAR II, a sparse-matrix, vectorized Gear-type code. To obtain SMVGEAR II, the original SMVGEAR code was modified to allow computation of different sets of chem...

Bayesian block-diagonal variable selection and model averaging

PubMed Central

Papaspiliopoulos, O.; Rossell, D.

2018-01-01

Summary We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for block-diagonal designs. Posterior probabilities for any number of models are obtained by evaluating a single one-dimensional integral, and other quantities of interest such as variable inclusion probabilities and model-averaged regression estimates are obtained by an adaptive, deterministic one-dimensional numerical integration. The overall computational cost scales linearly with the number of blocks, which can be processed in parallel, and exponentially with the block size, rendering it most adequate in situations where predictors are organized in many moderately-sized blocks. For general designs, we approximate the Gram matrix by a block-diagonal matrix using spectral clustering and propose an iterative algorithm that capitalizes on the block-diagonal algorithms to explore efficiently the model space. All methods proposed in this paper are implemented in the R library mombf. PMID:29861501

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.

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

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models.

PubMed

Dumont, Cyrielle; Lestini, Giulia; Le Nagard, Hervé; Mentré, France; Comets, Emmanuelle; Nguyen, Thu Thuy; Group, For The Pfim

2018-03-01

Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr. Copyright © 2018 Elsevier B.V. All rights reserved.

Bayesian design criteria: computation, comparison, and application to a pharmacokinetic and a pharmacodynamic model.

PubMed

Merlé, Y; Mentré, F

1995-02-01

In this paper 3 criteria to design experiments for Bayesian estimation of the parameters of nonlinear models with respect to their parameters, when a prior distribution is available, are presented: the determinant of the Bayesian information matrix, the determinant of the pre-posterior covariance matrix, and the expected information provided by an experiment. A procedure to simplify the computation of these criteria is proposed in the case of continuous prior distributions and is compared with the criterion obtained from a linearization of the model about the mean of the prior distribution for the parameters. This procedure is applied to two models commonly encountered in the area of pharmacokinetics and pharmacodynamics: the one-compartment open model with bolus intravenous single-dose injection and the Emax model. They both involve two parameters. Additive as well as multiplicative gaussian measurement errors are considered with normal prior distributions. Various combinations of the variances of the prior distribution and of the measurement error are studied. Our attention is restricted to designs with limited numbers of measurements (1 or 2 measurements). This situation often occurs in practice when Bayesian estimation is performed. The optimal Bayesian designs that result vary with the variances of the parameter distribution and with the measurement error. The two-point optimal designs sometimes differ from the D-optimal designs for the mean of the prior distribution and may consist of replicating measurements. For the studied cases, the determinant of the Bayesian information matrix and its linearized form lead to the same optimal designs. In some cases, the pre-posterior covariance matrix can be far from its lower bound, namely, the inverse of the Bayesian information matrix, especially for the Emax model and a multiplicative measurement error. The expected information provided by the experiment and the determinant of the pre-posterior covariance matrix generally lead to the same designs except for the Emax model and the multiplicative measurement error. Results show that these criteria can be easily computed and that they could be incorporated in modules for designing experiments.

VizieR Online Data Catalog: Giant HII regions BOND abundances (Vale Asari+, 2016)

NASA Astrophysics Data System (ADS)

Vale Asari, N.; Stasinska, G.; Morisset, C.; Cid Fernandes, R.

2017-10-01

BOND determines nitrogen and oxygen gas-phase abundances by using strong and semistrong lines and comparing them to a grid of photoionization models in a Bayesian framework. The code is written in python and its source is publicly available at http://bond.ufsc.br. The grid of models presented here is included in the 3MdB data base (Morisset, Delgado-Inglada & Flores-Fajardo 2015RMxAA..51..103M, see https://sites.google.com/site/mexicanmillionmodels/) under the reference 'BOND'. The Bayesian posterior probability calculated by bond stands on two pillars: our grid of models and our choice of observational constraints (from which we calculate our likelihoods). We discuss each of these in turn. (2 data files).

Top quark produced through the electroweak force: Discovery using the matrix element analysis and search for heavy gauge bosons using boosted decision trees

NASA Astrophysics Data System (ADS)

Pangilinan, Monica

The top quark produced through the electroweak channel provides a direct measurement of the Vtb element in the CKM matrix which can be viewed as a transition rate of a top quark to a bottom quark. This production channel of top quark is also sensitive to different theories beyond the Standard Model such as heavy charged gauged bosons termed W'. This thesis measures the cross section of the electroweak produced top quark using a technique based on using the matrix elements of the processes under consideration. The technique is applied to 2.3 fb--1 of data from the DO detector. From a comparison of the matrix element discriminants between data and the signal and background model using Bayesian statistics, we measure the cross section of the top quark produced through the electroweak mechanism spp¯→ tb+X,tqb+X=4.30+0.98-1.2 0pb The measured result corresponds to a 4.9sigma Gaussian-equivalent significance. By combining this analysis with other analyses based on the Bayesian Neural Network (BNN) and Boosted Decision Tree (BDT) method, the measured cross section is 3.94 +/- 0.88 pb with a significance of 5.0sigma, resulting in the discovery of electroweak produced top quarks. Using this measured cross section and constraining |Vtb| < 1, the 95% confidence level (C.L.) lower limit is |Vtb| > 0.78. Additionally, a search is made for the production of W' using the same samples from the electroweak produced top quark. An analysis based on the BDT method is used to separate the signal from expected backgrounds. No significant excess is found and 95% C.L. upper limits on the production cross section are set for W' with masses within 600--950 GeV. For four general models of W' boson production using decay channel W' → tb¯, the lower mass limits are the following: M( W'L with SM couplings) > 840 GeV; M( W'R ) > 880 GeV or 890 GeV if the right-handed neutrino is lighter or heavier than W'R ; and M( W'L+R ) > 915 GeV.

Handheld Synthetic Array Final Report, Part B

DTIC Science & Technology

2014-12-01

Multiple Model IMU Inertial Measurement Unit 4/154 IEEE Institute of Electrical and Electronics Engineers KF Kalman Filter KL Kullback - Leibler LAMBDA...important metric in information theory is the input–output mutual information (MI) that is used as an indicator of how much coded information can be...tracking using best- fitting Gaussian distributions,” Proc. Int. Conf. Inform . Fusion, pp. 1–8, 2005. [liv] L. Svensson, “On the Bayesian Cramér-Rao

2DRMP: A suite of two-dimensional R-matrix propagation codes

NASA Astrophysics Data System (ADS)

Scott, N. S.; Scott, M. P.; Burke, P. G.; Stitt, T.; Faro-Maza, V.; Denis, C.; Maniopoulou, A.

2009-12-01

The R-matrix method has proved to be a remarkably stable, robust and efficient technique for solving the close-coupling equations that arise in electron and photon collisions with atoms, ions and molecules. During the last thirty-four years a series of related R-matrix program packages have been published periodically in CPC. These packages are primarily concerned with low-energy scattering where the incident energy is insufficient to ionise the target. In this paper we describe 2DRMP, a suite of two-dimensional R-matrix propagation programs aimed at creating virtual experiments on high performance and grid architectures to enable the study of electron scattering from H-like atoms and ions at intermediate energies. Program summaryProgram title: 2DRMP Catalogue identifier: AEEA_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEA_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 196 717 No. of bytes in distributed program, including test data, etc.: 3 819 727 Distribution format: tar.gz Programming language: Fortran 95, MPI Computer: Tested on CRAY XT4 [1]; IBM eServer 575 [2]; Itanium II cluster [3] Operating system: Tested on UNICOS/lc [1]; IBM AIX [2]; Red Hat Linux Enterprise AS [3] Has the code been vectorised or parallelised?: Yes. 16 cores were used for small test run Classification: 2.4 External routines: BLAS, LAPACK, PBLAS, ScaLAPACK Subprograms used: ADAZ_v1_1 Nature of problem: 2DRMP is a suite of programs aimed at creating virtual experiments on high performance architectures to enable the study of electron scattering from H-like atoms and ions at intermediate energies. Solution method: Two-dimensional R-matrix propagation theory. The (r,r) space of the internal region is subdivided into a number of subregions. Local R-matrices are constructed within each subregion and used to propagate a global R-matrix, ℜ, across the internal region. On the boundary of the internal region ℜ is transformed onto the IERM target state basis. Thus, the two-dimensional R-matrix propagation technique transforms an intractable problem into a series of tractable problems enabling the internal region to be extended far beyond that which is possible with the standard one-sector codes. A distinctive feature of the method is that both electrons are treated identically and the R-matrix basis states are constructed to allow for both electrons to be in the continuum. The subregion size is flexible and can be adjusted to accommodate the number of cores available. Restrictions: The implementation is currently restricted to electron scattering from H-like atoms and ions. Additional comments: The programs have been designed to operate on serial computers and to exploit the distributed memory parallelism found on tightly coupled high performance clusters and supercomputers. 2DRMP has been systematically and comprehensively documented using ROBODoc [4] which is an API documentation tool that works by extracting specially formatted headers from the program source code and writing them to documentation files. Running time: The wall clock running time for the small test run using 16 cores and performed on [3] is as follows: bp (7 s); rint2 (34 s); newrd (32 s); diag (21 s); amps (11 s); prop (24 s). References:HECToR, CRAY XT4 running UNICOS/lc, http://www.hector.ac.uk/, accessed 22 July, 2009. HPCx, IBM eServer 575 running IBM AIX, http://www.hpcx.ac.uk/, accessed 22 July, 2009. HP Cluster, Itanium II cluster running Red Hat Linux Enterprise AS, Queen s University Belfast, http://www.qub.ac.uk/directorates/InformationServices/Research/HighPerformanceComputing/Services/Hardware/HPResearch/, accessed 22 July, 2009. Automating Software Documentation with ROBODoc, http://www.xs4all.nl/~rfsber/Robo/, accessed 22 July, 2009.

Ehf Multiple Beam Antennas.

DTIC Science & Technology

1981-09-21

acknowledge and thank A. R. Hislop and D. L. Saul, Code 9262, for their work on tbh mixer design and D. L. Chappelle and K. S. Maynard, Code 8124, for...MTT-28, p 555-563, June 1980 . 33 (a) Mixer matrix, 7 boards, 6-5 mixers, 1-7 mixers. (b) LO power split to boards, 7-way. rN’ o, Ro (c) N-way power...1966. 9. Saleh, A.A.M., Planar Electrically Symmetric N-Way Hybrid Power Dividers/ Combiners, IEEE T-MTT-28, p 555-563, June 1980 . 55

Further consideration of the phylogeny of some "traditional" heterotrichs (Protista, Ciliophora) of uncertain affinities, based on new sequences of the small subunit rRNA gene.

PubMed

Miao, Miao; Song, Weibo; Clamp, John C; Al-Rasheid, Khaled A S; Al-Khedhairy, Abdulaziz A; Al-Arifi, Saud

2009-01-01

The systematic relationships and taxonomic positions of the traditional heterotrich genera Condylostentor, Climacostomum, Fabrea, Folliculina, Peritromus, and Condylostoma, as well as the licnophorid genus Licnophora, were re-examined using new data from sequences of the gene coding for small subunit ribosomal RNA. Trees constructed using distance-matrix, Bayesian inference, and maximum-parsimony methods all showed the following relationships: (1) the "traditional" heterotrichs consist of several paraphyletic groups, including the current classes Heterotrichea, Armophorea and part of the Spirotrichea; (2) the class Heterotrichea was confirmed as a monophyletic assemblage based on our analyses of 31 taxa, and the genus Peritromus was demonstrated to be a peripheral group; (3) the genus Licnophora occupied an isolated branch on one side of the deepest divergence in the subphylum Intramacronucleata and was closely affiliated with spirotrichs, armophoreans, and clevelandellids; (4) Condylostentor, a recently defined genus with several truly unique morphological features, is more closely related to Condylostoma than to Stentor; (5) Folliculina, Eufolliculina, and Maristentor always clustered together with high bootstrap support; and (6) Climacostomum occupied a paraphyletic position distant from Fabrea, showing a close relationship with Condylostomatidae and Chattonidiidae despite of modest support.

Getting more from accuracy and response time data: methods for fitting the linear ballistic accumulator.

PubMed

Donkin, Chris; Averell, Lee; Brown, Scott; Heathcote, Andrew

2009-11-01

Cognitive models of the decision process provide greater insight into response time and accuracy than do standard ANOVA techniques. However, such models can be mathematically and computationally difficult to apply. We provide instructions and computer code for three methods for estimating the parameters of the linear ballistic accumulator (LBA), a new and computationally tractable model of decisions between two or more choices. These methods-a Microsoft Excel worksheet, scripts for the statistical program R, and code for implementation of the LBA into the Bayesian sampling software WinBUGS-vary in their flexibility and user accessibility. We also provide scripts in R that produce a graphical summary of the data and model predictions. In a simulation study, we explored the effect of sample size on parameter recovery for each method. The materials discussed in this article may be downloaded as a supplement from http://brm.psychonomic-journals.org/content/supplemental.

Generalized Reich-Moore R-matrix approximation

NASA Astrophysics Data System (ADS)

Arbanas, Goran; Sobes, Vladimir; Holcomb, Andrew; Ducru, Pablo; Pigni, Marco; Wiarda, Dorothea

2017-09-01

A conventional Reich-Moore approximation (RMA) of R-matrix is generalized into a manifestly unitary form by introducing a set of resonant capture channels treated explicitly in a generalized, reduced R-matrix. A dramatic reduction of channel space witnessed in conventional RMA, from Nc × Nc full R-matrix to Np × Np reduced R-matrix, where Nc = Np + Nγ, Np and Nγ denoting the number of particle and γ-ray channels, respectively, is due to Np < Nγ. A corresponding reduction of channel space in generalized RMA (GRMA) is from Nc × Nc full R-matrix to N × N, where N = Np + N, and where N is the number of capture channels defined in GRMA. We show that N = Nλ where Nλ is the number of R-matrix levels. This reduction in channel space, although not as dramatic as in the conventional RMA, could be significant for medium and heavy nuclides where N < Nγ. The resonant capture channels defined by GRMA accommodate level-level interference (via capture channels) neglected in conventional RMA. The expression for total capture cross section in GRMA is formally equal to that of the full Nc × NcR-matrix. This suggests that GRMA could yield improved nuclear data evaluations in the resolved resonance range at a cost of introducing N(N - 1)/2 resonant capture width parameters relative to conventional RMA. Manifest unitarity of GRMA justifies a method advocated by Fröhner and implemented in the SAMMY nuclear data evaluation code for enforcing unitarity of conventional RMA. Capture widths of GRMA are exactly convertible into alternative R-matrix parameters via Brune tranform. Application of idealized statistical methods to GRMA shows that variance among conventional RMA capture widths in extant RMA evaluations could be used to estimate variance among off-diagonal elements neglected by conventional RMA. Significant departure of capture widths from an idealized distribution may indicate the presence of underlying doorway states.

PROFIT: Bayesian profile fitting of galaxy images

NASA Astrophysics Data System (ADS)

Robotham, A. S. G.; Taranu, D. S.; Tobar, R.; Moffett, A.; Driver, S. P.

2017-04-01

We present PROFIT, a new code for Bayesian two-dimensional photometric galaxy profile modelling. PROFIT consists of a low-level C++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (PROFIT) and PYTHON (PyProFit) interfaces (available at github.com/ICRAR/libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit, respectively). R PROFIT is also available pre-built from CRAN; however, this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two-dimensional integration for a useful number of profiles, including Sérsic, Core-Sérsic, broken-exponential, Ferrer, Moffat, empirical King, point-source, and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and GALFIT. libprofit is both faster and more accurate than GALFIT at integrating the ubiquitous Sérsic profile for the most common values of the Sérsic index n (0.5 < n < 8). The high-level fitting code PROFIT is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fitting parameters from fitting images from different sources (SDSS versus KiDS) than from using different codes (PROFIT versus GALFIT). A large suite of Monte Carlo-simulated images are used to assess prospects for automated bulge-disc decomposition with PROFIT on SDSS, KiDS, and future LSST imaging. We find that the biggest increases in fit quality come from moving from SDSS- to KiDS-quality data, with less significant gains moving from KiDS to LSST.

Bayesian variable selection for post-analytic interrogation of susceptibility loci.

PubMed

Chen, Siying; Nunez, Sara; Reilly, Muredach P; Foulkes, Andrea S

2017-06-01

Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al., 2010; Willer et al., 2013) meta-analysis summary statistics for low-density lipoprotein cholesterol (LDL-C). The analysis reveals 33 PCGs and three lncRNAs across 11 loci with >50% posterior probabilities for inclusion in an additive model of association. The findings are consistent with previous reports, while providing some new insight into the architecture of LDL-cholesterol to be investigated further. As genomic taxonomies continue to evolve, additional classes such as enhancer elements and splicing regions, can easily be layered into the proposed analysis framework. Moreover, application of this approach to alternative publicly available meta-analysis resources, or more generally as a post-analytic strategy to further interrogate regions that are identified through single point analysis, is straightforward. All coding examples are implemented in R version 3.2.1 and provided as supplemental material. © 2016, The International Biometric Society.

A combined Fuzzy and Naive Bayesian strategy can be used to assign event codes to injury narratives.

PubMed

Marucci-Wellman, H; Lehto, M; Corns, H

2011-12-01

Bayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used to either classify a narrative or select it for manual review. Injury narratives were extracted from claims filed with a worker's compensation insurance provider between January 2002 and December 2004. Narratives were separated into a training set (n=11,000) and prediction set (n=3,000). Expert coders assigned two-digit Bureau of Labor Statistics Occupational Injury and Illness Classification event codes to each narrative. Fuzzy and Naïve Bayesian models were developed using manually classified cases in the training set. Two semi-automatic machine coding strategies were evaluated. The first strategy assigned cases for manual review if the Fuzzy and Naïve models disagreed on the classification. The second strategy selected additional cases for manual review from the Agree dataset using prediction strength to reach a level of 50% computer coding and 50% manual coding. When agreement alone was used as the filtering strategy, the majority were coded by the computer (n=1,928, 64%) leaving 36% for manual review. The overall combined (human plus computer) sensitivity was 0.90 and positive predictive value (PPV) was >0.90 for 11 of 18 2-digit event categories. Implementing the 2nd strategy improved results with an overall sensitivity of 0.95 and PPV >0.90 for 17 of 18 categories. A combined Naïve-Fuzzy Bayesian approach can classify some narratives with high accuracy and identify others most beneficial for manual review, reducing the burden on human coders.

Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks

NASA Astrophysics Data System (ADS)

Schmit, C. J.; Pritchard, J. R.

2018-03-01

Next generation radio experiments such as LOFAR, HERA, and SKA are expected to probe the Epoch of Reionization (EoR) and claim a first direct detection of the cosmic 21cm signal within the next decade. Data volumes will be enormous and can thus potentially revolutionize our understanding of the early Universe and galaxy formation. However, numerical modelling of the EoR can be prohibitively expensive for Bayesian parameter inference and how to optimally extract information from incoming data is currently unclear. Emulation techniques for fast model evaluations have recently been proposed as a way to bypass costly simulations. We consider the use of artificial neural networks as a blind emulation technique. We study the impact of training duration and training set size on the quality of the network prediction and the resulting best-fitting values of a parameter search. A direct comparison is drawn between our emulation technique and an equivalent analysis using 21CMMC. We find good predictive capabilities of our network using training sets of as low as 100 model evaluations, which is within the capabilities of fully numerical radiative transfer codes.

A Study of the Critical Factors Controlling the Synthesis of Ceramic Matrix Composites from Preceramic Polymers

DTIC Science & Technology

1990-12-15

THE SYNTHESIS OF CERAMIC MATRIX COMPOSITES PE - 61102F FROM PRECERAMIC POLYMERS PR -9999 6. AUTHOR(S) TA - 99 J. R. Strife(l), J. P. Wesson(1 ), and H...stability at temperatures up to 15000 C. 14. SUBJECT TERMS 15. NUMBER OF PAGES 49 C- SiC composites vinylmethylsilane 16. PRICE CODE polymer precursor...vapor infiltration of fibrous preforms. More recently, the conversion of preceramic polymers as a matrix synthesis process is being considered. This

BSR: B-spline atomic R-matrix codes

NASA Astrophysics Data System (ADS)

Zatsarinny, Oleg

2006-02-01

BSR is a general program to calculate atomic continuum processes using the B-spline R-matrix method, including electron-atom and electron-ion scattering, and radiative processes such as bound-bound transitions, photoionization and polarizabilities. The calculations can be performed in LS-coupling or in an intermediate-coupling scheme by including terms of the Breit-Pauli Hamiltonian. New version program summaryTitle of program: BSR Catalogue identifier: ADWY Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADWY Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computers on which the program has been tested: Microway Beowulf cluster; Compaq Beowulf cluster; DEC Alpha workstation; DELL PC Operating systems under which the new version has been tested: UNIX, Windows XP Programming language used: FORTRAN 95 Memory required to execute with typical data: Typically 256-512 Mwords. Since all the principal dimensions are allocatable, the available memory defines the maximum complexity of the problem No. of bits in a word: 8 No. of processors used: 1 Has the code been vectorized or parallelized?: no No. of lines in distributed program, including test data, etc.: 69 943 No. of bytes in distributed program, including test data, etc.: 746 450 Peripherals used: scratch disk store; permanent disk store Distribution format: tar.gz Nature of physical problem: This program uses the R-matrix method to calculate electron-atom and electron-ion collision processes, with options to calculate radiative data, photoionization, etc. The calculations can be performed in LS-coupling or in an intermediate-coupling scheme, with options to include Breit-Pauli terms in the Hamiltonian. Method of solution: The R-matrix method is used [P.G. Burke, K.A. Berrington, Atomic and Molecular Processes: An R-Matrix Approach, IOP Publishing, Bristol, 1993; P.G. Burke, W.D. Robb, Adv. At. Mol. Phys. 11 (1975) 143; K.A. Berrington, W.B. Eissner, P.H. Norrington, Comput. Phys. Comm. 92 (1995) 290].

A Viscoelastic-Plastic Constitutive Model with a Finite Element Solution Methodology

DTIC Science & Technology

1978-06-01

where - r3 K f BT D B dv (4-15) • ,re E,,rae v ’,vp ,vp w F BT dv (4-17)A -vp -Vp 84 ii T In the above, K is the global viscoelastic stiffness matrix anl ...Code C4AA Port Hueneme. CA NAVSE ASYSCOM Code OOC (LT R. MacDougisl). Washington DC NAVSEC Code 6034 1 Library). Washington DC NAVSEC61RLACT PWO. Torni...ESEARCH CO LA HABRA, CA iBROOKSi 0ONCRFE It Il FCH-NoIOGY CORP. TACOMA. ’A At( ANL )ESONi ((tNRAI) ASSOC. Van NuNs CA iA. Luisonit I)RA Vt COR(P I’muitt

Bayesian logistic regression approaches to predict incorrect DRG assignment.

PubMed

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Dirac R-matrix calculations of photoionization cross sections of Ni XII and atomic structure data of Ni XIII

NASA Astrophysics Data System (ADS)

Nazir, R. T.; Bari, M. A.; Bilal, M.; Sardar, S.; Nasim, M. H.; Salahuddin, M.

2017-02-01

We performed R-matrix calculations for photoionization cross sections of the two ground state configuration 3s23p5 (^2P^o3/2,1/2) levels and 12 excited states of Ni XII using relativistic Dirac Atomic R-matrix Codes (DARC) across the photon energy range between the ionizations thresholds of the corresponding states and well above the thresholds of the last level of the Ni XIII target ion. Generally, a good agreement is obtained between our results and the earlier theoretical photoionization cross sections. Moreover, we have used two independent fully relativistic GRASP and FAC codes to calculate fine-structure energy levels, wavelengths, oscillator strengths, transitions rates among the lowest 48 levels belonging to the configuration (3s23p4, 3s3p5, 3p6, 3s23p33d) in Ni XIII. Additionally, radiative lifetimes of all the excited states of Ni XIII are presented. Our results of the atomic structure of Ni XIII show good agreement with other theoretical and experimental results available in the literature. A good agreement is found between our calculated lifetimes and the experimental ones. Our present results are useful for plasma diagnostic of fusion and astrophysical plasmas.

Benchmark Testing of a New 56Fe Evaluation for Criticality Safety Applications

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

Leal, Luiz C; Ivanov, E.

2015-01-01

The SAMMY code was used to evaluate resonance parameters of the 56Fe cross section in the resolved resonance energy range of 0–2 MeV using transmission data, capture, elastic, inelastic, and double differential elastic cross sections. The resonance analysis was performed with the code SAMMY that fits R-matrix resonance parameters using the generalized least-squares technique (Bayes’ theory). The evaluation yielded a set of resonance parameters that reproduced the experimental data very well, along with a resonance parameter covariance matrix for data uncertainty calculations. Benchmark tests were conducted to assess the evaluation performance in benchmark calculations.

Updated User's Guide for Sammy: Multilevel R-Matrix Fits to Neutron Data Using Bayes' Equations

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

Larson, Nancy M

2008-10-01

In 1980 the multilevel multichannel R-matrix code SAMMY was released for use in analysis of neutron-induced cross section data at the Oak Ridge Electron Linear Accelerator. Since that time, SAMMY has evolved to the point where it is now in use around the world for analysis of many different types of data. SAMMY is not limited to incident neutrons but can also be used for incident protons, alpha particles, or other charged particles; likewise, Coulomb exit hannels can be included. Corrections for a wide variety of experimental conditions are available in the code: Doppler and resolution broadening, multiple-scattering corrections formore » capture or reaction yields, normalizations and backgrounds, to name but a few. The fitting procedure is Bayes' method, and data and parameter covariance matrices are properly treated within the code. Pre- and post-processing capabilities are also available, including (but not limited to) connections with the Evaluated Nuclear Data Files. Though originally designed for use in the resolved resonance region, SAMMY also includes a treatment for data analysis in the unresolved resonance region.« less

Bayesian Ising approximation for learning dictionaries of multispike timing patterns in premotor neurons

NASA Astrophysics Data System (ADS)

Hernandez Lahme, Damian; Sober, Samuel; Nemenman, Ilya

Important questions in computational neuroscience are whether, how much, and how information is encoded in the precise timing of neural action potentials. We recently demonstrated that, in the premotor cortex during vocal control in songbirds, spike timing is far more informative about upcoming behavior than is spike rate (Tang et al, 2014). However, identification of complete dictionaries that relate spike timing patterns with the controled behavior remains an elusive problem. Here we present a computational approach to deciphering such codes for individual neurons in the songbird premotor area RA, an analog of mammalian primary motor cortex. Specifically, we analyze which multispike patterns of neural activity predict features of the upcoming vocalization, and hence are important codewords. We use a recently introduced Bayesian Ising Approximation, which properly accounts for the fact that many codewords overlap and hence are not independent. Our results show which complex, temporally precise multispike combinations are used by individual neurons to control acoustic features of the produced song, and that these code words are different across individual neurons and across different acoustic features. This work was supported, in part, by JSMF Grant 220020321, NSF Grant 1208126, NIH Grant NS084844 and NIH Grant 1 R01 EB022872.

A Bayesian approach to multivariate measurement system assessment

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

Hamada, Michael Scott

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

A Bayesian approach to multivariate measurement system assessment

DOE PAGES

Hamada, Michael Scott

2016-07-01

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

Resonance region measurements of dysprosium and rhenium

NASA Astrophysics Data System (ADS)

Leinweber, Gregory; Block, Robert C.; Epping, Brian E.; Barry, Devin P.; Rapp, Michael J.; Danon, Yaron; Donovan, Timothy J.; Landsberger, Sheldon; Burke, John A.; Bishop, Mary C.; Youmans, Amanda; Kim, Guinyun N.; Kang, yeong-rok; Lee, Man Woo; Drindak, Noel J.

2017-09-01

Neutron capture and transmission measurements have been performed, and resonance parameter analysis has been completed for dysprosium, Dy, and rhenium, Re. The 60 MeV electron accelerator at RPI Gaerttner LINAC Center produced neutrons in the thermal and epithermal energy regions for these measurements. Transmission measurements were made using 6Li glass scintillation detectors. The neutron capture measurements were made with a 16-segment NaI multiplicity detector. The detectors for all experiments were located at ≈25 m except for thermal transmission, which was done at ≈15 m. The dysprosium samples included one highly enriched 164Dy metal, 6 liquid solutions of enriched 164Dy, two natural Dy metals. The Re samples were natural metals. Their capture yield normalizations were corrected for their high gamma attenuation. The multi-level R-matrix Bayesian computer code SAMMY was used to extract the resonance parameters from the data. 164Dy resonance data were analyzed up to 550 eV, other Dy isotopes up to 17 eV, and Re resonance data up to 1 keV. Uncertainties due to resolution function, flight path, burst width, sample thickness, normalization, background, and zero time were estimated and propagated using SAMMY. An additional check of sample-to-sample consistency is presented as an estimate of uncertainty. The thermal total cross sections and neutron capture resonance integrals of 164Dy and Re were determined from the resonance parameters. The NJOY and INTER codes were used to process and integrate the cross sections. Plots of the data, fits, and calculations using ENDF/B-VII.1 resonance parameters are presented.

The Islamic State Battle Plan: Press Release Natural Language Processing

DTIC Science & Technology

2016-06-01

Processing, text mining , corpus, generalized linear model, cascade, R Shiny, leaflet, data visualization 15. NUMBER OF PAGES 83 16. PRICE CODE...Terrorism and Responses to Terrorism TDM Term Document Matrix TF Term Frequency TF-IDF Term Frequency-Inverse Document Frequency tm text mining (R...package=leaflet. Feinerer I, Hornik K (2015) Text Mining Package “tm,” Version 0.6-2. (Jul 3) https://cran.r-project.org/web/packages/tm/tm.pdf

Bayesian models: A statistical primer for ecologists

USGS Publications Warehouse

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

The complete mitochondrial genome of Papilio glaucus and its phylogenetic implications.

PubMed

Shen, Jinhui; Cong, Qian; Grishin, Nick V

2015-09-01

Due to the intriguing morphology, lifecycle, and diversity of butterflies and moths, Lepidoptera are emerging as model organisms for the study of genetics, evolution and speciation. The progress of these studies relies on decoding Lepidoptera genomes, both nuclear and mitochondrial. Here we describe a protocol to obtain mitogenomes from Next Generation Sequencing reads performed for whole-genome sequencing and report the complete mitogenome of Papilio (Pterourus) glaucus. The circular mitogenome is 15,306 bp in length and rich in A and T. It contains 13 protein-coding genes (PCGs), 22 transfer-RNA-coding genes (tRNA), and 2 ribosomal-RNA-coding genes (rRNA), with a gene order typical for mitogenomes of Lepidoptera. We performed phylogenetic analyses based on PCG and RNA-coding genes or protein sequences using Bayesian Inference and Maximum Likelihood methods. The phylogenetic trees consistently show that among species with available mitogenomes Papilio glaucus is the closest to Papilio (Agehana) maraho from Asia.

The fourfold way of the genetic code.

PubMed

Jiménez-Montaño, Miguel Angel

2009-11-01

We describe a compact representation of the genetic code that factorizes the table in quartets. It represents a "least grammar" for the genetic language. It is justified by the Klein-4 group structure of RNA bases and codon doublets. The matrix of the outer product between the column-vector of bases and the corresponding row-vector V(T)=(C G U A), considered as signal vectors, has a block structure consisting of the four cosets of the KxK group of base transformations acting on doublet AA. This matrix, translated into weak/strong (W/S) and purine/pyrimidine (R/Y) nucleotide classes, leads to a code table with mixed and unmixed families in separate regions. A basic difference between them is the non-commuting (R/Y) doublets: AC/CA, GU/UG. We describe the degeneracy in the canonical code and the systematic changes in deviant codes in terms of the divisors of 24, employing modulo multiplication groups. We illustrate binary sub-codes characterizing mutations in the quartets. We introduce a decision-tree to predict the mode of tRNA recognition corresponding to each codon, and compare our result with related findings by Jestin and Soulé [Jestin, J.-L., Soulé, C., 2007. Symmetries by base substitutions in the genetic code predict 2' or 3' aminoacylation of tRNAs. J. Theor. Biol. 247, 391-394], and the rearrangements of the table by Delarue [Delarue, M., 2007. An asymmetric underlying rule in the assignment of codons: possible clue to a quick early evolution of the genetic code via successive binary choices. RNA 13, 161-169] and Rodin and Rodin [Rodin, S.N., Rodin, A.S., 2008. On the origin of the genetic code: signatures of its primordial complementarity in tRNAs and aminoacyl-tRNA synthetases. Heredity 100, 341-355], respectively.

An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code, with Application to WASP-12b

NASA Astrophysics Data System (ADS)

Harrington, Joseph; Blecic, Jasmina; Cubillos, Patricio; Rojo, Patricio; Loredo, Thomas J.; Bowman, M. Oliver; Foster, Andrew S. D.; Stemm, Madison M.; Lust, Nate B.

2015-01-01

Atmospheric retrievals for solar-system planets typically fit, either with a minimizer or by eye, a synthetic spectrum to high-resolution (Δλ/λ ~ 1000-100,000) data with S/N > 100 per point. In contrast, exoplanet data often have S/N ~ 10 per point, and may have just a few points representing bandpasses larger than 1 um. To derive atmospheric constraints and robust parameter uncertainty estimates from such data requires a Bayesian approach. To date there are few investigators with the relevant codes, none of which are publicly available. We are therefore pleased to announce the open-source Bayesian Atmospheric Radiative Transfer (BART) code. BART uses a Bayesian phase-space explorer to drive a radiative-transfer model through the parameter phase space, producing the most robust estimates available for the thermal profile and chemical abundances in the atmosphere. We present an overview of the code and an initial application to Spitzer eclipse data for WASP-12b. We invite the community to use and improve BART via the open-source development site GitHub.com. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code, and Application to WASP-12b

NASA Astrophysics Data System (ADS)

Harrington, Joseph; Blecic, Jasmina; Cubillos, Patricio; Rojo, Patricio M.; Loredo, Thomas J.; Bowman, Matthew O.; Foster, Andrew S.; Stemm, Madison M.; Lust, Nate B.

2014-11-01

Atmospheric retrievals for solar-system planets typically fit, either with a minimizer or by eye, a synthetic spectrum to high-resolution (Δλ/λ ~ 1000-100,000) data with S/N > 100 per point. In contrast, exoplanet data often have S/N ~ 10 per point, and may have just a few points representing bandpasses larger than 1 um. To derive atmospheric constraints and robust parameter uncertainty estimates from such data requires a Bayesian approach. To date there are few investigators with the relevant codes, none of which are publicly available. We are therefore pleased to announce the open-source Bayesian Atmospheric Radiative Transfer (BART) code. BART uses a Bayesian phase-space explorer to drive a radiative-transfer model through the parameter phase space, producing the most robust estimates available for the thermal profile and chemical abundances in the atmosphere. We present an overview of the code and an initial application to Spitzer eclipse data for WASP-12b. We invite the community to use and improve BART via the open-source development site GitHub.com. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

A spatiotemporal model of ecological and sociological ...

EPA Pesticide Factsheets

Background/Question/Methods Suffolk County, New York is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a robust system of light and gravid traps used for mosquito collection and disease monitoring. Since 2010, there have been 55 confirmed human cases of WNV in Suffolk County, resulting in 3 deaths. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV we developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008-2014 using the R package 'R-INLA' which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The INLA SPDE allows for simultaneous fitting of temporal parameters and a spatial covariance matrix, while incorporating multiple likelihood functions and running in standard R statistical software on a typical home computer. Results/Conclusions We found that land cover classified as open water or woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two weeks lag was associated with a strong positive impact, while mean precipitation at no lag and

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients.

PubMed

Green, Nancy

2005-04-01

We developed a Bayesian network coding scheme for annotating biomedical content in layperson-oriented clinical genetics documents. The coding scheme supports the representation of probabilistic and causal relationships among concepts in this domain, at a high enough level of abstraction to capture commonalities among genetic processes and their relationship to health. We are using the coding scheme to annotate a corpus of genetic counseling patient letters as part of the requirements analysis and knowledge acquisition phase of a natural language generation project. This paper describes the coding scheme and presents an evaluation of intercoder reliability for its tag set. In addition to giving examples of use of the coding scheme for analysis of discourse and linguistic features in this genre, we suggest other uses for it in analysis of layperson-oriented text and dialogue in medical communication.

Bayesian operational modal analysis with asynchronous data, Part II: Posterior uncertainty

NASA Astrophysics Data System (ADS)

Zhu, Yi-Chen; Au, Siu-Kui

2018-01-01

A Bayesian modal identification method has been proposed in the companion paper that allows the most probable values of modal parameters to be determined using asynchronous ambient vibration data. This paper investigates the identification uncertainty of modal parameters in terms of their posterior covariance matrix. Computational issues are addressed. Analytical expressions are derived to allow the posterior covariance matrix to be evaluated accurately and efficiently. Synthetic, laboratory and field data examples are presented to verify the consistency, investigate potential modelling error and demonstrate practical applications.

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models

PubMed Central

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A.; Valdés-Hernández, Pedro A.; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A.

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website. PMID:29200994

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models.

PubMed

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A; Valdés-Hernández, Pedro A; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website.

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

A generative model of whole-brain effective connectivity.

PubMed

Frässle, Stefan; Lomakina, Ekaterina I; Kasper, Lars; Manjaly, Zina M; Leff, Alex; Pruessmann, Klaas P; Buhmann, Joachim M; Stephan, Klaas E

2018-05-25

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data - in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling - for example, for phenotyping individual patients in terms of whole-brain network structure. Copyright © 2018. Published by Elsevier Inc.

Relativistic R-matrix calculations for photoionization cross-sections of C IV: implications for photorecombination of C V

NASA Astrophysics Data System (ADS)

Sardar, Shahid; Xu, Xin; Xu, Long-Quan; Zhu, Lin-Fan

2018-02-01

In this paper we present photoionization cross-sections of the ground and excited states of Li-like carbon (C IV) in the framework of fully relativistic R-matrix formalism as implemented in Dirac atomic R-matrix code. For target wavefunctions expansion, Multiconfiguration Dirac Hartree Fock calculations are performed for the lowest 17 target states of He-like carbon (C V) arising from 1s2 and 1snl, with n = 2, 3 and l = s, p, d configurations. Our target energy levels and transition parameters belonging to these levels are ascertained to be in excellent agreement with the experimental and the well-established theoretical results. We use the principle of detailed balance to get the photorecombination (PR) cross-sections of the ground state of C V. Both photoionization and PR cross-sections manifest important KLL and KLM resonance structures which are in very good agreement with the accurate measurements at Advanced Light Source (ion photon end beam station) and CRYRING (synchrotron storage ring).

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.

The influence of ignoring secondary structure on divergence time estimates from ribosomal RNA genes.

PubMed

Dohrmann, Martin

2014-02-01

Genes coding for ribosomal RNA molecules (rDNA) are among the most popular markers in molecular phylogenetics and evolution. However, coevolution of sites that code for pairing regions (stems) in the RNA secondary structure can make it challenging to obtain accurate results from such loci. While the influence of ignoring secondary structure on multiple sequence alignment and tree topology has been investigated in numerous studies, its effect on molecular divergence time estimates is still poorly known. Here, I investigate this issue in Bayesian Markov Chain Monte Carlo (BMCMC) and penalized likelihood (PL) frameworks, using empirical datasets from dragonflies (Odonata: Anisoptera) and glass sponges (Porifera: Hexactinellida). My results indicate that highly biased inferences under substitution models that ignore secondary structure only occur if maximum-likelihood estimates of branch lengths are used as input to PL dating, whereas in a BMCMC framework and in PL dating based on Bayesian consensus branch lengths, the effect is far less severe. I conclude that accounting for coevolution of paired sites in molecular dating studies is not as important as previously suggested, as long as the estimates are based on Bayesian consensus branch lengths instead of ML point estimates. This finding is especially relevant for studies where computational limitations do not allow the use of secondary-structure specific substitution models, or where accurate consensus structures cannot be predicted. I also found that the magnitude and direction (over- vs. underestimating node ages) of bias in age estimates when secondary structure is ignored was not distributed randomly across the nodes of the phylogenies, a phenomenon that requires further investigation. Copyright © 2013 Elsevier Inc. All rights reserved.

Approximate method of variational Bayesian matrix factorization/completion with sparse prior

NASA Astrophysics Data System (ADS)

Kawasumi, Ryota; Takeda, Koujin

2018-05-01

We derive the analytical expression of a matrix factorization/completion solution by the variational Bayes method, under the assumption that the observed matrix is originally the product of low-rank, dense and sparse matrices with additive noise. We assume the prior of a sparse matrix is a Laplace distribution by taking matrix sparsity into consideration. Then we use several approximations for the derivation of a matrix factorization/completion solution. By our solution, we also numerically evaluate the performance of a sparse matrix reconstruction in matrix factorization, and completion of a missing matrix element in matrix completion.

A Bayesian approach to estimating variance components within a multivariate generalizability theory framework.

PubMed

Jiang, Zhehan; Skorupski, William

2017-12-12

In many behavioral research areas, multivariate generalizability theory (mG theory) has been typically used to investigate the reliability of certain multidimensional assessments. However, traditional mG-theory estimation-namely, using frequentist approaches-has limits, leading researchers to fail to take full advantage of the information that mG theory can offer regarding the reliability of measurements. Alternatively, Bayesian methods provide more information than frequentist approaches can offer. This article presents instructional guidelines on how to implement mG-theory analyses in a Bayesian framework; in particular, BUGS code is presented to fit commonly seen designs from mG theory, including single-facet designs, two-facet crossed designs, and two-facet nested designs. In addition to concrete examples that are closely related to the selected designs and the corresponding BUGS code, a simulated dataset is provided to demonstrate the utility and advantages of the Bayesian approach. This article is intended to serve as a tutorial reference for applied researchers and methodologists conducting mG-theory studies.

BMDS: A Collection of R Functions for Bayesian Multidimensional Scaling

ERIC Educational Resources Information Center

Okada, Kensuke; Shigemasu, Kazuo

2009-01-01

Bayesian multidimensional scaling (MDS) has attracted a great deal of attention because: (1) it provides a better fit than do classical MDS and ALSCAL; (2) it provides estimation errors of the distances; and (3) the Bayesian dimension selection criterion, MDSIC, provides a direct indication of optimal dimensionality. However, Bayesian MDS is not…

The Variability and Interpretation of Earthquake Source Mechanisms in The Geysers Geothermal Field From a Bayesian Standpoint Based on the Choice of a Noise Model

NASA Astrophysics Data System (ADS)

Mustać, Marija; Tkalčić, Hrvoje; Burky, Alexander L.

2018-01-01

Moment tensor (MT) inversion studies of events in The Geysers geothermal field mostly focused on microseismicity and found a large number of earthquakes with significant non-double-couple (non-DC) seismic radiation. Here we concentrate on the largest events in the area in recent years using a hierarchical Bayesian MT inversion. Initially, we show that the non-DC components of the MT can be reliably retrieved using regional waveform data from a small number of stations. Subsequently, we present results for a number of events and show that accounting for noise correlations can lead to retrieval of a lower isotropic (ISO) component and significantly different focal mechanisms. We compute the Bayesian evidence to compare solutions obtained with different assumptions of the noise covariance matrix. Although a diagonal covariance matrix produces a better waveform fit, inversions that account for noise correlations via an empirically estimated noise covariance matrix account for interdependences of data errors and are preferred from a Bayesian point of view. This implies that improper treatment of data noise in waveform inversions can result in fitting the noise and misinterpreting the non-DC components. Finally, one of the analyzed events is characterized as predominantly DC, while the others still have significant non-DC components, probably as a result of crack opening, which is a reasonable hypothesis for The Geysers geothermal field geological setting.

Single nucleotide polymorphism-specific regulation of matrix metalloproteinase-9 by multiple miRNAs targeting the coding exon

PubMed Central

Duellman, Tyler; Warren, Christopher; Yang, Jay

2014-01-01

Microribonucleic acids (miRNAs) work with exquisite specificity and are able to distinguish a target from a non-target based on a single nucleotide mismatch in the core nucleotide domain. We questioned whether miRNA regulation of gene expression could occur in a single nucleotide polymorphism (SNP)-specific manner, manifesting as a post-transcriptional control of expression of genetic polymorphisms. In our recent study of the functional consequences of matrix metalloproteinase (MMP)-9 SNPs, we discovered that expression of a coding exon SNP in the pro-domain of the protein resulted in a profound decrease in the secreted protein. This missense SNP results in the N38S amino acid change and a loss of an N-glycosylation site. A systematic study demonstrated that the loss of secreted protein was due not to the loss of an N-glycosylation site, but rather an SNP-specific targeting by miR-671-3p and miR-657. Bioinformatics analysis identified 41 SNP-specific miRNA targeting MMP-9 SNPs, mostly in the coding exon and an extension of the analysis to chromosome 20, where the MMP-9 gene is located, suggesting that SNP-specific miRNAs targeting the coding exon are prevalent. This selective post-transcriptional regulation of a target messenger RNA harboring genetic polymorphisms by miRNAs offers an SNP-dependent post-transcriptional regulatory mechanism, allowing for polymorphic-specific differential gene regulation. PMID:24627221

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.

R-Matrix Codes for Charged-Particle Induced Reactions in the Resolved Resonance Region (3), Summary Report of an IAEA Consultants' Meeting IAEA Headquarters, Vienna, Austria, 28-30 June 2017

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

Leeb, Helmut; Dimitriou, Paraskevi; Thompson, Ian

A Consultants Meeting was held at the IAEA Headquarters, from 28 to 30 June 2017, to discuss the results of a test exercise that had been defined and assigned to all participants of the previous meeting held in December 2016. Five codes were used in this exercise: AMUR, AZURE2, RAC, SFRESCO and SAMMY. The results obtained from these codes were compared and further actions were proposed. Participants’ presentations and technical discussions, as well as proposed additional actions have been summarized in this report.

The First Mitochondrial Genome for Caddisfly (Insecta: Trichoptera) with Phylogenetic Implications

PubMed Central

Wang, Yuyu; Liu, Xingyue; Yang, Ding

2014-01-01

The Trichoptera (caddisflies) is a holometabolous insect order with 14,300 described species forming the second most species-rich monophyletic group of animals in freshwater. Hitherto, there is no mitochondrial genome reported of this order. Herein, we describe the complete mitochondrial (mt) genome of a caddisfly species, Eubasilissa regina (McLachlan, 1871). A phylogenomic analysis was carried out based on the mt genomic sequences of 13 mt protein coding genes (PCGs) and two rRNA genes of 24 species belonging to eight holometabolous orders. Both maximum likelihood and Bayesian inference analyses highly support the sister relationship between Trichoptera and Lepidoptera. PMID:24391451

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

BONNSAI: correlated stellar observables in Bayesian methods

NASA Astrophysics Data System (ADS)

Schneider, F. R. N.; Castro, N.; Fossati, L.; Langer, N.; de Koter, A.

2017-02-01

In an era of large spectroscopic surveys of stars and big data, sophisticated statistical methods become more and more important in order to infer fundamental stellar parameters such as mass and age. Bayesian techniques are powerful methods because they can match all available observables simultaneously to stellar models while taking prior knowledge properly into account. However, in most cases it is assumed that observables are uncorrelated which is generally not the case. Here, we include correlations in the Bayesian code Bonnsai by incorporating the covariance matrix in the likelihood function. We derive a parametrisation of the covariance matrix that, in addition to classical uncertainties, only requires the specification of a correlation parameter that describes how observables co-vary. Our correlation parameter depends purely on the method with which observables have been determined and can be analytically derived in some cases. This approach therefore has the advantage that correlations can be accounted for even if information for them are not available in specific cases but are known in general. Because the new likelihood model is a better approximation of the data, the reliability and robustness of the inferred parameters are improved. We find that neglecting correlations biases the most likely values of inferred stellar parameters and affects the precision with which these parameters can be determined. The importance of these biases depends on the strength of the correlations and the uncertainties. For example, we apply our technique to massive OB stars, but emphasise that it is valid for any type of stars. For effective temperatures and surface gravities determined from atmosphere modelling, we find that masses can be underestimated on average by 0.5σ and mass uncertainties overestimated by a factor of about 2 when neglecting correlations. At the same time, the age precisions are underestimated over a wide range of stellar parameters. We conclude that accounting for correlations is essential in order to derive reliable stellar parameters including robust uncertainties and will be vital when entering an era of precision stellar astrophysics thanks to the Gaia satellite.

GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

NASA Astrophysics Data System (ADS)

Takaishi, Tetsuya

2015-01-01

The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus performed on the GPU for speedup. The GPU code is developed with CUDA Fortran. We compare the computational time in performing the HMC algorithm on GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz) and find that the GPU can be up to 17 times faster than the CPU. We also code the program with OpenACC and find that appropriate coding can achieve the similar speedup with CUDA Fortran.

Recognizing Uncertainty in the Q-Matrix via a Bayesian Extension of the DINA Model

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2012-01-01

In the typical application of a cognitive diagnosis model, the Q-matrix, which reflects the theory with respect to the skills indicated by the items, is assumed to be known. However, the Q-matrix is usually determined by expert judgment, and so there can be uncertainty about some of its elements. Here it is shown that this uncertainty can be…

bnstruct: an R package for Bayesian Network structure learning in the presence of missing data.

PubMed

Franzin, Alberto; Sambo, Francesco; Di Camillo, Barbara

2017-04-15

A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks, particularly the manipulation of missing data, which is a common situation in practice. The software is implemented in R and C and is available on CRAN under a GPL licence. francesco.sambo@unipd.it. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

APPLICATION OF BAYESIAN MONTE CARLO ANALYSIS TO A LAGRANGIAN PHOTOCHEMICAL AIR QUALITY MODEL. (R824792)

EPA Science Inventory

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

DOE PAGES

Wang, Liming; Huang, Jiaji; Yuan, Xin; ...

2015-09-17

The measurement matrix employed in compressive sensing typically cannot be known precisely a priori and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be estimated jointly. This is of interest as well when the measurement matrix may change as a function of the details of what is measured. This problem has been considered recently for Gaussian measurement noise, and here we develop this idea with application to Poisson systems. A collaborative maximum likelihood algorithm and alternating proximal gradient algorithm are proposed, and associated theoretical performance guarantees are establishedmore » based on newly derived concentration-of-measure results. A Bayesian model is then introduced, to improve flexibility and generality. Connections between the maximum likelihood methods and the Bayesian model are developed, and example results are presented for a real compressive X-ray imaging system.« less

Energy levels, radiative rates and electron impact excitation rates for transitions in He-like Ga XXX, Ge XXXI, As XXXII, Se XXXIII and Br XXXIV

NASA Astrophysics Data System (ADS)

Aggarwal, Kanti M.; Keenan, Francis P.

2013-04-01

We report calculations of energy levels, radiative rates and electron impact excitation cross sections and rates for transitions in He-like Ga XXX, Ge XXXI, As XXXII, Se XXXIII and Br XXXIV. The grasp (general-purpose relativistic atomic structure package) is adopted for calculating energy levels and radiative rates. For determining the collision strengths, and subsequently the excitation rates, the Dirac atomic R-matrix code (darc) is used. Oscillator strengths, radiative rates and line strengths are reported for all E1, E2, M1 and M2 transitions among the lowest 49 levels of each ion. Additionally, theoretical lifetimes are provided for all 49 levels of the above five ions. Collision strengths are averaged over a Maxwellian velocity distribution and the effective collision strengths obtained listed over a wide temperature range up to 108 K. Comparisons are made with similar data obtained using the flexible atomic code (fac) to highlight the importance of resonances, included in calculations with darc, in the determination of effective collision strengths. Discrepancies between the collision strengths from darc and fac, particularly for some forbidden transitions, are also discussed. Finally, discrepancies between the present results for effective collision strengths with the darc code and earlier semi-relativistic R-matrix data are noted over a wide range of electron temperatures for many transitions in all ions.

A Bayesian method for detecting pairwise associations in compositional data

PubMed Central

Ventz, Steffen; Huttenhower, Curtis

2017-01-01

Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats. PMID:29140991

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.

BayeSED: A General Approach to Fitting the Spectral Energy Distribution of Galaxies

NASA Astrophysics Data System (ADS)

Han, Yunkun; Han, Zhanwen

2014-11-01

We present a newly developed version of BayeSED, a general Bayesian approach to the spectral energy distribution (SED) fitting of galaxies. The new BayeSED code has been systematically tested on a mock sample of galaxies. The comparison between the estimated and input values of the parameters shows that BayeSED can recover the physical parameters of galaxies reasonably well. We then applied BayeSED to interpret the SEDs of a large Ks -selected sample of galaxies in the COSMOS/UltraVISTA field with stellar population synthesis models. Using the new BayeSED code, a Bayesian model comparison of stellar population synthesis models has been performed for the first time. We found that the 2003 model by Bruzual & Charlot, statistically speaking, has greater Bayesian evidence than the 2005 model by Maraston for the Ks -selected sample. In addition, while setting the stellar metallicity as a free parameter obviously increases the Bayesian evidence of both models, varying the initial mass function has a notable effect only on the Maraston model. Meanwhile, the physical parameters estimated with BayeSED are found to be generally consistent with those obtained using the popular grid-based FAST code, while the former parameters exhibit more natural distributions. Based on the estimated physical parameters of the galaxies in the sample, we qualitatively classified the galaxies in the sample into five populations that may represent galaxies at different evolution stages or in different environments. We conclude that BayeSED could be a reliable and powerful tool for investigating the formation and evolution of galaxies from the rich multi-wavelength observations currently available. A binary version of the BayeSED code parallelized with Message Passing Interface is publicly available at https://bitbucket.org/hanyk/bayesed.

BayeSED: A GENERAL APPROACH TO FITTING THE SPECTRAL ENERGY DISTRIBUTION OF GALAXIES

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

Han, Yunkun; Han, Zhanwen, E-mail: hanyk@ynao.ac.cn, E-mail: zhanwenhan@ynao.ac.cn

2014-11-01

We present a newly developed version of BayeSED, a general Bayesian approach to the spectral energy distribution (SED) fitting of galaxies. The new BayeSED code has been systematically tested on a mock sample of galaxies. The comparison between the estimated and input values of the parameters shows that BayeSED can recover the physical parameters of galaxies reasonably well. We then applied BayeSED to interpret the SEDs of a large K{sub s} -selected sample of galaxies in the COSMOS/UltraVISTA field with stellar population synthesis models. Using the new BayeSED code, a Bayesian model comparison of stellar population synthesis models has beenmore » performed for the first time. We found that the 2003 model by Bruzual and Charlot, statistically speaking, has greater Bayesian evidence than the 2005 model by Maraston for the K{sub s} -selected sample. In addition, while setting the stellar metallicity as a free parameter obviously increases the Bayesian evidence of both models, varying the initial mass function has a notable effect only on the Maraston model. Meanwhile, the physical parameters estimated with BayeSED are found to be generally consistent with those obtained using the popular grid-based FAST code, while the former parameters exhibit more natural distributions. Based on the estimated physical parameters of the galaxies in the sample, we qualitatively classified the galaxies in the sample into five populations that may represent galaxies at different evolution stages or in different environments. We conclude that BayeSED could be a reliable and powerful tool for investigating the formation and evolution of galaxies from the rich multi-wavelength observations currently available. A binary version of the BayeSED code parallelized with Message Passing Interface is publicly available at https://bitbucket.org/hanyk/bayesed.« less

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

Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: a case for the second derivative

PubMed Central

Bickel, David R.; Montazeri, Zahra; Hsieh, Pei-Chun; Beatty, Mary; Lawit, Shai J.; Bate, Nicholas J.

2009-01-01

Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made. Results: The representation of a sufficiently large portion of genome enables computation of an upper bound on how much confidence one may place in influences between genes on the basis of expression data. Information about which genes encode transcription factors is not necessary but may be incorporated if available. The methodology is generalized to cover cases in which expression measurements are missing for many of the genes that might control the transcription of the genes of interest. The assumption that the gene expression level is roughly proportional to the rate of translation led to better empirical performance than did either the assumption that the gene expression level is roughly proportional to the protein level or the Bayesian model average of both assumptions. Availability: http://www.oisb.ca points to R code implementing the methods (R Development Core Team 2004). Contact: dbickel@uottawa.ca Supplementary information: http://www.davidbickel.com PMID:19218351

Reliability analysis using an exponential power model with bathtub-shaped failure rate function: a Bayes study.

PubMed

Shehla, Romana; Khan, Athar Ali

2016-01-01

Models with bathtub-shaped hazard function have been widely accepted in the field of reliability and medicine and are particularly useful in reliability related decision making and cost analysis. In this paper, the exponential power model capable of assuming increasing as well as bathtub-shape, is studied. This article makes a Bayesian study of the same model and simultaneously shows how posterior simulations based on Markov chain Monte Carlo algorithms can be straightforward and routine in R. The study is carried out for complete as well as censored data, under the assumption of weakly-informative priors for the parameters. In addition to this, inference interest focuses on the posterior distribution of non-linear functions of the parameters. Also, the model has been extended to include continuous explanatory variables and R-codes are well illustrated. Two real data sets are considered for illustrative purposes.

VizieR Online Data Catalog: Massive stars in 30 Dor (Schneider+, 2018)

NASA Astrophysics Data System (ADS)

Schneider, F. R. N.; Sana, H.; Evans, C. J.; Bestenlehner, J. M.; Castro, N.; Fossati, L.; Grafener, G.; Langer, N.; Ramirez-Agudelo, O. H.; Sabin-Sanjulian, C.; Simon-Diaz, S.; Tramper, F.; Crowther, P. A.; de Koter, A.; de Mink, S. E.; Dufton, P. L.; Garcia, M.; Gieles, M.; Henault-Brunet, V.; Herrero, A.; Izzard, R. G.; Kalari, V.; Lennon, D. J.; Apellaniz, J. M.; Markova, N.; Najarro, F.; Podsiadlowski, P.; Puls, J.; Taylor, W. D.; van Loon, J. T.; Vink, J. S.; Norman, C.

2018-02-01

Through the use of the Fibre Large Array Multi Element Spectrograph (FLAMES) on the Very Large Telescope (VLT), the VLT-FLAMES Tarantula Survey (VFTS) has obtained optical spectra of ~800 massive stars in 30 Dor, avoiding the core region of the dense star cluster R136 because of difficulties with crowding. Repeated observations at multiple epochs allow determination of the orbital motion of potentially binary objects. For a sample of 452 apparently single stars, robust stellar parameters-such as effective temperatures, luminosities, surface gravities, and projected rotational velocities-are determined by modeling the observed spectra. Composite spectra of visual multiple systems and spectroscopic binaries are not considered here because their parameters cannot be reliably inferred from the VFTS data. To match the derived atmospheric parameters of the apparently single VFTS stars to stellar evolutionary models, we use the Bayesian code Bonnsai. (2 data files).

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

Bayesian Inference in the Modern Design of Experiments

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2008-01-01

This paper provides an elementary tutorial overview of Bayesian inference and its potential for application in aerospace experimentation in general and wind tunnel testing in particular. Bayes Theorem is reviewed and examples are provided to illustrate how it can be applied to objectively revise prior knowledge by incorporating insights subsequently obtained from additional observations, resulting in new (posterior) knowledge that combines information from both sources. A logical merger of Bayesian methods and certain aspects of Response Surface Modeling is explored. Specific applications to wind tunnel testing, computational code validation, and instrumentation calibration are discussed.

Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library

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

Bates, Cameron Russell; Mckigney, Edward Allen

The use of Bayesian inference in data analysis has become the standard for large scienti c experiments [1, 2]. The Monte Carlo Codes Group(XCP-3) at Los Alamos has developed a simple set of algorithms currently implemented in C++ and Python to easily perform at-prior Markov Chain Monte Carlo Bayesian inference with pure Metropolis sampling. These implementations are designed to be user friendly and extensible for customization based on speci c application requirements. This document describes the algorithmic choices made and presents two use cases.

General linear codes for fault-tolerant matrix operations on processor arrays

NASA Technical Reports Server (NTRS)

Nair, V. S. S.; Abraham, J. A.

1988-01-01

Various checksum codes have been suggested for fault-tolerant matrix computations on processor arrays. Use of these codes is limited due to potential roundoff and overflow errors. Numerical errors may also be misconstrued as errors due to physical faults in the system. In this a set of linear codes is identified which can be used for fault-tolerant matrix operations such as matrix addition, multiplication, transposition, and LU-decomposition, with minimum numerical error. Encoding schemes are given for some of the example codes which fall under the general set of codes. With the help of experiments, a rule of thumb for the selection of a particular code for a given application is derived.

The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation in R.

PubMed

Pang, Haotian; Liu, Han; Vanderbei, Robert

2014-02-01

We develop an R package fastclime for solving a family of regularized linear programming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example, one use of our LP solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained L 1 Minimization Estimator). Compared with existing packages for this problem such as clime and flare, our package has three advantages: (1) it efficiently calculates the full piecewise-linear regularization path; (2) it provides an accurate dual certificate as stopping criterion; (3) it is completely coded in C and is highly portable. This package is designed to be useful to statisticians and machine learning researchers for solving a wide range of problems.

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

Inner-shell photoionization of atomic chlorine near the 2p-1 edge: a Breit-Pauli R-matrix calculation

NASA Astrophysics Data System (ADS)

Felfli, Z.; Deb, N. C.; Manson, S. T.; Hibbert, A.; Msezane, A. Z.

2009-05-01

An R-matrix calculation which takes into account relativistic effects via the Breit-Pauli (BP) operator is performed for photoionization cross sections of atomic Cl near the 2p threshold. The wavefunctions are constructed with orbitals generated from a careful large scale configuration interaction (CI) calculation with relativistic corrections using the CIV3 code of Hibbert [1] and Glass and Hibbert [2]. The results are contrasted with the calculation of Martins [3], which uses a CI with relativistic corrections, and compared with the most recent measurements [4]. [1] A. Hibbert, Comput. Phys. Commun. 9, 141 (1975) [2] R. Glass and A. Hibbert, Comput. Phys. Commun. 16, 19 (1978) [3] M. Martins, J. Phys. B 34, 1321 (2001) [4] D. Lindle et al (private communication) Research supported by U.S. DOE, Division of Chemical Sciences, NSF and CAU CFNM, NSF-CREST Program. Computing facilities at Queen's University of Belfast, UK and of DOE Office of Science, NERSC are appreciated.

MATline: a job-exposure matrix for carcinogenic chemicals.

PubMed

Gilardi, Luisella; Falcone, Umberto; Santoro, Silvano; Coffano, Elena

2008-01-01

MATline is a tool that can be used to predict which industrial processes can be expected to involve the use of a substance that is considered carcinogenic as documented in the literature. The database includes agents carrying risk phrases R45, R49 and R40 according to the method of classification adopted by the EU and/or agents in categories 1, 2A and 2B as classified by the International Agency for Research on Cancer (IARC). Each agent is associated with a list of industrial processes coded according to the tariff headings used by the National Institute of Insurance against Occupational Injuries and Diseases (Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro, INAIL). The main sources of information are the IARC Monographs and databases available through the National Library of Medicine's TOXNET portal. The matrix currently includes 600 carcinogenic agents, 23 classes of agents and some 7000 links between agents and industrial processes. MATline can be viewed on the www.dors.it website.

More box codes

NASA Technical Reports Server (NTRS)

Solomon, G.

1992-01-01

A new investigation shows that, starting from the BCH (21,15;3) code represented as a 7 x 3 matrix and adding a row and column to add even parity, one obtains an 8 x 4 matrix (32,15;8) code. An additional dimension is obtained by specifying odd parity on the rows and even parity on the columns, i.e., adjoining to the 8 x 4 matrix, the matrix, which is zero except for the fourth column (of all ones). Furthermore, any seven rows and three columns will form the BCH (21,15;3) code. This box code has the same weight structure as the quadratic residue and BCH codes of the same dimensions. Whether there exists an algebraic isomorphism to either code is as yet unknown.

Bayesian Atmospheric Radiative Transfer (BART)Thermochemical Equilibrium Abundance (TEA) Code and Application to WASP-43b

NASA Astrophysics Data System (ADS)

Blecic, Jasmina; Harrington, Joseph; Bowman, Matthew O.; Cubillos, Patricio E.; Stemm, Madison; Foster, Andrew

2014-11-01

We present a new, open-source, Thermochemical Equilibrium Abundances (TEA) code that calculates the abundances of gaseous molecular species. TEA uses the Gibbs-free-energy minimization method with an iterative Lagrangian optimization scheme. It initializes the radiative-transfer calculation in our Bayesian Atmospheric Radiative Transfer (BART) code. Given elemental abundances, TEA calculates molecular abundances for a particular temperature and pressure or a list of temperature-pressure pairs. The code is tested against the original method developed by White at al. (1958), the analytic method developed by Burrows and Sharp (1999), and the Newton-Raphson method implemented in the open-source Chemical Equilibrium with Applications (CEA) code. TEA is written in Python and is available to the community via the open-source development site GitHub.com. We also present BART applied to eclipse depths of WASP-43b exoplanet, constraining atmospheric thermal and chemical parameters. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2016-12-01

In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

Learning coefficient of generalization error in Bayesian estimation and vandermonde matrix-type singularity.

PubMed

Aoyagi, Miki; Nagata, Kenji

2012-06-01

The term algebraic statistics arises from the study of probabilistic models and techniques for statistical inference using methods from algebra and geometry (Sturmfels, 2009 ). The purpose of our study is to consider the generalization error and stochastic complexity in learning theory by using the log-canonical threshold in algebraic geometry. Such thresholds correspond to the main term of the generalization error in Bayesian estimation, which is called a learning coefficient (Watanabe, 2001a , 2001b ). The learning coefficient serves to measure the learning efficiencies in hierarchical learning models. In this letter, we consider learning coefficients for Vandermonde matrix-type singularities, by using a new approach: focusing on the generators of the ideal, which defines singularities. We give tight new bound values of learning coefficients for the Vandermonde matrix-type singularities and the explicit values with certain conditions. By applying our results, we can show the learning coefficients of three-layered neural networks and normal mixture models.

[The design and experiment of complementary S coding matrix based on digital micromirror spectrometer].

PubMed

Zhang, Zhi-Hai; Gao, Ling-Xiao; Guo, Yuan-Jun; Wang, Wei; Mo, Xiang-Xia

2012-12-01

The template selection is essential in the application of digital micromirror spectrometer. The best theoretical coding H-matrix is not widely used due to acyclic, complex coding and difficult achievement. The noise ratio of best practical S-matrix for improvement is slightly inferior to matrix H. So we designed a new type complementary S-matrix. Through studying its noise improvement theory, the algorithm is proved to have the advantages of both H-matrix and S-matrix. The experiments proved that the SNR can be increased 2.05 times than S-template.

Electron density diagnostics for gaseous nebulae involving the O 4 intercombination lines near 1400 A

NASA Technical Reports Server (NTRS)

Keenan, F. P.; Conlon, E. S.; Bowden, D. A.; Feibelman, W. A.; Pradhan, Anil K.

1992-01-01

Theoretical O IV electron density sensitive emission line ratios, determined using electron impact excitation rates calculated with the R-matrix code, are presented for R(sub 1) = I(1407.4 A)/I(1401.2 A), R(sub 2) = I(1404.8 A)/I(1401.2A), R(sub 3) = I(1399.8 A)/(1401.2 A), and R(sub 4) = I(1397.2 A)/I(1401.2 A). The observed values of R(sub 1)-R(sub 4), measured from high resolution spectra obtained with the International Ultraviolet Explorer (IUE) satellite, lead to electron densities that are compatible, and which are also in good agreement with those deduced from line ratios in other species. This provides observational support for the accuracy of the atomic data adopted in the present calculations.

New Insights into the Lake Chad Basin Population Structure Revealed by High-Throughput Genotyping of Mitochondrial DNA Coding SNPs

PubMed Central

Černý, Viktor; Carracedo, Ángel

2011-01-01

Background Located in the Sudan belt, the Chad Basin forms a remarkable ecosystem, where several unique agricultural and pastoral techniques have been developed. Both from an archaeological and a genetic point of view, this region has been interpreted to be the center of a bidirectional corridor connecting West and East Africa, as well as a meeting point for populations coming from North Africa through the Saharan desert. Methodology/Principal Findings Samples from twelve ethnic groups from the Chad Basin (n = 542) have been high-throughput genotyped for 230 coding region mitochondrial DNA (mtDNA) Single Nucleotide Polymorphisms (mtSNPs) using Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight (MALDI-TOF) mass spectrometry. This set of mtSNPs allowed for much better phylogenetic resolution than previous studies of this geographic region, enabling new insights into its population history. Notable haplogroup (hg) heterogeneity has been observed in the Chad Basin mirroring the different demographic histories of these ethnic groups. As estimated using a Bayesian framework, nomadic populations showed negative growth which was not always correlated to their estimated effective population sizes. Nomads also showed lower diversity values than sedentary groups. Conclusions/Significance Compared to sedentary population, nomads showed signals of stronger genetic drift occurring in their ancestral populations. These populations, however, retained more haplotype diversity in their hypervariable segments I (HVS-I), but not their mtSNPs, suggesting a more ancestral ethnogenesis. Whereas the nomadic population showed a higher Mediterranean influence signaled mainly by sub-lineages of M1, R0, U6, and U5, the other populations showed a more consistent sub-Saharan pattern. Although lifestyle may have an influence on diversity patterns and hg composition, analysis of molecular variance has not identified these differences. The present study indicates that analysis of mtSNPs at high resolution could be a fast and extensive approach for screening variation in population studies where labor-intensive techniques such as entire genome sequencing remain unfeasible. PMID:21533064

Fully relativistic B-spline R-matrix calculations for electron collisions with xenon

NASA Astrophysics Data System (ADS)

Bartschat, Klaus; Zatsarinny, Oleg

2009-05-01

We have applied our recently developed fully relativistic Dirac B-spline R-matrix (DBSR) code [1] to calculate electron scattering from xenon atoms. Results from a 31-state close-coupling model for the excitation function of the metastable (5p^5 6s) J=0,2 states show excellent agreement with experiment [2], thereby presenting a significant improvement over the most sophisticated previous Breit-Pauli calculations [3,4]. This allows for a detailed and reliable analysis of the resonance structure. The same model is currently being used to calculate electron-impact excitation from the metastable J=2 state. The results will be compared with recent experimental data [5] and predictions from other theoretical models [6,7]. [1] O. Zatsarinny and K. Bartschat, Phys. Rev. A 77 (2008) 062701. [2] S. J. Buckman et al., J. Phys. B 16 (1983) 4219. [3] A. N. Grum-Grzhimailo and K. Bartschat, J. Phys. B 35 (2002) 3479. [4] M. Allan et al., Phys. Rev. A 74 (2006) 030701(R). [5] R. O. Jung et al., Phys. Rev. A 72 (2005) 022723. [6] R. Srivastava et al., Phys. Rev. A 74 (2006) 012715. [7] J. Jiang et al., J. Phys. B 41 (2008) 245204.

Multilevel modeling of single-case data: A comparison of maximum likelihood and Bayesian estimation.

PubMed

Moeyaert, Mariola; Rindskopf, David; Onghena, Patrick; Van den Noortgate, Wim

2017-12-01

The focus of this article is to describe Bayesian estimation, including construction of prior distributions, and to compare parameter recovery under the Bayesian framework (using weakly informative priors) and the maximum likelihood (ML) framework in the context of multilevel modeling of single-case experimental data. Bayesian estimation results were found similar to ML estimation results in terms of the treatment effect estimates, regardless of the functional form and degree of information included in the prior specification in the Bayesian framework. In terms of the variance component estimates, both the ML and Bayesian estimation procedures result in biased and less precise variance estimates when the number of participants is small (i.e., 3). By increasing the number of participants to 5 or 7, the relative bias is close to 5% and more precise estimates are obtained for all approaches, except for the inverse-Wishart prior using the identity matrix. When a more informative prior was added, more precise estimates for the fixed effects and random effects were obtained, even when only 3 participants were included. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The complete mitochondrial genome of lesser long-tailed Hamster Cricetulus longicaudatus (Milne-Edwards, 1867) and phylogenetic implications.

PubMed

Zhang, Ziqi; Sun, Tong; Kang, Chunlan; Liu, Yang; Liu, Shaoying; Yue, Bisong; Zeng, Tao

2016-01-01

The complete mitochondrial genome sequence of Cricetulus longicaudatus (Rodentia Cricetidae: Cricetinae) was determined and was deposited in GenBank (GenBank accession no. KM067270). The mitochondrial genome of C. longicaudatus was 16,302 bp in length and contained 13 protein-coding genes, 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes and one control region, with an identical order to that of other rodents' mitochondrial genomes. The phylogenetic analysis was performed with Bayesian inference based on the concatenated nucleotide sequence of 12 protein-coding genes on the heavy strand. The result showed that these species from Cricetidae and its two subfamilies (Cricetinae and Arvicolines) formed solid monophyletic group, respectively. The Cricetulus had close phylogenetic relationship with Tscherskia among three genera (Cricetulus, Cricetulus and Mesocricetus). Neodon irene and Myodes regulus were embedded in Microtus and Eothenomys, respectively. The unusual phylogenetic positions of Neodon irene and Myodes regulus remain further study in the future.

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.

Relativistic R -matrix calculations for the electron-impact excitation of neutral molybdenum

NASA Astrophysics Data System (ADS)

Smyth, R. T.; Johnson, C. A.; Ennis, D. A.; Loch, S. D.; Ramsbottom, C. A.; Ballance, C. P.

2017-10-01

A recent PISCES-B Mod experiment [Nishijima et al., J. Phys. B 43, 225701 (2010), 10.1088/0953-4075/43/22/225701] has revealed up to a factor of 5 discrepancy between measurement and the two existing theoretical models [Badnell et al., J. Phys. B 29, 3683 (1996), 10.1088/0953-4075/29/16/014; Bartschat et al., J. Phys. B 35, 2899 (2002), 10.1088/0953-4075/35/13/305], providing important diagnostics for Mo i. In the following paper we address this issue by employing a relativistic atomic structure and R -matrix scattering calculations to improve upon the available models for future applications and benchmark results against a recent Compact Toroidal Hybrid experiment [Hartwell et al., Fusion Sci. Technol. 72, 76 (2017), 10.1080/15361055.2017.1291046]. We determine the atomic structure of Mo i using grasp0, which implements the multiconfigurational Dirac-Fock method. Fine structure energies and radiative transition rates are presented and compared to existing experimental and theoretical values. The electron-impact excitation of Mo i is investigated using the relativistic R -matrix method and the parallel versions of the Dirac atomic R -matrix codes. Electron-impact excitation cross sections are presented and compared to the few available theoretical cross sections. Throughout, our emphasis is on improving the results for the z 1,2,3o5P →a S52,z 2,3,4o7P → a S73 and y 2,3,4o7P → a S73 electric dipole transitions of particular relevance for diagnostic work.

A parallel algorithm for Hamiltonian matrix construction in electron-molecule collision calculations: MPI-SCATCI

NASA Astrophysics Data System (ADS)

Al-Refaie, Ahmed F.; Tennyson, Jonathan

2017-12-01

Construction and diagonalization of the Hamiltonian matrix is the rate-limiting step in most low-energy electron - molecule collision calculations. Tennyson (1996) implemented a novel algorithm for Hamiltonian construction which took advantage of the structure of the wavefunction in such calculations. This algorithm is re-engineered to make use of modern computer architectures and the use of appropriate diagonalizers is considered. Test calculations demonstrate that significant speed-ups can be gained using multiple CPUs. This opens the way to calculations which consider higher collision energies, larger molecules and / or more target states. The methodology, which is implemented as part of the UK molecular R-matrix codes (UKRMol and UKRMol+) can also be used for studies of bound molecular Rydberg states, photoionization and positron-molecule collisions.

The Bayesian Cramér-Rao lower bound in Astrometry

NASA Astrophysics Data System (ADS)

Mendez, R. A.; Echeverria, A.; Silva, J.; Orchard, M.

2018-01-01

A determination of the highest precision that can be achieved in the measurement of the location of a stellar-like object has been a topic of permanent interest by the astrometric community. The so-called (parametric, or non-Bayesian) Cramér-Rao (CR hereafter) bound provides a lower bound for the variance with which one could estimate the position of a point source. This has been studied recently by Mendez et al. (2013, 2014, 2015). In this work we present a different approach to the same problem (Echeverria et al. 2016), using a Bayesian CR setting which has a number of advantages over the parametric scenario.

The Bayesian Cramér-Rao lower bound in Astrometry

NASA Astrophysics Data System (ADS)

Mendez, R. A.; Echeverria, A.; Silva, J.; Orchard, M.

2017-07-01

A determination of the highest precision that can be achieved in the measurement of the location of a stellar-like object has been a topic of permanent interest by the astrometric community. The so-called (parametric, or non-Bayesian) Cramér-Rao (CR hereafter) bound provides a lower bound for the variance with which one could estimate the position of a point source. This has been studied recently by Mendez and collaborators (2014, 2015). In this work we present a different approach to the same problem (Echeverria et al. 2016), using a Bayesian CR setting which has a number of advantages over the parametric scenario.

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.

SynergyFinder: a web application for analyzing drug combination dose-response matrix data.

PubMed

Ianevski, Aleksandr; He, Liye; Aittokallio, Tero; Tang, Jing

2017-08-01

Rational design of drug combinations has become a promising strategy to tackle the drug sensitivity and resistance problem in cancer treatment. To systematically evaluate the pre-clinical significance of pairwise drug combinations, functional screening assays that probe combination effects in a dose-response matrix assay are commonly used. To facilitate the analysis of such drug combination experiments, we implemented a web application that uses key functions of R-package SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user-friendly interface for visualizing the drug combination landscapes in an interactive manner. The SynergyFinder web application is freely accessible at https://synergyfinder.fimm.fi ; The R-package and its source-code are freely available at http://bioconductor.org/packages/release/bioc/html/synergyfinder.html . jing.tang@helsinki.fi. © The Author(s) 2017. Published by Oxford University Press.

Cyber-T web server: differential analysis of high-throughput data.

PubMed

Kayala, Matthew A; Baldi, Pierre

2012-07-01

The Bayesian regularization method for high-throughput differential analysis, described in Baldi and Long (A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001: 17: 509-519) and implemented in the Cyber-T web server, is one of the most widely validated. Cyber-T implements a t-test using a Bayesian framework to compute a regularized variance of the measurements associated with each probe under each condition. This regularized estimate is derived by flexibly combining the empirical measurements with a prior, or background, derived from pooling measurements associated with probes in the same neighborhood. This approach flexibly addresses problems associated with low replication levels and technology biases, not only for DNA microarrays, but also for other technologies, such as protein arrays, quantitative mass spectrometry and next-generation sequencing (RNA-seq). Here we present an update to the Cyber-T web server, incorporating several useful new additions and improvements. Several preprocessing data normalization options including logarithmic and (Variance Stabilizing Normalization) VSN transforms are included. To augment two-sample t-tests, a one-way analysis of variance is implemented. Several methods for multiple tests correction, including standard frequentist methods and a probabilistic mixture model treatment, are available. Diagnostic plots allow visual assessment of the results. The web server provides comprehensive documentation and example data sets. The Cyber-T web server, with R source code and data sets, is publicly available at http://cybert.ics.uci.edu/.

The association between patient-therapist MATRIX congruence and treatment outcome.

PubMed

Mendlovic, Shlomo; Saad, Amit; Roll, Uri; Ben Yehuda, Ariel; Tuval-Mashiah, Rivka; Atzil-Slonim, Dana

2018-03-14

The present study aimed to examine the association between patient-therapist micro-level congruence/incongruence ratio and psychotherapeutic outcome. Nine good- and nine poor-outcome psychodynamic treatments (segregated by comparing pre- and post-treatment BDI-II) were analyzed (N = 18) moment by moment using the MATRIX (total number of MATRIX codes analyzed = 11,125). MATRIX congruence was defined as similar adjacent MATRIX codes. the congruence/incongruence ratio tended to increase as the treatment progressed only in good-outcome treatments. Progression of MATRIX codes' congruence/incongruence ratio is associated with good outcome of psychotherapy.

Evaluation of a neutron spectrum from Bonner spheres measurements using a Bayesian parameter estimation combined with the traditional unfolding methods

NASA Astrophysics Data System (ADS)

Mazrou, H.; Bezoubiri, F.

2018-07-01

In this work, a new program developed under MATLAB environment and supported by the Bayesian software WinBUGS has been combined to the traditional unfolding codes namely MAXED and GRAVEL, to evaluate a neutron spectrum from the Bonner spheres measured counts obtained around a shielded 241AmBe based-neutron irradiator located at a Secondary Standards Dosimetry Laboratory (SSDL) at CRNA. In the first step, the results obtained by the standalone Bayesian program, using a parametric neutron spectrum model based on a linear superposition of three components namely: a thermal-Maxwellian distribution, an epithermal (1/E behavior) and a kind of a Watt fission and Evaporation models to represent the fast component, were compared to those issued from MAXED and GRAVEL assuming a Monte Carlo default spectrum. Through the selection of new upper limits for some free parameters, taking into account the physical characteristics of the irradiation source, of both considered models, good agreement was obtained for investigated integral quantities i.e. fluence rate and ambient dose equivalent rate compared to MAXED and GRAVEL results. The difference was generally below 4% for investigated parameters suggesting, thereby, the reliability of the proposed models. In the second step, the Bayesian results obtained from the previous calculations were used, as initial guess spectra, for the traditional unfolding codes, MAXED and GRAVEL to derive the solution spectra. Here again the results were in very good agreement, confirming the stability of the Bayesian solution.

Mitochondrial genome of Pteronotus personatus (Chiroptera: Mormoopidae): comparison with selected bats and phylogenetic considerations.

PubMed

López-Wilchis, Ricardo; Del Río-Portilla, Miguel Ángel; Guevara-Chumacero, Luis Manuel

2017-02-01

We described the complete mitochondrial genome (mitogenome) of the Wagner's mustached bat, Pteronotus personatus, a species belonging to the family Mormoopidae, and compared it with other published mitogenomes of bats (Chiroptera). The mitogenome of P. personatus was 16,570 bp long and contained a typically conserved structure including 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA genes, and one control region (D-loop). Most of the genes were encoded on the H-strand, except for eight tRNA and the ND6 genes. The order of protein-coding and rRNA genes was highly conserved in all mitogenomes. All protein-coding genes started with an ATG codon, except for ND2, ND3, and ND5, which initiated with ATA, and terminated with the typical stop codon TAA/TAG or the codon AGA. Phylogenetic trees constructed using Maximum Parsimony, Maximum Likelihood, and Bayesian inference methods showed an identical topology and indicated the monophyly of different families of bats (Mormoopidae, Phyllostomidae, Vespertilionidae, Rhinolophidae, and Pteropopidae) and the existence of two major clades corresponding to the suborders Yangochiroptera and Yinpterochiroptera. The mitogenome sequence provided here will be useful for further phylogenetic analyses and population genetic studies in mormoopid bats.

Complete mitochondrial genome of Cuora trifasciata (Chinese three-striped box turtle), and a comparative analysis with other box turtles.

PubMed

Li, Wei; Zhang, Xin-Cheng; Zhao, Jian; Shi, Yan; Zhu, Xin-Ping

2015-01-25

Cuora trifasciata has become one of the most critically endangered species in the world. The complete mitochondrial genome of C. trifasciata (Chinese three-striped box turtle) was determined in this study. Its mitochondrial genome is a 16,575-bp-long circular molecule that consists of 37 genes that are typically found in other vertebrates. And the basic characteristics of the C. trifasciata mitochondrial genome were also determined. Moreover, a comparison of C. trifasciata with Cuora cyclornata, Cuora pani and Cuora aurocapitata indicated that the four mitogenomics differed in length, codons, overlaps, 13 protein-coding genes (PCGs), ND3, rRNA genes, control region, and other aspects. Phylogenetic analysis with Bayesian inference and maximum likelihood based on 12 protein-coding genes of the genus Cuora indicated the phylogenetic position of C. trifasciata within Cuora. The phylogenetic analysis also showed that C. trifasciata from Vietnam and China formed separate monophyletic clades with different Cuora species. The results of nucleotide base compositions, protein-coding genes and phylogenetic analysis showed that C. trifasciata from these two countries may represent different Cuora species. Copyright © 2014 Elsevier B.V. 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

The mitochondrial genome of Elodia flavipalpis Aldrich (Diptera: Tachinidae) and the evolutionary timescale of Tachinid flies.

PubMed

Zhao, Zhe; Su, Tian-Juan; Chesters, Douglas; Wang, Shi-di; Ho, Simon Y W; Zhu, Chao-Dong; Chen, Xiao-Lin; Zhang, Chun-Tian

2013-01-01

Tachinid flies are natural enemies of many lepidopteran and coleopteran pests of forests, crops, and fruit trees. In order to address the lack of genetic data in this economically important group, we sequenced the complete mitochondrial genome of the Palaearctic tachinid fly Elodia flavipalpis Aldrich, 1933. Usually found in Northern China and Japan, this species is one of the primary natural enemies of the leaf-roller moths (Tortricidae), which are major pests of various fruit trees. The 14,932-bp mitochondrial genome was typical of Diptera, with 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes. However, its control region is only 105 bp in length, which is the shortest found so far in flies. In order to estimate dipteran evolutionary relationships, we conducted a phylogenetic analysis of 58 mitochondrial genomes from 23 families. Maximum-likelihood and Bayesian methods supported the monophyly of both Tachinidae and superfamily Oestroidea. Within the subsection Calyptratae, Muscidae was inferred as the sister group to Oestroidea. Within Oestroidea, Calliphoridae and Sarcophagidae formed a sister clade to Oestridae and Tachinidae. Using a Bayesian relaxed clock calibrated with fossil data, we estimated that Tachinidae originated in the middle Eocene.

The Mitochondrial Genome of Elodia flavipalpis Aldrich (Diptera: Tachinidae) and the Evolutionary Timescale of Tachinid Flies

PubMed Central

Zhao, Zhe; Su, Tian-juan; Chesters, Douglas; Wang, Shi-di; Ho, Simon Y. W.; Zhu, Chao-dong; Chen, Xiao-lin; Zhang, Chun-tian

2013-01-01

Tachinid flies are natural enemies of many lepidopteran and coleopteran pests of forests, crops, and fruit trees. In order to address the lack of genetic data in this economically important group, we sequenced the complete mitochondrial genome of the Palaearctic tachinid fly Elodia flavipalpis Aldrich, 1933. Usually found in Northern China and Japan, this species is one of the primary natural enemies of the leaf-roller moths (Tortricidae), which are major pests of various fruit trees. The 14,932-bp mitochondrial genome was typical of Diptera, with 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes. However, its control region is only 105 bp in length, which is the shortest found so far in flies. In order to estimate dipteran evolutionary relationships, we conducted a phylogenetic analysis of 58 mitochondrial genomes from 23 families. Maximum-likelihood and Bayesian methods supported the monophyly of both Tachinidae and superfamily Oestroidea. Within the subsection Calyptratae, Muscidae was inferred as the sister group to Oestroidea. Within Oestroidea, Calliphoridae and Sarcophagidae formed a sister clade to Oestridae and Tachinidae. Using a Bayesian relaxed clock calibrated with fossil data, we estimated that Tachinidae originated in the middle Eocene. PMID:23626734

Eddington's demon: inferring galaxy mass functions and other distributions from uncertain data

NASA Astrophysics Data System (ADS)

Obreschkow, D.; Murray, S. G.; Robotham, A. S. G.; Westmeier, T.

2018-03-01

We present a general modified maximum likelihood (MML) method for inferring generative distribution functions from uncertain and biased data. The MML estimator is identical to, but easier and many orders of magnitude faster to compute than the solution of the exact Bayesian hierarchical modelling of all measurement errors. As a key application, this method can accurately recover the mass function (MF) of galaxies, while simultaneously dealing with observational uncertainties (Eddington bias), complex selection functions and unknown cosmic large-scale structure. The MML method is free of binning and natively accounts for small number statistics and non-detections. Its fast implementation in the R-package dftools is equally applicable to other objects, such as haloes, groups, and clusters, as well as observables other than mass. The formalism readily extends to multidimensional distribution functions, e.g. a Choloniewski function for the galaxy mass-angular momentum distribution, also handled by dftools. The code provides uncertainties and covariances for the fitted model parameters and approximate Bayesian evidences. We use numerous mock surveys to illustrate and test the MML method, as well as to emphasize the necessity of accounting for observational uncertainties in MFs of modern galaxy surveys.

MC1R Genotype and Plumage Colouration in the Zebra Finch (Taeniopygia guttata): Population Structure Generates Artefactual Associations

PubMed Central

Hoffman, Joseph I.; Krause, E. Tobias; Lehmann, Katrin; Krüger, Oliver

2014-01-01

Polymorphisms at the melanocortin-1 receptor (MC1R) gene have been linked to coloration in many vertebrate species. However, the potentially confounding influence of population structure has rarely been controlled for. We explored the role of the MC1R in a model avian system by sequencing the coding region in 162 zebra finches comprising 79 wild type and 83 white individuals from five stocks. Allelic counts differed significantly between the two plumage morphs at multiple segregating sites, but these were mostly synonymous. To provide a control, the birds were genotyped at eight microsatellites and subjected to Bayesian cluster analysis, revealing two distinct groups. We therefore crossed wild type with white individuals and backcrossed the F1s with white birds. No significant associations were detected in the resulting offspring, suggesting that our original findings were a byproduct of genome-wide divergence. Our results are consistent with a previous study that found no association between MC1R polymorphism and plumage coloration in leaf warblers. They also contribute towards a growing body of evidence suggesting that care should be taken to quantify, and where necessary control for, population structure in association studies. PMID:24489736

Asteroid orbital error analysis: Theory and application

NASA Technical Reports Server (NTRS)

Muinonen, K.; Bowell, Edward

1992-01-01

We present a rigorous Bayesian theory for asteroid orbital error estimation in which the probability density of the orbital elements is derived from the noise statistics of the observations. For Gaussian noise in a linearized approximation the probability density is also Gaussian, and the errors of the orbital elements at a given epoch are fully described by the covariance matrix. The law of error propagation can then be applied to calculate past and future positional uncertainty ellipsoids (Cappellari et al. 1976, Yeomans et al. 1987, Whipple et al. 1991). To our knowledge, this is the first time a Bayesian approach has been formulated for orbital element estimation. In contrast to the classical Fisherian school of statistics, the Bayesian school allows a priori information to be formally present in the final estimation. However, Bayesian estimation does give the same results as Fisherian estimation when no priori information is assumed (Lehtinen 1988, and reference therein).

Expression of versican 3'-untranslated region modulates endogenous microRNA functions.

PubMed

Lee, Daniel Y; Jeyapalan, Zina; Fang, Ling; Yang, Jennifer; Zhang, Yaou; Yee, Albert Y; Li, Minhui; Du, William W; Shatseva, Tatiana; Yang, Burton B

2010-10-25

Mature microRNAs (miRNAs) are single-stranded RNAs that regulate post-transcriptional gene expression. In our previous study, we have shown that versican 3'UTR, a fragment of non-coding transcript, has the ability to antagonize miR-199a-3p function thereby regulating expression of the matrix proteins versican and fibronectin, and thus resulting in enhanced cell-cell adhesion and organ adhesion. However, the impact of this non-coding fragment on tumorigenesis is yet to be determined. Using computational prediction confirmed with in vitro and in vivo experiments, we report that the expression of versican 3'UTR not only antagonizes miR-199a-3p but can also lower its steady state expression. We found that expression of versican 3'UTR in a mouse breast carcinoma cell line, 4T1, decreased miR-199a-3p levels. The decrease in miRNA activity consequently translated into differences in tumor growth. Computational analysis indicated that both miR-199a-3p and miR-144 targeted a cell cycle regulator, Rb1. In addition, miR-144 and miR-136, which have also been shown to interact with versican 3'UTR, was found to target PTEN. Expression of Rb1 and PTEN were up-regulated synergistically in vitro and in vivo, suggesting that the 3'UTR binds and modulates miRNA activities, freeing Rb1 and PTEN mRNAs for translation. In tumor formation assays, cells transfected with the 3'UTR formed smaller tumors compared with cells transfected with a control vector. Our results demonstrated that a 3'UTR fragment can be used to modulate miRNA functions. Our study also suggests that miRNAs in the cancer cells are more susceptible to degradation, due to its interaction with a non-coding 3'UTR. This non-coding component of mRNA may be used retrospectively to modulate miRNA activities.

Case studies in Bayesian microbial risk assessments.

PubMed

Kennedy, Marc C; Clough, Helen E; Turner, Joanne

2009-12-21

The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates. We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from a dietary survey. Finally we accounted for uncertainty in the dose-response relationship and uncertainty due to limited incidence data to derive uncertainty about yearly incidences of illness in young children. Options for altering the risk were considered by running the model with different hypothetical policy-driven exposure scenarios. In the second case study we illustrate an efficient Bayesian sensitivity analysis for identifying the most important parameters of a complex computer code that simulated VTEC O157 prevalence within a managed dairy herd. This was carried out in 2 stages, first to screen out the unimportant inputs, then to perform a more detailed analysis on the remaining inputs. The method works by building a Bayesian statistical approximation to the computer code using a number of known code input/output pairs (training runs). We estimated that the expected total number of children aged 1.5-4.5 who become ill due to VTEC O157 in milk is 8.6 per year, with 95% uncertainty interval (0,11.5). The most extreme policy we considered was banning on-farm pasteurisation of milk, which reduced the estimate to 6.4 with 95% interval (0,11). In the second case study the effective number of inputs was reduced from 30 to 7 in the screening stage, and just 2 inputs were found to explain 82.8% of the output variance. A combined total of 500 runs of the computer code were used. These case studies illustrate the use of Bayesian statistics to perform detailed uncertainty and sensitivity analyses, integrating multiple information sources in a way that is both rigorous and efficient.

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.

spMC: an R-package for 3D lithological reconstructions based on spatial Markov chains

NASA Astrophysics Data System (ADS)

Sartore, Luca; Fabbri, Paolo; Gaetan, Carlo

2016-09-01

The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation/prediction located in a plain site of Northeastern Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, simulation methods based on most known prediction methods (as indicator Kriging and CoKriging) were implemented in spMC package. Moreover, other more advanced methods are available for simulations, e.g. path methods and Bayesian procedures, that exploit the maximum entropy. Since the spMC package was developed for intensive geostatistical computations, part of the code is implemented for parallel computations via the OpenMP constructs. A final analysis of this computational efficiency compares the simulation/prediction algorithms by using different numbers of CPU cores, and considering the example data set of the case study included in the package.

Photo-z-SQL: Integrated, flexible photometric redshift computation in a database

NASA Astrophysics Data System (ADS)

Beck, R.; Dobos, L.; Budavári, T.; Szalay, A. S.; Csabai, I.

2017-04-01

We present a flexible template-based photometric redshift estimation framework, implemented in C#, that can be seamlessly integrated into a SQL database (or DB) server and executed on-demand in SQL. The DB integration eliminates the need to move large photometric datasets outside a database for redshift estimation, and utilizes the computational capabilities of DB hardware. The code is able to perform both maximum likelihood and Bayesian estimation, and can handle inputs of variable photometric filter sets and corresponding broad-band magnitudes. It is possible to take into account the full covariance matrix between filters, and filter zero points can be empirically calibrated using measurements with given redshifts. The list of spectral templates and the prior can be specified flexibly, and the expensive synthetic magnitude computations are done via lazy evaluation, coupled with a caching of results. Parallel execution is fully supported. For large upcoming photometric surveys such as the LSST, the ability to perform in-place photo-z calculation would be a significant advantage. Also, the efficient handling of variable filter sets is a necessity for heterogeneous databases, for example the Hubble Source Catalog, and for cross-match services such as SkyQuery. We illustrate the performance of our code on two reference photo-z estimation testing datasets, and provide an analysis of execution time and scalability with respect to different configurations. The code is available for download at https://github.com/beckrob/Photo-z-SQL.

VizieR Online Data Catalog: Planetary atmosphere radiative transport code (Garcia Munoz+ 2015)

NASA Astrophysics Data System (ADS)

Garcia Munoz, A.; Mills, F. P.

2014-08-01

Files are: * readme.txt * Input files: INPUThazeL.txt, INPUTL13.txt, INPUT_L60.txt; they contain explanations to the input parameters. Copy INPUT_XXXX.txt into INPUT.dat to execute some of the examples described in the reference. * Files with scattering matrix properties: phFhazeL.txt, phFL13.txt, phF_L60.txt * Script for compilation in GFortran (myscript) (10 data files).

Resonance Parameter Adjustment Based on Integral Experiments

DOE PAGES

Sobes, Vladimir; Leal, Luiz; Arbanas, Goran; ...

2016-06-02

Our project seeks to allow coupling of differential and integral data evaluation in a continuous-energy framework and to use the generalized linear least-squares (GLLS) methodology in the TSURFER module of the SCALE code package to update the parameters of a resolved resonance region evaluation. We recognize that the GLLS methodology in TSURFER is identical to the mathematical description of a Bayesian update in SAMMY, the SAMINT code was created to use the mathematical machinery of SAMMY to update resolved resonance parameters based on integral data. Traditionally, SAMMY used differential experimental data to adjust nuclear data parameters. Integral experimental data, suchmore » as in the International Criticality Safety Benchmark Experiments Project, remain a tool for validation of completed nuclear data evaluations. SAMINT extracts information from integral benchmarks to aid the nuclear data evaluation process. Later, integral data can be used to resolve any remaining ambiguity between differential data sets, highlight troublesome energy regions, determine key nuclear data parameters for integral benchmark calculations, and improve the nuclear data covariance matrix evaluation. Moreover, SAMINT is not intended to bias nuclear data toward specific integral experiments but should be used to supplement the evaluation of differential experimental data. Using GLLS ensures proper weight is given to the differential data.« less

BAYESIAN METHODS FOR REGIONAL-SCALE EUTROPHICATION MODELS. (R830887)

EPA Science Inventory

We demonstrate a Bayesian classification and regression tree (CART) approach to link multiple environmental stressors to biological responses and quantify uncertainty in model predictions. Such an approach can: (1) report prediction uncertainty, (2) be consistent with the amou...

Joint design of QC-LDPC codes for coded cooperation system with joint iterative decoding

NASA Astrophysics Data System (ADS)

Zhang, Shunwai; Yang, Fengfan; Tang, Lei; Ejaz, Saqib; Luo, Lin; Maharaj, B. T.

2016-03-01

In this paper, we investigate joint design of quasi-cyclic low-density-parity-check (QC-LDPC) codes for coded cooperation system with joint iterative decoding in the destination. First, QC-LDPC codes based on the base matrix and exponent matrix are introduced, and then we describe two types of girth-4 cycles in QC-LDPC codes employed by the source and relay. In the equivalent parity-check matrix corresponding to the jointly designed QC-LDPC codes employed by the source and relay, all girth-4 cycles including both type I and type II are cancelled. Theoretical analysis and numerical simulations show that the jointly designed QC-LDPC coded cooperation well combines cooperation gain and channel coding gain, and outperforms the coded non-cooperation under the same conditions. Furthermore, the bit error rate performance of the coded cooperation employing jointly designed QC-LDPC codes is better than those of random LDPC codes and separately designed QC-LDPC codes over AWGN channels.

Mads.jl

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

Vesselinov, Velimir; O'Malley, Daniel; Lin, Youzuo

2016-07-01

Mads.jl (Model analysis and decision support in Julia) is a code that streamlines the process of using data and models for analysis and decision support. It is based on another open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11- 035). Mads.jl can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. It enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. The code also can use a series of alternative adaptive computational techniques for Bayesian sampling, Monte Carlo,more » and Bayesian Information-Gap Decision Theory. The code is implemented in the Julia programming language, and has high-performance (parallel) and memory management capabilities. The code uses a series of third party modules developed by others. The code development will also include contributions to the existing third party modules written in Julia; this contributions will be important for the efficient implementation of the algorithm used by Mads.jl. The code also uses a series of LANL developed modules that are developed by Dan O'Malley; these modules will be also a part of the Mads.jl release. Mads.jl will be released under GPL V3 license. The code will be distributed as a Git repo at gitlab.com and github.com. Mads.jl manual and documentation will be posted at madsjulia.lanl.gov.« less

Efficient Matrix Models for Relational Learning

DTIC Science & Technology

2009-10-01

74 4.5.3 Comparison to pLSI- pHITS . . . . . . . . . . . . . . . . . . . . 76 5 Hierarchical Bayesian Collective...Behaviour of Newton vs. Stochastic Newton on a three-factor model. 4.5.3 Comparison to pLSI- pHITS Caveat: Collective Matrix Factorization makes no guarantees...leads to better results; and another where a co-clustering model, pLSI- pHITS , has the advantage. pLSI- pHITS [24] is a relational clustering technique

EFFICIENT MODEL-FITTING AND MODEL-COMPARISON FOR HIGH-DIMENSIONAL BAYESIAN GEOSTATISTICAL MODELS. (R826887)

EPA Science Inventory

Geostatistical models are appropriate for spatially distributed data measured at irregularly spaced locations. We propose an efficient Markov chain Monte Carlo (MCMC) algorithm for fitting Bayesian geostatistical models with substantial numbers of unknown parameters to sizable...

Cross-view gait recognition using joint Bayesian

NASA Astrophysics Data System (ADS)

Li, Chao; Sun, Shouqian; Chen, Xiaoyu; Min, Xin

2017-07-01

Human gait, as a soft biometric, helps to recognize people by walking. To further improve the recognition performance under cross-view condition, we propose Joint Bayesian to model the view variance. We evaluated our prosed method with the largest population (OULP) dataset which makes our result reliable in a statically way. As a result, we confirmed our proposed method significantly outperformed state-of-the-art approaches for both identification and verification tasks. Finally, sensitivity analysis on the number of training subjects was conducted, we find Joint Bayesian could achieve competitive results even with a small subset of training subjects (100 subjects). For further comparison, experimental results, learning models, and test codes are available.

Population-level differences in disease transmission: A Bayesian analysis of multiple smallpox epidemics

PubMed Central

Elderd, Bret D.; Dwyer, Greg; Dukic, Vanja

2013-01-01

Estimates of a disease’s basic reproductive rate R0 play a central role in understanding outbreaks and planning intervention strategies. In many calculations of R0, a simplifying assumption is that different host populations have effectively identical transmission rates. This assumption can lead to an underestimate of the overall uncertainty associated with R0, which, due to the non-linearity of epidemic processes, may result in a mis-estimate of epidemic intensity and miscalculated expenditures associated with public-health interventions. In this paper, we utilize a Bayesian method for quantifying the overall uncertainty arising from differences in population-specific basic reproductive rates. Using this method, we fit spatial and non-spatial susceptible-exposed-infected-recovered (SEIR) models to a series of 13 smallpox outbreaks. Five outbreaks occurred in populations that had been previously exposed to smallpox, while the remaining eight occurred in Native-American populations that were naïve to the disease at the time. The Native-American outbreaks were close in a spatial and temporal sense. Using Bayesian Information Criterion (BIC), we show that the best model includes population-specific R0 values. These differences in R0 values may, in part, be due to differences in genetic background, social structure, or food and water availability. As a result of these inter-population differences, the overall uncertainty associated with the “population average” value of smallpox R0 is larger, a finding that can have important consequences for controlling epidemics. In general, Bayesian hierarchical models are able to properly account for the uncertainty associated with multiple epidemics, provide a clearer understanding of variability in epidemic dynamics, and yield a better assessment of the range of potential risks and consequences that decision makers face. PMID:24021521

User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.

NASA Technical Reports Server (NTRS)

Reddy, C. J.

2000-01-01

PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.

Surface acoustic wave coding for orthogonal frequency coded devices

NASA Technical Reports Server (NTRS)

Malocha, Donald (Inventor); Kozlovski, Nikolai (Inventor)

2011-01-01

Methods and systems for coding SAW OFC devices to mitigate code collisions in a wireless multi-tag system. Each device producing plural stepped frequencies as an OFC signal with a chip offset delay to increase code diversity. A method for assigning a different OCF to each device includes using a matrix based on the number of OFCs needed and the number chips per code, populating each matrix cell with OFC chip, and assigning the codes from the matrix to the devices. The asynchronous passive multi-tag system includes plural surface acoustic wave devices each producing a different OFC signal having the same number of chips and including a chip offset time delay, an algorithm for assigning OFCs to each device, and a transceiver to transmit an interrogation signal and receive OFC signals in response with minimal code collisions during transmission.

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

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

Chowdhary, Kenny; Najm, Habib N.

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition)more » on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.« less

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

DOE PAGES

Chowdhary, Kenny; Najm, Habib N.

2016-04-13

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition)more » on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.« less

Soft decoding a self-dual (48, 24; 12) code

NASA Technical Reports Server (NTRS)

Solomon, G.

1993-01-01

A self-dual (48,24;12) code comes from restricting a binary cyclic (63,18;36) code to a 6 x 7 matrix, adding an eighth all-zero column, and then adjoining six dimensions to this extended 6 x 8 matrix. These six dimensions are generated by linear combinations of row permutations of a 6 x 8 matrix of weight 12, whose sums of rows and columns add to one. A soft decoding using these properties and approximating maximum likelihood is presented here. This is preliminary to a possible soft decoding of the box (72,36;15) code that promises a 7.7-dB theoretical coding under maximum likelihood.

A Rapid Item-Search Procedure for Bayesian Adaptive Testing.

DTIC Science & Technology

1977-05-01

properties of the • procedure , they migh t well introduce undesirable psychological effects on test scores (e.g., Betz & Weiss , 1976r.’ , 1976b...ge of results and adaptive ability test .~~~~ (Research Rep . 76—4). Minneapolis: University of Minnesota , Departmen t of Psychology , Psychometric...t~~[AH ~~~ ~~~~ r _ _ _ _ A RAPID ITEM -SEARC H PROCEDURE FOR BAYESIAN ADAPTIVE TESTING C. David Vale d D D Can David J . Weiss RESEARCH REPORT 77-n

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.

Drug-target interaction prediction: A Bayesian ranking approach.

PubMed

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug-centric repositioning scenarios. BRDTI Software and supplementary materials are available online at www.ksi.mff.cuni.cz/∼peska/BRDTI. Copyright © 2017 Elsevier B.V. All rights reserved.

A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging

PubMed Central

Zhang, Yin; Zhang, Yongchao; Huang, Yulin; Yang, Jianyu

2017-01-01

This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model. Then, based on the Bayesian criterion, the widely-used sparse regularization is considered as the penalty term to recover the target distribution. The derivation of the cost function is described, and finally, an iterative expression for minimizing this function is presented. Alternatively, this paper discusses how to estimate the single parameter of Gaussian noise. With the advantage of a more accurate model, the proposed sparse Bayesian approach enjoys a lower model error. Meanwhile, when compared with the conventional superresolution methods, the proposed approach shows high cross-range resolution and small location error. The superresolution results for the simulated point target, scene data, and real measured data are presented to demonstrate the superior performance of the proposed approach. PMID:28604583

Exact Bayesian Inference for Phylogenetic Birth-Death Models.

PubMed

Parag, K V; Pybus, O G

2018-04-26

Inferring the rates of change of a population from a reconstructed phylogeny of genetic sequences is a central problem in macro-evolutionary biology, epidemiology, and many other disciplines. A popular solution involves estimating the parameters of a birth-death process (BDP), which links the shape of the phylogeny to its birth and death rates. Modern BDP estimators rely on random Markov chain Monte Carlo (MCMC) sampling to infer these rates. Such methods, while powerful and scalable, cannot be guaranteed to converge, leading to results that may be hard to replicate or difficult to validate. We present a conceptually and computationally different parametric BDP inference approach using flexible and easy to implement Snyder filter (SF) algorithms. This method is deterministic so its results are provable, guaranteed, and reproducible. We validate the SF on constant rate BDPs and find that it solves BDP likelihoods known to produce robust estimates. We then examine more complex BDPs with time-varying rates. Our estimates compare well with a recently developed parametric MCMC inference method. Lastly, we performmodel selection on an empirical Agamid species phylogeny, obtaining results consistent with the literature. The SF makes no approximations, beyond those required for parameter quantisation and numerical integration, and directly computes the posterior distribution of model parameters. It is a promising alternative inference algorithm that may serve either as a standalone Bayesian estimator or as a useful diagnostic reference for validating more involved MCMC strategies. The Snyder filter is implemented in Matlab and the time-varying BDP models are simulated in R. The source code and data are freely available at https://github.com/kpzoo/snyder-birth-death-code. kris.parag@zoo.ox.ac.uk. Supplementary material is available at Bioinformatics online.

Application of Quantum Gauss-Jordan Elimination Code to Quantum Secret Sharing Code

NASA Astrophysics Data System (ADS)

Diep, Do Ngoc; Giang, Do Hoang; Phu, Phan Huy

2017-12-01

The QSS codes associated with a MSP code are based on finding an invertible matrix V, solving the system vATMB (s a) = s. We propose a quantum Gauss-Jordan Elimination Procedure to produce such a pivotal matrix V by using the Grover search code. The complexity of solving is of square-root order of the cardinal number of the unauthorized set √ {2^{|B|}}.

Application of Quantum Gauss-Jordan Elimination Code to Quantum Secret Sharing Code

NASA Astrophysics Data System (ADS)

Diep, Do Ngoc; Giang, Do Hoang; Phu, Phan Huy

2018-03-01

The QSS codes associated with a MSP code are based on finding an invertible matrix V, solving the system vATMB (s a)=s. We propose a quantum Gauss-Jordan Elimination Procedure to produce such a pivotal matrix V by using the Grover search code. The complexity of solving is of square-root order of the cardinal number of the unauthorized set √ {2^{|B|}}.

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.

PubMed

Baptista, Helena; Mendes, Jorge M; MacNab, Ying C; Xavier, Miguel; Caldas-de-Almeida, José

2016-08-01

Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by "similarity" with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models. © The Author(s) 2016.

Bit Error Probability for Maximum Likelihood Decoding of Linear Block Codes

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc P. C.; Rhee, Dojun

1996-01-01

In this paper, the bit error probability P(sub b) for maximum likelihood decoding of binary linear codes is investigated. The contribution of each information bit to P(sub b) is considered. For randomly generated codes, it is shown that the conventional approximation at high SNR P(sub b) is approximately equal to (d(sub H)/N)P(sub s), where P(sub s) represents the block error probability, holds for systematic encoding only. Also systematic encoding provides the minimum P(sub b) when the inverse mapping corresponding to the generator matrix of the code is used to retrieve the information sequence. The bit error performances corresponding to other generator matrix forms are also evaluated. Although derived for codes with a generator matrix randomly generated, these results are shown to provide good approximations for codes used in practice. Finally, for decoding methods which require a generator matrix with a particular structure such as trellis decoding or algebraic-based soft decision decoding, equivalent schemes that reduce the bit error probability are discussed.

Hypercube matrix computation task

NASA Technical Reports Server (NTRS)

Calalo, Ruel H.; Imbriale, William A.; Jacobi, Nathan; Liewer, Paulett C.; Lockhart, Thomas G.; Lyzenga, Gregory A.; Lyons, James R.; Manshadi, Farzin; Patterson, Jean E.

1988-01-01

A major objective of the Hypercube Matrix Computation effort at the Jet Propulsion Laboratory (JPL) is to investigate the applicability of a parallel computing architecture to the solution of large-scale electromagnetic scattering problems. Three scattering analysis codes are being implemented and assessed on a JPL/California Institute of Technology (Caltech) Mark 3 Hypercube. The codes, which utilize different underlying algorithms, give a means of evaluating the general applicability of this parallel architecture. The three analysis codes being implemented are a frequency domain method of moments code, a time domain finite difference code, and a frequency domain finite elements code. These analysis capabilities are being integrated into an electromagnetics interactive analysis workstation which can serve as a design tool for the construction of antennas and other radiating or scattering structures. The first two years of work on the Hypercube Matrix Computation effort is summarized. It includes both new developments and results as well as work previously reported in the Hypercube Matrix Computation Task: Final Report for 1986 to 1987 (JPL Publication 87-18).

Matrix-Product-State Algorithm for Finite Fractional Quantum Hall Systems

NASA Astrophysics Data System (ADS)

Liu, Zhao; Bhatt, R. N.

2015-09-01

Exact diagonalization is a powerful tool to study fractional quantum Hall (FQH) systems. However, its capability is limited by the exponentially increasing computational cost. In order to overcome this difficulty, density-matrix-renormalization-group (DMRG) algorithms were developed for much larger system sizes. Very recently, it was realized that some model FQH states have exact matrix-product-state (MPS) representation. Motivated by this, here we report a MPS code, which is closely related to, but different from traditional DMRG language, for finite FQH systems on the cylinder geometry. By representing the many-body Hamiltonian as a matrix-product-operator (MPO) and using single-site update and density matrix correction, we show that our code can efficiently search the ground state of various FQH systems. We also compare the performance of our code with traditional DMRG. The possible generalization of our code to infinite FQH systems and other physical systems is also discussed.

A computer code for calculations in the algebraic collective model of the atomic nucleus

NASA Astrophysics Data System (ADS)

Welsh, T. A.; Rowe, D. J.

2016-03-01

A Maple code is presented for algebraic collective model (ACM) calculations. The ACM is an algebraic version of the Bohr model of the atomic nucleus, in which all required matrix elements are derived by exploiting the model's SU(1 , 1) × SO(5) dynamical group. This paper reviews the mathematical formulation of the ACM, and serves as a manual for the code. The code enables a wide range of model Hamiltonians to be analysed. This range includes essentially all Hamiltonians that are rational functions of the model's quadrupole moments qˆM and are at most quadratic in the corresponding conjugate momenta πˆN (- 2 ≤ M , N ≤ 2). The code makes use of expressions for matrix elements derived elsewhere and newly derived matrix elements of the operators [ π ˆ ⊗ q ˆ ⊗ π ˆ ] 0 and [ π ˆ ⊗ π ˆ ] LM. The code is made efficient by use of an analytical expression for the needed SO(5)-reduced matrix elements, and use of SO(5) ⊃ SO(3) Clebsch-Gordan coefficients obtained from precomputed data files provided with the code.

The first complete organellar genomes of an Antarctic red alga, Pyropia endiviifolia: insights into its genome architecture and phylogenetic position within genus Pyropia (Bangiales, Rhodophyta)

NASA Astrophysics Data System (ADS)

Xu, Kuipeng; Tang, Xianghai; Bi, Guiqi; Cao, Min; Wang, Lu; Mao, Yunxiang

2017-08-01

Pyropia species grow in the intertidal zone and are cold-water adapted. To date, most of the information about the whole plastid and mitochondrial genomes (ptDNA and mtDNA) of this genus is limited to Northern Hemisphere species. Here, we report the sequencing of the ptDNA and mtDNA of the Antarctic red alga Pyropia endiviifolia using the Illumina platform. The plastid genome (195 784 bp, 33.28% GC content) contains 210 protein-coding genes, 37 tRNA genes and 6 rRNA genes. The mitochondrial genome (34 603 bp, 30.5% GC content) contains 26 protein-coding genes, 25 tRNA genes and 2 rRNA genes. Our results suggest that the organellar genomes of Py. endiviifolia have a compact organization. Although the collinearity of these genomes is conserved compared with other Pyropia species, the genome sizes show significant differences, mainly because of the different copy numbers of rDNA operons in the ptDNA and group II introns in the mtDNA. The other Pyropia species have 2u20133 distinct intronic ORFs in their cox 1 genes, but Py. endiviifolia has no introns in its cox 1 gene. This has led to a smaller mtDNA than in other Pyropia species. The phylogenetic relationships within Pyropia were examined using concatenated gene sets from most of the available organellar genomes with both the maximum likelihood and Bayesian methods. The analysis revealed a sister taxa affiliation between the Antarctic species Py. endiviifolia and the North American species Py. kanakaensis.

LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models

USGS Publications Warehouse

Winslow, Luke; Zwart, Jacob A.; Batt, Ryan D.; Dugan, Hilary; Woolway, R. Iestyn; Corman, Jessica; Hanson, Paul C.; Read, Jordan S.

2016-01-01

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.

Genotypic Characterization of Rickettsia bellii Reveals Distinct Lineages in the United States and South America.

PubMed

Krawczak, Felipe S; Labruna, Marcelo B; Hecht, Joy A; Paddock, Christopher D; Karpathy, Sandor E

2018-01-01

The bacterium Rickettsia bellii belongs to a basal group of rickettsiae that diverged prior to the pathogenic spotted fever group and typhus group Rickettsia species. Despite a diverse representation of R. bellii across more than 25 species of hard and soft ticks in the American continent, phylogeographical relationships among strains of this basal group- Rickettsia species are unknown; the work described here explores these relationships. DNA was extracted from 30 R. bellii tick isolates: 15 from the United States, 14 from Brazil, and 1 from Argentina. A total of 2,269 aligned nucleotide sites of 3 protein coding genes ( glt A, atp A, and cox A) and 2 intergenic regions ( rpm E -tRN A fmet and RC1027-xth A 2 ) were concatenated and subjected to phylogenetic analysis by Bayesian methods. Results showed a separation of almost all isolates between North and South Americas, suggesting that they have radiated within their respective continents. Phylogenetic positions of the 30 isolates could be a result of not only their geographical origin but also the tick hosts they have coevolved with. Whether R. bellii originated with ticks in North or South America remains obscure, as our analyses did not show evidence for greater genetic divergence of R. bellii in either continent.

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...

Pig Data and Bayesian Inference on Multinomial Probabilities

ERIC Educational Resources Information Center

Kern, John C.

2006-01-01

Bayesian inference on multinomial probabilities is conducted based on data collected from the game Pass the Pigs[R]. Prior information on these probabilities is readily available from the instruction manual, and is easily incorporated in a Dirichlet prior. Posterior analysis of the scoring probabilities quantifies the discrepancy between empirical…

A new software for deformation source optimization, the Bayesian Earthquake Analysis Tool (BEAT)

NASA Astrophysics Data System (ADS)

Vasyura-Bathke, H.; Dutta, R.; Jonsson, S.; Mai, P. M.

2017-12-01

Modern studies of crustal deformation and the related source estimation, including magmatic and tectonic sources, increasingly use non-linear optimization strategies to estimate geometric and/or kinematic source parameters and often consider both jointly, geodetic and seismic data. Bayesian inference is increasingly being used for estimating posterior distributions of deformation source model parameters, given measured/estimated/assumed data and model uncertainties. For instance, some studies consider uncertainties of a layered medium and propagate these into source parameter uncertainties, while others use informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed to efficiently explore the high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational burden of these methods is high and estimation codes are rarely made available along with the published results. Even if the codes are accessible, it is usually challenging to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in deformation source estimations, we undertook the effort of developing BEAT, a python package that comprises all the above-mentioned features in one single programing environment. The package builds on the pyrocko seismological toolbox (www.pyrocko.org), and uses the pymc3 module for Bayesian statistical model fitting. BEAT is an open-source package (https://github.com/hvasbath/beat), and we encourage and solicit contributions to the project. Here, we present our strategy for developing BEAT and show application examples; especially the effect of including the model prediction uncertainty of the velocity model in following source optimizations: full moment tensor, Mogi source, moderate strike-slip earth-quake.

Bayesian Approach to the Joint Inversion of Gravity and Magnetic Data, with Application to the Ismenius Area of Mars

NASA Technical Reports Server (NTRS)

Jewell, Jeffrey B.; Raymond, C.; Smrekar, S.; Millbury, C.

2004-01-01

This viewgraph presentation reviews a Bayesian approach to the inversion of gravity and magnetic data with specific application to the Ismenius Area of Mars. Many inverse problems encountered in geophysics and planetary science are well known to be non-unique (i.e. inversion of gravity the density structure of a body). In hopes of reducing the non-uniqueness of solutions, there has been interest in the joint analysis of data. An example is the joint inversion of gravity and magnetic data, with the assumption that the same physical anomalies generate both the observed magnetic and gravitational anomalies. In this talk, we formulate the joint analysis of different types of data in a Bayesian framework and apply the formalism to the inference of the density and remanent magnetization structure for a local region in the Ismenius area of Mars. The Bayesian approach allows prior information or constraints in the solutions to be incorporated in the inversion, with the "best" solutions those whose forward predictions most closely match the data while remaining consistent with assumed constraints. The application of this framework to the inversion of gravity and magnetic data on Mars reveals two typical challenges - the forward predictions of the data have a linear dependence on some of the quantities of interest, and non-linear dependence on others (termed the "linear" and "non-linear" variables, respectively). For observations with Gaussian noise, a Bayesian approach to inversion for "linear" variables reduces to a linear filtering problem, with an explicitly computable "error" matrix. However, for models whose forward predictions have non-linear dependencies, inference is no longer given by such a simple linear problem, and moreover, the uncertainty in the solution is no longer completely specified by a computable "error matrix". It is therefore important to develop methods for sampling from the full Bayesian posterior to provide a complete and statistically consistent picture of model uncertainty, and what has been learned from observations. We will discuss advanced numerical techniques, including Monte Carlo Markov

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).

PyMC: Bayesian Stochastic Modelling in Python

PubMed Central

Patil, Anand; Huard, David; Fonnesbeck, Christopher J.

2010-01-01

This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques. PMID:21603108

Bayesian inference for psychology. Part II: Example applications with JASP.

PubMed

Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D

2018-02-01

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

MATRIX DISCRIMINANT ANALYSIS WITH APPLICATION TO COLORIMETRIC SENSOR ARRAY DATA

PubMed Central

Suslick, Kenneth S.

2014-01-01

With the rapid development of nano-technology, a “colorimetric sensor array” (CSA) which is referred to as an optical electronic nose has been developed for the identification of toxicants. Unlike traditional sensors which rely on a single chemical interaction, CSA can measure multiple chemical interactions by using chemo-responsive dyes. The color changes of the chemo-responsive dyes are recorded before and after exposure to toxicants and serve as a template for classification. The color changes are digitalized in the form of a matrix with rows representing dye effects and columns representing the spectrum of colors. Thus, matrix-classification methods are highly desirable. In this article, we develop a novel classification method, matrix discriminant analysis (MDA), which is a generalization of linear discriminant analysis (LDA) for the data in matrix form. By incorporating the intrinsic matrix-structure of the data in discriminant analysis, the proposed method can improve CSA’s sensitivity and more importantly, specificity. A penalized MDA method, PMDA, is also introduced to further incorporate sparsity structure in discriminant function. Numerical studies suggest that the proposed MDA and PMDA methods outperform LDA and other competing discriminant methods for matrix predictors. The asymptotic consistency of MDA is also established. R code and data are available online as supplementary material. PMID:26783371

Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding.

PubMed

Xu, Yun; Muhamadali, Howbeer; Sayqal, Ali; Dixon, Neil; Goodacre, Royston

2016-10-28

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a "pure" regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding.

Expression of Versican 3′-Untranslated Region Modulates Endogenous MicroRNA Functions

PubMed Central

Lee, Daniel Y.; Jeyapalan, Zina; Fang, Ling; Yang, Jennifer; Zhang, Yaou; Yee, Albert Y.; Li, Minhui; Du, William W.; Shatseva, Tatiana; Yang, Burton B.

2010-01-01

Background Mature microRNAs (miRNAs) are single-stranded RNAs that regulate post-transcriptional gene expression. In our previous study, we have shown that versican 3′UTR, a fragment of non-coding transcript, has the ability to antagonize miR-199a-3p function thereby regulating expression of the matrix proteins versican and fibronectin, and thus resulting in enhanced cell-cell adhesion and organ adhesion. However, the impact of this non-coding fragment on tumorigenesis is yet to be determined. Methods and Findings Using computational prediction confirmed with in vitro and in vivo experiments, we report that the expression of versican 3′UTR not only antagonizes miR-199a-3p but can also lower its steady state expression. We found that expression of versican 3′UTR in a mouse breast carcinoma cell line, 4T1, decreased miR-199a-3p levels. The decrease in miRNA activity consequently translated into differences in tumor growth. Computational analysis indicated that both miR-199a-3p and miR-144 targeted a cell cycle regulator, Rb1. In addition, miR-144 and miR-136, which have also been shown to interact with versican 3′UTR, was found to target PTEN. Expression of Rb1 and PTEN were up-regulated synergistically in vitro and in vivo, suggesting that the 3′UTR binds and modulates miRNA activities, freeing Rb1 and PTEN mRNAs for translation. In tumor formation assays, cells transfected with the 3′UTR formed smaller tumors compared with cells transfected with a control vector. Conclusion Our results demonstrated that a 3′UTR fragment can be used to modulate miRNA functions. Our study also suggests that miRNAs in the cancer cells are more susceptible to degradation, due to its interaction with a non-coding 3′UTR. This non-coding component of mRNA may be used retrospectively to modulate miRNA activities. PMID:21049042

A product Pearson-type VII density distribution

NASA Astrophysics Data System (ADS)

Nadarajah, Saralees; Kotz, Samuel

2008-01-01

The Pearson-type VII distributions (containing the Student's t distributions) are becoming increasing prominent and are being considered as competitors to the normal distribution. Motivated by real examples in decision sciences, Bayesian statistics, probability theory and Physics, a new Pearson-type VII distribution is introduced by taking the product of two Pearson-type VII pdfs. Various structural properties of this distribution are derived, including its cdf, moments, mean deviation about the mean, mean deviation about the median, entropy, asymptotic distribution of the extreme order statistics, maximum likelihood estimates and the Fisher information matrix. Finally, an application to a Bayesian testing problem is illustrated.

Improvement of Mishchenko's T-matrix code for absorbing particles.

PubMed

Moroz, Alexander

2005-06-10

The use of Gaussian elimination with backsubstitution for matrix inversion in scattering theories is discussed. Within the framework of the T-matrix method (the state-of-the-art code by Mishchenko is freely available at http://www.giss.nasa.gov/-crmim), it is shown that the domain of applicability of Mishchenko's FORTRAN 77 (F77) code can be substantially expanded in the direction of strongly absorbing particles where the current code fails to converge. Such an extension is especially important if the code is to be used in nanoplasmonic or nanophotonic applications involving metallic particles. At the same time, convergence can also be achieved for large nonabsorbing particles, in which case the non-Numerical Algorithms Group option of Mishchenko's code diverges. Computer F77 implementation of Mishchenko's code supplemented with Gaussian elimination with backsubstitution is freely available at http://www.wave-scattering.com.

Bayesian statistics and Monte Carlo methods

NASA Astrophysics Data System (ADS)

Koch, K. R.

2018-03-01

The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes' theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. TheMonte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum.

Joint three-dimensional inversion of coupled groundwater flow and heat transfer based on automatic differentiation: sensitivity calculation, verification, and synthetic examples

NASA Astrophysics Data System (ADS)

Rath, V.; Wolf, A.; Bücker, H. M.

2006-10-01

Inverse methods are useful tools not only for deriving estimates of unknown parameters of the subsurface, but also for appraisal of the thus obtained models. While not being neither the most general nor the most efficient methods, Bayesian inversion based on the calculation of the Jacobian of a given forward model can be used to evaluate many quantities useful in this process. The calculation of the Jacobian, however, is computationally expensive and, if done by divided differences, prone to truncation error. Here, automatic differentiation can be used to produce derivative code by source transformation of an existing forward model. We describe this process for a coupled fluid flow and heat transport finite difference code, which is used in a Bayesian inverse scheme to estimate thermal and hydraulic properties and boundary conditions form measured hydraulic potentials and temperatures. The resulting derivative code was validated by comparison to simple analytical solutions and divided differences. Synthetic examples from different flow regimes demonstrate the use of the inverse scheme, and its behaviour in different configurations.

Robust, Adaptive Functional Regression in Functional Mixed Model Framework.

PubMed

Zhu, Hongxiao; Brown, Philip J; Morris, Jeffrey S

2011-09-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets.

Robust, Adaptive Functional Regression in Functional Mixed Model Framework

PubMed Central

Zhu, Hongxiao; Brown, Philip J.; Morris, Jeffrey S.

2012-01-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets. PMID:22308015

Minimizing embedding impact in steganography using trellis-coded quantization

NASA Astrophysics Data System (ADS)

Filler, Tomáš; Judas, Jan; Fridrich, Jessica

2010-01-01

In this paper, we propose a practical approach to minimizing embedding impact in steganography based on syndrome coding and trellis-coded quantization and contrast its performance with bounds derived from appropriate rate-distortion bounds. We assume that each cover element can be assigned a positive scalar expressing the impact of making an embedding change at that element (single-letter distortion). The problem is to embed a given payload with minimal possible average embedding impact. This task, which can be viewed as a generalization of matrix embedding or writing on wet paper, has been approached using heuristic and suboptimal tools in the past. Here, we propose a fast and very versatile solution to this problem that can theoretically achieve performance arbitrarily close to the bound. It is based on syndrome coding using linear convolutional codes with the optimal binary quantizer implemented using the Viterbi algorithm run in the dual domain. The complexity and memory requirements of the embedding algorithm are linear w.r.t. the number of cover elements. For practitioners, we include detailed algorithms for finding good codes and their implementation. Finally, we report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel.

Bayesian inference of T Tauri star properties using multi-wavelength survey photometry

NASA Astrophysics Data System (ADS)

Barentsen, Geert; Vink, J. S.; Drew, J. E.; Sale, S. E.

2013-03-01

There are many pertinent open issues in the area of star and planet formation. Large statistical samples of young stars across star-forming regions are needed to trigger a breakthrough in our understanding, but most optical studies are based on a wide variety of spectrographs and analysis methods, which introduces large biases. Here we show how graphical Bayesian networks can be employed to construct a hierarchical probabilistic model which allows pre-main-sequence ages, masses, accretion rates and extinctions to be estimated using two widely available photometric survey data bases (Isaac Newton Telescope Photometric Hα Survey r'/Hα/i' and Two Micron All Sky Survey J-band magnitudes). Because our approach does not rely on spectroscopy, it can easily be applied to ho-mogeneously study the large number of clusters for which Gaia will yield membership lists. We explain how the analysis is carried out using the Markov chain Monte Carlo method and provide PYTHON source code. We then demonstrate its use on 587 known low-mass members of the star-forming region NGC 2264 (Cone Nebula), arriving at a median age of 3.0 Myr, an accretion fraction of 20 ± 2 per cent and a median accretion rate of 10-8.4 M⊙ yr-1. The Bayesian analysis formulated in this work delivers results which are in agreement with spectroscopic studies already in the literature, but achieves this with great efficiency by depending only on photometry. It is a significant step forward from previous photometric studies because the probabilistic approach ensures that nuisance parameters, such as extinction and distance, are fully included in the analysis with a clear picture on any degeneracies.

New subspace methods for ATR

NASA Astrophysics Data System (ADS)

Zhang, Peng; Peng, Jing; Sims, S. Richard F.

2005-05-01

In ATR applications, each feature is a convolution of an image with a filter. It is important to use most discriminant features to produce compact representations. We propose two novel subspace methods for dimension reduction to address limitations associated with Fukunaga-Koontz Transform (FKT). The first method, Scatter-FKT, assumes that target is more homogeneous, while clutter can be anything other than target and anywhere. Thus, instead of estimating a clutter covariance matrix, Scatter-FKT computes a clutter scatter matrix that measures the spread of clutter from the target mean. We choose dimensions along which the difference in variation between target and clutter is most pronounced. When the target follows a Gaussian distribution, Scatter-FKT can be viewed as a generalization of FKT. The second method, Optimal Bayesian Subspace, is derived from the optimal Bayesian classifier. It selects dimensions such that the minimum Bayes error rate can be achieved. When both target and clutter follow Gaussian distributions, OBS computes optimal subspace representations. We compare our methods against FKT using character image as well as IR data.

A BAYESIAN SPATIAL AND TEMPORAL MODELING APPROACH TO MAPPING GEOGRAPHIC VARIATION IN MORTALITY RATES FOR SUBNATIONAL AREAS WITH R-INLA.

PubMed

Khana, Diba; Rossen, Lauren M; Hedegaard, Holly; Warner, Margaret

2018-01-01

Hierarchical Bayes models have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of mortality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level mortality rates based on fewer than 20 deaths are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatio-temporal variation in less common causes of mortality outcomes such as suicide rates (SRs) at the county level using direct estimates. Existing Bayesian spatio-temporal modeling strategies can be applied via Integrated Nested Laplace Approximation (INLA) in R to a large number of rare causes of mortality outcomes to enable examination of spatio-temporal variations on smaller geographic scales such as counties. This method allows examination of spatiotemporal variation across the entire U.S., even where the data are sparse. We used mortality data from 2005-2015 to explore spatiotemporal variation in SRs, as one particular application of the Bayesian spatio-temporal modeling strategy in R-INLA to predict year and county-specific SRs. Specifically, hierarchical Bayesian spatio-temporal models were implemented with spatially structured and unstructured random effects, correlated time effects, time varying confounders and space-time interaction terms in the software R-INLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation.

HAT-P-16b: A Bayesian Atmospheric Retrieval

NASA Astrophysics Data System (ADS)

McIntyre, Kathleen; Harrington, Joseph; Blecic, Jasmina; Cubillos, Patricio; Challener, Ryan; Bakos, Gaspar

2017-10-01

HAT-P-16b is a hot (equilibrium temperature 1626 ± 40 K, assuming zero Bond albedo and efficient energy redistribution), 4.19 ± 0.09 Jupiter-mass exoplanet orbiting an F8 star every 2.775960 ± 0.000003 days (Buchhave et al 2010). We observed two secondary eclipses of HAT-P-16b using the 3.6 μm and 4.5 μm channels of the Spitzer Space Telescope's Infrared Array Camera (program ID 60003). We applied our Photometry for Orbits, Eclipses, and Transits (POET) code to produce normalized eclipse light curves, and our Bayesian Atmospheric Radiative Transfer (BART) code to constrain the temperature-pressure profiles and atmospheric molecular abundances of the planet. Spitzer is operated by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G.

Laminar fMRI and computational theories of brain function.

PubMed

Stephan, K E; Petzschner, F H; Kasper, L; Bayer, J; Wellstein, K V; Stefanics, G; Pruessmann, K P; Heinzle, J

2017-11-02

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Bayesian hypothesis testing for human threat conditioning research: an introduction and the condir R package

PubMed Central

Krypotos, Angelos-Miltiadis; Klugkist, Irene; Engelhard, Iris M.

2017-01-01

ABSTRACT Threat conditioning procedures have allowed the experimental investigation of the pathogenesis of Post-Traumatic Stress Disorder. The findings of these procedures have also provided stable foundations for the development of relevant intervention programs (e.g. exposure therapy). Statistical inference of threat conditioning procedures is commonly based on p-values and Null Hypothesis Significance Testing (NHST). Nowadays, however, there is a growing concern about this statistical approach, as many scientists point to the various limitations of p-values and NHST. As an alternative, the use of Bayes factors and Bayesian hypothesis testing has been suggested. In this article, we apply this statistical approach to threat conditioning data. In order to enable the easy computation of Bayes factors for threat conditioning data we present a new R package named condir, which can be used either via the R console or via a Shiny application. This article provides both a non-technical introduction to Bayesian analysis for researchers using the threat conditioning paradigm, and the necessary tools for computing Bayes factors easily. PMID:29038683

Exploring Hill Ciphers with Graphing Calculators.

ERIC Educational Resources Information Center

St. John, Dennis

1998-01-01

Explains how to code and decode messages using Hill ciphers which combine matrix multiplication and modular arithmetic. Discusses how a graphing calculator can facilitate the matrix and modular arithmetic used in the coding and decoding procedures. (ASK)

Bayesian accounts of covert selective attention: A tutorial review.

PubMed

Vincent, Benjamin T

2015-05-01

Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. Graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

Bayesian analysis of caustic-crossing microlensing events

NASA Astrophysics Data System (ADS)

Cassan, A.; Horne, K.; Kains, N.; Tsapras, Y.; Browne, P.

2010-06-01

Aims: Caustic-crossing binary-lens microlensing events are important anomalous events because they are capable of detecting an extrasolar planet companion orbiting the lens star. Fast and robust modelling methods are thus of prime interest in helping to decide whether a planet is detected by an event. Cassan introduced a new set of parameters to model binary-lens events, which are closely related to properties of the light curve. In this work, we explain how Bayesian priors can be added to this framework, and investigate on interesting options. Methods: We develop a mathematical formulation that allows us to compute analytically the priors on the new parameters, given some previous knowledge about other physical quantities. We explicitly compute the priors for a number of interesting cases, and show how this can be implemented in a fully Bayesian, Markov chain Monte Carlo algorithm. Results: Using Bayesian priors can accelerate microlens fitting codes by reducing the time spent considering physically implausible models, and helps us to discriminate between alternative models based on the physical plausibility of their parameters.

Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions: A Bayesian Belief Network Application

PubMed Central

Schmitt, Laetitia Helene Marie; Brugere, Cecile

2013-01-01

Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development. PMID:24155876

A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Ma, Jun; Yang, Gang; Du, Bo; Zhang, Liangpei

2017-06-01

A new Bayesian method named Poisson Nonnegative Matrix Factorization with Parameter Subspace Clustering Constraint (PNMF-PSCC) has been presented to extract endmembers from Hyperspectral Imagery (HSI). First, the method integrates the liner spectral mixture model with the Bayesian framework and it formulates endmember extraction into a Bayesian inference problem. Second, the Parameter Subspace Clustering Constraint (PSCC) is incorporated into the statistical program to consider the clustering of all pixels in the parameter subspace. The PSCC could enlarge differences among ground objects and helps finding endmembers with smaller spectrum divergences. Meanwhile, the PNMF-PSCC method utilizes the Poisson distribution as the prior knowledge of spectral signals to better explain the quantum nature of light in imaging spectrometer. Third, the optimization problem of PNMF-PSCC is formulated into maximizing the joint density via the Maximum A Posterior (MAP) estimator. The program is finally solved by iteratively optimizing two sub-problems via the Alternating Direction Method of Multipliers (ADMM) framework and the FURTHESTSUM initialization scheme. Five state-of-the art methods are implemented to make comparisons with the performance of PNMF-PSCC on both the synthetic and real HSI datasets. Experimental results show that the PNMF-PSCC outperforms all the five methods in Spectral Angle Distance (SAD) and Root-Mean-Square-Error (RMSE), and especially it could identify good endmembers for ground objects with smaller spectrum divergences.

Bayesian Multi-Trait Analysis Reveals a Useful Tool to Increase Oil Concentration and to Decrease Toxicity in Jatropha curcas L.

PubMed Central

Silva Junqueira, Vinícius; de Azevedo Peixoto, Leonardo; Galvêas Laviola, Bruno; Lopes Bhering, Leonardo; Mendonça, Simone; Agostini Costa, Tania da Silveira; Antoniassi, Rosemar

2016-01-01

The biggest challenge for jatropha breeding is to identify superior genotypes that present high seed yield and seed oil content with reduced toxicity levels. Therefore, the objective of this study was to estimate genetic parameters for three important traits (weight of 100 seed, oil seed content, and phorbol ester concentration), and to select superior genotypes to be used as progenitors in jatropha breeding. Additionally, the genotypic values and the genetic parameters estimated under the Bayesian multi-trait approach were used to evaluate different selection indices scenarios of 179 half-sib families. Three different scenarios and economic weights were considered. It was possible to simultaneously reduce toxicity and increase seed oil content and weight of 100 seed by using index selection based on genotypic value estimated by the Bayesian multi-trait approach. Indeed, we identified two families that present these characteristics by evaluating genetic diversity using the Ward clustering method, which suggested nine homogenous clusters. Future researches must integrate the Bayesian multi-trait methods with realized relationship matrix, aiming to build accurate selection indices models. PMID:27281340

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.

Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization

ERIC Educational Resources Information Center

Gelman, Andrew; Lee, Daniel; Guo, Jiqiang

2015-01-01

Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…

Using SAS PROC MCMC for Item Response Theory Models

ERIC Educational Resources Information Center

Ames, Allison J.; Samonte, Kelli

2015-01-01

Interest in using Bayesian methods for estimating item response theory models has grown at a remarkable rate in recent years. This attentiveness to Bayesian estimation has also inspired a growth in available software such as WinBUGS, R packages, BMIRT, MPLUS, and SAS PROC MCMC. This article intends to provide an accessible overview of Bayesian…

From individual to population level effects of toxicants in the tubicifid Branchiura sowerbyi using threshold effect models in a Bayesian framework.

PubMed

Ducrot, Virginie; Billoir, Elise; Péry, Alexandre R R; Garric, Jeanne; Charles, Sandrine

2010-05-01

Effects of zinc were studied in the freshwater worm Branchiura sowerbyi using partial and full life-cycle tests. Only newborn and juveniles were sensitive to zinc, displaying effects on survival, growth, and age at first brood at environmentally relevant concentrations. Threshold effect models were proposed to assess toxic effects on individuals. They were fitted to life-cycle test data using Bayesian inference and adequately described life-history trait data in exposed organisms. The daily asymptotic growth rate of theoretical populations was then simulated with a matrix population model, based upon individual-level outputs. Population-level outputs were in accordance with existing literature for controls. Working in a Bayesian framework allowed incorporating parameter uncertainty in the simulation of the population-level response to zinc exposure, thus increasing the relevance of test results in the context of ecological risk assessment.

F-MAP: A Bayesian approach to infer the gene regulatory network using external hints

PubMed Central

Shahdoust, Maryam; Mahjub, Hossein; Sadeghi, Mehdi

2017-01-01

The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches. PMID:28938012

Sparse Bayesian learning for DOA estimation with mutual coupling.

PubMed

Dai, Jisheng; Hu, Nan; Xu, Weichao; Chang, Chunqi

2015-10-16

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

A (72, 36; 15) box code

NASA Technical Reports Server (NTRS)

Solomon, G.

1993-01-01

A (72,36;15) box code is constructed as a 9 x 8 matrix whose columns add to form an extended BCH-Hamming (8,4;4) code and whose rows sum to odd or even parity. The newly constructed code, due to its matrix form, is easily decodable for all seven-error and many eight-error patterns. The code comes from a slight modification in the parity (eighth) dimension of the Reed-Solomon (8,4;5) code over GF(512). Error correction uses the row sum parity information to detect errors, which then become erasures in a Reed-Solomon correction algorithm.

Noniterative MAP reconstruction using sparse matrix representations.

PubMed

Cao, Guangzhi; Bouman, Charles A; Webb, Kevin J

2009-09-01

We present a method for noniterative maximum a posteriori (MAP) tomographic reconstruction which is based on the use of sparse matrix representations. Our approach is to precompute and store the inverse matrix required for MAP reconstruction. This approach has generally not been used in the past because the inverse matrix is typically large and fully populated (i.e., not sparse). In order to overcome this problem, we introduce two new ideas. The first idea is a novel theory for the lossy source coding of matrix transformations which we refer to as matrix source coding. This theory is based on a distortion metric that reflects the distortions produced in the final matrix-vector product, rather than the distortions in the coded matrix itself. The resulting algorithms are shown to require orthonormal transformations of both the measurement data and the matrix rows and columns before quantization and coding. The second idea is a method for efficiently storing and computing the required orthonormal transformations, which we call a sparse-matrix transform (SMT). The SMT is a generalization of the classical FFT in that it uses butterflies to compute an orthonormal transform; but unlike an FFT, the SMT uses the butterflies in an irregular pattern, and is numerically designed to best approximate the desired transforms. We demonstrate the potential of the noniterative MAP reconstruction with examples from optical tomography. The method requires offline computation to encode the inverse transform. However, once these offline computations are completed, the noniterative MAP algorithm is shown to reduce both storage and computation by well over two orders of magnitude, as compared to a linear iterative reconstruction methods.

The complete mitochondrial genome of the styloperlid stonefly species Styloperla spinicercia Wu (Insecta: Plecoptera) with family-level phylogenetic analyses of the Pteronarcyoidea.

PubMed

Wang, Ying; Cao, Jinjun; Li, Weihai

2017-03-13

We present the complete mitochondrial (mt) genome sequence of the stonefly, Styloperla spinicercia Wu, 1935 (Plecoptera: Styloperlidae), the type species of the genus Styloperla and the first complete mt genome for the family Styloperlidae. The genome is circular, 16,129 base pairs long, has an A+T content of 70.7%, and contains 37 genes including the large and small ribosomal RNA (rRNA) subunits, 13 protein coding genes (PCGs), 22 tRNA genes and a large non-coding region (CR). All of the PCGs use the standard initiation codon ATN except ND1 and ND5, which start with TTG and GTG. Twelve of the PCGs stop with conventional terminal codons TAA and TAG, except ND5 which shows an incomplete terminator signal T. All tRNAs have the classic clover-leaf structures with the dihydrouridine (DHU) arm of tRNASer(AGN) forming a simple loop. Secondary structures of the two ribosomal RNAs are presented with reference to previous models. The structural elements and the variable numbers of tandem repeats are described within the control region. Phylogenetic analyses using both Bayesian (BI) and Maximum Likelihood (ML) methods support the previous hypotheses regarding family level relationships within the Pteronarcyoidea. The genetic distance calculated based on 13 PCGs and two rRNAs between Styloperla sp. and S. spinicercia is provided and interspecific divergence is discussed.

Stabilization of the SIESTA MHD Equilibrium Code Using Rapid Cholesky Factorization

NASA Astrophysics Data System (ADS)

Hirshman, S. P.; D'Azevedo, E. A.; Seal, S. K.

2016-10-01

The SIESTA MHD equilibrium code solves the discretized nonlinear MHD force F ≡ J X B - ∇p for a 3D plasma which may contain islands and stochastic regions. At each nonlinear evolution step, it solves a set of linearized MHD equations which can be written r ≡ Ax - b = 0, where A is the linearized MHD Hessian matrix. When the solution norm | x| is small enough, the nonlinear force norm will be close to the linearized force norm | r| 0 obtained using preconditioned GMRES. In many cases, this procedure works well and leads to a vanishing nonlinear residual (equilibrium) after several iterations in SIESTA. In some cases, however, | x|>1 results and the SIESTA code has to be restarted to obtain nonlinear convergence. In order to make SIESTA more robust and avoid such restarts, we have implemented a new rapid QR factorization of the Hessian which allows us to rapidly and accurately solve the least-squares problem AT r = 0, subject to the condition | x|<1. This avoids large contributions to the nonlinear force terms and in general makes the convergence sequence of SIESTA much more stable. The innovative rapid QR method is based on a pairwise row factorization of the tri-diagonal Hessian. It provides a complete Cholesky factorization while preserving the memory allocation of A. This work was supported by the U.S. D.O.E. contract DE-AC05-00OR22725.

A Constrained Bayesian Approach for Testing TTBT Compliance

DTIC Science & Technology

1992-03-06

These figures show that the treatment of the covariance matrix is very im- portant. In many of the previous figures the power curves of all four...Drawer 719 Richland, WA 99352 Santa Barbara, CA 93102 Prof Stale Fla. Dr. James Hannon AplidSceces Bilding Lawrence Uivermare National Labory Uveity

Maximum entropy formalism for the analytic continuation of matrix-valued Green's functions

NASA Astrophysics Data System (ADS)

Kraberger, Gernot J.; Triebl, Robert; Zingl, Manuel; Aichhorn, Markus

2017-10-01

We present a generalization of the maximum entropy method to the analytic continuation of matrix-valued Green's functions. To treat off-diagonal elements correctly based on Bayesian probability theory, the entropy term has to be extended for spectral functions that are possibly negative in some frequency ranges. In that way, all matrix elements of the Green's function matrix can be analytically continued; we introduce a computationally cheap element-wise method for this purpose. However, this method cannot ensure important constraints on the mathematical properties of the resulting spectral functions, namely positive semidefiniteness and Hermiticity. To improve on this, we present a full matrix formalism, where all matrix elements are treated simultaneously. We show the capabilities of these methods using insulating and metallic dynamical mean-field theory (DMFT) Green's functions as test cases. Finally, we apply the methods to realistic material calculations for LaTiO3, where off-diagonal matrix elements in the Green's function appear due to the distorted crystal structure.

Carbon Nanotube Growth Rate Regression using Support Vector Machines and Artificial Neural Networks

DTIC Science & Technology

2014-03-27

intensity D peak. Reprinted with permission from [38]. The SVM classifier is trained using custom written Java code leveraging the Sequential Minimal...Society Encog is a machine learning framework for Java , C++ and .Net applications that supports Bayesian Networks, Hidden Markov Models, SVMs and ANNs [13...SVM classifiers are trained using Weka libraries and leveraging custom written Java code. The data set is created as an Attribute Relationship File

Virtual Representation of IID Observations in Bayesian Belief Networks

DTIC Science & Technology

1994-04-01

programs for structuring and using Bayesian inference include ERGO ( Noetic Systems, Inc., 1991) and HUGIN (Andersen, Jensen, Olesen, & Jensen, 1989...Nichols, S.. Chipman, & R. Brennan (Eds.), Cognitively diagnostic assessment. Hillsdale, NJ: Erlbaum. Noetic Systems, Inc. (1991). ERGO [computer...Dr Geore Eageiard Jr Chicago IL 60612 US Naval Academy Division of Educational Studies Annapolis MD 21402-5002 Emory University Dr Janice Gifford 210

Overcoming the effects of rogue taxa: Evolutionary relationships of the bee flies

PubMed Central

Trautwein, Michelle D.; Wiegmann, Brian M.; Yeates, David K

2011-01-01

Bombyliidae (5000 sp.), or bee flies, are a lower brachyceran family of flower-visiting flies that, as larvae, act as parasitoids of other insects. The evolutionary relationships are known from a morphological analysis that yielded minimal support for higher-level groupings. We use the protein-coding gene CAD and 28S rDNA to determine phylogeny and to test the monophyly of existing subfamilies, the divisions Tomophtalmae, and ‘the sand chamber subfamilies’. Additionally, we demonstrate that consensus networks can be used to identify rogue taxa in a Bayesian framework. Pruning rogue taxa post-analysis from the final tree distribution results in increased posterior probabilities. We find 8 subfamilies to be monophyletic and the subfamilies Heterotropinae and Mythicomyiinae to be the earliest diverging lineages. The large subfamily Bombyliinae is found to be polyphyletic and our data does not provide evidence for the monophyly of Tomophthalmae or the ‘sand chamber subfamilies’. PMID:21686308

Bayesian, maximum parsimony and UPGMA models for inferring the phylogenies of antelopes using mitochondrial markers.

PubMed

Khan, Haseeb A; Arif, Ibrahim A; Bahkali, Ali H; Al Farhan, Ahmad H; Al Homaidan, Ali A

2008-10-06

This investigation was aimed to compare the inference of antelope phylogenies resulting from the 16S rRNA, cytochrome-b (cyt-b) and d-loop segments of mitochondrial DNA using three different computational models including Bayesian (BA), maximum parsimony (MP) and unweighted pair group method with arithmetic mean (UPGMA). The respective nucleotide sequences of three Oryx species (Oryx leucoryx, Oryx dammah and Oryx gazella) and an out-group (Addax nasomaculatus) were aligned and subjected to BA, MP and UPGMA models for comparing the topologies of respective phylogenetic trees. The 16S rRNA region possessed the highest frequency of conserved sequences (97.65%) followed by cyt-b (94.22%) and d-loop (87.29%). There were few transitions (2.35%) and none transversions in 16S rRNA as compared to cyt-b (5.61% transitions and 0.17% transversions) and d-loop (11.57% transitions and 1.14% transversions) while comparing the four taxa. All the three mitochondrial segments clearly differentiated the genus Addax from Oryx using the BA or UPGMA models. The topologies of all the gamma-corrected Bayesian trees were identical irrespective of the marker type. The UPGMA trees resulting from 16S rRNA and d-loop sequences were also identical (Oryx dammah grouped with Oryx leucoryx) to Bayesian trees except that the UPGMA tree based on cyt-b showed a slightly different phylogeny (Oryx dammah grouped with Oryx gazella) with a low bootstrap support. However, the MP model failed to differentiate the genus Addax from Oryx. These findings demonstrate the efficiency and robustness of BA and UPGMA methods for phylogenetic analysis of antelopes using mitochondrial markers.

Bayesian, Maximum Parsimony and UPGMA Models for Inferring the Phylogenies of Antelopes Using Mitochondrial Markers

PubMed Central

Khan, Haseeb A.; Arif, Ibrahim A.; Bahkali, Ali H.; Al Farhan, Ahmad H.; Al Homaidan, Ali A.

2008-01-01

This investigation was aimed to compare the inference of antelope phylogenies resulting from the 16S rRNA, cytochrome-b (cyt-b) and d-loop segments of mitochondrial DNA using three different computational models including Bayesian (BA), maximum parsimony (MP) and unweighted pair group method with arithmetic mean (UPGMA). The respective nucleotide sequences of three Oryx species (Oryx leucoryx, Oryx dammah and Oryx gazella) and an out-group (Addax nasomaculatus) were aligned and subjected to BA, MP and UPGMA models for comparing the topologies of respective phylogenetic trees. The 16S rRNA region possessed the highest frequency of conserved sequences (97.65%) followed by cyt-b (94.22%) and d-loop (87.29%). There were few transitions (2.35%) and none transversions in 16S rRNA as compared to cyt-b (5.61% transitions and 0.17% transversions) and d-loop (11.57% transitions and 1.14% transversions) while comparing the four taxa. All the three mitochondrial segments clearly differentiated the genus Addax from Oryx using the BA or UPGMA models. The topologies of all the gamma-corrected Bayesian trees were identical irrespective of the marker type. The UPGMA trees resulting from 16S rRNA and d-loop sequences were also identical (Oryx dammah grouped with Oryx leucoryx) to Bayesian trees except that the UPGMA tree based on cyt-b showed a slightly different phylogeny (Oryx dammah grouped with Oryx gazella) with a low bootstrap support. However, the MP model failed to differentiate the genus Addax from Oryx. These findings demonstrate the efficiency and robustness of BA and UPGMA methods for phylogenetic analysis of antelopes using mitochondrial markers. PMID:19204824

Mechanisms of motivational interviewing in health promotion: a Bayesian mediation analysis

PubMed Central

2012-01-01

Background Counselor behaviors that mediate the efficacy of motivational interviewing (MI) are not well understood, especially when applied to health behavior promotion. We hypothesized that client change talk mediates the relationship between counselor variables and subsequent client behavior change. Methods Purposeful sampling identified individuals from a prospective randomized worksite trial using an MI intervention to promote firefighters’ healthy diet and regular exercise that increased dietary intake of fruits and vegetables (n = 21) or did not increase intake of fruits and vegetables (n = 22). MI interactions were coded using the Motivational Interviewing Skill Code (MISC 2.1) to categorize counselor and firefighter verbal utterances. Both Bayesian and frequentist mediation analyses were used to investigate whether client change talk mediated the relationship between counselor skills and behavior change. Results Counselors’ global spirit, empathy, and direction and MI-consistent behavioral counts (e.g., reflections, open questions, affirmations, emphasize control) significantly correlated with firefighters’ total client change talk utterances (rs = 0.42, 0.40, 0.30, and 0.61, respectively), which correlated significantly with their fruit and vegetable intake increase (r = 0.33). Both Bayesian and frequentist mediation analyses demonstrated that findings were consistent with hypotheses, such that total client change talk mediated the relationship between counselor’s skills—MI-consistent behaviors [Bayesian mediated effect: αβ = .06 (.03), 95% CI = .02, .12] and MI spirit [Bayesian mediated effect: αβ = .06 (.03), 95% CI = .01, .13]—and increased fruit and vegetable consumption. Conclusion Motivational interviewing is a resource- and time-intensive intervention, and is currently being applied in many arenas. Previous research has identified the importance of counselor behaviors and client change talk in the treatment of substance use disorders. Our results indicate that similar mechanisms may underlie the effects of MI for dietary change. These results inform MI training and application by identifying those processes critical for MI success in health promotion domains. PMID:22681874

Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases.

PubMed

Friede, Tim; Röver, Christian; Wandel, Simon; Neuenschwander, Beat

2017-07-01

Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method (mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses with priors covering plausible heterogeneity values; R code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes. © 2016 The Authors. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Low-Density Parity-Check Code Design Techniques to Simplify Encoding

NASA Astrophysics Data System (ADS)

Perez, J. M.; Andrews, K.

2007-11-01

This work describes a method for encoding low-density parity-check (LDPC) codes based on the accumulate-repeat-4-jagged-accumulate (AR4JA) scheme, using the low-density parity-check matrix H instead of the dense generator matrix G. The use of the H matrix to encode allows a significant reduction in memory consumption and provides the encoder design a great flexibility. Also described are new hardware-efficient codes, based on the same kind of protographs, which require less memory storage and area, allowing at the same time a reduction in the encoding delay.

Enhancement of the Probabilistic CEramic Matrix Composite ANalyzer (PCEMCAN) Computer Code

NASA Technical Reports Server (NTRS)

Shah, Ashwin

2000-01-01

This report represents a final technical report for Order No. C-78019-J entitled "Enhancement of the Probabilistic Ceramic Matrix Composite Analyzer (PCEMCAN) Computer Code." The scope of the enhancement relates to including the probabilistic evaluation of the D-Matrix terms in MAT2 and MAT9 material properties card (available in CEMCAN code) for the MSC/NASTRAN. Technical activities performed during the time period of June 1, 1999 through September 3, 1999 have been summarized, and the final version of the enhanced PCEMCAN code and revisions to the User's Manual is delivered along with. Discussions related to the performed activities were made to the NASA Project Manager during the performance period. The enhanced capabilities have been demonstrated using sample problems.

A new Bayesian Earthquake Analysis Tool (BEAT)

NASA Astrophysics Data System (ADS)

Vasyura-Bathke, Hannes; Dutta, Rishabh; Jónsson, Sigurjón; Mai, Martin

2017-04-01

Modern earthquake source estimation studies increasingly use non-linear optimization strategies to estimate kinematic rupture parameters, often considering geodetic and seismic data jointly. However, the optimization process is complex and consists of several steps that need to be followed in the earthquake parameter estimation procedure. These include pre-describing or modeling the fault geometry, calculating the Green's Functions (often assuming a layered elastic half-space), and estimating the distributed final slip and possibly other kinematic source parameters. Recently, Bayesian inference has become popular for estimating posterior distributions of earthquake source model parameters given measured/estimated/assumed data and model uncertainties. For instance, some research groups consider uncertainties of the layered medium and propagate these to the source parameter uncertainties. Other groups make use of informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed that efficiently explore the often high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational demands of these methods are high and estimation codes are rarely distributed along with the published results. Even if codes are made available, it is often difficult to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in earthquake source estimations, we undertook the effort of producing BEAT, a python package that comprises all the above-mentioned features in one single programing environment. The package is build on top of the pyrocko seismological toolbox (www.pyrocko.org) and makes use of the pymc3 module for Bayesian statistical model fitting. BEAT is an open-source package (https://github.com/hvasbath/beat) and we encourage and solicit contributions to the project. In this contribution, we present our strategy for developing BEAT, show application examples, and discuss future developments.

From least squares to multilevel modeling: A graphical introduction to Bayesian inference

NASA Astrophysics Data System (ADS)

Loredo, Thomas J.

2016-01-01

This tutorial presentation will introduce some of the key ideas and techniques involved in applying Bayesian methods to problems in astrostatistics. The focus will be on the big picture: understanding the foundations (interpreting probability, Bayes's theorem, the law of total probability and marginalization), making connections to traditional methods (propagation of errors, least squares, chi-squared, maximum likelihood, Monte Carlo simulation), and highlighting problems where a Bayesian approach can be particularly powerful (Poisson processes, density estimation and curve fitting with measurement error). The "graphical" component of the title reflects an emphasis on pictorial representations of some of the math, but also on the use of graphical models (multilevel or hierarchical models) for analyzing complex data. Code for some examples from the talk will be available to participants, in Python and in the Stan probabilistic programming language.

Evidence that multiple genetic variants of MC4R play a functional role in the regulation of energy expenditure and appetite in Hispanic children1234

PubMed Central

Cole, Shelley A; Voruganti, V Saroja; Cai, Guowen; Haack, Karin; Kent, Jack W; Blangero, John; Comuzzie, Anthony G; McPherson, John D; Gibbs, Richard A

2010-01-01

Background: Melanocortin-4-receptor (MC4R) haploinsufficiency is the most common form of monogenic obesity; however, the frequency of MC4R variants and their functional effects in general populations remain uncertain. Objective: The aim was to identify and characterize the effects of MC4R variants in Hispanic children. Design: MC4R was resequenced in 376 parents, and the identified single nucleotide polymorphisms (SNPs) were genotyped in 613 parents and 1016 children from the Viva la Familia cohort. Measured genotype analysis (MGA) tested associations between SNPs and phenotypes. Bayesian quantitative trait nucleotide (BQTN) analysis was used to infer the most likely functional polymorphisms influencing obesity-related traits. Results: Seven rare SNPs in coding and 18 SNPs in flanking regions of MC4R were identified. MGA showed suggestive associations between MC4R variants and body size, adiposity, glucose, insulin, leptin, ghrelin, energy expenditure, physical activity, and food intake. BQTN analysis identified SNP 1704 in a predicted micro-RNA target sequence in the downstream flanking region of MC4R as a strong, probable functional variant influencing total, sedentary, and moderate activities with posterior probabilities of 1.0. SNP 2132 was identified as a variant with a high probability (1.0) of exerting a functional effect on total energy expenditure and sleeping metabolic rate. SNP rs34114122 was selected as having likely functional effects on the appetite hormone ghrelin, with a posterior probability of 0.81. Conclusion: This comprehensive investigation provides strong evidence that MC4R genetic variants are likely to play a functional role in the regulation of weight, not only through energy intake but through energy expenditure. PMID:19889825

Bayesian conditional-independence modeling of the AIDS epidemic in England and Wales

NASA Astrophysics Data System (ADS)

Gilks, Walter R.; De Angelis, Daniela; Day, Nicholas E.

We describe the use of conditional-independence modeling, Bayesian inference and Markov chain Monte Carlo, to model and project the HIV-AIDS epidemic in homosexual/bisexual males in England and Wales. Complexity in this analysis arises through selectively missing data, indirectly observed underlying processes, and measurement error. Our emphasis is on presentation and discussion of the concepts, not on the technicalities of this analysis, which can be found elsewhere [D. De Angelis, W.R. Gilks, N.E. Day, Bayesian projection of the the acquired immune deficiency syndrome epidemic (with discussion), Applied Statistics, in press].

Stochastic determination of matrix determinants

NASA Astrophysics Data System (ADS)

Dorn, Sebastian; Enßlin, Torsten A.

2015-07-01

Matrix determinants play an important role in data analysis, in particular when Gaussian processes are involved. Due to currently exploding data volumes, linear operations—matrices—acting on the data are often not accessible directly but are only represented indirectly in form of a computer routine. Such a routine implements the transformation a data vector undergoes under matrix multiplication. While efficient probing routines to estimate a matrix's diagonal or trace, based solely on such computationally affordable matrix-vector multiplications, are well known and frequently used in signal inference, there is no stochastic estimate for its determinant. We introduce a probing method for the logarithm of a determinant of a linear operator. Our method rests upon a reformulation of the log-determinant by an integral representation and the transformation of the involved terms into stochastic expressions. This stochastic determinant determination enables large-size applications in Bayesian inference, in particular evidence calculations, model comparison, and posterior determination.

Stochastic determination of matrix determinants.

PubMed

Dorn, Sebastian; Ensslin, Torsten A

2015-07-01

Matrix determinants play an important role in data analysis, in particular when Gaussian processes are involved. Due to currently exploding data volumes, linear operations-matrices-acting on the data are often not accessible directly but are only represented indirectly in form of a computer routine. Such a routine implements the transformation a data vector undergoes under matrix multiplication. While efficient probing routines to estimate a matrix's diagonal or trace, based solely on such computationally affordable matrix-vector multiplications, are well known and frequently used in signal inference, there is no stochastic estimate for its determinant. We introduce a probing method for the logarithm of a determinant of a linear operator. Our method rests upon a reformulation of the log-determinant by an integral representation and the transformation of the involved terms into stochastic expressions. This stochastic determinant determination enables large-size applications in Bayesian inference, in particular evidence calculations, model comparison, and posterior determination.

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.

PubMed

Lopes, J S; Arenas, M; Posada, D; Beaumont, M A

2014-03-01

The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

Perceptually-Based Adaptive JPEG Coding

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Rosenholtz, Ruth; Null, Cynthia H. (Technical Monitor)

1996-01-01

An extension to the JPEG standard (ISO/IEC DIS 10918-3) allows spatial adaptive coding of still images. As with baseline JPEG coding, one quantization matrix applies to an entire image channel, but in addition the user may specify a multiplier for each 8 x 8 block, which multiplies the quantization matrix, yielding the new matrix for the block. MPEG 1 and 2 use much the same scheme, except there the multiplier changes only on macroblock boundaries. We propose a method for perceptual optimization of the set of multipliers. We compute the perceptual error for each block based upon DCT quantization error adjusted according to contrast sensitivity, light adaptation, and contrast masking, and pick the set of multipliers which yield maximally flat perceptual error over the blocks of the image. We investigate the bitrate savings due to this adaptive coding scheme and the relative importance of the different sorts of masking on adaptive coding.

Parallel Computation of the Jacobian Matrix for Nonlinear Equation Solvers Using MATLAB

NASA Technical Reports Server (NTRS)

Rose, Geoffrey K.; Nguyen, Duc T.; Newman, Brett A.

2017-01-01

Demonstrating speedup for parallel code on a multicore shared memory PC can be challenging in MATLAB due to underlying parallel operations that are often opaque to the user. This can limit potential for improvement of serial code even for the so-called embarrassingly parallel applications. One such application is the computation of the Jacobian matrix inherent to most nonlinear equation solvers. Computation of this matrix represents the primary bottleneck in nonlinear solver speed such that commercial finite element (FE) and multi-body-dynamic (MBD) codes attempt to minimize computations. A timing study using MATLAB's Parallel Computing Toolbox was performed for numerical computation of the Jacobian. Several approaches for implementing parallel code were investigated while only the single program multiple data (spmd) method using composite objects provided positive results. Parallel code speedup is demonstrated but the goal of linear speedup through the addition of processors was not achieved due to PC architecture.

Fine-structure excitation of Fe II and Fe III due to collisions with electrons

NASA Astrophysics Data System (ADS)

Wan, Yier; Qi, Yueying; Favreau, Connor; Loch, Stuart; Stancil, P.; Ballance, Connor; McLaughlin, Brendan

2018-06-01

Atomic data of iron peak elements are of great importance in astronomical observations. Among all the ionization stages of iron, Fe II and Fe III are of particular importance because of the high cosmic abundance, relatively low ionization potential and complex open d-shell atomic structure. Fe II and Fe III emission are observed from nearly all classes of astronomical objects over a wide spectral range from the infrared to the ultraviolet. To meaningfully interpret these spectra, astronomers have to employ highly complex modeling codes with reliable collision data to simulate the astrophysical observations. The major aim of this work is to provide reliable atomic data for diagnostics. We present new collision strengths and effective collisions for electron impact excitation of Fe II and Fe III for the forbidden transitions among the fine-structure levels of the ground terms. A very fine energy mesh is used for the collision strengths and the effective collision strengths are calculated over a wide range of electron temperatures of astrophysical importance (10-2000 K). The configuration interaction state wave functions are generated with a scaled Thomas-Fermi-Dirac-Amaldi (TFDA) potential, while the R-matrix plus intermediate coupling frame transformation (ICFT), Breit-Pauli R-matrix and Dirac R-matrix packages are used to obtain collision strengths. Influences of the different methods and configuration expansions on the collisional data are discussed. Comparison is made with earlier theoretical work and differences are found to occur at the low temperatures considered here.This work was funded by NASA grant NNX15AE47G.

Are we witnessing the epoch of reionisation at z = 7.1 from the spectrum of J1120+0641?

NASA Astrophysics Data System (ADS)

Greig, Bradley; Mesinger, Andrei; Haiman, Zoltán; Simcoe, Robert A.

2017-04-01

We quantify the presence of Lyα damping wing absorption from a partially neutral intergalactic medium (IGM) in the spectrum of the z = 7.08 QSO, ULASJ1120+0641. Using a Bayesian framework, we simultaneously account for uncertainties in: (I) the intrinsic QSO emission spectrum; and (II) the distribution of cosmic H I patches during the epoch of reionization (EoR). For (I), we use a new intrinsic Lyα emission line reconstruction method, sampling a covariance matrix of emission line properties built from a large data base of moderate-z QSOs. For (II), we use the Evolution of 21-cm Structure (EOS; Mesinger et al.) simulations, which span a range of physically motivated EoR models. We find strong evidence for the presence of damping wing absorption redward of Lyα (where there is no contamination from the Lyα forest). Our analysis implies that the EoR is not yet complete by z = 7.1, with the volume-weighted IGM neutral fraction constrained to \\bar{x}_{H I} = 0.40^{+0.21 }_{ -0.19} at 1σ (\\bar{x}_{H I} = 0.40^{+0.41 }_{ -0.32} at 2σ). This result is insensitive to the EoR morphology. Our detection of significant neutral H I in the IGM at z = 7.1 is consistent with the latest Planck 2016 measurements of the CMB Thompson scattering optical depth.

Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference

PubMed Central

Cuevas Rivera, Dario; Bitzer, Sebastian; Kiebel, Stefan J.

2015-01-01

The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an ‘intelligent coincidence detector’, which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena. PMID:26451888

Bayesian ISOLA: new tool for automated centroid moment tensor inversion

NASA Astrophysics Data System (ADS)

Vackář, Jiří; Burjánek, Jan; Gallovič, František; Zahradník, Jiří; Clinton, John

2017-04-01

Focal mechanisms are important for understanding seismotectonics of a region, and they serve as a basic input for seismic hazard assessment. Usually, the point source approximation and the moment tensor (MT) are used. We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances and high signal-to-noise are rejected, and full-waveform inversion in a space-time grid around a provided hypocenter. The method is innovative in the following aspects: (i) The CMT inversion is fully automated, no user interaction is required, although the details of the process can be visually inspected latter on many figures which are automatically plotted.(ii) The automated process includes detection of disturbances based on MouseTrap code, so disturbed recordings do not affect inversion.(iii) A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequencies.(iv) Bayesian approach is used, so not only the best solution is obtained, but also the posterior probability density function.(v) A space-time grid search effectively combined with the least-squares inversion of moment tensor components speeds up the inversion and allows to obtain more accurate results compared to stochastic methods. The method has been tested on synthetic and observed data. It has been tested by comparison with manually processed moment tensors of all events greater than M≥3 in the Swiss catalogue over 16 years using data available at the Swiss data center (http://arclink.ethz.ch). The quality of the results of the presented automated process is comparable with careful manual processing of data. The software package programmed in Python has been designed to be as versatile as possible in order to be applicable in various networks ranging from local to regional. The method can be applied either to the everyday network data flow, or to process large previously existing earthquake catalogues and data sets.

On the effective implementation of a boundary element code on graphics processing units unsing an out-of-core LU algorithm

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

D'Azevedo, Ed F; Nintcheu Fata, Sylvain

2012-01-01

A collocation boundary element code for solving the three-dimensional Laplace equation, publicly available from \\url{http://www.intetec.org}, has been adapted to run on an Nvidia Tesla general purpose graphics processing unit (GPU). Global matrix assembly and LU factorization of the resulting dense matrix were performed on the GPU. Out-of-core techniques were used to solve problems larger than available GPU memory. The code achieved over eight times speedup in matrix assembly and about 56~Gflops/sec in the LU factorization using only 512~Mbytes of GPU memory. Details of the GPU implementation and comparisons with the standard sequential algorithm are included to illustrate the performance ofmore » the GPU code.« less

Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models

ERIC Educational Resources Information Center

Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent

2015-01-01

When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (S) of group-level varying coefficients are often degenerate. One can do better, even from…

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Enhanced optical alignment of a digital micro mirror device through Bayesian adaptive exploration

NASA Astrophysics Data System (ADS)

Wynne, Kevin B.; Knuth, Kevin H.; Petruccelli, Jonathan

2017-12-01

As the use of Digital Micro Mirror Devices (DMDs) becomes more prevalent in optics research, the ability to precisely locate the Fourier "footprint" of an image beam at the Fourier plane becomes a pressing need. In this approach, Bayesian adaptive exploration techniques were employed to characterize the size and position of the beam on a DMD located at the Fourier plane. It couples a Bayesian inference engine with an inquiry engine to implement the search. The inquiry engine explores the DMD by engaging mirrors and recording light intensity values based on the maximization of the expected information gain. Using the data collected from this exploration, the Bayesian inference engine updates the posterior probability describing the beam's characteristics. The process is iterated until the beam is located to within the desired precision. This methodology not only locates the center and radius of the beam with remarkable precision but accomplishes the task in far less time than a brute force search. The employed approach has applications to system alignment for both Fourier processing and coded aperture design.

Metal Matrix Laminate Tailoring (MMLT) code: User's manual

NASA Technical Reports Server (NTRS)

Murthy, P. L. N.; Morel, M. R.; Saravanos, D. A.

1993-01-01

The User's Manual for the Metal Matrix Laminate Tailoring (MMLT) program is presented. The code is capable of tailoring the fabrication process, constituent characteristics, and laminate parameters (individually or concurrently) for a wide variety of metal matrix composite (MMC) materials, to improve the performance and identify trends or behavior of MMC's under different thermo-mechanical loading conditions. This document is meant to serve as a guide in the use of the MMLT code. Detailed explanations of the composite mechanics and tailoring analysis are beyond the scope of this document, and may be found in the references. MMLT was developed by the Structural Mechanics Branch at NASA Lewis Research Center (LeRC).

Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors.

PubMed

Peterson, Christine; Vannucci, Marina; Karakas, Cemal; Choi, William; Ma, Lihua; Maletić-Savatić, Mirjana

2013-10-01

Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation.

Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors

PubMed Central

PETERSON, CHRISTINE; VANNUCCI, MARINA; KARAKAS, CEMAL; CHOI, WILLIAM; MA, LIHUA; MALETIĆ-SAVATIĆ, MIRJANA

2014-01-01

Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation. PMID:24533172

Wavelet extractor: A Bayesian well-tie and wavelet extraction program

NASA Astrophysics Data System (ADS)

Gunning, James; Glinsky, Michael E.

2006-06-01

We introduce a new open-source toolkit for the well-tie or wavelet extraction problem of estimating seismic wavelets from seismic data, time-to-depth information, and well-log suites. The wavelet extraction model is formulated as a Bayesian inverse problem, and the software will simultaneously estimate wavelet coefficients, other parameters associated with uncertainty in the time-to-depth mapping, positioning errors in the seismic imaging, and useful amplitude-variation-with-offset (AVO) related parameters in multi-stack extractions. It is capable of multi-well, multi-stack extractions, and uses continuous seismic data-cube interpolation to cope with the problem of arbitrary well paths. Velocity constraints in the form of checkshot data, interpreted markers, and sonic logs are integrated in a natural way. The Bayesian formulation allows computation of full posterior uncertainties of the model parameters, and the important problem of the uncertain wavelet span is addressed uses a multi-model posterior developed from Bayesian model selection theory. The wavelet extraction tool is distributed as part of the Delivery seismic inversion toolkit. A simple log and seismic viewing tool is included in the distribution. The code is written in Java, and thus platform independent, but the Seismic Unix (SU) data model makes the inversion particularly suited to Unix/Linux environments. It is a natural companion piece of software to Delivery, having the capacity to produce maximum likelihood wavelet and noise estimates, but will also be of significant utility to practitioners wanting to produce wavelet estimates for other inversion codes or purposes. The generation of full parameter uncertainties is a crucial function for workers wishing to investigate questions of wavelet stability before proceeding to more advanced inversion studies.

PCEMCAN - Probabilistic Ceramic Matrix Composites Analyzer: User's Guide, Version 1.0

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Mital, Subodh K.; Murthy, Pappu L. N.

1998-01-01

PCEMCAN (Probabalistic CEramic Matrix Composites ANalyzer) is an integrated computer code developed at NASA Lewis Research Center that simulates uncertainties associated with the constituent properties, manufacturing process, and geometric parameters of fiber reinforced ceramic matrix composites and quantifies their random thermomechanical behavior. The PCEMCAN code can perform the deterministic as well as probabilistic analyses to predict thermomechanical properties. This User's guide details the step-by-step procedure to create input file and update/modify the material properties database required to run PCEMCAN computer code. An overview of the geometric conventions, micromechanical unit cell, nonlinear constitutive relationship and probabilistic simulation methodology is also provided in the manual. Fast probability integration as well as Monte-Carlo simulation methods are available for the uncertainty simulation. Various options available in the code to simulate probabilistic material properties and quantify sensitivity of the primitive random variables have been described. The description of deterministic as well as probabilistic results have been described using demonstration problems. For detailed theoretical description of deterministic and probabilistic analyses, the user is referred to the companion documents "Computational Simulation of Continuous Fiber-Reinforced Ceramic Matrix Composite Behavior," NASA TP-3602, 1996 and "Probabilistic Micromechanics and Macromechanics for Ceramic Matrix Composites", NASA TM 4766, June 1997.

Ancient DNA sequence revealed by error-correcting codes.

PubMed

Brandão, Marcelo M; Spoladore, Larissa; Faria, Luzinete C B; Rocha, Andréa S L; Silva-Filho, Marcio C; Palazzo, Reginaldo

2015-07-10

A previously described DNA sequence generator algorithm (DNA-SGA) using error-correcting codes has been employed as a computational tool to address the evolutionary pathway of the genetic code. The code-generated sequence alignment demonstrated that a residue mutation revealed by the code can be found in the same position in sequences of distantly related taxa. Furthermore, the code-generated sequences do not promote amino acid changes in the deviant genomes through codon reassignment. A Bayesian evolutionary analysis of both code-generated and homologous sequences of the Arabidopsis thaliana malate dehydrogenase gene indicates an approximately 1 MYA divergence time from the MDH code-generated sequence node to its paralogous sequences. The DNA-SGA helps to determine the plesiomorphic state of DNA sequences because a single nucleotide alteration often occurs in distantly related taxa and can be found in the alternative codon patterns of noncanonical genetic codes. As a consequence, the algorithm may reveal an earlier stage of the evolution of the standard code.

Ancient DNA sequence revealed by error-correcting codes

PubMed Central

Brandão, Marcelo M.; Spoladore, Larissa; Faria, Luzinete C. B.; Rocha, Andréa S. L.; Silva-Filho, Marcio C.; Palazzo, Reginaldo

2015-01-01

A previously described DNA sequence generator algorithm (DNA-SGA) using error-correcting codes has been employed as a computational tool to address the evolutionary pathway of the genetic code. The code-generated sequence alignment demonstrated that a residue mutation revealed by the code can be found in the same position in sequences of distantly related taxa. Furthermore, the code-generated sequences do not promote amino acid changes in the deviant genomes through codon reassignment. A Bayesian evolutionary analysis of both code-generated and homologous sequences of the Arabidopsis thaliana malate dehydrogenase gene indicates an approximately 1 MYA divergence time from the MDH code-generated sequence node to its paralogous sequences. The DNA-SGA helps to determine the plesiomorphic state of DNA sequences because a single nucleotide alteration often occurs in distantly related taxa and can be found in the alternative codon patterns of noncanonical genetic codes. As a consequence, the algorithm may reveal an earlier stage of the evolution of the standard code. PMID:26159228

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 component separation: The Planck experience

NASA Astrophysics Data System (ADS)

Wehus, Ingunn Kathrine; Eriksen, Hans Kristian

2018-05-01

Bayesian component separation techniques have played a central role in the data reduction process of Planck. The most important strength of this approach is its global nature, in which a parametric and physical model is fitted to the data. Such physical modeling allows the user to constrain very general data models, and jointly probe cosmological, astrophysical and instrumental parameters. This approach also supports statistically robust goodness-of-fit tests in terms of data-minus-model residual maps, which are essential for identifying residual systematic effects in the data. The main challenges are high code complexity and computational cost. Whether or not these costs are justified for a given experiment depends on its final uncertainty budget. We therefore predict that the importance of Bayesian component separation techniques is likely to increase with time for intensity mapping experiments, similar to what has happened in the CMB field, as observational techniques mature, and their overall sensitivity improves.

Construction of self-dual codes in the Rosenbloom-Tsfasman metric

NASA Astrophysics Data System (ADS)

Krisnawati, Vira Hari; Nisa, Anzi Lina Ukhtin

2017-12-01

Linear code is a very basic code and very useful in coding theory. Generally, linear code is a code over finite field in Hamming metric. Among the most interesting families of codes, the family of self-dual code is a very important one, because it is the best known error-correcting code. The concept of Hamming metric is develop into Rosenbloom-Tsfasman metric (RT-metric). The inner product in RT-metric is different from Euclid inner product that is used to define duality in Hamming metric. Most of the codes which are self-dual in Hamming metric are not so in RT-metric. And, generator matrix is very important to construct a code because it contains basis of the code. Therefore in this paper, we give some theorems and methods to construct self-dual codes in RT-metric by considering properties of the inner product and generator matrix. Also, we illustrate some examples for every kind of the construction.

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

NASA Technical Reports Server (NTRS)

Hoover, Richard B.; Storrie-Lombardi, Michael C.

2004-01-01

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Long Non-Coding RNA HOXA-AS2 Regulates Malignant Glioma Behaviors and Vasculogenic Mimicry Formation via the MiR-373/EGFR Axis.

PubMed

Gao, Yana; Yu, Hai; Liu, Yunhui; Liu, Xiaobai; Zheng, Jian; Ma, Jun; Gong, Wei; Chen, Jiajia; Zhao, Lini; Tian, Yu; Xue, Yixue

2018-01-01

Vasculogenic mimicry (VM) has been reported to be a novel glioma neovascularization process. Anti-VM therapy provides new insight into glioma clinical management. In this study, we revealed the role of the long non-coding RNA HOXA cluster antisense RNA 2 (HOXA-AS2) in malignant glioma behaviors and VM formation. Quantitative real-time PCR was performed to determine the expression levels of HOXA-AS2 in glioma samples and glioblastoma cell lines. CD34-periodic acid-Schiff dual-staining was performed to assess VM in glioma samples. CCK-8, transwell, and Matrigel tube formation assays were performed to measure the effects of HOXA-AS2 knockdown on cell viability, migration, invasion, and VM tube formation, respectively. RNA immunoprecipitation, dual-luciferase reporter and Western blot assays were performed to explore the molecular mechanisms underlying the functions of HOXS-AS2 in glioblastoma cells. A nude mouse xenograft model was used to investigate the role of HOXA-AS2 in xenograft glioma growth and VM density. Student's t-tests, one-way ANOVAs followed by Bonferroni posthoc tests, and chi-square tests were used for the statistical analyses. HOXA-AS2 was upregulated in glioma samples and cell lines and was positively correlated with VM. HOXA-AS2 knockdown attenuated cell viability, migration, invasion, and VM formation in glioma cells and inhibited the expression of vascular endothelial-cadherin (VE-cadherin), as well as the expression and activity of matrix metalloproteinase matrix metalloproteinase (MMP)-2 and MMP-9. miR-373 was downregulated in glioma samples and cell lines and suppressed malignancy in glioblastoma cells. HOXA-AS2 bound to miR-373 and negatively regulated its expression. Epidermal growth factor receptor (EGFR), a target of miR-373, increased the expression levels of VE-cadherin, as well as the expression and activity levels of MMP-2 and MMP-9, via activating phosphatidylinositol 3-kinase/serine/threonine kinase pathways. HOXA-AS2 knockdown combined with miR-373 overexpression yielded optimal tumor suppressive effects and the lowest VM density in vivo. HOXA-AS2 knockdown inhibited malignant glioma behaviors and VM formation via the miR-373/EGFR axis. © 2018 The Author(s). Published by S. Karger AG, Basel.

Implication of adaptive smoothness constraint and Helmert variance component estimation in seismic slip inversion

NASA Astrophysics Data System (ADS)

Fan, Qingbiao; Xu, Caijun; Yi, Lei; Liu, Yang; Wen, Yangmao; Yin, Zhi

2017-10-01

When ill-posed problems are inverted, the regularization process is equivalent to adding constraint equations or prior information from a Bayesian perspective. The veracity of the constraints (or the regularization matrix R) significantly affects the solution, and a smoothness constraint is usually added in seismic slip inversions. In this paper, an adaptive smoothness constraint (ASC) based on the classic Laplacian smoothness constraint (LSC) is proposed. The ASC not only improves the smoothness constraint, but also helps constrain the slip direction. A series of experiments are conducted in which different magnitudes of noise are imposed and different densities of observation are assumed, and the results indicated that the ASC was superior to the LSC. Using the proposed ASC, the Helmert variance component estimation method is highlighted as the best for selecting the regularization parameter compared with other methods, such as generalized cross-validation or the mean squared error criterion method. The ASC may also benefit other ill-posed problems in which a smoothness constraint is required.

MiR-34a/miR-93 target c-Ski to modulate the proliferaton of rat cardiac fibroblasts and extracellular matrix deposition in vivo and in vitro.

PubMed

Zhang, Chengliang; Zhang, Yanfeng; Zhu, Hong; Hu, Jiajia; Xie, Zhongshang

2018-06-01

Cardiac fibrosis is associated with diverse heart diseases. In response to different pathological irritants, cardiac fibroblasts may be induced to proliferate and differentiate into cardiac myofibroblasts, thus contributing to cardiac fibrosis. TGF-β signaling is implicated in the development of heart failure through the induction of cardiac fibrosis. C-Ski, an inhibitory regulator of TGF-β signaling, has been reported to suppress TGF-β1-induced human cardiac fibroblasts' proliferation and ECM protein increase; however, the underlying molecular mechanism needs further investigation. In the present study, we demonstrated that c-Ski could ameliorate isoproterenol (ISO)-induced rat myocardial fibrosis model and TGF-β1-induced primary rat cardiac fibroblasts' proliferation, as well as extracellular matrix (ECM) deposition. The protein level of c-Ski was dramatically decreased in cardiac fibrosis and TGF-β1-stimulated primary rat cardiac fibroblasts. In recent decades, a family of small non-coding RNA, namely miRNAs, has been reported to regulate gene expression by interacting with diverse mRNAs and inducing either translational suppression or mRNA degradation. Herein, we selected miR-34a and miR-93 as candidate miRNAs that might target to regulate c-Ski expression. After confirming that miR-34a/miR-93 targeted c-Ski to inhibit its expression, we also revealed that miR-34a/miR-93 affected TGF-β1-induced fibroblasts' proliferation and ECM deposition through c-Ski. Taken together, we demonstrated a miR-34a/miR-93-c-Ski axis which modulates TGF-β1- and ISO-induced cardiac fibrosis in vitro and in vivo; targeting the inhibitory factors of c-Ski to rescue its expression may be a promising strategy for the treatment of cardiac fibrosis. Copyright © 2018 Elsevier Inc. All rights reserved.

Bayesian Atmospheric Radiative Transfer (BART): Model, Statistics Driver, and Application to HD 209458b

NASA Astrophysics Data System (ADS)

Cubillos, Patricio; Harrington, Joseph; Blecic, Jasmina; Stemm, Madison M.; Lust, Nate B.; Foster, Andrew S.; Rojo, Patricio M.; Loredo, Thomas J.

2014-11-01

Multi-wavelength secondary-eclipse and transit depths probe the thermo-chemical properties of exoplanets. In recent years, several research groups have developed retrieval codes to analyze the existing data and study the prospects of future facilities. However, the scientific community has limited access to these packages. Here we premiere the open-source Bayesian Atmospheric Radiative Transfer (BART) code. We discuss the key aspects of the radiative-transfer algorithm and the statistical package. The radiation code includes line databases for all HITRAN molecules, high-temperature H2O, TiO, and VO, and includes a preprocessor for adding additional line databases without recompiling the radiation code. Collision-induced absorption lines are available for H2-H2 and H2-He. The parameterized thermal and molecular abundance profiles can be modified arbitrarily without recompilation. The generated spectra are integrated over arbitrary bandpasses for comparison to data. BART's statistical package, Multi-core Markov-chain Monte Carlo (MC3), is a general-purpose MCMC module. MC3 implements the Differental-evolution Markov-chain Monte Carlo algorithm (ter Braak 2006, 2009). MC3 converges 20-400 times faster than the usual Metropolis-Hastings MCMC algorithm, and in addition uses the Message Passing Interface (MPI) to parallelize the MCMC chains. We apply the BART retrieval code to the HD 209458b data set to estimate the planet's temperature profile and molecular abundances. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

PubMed Central

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

NASA Astrophysics Data System (ADS)

Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

2015-03-01

We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

The First Mitochondrial Genome for the Superfamily Hagloidea and Implications for Its Systematic Status in Ensifera

PubMed Central

Zhou, Zhijun; Shi, Fuming; Zhao, Ling

2014-01-01

Hagloidea Handlirsch, 1906 was an ancient group of Ensifera, that was much more diverse in the past extending at least into the Triassic, apparently diminishing in diversity through the Cretaceous, and now only represented by a few extant species. In this paper, we report the complete mitochondrial genome (mitogenome) of Tarragoilus diuturnus Gorochov, 2001, representing the first mitogenome of the superfamily Hagloidea. The size of the entire mitogenome of T. diuturnus is 16144 bp, containing 13 protein-coding genes (PCGs), 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes and one control region. The order and orientation of the gene arrangement pattern is identical to that of D. yakuba and most ensiferans species. A phylogenomic analysis was carried out based on the concatenated dataset of 13 PCGs and 2 rRNA genes from mitogenome sequences of 15 ensiferan species, comprising four superfamilies Grylloidea, Tettigonioidae, Rhaphidophoroidea and Hagloidea. Both maximum likelihood and Bayesian inference analyses strongly support Hagloidea T. diuturnus and Rhaphidophoroidea Troglophilus neglectus as forming a monophyletic group, sister to the Tettigonioidea. The relationships among four superfamilies of Ensifera were (Grylloidea, (Tettigonioidea, (Hagloidea, Rhaphidophoroidea))). PMID:24465850

A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.

PubMed

Cao, Cheng; Akalin Acar, Zeynep; Kreutz-Delgado, Kenneth; Makeig, Scott

2012-01-01

Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.

Bayesian Analogy with Relational Transformations

ERIC Educational Resources Information Center

Lu, Hongjing; Chen, Dawn; Holyoak, Keith J.

2012-01-01

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy…

The analysis of convolutional codes via the extended Smith algorithm

NASA Technical Reports Server (NTRS)

Mceliece, R. J.; Onyszchuk, I.

1993-01-01

Convolutional codes have been the central part of most error-control systems in deep-space communication for many years. Almost all such applications, however, have used the restricted class of (n,1), also known as 'rate 1/n,' convolutional codes. The more general class of (n,k) convolutional codes contains many potentially useful codes, but their algebraic theory is difficult and has proved to be a stumbling block in the evolution of convolutional coding systems. In this article, the situation is improved by describing a set of practical algorithms for computing certain basic things about a convolutional code (among them the degree, the Forney indices, a minimal generator matrix, and a parity-check matrix), which are usually needed before a system using the code can be built. The approach is based on the classic Forney theory for convolutional codes, together with the extended Smith algorithm for polynomial matrices, which is introduced in this article.

Innovative Bayesian and Parsimony Phylogeny of Dung Beetles (Coleoptera, Scarabaeidae, Scarabaeinae) Enhanced by Ontology-Based Partitioning of Morphological Characters

PubMed Central

Tarasov, Sergei; Génier, François

2015-01-01

Scarabaeine dung beetles are the dominant dung feeding group of insects and are widely used as model organisms in conservation, ecology and developmental biology. Due to the conflicts among 13 recently published phylogenies dealing with the higher-level relationships of dung beetles, the phylogeny of this lineage remains largely unresolved. In this study, we conduct rigorous phylogenetic analyses of dung beetles, based on an unprecedented taxon sample (110 taxa) and detailed investigation of morphology (205 characters). We provide the description of morphology and thoroughly illustrate the used characters. Along with parsimony, traditionally used in the analysis of morphological data, we also apply the Bayesian method with a novel approach that uses anatomy ontology for matrix partitioning. This approach allows for heterogeneity in evolutionary rates among characters from different anatomical regions. Anatomy ontology generates a number of parameter-partition schemes which we compare using Bayes factor. We also test the effect of inclusion of autapomorphies in the morphological analysis, which hitherto has not been examined. Generally, schemes with more parameters were favored in the Bayesian comparison suggesting that characters located on different body regions evolve at different rates and that partitioning of the data matrix using anatomy ontology is reasonable; however, trees from the parsimony and all the Bayesian analyses were quite consistent. The hypothesized phylogeny reveals many novel clades and provides additional support for some clades recovered in previous analyses. Our results provide a solid basis for a new classification of dung beetles, in which the taxonomic limits of the tribes Dichotomiini, Deltochilini and Coprini are restricted and many new tribes must be described. Based on the consistency of the phylogeny with biogeography, we speculate that dung beetles may have originated in the Mesozoic contrary to the traditional view pointing to a Cenozoic origin. PMID:25781019

Unsupervised Bayesian linear unmixing of gene expression microarrays.

PubMed

Bazot, Cécile; Dobigeon, Nicolas; Tourneret, Jean-Yves; Zaas, Aimee K; Ginsburg, Geoffrey S; Hero, Alfred O

2013-03-19

This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor.

A Multiple Sphere T-Matrix Fortran Code for Use on Parallel Computer Clusters

NASA Technical Reports Server (NTRS)

Mackowski, D. W.; Mishchenko, M. I.

2011-01-01

A general-purpose Fortran-90 code for calculation of the electromagnetic scattering and absorption properties of multiple sphere clusters is described. The code can calculate the efficiency factors and scattering matrix elements of the cluster for either fixed or random orientation with respect to the incident beam and for plane wave or localized- approximation Gaussian incident fields. In addition, the code can calculate maps of the electric field both interior and exterior to the spheres.The code is written with message passing interface instructions to enable the use on distributed memory compute clusters, and for such platforms the code can make feasible the calculation of absorption, scattering, and general EM characteristics of systems containing several thousand spheres.

10Gbps 2D MGC OCDMA Code over FSO Communication System

NASA Astrophysics Data System (ADS)

Professor Urmila Bhanja, Associate, Dr.; Khuntia, Arpita; Alamasety Swati, (Student

2017-08-01

Currently, wide bandwidth signal dissemination along with low latency is a leading requisite in various applications. Free space optical wireless communication has introduced as a realistic technology for bridging the gap in present high data transmission fiber connectivity and as a provisional backbone for rapidly deployable wireless communication infrastructure. The manuscript highlights on the implementation of 10Gbps SAC-OCDMA FSO communications using modified two dimensional Golomb code (2D MGC) that possesses better auto correlation, minimum cross correlation and high cardinality. A comparison based on pseudo orthogonal (PSO) matrix code and modified two dimensional Golomb code (2D MGC) is developed in the proposed SAC OCDMA-FSO communication module taking different parameters into account. The simulative outcome signifies that the communication radius is bounded by the multiple access interference (MAI). In this work, a comparison is made in terms of bit error rate (BER), and quality factor (Q) based on modified two dimensional Golomb code (2D MGC) and PSO matrix code. It is observed that the 2D MGC yields better results compared to the PSO matrix code. The simulation results are validated using optisystem version 14.

Resonant scattering experiments with radioactive nuclear beams - Recent results and future plans

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

Teranishi, T.; Sakaguchi, S.; Uesaka, T.

2013-04-19

Resonant scattering with low-energy radioactive nuclear beams of E < 5 MeV/u have been studied at CRIB of CNS and at RIPS of RIKEN. As an extension to the present experimental technique, we will install an advanced polarized proton target for resonant scattering experiments. A Monte-Carlo simulation was performed to study the feasibility of future experiments with the polarized target. In the Monte-Carlo simulation, excitation functions and analyzing powers were calculated using a newly developed R-matrix calculation code. A project of a small-scale radioactive beam facility at Kyushu University is also briefly described.

Processing Motion: Using Code to Teach Newtonian Physics

NASA Astrophysics Data System (ADS)

Massey, M. Ryan

Prior to instruction, students often possess a common-sense view of motion, which is inconsistent with Newtonian physics. Effective physics lessons therefore involve conceptual change. To provide a theoretical explanation for concepts and how they change, the triangulation model brings together key attributes of prototypes, exemplars, theories, Bayesian learning, ontological categories, and the causal model theory. The triangulation model provides a theoretical rationale for why coding is a viable method for physics instruction. As an experiment, thirty-two adolescent students participated in summer coding academies to learn how to design Newtonian simulations. Conceptual and attitudinal data was collected using the Force Concept Inventory and the Colorado Learning Attitudes about Science Survey. Results suggest that coding is an effective means for teaching Newtonian physics.

Snapshot Mueller polarimetry for biomedical diagnostic related to human liver fibrosis: evaluation of the method for biomedical assessments

NASA Astrophysics Data System (ADS)

Babilotte, P.; Dubreuil, M.; Rivet, S.; Lijour, Y.; Sevrain, D.; Martin, L.; Le Brun, G.; Le Grand, Y.; Le Jeune, B.

2011-10-01

Human liver biopsy samples, consisting into a 16 μm thickness biomaterial chemically fixed into a formaldehyde matrix, and stained by red picrosirius dye, are analysed for different states of fibrosis degeneration. Polarimetric methods, and specially Mueller polarimetry based on wavelength coding, have been qualified as an efficient tool to describe many different biological aspects. The polarimetric characteristics of the media, extracted from a Lu and Chipman decomposition1, 2 of their Mueller Matrix (MM), are correlated with the degeneracy level of tissue. Different works and results linked to the clinical analysis will be presented and compared to previous performed works.3 Polarimetric imaging will be presented and compared with SHG measurements. A statistical analysis of the distribution of polarimetric parameters (such as the retardance R and depolarisation Pd) will be presented too, in order to characterise the liver fibrosis level into the biomaterial under study.

Non-Insertional Code-Switching in English-Japanese Bilingual Children: Alternation and Congruent Lexicalisation

ERIC Educational Resources Information Center

Namba, Kazuhiko

2012-01-01

This article investigates English-Japanese children's code-switching (CS) from the structural point of view. Muysken categorises it into three types, that is, insertion, alternation and congruent lexicalisation. Regarding insertion, using Myers-Scotton's matrix language frame (MLF) model, for example, the matrix language (ML) of a bilingual clause…

Bare Forms and Lexical Insertions in Code-Switching: A Processing-Based Account

ERIC Educational Resources Information Center

Owens, Jonathan

2005-01-01

Bare forms (or [slashed O] forms), uninflected lexical L2 insertions in contexts where the matrix language expects morphological marking, have been recognized as an anomaly in different approaches to code-switching. Myers-Scotton (1997, 2002) has explained their existence in terms of structural incongruity between the matrix and embedded…

Overcoming Challenges in Kinetic Modeling of Magnetized Plasmas and Vacuum Electronic Devices

NASA Astrophysics Data System (ADS)

Omelchenko, Yuri; Na, Dong-Yeop; Teixeira, Fernando

2017-10-01

We transform the state-of-the art of plasma modeling by taking advantage of novel computational techniques for fast and robust integration of multiscale hybrid (full particle ions, fluid electrons, no displacement current) and full-PIC models. These models are implemented in 3D HYPERS and axisymmetric full-PIC CONPIC codes. HYPERS is a massively parallel, asynchronous code. The HYPERS solver does not step fields and particles synchronously in time but instead executes local variable updates (events) at their self-adaptive rates while preserving fundamental conservation laws. The charge-conserving CONPIC code has a matrix-free explicit finite-element (FE) solver based on a sparse-approximate inverse (SPAI) algorithm. This explicit solver approximates the inverse FE system matrix (``mass'' matrix) using successive sparsity pattern orders of the original matrix. It does not reduce the set of Maxwell's equations to a vector-wave (curl-curl) equation of second order but instead utilizes the standard coupled first-order Maxwell's system. We discuss the ability of our codes to accurately and efficiently account for multiscale physical phenomena in 3D magnetized space and laboratory plasmas and axisymmetric vacuum electronic devices.

Toric Calabi-Yau threefolds as quantum integrable systems. R-matrix and RTT relations

NASA Astrophysics Data System (ADS)

Awata, Hidetoshi; Kanno, Hiroaki; Mironov, Andrei; Morozov, Alexei; Morozov, Andrey; Ohkubo, Yusuke; Zenkevich, Yegor

2016-10-01

R-matrix is explicitly constructed for simplest representations of the Ding-Iohara-Miki algebra. Calculation is straightforward and significantly simpler than the one through the universal R-matrix used for a similar calculation in the Yangian case by A. Smirnov but less general. We investigate the interplay between the R-matrix structure and the structure of DIM algebra intertwiners, i.e. of refined topological vertices and show that the R-matrix is diagonalized by the action of the spectral duality belonging to the SL(2, ℤ) group of DIM algebra automorphisms. We also construct the T-operators satisfying the RTT relations with the R-matrix from refined amplitudes on resolved conifold. We thus show that topological string theories on the toric Calabi-Yau threefolds can be naturally interpreted as lattice integrable models. Integrals of motion for these systems are related to q-deformation of the reflection matrices of the Liouville/Toda theories.

Bayesian Latent Class Analysis Tutorial.

PubMed

Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca

2018-01-01

This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

Decoding and optimized implementation of SECDED codes over GF(q)

DOEpatents

Ward, H. Lee; Ganti, Anand; Resnick, David R

2013-10-22

A plurality of columns for a check matrix that implements a distance d linear error correcting code are populated by providing a set of vectors from which to populate the columns, and applying to the set of vectors a filter operation that reduces the set by eliminating therefrom all vectors that would, if used to populate the columns, prevent the check matrix from satisfying a column-wise linear independence requirement associated with check matrices of distance d linear codes. One of the vectors from the reduced set may then be selected to populate one of the columns. The filtering and selecting repeats iteratively until either all of the columns are populated or the number of currently unpopulated columns exceeds the number of vectors in the reduced set. Columns for the check matrix may be processed to reduce the amount of logic needed to implement the check matrix in circuit logic.

Design, decoding and optimized implementation of SECDED codes over GF(q)

DOEpatents

Ward, H Lee; Ganti, Anand; Resnick, David R

2014-06-17

A plurality of columns for a check matrix that implements a distance d linear error correcting code are populated by providing a set of vectors from which to populate the columns, and applying to the set of vectors a filter operation that reduces the set by eliminating therefrom all vectors that would, if used to populate the columns, prevent the check matrix from satisfying a column-wise linear independence requirement associated with check matrices of distance d linear codes. One of the vectors from the reduced set may then be selected to populate one of the columns. The filtering and selecting repeats iteratively until either all of the columns are populated or the number of currently unpopulated columns exceeds the number of vectors in the reduced set. Columns for the check matrix may be processed to reduce the amount of logic needed to implement the check matrix in circuit logic.

Decoding and optimized implementation of SECDED codes over GF(q)

DOEpatents

Ward, H Lee; Ganti, Anand; Resnick, David R

2014-11-18

A plurality of columns for a check matrix that implements a distance d linear error correcting code are populated by providing a set of vectors from which to populate the columns, and applying to the set of vectors a filter operation that reduces the set by eliminating therefrom all vectors that would, if used to populate the columns, prevent the check matrix from satisfying a column-wise linear independence requirement associated with check matrices of distance d linear codes. One of the vectors from the reduced set may then be selected to populate one of the columns. The filtering and selecting repeats iteratively until either all of the columns are populated or the number of currently unpopulated columns exceeds the number of vectors in the reduced set. Columns for the check matrix may be processed to reduce the amount of logic needed to implement the check matrix in circuit logic.

A Novel Mgp-Cre Knock-In Mouse Reveals an Anticalcification/Antistiffness Candidate Gene in the Trabecular Meshwork and Peripapillary Scleral Region.

PubMed

Borrás, Teresa; Smith, Matthew H; Buie, LaKisha K

2015-04-01

Soft tissue calcification is a pathological condition. Matrix Gla (MGP) is a potent mineralization inhibitor secreted by cartilage chondrocytes and arteries' vascular smooth muscle cells. Mgp knock-out mice die at 6 weeks due to massive arterial calcification. Arterial calcification results in arterial stiffness and higher systolic blood pressure. Intriguingly, MGP was highly abundant in trabecular meshwork (TM). Because tissue stiffness is relevant to glaucoma, we investigated which additional eye tissues use Mgp's function using knock-in mice. An Mgp-Cre-recombinase coding sequence (Cre) knock-in mouse, containing Mgp DNA plus an internal ribosomal entry site (IRES)-Cre-cassette was generated by homologous recombination. Founders were crossed with Cre-mediated reporter mouse R26R-lacZ. Their offspring expresses lacZ where Mgp is transcribed. Eyes from MgpCre/+;R26RlacZ/+ (Mgp-lacZ knock-in) and controls, 1 to 8 months were assayed for β-gal enzyme histochemistry. As expected, Mgp-lacZ knock-in's TM was intensely blue. In addition, this mouse revealed high specific expression in the sclera, particularly in the peripapillary scleral region (ppSC). Ciliary muscle and sclera above the TM were also positive. Scleral staining was located immediately underneath the choroid (chondrocyte layer), began midsclera and was remarkably high in the ppSC. Cornea, iris, lens, ciliary body, and retina were negative. All mice exhibited similar staining patterns. All controls were negative. Matrix Gla's restricted expression to glaucoma-associated tissues from anterior and posterior segments suggests its involvement in the development of the disease. Matrix Gla's anticalcification/antistiffness properties in the vascular tissue, together with its high TM and ppCS expression, place this gene as a strong candidate for TM's softness and sclera's stiffness regulation in glaucoma.

Bayesian Methods and Confidence Intervals for Automatic Target Recognition of SAR Canonical Shapes

DTIC Science & Technology

2014-03-27

and DirectX [22]. The CUDA platform was developed by the NVIDIA Corporation to allow programmers access to the computational capabilities of the...were used for the intense repetitive computations. Developing CUDA software requires writing code for specialized compilers provided by NVIDIA and

Building Process Improvement Business Cases Using Bayesian Belief Networks and Monte Carlo Simulation

DTIC Science & Technology

2009-07-01

simulation. The pilot described in this paper used this two-step approach within a Define, Measure, Analyze, Improve, and Control ( DMAIC ) framework to...networks, BBN, Monte Carlo simulation, DMAIC , Six Sigma, business case 15. NUMBER OF PAGES 35 16. PRICE CODE 17. SECURITY CLASSIFICATION OF

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

PubMed

Kadi, Adiveppa S; Avaradi, Shivakumari R

2015-01-01

Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns. We have used Bayesian approach to quantify the disease outbreak through key epidemiological parameter basic reproduction number (R0), using effective contacts, defined as sum of the product of incidence cases and probability of generation time distribution. We have estimated R0 from daily case incidence data for pandemic influenza A/H1N1 2009 in India, for the initial phase. The estimated R0 with 95% credible interval is consistent with several other studies on the same strain. Through sensitivity analysis our study indicates that infectiousness affects the estimate of R0. Basic reproduction number R0 provides the useful information to the public health system to do some effort in controlling the disease by using mitigation strategies like vaccination, quarantine, and so forth.

The Universal R-Matrix for the Jordanian Deformation of sl(2), and the Contracted Forms of so(4)

NASA Astrophysics Data System (ADS)

Shariati, A.; Aghamohammadi, A.; Khorrami, M.

We introduce a universal R-matrix for the Jordanian deformation of U(sl(2)). Using Uh(so(4))=Uh(sl(2)) ⊕ U-h(sl(2)), we obtain the universal R-matrix for Uh(so(4)). Applying the graded contractions on the universal R-matrix of Uh(so(4)), we show that there exist three distinct R-matrices for all the contracted algebras. It is shown that Uh(sl(2)), Uh(so(4)), and all of these contracted algebras are triangular.

Consumption of a durable termite bait matrix by subterranean termites (Isoptera: Rhinotermitidae) and resulting insecticidal activity.

PubMed

Hamm, Ronda L; DeMark, Joseph J; Chin-Heady, Eva; Tolley, Mike P

2013-04-01

A novel durable termite bait was developed to enable continuous bait availability and lengthen the monitoring interval to 1 year. Laboratory studies were conducted to determine the palatability and insecticidal activity of this bait to Reticulitermes flavipes (Kollar), R. virginicus (Banks), R. hesperus Banks, Coptotermes formosanus Shiraki and Heterotermes aureus (Synder). Consumption of the blank durable bait matrix was significantly higher than consumption of a blank preferred textured cellulose matrix (PTC) by R. virginicus, R. flavipes and C. formosanus. R. flavipes, R. hesperus and H. aureus consumed significantly more durable bait than PTC when both contained the active ingredient noviflumuron. All bait treatments resulted in significant mortality relative to the untreated controls. Survivorship of R. virginicus, C. formosanus and H. aureus was 2% or less and not significantly different between the durable bait and PTC treatments containing noviflumuron. The durable bait matrix lagged behind the PTC matrix in mortality over time for all species tested except H. aureus. The durable bait was highly palatable and effective in inducing mortality to R. flavipes, R. virginicus, R. hesperus, C. formosanus and H. aureus in the laboratory. This unique bait matrix will be available to termites continuously and allows for an annual monitoring interval. The durability of this bait matrix is unprecedented, allowing for bait to remain active for years and thus providing continuous structural protection. © 2012 Society of Chemical Industry.

Representing k-graphs as Matrix Algebras

NASA Astrophysics Data System (ADS)

Rosjanuardi, R.

2018-05-01

For any commutative unital ring R and finitely aligned k-graph Λ with |Λ| < ∞ without cycles, we can realise Kumjian-Pask algebra KP R (Λ) as a direct sum of of matrix algebra over some vertices v with properties ν = νΛ, i.e: ⊕ νΛ=ν M |Λv|(R). When there is only a single vertex ν ∈ Λ° such that ν = νΛ, we can realise the Kumjian-Pask algebra as the matrix algebra M |ΛV|(R). Hence the matrix algebra M |vΛ|(R) can be regarded as a representation of the k-graph Λ. In this talk we will figure out the relation between finitely aligned k-graph and matrix algebra.

Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

PubMed Central

Ahn, Woo-Young; Haines, Nathaniel; Zhang, Lei

2017-01-01

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations. PMID:29601060

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node

PubMed Central

Cai, Zhipeng; Zou, Fumin; Zhang, Xiangyu

2018-01-01

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption. PMID:29599945

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node.

PubMed

Luo, Kan; Cai, Zhipeng; Du, Keqin; Zou, Fumin; Zhang, Xiangyu; Li, Jianqing

2018-01-01

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.

Parallel filtering in global gyrokinetic simulations

NASA Astrophysics Data System (ADS)

Jolliet, S.; McMillan, B. F.; Villard, L.; Vernay, T.; Angelino, P.; Tran, T. M.; Brunner, S.; Bottino, A.; Idomura, Y.

2012-02-01

In this work, a Fourier solver [B.F. McMillan, S. Jolliet, A. Bottino, P. Angelino, T.M. Tran, L. Villard, Comp. Phys. Commun. 181 (2010) 715] is implemented in the global Eulerian gyrokinetic code GT5D [Y. Idomura, H. Urano, N. Aiba, S. Tokuda, Nucl. Fusion 49 (2009) 065029] and in the global Particle-In-Cell code ORB5 [S. Jolliet, A. Bottino, P. Angelino, R. Hatzky, T.M. Tran, B.F. McMillan, O. Sauter, K. Appert, Y. Idomura, L. Villard, Comp. Phys. Commun. 177 (2007) 409] in order to reduce the memory of the matrix associated with the field equation. This scheme is verified with linear and nonlinear simulations of turbulence. It is demonstrated that the straight-field-line angle is the coordinate that optimizes the Fourier solver, that both linear and nonlinear turbulent states are unaffected by the parallel filtering, and that the k∥ spectrum is independent of plasma size at fixed normalized poloidal wave number.

The Use of a Bayesian Hierarchy to Develop and Validate a Co-Morbidity Score to Predict Mortality for Linked Primary and Secondary Care Data from the NHS in England

PubMed Central

Card, Tim R.; West, Joe

2016-01-01

Background We have assessed whether the linkage between routine primary and secondary care records provided an opportunity to develop an improved population based co-morbidity score with the combined information on co-morbidities from both health care settings. Methods We extracted all people older than 20 years at the start of 2005 within the linkage between the Hospital Episodes Statistics, Clinical Practice Research Datalink, and Office for National Statistics death register in England. A random 50% sample was used to identify relevant diagnostic codes using a Bayesian hierarchy to share information between similar Read and ICD 10 code groupings. Internal validation of the score was performed in the remaining 50% and discrimination was assessed using Harrell’s C statistic. Comparisons were made over time, age, and consultation rate with the Charlson and Elixhauser indexes. Results 657,264 people were followed up from the 1st January 2005. 98 groupings of codes were derived from the Bayesian hierarchy, and 37 had an adjusted weighting of greater than zero in the Cox proportional hazards model. 11 of these groupings had a different weighting dependent on whether they were coded from hospital or primary care. The C statistic reduced from 0.88 (95% confidence interval 0.88–0.88) in the first year of follow up, to 0.85 (0.85–0.85) including all 5 years. When we stratified the linked score by consultation rate the association with mortality remained consistent, but there was a significant interaction with age, with improved discrimination and fit in those under 50 years old (C = 0.85, 0.83–0.87) compared to the Charlson (C = 0.79, 0.77–0.82) or Elixhauser index (C = 0.81, 0.79–0.83). Conclusions The use of linked population based primary and secondary care data developed a co-morbidity score that had improved discrimination, particularly in younger age groups, and had a greater effect when adjusting for co-morbidity than existing scores. PMID:27788230

The effect of iconicity of visual displays on statistical reasoning: evidence in favor of the null hypothesis.

PubMed

Sirota, Miroslav; Kostovičová, Lenka; Juanchich, Marie

2014-08-01

Knowing which properties of visual displays facilitate statistical reasoning bears practical and theoretical implications. Therefore, we studied the effect of one property of visual diplays - iconicity (i.e., the resemblance of a visual sign to its referent) - on Bayesian reasoning. Two main accounts of statistical reasoning predict different effect of iconicity on Bayesian reasoning. The ecological-rationality account predicts a positive iconicity effect, because more highly iconic signs resemble more individuated objects, which tap better into an evolutionary-designed frequency-coding mechanism that, in turn, facilitates Bayesian reasoning. The nested-sets account predicts a null iconicity effect, because iconicity does not affect the salience of a nested-sets structure-the factor facilitating Bayesian reasoning processed by a general reasoning mechanism. In two well-powered experiments (N = 577), we found no support for a positive iconicity effect across different iconicity levels that were manipulated in different visual displays (meta-analytical overall effect: log OR = -0.13, 95% CI [-0.53, 0.28]). A Bayes factor analysis provided strong evidence in favor of the null hypothesis-the null iconicity effect. Thus, these findings corroborate the nested-sets rather than the ecological-rationality account of statistical reasoning.

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

PubMed Central

Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang

2013-01-01

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941

Evaluation of the validity of job exposure matrix for psychosocial factors at work.

PubMed

Solovieva, Svetlana; Pensola, Tiina; Kausto, Johanna; Shiri, Rahman; Heliövaara, Markku; Burdorf, Alex; Husgafvel-Pursiainen, Kirsti; Viikari-Juntura, Eira

2014-01-01

To study the performance of a developed job exposure matrix (JEM) for the assessment of psychosocial factors at work in terms of accuracy, possible misclassification bias and predictive ability to detect known associations with depression and low back pain (LBP). We utilized two large population surveys (the Health 2000 Study and the Finnish Work and Health Surveys), one to construct the JEM and another to test matrix performance. In the first study, information on job demands, job control, monotonous work and social support at work was collected via face-to-face interviews. Job strain was operationalized based on job demands and job control using quadrant approach. In the second study, the sensitivity and specificity were estimated applying a Bayesian approach. The magnitude of misclassification error was examined by calculating the biased odds ratios as a function of the sensitivity and specificity of the JEM and fixed true prevalence and odds ratios. Finally, we adjusted for misclassification error the observed associations between JEM measures and selected health outcomes. The matrix showed a good accuracy for job control and job strain, while its performance for other exposures was relatively low. Without correction for exposure misclassification, the JEM was able to detect the association between job strain and depression in men and between monotonous work and LBP in both genders. Our results suggest that JEM more accurately identifies occupations with low control and high strain than those with high demands or low social support. Overall, the present JEM is a useful source of job-level psychosocial exposures in epidemiological studies lacking individual-level exposure information. Furthermore, we showed the applicability of a Bayesian approach in the evaluation of the performance of the JEM in a situation where, in practice, no gold standard of exposure assessment exists.

Bayesian Recurrent Neural Network for Language Modeling.

PubMed

Chien, Jen-Tzung; Ku, Yuan-Chu

2016-02-01

A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.

Matrix product operators, matrix product states, and ab initio density matrix renormalization group algorithms

NASA Astrophysics Data System (ADS)

Chan, Garnet Kin-Lic; Keselman, Anna; Nakatani, Naoki; Li, Zhendong; White, Steven R.

2016-07-01

Current descriptions of the ab initio density matrix renormalization group (DMRG) algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of matrix product states and matrix product operators. The same algorithm can appear dramatically different when written in the two different vocabularies. In this work, we carefully describe the translation between the two languages in several contexts. First, we describe how to efficiently implement the ab initio DMRG sweep using a matrix product operator based code, and the equivalence to the original renormalized operator implementation. Next we describe how to implement the general matrix product operator/matrix product state algebra within a pure renormalized operator-based DMRG code. Finally, we discuss two improvements of the ab initio DMRG sweep algorithm motivated by matrix product operator language: Hamiltonian compression, and a sum over operators representation that allows for perfect computational parallelism. The connections and correspondences described here serve to link the future developments with the past and are important in the efficient implementation of continuing advances in ab initio DMRG and related algorithms.

Matrix product operators, matrix product states, and ab initio density matrix renormalization group algorithms.

PubMed

Chan, Garnet Kin-Lic; Keselman, Anna; Nakatani, Naoki; Li, Zhendong; White, Steven R

2016-07-07

Current descriptions of the ab initio density matrix renormalization group (DMRG) algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of matrix product states and matrix product operators. The same algorithm can appear dramatically different when written in the two different vocabularies. In this work, we carefully describe the translation between the two languages in several contexts. First, we describe how to efficiently implement the ab initio DMRG sweep using a matrix product operator based code, and the equivalence to the original renormalized operator implementation. Next we describe how to implement the general matrix product operator/matrix product state algebra within a pure renormalized operator-based DMRG code. Finally, we discuss two improvements of the ab initio DMRG sweep algorithm motivated by matrix product operator language: Hamiltonian compression, and a sum over operators representation that allows for perfect computational parallelism. The connections and correspondences described here serve to link the future developments with the past and are important in the efficient implementation of continuing advances in ab initio DMRG and related algorithms.

Berry phase and entanglement of three qubits in a new Yang-Baxter system

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

Hu Taotao; Xue Kang; Wu Chunfeng

2009-08-15

In this paper we construct a new 8x8M matrix from the 4x4M matrix, where M/M is the image of the braid group representation. The 8x8M matrix and the 4x4M matrix both satisfy extraspecial 2-group algebra relations. By Yang-Baxteration approach, we derive a unitary R({theta},{phi}) matrix from the M matrix with parameters {phi} and {theta}. Three-qubit entangled states can be generated by using the R({theta},{phi}) matrix. A Hamiltonian for three qubits is constructed from the unitary R({theta},{phi}) matrix. We then study the entanglement and Berry phase of the Yang-Baxter system.

Analysis of the LSC microbunching instability in MaRIE linac reference design

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

Yampolsky, Nikolai

In this report we estimate the effect of the microbunching instability in the MaRIE XFEL linac. The reference design for the linac is described in a separate report. The parameters of the L1, L2, and L3 linacs as well as BC1 and BC2 bunch compressors were the same as in the referenced report. The beam dynamics was assumed to be linear along the accelerator (which is a reasonable assumption for estimating the effect of the microbunching instability). The parameters of the bunch also match the parameters described in the referenced report. Additionally, it was assumed that the beam radius ismore » equal to R = 100 m and does not change along linac. This assumption needs to be revisited at later studies. The beam dynamics during acceleration was accounted in the matrix formalism using a Matlab code. The input parameters for the linacs are: RF peak gradient, RF frequency, RF phase, linac length, and initial beam energy. The energy gain and the imposed chirp are calculated based on the RF parameters self-consistently. The bunch compressors are accounted in the matrix formalism as well. Each chicane is characterized by the beam energy and the R56 matrix element. It was confirmed that the linac and beam parameters described previously provide two-stage bunch compression with compression ratios of 10 and 20 resulting in the bunch of 3kA peak current.« less

LncRNA MALAT1 sponges miR-204 to promote osteoblast differentiation of human aortic valve interstitial cells through up-regulating Smad4.

PubMed

Xiao, Xiaoxiong; Zhou, Tingwen; Guo, Shichao; Guo, Chao; Zhang, Qiao; Dong, Nianguo; Wang, Yongjun

2017-09-15

Emerging evidences have indicated that long non-coding RNAs (lncRNAs) play vital roles in cardiovascular physiology and pathology. The lncRNA MALAT1, a highly abundant and conserved imprinted gene, has been implicated in many cardiovascular diseases. However, the function of MALAT1 in calcific aortic valve disease (CAVD) remains unknown. This study sought to document the function and underlying mechanism of MALAT1 in regulating CAVD. Protein level was determined by immunoblotting and immunofluorescence staining. MALAT1, miR-204 and mRNA expressions were detected by qRT-PCR. Mineralized bone matrix formation was assessed by Alizarin Red staining. The interaction between MALAT1 and miR-204 was studied using luciferase reporter assay, RNA pull-down assay and RNA-binding protein immunoprecipitation assay. Ectopic expression of MALAT1 was observed in calcific valves and after osteogenic induction in human aortic valve interstitial cells (VICs). In vitro experiments revealed that MALAT1 acted as a positive regulator of osteogenic differentiation by repressing miR-204 expression and activity and thereby promoting expression of osteoblast-specific markers, including alkaline phosphatase, mineralized bone matrix formation and osteocalcin. Mechanistically, we identified Smad4 as a direct target of miR-204. Importantly, MALAT1 could directly interact with miR-204 and overexpression of miR-204 efficiently reversed the upregulation of Smad4 induced by MALAT1. Thus, MALAT1 positively regulated the expression of Smad4 through sponging miR-204, and promoted osteogenic differentiation of VICs. Our study provides novel mechanistic insights into a critical role for lncRNA MALAT1 as a miRNA sponge in CAVD and sheds new light on lncRNA-directed diagnostics and therapeutics in CAVD. Copyright © 2017. Published by Elsevier B.V.

Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research.

PubMed

Waller, Niels G

2016-01-01

For a fixed set of standardized regression coefficients and a fixed coefficient of determination (R-squared), an infinite number of predictor correlation matrices will satisfy the implied quadratic form. I call such matrices fungible correlation matrices. In this article, I describe an algorithm for generating positive definite (PD), positive semidefinite (PSD), or indefinite (ID) fungible correlation matrices that have a random or fixed smallest eigenvalue. The underlying equations of this algorithm are reviewed from both algebraic and geometric perspectives. Two simulation studies illustrate that fungible correlation matrices can be profitably used in Monte Carlo research. The first study uses PD fungible correlation matrices to compare penalized regression algorithms. The second study uses ID fungible correlation matrices to compare matrix-smoothing algorithms. R code for generating fungible correlation matrices is presented in the supplemental materials.

An Introduction to Kristof's Theorem for Solving Least-Square Optimization Problems Without Calculus.

PubMed

Waller, Niels

2018-01-01

Kristof's Theorem (Kristof, 1970 ) describes a matrix trace inequality that can be used to solve a wide-class of least-square optimization problems without calculus. Considering its generality, it is surprising that Kristof's Theorem is rarely used in statistics and psychometric applications. The underutilization of this method likely stems, in part, from the mathematical complexity of Kristof's ( 1964 , 1970 ) writings. In this article, I describe the underlying logic of Kristof's Theorem in simple terms by reviewing four key mathematical ideas that are used in the theorem's proof. I then show how Kristof's Theorem can be used to provide novel derivations to two cognate models from statistics and psychometrics. This tutorial includes a glossary of technical terms and an online supplement with R (R Core Team, 2017 ) code to perform the calculations described in the text.

Low complexity Reed-Solomon-based low-density parity-check design for software defined optical transmission system based on adaptive puncturing decoding algorithm

NASA Astrophysics Data System (ADS)

Pan, Xiaolong; Liu, Bo; Zheng, Jianglong; Tian, Qinghua

2016-08-01

We propose and demonstrate a low complexity Reed-Solomon-based low-density parity-check (RS-LDPC) code with adaptive puncturing decoding algorithm for elastic optical transmission system. Partial received codes and the relevant column in parity-check matrix can be punctured to reduce the calculation complexity by adaptive parity-check matrix during decoding process. The results show that the complexity of the proposed decoding algorithm is reduced by 30% compared with the regular RS-LDPC system. The optimized code rate of the RS-LDPC code can be obtained after five times iteration.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

An efficient MPI/OpenMP parallelization of the Hartree–Fock–Roothaan method for the first generation of Intel® Xeon Phi™ processor architecture

DOE PAGES

Mironov, Vladimir; Moskovsky, Alexander; D’Mello, Michael; ...

2017-10-04

The Hartree-Fock (HF) method in the quantum chemistry package GAMESS represents one of the most irregular algorithms in computation today. Major steps in the calculation are the irregular computation of electron repulsion integrals (ERIs) and the building of the Fock matrix. These are the central components of the main Self Consistent Field (SCF) loop, the key hotspot in Electronic Structure (ES) codes. By threading the MPI ranks in the official release of the GAMESS code, we not only speed up the main SCF loop (4x to 6x for large systems), but also achieve a significant (>2x) reduction in the overallmore » memory footprint. These improvements are a direct consequence of memory access optimizations within the MPI ranks. We benchmark our implementation against the official release of the GAMESS code on the Intel R Xeon PhiTM supercomputer. Here, scaling numbers are reported on up to 7,680 cores on Intel Xeon Phi coprocessors.« less

Constructing a Pre-Emptive System Based on a Multidimentional Matrix and Autocompletion to Improve Diagnostic Coding in Acute Care Hospitals.

PubMed

Noussa-Yao, Joseph; Heudes, Didier; Escudie, Jean-Baptiste; Degoulet, Patrice

2016-01-01

Short-stay MSO (Medicine, Surgery, Obstetrics) hospitalization activities in public and private hospitals providing public services are funded through charges for the services provided (T2A in French). Coding must be well matched to the severity of the patient's condition, to ensure that appropriate funding is provided to the hospital. We propose the use of an autocompletion process and multidimensional matrix, to help physicians to improve the expression of information and to optimize clinical coding. With this approach, physicians without knowledge of the encoding rules begin from a rough concept, which is gradually refined through semantic proximity and uses information on the associated codes stemming of optimized knowledge bases of diagnosis code.

Analysis of the Bayesian Cramér-Rao lower bound in astrometry. Studying the impact of prior information in the location of an object

NASA Astrophysics Data System (ADS)

Echeverria, Alex; Silva, Jorge F.; Mendez, Rene A.; Orchard, Marcos

2016-10-01

Context. The best precision that can be achieved to estimate the location of a stellar-like object is a topic of permanent interest in the astrometric community. Aims: We analyze bounds for the best position estimation of a stellar-like object on a CCD detector array in a Bayesian setting where the position is unknown, but where we have access to a prior distribution. In contrast to a parametric setting where we estimate a parameter from observations, the Bayesian approach estimates a random object (I.e., the position is a random variable) from observations that are statistically dependent on the position. Methods: We characterize the Bayesian Cramér-Rao (CR) that bounds the minimum mean square error (MMSE) of the best estimator of the position of a point source on a linear CCD-like detector, as a function of the properties of detector, the source, and the background. Results: We quantify and analyze the increase in astrometric performance from the use of a prior distribution of the object position, which is not available in the classical parametric setting. This gain is shown to be significant for various observational regimes, in particular in the case of faint objects or when the observations are taken under poor conditions. Furthermore, we present numerical evidence that the MMSE estimator of this problem tightly achieves the Bayesian CR bound. This is a remarkable result, demonstrating that all the performance gains presented in our analysis can be achieved with the MMSE estimator. Conclusions: The Bayesian CR bound can be used as a benchmark indicator of the expected maximum positional precision of a set of astrometric measurements in which prior information can be incorporated. This bound can be achieved through the conditional mean estimator, in contrast to the parametric case where no unbiased estimator precisely reaches the CR bound.

cosmoabc: Likelihood-free inference for cosmology

NASA Astrophysics Data System (ADS)

Ishida, Emille E. O.; Vitenti, Sandro D. P.; Penna-Lima, Mariana; Trindade, Arlindo M.; Cisewski, Jessi; M.; de Souza, Rafael; Cameron, Ewan; Busti, Vinicius C.

2015-05-01

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.

Inferring the Growth of Massive Galaxies Using Bayesian Spectral Synthesis Modeling

NASA Astrophysics Data System (ADS)

Stillman, Coley Michael; Poremba, Megan R.; Moustakas, John

2018-01-01

The most massive galaxies in the universe are typically found at the centers of massive galaxy clusters. Studying these galaxies can provide valuable insight into the hierarchical growth of massive dark matter halos. One of the key challenges of measuring the stellar mass growth of massive galaxies is converting the measured light profiles into stellar mass. We use Prospector, a state-of-the-art Bayesian spectral synthesis modeling code, to infer the total stellar masses of a pilot sample of massive central galaxies selected from the Sloan Digital Sky Survey. We compare our stellar mass estimates to previous measurements, and present some of the quantitative diagnostics provided by Prospector.

A computer program for estimation from incomplete multinomial data

NASA Technical Reports Server (NTRS)

Credeur, K. R.

1978-01-01

Coding is given for maximum likelihood and Bayesian estimation of the vector p of multinomial cell probabilities from incomplete data. Also included is coding to calculate and approximate elements of the posterior mean and covariance matrices. The program is written in FORTRAN 4 language for the Control Data CYBER 170 series digital computer system with network operating system (NOS) 1.1. The program requires approximately 44000 octal locations of core storage. A typical case requires from 72 seconds to 92 seconds on CYBER 175 depending on the value of the prior parameter.

Implementation of a kappa-epsilon turbulence model to RPLUS3D code

NASA Technical Reports Server (NTRS)

Chitsomboon, Tawit

1992-01-01

The RPLUS3D code has been developed at the NASA Lewis Research Center to support the National Aerospace Plane (NASP) project. The code has the ability to solve three dimensional flowfields with finite rate combustion of hydrogen and air. The combustion process of the hydrogen-air system are simulated by an 18 reaction path, 8 species chemical kinetic mechanism. The code uses a Lower-Upper (LU) decomposition numerical algorithm as its basis, making it a very efficient and robust code. Except for the Jacobian matrix for the implicit chemistry source terms, there is no inversion of a matrix even though a fully implicit numerical algorithm is used. A k-epsilon turbulence model has recently been incorporated into the code. Initial validations have been conducted for a flow over a flat plate. Results of the validation studies are shown. Some difficulties in implementing the k-epsilon equations to the code are also discussed.

Implementation of a kappa-epsilon turbulence model to RPLUS3D code

NASA Astrophysics Data System (ADS)

Chitsomboon, Tawit

1992-02-01

The RPLUS3D code has been developed at the NASA Lewis Research Center to support the National Aerospace Plane (NASP) project. The code has the ability to solve three dimensional flowfields with finite rate combustion of hydrogen and air. The combustion process of the hydrogen-air system are simulated by an 18 reaction path, 8 species chemical kinetic mechanism. The code uses a Lower-Upper (LU) decomposition numerical algorithm as its basis, making it a very efficient and robust code. Except for the Jacobian matrix for the implicit chemistry source terms, there is no inversion of a matrix even though a fully implicit numerical algorithm is used. A k-epsilon turbulence model has recently been incorporated into the code. Initial validations have been conducted for a flow over a flat plate. Results of the validation studies are shown. Some difficulties in implementing the k-epsilon equations to the code are also discussed.

A denoising algorithm for CT image using low-rank sparse coding

NASA Astrophysics Data System (ADS)

Lei, Yang; Xu, Dong; Zhou, Zhengyang; Wang, Tonghe; Dong, Xue; Liu, Tian; Dhabaan, Anees; Curran, Walter J.; Yang, Xiaofeng

2018-03-01

We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.

Higher level phylogeny and the first divergence time estimation of Heteroptera (Insecta: Hemiptera) based on multiple genes.

PubMed

Li, Min; Tian, Ying; Zhao, Ying; Bu, Wenjun

2012-01-01

Heteroptera, or true bugs, are the largest, morphologically diverse and economically important group of insects with incomplete metamorphosis. However, the phylogenetic relationships within Heteroptera are still in dispute and most of the previous studies were based on morphological characters or with single gene (partial or whole 18S rDNA). Besides, so far, divergence time estimates for Heteroptera totally rely on the fossil record, while no studies have been performed on molecular divergence rates. Here, for the first time, we used maximum parsimony (MP), maximum likelihood (ML) and Bayesian inference (BI) with multiple genes (18S rDNA, 28S rDNA, 16S rDNA and COI) to estimate phylogenetic relationships among the infraorders, and meanwhile, the Penalized Likelihood (r8s) and Bayesian (BEAST) molecular dating methods were employed to estimate divergence time of higher taxa of this suborder. Major results of the present study included: Nepomorpha was placed as the most basal clade in all six trees (MP trees, ML trees and Bayesian trees of nuclear gene data and four-gene combined data, respectively) with full support values. The sister-group relationship of Cimicomorpha and Pentatomomorpha was also strongly supported. Nepomorpha originated in early Triassic and the other six infraorders originated in a very short period of time in middle Triassic. Cimicomorpha and Pentatomomorpha underwent a radiation at family level in Cretaceous, paralleling the proliferation of the flowering plants. Our results indicated that the higher-group radiations within hemimetabolous Heteroptera were simultaneously with those of holometabolous Coleoptera and Diptera which took place in the Triassic. While the aquatic habitat was colonized by Nepomorpha already in the Triassic, the Gerromorpha independently adapted to the semi-aquatic habitat in the Early Jurassic.

Higher Level Phylogeny and the First Divergence Time Estimation of Heteroptera (Insecta: Hemiptera) Based on Multiple Genes

PubMed Central

Zhao, Ying; Bu, Wenjun

2012-01-01

Heteroptera, or true bugs, are the largest, morphologically diverse and economically important group of insects with incomplete metamorphosis. However, the phylogenetic relationships within Heteroptera are still in dispute and most of the previous studies were based on morphological characters or with single gene (partial or whole 18S rDNA). Besides, so far, divergence time estimates for Heteroptera totally rely on the fossil record, while no studies have been performed on molecular divergence rates. Here, for the first time, we used maximum parsimony (MP), maximum likelihood (ML) and Bayesian inference (BI) with multiple genes (18S rDNA, 28S rDNA, 16S rDNA and COI) to estimate phylogenetic relationships among the infraorders, and meanwhile, the Penalized Likelihood (r8s) and Bayesian (BEAST) molecular dating methods were employed to estimate divergence time of higher taxa of this suborder. Major results of the present study included: Nepomorpha was placed as the most basal clade in all six trees (MP trees, ML trees and Bayesian trees of nuclear gene data and four-gene combined data, respectively) with full support values. The sister-group relationship of Cimicomorpha and Pentatomomorpha was also strongly supported. Nepomorpha originated in early Triassic and the other six infraorders originated in a very short period of time in middle Triassic. Cimicomorpha and Pentatomomorpha underwent a radiation at family level in Cretaceous, paralleling the proliferation of the flowering plants. Our results indicated that the higher-group radiations within hemimetabolous Heteroptera were simultaneously with those of holometabolous Coleoptera and Diptera which took place in the Triassic. While the aquatic habitat was colonized by Nepomorpha already in the Triassic, the Gerromorpha independently adapted to the semi-aquatic habitat in the Early Jurassic. PMID:22384163

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

Sechin, Ivan, E-mail: shnbuz@gmail.com, E-mail: zotov@mi.ras.ru; ITEP, B. Cheremushkinskaya Str. 25, Moscow 117218; Zotov, Andrei, E-mail: shnbuz@gmail.com, E-mail: zotov@mi.ras.ru

In this paper we propose versions of the associative Yang-Baxter equation and higher order R-matrix identities which can be applied to quantum dynamical R-matrices. As is known quantum non-dynamical R-matrices of Baxter-Belavin type satisfy this equation. Together with unitarity condition and skew-symmetry it provides the quantum Yang-Baxter equation and a set of identities useful for different applications in integrable systems. The dynamical R-matrices satisfy the Gervais-Neveu-Felder (or dynamical Yang-Baxter) equation. Relation between the dynamical and non-dynamical cases is described by the IRF (interaction-round-a-face)-Vertex transformation. An alternative approach to quantum (semi-)dynamical R-matrices and related quantum algebras was suggested by Arutyunov, Chekhov,more » and Frolov (ACF) in their study of the quantum Ruijsenaars-Schneider model. The purpose of this paper is twofold. First, we prove that the ACF elliptic R-matrix satisfies the associative Yang-Baxter equation with shifted spectral parameters. Second, we directly prove a simple relation of the IRF-Vertex type between the Baxter-Belavin and the ACF elliptic R-matrices predicted previously by Avan and Rollet. It provides the higher order R-matrix identities and an explanation of the obtained equations through those for non-dynamical R-matrices. As a by-product we also get an interpretation of the intertwining transformation as matrix extension of scalar theta function likewise R-matrix is interpreted as matrix extension of the Kronecker function. Relations to the Gervais-Neveu-Felder equation and identities for the Felder’s elliptic R-matrix are also discussed.« less

ICAN/PART: Particulate composite analyzer, user's manual and verification studies

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Murthy, Pappu L. N.; Mital, Subodh K.

1996-01-01

A methodology for predicting the equivalent properties and constituent microstresses for particulate matrix composites, based on the micromechanics approach, is developed. These equations are integrated into a computer code developed to predict the equivalent properties and microstresses of fiber reinforced polymer matrix composites to form a new computer code, ICAN/PART. Details of the flowchart, input and output for ICAN/PART are described, along with examples of the input and output. Only the differences between ICAN/PART and the original ICAN code are described in detail, and the user is assumed to be familiar with the structure and usage of the original ICAN code. Detailed verification studies, utilizing dim dimensional finite element and boundary element analyses, are conducted in order to verify that the micromechanics methodology accurately models the mechanics of particulate matrix composites. ne equivalent properties computed by ICAN/PART fall within bounds established by the finite element and boundary element results. Furthermore, constituent microstresses computed by ICAN/PART agree in average sense with results computed using the finite element method. The verification studies indicate that the micromechanics programmed into ICAN/PART do indeed accurately model the mechanics of particulate matrix composites.

The Cause of Category-Based Distortions in Spatial Memory: A Distribution Analysis

ERIC Educational Resources Information Center

Sampaio, Cristina; Wang, Ranxiao Frances

2017-01-01

Recall of remembered locations reliably reflects a compromise between a target's true position and its region's prototypical position. The effect is quite robust, and a standard interpretation for these data is that the metric and categorical codings blend in a Bayesian combinatory fashion. However, there has been no direct experimental evidence…

Assessing Argumentative Representation with Bayesian Network Models in Debatable Social Issues

ERIC Educational Resources Information Center

Zhang, Zhidong; Lu, Jingyan

2014-01-01

This study seeks to obtain argumentation models, which represent argumentative processes and an assessment structure in secondary school debatable issues in the social sciences. The argumentation model was developed based on mixed methods, a combination of both theory-driven and data-driven methods. The coding system provided a combing point by…

pyblocxs: Bayesian Low-Counts X-ray Spectral Analysis in Sherpa

NASA Astrophysics Data System (ADS)

Siemiginowska, A.; Kashyap, V.; Refsdal, B.; van Dyk, D.; Connors, A.; Park, T.

2011-07-01

Typical X-ray spectra have low counts and should be modeled using the Poisson distribution. However, χ2 statistic is often applied as an alternative and the data are assumed to follow the Gaussian distribution. A variety of weights to the statistic or a binning of the data is performed to overcome the low counts issues. However, such modifications introduce biases or/and a loss of information. Standard modeling packages such as XSPEC and Sherpa provide the Poisson likelihood and allow computation of rudimentary MCMC chains, but so far do not allow for setting a full Bayesian model. We have implemented a sophisticated Bayesian MCMC-based algorithm to carry out spectral fitting of low counts sources in the Sherpa environment. The code is a Python extension to Sherpa and allows to fit a predefined Sherpa model to high-energy X-ray spectral data and other generic data. We present the algorithm and discuss several issues related to the implementation, including flexible definition of priors and allowing for variations in the calibration information.

A Bayesian Approach for Measurements of Stray Neutrons at Proton Therapy Facilities: Quantifying Neutron Dose Uncertainty.

PubMed

Dommert, M; Reginatto, M; Zboril, M; Fiedler, F; Helmbrecht, S; Enghardt, W; Lutz, B

2017-11-28

Bonner sphere measurements are typically analyzed using unfolding codes. It is well known that it is difficult to get reliable estimates of uncertainties for standard unfolding procedures. An alternative approach is to analyze the data using Bayesian parameter estimation. This method provides reliable estimates of the uncertainties of neutron spectra leading to rigorous estimates of uncertainties of the dose. We extend previous Bayesian approaches and apply the method to stray neutrons in proton therapy environments by introducing a new parameterized model which describes the main features of the expected neutron spectra. The parameterization is based on information that is available from measurements and detailed Monte Carlo simulations. The validity of this approach has been validated with results of an experiment using Bonner spheres carried out at the experimental hall of the OncoRay proton therapy facility in Dresden. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2002-01-01

NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

Stress and Damage in Polymer Matrix Composite Materials Due to Material Degradation at High Temperatures

NASA Technical Reports Server (NTRS)

McManus, Hugh L.; Chamis, Christos C.

1996-01-01

This report describes analytical methods for calculating stresses and damage caused by degradation of the matrix constituent in polymer matrix composite materials. Laminate geometry, material properties, and matrix degradation states are specified as functions of position and time. Matrix shrinkage and property changes are modeled as functions of the degradation states. The model is incorporated into an existing composite mechanics computer code. Stresses, strains, and deformations at the laminate, ply, and micro levels are calculated, and from these calculations it is determined if there is failure of any kind. The rationale for the model (based on published experimental work) is presented, its integration into the laminate analysis code is outlined, and example results are given, with comparisons to existing material and structural data. The mechanisms behind the changes in properties and in surface cracking during long-term aging of polyimide matrix composites are clarified. High-temperature-material test methods are also evaluated.

Strategies for vectorizing the sparse matrix vector product on the CRAY XMP, CRAY 2, and CYBER 205

NASA Technical Reports Server (NTRS)

Bauschlicher, Charles W., Jr.; Partridge, Harry

1987-01-01

Large, randomly sparse matrix vector products are important in a number of applications in computational chemistry, such as matrix diagonalization and the solution of simultaneous equations. Vectorization of this process is considered for the CRAY XMP, CRAY 2, and CYBER 205, using a matrix of dimension of 20,000 with from 1 percent to 6 percent nonzeros. Efficient scatter/gather capabilities add coding flexibility and yield significant improvements in performance. For the CYBER 205, it is shown that minor changes in the IO can reduce the CPU time by a factor of 50. Similar changes in the CRAY codes make a far smaller improvement.

The Generation of Field Sensitive Interface States in Commercial CMOS Devices.

DTIC Science & Technology

1984-05-31

R. Hevey ATTN: STEWS -TE-AN, A. De La Paz ATTN: Code 6816, R. Lambert ATTN: STEWS -TE-AN, J. Meason ATTN: STEWS -TE-AN, R. Dutchover Naval Surface...Weapons Center ATTN: STEWS -TE-AN, R. Hays ATTN: Code F30 ATTN: STEWS -TE-N, K. Cummings ATTN: Code F31 ATTN: STEWS -TE-N, T. Arellanes ATTN: Code F31, F...Warnock ATTN: STEWS -TE-NT, M. Squires ATTN: Code F31, K. Caudle ATTN: Code WA-52, R. Smith USA Missile Command ATTN: F31, J. Downs ATTN: AMSMI-SF, G

Prevalence of the prion protein gene E211K variant in U.S. cattle

PubMed Central

Heaton, Michael P; Keele, John W; Harhay, Gregory P; Richt, Jürgen A; Koohmaraie, Mohammad; Wheeler, Tommy L; Shackelford, Steven D; Casas, Eduardo; King, D Andy; Sonstegard, Tad S; Van Tassell, Curtis P; Neibergs, Holly L; Chase, Chad C; Kalbfleisch, Theodore S; Smith, Timothy PL; Clawson, Michael L; Laegreid, William W

2008-01-01

Background In 2006, an atypical U.S. case of bovine spongiform encephalopathy (BSE) was discovered in Alabama and later reported to be polymorphic for glutamate (E) and lysine (K) codons at position 211 in the bovine prion protein gene (Prnp) coding sequence. A bovine E211K mutation is important because it is analogous to the most common pathogenic mutation in humans (E200K) which causes hereditary Creutzfeldt – Jakob disease, an autosomal dominant form of prion disease. The present report describes a high-throughput matrix-associated laser desorption/ionization-time-of-flight mass spectrometry assay for scoring the Prnp E211K variant and its use to determine an upper limit for the K211 allele frequency in U.S. cattle. Results The K211 allele was not detected in 6062 cattle, including those from five commercial beef processing plants (3892 carcasses) and 2170 registered cattle from 42 breeds. Multiple nearby polymorphisms in Prnp coding sequence of 1456 diverse purebred cattle (42 breeds) did not interfere with scoring E211 or K211 alleles. Based on these results, the upper bounds for prevalence of the E211K variant was estimated to be extremely low, less than 1 in 2000 cattle (Bayesian analysis based on 95% quantile of the posterior distribution with a uniform prior). Conclusion No groups or breeds of U.S. cattle are presently known to harbor the Prnp K211 allele. Because a carrier was not detected, the number of additional atypical BSE cases with K211 will also be vanishingly low. PMID:18625065

Using SAS PROC MCMC for Item Response Theory Models

PubMed Central

Samonte, Kelli

2014-01-01

Interest in using Bayesian methods for estimating item response theory models has grown at a remarkable rate in recent years. This attentiveness to Bayesian estimation has also inspired a growth in available software such as WinBUGS, R packages, BMIRT, MPLUS, and SAS PROC MCMC. This article intends to provide an accessible overview of Bayesian methods in the context of item response theory to serve as a useful guide for practitioners in estimating and interpreting item response theory (IRT) models. Included is a description of the estimation procedure used by SAS PROC MCMC. Syntax is provided for estimation of both dichotomous and polytomous IRT models, as well as a discussion on how to extend the syntax to accommodate more complex IRT models. PMID:29795834

An efficient method for model refinement in diffuse optical tomography

NASA Astrophysics Data System (ADS)

Zirak, A. R.; Khademi, M.

2007-11-01

Diffuse optical tomography (DOT) is a non-linear, ill-posed, boundary value and optimization problem which necessitates regularization. Also, Bayesian methods are suitable owing to measurements data are sparse and correlated. In such problems which are solved with iterative methods, for stabilization and better convergence, the solution space must be small. These constraints subject to extensive and overdetermined system of equations which model retrieving criteria specially total least squares (TLS) must to refine model error. Using TLS is limited to linear systems which is not achievable when applying traditional Bayesian methods. This paper presents an efficient method for model refinement using regularized total least squares (RTLS) for treating on linearized DOT problem, having maximum a posteriori (MAP) estimator and Tikhonov regulator. This is done with combination Bayesian and regularization tools as preconditioner matrices, applying them to equations and then using RTLS to the resulting linear equations. The preconditioning matrixes are guided by patient specific information as well as a priori knowledge gained from the training set. Simulation results illustrate that proposed method improves the image reconstruction performance and localize the abnormally well.

Genome-wide identification of conserved intronic non-coding sequences using a Bayesian segmentation approach.

PubMed

Algama, Manjula; Tasker, Edward; Williams, Caitlin; Parslow, Adam C; Bryson-Richardson, Robert J; Keith, Jonathan M

2017-03-27

Computational identification of non-coding RNAs (ncRNAs) is a challenging problem. We describe a genome-wide analysis using Bayesian segmentation to identify intronic elements highly conserved between three evolutionarily distant vertebrate species: human, mouse and zebrafish. We investigate the extent to which these elements include ncRNAs (or conserved domains of ncRNAs) and regulatory sequences. We identified 655 deeply conserved intronic sequences in a genome-wide analysis. We also performed a pathway-focussed analysis on genes involved in muscle development, detecting 27 intronic elements, of which 22 were not detected in the genome-wide analysis. At least 87% of the genome-wide and 70% of the pathway-focussed elements have existing annotations indicative of conserved RNA secondary structure. The expression of 26 of the pathway-focused elements was examined using RT-PCR, providing confirmation that they include expressed ncRNAs. Consistent with previous studies, these elements are significantly over-represented in the introns of transcription factors. This study demonstrates a novel, highly effective, Bayesian approach to identifying conserved non-coding sequences. Our results complement previous findings that these sequences are enriched in transcription factors. However, in contrast to previous studies which suggest the majority of conserved sequences are regulatory factor binding sites, the majority of conserved sequences identified using our approach contain evidence of conserved RNA secondary structures, and our laboratory results suggest most are expressed. Functional roles at DNA and RNA levels are not mutually exclusive, and many of our elements possess evidence of both. Moreover, ncRNAs play roles in transcriptional and post-transcriptional regulation, and this may contribute to the over-representation of these elements in introns of transcription factors. We attribute the higher sensitivity of the pathway-focussed analysis compared to the genome-wide analysis to improved alignment quality, suggesting that enhanced genomic alignments may reveal many more conserved intronic sequences.

Matlab Based LOCO

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

Portmann, Greg; /LBL, Berkeley; Safranek, James

The LOCO algorithm has been used by many accelerators around the world. Although the uses for LOCO vary, the most common use has been to find calibration errors and correct the optics functions. The light source community in particular has made extensive use of the LOCO algorithms to tightly control the beta function and coupling. Maintaining high quality beam parameters requires constant attention so a relatively large effort was put into software development for the LOCO application. The LOCO code was originally written in FORTRAN. This code worked fine but it was somewhat awkward to use. For instance, the FORTRANmore » code itself did not calculate the model response matrix. It required a separate modeling code such as MAD to calculate the model matrix then one manually loads the data into the LOCO code. As the number of people interested in LOCO grew, it required making it easier to use. The decision to port LOCO to Matlab was relatively easy. It's best to use a matrix programming language with good graphics capability; Matlab was also being used for high level machine control; and the accelerator modeling code AT, [5], was already developed for Matlab. Since LOCO requires collecting and processing a relative large amount of data, it is very helpful to have the LOCO code compatible with the high level machine control, [3]. A number of new features were added while porting the code from FORTRAN and new methods continue to evolve, [7][9]. Although Matlab LOCO was written with AT as the underlying tracking code, a mechanism to connect to other modeling codes has been provided.« less

Modeling of stress/strain behavior of fiber-reinforced ceramic matrix composites including stress redistribution

NASA Technical Reports Server (NTRS)

Mital, Subodh K.; Murthy, Pappu L. N.; Chamis, Christos C.

1994-01-01

A computational simulation procedure is presented for nonlinear analyses which incorporates microstress redistribution due to progressive fracture in ceramic matrix composites. This procedure facilitates an accurate simulation of the stress-strain behavior of ceramic matrix composites up to failure. The nonlinearity in the material behavior is accounted for at the constituent (fiber/matrix/interphase) level. This computational procedure is a part of recent upgrades to CEMCAN (Ceramic Matrix Composite Analyzer) computer code. The fiber substructuring technique in CEMCAN is used to monitor the damage initiation and progression as the load increases. The room-temperature tensile stress-strain curves for SiC fiber reinforced reaction-bonded silicon nitride (RBSN) matrix unidirectional and angle-ply laminates are simulated and compared with experimentally observed stress-strain behavior. Comparison between the predicted stress/strain behavior and experimental stress/strain curves is good. Collectively the results demonstrate that CEMCAN computer code provides the user with an effective computational tool to simulate the behavior of ceramic matrix composites.

Absolute Single Photoionization Cross Sections of Se^3+ For the Determination of Elemental Abundances in Planetary Nebulae

NASA Astrophysics Data System (ADS)

Esteves, David; Sterling, Nicholas; Aguilar, Alex; Kilcoyne, A. L. David; Phaneuf, Ronald; Bilodeau, Rene; Red, Eddie; McLaughlin, Brendan; Norrington, Patrick; Balance, Connor

2009-05-01

Numerical simulations show that derived elemental abundances in astrophysical nebulae can be uncertain by factors of two or more due to atomic data uncertainties alone, and of these uncertainties, absolute photoionization cross sections are the most important. Absolute single photoionization cross sections for Se^3+ ions have been measured from 42 eV to 56 eV at the ALS using the merged beams photo-ion technique. Theoretical photoionization cross section calculations were also performed for these ions using the state-of-the-art fully relativistic Dirac R-matrix code (DARC). The calculations show encouraging agreement with the experimental measurements.

Recursive Bayesian recurrent neural networks for time-series modeling.

PubMed

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects.

PubMed

Henschel, Volkmar; Engel, Jutta; Hölzel, Dieter; Mansmann, Ulrich

2009-02-10

Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome. Likelihood inference for models which additionally include random effects are not available at all. Developed algorithms bear problems for practical users like: matrix inversion, slow convergence, no assessment of statistical uncertainty. MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework. Two examples from clinical practice demonstrate the handling of clustered interval censored event times as well as multilayer random effects for inter-institutional quality assessment. The software developed is called survBayes and is freely available at CRAN. The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.

Purification of pre-miR-29 by a new O-phospho-l-tyrosine affinity chromatographic strategy optimized using design of experiments.

PubMed

Afonso, Adriana; Pereira, Patrícia; Queiroz, João A; Sousa, Ângela; Sousa, Fani

2014-05-23

MicroRNAs are the most studied small non-coding RNA molecules that are involved in post-transcriptional regulation of target genes. Their role in Alzheimer's disease is being studied and explored in order to develop a new therapeutic strategy based on specific gene silencing. This disease is characterized by protein deposits, mainly deposits of extracellular Aβ plaques, produced upon endoproteolytic cleavage of APP by ß-site APP-cleaving enzyme 1 (BACE1). Recent studies have shown that particularly miR-29 cluster can be involved in the decrease of Aβ plaques production, by acting on BACE1 expression silencing. In order to use this microRNA as potential therapeutic it is essential to guarantee its purity, stability and integrity. Hence, the main purpose of this study was the development of a new affinity chromatographic strategy by using an O-phospho-l-tyrosine matrix and applying Box-Behnken design (BBD) to obtain pre-miR-29 with high purity degree and yield, envisioning its application in gene therapy. Thus, after process optimization the best results were achieved with a decreasing ammonium sulfate gradient in 10mM Tris buffer, pH 8 (1.6M (NH4)2SO4, 1.11M (NH4)2SO4 and 0M (NH4)2SO4), at 16°C. These experimental conditions allowed the recovery of pre-miR-29 with 52% of purity and 71% of recovery yield. The O-phospho-l-tyrosine matrix was initially chosen to mimic the natural interactions that occur inside the cell, and in fact it was proved a satisfactory selectivity for pre-miR-29. Also the innovative application of BBD for this strategy was efficient (R(2)=0.98 for % relative recovery and R(2)=0.93 for % relative purity) and essential to achieve best purification results in short time, saving lab resources. Copyright © 2014 Elsevier B.V. All rights reserved.

A Bayesian Inferential Approach to Quantify the Transmission Intensity of Disease Outbreak

PubMed Central

Kadi, Adiveppa S.; Avaradi, Shivakumari R.

2015-01-01

Background. Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns. Method. We have used Bayesian approach to quantify the disease outbreak through key epidemiological parameter basic reproduction number (R 0), using effective contacts, defined as sum of the product of incidence cases and probability of generation time distribution. We have estimated R 0 from daily case incidence data for pandemic influenza A/H1N1 2009 in India, for the initial phase. Result. The estimated R 0 with 95% credible interval is consistent with several other studies on the same strain. Through sensitivity analysis our study indicates that infectiousness affects the estimate of R 0. Conclusion. Basic reproduction number R 0 provides the useful information to the public health system to do some effort in controlling the disease by using mitigation strategies like vaccination, quarantine, and so forth. PMID:25784956

PNPN Latchup in Bipolar LSI Devices.

DTIC Science & Technology

1982-01-01

6611, J. Ritter ATTN: STEWS -TE-N, T. Arellanes ATTN: Code 6612, G. McLane ATTN: STEWS -TE-AN, R. Dutchover ATTN: Code 6816, H. Hughes ATTN: STEWS -TE...AN, R. Hays ATTN: Code 6653, A. Namenson * ATTN: STEWS -TE-N, K. Cummings ATTN: Code 6611, L. August ATTN: STEWS -TE-NT, M. Squires ATTN: Code 6813, J...Killiany ATTN: STEWS -TE-AN, J. Meason ATTN: Code 6600, J. Schriempf ATTN: STEWS -TE-AN, A. De La Paz ATTN: Code 6610, R. Marlow USA MATTN: Code 2627USA

eDNAoccupancy: An R package for multi-scale occupancy modeling of environmental DNA data

USGS Publications Warehouse

Dorazio, Robert; Erickson, Richard A.

2017-01-01

In this article we describe eDNAoccupancy, an R package for fitting Bayesian, multi-scale occupancy models. These models are appropriate for occupancy surveys that include three, nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit, and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). eDNAoccupancy allows users to specify and fit multi-scale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analyzing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.

miR-203 and miR-320 Regulate Bone Morphogenetic Protein-2-Induced Osteoblast Differentiation by Targeting Distal-Less Homeobox 5 (Dlx5).

PubMed

Laxman, Navya; Mallmin, Hans; Nilsson, Olle; Kindmark, Andreas

2016-12-23

MicroRNAs (miRNAs) are a family of small, non-coding RNAs (17-24 nucleotides), which regulate gene expression either by the degradation of the target mRNAs or inhibiting the translation of genes. Recent studies have indicated that miRNA plays an important role in regulating osteoblast differentiation. In this study, we identified miR-203 and miR-320b as important miRNAs modulating osteoblast differentiation. We identified Dlx5 as potential common target by prediction algorithms and confirmed this by knock-down and over expression of the miRNAs and assessing Dlx5 at mRNA and protein levels and specificity was verified by luciferase reporter assays. We examined the effect of miR-203 and miR-320b on osteoblast differentiation by transfecting with pre- and anti-miRs. Over-expression of miR-203 and miR-320b inhibited osteoblast differentiation, whereas inhibition of miR-203 and miR-320b stimulated alkaline phosphatase activity and matrix mineralization. We show that miR-203 and miR-320b negatively regulate BMP-2-induced osteoblast differentiation by suppressing Dlx5 , which in turn suppresses the downstream osteogenic master transcription factor Runx2 and Osx and together they suppress osteoblast differentiation. Taken together, we propose a role for miR-203 and miR-320b in modulating bone metabolism.

miR-203 and miR-320 Regulate Bone Morphogenetic Protein-2-Induced Osteoblast Differentiation by Targeting Distal-Less Homeobox 5 (Dlx5)

PubMed Central

Laxman, Navya; Mallmin, Hans; Nilsson, Olle; Kindmark, Andreas

2016-01-01

MicroRNAs (miRNAs) are a family of small, non-coding RNAs (17–24 nucleotides), which regulate gene expression either by the degradation of the target mRNAs or inhibiting the translation of genes. Recent studies have indicated that miRNA plays an important role in regulating osteoblast differentiation. In this study, we identified miR-203 and miR-320b as important miRNAs modulating osteoblast differentiation. We identified Dlx5 as potential common target by prediction algorithms and confirmed this by knock-down and over expression of the miRNAs and assessing Dlx5 at mRNA and protein levels and specificity was verified by luciferase reporter assays. We examined the effect of miR-203 and miR-320b on osteoblast differentiation by transfecting with pre- and anti-miRs. Over-expression of miR-203 and miR-320b inhibited osteoblast differentiation, whereas inhibition of miR-203 and miR-320b stimulated alkaline phosphatase activity and matrix mineralization. We show that miR-203 and miR-320b negatively regulate BMP-2-induced osteoblast differentiation by suppressing Dlx5, which in turn suppresses the downstream osteogenic master transcription factor Runx2 and Osx and together they suppress osteoblast differentiation. Taken together, we propose a role for miR-203 and miR-320b in modulating bone metabolism. PMID:28025541

Pseudoinverse Decoding Process in Delay-Encoded Synthetic Transmit Aperture Imaging.

PubMed

Gong, Ping; Kolios, Michael C; Xu, Yuan

2016-09-01

Recently, we proposed a new method to improve the signal-to-noise ratio of the prebeamformed radio-frequency data in synthetic transmit aperture (STA) imaging: the delay-encoded STA (DE-STA) imaging. In the decoding process of DE-STA, the equivalent STA data were obtained by directly inverting the coding matrix. This is usually regarded as an ill-posed problem, especially under high noise levels. Pseudoinverse (PI) is usually used instead for seeking a more stable inversion process. In this paper, we apply singular value decomposition to the coding matrix to conduct the PI. Our numerical studies demonstrate that the singular values of the coding matrix have a special distribution, i.e., all the values are the same except for the first and last ones. We compare the PI in two cases: complete PI (CPI), where all the singular values are kept, and truncated PI (TPI), where the last and smallest singular value is ignored. The PI (both CPI and TPI) DE-STA processes are tested against noise with both numerical simulations and experiments. The CPI and TPI can restore the signals stably, and the noise mainly affects the prebeamformed signals corresponding to the first transmit channel. The difference in the overall enveloped beamformed image qualities between the CPI and TPI is negligible. Thus, it demonstrates that DE-STA is a relatively stable encoding and decoding technique. Also, according to the special distribution of the singular values of the coding matrix, we propose a new efficient decoding formula that is based on the conjugate transpose of the coding matrix. We also compare the computational complexity of the direct inverse and the new formula.

Simulation of Fatigue Behavior of High Temperature Metal Matrix Composites

NASA Technical Reports Server (NTRS)

Tong, Mike T.; Singhal, Suren N.; Chamis, Christos C.; Murthy, Pappu L. N.

1996-01-01

A generalized relatively new approach is described for the computational simulation of fatigue behavior of high temperature metal matrix composites (HT-MMCs). This theory is embedded in a specialty-purpose computer code. The effectiveness of the computer code to predict the fatigue behavior of HT-MMCs is demonstrated by applying it to a silicon-fiber/titanium-matrix HT-MMC. Comparative results are shown for mechanical fatigue, thermal fatigue, thermomechanical (in-phase and out-of-phase) fatigue, as well as the effects of oxidizing environments on fatigue life. These results show that the new approach reproduces available experimental data remarkably well.

Quantum Double of Yangian of strange Lie superalgebra Qn and multiplicative formula for universal R-matrix

NASA Astrophysics Data System (ADS)

Stukopin, Vladimir

2018-02-01

Main result is the multiplicative formula for universal R-matrix for Quantum Double of Yangian of strange Lie superalgebra Qn type. We introduce the Quantum Double of the Yangian of the strange Lie superalgebra Qn and define its PBW basis. We compute the Hopf pairing for the generators of the Yangian Double. From the Hopf pairing formulas we derive a factorized multiplicative formula for the universal R-matrix of the Yangian Double of the Lie superalgebra Qn . After them we obtain coefficients in this multiplicative formula for universal R-matrix.

MESHMAKER (MM) V1.5

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

MORIDIS, GEORGE

2016-05-02

MeshMaker v1.5 is a code that describes the system geometry and discretizes the domain in problems of flow and transport through porous and fractured media that are simulated using the TOUGH+ [Moridis and Pruess, 2014] or TOUGH2 [Pruess et al., 1999; 2012] families of codes. It is a significantly modified and drastically enhanced version of an earlier simpler facility that was embedded in the TOUGH2 codes [Pruess et al., 1999; 2012], from which it could not be separated. The code (MeshMaker.f90) is a stand-alone product written in FORTRAN 95/2003, is written according to the tenets of Object-Oriented Programming, has amore » modular structure and can perform a number of mesh generation and processing operations. It can generate two-dimensional radially symmetric (r,z) meshes, and one-, two-, and three-dimensional rectilinear (Cartesian) grids in (x,y,z). The code generates the file MESH, which includes all the elements and connections that describe the discretized simulation domain and conforming to the requirements of the TOUGH+ and TOUGH2 codes. Multiple-porosity processing for simulation of flow in naturally fractured reservoirs can be invoked by means of a keyword MINC, which stands for Multiple INteracting Continua. The MINC process operates on the data of the primary (porous medium) mesh as provided on disk file MESH, and generates a secondary mesh containing fracture and matrix elements with identical data formats on file MINC.« less

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

PubMed

Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio

2014-05-10

Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

PubMed Central

2014-01-01

Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957

High-SNR spectrum measurement based on Hadamard encoding and sparse reconstruction

NASA Astrophysics Data System (ADS)

Wang, Zhaoxin; Yue, Jiang; Han, Jing; Li, Long; Jin, Yong; Gao, Yuan; Li, Baoming

2017-12-01

The denoising capabilities of the H-matrix and cyclic S-matrix based on the sparse reconstruction, employed in the Pixel of Focal Plane Coded Visible Spectrometer for spectrum measurement are investigated, where the spectrum is sparse in a known basis. In the measurement process, the digital micromirror device plays an important role, which implements the Hadamard coding. In contrast with Hadamard transform spectrometry, based on the shift invariability, this spectrometer may have the advantage of a high efficiency. Simulations and experiments show that the nonlinear solution with a sparse reconstruction has a better signal-to-noise ratio than the linear solution and the H-matrix outperforms the cyclic S-matrix whether the reconstruction method is nonlinear or linear.

Gene copy number variation and its significance in cyanobacterial phylogeny

PubMed Central

2012-01-01

Background In eukaryotes, variation in gene copy numbers is often associated with deleterious effects, but may also have positive effects. For prokaryotes, studies on gene copy number variation are rare. Previous studies have suggested that high numbers of rRNA gene copies can be advantageous in environments with changing resource availability, but further association of gene copies and phenotypic traits are not documented. We used one of the morphologically most diverse prokaryotic phyla to test whether numbers of gene copies are associated with levels of cell differentiation. Results We implemented a search algorithm that identified 44 genes with highly conserved copies across 22 fully sequenced cyanobacterial taxa. For two very basal cyanobacterial species, Gloeobacter violaceus and a thermophilic Synechococcus species, distinct phylogenetic positions previously found were supported by identical protein coding gene copy numbers. Furthermore, we found that increased ribosomal gene copy numbers showed a strong correlation to cyanobacteria capable of terminal cell differentiation. Additionally, we detected extremely low variation of 16S rRNA sequence copies within the cyanobacteria. We compared our results for 16S rRNA to three other eubacterial phyla (Chroroflexi, Spirochaetes and Bacteroidetes). Based on Bayesian phylogenetic inference and the comparisons of genetic distances, we could confirm that cyanobacterial 16S rRNA paralogs and orthologs show significantly stronger conservation than found in other eubacterial phyla. Conclusions A higher number of ribosomal operons could potentially provide an advantage to terminally differentiated cyanobacteria. Furthermore, we suggest that 16S rRNA gene copies in cyanobacteria are homogenized by both concerted evolution and purifying selection. In addition, the small ribosomal subunit in cyanobacteria appears to evolve at extraordinary slow evolutionary rates, an observation that has been made previously for morphological characteristics of cyanobacteria. PMID:22894826

Bayesian geostatistical modelling of soil-transmitted helminth survey data in the People's Republic of China.

PubMed

Lai, Ying-Si; Zhou, Xiao-Nong; Utzinger, Jürg; Vounatsou, Penelope

2013-12-18

Soil-transmitted helminth infections affect tens of millions of individuals in the People's Republic of China (P.R. China). There is a need for high-resolution estimates of at-risk areas and number of people infected to enhance spatial targeting of control interventions. However, such information is not yet available for P.R. China. A geo-referenced database compiling surveys pertaining to soil-transmitted helminthiasis, carried out from 2000 onwards in P.R. China, was established. Bayesian geostatistical models relating the observed survey data with potential climatic, environmental and socioeconomic predictors were developed and used to predict at-risk areas at high spatial resolution. Predictors were extracted from remote sensing and other readily accessible open-source databases. Advanced Bayesian variable selection methods were employed to develop a parsimonious model. Our results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) of the roundworm Ascaris lumbricoides infection was predicted for large areas of Guizhou province, the southern part of Hubei and Sichuan provinces, while the northern part and the south-eastern coastal-line areas of P.R. China had low prevalence (<5%). High infection prevalence (>20%) with hookworm was found in Hainan, the eastern part of Sichuan and the southern part of Yunnan provinces. High infection prevalence (>20%) with the whipworm Trichuris trichiura was found in a few small areas of south P.R. China. Very low prevalence (<0.1%) of hookworm and whipworm infections were predicted for the northern parts of P.R. China. We present the first model-based estimates for soil-transmitted helminth infections throughout P.R. China at high spatial resolution. Our prediction maps provide useful information for the spatial targeting of soil-transmitted helminthiasis control interventions and for long-term monitoring and surveillance in the frame of enhanced efforts to control and eliminate the public health burden of these parasitic worm infections.

Bayesian geostatistical modelling of soil-transmitted helminth survey data in the People’s Republic of China

PubMed Central

2013-01-01

Background Soil-transmitted helminth infections affect tens of millions of individuals in the People’s Republic of China (P.R. China). There is a need for high-resolution estimates of at-risk areas and number of people infected to enhance spatial targeting of control interventions. However, such information is not yet available for P.R. China. Methods A geo-referenced database compiling surveys pertaining to soil-transmitted helminthiasis, carried out from 2000 onwards in P.R. China, was established. Bayesian geostatistical models relating the observed survey data with potential climatic, environmental and socioeconomic predictors were developed and used to predict at-risk areas at high spatial resolution. Predictors were extracted from remote sensing and other readily accessible open-source databases. Advanced Bayesian variable selection methods were employed to develop a parsimonious model. Results Our results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) of the roundworm Ascaris lumbricoides infection was predicted for large areas of Guizhou province, the southern part of Hubei and Sichuan provinces, while the northern part and the south-eastern coastal-line areas of P.R. China had low prevalence (<5%). High infection prevalence (>20%) with hookworm was found in Hainan, the eastern part of Sichuan and the southern part of Yunnan provinces. High infection prevalence (>20%) with the whipworm Trichuris trichiura was found in a few small areas of south P.R. China. Very low prevalence (<0.1%) of hookworm and whipworm infections were predicted for the northern parts of P.R. China. Conclusions We present the first model-based estimates for soil-transmitted helminth infections throughout P.R. China at high spatial resolution. Our prediction maps provide useful information for the spatial targeting of soil-transmitted helminthiasis control interventions and for long-term monitoring and surveillance in the frame of enhanced efforts to control and eliminate the public health burden of these parasitic worm infections. PMID:24350825

Valence and L-shell photoionization of Cl-like argon using R-matrix techniques

NASA Astrophysics Data System (ADS)

Tyndall, N. B.; Ramsbottom, C. A.; Ballance, C. P.; Hibbert, A.

2016-02-01

Photoionization cross-sections are obtained using the relativistic Dirac Atomic R-matrix Codes (DARC) for all valence and L-shell energy ranges between 27 and 270 eV. A total of 557 levels arising from the dominant configurations 3s23p4, 3s3p5, 3p6, 3s23p3[3d, 4s, 4p], 3p53d, 3s23p23d2, 3s3p43d, 3s3p33d2 and 2s22p53s23p5 have been included in the target wavefunction representation of the Ar III ion, including up to 4p in the orbital basis. We also performed a smaller Breit-Pauli (BP) calculation containing the lowest 124 levels. Direct comparisons are made with previous theoretical and experimental work for both valence shell and L-shell photoionization. Excellent agreement was found for transitions involving the 2Po initial state to all allowed final states for both calculations across a range of photon energies. A number of resonant states have been identified to help analyse and explain the nature of the spectra at photon energies between 250 and 270 eV.

Using a multifrontal sparse solver in a high performance, finite element code

NASA Technical Reports Server (NTRS)

King, Scott D.; Lucas, Robert; Raefsky, Arthur

1990-01-01

We consider the performance of the finite element method on a vector supercomputer. The computationally intensive parts of the finite element method are typically the individual element forms and the solution of the global stiffness matrix both of which are vectorized in high performance codes. To further increase throughput, new algorithms are needed. We compare a multifrontal sparse solver to a traditional skyline solver in a finite element code on a vector supercomputer. The multifrontal solver uses the Multiple-Minimum Degree reordering heuristic to reduce the number of operations required to factor a sparse matrix and full matrix computational kernels (e.g., BLAS3) to enhance vector performance. The net result in an order-of-magnitude reduction in run time for a finite element application on one processor of a Cray X-MP.

A Bayesian framework for knowledge attribution: evidence from semantic integration.

PubMed

Powell, Derek; Horne, Zachary; Pinillos, N Ángel; Holyoak, Keith J

2015-06-01

We propose a Bayesian framework for the attribution of knowledge, and apply this framework to generate novel predictions about knowledge attribution for different types of "Gettier cases", in which an agent is led to a justified true belief yet has made erroneous assumptions. We tested these predictions using a paradigm based on semantic integration. We coded the frequencies with which participants falsely recalled the word "thought" as "knew" (or a near synonym), yielding an implicit measure of conceptual activation. Our experiments confirmed the predictions of our Bayesian account of knowledge attribution across three experiments. We found that Gettier cases due to counterfeit objects were not treated as knowledge (Experiment 1), but those due to intentionally-replaced evidence were (Experiment 2). Our findings are not well explained by an alternative account focused only on luck, because accidentally-replaced evidence activated the knowledge concept more strongly than did similar false belief cases (Experiment 3). We observed a consistent pattern of results across a number of different vignettes that varied the quality and type of evidence available to agents, the relative stakes involved, and surface details of content. Accordingly, the present findings establish basic phenomena surrounding people's knowledge attributions in Gettier cases, and provide explanations of these phenomena within a Bayesian framework. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatially constrained Bayesian inversion of frequency- and time-domain electromagnetic data from the Tellus projects

NASA Astrophysics Data System (ADS)

Kiyan, Duygu; Rath, Volker; Delhaye, Robert

2017-04-01

The frequency- and time-domain airborne electromagnetic (AEM) data collected under the Tellus projects of the Geological Survey of Ireland (GSI) which represent a wealth of information on the multi-dimensional electrical structure of Ireland's near-surface. Our project, which was funded by GSI under the framework of their Short Call Research Programme, aims to develop and implement inverse techniques based on various Bayesian methods for these densely sampled data. We have developed a highly flexible toolbox using Python language for the one-dimensional inversion of AEM data along the flight lines. The computational core is based on an adapted frequency- and time-domain forward modelling core derived from the well-tested open-source code AirBeo, which was developed by the CSIRO (Australia) and the AMIRA consortium. Three different inversion methods have been implemented: (i) Tikhonov-type inversion including optimal regularisation methods (Aster el al., 2012; Zhdanov, 2015), (ii) Bayesian MAP inversion in parameter and data space (e.g. Tarantola, 2005), and (iii) Full Bayesian inversion with Markov Chain Monte Carlo (Sambridge and Mosegaard, 2002; Mosegaard and Sambridge, 2002), all including different forms of spatial constraints. The methods have been tested on synthetic and field data. This contribution will introduce the toolbox and present case studies on the AEM data from the Tellus projects.

XDATA

DTIC Science & Technology

2017-05-01

Parallelizing PINT The main focus of our research into the parallelization of the PINT algorithm has been to find appropriately scalable matrix math algorithms...leading eigenvector of the adjacency matrix of the pairwise affinity graph. We reviewed the matrix math implementation currently being used in PINT and...the new versions support a feature called matrix.distributed, which is some level of support for distributed matrix math ; however our code is not

Progressive Failure And Life Prediction of Ceramic and Textile Composites

NASA Technical Reports Server (NTRS)

Xue, David Y.; Shi, Yucheng; Katikala, Madhu; Johnston, William M., Jr.; Card, Michael F.

1998-01-01

An engineering approach to predict the fatigue life and progressive failure of multilayered composite and textile laminates is presented. Analytical models which account for matrix cracking, statistical fiber failures and nonlinear stress-strain behavior have been developed for both composites and textiles. The analysis method is based on a combined micromechanics, fracture mechanics and failure statistics analysis. Experimentally derived empirical coefficients are used to account for the interface of fiber and matrix, fiber strength, and fiber-matrix stiffness reductions. Similar approaches were applied to textiles using Repeating Unit Cells. In composite fatigue analysis, Walker's equation is applied for matrix fatigue cracking and Heywood's formulation is used for fiber strength fatigue degradation. The analysis has been compared with experiment with good agreement. Comparisons were made with Graphite-Epoxy, C/SiC and Nicalon/CAS composite materials. For textile materials, comparisons were made with triaxial braided and plain weave materials under biaxial or uniaxial tension. Fatigue predictions were compared with test data obtained from plain weave C/SiC materials tested at AS&M. Computer codes were developed to perform the analysis. Composite Progressive Failure Analysis for Laminates is contained in the code CPFail. Micromechanics Analysis for Textile Composites is contained in the code MicroTex. Both codes were adapted to run as subroutines for the finite element code ABAQUS and CPFail-ABAQUS and MicroTex-ABAQUS. Graphic user interface (GUI) was developed to connect CPFail and MicroTex with ABAQUS.

BATMAN--an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model.

PubMed

Hao, Jie; Astle, William; De Iorio, Maria; Ebbels, Timothy M D

2012-08-01

Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolites involve either an expert manual peak fitting or extra pre-processing steps, such as peak alignment and binning. Peak fitting is very time consuming and is subject to human error. Conversely, alignment and binning can introduce artefacts and limit immediate biological interpretation of models. We present the Bayesian automated metabolite analyser for NMR spectra (BATMAN), an R package that deconvolutes peaks from one-dimensional NMR spectra, automatically assigns them to specific metabolites from a target list and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov chain Monte Carlo algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced error compared with conventional numerical integration and comparable to manual deconvolution by experienced spectroscopists. http://www1.imperial.ac.uk/medicine/people/t.ebbels/ t.ebbels@imperial.ac.uk.

R-matrix-valued Lax pairs and long-range spin chains

NASA Astrophysics Data System (ADS)

Sechin, I.; Zotov, A.

2018-06-01

In this paper we discuss R-matrix-valued Lax pairs for slN Calogero-Moser model and their relation to integrable quantum long-range spin chains of the Haldane-Shastry-Inozemtsev type. First, we construct the R-matrix-valued Lax pairs for the third flow of the classical Calogero-Moser model. Then we notice that the scalar parts (in the auxiliary space) of the M-matrices corresponding to the second and third flows have form of special spin exchange operators. The freezing trick restricts them to quantum Hamiltonians of long-range spin chains. We show that for a special choice of the R-matrix these Hamiltonians reproduce those for the Inozemtsev chain. In the general case related to the Baxter's elliptic R-matrix we obtain a natural anisotropic extension of the Inozemtsev chain. Commutativity of the Hamiltonians is verified numerically. Trigonometric limits lead to the Haldane-Shastry chains and their anisotropic generalizations.

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

NASA Technical Reports Server (NTRS)

Solakiewiz, Richard; Koshak, William

2008-01-01

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Towards Breaking the Histone Code – Bayesian Graphical Models for Histone Modifications

PubMed Central

Mitra, Riten; Müller, Peter; Liang, Shoudan; Xu, Yanxun; Ji, Yuan

2013-01-01

Background Histones are proteins that wrap DNA around in small spherical structures called nucleosomes. Histone modifications (HMs) refer to the post-translational modifications to the histone tails. At a particular genomic locus, each of these HMs can either be present or absent, and the combinatory patterns of the presence or absence of multiple HMs, or the ‘histone codes,’ are believed to co-regulate important biological processes. We aim to use raw data on HM markers at different genomic loci to (1) decode the complex biological network of HMs in a single region and (2) demonstrate how the HM networks differ in different regulatory regions. We suggest that these differences in network attributes form a significant link between histones and genomic functions. Methods and Results We develop a powerful graphical model under Bayesian paradigm. Posterior inference is fully probabilistic, allowing us to compute the probabilities of distinct dependence patterns of the HMs using graphs. Furthermore, our model-based framework allows for easy but important extensions for inference on differential networks under various conditions, such as the different annotations of the genomic locations (e.g., promoters versus insulators). We applied these models to ChIP-Seq data based on CD4+ T lymphocytes. The results confirmed many existing findings and provided a unified tool to generate various promising hypotheses. Differential network analyses revealed new insights on co-regulation of HMs of transcriptional activities in different genomic regions. Conclusions The use of Bayesian graphical models and borrowing strength across different conditions provide high power to infer histone networks and their differences. PMID:23748248

Phylogenetic Analysis of Myobia musculi (Schranck, 1781) by Using the 18S Small Ribosomal Subunit Sequence

PubMed Central

Feldman, Sanford H; Ntenda, Abraham M

2011-01-01

We used high-fidelity PCR to amplify 2 overlapping regions of the ribosomal gene complex from the rodent fur mite Myobia musculi. The amplicons encompassed a large portion of the mite's ribosomal gene complex spanning 3128 nucleotides containing the entire 18S rRNA, internal transcribed spacer (ITS) 1, 5.8S rRNA, ITS2, and a portion of the 5′-end of the 28S rRNA. M. musculi’s 179-nucleotide 5.8S rRNA nucleotide sequence was not conserved, so this region was identified by conservation of rRNA secondary structure. Maximum likelihood and Bayesian inference phylogenetic analyses were performed by using multiple sequence alignment consisting of 1524 nucleotides of M. musculi 18S rRNA and homologous sequences from 42 prostigmatid mites and the tick Dermacentor andersoni. The phylograms produced by both methods were in agreement regarding terminal, secondary, and some tertiary phylogenetic relationships among mites. Bayesian inference discriminated most infraordinal relationships between Eleutherengona and Parasitengona mites in the suborder Anystina. Basal relationships between suborders Anystina and Eupodina historically determined by comparing differences in anatomic characteristics were less well-supported by our molecular analysis. Our results recapitulated similar 18S rRNA sequence analyses recently reported. Our study supports M. musculi as belonging to the suborder Anystina, infraorder Eleutherenona, and superfamily Cheyletoidea. PMID:22330574

Modeling and Simulation of a Non-Coherent Frequency Shift Keying Transceiver Using a Field Programmable Gate Array (FPGA)

DTIC Science & Technology

2008-09-01

Convolutional Encoder Block Diagram of code rate 1 2 r = and...most commonly used along with block codes . They were introduced in 1955 by Elias [7]. Convolutional codes are characterized by the code rate kr n... convolutional code for 1 2 r = and = 3κ , namely [7 5], is used. Figure 2 Convolutional Encoder Block Diagram of code rate 1 2 r = and

Photoionization of the valence shells of the neutral tungsten atom

NASA Astrophysics Data System (ADS)

Ballance, C. P.; McLaughlin, B. M.

2015-04-01

Results from large-scale theoretical cross section calculations for the total photoionization (PI) of the 4f, 5s, 5p and 6s orbitals of the neutral tungsten atom using the Dirac Coulomb R-matrix approximation (DARC: Dirac-atomic R-matrix codes) are presented. Comparisons are made with previous theoretical methods and prior experimental measurements. In previous experiments a time-resolved dual laser approach was employed for the photo-absorption of metal vapours and photo-absorption measurements on tungsten in a solid, using synchrotron radiation. The lowest ground state level of neutral tungsten is 5{{p}6}5{{d}4}6{{s}2}{{ }5}{{D}J}, with J = 0, and requires only a single dipole matrix for PI. To make a meaningful comparison with existing experimental measurements, we statistically average the large-scale theoretical PI cross sections from the levels associated with the ground state 5{{p}6}5{{d}4}6{{s}2}{{ }5}{{D}J} (J = 0, 1, 2, 3, 4) levels and the 5{{d}5}6{{s} 7}{{S}3} excited metastable level. As the experiments have a self-evident metastable component in their ground state measurement, averaging over the initial levels allows for a more consistent and realistic comparison to be made. In the wider context, the absence of many detailed electron-impact excitation (EIE) experiments for tungsten and its multi-charged ion stages allows current PI measurements and theory to provide a road-map for future EIE, ionization and di-electronic cross section calculations by identifying the dominant resonance structure and features across an energy range of hundreds of eV.

Stress and Damage in Polymer Matrix Composite Materials Due to Material Degradation at High Temperatures

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

Mcmanus, H.L.; Chamis, C.C.

1996-01-01

This report describes analytical methods for calculating stresses and damage caused by degradation of the matrix constituent in polymer matrix composite materials. Laminate geometry, material properties, and matrix degradation states are specified as functions of position and time. Matrix shrinkage and property changes are modeled as functions of the degradation states. The model is incorporated into an existing composite mechanics computer code. Stresses, strains, and deformations at the laminate, ply, and micro levels are calculated, and from these calculations it is determined if there is failure of any kind. The rationale for the model (based on published experimental work) ismore » presented, its integration into the laminate analysis code is outlined, and example results are given, with comparisons to existing material and structural data. The mechanisms behind the changes in properties and in surface cracking during long-term aging of polyimide matrix composites are clarified. High-temperature-material test methods are also evaluated.« less

Similarity preserving low-rank representation for enhanced data representation and effective subspace learning.

PubMed

Zhang, Zhao; Yan, Shuicheng; Zhao, Mingbo

2014-05-01

Latent Low-Rank Representation (LatLRR) delivers robust and promising results for subspace recovery and feature extraction through mining the so-called hidden effects, but the locality of both similar principal and salient features cannot be preserved in the optimizations. To solve this issue for achieving enhanced performance, a boosted version of LatLRR, referred to as Regularized Low-Rank Representation (rLRR), is proposed through explicitly including an appropriate Laplacian regularization that can maximally preserve the similarity among local features. Resembling LatLRR, rLRR decomposes given data matrix from two directions by seeking a pair of low-rank matrices. But the similarities of principal and salient features can be effectively preserved by rLRR. As a result, the correlated features are well grouped and the robustness of representations is also enhanced. Based on the outputted bi-directional low-rank codes by rLRR, an unsupervised subspace learning framework termed Low-rank Similarity Preserving Projections (LSPP) is also derived for feature learning. The supervised extension of LSPP is also discussed for discriminant subspace learning. The validity of rLRR is examined by robust representation and decomposition of real images. Results demonstrated the superiority of our rLRR and LSPP in comparison to other related state-of-the-art algorithms. Copyright © 2014 Elsevier Ltd. All rights reserved.

Association of Genetic Variants of Small Non-Coding RNAs with Survival in Colorectal Cancer

PubMed Central

Pao, Jiunn-Bey; Lu, Te-Ling; Ting, Wen-Chien; Chen, Lu-Min; Bao, Bo-Ying

2018-01-01

Background: Single nucleotide polymorphisms (SNPs) of small non-coding RNAs (sncRNAs) can influence sncRNA function and target gene expression to mediate the risk of certain diseases. The aim of the present study was to evaluate the prognostic relevance of sncRNA SNPs for colorectal cancer, which has not been well characterized to date. Methods: We comprehensively examined 31 common SNPs of sncRNAs, and assessed the impact of these variants on survival in a cohort of 188 patients with colorectal cancer. Results: Three SNPs were significantly associated with survival of patients with colorectal cancer after correction for multiple testing, and two of the SNPs (hsa-mir-196a-2 rs11614913 and U85 rs714775) remained significant in multivariate analyses. Additional in silico analysis provided further evidence of this association, since the expression levels of the target genes of the hsa-miR-196a (HOXA7, HOXB8, and AKT1) were significantly correlated with colorectal cancer progression. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that hsa-miR-196a is associated with well-known oncogenic pathways, including cellular protein modification process, mitotic cell cycle, adherens junction, and extracellular matrix receptor interaction pathways. Conclusion: Our results suggest that SNPs of sncRNAs could play a critical role in cancer progression, and that hsa-miR-196a might be a valuable biomarker or therapeutic target for colorectal cancer patients. PMID:29483812

An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes

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

Lewis, Allison, E-mail: lewis.allison10@gmail.com; Smith, Ralph; Williams, Brian

For many simulation models, it can be prohibitively expensive or physically infeasible to obtain a complete set of experimental data to calibrate model parameters. In such cases, one can alternatively employ validated higher-fidelity codes to generate simulated data, which can be used to calibrate the lower-fidelity code. In this paper, we employ an information-theoretic framework to determine the reduction in parameter uncertainty that is obtained by evaluating the high-fidelity code at a specific set of design conditions. These conditions are chosen sequentially, based on the amount of information that they contribute to the low-fidelity model parameters. The goal is tomore » employ Bayesian experimental design techniques to minimize the number of high-fidelity code evaluations required to accurately calibrate the low-fidelity model. We illustrate the performance of this framework using heat and diffusion examples, a 1-D kinetic neutron diffusion equation, and a particle transport model, and include initial results from the integration of the high-fidelity thermal-hydraulics code Hydra-TH with a low-fidelity exponential model for the friction correlation factor.« less

CTPPL: A Continuous Time Probabilistic Programming Language

DTIC Science & Technology

2009-07-01

recent years there has been a flurry of interest in continuous time models, mostly focused on continuous time Bayesian networks ( CTBNs ) [Nodelman, 2007... CTBNs are built on homogenous Markov processes. A homogenous Markov pro- cess is a finite state, continuous time process, consisting of an initial...q1 : xn()] ... Some state transitions can produce emissions. In a CTBN , each variable has a conditional inten- sity matrix Qu for every combination of

Bayesian network meta-analysis of root coverage procedures: ranking efficacy and identification of best treatment.

PubMed

Buti, Jacopo; Baccini, Michela; Nieri, Michele; La Marca, Michele; Pini-Prato, Giovan P

2013-04-01

The aim of this work was to conduct a Bayesian network meta-analysis (NM) of randomized controlled trials (RCTs) to establish a ranking in efficacy and the best technique for coronally advanced flap (CAF)-based root coverage procedures. A literature search on PubMed, Cochrane libraries, EMBASE, and hand-searched journals until June 2012 was conducted to identify RCTs on treatments of Miller Class I and II gingival recessions with at least 6 months of follow-up. The treatment outcomes were recession reduction (RecRed), clinical attachment gain (CALgain), keratinized tissue gain (KTgain), and complete root coverage (CRC). Twenty-nine studies met the inclusion criteria, 20 of which were classified as at high risk of bias. The CAF+connective tissue graft (CTG) combination ranked highest in effectiveness for RecRed (Probability of being the best = 40%) and CALgain (Pr = 33%); CAF+enamel matrix derivative (EMD) was slightly better for CRC; CAF+Collagen Matrix (CM) appeared effective for KTgain (Pr = 69%). Network inconsistency was low for all outcomes excluding CALgain. CAF+CTG might be considered the gold standard in root coverage procedures. The low amount of inconsistency gives support to the reliability of the present findings. © 2012 John Wiley & Sons A/S.

Discriminative Relational Topic Models.

PubMed

Chen, Ning; Zhu, Jun; Xia, Fei; Zhang, Bo

2015-05-01

Relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents for document networks, and they have shown promise on predicting network structures and discovering latent topic representations. However, existing RTMs have limitations in both the restricted model expressiveness and incapability of dealing with imbalanced network data. To expand the scope and improve the inference accuracy of RTMs, this paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference (RegBayes) with a regularization parameter to deal with the imbalanced link structure issue in real networks and improve the discriminative ability of learned latent representations; and 3) instead of doing variational approximation with strict mean-field assumptions, we present collapsed Gibbs sampling algorithms for the generalized relational topic models by exploring data augmentation without making restricting assumptions. Under the generic RegBayes framework, we carefully investigate two popular discriminative loss functions, namely, the logistic log-loss and the max-margin hinge loss. Experimental results on several real network datasets demonstrate the significance of these extensions on improving prediction performance.

The Linear Parameters and the Decoupling Matrix for Linearly Coupled Motion in 6 Dimensional Phase Space

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

Parzen, George

It will be shown that starting from a coordinate system where the 6 phase space coordinates are linearly coupled, one can go to a new coordinate system, where the motion is uncoupled, by means of a linear transformation. The original coupled coordinates and the new uncoupled coordinates are related by a 6 x 6 matrix, R. R will be called the decoupling matrix. It will be shown that of the 36 elements of the 6 x 6 decoupling matrix R, only 12 elements are independent. This may be contrasted with the results for motion in 4- dimensional phase space, wheremore » R has 4 independent elements. A set of equations is given from which the 12 elements of R can be computed from the one period transfer matrix. This set of equations also allows the linear parameters, the β i,α i, i = 1, 3, for the uncoupled coordinates, to be computed from the one period transfer matrix. An alternative procedure for computing the linear parameters,β i,α i, i = 1, 3, and the 12 independent elements of the decoupling matrix R is also given which depends on computing the eigenvectors of the one period transfer matrix. These results can be used in a tracking program, where the one period transfer matrix can be computed by multiplying the transfer matrices of all the elements in a period, to compute the linear parameters α i and β i, i = 1, 3, and the elements of the decoupling matrix R. The procedure presented here for studying coupled motion in 6-dimensional phase space can also be applied to coupled motion in 4-dimensional phase space, where it may be a useful alternative procedure to the procedure presented by Edwards and Teng. In particular, it gives a simpler programing procedure for computing the beta functions and the emittances for coupled motion in 4-dimensional phase space.« less

Bayesian and Phylogenic Approaches for Studying Relationships among Table Olive Cultivars.

PubMed

Ben Ayed, Rayda; Ennouri, Karim; Ben Amar, Fathi; Moreau, Fabienne; Triki, Mohamed Ali; Rebai, Ahmed

2017-08-01

To enhance table olive tree authentication, relationship, and productivity, we consider the analysis of 18 worldwide table olive cultivars (Olea europaea L.) based on morphological, biological, and physicochemical markers analyzed by bioinformatic and biostatistic tools. Accordingly, we assess the relationships between the studied varieties, on the one hand, and the potential productivity-quantitative parameter links on the other hand. The bioinformatic analysis based on the graphical representation of the matrix of Euclidean distances, the principal components analysis, unweighted pair group method with arithmetic mean, and principal coordinate analysis (PCoA) revealed three major clusters which were not correlated with the geographic origin. The statistical analysis based on Kendall's and Spearman correlation coefficients suggests two highly significant associations with both fruit color and pollinization and the productivity character. These results are confirmed by the multiple linear regression prediction models. In fact, based on the coefficient of determination (R 2 ) value, the best model demonstrated the power of the pollinization on the tree productivity (R 2  = 0.846). Moreover, the derived directed acyclic graph showed that only two direct influences are detected: effect of tolerance on fruit and stone symmetry on side and effect of tolerance on stone form and oil content on the other side. This work provides better understanding of the diversity available in worldwide table olive cultivars and supplies an important contribution for olive breeding and authenticity.

Drug delivery optimization through Bayesian networks.

PubMed Central

Bellazzi, R.

1992-01-01

This paper describes how Bayesian Networks can be used in combination with compartmental models to plan Recombinant Human Erythropoietin (r-HuEPO) delivery in the treatment of anemia of chronic uremic patients. Past measurements of hematocrit or hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of the erythropoiesis. This adaptive process allows more accurate patient-specific predictions, and hence a more rational dosage planning. We describe a drug delivery optimization protocol, based on our approach. Some results obtained on real data are presented. PMID:1482938

The Tetrahedral Zamolodchikov Algebra and the {AdS_5× S^5} S-matrix

NASA Astrophysics Data System (ADS)

Mitev, Vladimir; Staudacher, Matthias; Tsuboi, Zengo

2017-08-01

The S-matrix of the {AdS_5× S^5} string theory is a tensor product of two centrally extended su{(2|2)\\ltimes R^2 S-matrices, each of which is related to the R-matrix of the Hubbard model. The R-matrix of the Hubbard model was first found by Shastry, who ingeniously exploited the fact that, for zero coupling, the Hubbard model can be decomposed into two XX models. In this article, we review and clarify this construction from the AdS/CFT perspective and investigate the implications this has for the {AdS_5× S^5} S-matrix.

A new response matrix for a 6LiI scintillator BSS system

NASA Astrophysics Data System (ADS)

Lacerda, M. A. S.; Méndez-Villafañe, R.; Lorente, A.; Ibañez, S.; Gallego, E.; Vega-Carrillo, H. R.

2017-10-01

A new response matrix was calculated for a Bonner Sphere Spectrometer (BSS) with a 6 LiI(Eu) scintillator, using the Monte Carlo N-Particle radiation transport code MCNPX. Responses were calculated for 6 spheres and the bare detector, for energies varying from 1.059E(-9) MeV to 105.9 MeV, with 20 equal-log(E)-width bins per energy decade, totalizing 221 energy groups. A comparison was done among the responses obtained in this work and other published elsewhere, for the same detector model. The calculated response functions were inserted in the response input file of the MAXED code and used to unfold the total and direct neutron spectra generated by the 241Am-Be source of the Universidad Politécnica de Madrid (UPM). These spectra were compared with those obtained using the same unfolding code with the Mares and Schraube matrix response.

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

NASA Astrophysics Data System (ADS)

Jennings, E.; Madigan, M.

2017-04-01

Given the complexity of modern cosmological parameter inference where we are faced with non-Gaussian data and noise, correlated systematics and multi-probe correlated datasets,the Approximate Bayesian Computation (ABC) method is a promising alternative to traditional Markov Chain Monte Carlo approaches in the case where the Likelihood is intractable or unknown. The ABC method is called "Likelihood free" as it avoids explicit evaluation of the Likelihood by using a forward model simulation of the data which can include systematics. We introduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler for parameter estimation. A key challenge in astrophysics is the efficient use of large multi-probe datasets to constrain high dimensional, possibly correlated parameter spaces. With this in mind astroABC allows for massive parallelization using MPI, a framework that handles spawning of processes across multiple nodes. A key new feature of astroABC is the ability to create MPI groups with different communicators, one for the sampler and several others for the forward model simulation, which speeds up sampling time considerably. For smaller jobs the Python multiprocessing option is also available. Other key features of this new sampler include: a Sequential Monte Carlo sampler; a method for iteratively adapting tolerance levels; local covariance estimate using scikit-learn's KDTree; modules for specifying optimal covariance matrix for a component-wise or multivariate normal perturbation kernel and a weighted covariance metric; restart files output frequently so an interrupted sampling run can be resumed at any iteration; output and restart files are backed up at every iteration; user defined distance metric and simulation methods; a module for specifying heterogeneous parameter priors including non-standard prior PDFs; a module for specifying a constant, linear, log or exponential tolerance level; well-documented examples and sample scripts. This code is hosted online at https://github.com/EliseJ/astroABC.

The numerical approach adopted in toba computer code for mass and heat transfer dynamic analysis of metal hydride hydrogen storage beds

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

El Osery, I.A.

1983-12-01

Modelling studies of metal hydride hydrogen storage beds is a part of an extensive R and D program conducted in Egypt on hydrogen energy. In this context two computer programs; namely RET and RET1; have been developed. In RET computer program, a cylindrical conduction bed model is considered and an approximate analytical solution is used for solution of the associated mass and heat transfer problem. This problem is solved in RET1 computer program numerically allowing more flexibility in operating conditions but still limited to cylindrical configuration with only two alternatives for heat exchange; either fluid is passing through tubes imbeddedmore » in the solid alloy matrix or solid rods are surrounded by annular fluid tubes. The present computer code TOBA is more flexible and realistic. It performs the mass and heat transfer dynamic analysis of metal hydride storage beds using a variety of geometrical and operating alternatives.« less

Is a matrix exponential specification suitable for the modeling of spatial correlation structures?

PubMed Central

Strauß, Magdalena E.; Mezzetti, Maura; Leorato, Samantha

2018-01-01

This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms. PMID:29492375

DATMAN: A reliability data analysis program using Bayesian updating

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

Becker, M.; Feltus, M.A.

1996-12-31

Preventive maintenance (PM) techniques focus on the prevention of failures, in particular, system components that are important to plant functions. Reliability-centered maintenance (RCM) improves on the PM techniques by introducing a set of guidelines by which to evaluate the system functions. It also minimizes intrusive maintenance, labor, and equipment downtime without sacrificing system performance when its function is essential for plant safety. Both the PM and RCM approaches require that system reliability data be updated as more component failures and operation time are acquired. Systems reliability and the likelihood of component failures can be calculated by Bayesian statistical methods, whichmore » can update these data. The DATMAN computer code has been developed at Penn State to simplify the Bayesian analysis by performing tedious calculations needed for RCM reliability analysis. DATMAN reads data for updating, fits a distribution that best fits the data, and calculates component reliability. DATMAN provides a user-friendly interface menu that allows the user to choose from several common prior and posterior distributions, insert new failure data, and visually select the distribution that matches the data most accurately.« less

Bayesian dynamic mediation analysis.

PubMed

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

PubMed

Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence

2010-11-09

Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

A pedagogical walkthrough of computational modeling and simulation of Wnt signaling pathway using static causal models in MATLAB.

PubMed

Sinha, Shriprakash

2016-12-01

Simulation study in systems biology involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition, who intend to work on the modeling of the pathway. This paucity might happen due to restrictive business policies which enforce an unwanted embargo on the sharing of important scientific knowledge. A tutorial introduction to computational modeling of Wnt signaling pathway in a human colorectal cancer dataset using static Bayesian network models is provided. The walkthrough might aid biologists/informaticians in understanding the design of computational experiments that is interleaved with exposition of the Matlab code and causal models from Bayesian network toolbox. The manuscript elucidates the coding contents of the advance article by Sinha (Integr. Biol. 6:1034-1048, 2014) and takes the reader in a step-by-step process of how (a) the collection and the transformation of the available biological information from literature is done, (b) the integration of the heterogeneous data and prior biological knowledge in the network is achieved, (c) the simulation study is designed, (d) the hypothesis regarding a biological phenomena is transformed into computational framework, and (e) results and inferences drawn using d -connectivity/separability are reported. The manuscript finally ends with a programming assignment to help the readers get hands-on experience of a perturbation project. Description of Matlab files is made available under GNU GPL v3 license at the Google code project on https://code.google.com/p/static-bn-for-wnt-signaling-pathway and https: //sites.google.com/site/shriprakashsinha/shriprakashsinha/projects/static-bn-for-wnt-signaling-pathway. Latest updates can be found in the latter website.

GPU-accelerated simulations of isolated black holes

NASA Astrophysics Data System (ADS)

Lewis, Adam G. M.; Pfeiffer, Harald P.

2018-05-01

We present a port of the numerical relativity code SpEC which is capable of running on NVIDIA GPUs. Since this code must be maintained in parallel with SpEC itself, a primary design consideration is to perform as few explicit code changes as possible. We therefore rely on a hierarchy of automated porting strategies. At the highest level we use TLoops, a C++ library of our design, to automatically emit CUDA code equivalent to tensorial expressions written into C++ source using a syntax similar to analytic calculation. Next, we trace out and cache explicit matrix representations of the numerous linear transformations in the SpEC code, which allows these to be performed on the GPU using pre-existing matrix-multiplication libraries. We port the few remaining important modules by hand. In this paper we detail the specifics of our port, and present benchmarks of it simulating isolated black hole spacetimes on several generations of NVIDIA GPU.

The feasibility of adapting a population-based asthma-specific job exposure matrix (JEM) to NHANES.

PubMed

McHugh, Michelle K; Symanski, Elaine; Pompeii, Lisa A; Delclos, George L

2010-12-01

To determine the feasibility of applying a job exposure matrix (JEM) for classifying exposures to 18 asthmagens in the National Health and Nutrition Examination Survey (NHANES), 1999-2004. We cross-referenced 490 National Center for Health Statistics job codes used to develop the 40 NHANES occupation groups with 506 JEM job titles and assessed homogeneity in asthmagen exposure across job codes within each occupation group. In total, 399 job codes corresponded to one JEM job title, 32 to more than one job title, and 59 were not in the JEM. Three occupation groups had the same asthmagen exposure across job codes, 11 had no asthmagen exposure, and 26 groups had heterogeneous exposures across jobs codes. The NHANES classification of occupations limits the use of the JEM to evaluate the association between workplace exposures and asthma and more refined occupational data are needed to enhance work-related injury/illness surveillance efforts.

Fast space-varying convolution using matrix source coding with applications to camera stray light reduction.

PubMed

Wei, Jianing; Bouman, Charles A; Allebach, Jan P

2014-05-01

Many imaging applications require the implementation of space-varying convolution for accurate restoration and reconstruction of images. Here, we use the term space-varying convolution to refer to linear operators whose impulse response has slow spatial variation. In addition, these space-varying convolution operators are often dense, so direct implementation of the convolution operator is typically computationally impractical. One such example is the problem of stray light reduction in digital cameras, which requires the implementation of a dense space-varying deconvolution operator. However, other inverse problems, such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. While space-invariant convolution can be efficiently implemented with the fast Fourier transform, this approach does not work for space-varying operators. So direct convolution is often the only option for implementing space-varying convolution. In this paper, we develop a general approach to the efficient implementation of space-varying convolution, and demonstrate its use in the application of stray light reduction. Our approach, which we call matrix source coding, is based on lossy source coding of the dense space-varying convolution matrix. Importantly, by coding the transformation matrix, we not only reduce the memory required to store it; we also dramatically reduce the computation required to implement matrix-vector products. Our algorithm is able to reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. Experimental results show that our method can dramatically reduce the computation required for stray light reduction while maintaining high accuracy.

R matrices of three-state Hamiltonians solvable by coordinate Bethe ansatz

NASA Astrophysics Data System (ADS)

Fonseca, T.; Frappat, L.; Ragoucy, E.

2015-01-01

We review some of the strategies that can be implemented to infer an R-matrix from the knowledge of its Hamiltonian. We apply them to the classification achieved in Crampé, Frappat, and Ragoucy, J. Phys. A 46, 405001 (2013), on three state U(1)-invariant Hamiltonians solvable by coordinate Bethe ansatz, focusing on models for which the S-matrix is not trivial. For the 19-vertex solutions, we recover the R-matrices of the well-known Zamolodchikov-Fateev and Izergin-Korepin models. We point out that the generalized Bariev Hamiltonian is related to both main and special branches studied by Martins in Nucl. Phys. B 874, 243 (2013), that we prove to generate the same Hamiltonian. The 19-vertex SpR model still resists to the analysis, although we are able to state some no-go theorems on its R-matrix. For 17-vertex Hamiltonians, we produce a new R-matrix.

On the equilibrium state of a small system with random matrix coupling to its environment

NASA Astrophysics Data System (ADS)

Lebowitz, J. L.; Pastur, L.

2015-07-01

We consider a random matrix model of interaction between a small n-level system, S, and its environment, a N-level heat reservoir, R. The interaction between S and R is modeled by a tensor product of a fixed n× n matrix and a N× N Hermitian random matrix. We show that under certain ‘macroscopicity’ conditions on R, the reduced density matrix of the system {{ρ }S}=T{{r}R}ρ S\\cup R(eq), is given by ρ S(c)˜ exp \\{-β {{H}S}\\}, where HS is the Hamiltonian of the isolated system. This holds for all strengths of the interaction and thus gives some justification for using ρ S(c) to describe some nano-systems, like biopolymers, in equilibrium with their environment (Seifert 2012 Rep. Prog. Phys. 75 126001). Our results extend those obtained previously in (Lebowitz and Pastur 2004 J. Phys. A: Math. Gen. 37 1517-34) (Lebowitz et al 2007 Contemporary Mathematics (Providence RI: American Mathematical Society) pp 199-218) for a special two-level system.

An A{sub r} threesome: Matrix models, 2d conformal field theories, and 4dN=2 gauge theories

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

Schiappa, Ricardo; Wyllard, Niclas

We explore the connections between three classes of theories: A{sub r} quiver matrix models, d=2 conformal A{sub r} Toda field theories, and d=4N=2 supersymmetric conformal A{sub r} quiver gauge theories. In particular, we analyze the quiver matrix models recently introduced by Dijkgraaf and Vafa (unpublished) and make detailed comparisons with the corresponding quantities in the Toda field theories and the N=2 quiver gauge theories. We also make a speculative proposal for how the matrix models should be modified in order for them to reproduce the instanton partition functions in quiver gauge theories in five dimensions.

A Taxonomic Reduced-Space Pollen Model for Paleoclimate Reconstruction

NASA Astrophysics Data System (ADS)

Wahl, E. R.; Schoelzel, C.

2010-12-01

Paleoenvironmental reconstruction from fossil pollen often attempts to take advantage of the rich taxonomic diversity in such data. Here, a taxonomically "reduced-space" reconstruction model is explored that would be parsimonious in introducing parameters needing to be estimated within a Bayesian Hierarchical Modeling context. This work involves a refinement of the traditional pollen ratio method. This method is useful when one (or a few) dominant pollen type(s) in a region have a strong positive correlation with a climate variable of interest and another (or a few) dominant pollen type(s) have a strong negative correlation. When, e.g., counts of pollen taxa a and b (r >0) are combined with pollen types c and d (r <0) to form ratios of the form (a + b) / (a + b + c + d), an appropriate estimation form is the binomial logistic generalized linear model (GLM). The GLM can readily model this relationship in the forward form, pollen = g(climate), which is more physically realistic than inverse models often used in paleoclimate reconstruction [climate = f(pollen)]. The specification of the model is: rnum Bin(n,p), where E(r|T) = p = exp(η)/[1+exp(η)], and η = α + β(T); r is the pollen ratio formed as above, rnum is the ratio numerator, n is the ratio denominator (i.e., the sum of pollen counts), the denominator-specific count is (n - rnum), and T is the temperature at each site corresponding to a specific value of r. Ecological and empirical screening identified the model (Spruce+Birch) / (Spruce+Birch+Oak+Hickory) for use in temperate eastern N. America. α and β were estimated using both "traditional" and Bayesian GLM algorithms (in R). Although it includes only four pollen types, the ratio model yields more explained variation ( 80%) in the pollen-temperature relationship of the study region than a 64-taxon modern analog technique (MAT). Thus, the new pollen ratio method represents an information-rich, reduced space data model that can be efficiently employed in a BHM framework. The ratio model can directly reconstruct past temperature by solving the GLM equations for T as a function of α, β, and E(r|T): T = {ln[E(r|T)/{1-E(r|T)}]-α}/β. To enable use in paleoreconstruction, the observed r values from fossil pollen data are, by assumption, treated as unbiased estimators of the true r value at each time sampled, which can be substituted for E(r|T). Uncertainty in this reconstruction is systematically evaluated in two parts: 1) the observed r values and their corresponding n values are input as parameters into the binomial distribution, Monte Carlo random pollen count draws are made, and a new ratio value is determined for each iteration; and 2) in the "traditional" GLM the estimated SEs for α and β are used with the α and β EV estimates to yield Monte Carlo random draws for each binomial draw (assuming α and β are Gaussian), in the Bayesian GLM random draws for α and β are taken directly from their estimated posterior distribution. Both methods yield nearly identical reconstructions from varved lakes in Wisconsin where the model has been tested; slightly narrower uncertainty ranges are produced by the Bayesian model. The Little Ice Age is readily identified. Pine:Oak and Fir:Oak versions of the model used in S. California show differences from MAT-based reconstructions.

Evaluation of the Validity of Job Exposure Matrix for Psychosocial Factors at Work

PubMed Central

Solovieva, Svetlana; Pensola, Tiina; Kausto, Johanna; Shiri, Rahman; Heliövaara, Markku; Burdorf, Alex; Husgafvel-Pursiainen, Kirsti; Viikari-Juntura, Eira

2014-01-01

Objective To study the performance of a developed job exposure matrix (JEM) for the assessment of psychosocial factors at work in terms of accuracy, possible misclassification bias and predictive ability to detect known associations with depression and low back pain (LBP). Materials and Methods We utilized two large population surveys (the Health 2000 Study and the Finnish Work and Health Surveys), one to construct the JEM and another to test matrix performance. In the first study, information on job demands, job control, monotonous work and social support at work was collected via face-to-face interviews. Job strain was operationalized based on job demands and job control using quadrant approach. In the second study, the sensitivity and specificity were estimated applying a Bayesian approach. The magnitude of misclassification error was examined by calculating the biased odds ratios as a function of the sensitivity and specificity of the JEM and fixed true prevalence and odds ratios. Finally, we adjusted for misclassification error the observed associations between JEM measures and selected health outcomes. Results The matrix showed a good accuracy for job control and job strain, while its performance for other exposures was relatively low. Without correction for exposure misclassification, the JEM was able to detect the association between job strain and depression in men and between monotonous work and LBP in both genders. Conclusions Our results suggest that JEM more accurately identifies occupations with low control and high strain than those with high demands or low social support. Overall, the present JEM is a useful source of job-level psychosocial exposures in epidemiological studies lacking individual-level exposure information. Furthermore, we showed the applicability of a Bayesian approach in the evaluation of the performance of the JEM in a situation where, in practice, no gold standard of exposure assessment exists. PMID:25268276

On the symbolic manipulation and code generation for elasto-plastic material matrices

NASA Technical Reports Server (NTRS)

Chang, T. Y.; Saleeb, A. F.; Wang, P. S.; Tan, H. Q.

1991-01-01

A computerized procedure for symbolic manipulations and FORTRAN code generation of an elasto-plastic material matrix for finite element applications is presented. Special emphasis is placed on expression simplifications during intermediate derivations, optimal code generation, and interface with the main program. A systematic procedure is outlined to avoid redundant algebraic manipulations. Symbolic expressions of the derived material stiffness matrix are automatically converted to RATFOR code which is then translated into FORTRAN statements through a preprocessor. To minimize the interface problem with the main program, a template file is prepared so that the translated FORTRAN statements can be merged into the file to form a subroutine (or a submodule). Three constitutive models; namely, von Mises plasticity, Drucker-Prager model, and a concrete plasticity model, are used as illustrative examples.

Making extreme computations possible with virtual machines

NASA Astrophysics Data System (ADS)

Reuter, J.; Chokoufe Nejad, B.; Ohl, T.

2016-10-01

State-of-the-art algorithms generate scattering amplitudes for high-energy physics at leading order for high-multiplicity processes as compiled code (in Fortran, C or C++). For complicated processes the size of these libraries can become tremendous (many GiB). We show that amplitudes can be translated to byte-code instructions, which even reduce the size by one order of magnitude. The byte-code is interpreted by a Virtual Machine with runtimes comparable to compiled code and a better scaling with additional legs. We study the properties of this algorithm, as an extension of the Optimizing Matrix Element Generator (O'Mega). The bytecode matrix elements are available as alternative input for the event generator WHIZARD. The bytecode interpreter can be implemented very compactly, which will help with a future implementation on massively parallel GPUs.

Optimizing the use of a sensor resource for opponent polarization coding

PubMed Central

Heras, Francisco J.H.

2017-01-01

Flies use specialized photoreceptors R7 and R8 in the dorsal rim area (DRA) to detect skylight polarization. R7 and R8 form a tiered waveguide (central rhabdomere pair, CRP) with R7 on top, filtering light delivered to R8. We examine how the division of a given resource, CRP length, between R7 and R8 affects their ability to code polarization angle. We model optical absorption to show how the length fractions allotted to R7 and R8 determine the rates at which they transduce photons, and correct these rates for transduction unit saturation. The rates give polarization signal and photon noise in R7, and in R8. Their signals are combined in an opponent unit, intrinsic noise added, and the unit’s output analysed to extract two measures of coding ability, number of discriminable polarization angles and mutual information. A very long R7 maximizes opponent signal amplitude, but codes inefficiently due to photon noise in the very short R8. Discriminability and mutual information are optimized by maximizing signal to noise ratio, SNR. At lower light levels approximately equal lengths of R7 and R8 are optimal because photon noise dominates. At higher light levels intrinsic noise comes to dominate and a shorter R8 is optimum. The optimum R8 length fractions falls to one third. This intensity dependent range of optimal length fractions corresponds to the range observed in different fly species and is not affected by transduction unit saturation. We conclude that a limited resource, rhabdom length, can be divided between two polarization sensors, R7 and R8, to optimize opponent coding. We also find that coding ability increases sub-linearly with total rhabdom length, according to the law of diminishing returns. Consequently, the specialized shorter central rhabdom in the DRA codes polarization twice as efficiently with respect to rhabdom length than the longer rhabdom used in the rest of the eye. PMID:28316880

Nonequilibrium chemistry boundary layer integral matrix procedure

NASA Technical Reports Server (NTRS)

Tong, H.; Buckingham, A. C.; Morse, H. L.

1973-01-01

The development of an analytic procedure for the calculation of nonequilibrium boundary layer flows over surfaces of arbitrary catalycities is described. An existing equilibrium boundary layer integral matrix code was extended to include nonequilibrium chemistry while retaining all of the general boundary condition features built into the original code. For particular application to the pitch-plane of shuttle type vehicles, an approximate procedure was developed to estimate the nonequilibrium and nonisentropic state at the edge of the boundary layer.

E-O Sensor Signal Recognition Simulation: Computer Code SPOT I.

DTIC Science & Technology

1978-10-01

scattering phase function PDCO , defined at the specified wavelength, given for each of the scattering angles defined. Currently, a maximum of sixty-four...PHASE MATRIX DATA IS DEFINED PDCO AVERAGE PROBABILITY FOR PHASE MATRIX DEFINITION NPROB PROBLEM NUMBER 54 Fig. 12. FLOWCHART for the SPOT Computer Code...El0.1 WLAM(N) Wavelength at which the aerosol single-scattering phase function set is defined (microns) 3 8El0.1 PDCO (N,I) Average probability for

ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

PubMed Central

Bottolo, Leonardo; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia

2011-01-01

Summary: ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html. Contact: l.bottolo@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21233165

Code-division multiple-access multiuser demodulator by using quantum fluctuations.

PubMed

Otsubo, Yosuke; Inoue, Jun-Ichi; Nagata, Kenji; Okada, Masato

2014-07-01

We examine the average-case performance of a code-division multiple-access (CDMA) multiuser demodulator in which quantum fluctuations are utilized to demodulate the original message within the context of Bayesian inference. The quantum fluctuations are built into the system as a transverse field in the infinite-range Ising spin glass model. We evaluate the performance measurements by using statistical mechanics. We confirm that the CDMA multiuser modulator using quantum fluctuations achieve roughly the same performance as the conventional CDMA multiuser modulator through thermal fluctuations on average. We also find that the relationship between the quality of the original information retrieval and the amplitude of the transverse field is somehow a "universal feature" in typical probabilistic information processing, viz., in image restoration, error-correcting codes, and CDMA multiuser demodulation.

Code-division multiple-access multiuser demodulator by using quantum fluctuations

NASA Astrophysics Data System (ADS)

Otsubo, Yosuke; Inoue, Jun-ichi; Nagata, Kenji; Okada, Masato

2014-07-01

We examine the average-case performance of a code-division multiple-access (CDMA) multiuser demodulator in which quantum fluctuations are utilized to demodulate the original message within the context of Bayesian inference. The quantum fluctuations are built into the system as a transverse field in the infinite-range Ising spin glass model. We evaluate the performance measurements by using statistical mechanics. We confirm that the CDMA multiuser modulator using quantum fluctuations achieve roughly the same performance as the conventional CDMA multiuser modulator through thermal fluctuations on average. We also find that the relationship between the quality of the original information retrieval and the amplitude of the transverse field is somehow a "universal feature" in typical probabilistic information processing, viz., in image restoration, error-correcting codes, and CDMA multiuser demodulation.

Evaluation of coded aperture radiation detectors using a Bayesian approach

NASA Astrophysics Data System (ADS)

Miller, Kyle; Huggins, Peter; Labov, Simon; Nelson, Karl; Dubrawski, Artur

2016-12-01

We investigate tradeoffs arising from the use of coded aperture gamma-ray spectrometry to detect and localize sources of harmful radiation in the presence of noisy background. Using an example application scenario of area monitoring and search, we empirically evaluate weakly supervised spectral, spatial, and hybrid spatio-spectral algorithms for scoring individual observations, and two alternative methods of fusing evidence obtained from multiple observations. Results of our experiments confirm the intuition that directional information provided by spectrometers masked with coded aperture enables gains in source localization accuracy, but at the expense of reduced probability of detection. Losses in detection performance can however be to a substantial extent reclaimed by using our new spatial and spatio-spectral scoring methods which rely on realistic assumptions regarding masking and its impact on measured photon distributions.

Inference on the Genetic Basis of Eye and Skin Color in an Admixed Population via Bayesian Linear Mixed Models.

PubMed

Lloyd-Jones, Luke R; Robinson, Matthew R; Moser, Gerhard; Zeng, Jian; Beleza, Sandra; Barsh, Gregory S; Tang, Hua; Visscher, Peter M

2017-06-01

Genetic association studies in admixed populations are underrepresented in the genomics literature, with a key concern for researchers being the adequate control of spurious associations due to population structure. Linear mixed models (LMMs) are well suited for genome-wide association studies (GWAS) because they account for both population stratification and cryptic relatedness and achieve increased statistical power by jointly modeling all genotyped markers. Additionally, Bayesian LMMs allow for more flexible assumptions about the underlying distribution of genetic effects, and can concurrently estimate the proportion of phenotypic variance explained by genetic markers. Using three recently published Bayesian LMMs, Bayes R, BSLMM, and BOLT-LMM, we investigate an existing data set on eye ( n = 625) and skin ( n = 684) color from Cape Verde, an island nation off West Africa that is home to individuals with a broad range of phenotypic values for eye and skin color due to the mix of West African and European ancestry. We use simulations to demonstrate the utility of Bayesian LMMs for mapping loci and studying the genetic architecture of quantitative traits in admixed populations. The Bayesian LMMs provide evidence for two new pigmentation loci: one for eye color ( AHRR ) and one for skin color ( DDB1 ). Copyright © 2017 by the Genetics Society of America.

Long term economic relationships from cointegration maps

NASA Astrophysics Data System (ADS)

Vicente, Renato; Pereira, Carlos de B.; Leite, Vitor B. P.; Caticha, Nestor

2007-07-01

We employ the Bayesian framework to define a cointegration measure aimed to represent long term relationships between time series. For visualization of these relationships we introduce a dissimilarity matrix and a map based on the sorting points into neighborhoods (SPIN) technique, which has been previously used to analyze large data sets from DNA arrays. We exemplify the technique in three data sets: US interest rates (USIR), monthly inflation rates and gross domestic product (GDP) growth rates.

Authorship Discovery in Blogs Using Bayesian Classification with Corrective Scaling

DTIC Science & Technology

2008-06-01

4 2.3 W. Fucks ’ Diagram of n-Syllable Word Frequencies . . . . . . . . . . . . . . 5 3.1 Confusion Matrix for All Test Documents of 500...of the books which scholars believed he had. • Wilhelm Fucks discriminated between authors using the average number of syllables per word and average...distance between equal-syllabled words [8]. Fucks , too, concluded that a study such as his reveals a “possibility of a quantitative classification

Constacyclic codes over the ring F_q+v{F}_q+v2F_q and their applications of constructing new non-binary quantum codes

NASA Astrophysics Data System (ADS)

Ma, Fanghui; Gao, Jian; Fu, Fang-Wei

2018-06-01

Let R={F}_q+v{F}_q+v2{F}_q be a finite non-chain ring, where q is an odd prime power and v^3=v. In this paper, we propose two methods of constructing quantum codes from (α +β v+γ v2)-constacyclic codes over R. The first one is obtained via the Gray map and the Calderbank-Shor-Steane construction from Euclidean dual-containing (α +β v+γ v2)-constacyclic codes over R. The second one is obtained via the Gray map and the Hermitian construction from Hermitian dual-containing (α +β v+γ v2)-constacyclic codes over R. As an application, some new non-binary quantum codes are obtained.

Exact Bayesian p-values for a test of independence in a 2 × 2 contingency table with missing data.

PubMed

Lin, Yan; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett; Lipshultz, Steven

2017-01-01

Altham (Altham PME. Exact Bayesian analysis of a 2 × 2 contingency table, and Fisher's "exact" significance test. J R Stat Soc B 1969; 31: 261-269) showed that a one-sided p-value from Fisher's exact test of independence in a 2 × 2 contingency table is equal to the posterior probability of negative association in the 2 × 2 contingency table under a Bayesian analysis using an improper prior. We derive an extension of Fisher's exact test p-value in the presence of missing data, assuming the missing data mechanism is ignorable (i.e., missing at random or completely at random). Further, we propose Bayesian p-values for a test of independence in a 2 × 2 contingency table with missing data using alternative priors; we also present results from a simulation study exploring the Type I error rate and power of the proposed exact test p-values. An example, using data on the association between blood pressure and a cardiac enzyme, is presented to illustrate the methods.

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

PubMed Central

Weiss, Scott T.

2014-01-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com. PMID:24922310

CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

PubMed

McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

2014-06-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

Bayesian Analysis of Biogeography when the Number of Areas is Large

PubMed Central

Landis, Michael J.; Matzke, Nicholas J.; Moore, Brian R.; Huelsenbeck, John P.

2013-01-01

Historical biogeography is increasingly studied from an explicitly statistical perspective, using stochastic models to describe the evolution of species range as a continuous-time Markov process of dispersal between and extinction within a set of discrete geographic areas. The main constraint of these methods is the computational limit on the number of areas that can be specified. We propose a Bayesian approach for inferring biogeographic history that extends the application of biogeographic models to the analysis of more realistic problems that involve a large number of areas. Our solution is based on a “data-augmentation” approach, in which we first populate the tree with a history of biogeographic events that is consistent with the observed species ranges at the tips of the tree. We then calculate the likelihood of a given history by adopting a mechanistic interpretation of the instantaneous-rate matrix, which specifies both the exponential waiting times between biogeographic events and the relative probabilities of each biogeographic change. We develop this approach in a Bayesian framework, marginalizing over all possible biogeographic histories using Markov chain Monte Carlo (MCMC). Besides dramatically increasing the number of areas that can be accommodated in a biogeographic analysis, our method allows the parameters of a given biogeographic model to be estimated and different biogeographic models to be objectively compared. Our approach is implemented in the program, BayArea. [ancestral area analysis; Bayesian biogeographic inference; data augmentation; historical biogeography; Markov chain Monte Carlo.] PMID:23736102

Generalizability of Evidence-Based Assessment Recommendations for Pediatric Bipolar Disorder

PubMed Central

Jenkins, Melissa M.; Youngstrom, Eric A.; Youngstrom, Jennifer Kogos; Feeny, Norah C.; Findling, Robert L.

2013-01-01

Bipolar disorder is frequently clinically diagnosed in youths who do not actually satisfy DSM-IV criteria, yet cases that would satisfy full DSM-IV criteria are often undetected clinically. Evidence-based assessment methods that incorporate Bayesian reasoning have demonstrated improved diagnostic accuracy, and consistency; however, their clinical utility is largely unexplored. The present study examines the effectiveness of promising evidence-based decision-making compared to the clinical gold standard. Participants were 562 youth, ages 5-17 and predominantly African American, drawn from a community mental health clinic. Research diagnoses combined semi-structured interview with youths’ psychiatric, developmental, and family mental health histories. Independent Bayesian estimates relied on published risk estimates from other samples discriminated bipolar diagnoses, Area Under Curve=.75, p<.00005. The Bayes and confidence ratings correlated rs =.30. Agreement about an evidence-based assessment intervention “threshold model” (wait/assess/treat) had K=.24, p<.05. No potential moderators of agreement between the Bayesian estimates and confidence ratings, including type of bipolar illness, were significant. Bayesian risk estimates were highly correlated with logistic regression estimates using optimal sample weights, r=.81, p<.0005. Clinical and Bayesian approaches agree in terms of overall concordance and deciding next clinical action, even when Bayesian predictions are based on published estimates from clinically and demographically different samples. Evidence-based assessment methods may be useful in settings that cannot routinely employ gold standard assessments, and they may help decrease rates of overdiagnosis while promoting earlier identification of true cases. PMID:22004538

msap: a tool for the statistical analysis of methylation-sensitive amplified polymorphism data.

PubMed

Pérez-Figueroa, A

2013-05-01

In this study msap, an R package which analyses methylation-sensitive amplified polymorphism (MSAP or MS-AFLP) data is presented. The program provides a deep analysis of epigenetic variation starting from a binary data matrix indicating the banding pattern between the isoesquizomeric endonucleases HpaII and MspI, with differential sensitivity to cytosine methylation. After comparing the restriction fragments, the program determines if each fragment is susceptible to methylation (representative of epigenetic variation) or if there is no evidence of methylation (representative of genetic variation). The package provides, in a user-friendly command line interface, a pipeline of different analyses of the variation (genetic and epigenetic) among user-defined groups of samples, as well as the classification of the methylation occurrences in those groups. Statistical testing provides support to the analyses. A comprehensive report of the analyses and several useful plots could help researchers to assess the epigenetic and genetic variation in their MSAP experiments. msap is downloadable from CRAN (http://cran.r-project.org/) and its own webpage (http://msap.r-forge.R-project.org/). The package is intended to be easy to use even for those people unfamiliar with the R command line environment. Advanced users may take advantage of the available source code to adapt msap to more complex analyses. © 2013 Blackwell Publishing Ltd.

Fatigue-life behavior and matrix fatigue crack spacing in unnotched SCS-6/Timetal 21S metal matrix composites

NASA Technical Reports Server (NTRS)

Ward, G. T.; Herrmann, D. J.; Hillberry, B. M.

1993-01-01

Fatigue tests of the SCS-6/Timetal 21S composite system were performed to characterize the fatigue behavior for unnotched conditions. The stress-life behavior of the unnotched (9/90)2s laminates was investigated for stress ratios of R = 0.1 and R = 0.3. The occurrence of matrix cracking was also examined in these specimens. This revealed multiple matrix crack initiation sites throughout the composite, as well as evenly spaced surface cracks along the length of the specimens. No difference in fatigue lives were observed for stress ratios of R = 0.1 and R = 0.3 when compared on a stress range basis. The unnotched SCS-6/Timetal 21S composites had shorter fatigue lives than the SCS-6/Ti-15-3 composites, however the neat Timetal 21S matrix material had a longer fatigue life than the neat Ti-15-3.

The linear parameters and the decoupling matrix for linearly coupled motion in 6 dimensional phase space. Informal report

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

Parzen, G.

It will be shown that starting from a coordinate system where the 6 phase space coordinates are linearly coupled, one can go to a new coordinate system, where the motion is uncoupled, by means of a linear transformation. The original coupled coordinates and the new uncoupled coordinates are related by a 6 {times} 6 matrix, R. R will be called the decoupling matrix. It will be shown that of the 36 elements of the 6 {times} 6 decoupling matrix R, only 12 elements are independent. This may be contrasted with the results for motion in 4-dimensional phase space, where Rmore » has 4 independent elements. A set of equations is given from which the 12 elements of R can be computed from the one period transfer matrix. This set of equations also allows the linear parameters, {beta}{sub i}, {alpha}{sub i} = 1, 3, for the uncoupled coordinates, to be computed from the one period transfer matrix. An alternative procedure for computing the linear parameters, the {beta}{sub i}, {alpha}{sub i} i = 1, 3, and the 12 independent elements of the decoupling matrix R is also given which depends on computing the eigenvectors of the one period transfer matrix. These results can be used in a tracking program, where the one period transfer matrix can be computed by multiplying the transfer matrices of all the elements in a period, to compute the linear parameters {alpha}{sub i} and {beta}{sub i}, i = 1, 3, and the elements of the decoupling matrix R. The procedure presented here for studying coupled motion in 6-dimensional phase space can also be applied to coupled motion in 4-dimensional phase space, where it may be a useful alternative procedure to the procedure presented by Edwards and Teng. In particular, it gives a simpler programming procedure for computing the beta functions and the emittances for coupled motion in 4-dimensional phase space.« less

The evolution of autodigestion in the mushroom family Psathyrellaceae (Agaricales) inferred from Maximum Likelihood and Bayesian methods.

PubMed

Nagy, László G; Urban, Alexander; Orstadius, Leif; Papp, Tamás; Larsson, Ellen; Vágvölgyi, Csaba

2010-12-01

Recently developed comparative phylogenetic methods offer a wide spectrum of applications in evolutionary biology, although it is generally accepted that their statistical properties are incompletely known. Here, we examine and compare the statistical power of the ML and Bayesian methods with regard to selection of best-fit models of fruiting-body evolution and hypothesis testing of ancestral states on a real-life data set of a physiological trait (autodigestion) in the family Psathyrellaceae. Our phylogenies are based on the first multigene data set generated for the family. Two different coding regimes (binary and multistate) and two data sets differing in taxon sampling density are examined. The Bayesian method outperformed Maximum Likelihood with regard to statistical power in all analyses. This is particularly evident if the signal in the data is weak, i.e. in cases when the ML approach does not provide support to choose among competing hypotheses. Results based on binary and multistate coding differed only modestly, although it was evident that multistate analyses were less conclusive in all cases. It seems that increased taxon sampling density has favourable effects on inference of ancestral states, while model parameters are influenced to a smaller extent. The model best fitting our data implies that the rate of losses of deliquescence equals zero, although model selection in ML does not provide proper support to reject three of the four candidate models. The results also support the hypothesis that non-deliquescence (lack of autodigestion) has been ancestral in Psathyrellaceae, and that deliquescent fruiting bodies represent the preferred state, having evolved independently several times during evolution. Copyright © 2010 Elsevier Inc. All rights reserved.

A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer’s Disease

PubMed Central

Wang, Xulong; Philip, Vivek M.; Ananda, Guruprasad; White, Charles C.; Malhotra, Ankit; Michalski, Paul J.; Karuturi, Krishna R. Murthy; Chintalapudi, Sumana R.; Acklin, Casey; Sasner, Michael; Bennett, David A.; De Jager, Philip L.; Howell, Gareth R.; Carter, Gregory W.

2018-01-01

Recent technical and methodological advances have greatly enhanced genome-wide association studies (GWAS). The advent of low-cost, whole-genome sequencing facilitates high-resolution variant identification, and the development of linear mixed models (LMM) allows improved identification of putatively causal variants. While essential for correcting false positive associations due to sample relatedness and population stratification, LMMs have commonly been restricted to quantitative variables. However, phenotypic traits in association studies are often categorical, coded as binary case-control or ordered variables describing disease stages. To address these issues, we have devised a method for genomic association studies that implements a generalized LMM (GLMM) in a Bayesian framework, called Bayes-GLMM. Bayes-GLMM has four major features: (1) support of categorical, binary, and quantitative variables; (2) cohesive integration of previous GWAS results for related traits; (3) correction for sample relatedness by mixed modeling; and (4) model estimation by both Markov chain Monte Carlo sampling and maximal likelihood estimation. We applied Bayes-GLMM to the whole-genome sequencing cohort of the Alzheimer’s Disease Sequencing Project. This study contains 570 individuals from 111 families, each with Alzheimer’s disease diagnosed at one of four confidence levels. Using Bayes-GLMM we identified four variants in three loci significantly associated with Alzheimer’s disease. Two variants, rs140233081 and rs149372995, lie between PRKAR1B and PDGFA. The coded proteins are localized to the glial-vascular unit, and PDGFA transcript levels are associated with Alzheimer’s disease-related neuropathology. In summary, this work provides implementation of a flexible, generalized mixed-model approach in a Bayesian framework for association studies. PMID:29507048

Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

DTIC Science & Technology

2005-07-25

analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for

Testing non-minimally coupled inflation with CMB data: a Bayesian analysis

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

Campista, Marcela; Benetti, Micol; Alcaniz, Jailson, E-mail: campista@on.br, E-mail: micolbenetti@on.br, E-mail: alcaniz@on.br

2017-09-01

We use the most recent cosmic microwave background (CMB) data to perform a Bayesian statistical analysis and discuss the observational viability of inflationary models with a non-minimal coupling, ξ, between the inflaton field and the Ricci scalar. We particularize our analysis to two examples of small and large field inflationary models, namely, the Coleman-Weinberg and the chaotic quartic potentials. We find that ( i ) the ξ parameter is closely correlated with the primordial amplitude ; ( ii ) although improving the agreement with the CMB data in the r − n {sub s} plane, where r is the tensor-to-scalarmore » ratio and n {sub s} the primordial spectral index, a non-null coupling is strongly disfavoured with respect to the minimally coupled standard ΛCDM model, since the upper bounds of the Bayes factor (odds) for ξ parameter are greater than 150:1.« less

Selectivity curves of the capture of mangrove crab (Ucides cordatus) on the northern coast of Brazil using bayesian inference.

PubMed

Furtado-Junior, I; Abrunhosa, F A; Holanda, F C A F; Tavares, M C S

2016-06-01

Fishing selectivity of the mangrove crab Ucides cordatus in the north coast of Brazil can be defined as the fisherman's ability to capture and select individuals from a certain size or sex (or a combination of these factors) which suggests an empirical selectivity. Considering this hypothesis, we calculated the selectivity curves for males and females crabs using the logit function of the logistic model in the formulation. The Bayesian inference consisted of obtaining the posterior distribution by applying the Markov chain Monte Carlo (MCMC) method to software R using the OpenBUGS, BRugs, and R2WinBUGS libraries. The estimated results of width average carapace selection for males and females compared with previous studies reporting the average width of the carapace of sexual maturity allow us to confirm the hypothesis that most mature individuals do not suffer from fishing pressure; thus, ensuring their sustainability.

Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability.

PubMed

Halimi, Abderrahim; Dobigeon, Nicolas; Tourneret, Jean-Yves

2015-12-01

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing, accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to consider their variability in the image. An additive noise is also considered in the proposed model, generalizing the normal compositional model. The proposed algorithm exploits the whole image to benefit from both spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated through simulations conducted on both synthetic and real data.

Imprints of spherical nontrivial topologies on the cosmic microwave background.

PubMed

Niarchou, Anastasia; Jaffe, Andrew

2007-08-24

The apparent low power in the cosmic microwave background (CMB) temperature anisotropy power spectrum derived from the Wilkinson Microwave Anisotropy Probe motivated us to consider the possibility of a nontrivial topology. We focus on simple spherical multiconnected manifolds and discuss their implications for the CMB in terms of the power spectrum, maps, and the correlation matrix. We perform a Bayesian model comparison against the fiducial best-fit cold dark matter model with a cosmological constant based both on the power spectrum and the correlation matrix to assess their statistical significance. We find that the first-year power spectrum shows a slight preference for the truncated cube space, but the three-year data show no evidence for any of these spaces.

Evolutionary Relations of Hexanchiformes Deep-Sea Sharks Elucidated by Whole Mitochondrial Genome Sequences

PubMed Central

Tanaka, Keiko; Tomita, Taketeru; Suzuki, Shingo; Hosomichi, Kazuyoshi; Sano, Kazumi; Doi, Hiroyuki; Kono, Azumi; Inoko, Hidetoshi; Kulski, Jerzy K.; Tanaka, Sho

2013-01-01

Hexanchiformes is regarded as a monophyletic taxon, but the morphological and genetic relationships between the five extant species within the order are still uncertain. In this study, we determined the whole mitochondrial DNA (mtDNA) sequences of seven sharks including representatives of the five Hexanchiformes, one squaliform, and one carcharhiniform and inferred the phylogenetic relationships among those species and 12 other Chondrichthyes (cartilaginous fishes) species for which the complete mitogenome is available. The monophyly of Hexanchiformes and its close relation with all other Squaliformes sharks were strongly supported by likelihood and Bayesian phylogenetic analysis of 13,749 aligned nucleotides of 13 protein coding genes and two rRNA genes that were derived from the whole mDNA sequences of the 19 species. The phylogeny suggested that Hexanchiformes is in the superorder Squalomorphi, Chlamydoselachus anguineus (frilled shark) is the sister species to all other Hexanchiformes, and the relations within Hexanchiformes are well resolved as Chlamydoselachus, (Notorynchus, (Heptranchias, (Hexanchus griseus, H. nakamurai))). Based on our phylogeny, we discussed evolutionary scenarios of the jaw suspension mechanism and gill slit numbers that are significant features in the sharks. PMID:24089661

Skew-t fits to mortality data--can a Gaussian-related distribution replace the Gompertz-Makeham as the basis for mortality studies?

PubMed

Clark, Jeremy S C; Kaczmarczyk, Mariusz; Mongiało, Zbigniew; Ignaczak, Paweł; Czajkowski, Andrzej A; Klęsk, Przemysław; Ciechanowicz, Andrzej

2013-08-01

Gompertz-related distributions have dominated mortality studies for 187 years. However, nonrelated distributions also fit well to mortality data. These compete with the Gompertz and Gompertz-Makeham data when applied to data with varying extents of truncation, with no consensus as to preference. In contrast, Gaussian-related distributions are rarely applied, despite the fact that Lexis in 1879 suggested that the normal distribution itself fits well to the right of the mode. Study aims were therefore to compare skew-t fits to Human Mortality Database data, with Gompertz-nested distributions, by implementing maximum likelihood estimation functions (mle2, R package bbmle; coding given). Results showed skew-t fits obtained lower Bayesian information criterion values than Gompertz-nested distributions, applied to low-mortality country data, including 1711 and 1810 cohorts. As Gaussian-related distributions have now been found to have almost universal application to error theory, one conclusion could be that a Gaussian-related distribution might replace Gompertz-related distributions as the basis for mortality studies.

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

NASA Astrophysics Data System (ADS)

Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

2017-06-01

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

Black box multigrid

NASA Technical Reports Server (NTRS)

Dendy, J. E., Jr.

1981-01-01

The black box multigrid (BOXMG) code, which only needs specification of the matrix problem for application in the multigrid method was investigated. It is contended that a major problem with the multigrid method is that each new grid configuration requires a major programming effort to develop a code that specifically handles that grid configuration. The SOR and ICCG methods only specify the matrix problem, no matter what the grid configuration. It is concluded that the BOXMG does everything else necessary to set up the auxiliary coarser problems to achieve a multigrid solution.

Development and Evaluation of an Order-N Formulation for Multi-Flexible Body Space Systems

NASA Technical Reports Server (NTRS)

Ghosh, Tushar K.; Quiocho, Leslie J.

2013-01-01

This paper presents development of a generic recursive Order-N algorithm for systems with rigid and flexible bodies, in tree or closed-loop topology, with N being the number of bodies of the system. Simulation results are presented for several test cases to verify and evaluate the performance of the code compared to an existing efficient dense mass matrix-based code. The comparison brought out situations where Order-N or mass matrix-based algorithms could be useful.

JOZSO, a computer code for calculating broad neutron resonances in phenomenological nuclear potentials

NASA Astrophysics Data System (ADS)

Baran, Á.; Noszály, Cs.; Vertse, T.

2018-07-01

A renewed version of the computer code GAMOW (Vertse et al., 1982) is given in which the difficulties in calculating broad neutron resonances are amended. New types of phenomenological neutron potentials with strict finite range are built in. Landscape of the S-matrix can be generated on a given domain of the complex wave number plane and S-matrix poles in the domain are localized. Normalized Gamow wave functions and trajectories of given poles can be calculated optionally.

Deconvolution using a neural network

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

Lehman, S.K.

1990-11-15

Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

Intelligent Decisions Need Intelligent Choice of Models and Data - a Bayesian Justifiability Analysis for Models with Vastly Different Complexity

NASA Astrophysics Data System (ADS)

Nowak, W.; Schöniger, A.; Wöhling, T.; Illman, W. A.

2016-12-01

Model-based decision support requires justifiable models with good predictive capabilities. This, in turn, calls for a fine adjustment between predictive accuracy (small systematic model bias that can be achieved with rather complex models), and predictive precision (small predictive uncertainties that can be achieved with simpler models with fewer parameters). The implied complexity/simplicity trade-off depends on the availability of informative data for calibration. If not available, additional data collection can be planned through optimal experimental design. We present a model justifiability analysis that can compare models of vastly different complexity. It rests on Bayesian model averaging (BMA) to investigate the complexity/performance trade-off dependent on data availability. Then, we disentangle the complexity component from the performance component. We achieve this by replacing actually observed data by realizations of synthetic data predicted by the models. This results in a "model confusion matrix". Based on this matrix, the modeler can identify the maximum model complexity that can be justified by the available (or planned) amount and type of data. As a side product, the matrix quantifies model (dis-)similarity. We apply this analysis to aquifer characterization via hydraulic tomography, comparing four models with a vastly different number of parameters (from a homogeneous model to geostatistical random fields). As a testing scenario, we consider hydraulic tomography data. Using subsets of these data, we determine model justifiability as a function of data set size. The test case shows that geostatistical parameterization requires a substantial amount of hydraulic tomography data to be justified, while a zonation-based model can be justified with more limited data set sizes. The actual model performance (as opposed to model justifiability), however, depends strongly on the quality of prior geological information.

Towards a formal genealogical classification of the Lezgian languages (North Caucasus): testing various phylogenetic methods on lexical data.

PubMed

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies.

Towards a Formal Genealogical Classification of the Lezgian Languages (North Caucasus): Testing Various Phylogenetic Methods on Lexical Data

PubMed Central

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies. PMID:25719456

Reading Through Paint

NASA Technical Reports Server (NTRS)

2004-01-01

Two-dimensional data matrix symbols, which contain encoded letters and numbers, are permanently etched on items for identification. They can store up to 100 times more information than traditional bar codes. While the symbols provide several advantages over bar codes, once they are covered by paint they can no longer be read by optical scanners. Since most products are painted eventually, this presents a problem for industries relying on the symbols for identification and tracking. In 1987, NASA s Marshall Space Flight Center began studying direct parts marking with matrix symbols in order to track millions of Space Shuttle parts. Advances in the technology proved that by incorporating magnetic properties into the paints, inks, and pastes used to apply the matrix symbols, the codes could be read by a magnetic scanner even after being covered with paint or other coatings. NASA received a patent for such a scanner in 1998, but the system it used for development was not portable and was too costly. A prototype was needed as a lead-in to a production model. In the summer of 2000, NASA began seeking companies to build a hand-held scanner that would detect the Read Through Paint data matrix identification marks containing magnetic materials through coatings.

Computational Infrastructure for Engine Structural Performance Simulation

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

1997-01-01

Select computer codes developed over the years to simulate specific aspects of engine structures are described. These codes include blade impact integrated multidisciplinary analysis and optimization, progressive structural fracture, quantification of uncertainties for structural reliability and risk, benefits estimation of new technology insertion and hierarchical simulation of engine structures made from metal matrix and ceramic matrix composites. Collectively these codes constitute a unique infrastructure readiness to credibly evaluate new and future engine structural concepts throughout the development cycle from initial concept, to design and fabrication, to service performance and maintenance and repairs, and to retirement for cause and even to possible recycling. Stated differently, they provide 'virtual' concurrent engineering for engine structures total-life-cycle-cost.

Risk mapping of clonorchiasis in the People’s Republic of China: A systematic review and Bayesian geostatistical analysis

PubMed Central

Lai, Ying-Si; Zhou, Xiao-Nong; Pan, Zhi-Heng; Utzinger, Jürg; Vounatsou, Penelope

2017-01-01

Background Clonorchiasis, one of the most important food-borne trematodiases, affects more than 12 million people in the People’s Republic of China (P.R. China). Spatially explicit risk estimates of Clonorchis sinensis infection are needed in order to target control interventions. Methodology Georeferenced survey data pertaining to infection prevalence of C. sinensis in P.R. China from 2000 onwards were obtained via a systematic review in PubMed, ISI Web of Science, Chinese National Knowledge Internet, and Wanfang Data from January 1, 2000 until January 10, 2016, with no restriction of language or study design. Additional disease data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention in Shanghai. Environmental and socioeconomic proxies were extracted from remote-sensing and other data sources. Bayesian variable selection was carried out to identify the most important predictors of C. sinensis risk. Geostatistical models were applied to quantify the association between infection risk and the predictors of the disease, and to predict the risk of infection across P.R. China at high spatial resolution (over a grid with grid cell size of 5×5 km). Principal findings We obtained clonorchiasis survey data at 633 unique locations in P.R. China. We observed that the risk of C. sinensis infection increased over time, particularly from 2005 onwards. We estimate that around 14.8 million (95% Bayesian credible interval 13.8–15.8 million) people in P.R. China were infected with C. sinensis in 2010. Highly endemic areas (≥ 20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi, and Heilongjiang. Conclusions/Significance Our results provide spatially relevant information for guiding clonorchiasis control interventions in P.R. China. The trend toward higher risk of C. sinensis infection in the recent past urges the Chinese government to pay more attention to the public health importance of clonorchiasis and to target interventions to high-risk areas. PMID:28253272

Improving the Accuracy of Planet Occurrence Rates from Kepler Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Hsu, Danley C.; Ford, Eric B.; Ragozzine, Darin; Morehead, Robert C.

2018-05-01

We present a new framework to characterize the occurrence rates of planet candidates identified by Kepler based on hierarchical Bayesian modeling, approximate Bayesian computing (ABC), and sequential importance sampling. For this study, we adopt a simple 2D grid in planet radius and orbital period as our model and apply our algorithm to estimate occurrence rates for Q1–Q16 planet candidates orbiting solar-type stars. We arrive at significantly increased planet occurrence rates for small planet candidates (R p < 1.25 R ⊕) at larger orbital periods (P > 80 day) compared to the rates estimated by the more common inverse detection efficiency method (IDEM). Our improved methodology estimates that the occurrence rate density of small planet candidates in the habitable zone of solar-type stars is {1.6}-0.5+1.2 per factor of 2 in planet radius and orbital period. Additionally, we observe a local minimum in the occurrence rate for strong planet candidates marginalized over orbital period between 1.5 and 2 R ⊕ that is consistent with previous studies. For future improvements, the forward modeling approach of ABC is ideally suited to incorporating multiple populations, such as planets, astrophysical false positives, and pipeline false alarms, to provide accurate planet occurrence rates and uncertainties. Furthermore, ABC provides a practical statistical framework for answering complex questions (e.g., frequency of different planetary architectures) and providing sound uncertainties, even in the face of complex selection effects, observational biases, and follow-up strategies. In summary, ABC offers a powerful tool for accurately characterizing a wide variety of astrophysical populations.

A phylogenetic framework for root lesion nematodes of the genus Pratylenchus (Nematoda): Evidence from 18S and D2-D3 expansion segments of 28S ribosomal RNA genes and morphological characters.

PubMed

Subbotin, Sergei A; Ragsdale, Erik J; Mullens, Teresa; Roberts, Philip A; Mundo-Ocampo, Manuel; Baldwin, James G

2008-08-01

The root lesion nematodes of the genus Pratylenchus Filipjev, 1936 are migratory endoparasites of plant roots, considered among the most widespread and important nematode parasites in a variety of crops. We obtained gene sequences from the D2 and D3 expansion segments of 28S rRNA partial and 18S rRNA from 31 populations belonging to 11 valid and two unidentified species of root lesion nematodes and five outgroup taxa. These datasets were analyzed using maximum parsimony and Bayesian inference. The alignments were generated using the secondary structure models for these molecules and analyzed with Bayesian inference under the standard models and the complex model, considering helices under the doublet model and loops and bulges under the general time reversible model. The phylogenetic informativeness of morphological characters is tested by reconstruction of their histories on rRNA based trees using parallel parsimony and Bayesian approaches. Phylogenetic and sequence analyses of the 28S D2-D3 dataset with 145 accessions for 28 species and 18S dataset with 68 accessions for 15 species confirmed among large numbers of geographical diverse isolates that most classical morphospecies are monophyletic. Phylogenetic analyses revealed at least six distinct major clades of examined Pratylenchus species and these clades are generally congruent with those defined by characters derived from lip patterns, numbers of lip annules, and spermatheca shape. Morphological results suggest the need for sophisticated character discovery and analysis for morphology based phylogenetics in nematodes.

Bayesian network analyses of resistance pathways against efavirenz and nevirapine

PubMed Central

Deforche, Koen; Camacho, Ricardo J.; Grossman, Zehave; Soares, Marcelo A.; Laethem, Kristel Van; Katzenstein, David A.; Harrigan, P. Richard; Kantor, Rami; Shafer, Robert; Vandamme, Anne-Mieke

2016-01-01

Objective To clarify the role of novel mutations selected by treatment with efavirenz or nevirapine, and investigate the influence of HIV-1 subtype on nonnucleoside reverse transcriptase inhibitor (nNRTI) resistance pathways. Design By finding direct dependencies between treatment-selected mutations, the involvement of these mutations as minor or major resistance mutations against efavirenz, nevirapine, or coadministrated nucleoside analogue reverse transcriptase inhibitors (NRTIs) is hypothesized. In addition, direct dependencies were investigated between treatment-selected mutations and polymorphisms, some of which are linked with subtype, and between NRTI and nNRTI resistance pathways. Methods Sequences from a large collaborative database of various subtypes were jointly analyzed to detect mutations selected by treatment. Using Bayesian network learning, direct dependencies were investigated between treatment-selected mutations, NRTI and nNRTI treatment history, and known NRTI resistance mutations. Results Several novel minor resistance mutations were found: 28K and 196R (for resistance against efavirenz), 101H and 138Q (nevirapine), and 31L (lamivudine). Robust interactions between NRTI mutations (65R, 74V, 75I/M, and 184V) and nNRTI resistance mutations (100I, 181C, 190E and 230L) may affect resistance development to particular treatment combinations. For example, an interaction between 65R and 181C predicts that the nevirapine and tenofovir and lamivudine/emtricitabine combination should be more prone to failure than efavirenz and tenofovir and lamivudine/emtricitabine. Conclusion Bayesian networks were helpful in untangling the selection of mutations by NRTI versus nNRTI treatment, and in discovering interactions between resistance mutations within and between these two classes of inhibitors. PMID:18832874

Tests of Exoplanet Atmospheric Radiative Transfer Codes

NASA Astrophysics Data System (ADS)

Harrington, Joseph; Challener, Ryan; DeLarme, Emerson; Cubillos, Patricio; Blecic, Jasmina; Foster, Austin; Garland, Justin

2016-10-01

Atmospheric radiative transfer codes are used both to predict planetary spectra and in retrieval algorithms to interpret data. Observational plans, theoretical models, and scientific results thus depend on the correctness of these calculations. Yet, the calculations are complex and the codes implementing them are often written without modern software-verification techniques. In the process of writing our own code, we became aware of several others with artifacts of unknown origin and even outright errors in their spectra. We present a series of tests to verify atmospheric radiative-transfer codes. These include: simple, single-line line lists that, when combined with delta-function abundance profiles, should produce a broadened line that can be verified easily; isothermal atmospheres that should produce analytically-verifiable blackbody spectra at the input temperatures; and model atmospheres with a range of complexities that can be compared to the output of other codes. We apply the tests to our own code, Bayesian Atmospheric Radiative Transfer (BART) and to several other codes. The test suite is open-source software. We propose this test suite as a standard for verifying current and future radiative transfer codes, analogous to the Held-Suarez test for general circulation models. This work was supported by NASA Planetary Atmospheres grant NX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G.

Myc-induced anchorage of the rDNA IGS region to nucleolar matrix modulates growth-stimulated changes in higher-order rDNA architecture

PubMed Central

Shiue, Chiou-Nan; Nematollahi-Mahani, Amir; Wright, Anthony P.H.

2014-01-01

Chromatin domain organization and the compartmentalized distribution of chromosomal regions are essential for packaging of deoxyribonucleic acid (DNA) in the eukaryotic nucleus as well as regulated gene expression. Nucleoli are the most prominent morphological structures of cell nuclei and nucleolar organization is coupled to cell growth. It has been shown that nuclear scaffold/matrix attachment regions often define the base of looped chromosomal domains in vivo and that they are thereby critical for correct chromosome architecture and gene expression. Here, we show regulated organization of mammalian ribosomal ribonucleic acid genes into distinct chromatin loops by tethering to nucleolar matrix via the non-transcribed inter-genic spacer region of the ribosomal DNA (rDNA). The rDNA gene loop structures are induced specifically upon growth stimulation and are dependent on the activity of the c-Myc protein. Matrix-attached rDNA genes are hypomethylated at the promoter and are thus available for transcriptional activation. rDNA genes silenced by methylation are not recruited to the matrix. c-Myc, which has been shown to induce rDNA transcription directly, is physically associated with rDNA gene looping structures and the intergenic spacer sequence in growing cells. Such a role of Myc proteins in gene activation has not been reported previously. PMID:24609384

Myc-induced anchorage of the rDNA IGS region to nucleolar matrix modulates growth-stimulated changes in higher-order rDNA architecture.

PubMed

Shiue, Chiou-Nan; Nematollahi-Mahani, Amir; Wright, Anthony P H

2014-05-01

Chromatin domain organization and the compartmentalized distribution of chromosomal regions are essential for packaging of deoxyribonucleic acid (DNA) in the eukaryotic nucleus as well as regulated gene expression. Nucleoli are the most prominent morphological structures of cell nuclei and nucleolar organization is coupled to cell growth. It has been shown that nuclear scaffold/matrix attachment regions often define the base of looped chromosomal domains in vivo and that they are thereby critical for correct chromosome architecture and gene expression. Here, we show regulated organization of mammalian ribosomal ribonucleic acid genes into distinct chromatin loops by tethering to nucleolar matrix via the non-transcribed inter-genic spacer region of the ribosomal DNA (rDNA). The rDNA gene loop structures are induced specifically upon growth stimulation and are dependent on the activity of the c-Myc protein. Matrix-attached rDNA genes are hypomethylated at the promoter and are thus available for transcriptional activation. rDNA genes silenced by methylation are not recruited to the matrix. c-Myc, which has been shown to induce rDNA transcription directly, is physically associated with rDNA gene looping structures and the intergenic spacer sequence in growing cells. Such a role of Myc proteins in gene activation has not been reported previously. © 2014 The Author(s). Published by Oxford University Press [on behalf of Nucleic Acids Research].

Limit analysis of hollow spheres or spheroids with Hill orthotropic matrix

NASA Astrophysics Data System (ADS)

Pastor, Franck; Pastor, Joseph; Kondo, Djimedo

2012-03-01

Recent theoretical studies of the literature are concerned by the hollow sphere or spheroid (confocal) problems with orthotropic Hill type matrix. They have been developed in the framework of the limit analysis kinematical approach by using very simple trial velocity fields. The present Note provides, through numerical upper and lower bounds, a rigorous assessment of the approximate criteria derived in these theoretical works. To this end, existing static 3D codes for a von Mises matrix have been easily extended to the orthotropic case. Conversely, instead of the non-obvious extension of the existing kinematic codes, a new original mixed approach has been elaborated on the basis of the plane strain structure formulation earlier developed by F. Pastor (2007). Indeed, such a formulation does not need the expressions of the unit dissipated powers. Interestingly, it delivers a numerical code better conditioned and notably more rapid than the previous one, while preserving the rigorous upper bound character of the corresponding numerical results. The efficiency of the whole approach is first demonstrated through comparisons of the results to the analytical upper bounds of Benzerga and Besson (2001) or Monchiet et al. (2008) in the case of spherical voids in the Hill matrix. Moreover, we provide upper and lower bounds results for the hollow spheroid with the Hill matrix which are compared to those of Monchiet et al. (2008).

The molecular systematics of blowflies and screwworm flies (Diptera: Calliphoridae) using 28S rRNA, COX1 and EF-1α: insights into the evolution of dipteran parasitism.

PubMed

McDonagh, Laura M; Stevens, Jamie R

2011-11-01

The Calliphoridae include some of the most economically significant myiasis-causing flies in the world - blowflies and screwworm flies - with many being notorious for their parasitism of livestock. However, despite more than 50 years of research, key taxonomic relationships within the family remain unresolved. This study utilizes nucleotide sequence data from the protein-coding genes COX1 (mitochondrial) and EF1α (nuclear), and the 28S rRNA (nuclear) gene, from 57 blowfly taxa to improve resolution of key evolutionary relationships within the family Calliphoridae. Bayesian phylogenetic inference was carried out for each single-gene data set, demonstrating significant topological difference between the three gene trees. Nevertheless, all gene trees supported a Calliphorinae-Luciliinae subfamily sister-lineage, with respect to Chrysomyinae. In addition, this study also elucidates the taxonomic and evolutionary status of several less well-studied groups, including the genus Bengalia (either within Calliphoridae or as a separate sister-family), genus Onesia (as a sister-genera to, or sub-genera within, Calliphora), genus Dyscritomyia and Lucilia bufonivora, a specialised parasite of frogs and toads. The occurrence of cross-species hybridisation within Calliphoridae is also further explored, focusing on the two economically significant species Lucilia cuprina and Lucilia sericata. In summary, this study represents the most comprehensive molecular phylogenetic analysis of family Calliphoridae undertaken to date.

An Empirical Mass Function Distribution

NASA Astrophysics Data System (ADS)

Murray, S. G.; Robotham, A. S. G.; Power, C.

2018-03-01

The halo mass function, encoding the comoving number density of dark matter halos of a given mass, plays a key role in understanding the formation and evolution of galaxies. As such, it is a key goal of current and future deep optical surveys to constrain the mass function down to mass scales that typically host {L}\\star galaxies. Motivated by the proven accuracy of Press–Schechter-type mass functions, we introduce a related but purely empirical form consistent with standard formulae to better than 4% in the medium-mass regime, {10}10{--}{10}13 {h}-1 {M}ȯ . In particular, our form consists of four parameters, each of which has a simple interpretation, and can be directly related to parameters of the galaxy distribution, such as {L}\\star . Using this form within a hierarchical Bayesian likelihood model, we show how individual mass-measurement errors can be successfully included in a typical analysis, while accounting for Eddington bias. We apply our form to a question of survey design in the context of a semi-realistic data model, illustrating how it can be used to obtain optimal balance between survey depth and angular coverage for constraints on mass function parameters. Open-source Python and R codes to apply our new form are provided at http://mrpy.readthedocs.org and https://cran.r-project.org/web/packages/tggd/index.html respectively.

Matrigel Basement Membrane Matrix influences expression of microRNAs in cancer cell lines

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

Price, Karina J.; School of Medicine and Pharmacology, University of Western Australia, Nedlands, WA 6008; Tsykin, Anna

2012-10-19

Highlights: Black-Right-Pointing-Pointer Matrigel alters cancer cell line miRNA expression relative to culture on plastic. Black-Right-Pointing-Pointer Many identified Matrigel-regulated miRNAs are implicated in cancer. Black-Right-Pointing-Pointer miR-1290, -210, -32 and -29b represent a Matrigel-induced miRNA signature. Black-Right-Pointing-Pointer miR-32 down-regulates Integrin alpha 5 (ITGA5) mRNA. -- Abstract: Matrigel is a medium rich in extracellular matrix (ECM) components used for three-dimensional cell culture and is known to alter cellular phenotypes and gene expression. microRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression and have roles in cancer. While miRNA profiles of numerous cell lines cultured on plastic have been reported, the influence ofmore » Matrigel-based culture on cancer cell miRNA expression is largely unknown. This study investigated the influence of Matrigel on the expression of miRNAs that might facilitate ECM-associated cancer cell growth. We performed miRNA profiling by microarray using two colon cancer cell lines (SW480 and SW620), identifying significant differential expression of miRNAs between cells cultured in Matrigel and on plastic. Many of these miRNAs have previously been implicated in cancer-related processes. A common Matrigel-induced miRNA signature comprised of up-regulated miR-1290 and miR-210 and down-regulated miR-29b and miR-32 was identified using RT-qPCR across five epithelial cancer cell lines (SW480, SW620, HT-29, A549 and MDA-MB-231). Experimental modulation of these miRNAs altered expression of their known target mRNAs involved in cell adhesion, proliferation and invasion, in colon cancer cell lines. Furthermore, ITGA5 was identified as a novel putative target of miR-32 that may facilitate cancer cell interactions with the ECM. We propose that culture of cancer cell lines in Matrigel more accurately recapitulates miRNA expression and function in cancer than culture on plastic and thus is a valuable approach to the in vitro study of miRNAs.« less

Bayesian Inference on Proportional Elections

PubMed Central

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259

Approximate Bayesian computation for spatial SEIR(S) epidemic models.

PubMed

Brown, Grant D; Porter, Aaron T; Oleson, Jacob J; Hinman, Jessica A

2018-02-01

Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bayesian inference on proportional elections.

PubMed

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

Bayesian evaluation of effect size after replicating an original study

PubMed Central

van Aert, Robbie C. M.; van Assen, Marcel A. L. M.

2017-01-01

The vast majority of published results in the literature is statistically significant, which raises concerns about their reliability. The Reproducibility Project Psychology (RPP) and Experimental Economics Replication Project (EE-RP) both replicated a large number of published studies in psychology and economics. The original study and replication were statistically significant in 36.1% in RPP and 68.8% in EE-RP suggesting many null effects among the replicated studies. However, evidence in favor of the null hypothesis cannot be examined with null hypothesis significance testing. We developed a Bayesian meta-analysis method called snapshot hybrid that is easy to use and understand and quantifies the amount of evidence in favor of a zero, small, medium and large effect. The method computes posterior model probabilities for a zero, small, medium, and large effect and adjusts for publication bias by taking into account that the original study is statistically significant. We first analytically approximate the methods performance, and demonstrate the necessity to control for the original study’s significance to enable the accumulation of evidence for a true zero effect. Then we applied the method to the data of RPP and EE-RP, showing that the underlying effect sizes of the included studies in EE-RP are generally larger than in RPP, but that the sample sizes of especially the included studies in RPP are often too small to draw definite conclusions about the true effect size. We also illustrate how snapshot hybrid can be used to determine the required sample size of the replication akin to power analysis in null hypothesis significance testing and present an easy to use web application (https://rvanaert.shinyapps.io/snapshot/) and R code for applying the method. PMID:28388646

Screened selection design for randomised phase II oncology trials: an example in chronic lymphocytic leukaemia

PubMed Central

2013-01-01

Background As there are limited patients for chronic lymphocytic leukaemia trials, it is important that statistical methodologies in Phase II efficiently select regimens for subsequent evaluation in larger-scale Phase III trials. Methods We propose the screened selection design (SSD), which is a practical multi-stage, randomised Phase II design for two experimental arms. Activity is first evaluated by applying Simon’s two-stage design (1989) on each arm. If both are active, the play-the-winner selection strategy proposed by Simon, Wittes and Ellenberg (SWE) (1985) is applied to select the superior arm. A variant of the design, Modified SSD, also allows the arm with the higher response rates to be recommended only if its activity rate is greater by a clinically-relevant value. The operating characteristics are explored via a simulation study and compared to a Bayesian Selection approach. Results Simulations showed that with the proposed SSD, it is possible to retain the sample size as required in SWE and obtain similar probabilities of selecting the correct superior arm of at least 90%; with the additional attractive benefit of reducing the probability of selecting ineffective arms. This approach is comparable to a Bayesian Selection Strategy. The Modified SSD performs substantially better than the other designs in selecting neither arm if the underlying rates for both arms are desirable but equivalent, allowing for other factors to be considered in the decision making process. Though its probability of correctly selecting a superior arm might be reduced, it still performs reasonably well. It also reduces the probability of selecting an inferior arm. Conclusions SSD provides an easy to implement randomised Phase II design that selects the most promising treatment that has shown sufficient evidence of activity, with available R codes to evaluate its operating characteristics. PMID:23819695

Screened selection design for randomised phase II oncology trials: an example in chronic lymphocytic leukaemia.

PubMed

Yap, Christina; Pettitt, Andrew; Billingham, Lucinda

2013-07-03

As there are limited patients for chronic lymphocytic leukaemia trials, it is important that statistical methodologies in Phase II efficiently select regimens for subsequent evaluation in larger-scale Phase III trials. We propose the screened selection design (SSD), which is a practical multi-stage, randomised Phase II design for two experimental arms. Activity is first evaluated by applying Simon's two-stage design (1989) on each arm. If both are active, the play-the-winner selection strategy proposed by Simon, Wittes and Ellenberg (SWE) (1985) is applied to select the superior arm. A variant of the design, Modified SSD, also allows the arm with the higher response rates to be recommended only if its activity rate is greater by a clinically-relevant value. The operating characteristics are explored via a simulation study and compared to a Bayesian Selection approach. Simulations showed that with the proposed SSD, it is possible to retain the sample size as required in SWE and obtain similar probabilities of selecting the correct superior arm of at least 90%; with the additional attractive benefit of reducing the probability of selecting ineffective arms. This approach is comparable to a Bayesian Selection Strategy. The Modified SSD performs substantially better than the other designs in selecting neither arm if the underlying rates for both arms are desirable but equivalent, allowing for other factors to be considered in the decision making process. Though its probability of correctly selecting a superior arm might be reduced, it still performs reasonably well. It also reduces the probability of selecting an inferior arm. SSD provides an easy to implement randomised Phase II design that selects the most promising treatment that has shown sufficient evidence of activity, with available R codes to evaluate its operating characteristics.

Supercomputer description of human lung morphology for imaging analysis.

PubMed

Martonen, T B; Hwang, D; Guan, X; Fleming, J S

1998-04-01

A supercomputer code that describes the three-dimensional branching structure of the human lung has been developed. The algorithm was written for the Cray C94. In our simulations, the human lung was divided into a matrix containing discrete volumes (voxels) so as to be compatible with analyses of SPECT images. The matrix has 3840 voxels. The matrix can be segmented into transverse, sagittal and coronal layers analogous to human subject examinations. The compositions of individual voxels were identified by the type and respective number of airways present. The code provides a mapping of the spatial positions of the almost 17 million airways in human lungs and unambiguously assigns each airway to a voxel. Thus, the clinician and research scientist in the medical arena have a powerful new tool to be used in imaging analyses. The code was designed to be integrated into diverse applications, including the interpretation of SPECT images, the design of inhalation exposure experiments and the targeted delivery of inhaled pharmacologic drugs.

A BAYESIAN ANALYSIS OF SPARROW. (R830883)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Cooperative Activated Transport of Dilute Penetrants in Viscous Molecular and Polymer Liquids

NASA Astrophysics Data System (ADS)

Schweizer, Kenneth; Zhang, Rui

We generalize the force-level Elastically Collective Nonlinear Langevin Equation theory of activated relaxation in one-component supercooled liquids to treat the hopping transport of a dilute penetrant in a dense hard sphere fluid. The new idea is to explicitly account for the coupling between penetrant displacement and a local matrix cage re-arrangement which facilitates its hopping. A temporal casuality condition is employed to self-consistently determine a dimensionless degree of matrix distortion relative to the penetrant jump distance using the dynamic free energy concept. Penetrant diffusion becomes increasingly coupled to the correlated matrix displacements for larger penetrant to matrix particle size ratio (R) and/or attraction strength (physical bonds), but depends weakly on matrix packing fraction. In the absence of attractions, a nearly exponential dependence of penetrant diffusivity on R is predicted in the intermediate range of 0.2

Linearized T-Matrix and Mie Scattering Computations

NASA Technical Reports Server (NTRS)

Spurr, R.; Wang, J.; Zeng, J.; Mishchenko, M. I.

2011-01-01

We present a new linearization of T-Matrix and Mie computations for light scattering by non-spherical and spherical particles, respectively. In addition to the usual extinction and scattering cross-sections and the scattering matrix outputs, the linearized models will generate analytical derivatives of these optical properties with respect to the real and imaginary parts of the particle refractive index, and (for non-spherical scatterers) with respect to the ''shape'' parameter (the spheroid aspect ratio, cylinder diameter/height ratio, Chebyshev particle deformation factor). These derivatives are based on the essential linearity of Maxwell's theory. Analytical derivatives are also available for polydisperse particle size distribution parameters such as the mode radius. The T-matrix formulation is based on the NASA Goddard Institute for Space Studies FORTRAN 77 code developed in the 1990s. The linearized scattering codes presented here are in FORTRAN 90 and will be made publicly available.

CEMCAN Software Enhanced for Predicting the Properties of Woven Ceramic Matrix Composites

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Mital, Subodh K.; DiCarlo, James A.

2000-01-01

Major advancements are needed in current high-temperature materials to meet the requirements of future space and aeropropulsion structural components. Ceramic matrix composites (CMC's) are one class of materials that are being evaluated as candidate materials for many high-temperature applications. Past efforts to improve the performance of CMC's focused primarily on improving the properties of the fiber, interfacial coatings, and matrix constituents as individual phases. Design and analysis tools must take into consideration the complex geometries, microstructures, and fabrication processes involved in these composites and must allow the composite properties to be tailored for optimum performance. Major accomplishments during the past year include the development and inclusion of woven CMC micromechanics methodology into the CEMCAN (Ceramic Matrix Composites Analyzer) computer code. The code enables one to calibrate a consistent set of constituent properties as a function of temperature with the aid of experimentally measured data.

Element analysis and calculation of the attenuation coefficients for gold, bronze and water matrixes using MCNP, WinXCom and experimental data

NASA Astrophysics Data System (ADS)

Esfandiari, M.; Shirmardi, S. P.; Medhat, M. E.

2014-06-01

In this study, element analysis and the mass attenuation coefficient for matrixes of gold, bronze and water with various impurities and the concentrations of heavy metals (Cu, Mn, Pb and Zn) are evaluated and calculated by the MCNP simulation code for photons emitted from Barium-133, Americium-241 and sources with energies between 1 and 100 keV. The MCNP data are compared with the experimental data and WinXCom code simulated results by Medhat. The results showed that the obtained results of bronze and gold matrix are in good agreement with the other methods for energies above 40 and 60 keV, respectively. However for water matrixes with various impurities, there is a good agreement between the three methods MCNP, WinXCom and the experimental one in low and high energies.

Substantial advantage of a combined Bayesian and genotyping approach in testosterone doping tests.

PubMed

Schulze, Jenny Jakobsson; Lundmark, Jonas; Garle, Mats; Ekström, Lena; Sottas, Pierre-Edouard; Rane, Anders

2009-03-01

Testosterone abuse is conventionally assessed by the urinary testosterone/epitestosterone (T/E) ratio, levels above 4.0 being considered suspicious. A deletion polymorphism in the gene coding for UGT2B17 is strongly associated with reduced testosterone glucuronide (TG) levels in urine. Many of the individuals devoid of the gene would not reach a T/E ratio of 4.0 after testosterone intake. Future test programs will most likely shift from population based- to individual-based T/E cut-off ratios using Bayesian inference. A longitudinal analysis is dependent on an individual's true negative baseline T/E ratio. The aim was to investigate whether it is possible to increase the sensitivity and specificity of the T/E test by addition of UGT2B17 genotype information in a Bayesian framework. A single intramuscular dose of 500mg testosterone enanthate was given to 55 healthy male volunteers with either two, one or no allele (ins/ins, ins/del or del/del) of the UGT2B17 gene. Urinary excretion of TG and the T/E ratio was measured during 15 days. The Bayesian analysis was conducted to calculate the individual T/E cut-off ratio. When adding the genotype information, the program returned lower individual cut-off ratios in all del/del subjects increasing the sensitivity of the test considerably. It will be difficult, if not impossible, to discriminate between a true negative baseline T/E value and a false negative one without knowledge of the UGT2B17 genotype. UGT2B17 genotype information is crucial, both to decide which initial cut-off ratio to use for an individual, and for increasing the sensitivity of the Bayesian analysis.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Intrasystem Analysis Program (IAP) code summaries

NASA Astrophysics Data System (ADS)

Dobmeier, J. J.; Drozd, A. L. S.; Surace, J. A.

1983-05-01

This report contains detailed descriptions and capabilities of the codes that comprise the Intrasystem Analysis Program. The four codes are: Intrasystem Electromagnetic Compatibility Analysis Program (IEMCAP), General Electromagnetic Model for the Analysis of Complex Systems (GEMACS), Nonlinear Circuit Analysis Program (NCAP), and Wire Coupling Prediction Models (WIRE). IEMCAP is used for computer-aided evaluation of electromagnetic compatibility (ECM) at all stages of an Air Force system's life cycle, applicable to aircraft, space/missile, and ground-based systems. GEMACS utilizes a Method of Moments (MOM) formalism with the Electric Field Integral Equation (EFIE) for the solution of electromagnetic radiation and scattering problems. The code employs both full matrix decomposition and Banded Matrix Iteration solution techniques and is expressly designed for large problems. NCAP is a circuit analysis code which uses the Volterra approach to solve for the transfer functions and node voltage of weakly nonlinear circuits. The Wire Programs deal with the Application of Multiconductor Transmission Line Theory to the Prediction of Cable Coupling for specific classes of problems.

A Measurement and Simulation Based Methodology for Cache Performance Modeling and Tuning

NASA Technical Reports Server (NTRS)

Waheed, Abdul; Yan, Jerry; Saini, Subhash (Technical Monitor)

1998-01-01

We present a cache performance modeling methodology that facilitates the tuning of uniprocessor cache performance for applications executing on shared memory multiprocessors by accurately predicting the effects of source code level modifications. Measurements on a single processor are initially used for identifying parts of code where cache utilization improvements may significantly impact the overall performance. Cache simulation based on trace-driven techniques can be carried out without gathering detailed address traces. Minimal runtime information for modeling cache performance of a selected code block includes: base virtual addresses of arrays, virtual addresses of variables, and loop bounds for that code block. Rest of the information is obtained from the source code. We show that the cache performance predictions are as reliable as those obtained through trace-driven simulations. This technique is particularly helpful to the exploration of various "what-if' scenarios regarding the cache performance impact for alternative code structures. We explain and validate this methodology using a simple matrix-matrix multiplication program. We then apply this methodology to predict and tune the cache performance of two realistic scientific applications taken from the Computational Fluid Dynamics (CFD) domain.

Hierarchical differences in population coding within auditory cortex.

PubMed

Downer, Joshua D; Niwa, Mamiko; Sutter, Mitchell L

2017-08-01

Most models of auditory cortical (AC) population coding have focused on primary auditory cortex (A1). Thus our understanding of how neural coding for sounds progresses along the cortical hierarchy remains obscure. To illuminate this, we recorded from two AC fields: A1 and middle lateral belt (ML) of rhesus macaques. We presented amplitude-modulated (AM) noise during both passive listening and while the animals performed an AM detection task ("active" condition). In both fields, neurons exhibit monotonic AM-depth tuning, with A1 neurons mostly exhibiting increasing rate-depth functions and ML neurons approximately evenly distributed between increasing and decreasing functions. We measured noise correlation ( r noise ) between simultaneously recorded neurons and found that whereas engagement decreased average r noise in A1, engagement increased average r noise in ML. This finding surprised us, because attentive states are commonly reported to decrease average r noise We analyzed the effect of r noise on AM coding in both A1 and ML and found that whereas engagement-related shifts in r noise in A1 enhance AM coding, r noise shifts in ML have little effect. These results imply that the effect of r noise differs between sensory areas, based on the distribution of tuning properties among the neurons within each population. A possible explanation of this is that higher areas need to encode nonsensory variables (e.g., attention, choice, and motor preparation), which impart common noise, thus increasing r noise Therefore, the hierarchical emergence of r noise -robust population coding (e.g., as we observed in ML) enhances the ability of sensory cortex to integrate cognitive and sensory information without a loss of sensory fidelity. NEW & NOTEWORTHY Prevailing models of population coding of sensory information are based on a limited subset of neural structures. An important and under-explored question in neuroscience is how distinct areas of sensory cortex differ in their population coding strategies. In this study, we compared population coding between primary and secondary auditory cortex. Our findings demonstrate striking differences between the two areas and highlight the importance of considering the diversity of neural structures as we develop models of population coding. Copyright © 2017 the American Physiological Society.

Bayesian Modeling of NMR Data: Quantifying Longitudinal Relaxation in Vivo, and in Vitro with a Tissue-Water-Relaxation Mimic (Crosslinked Bovine Serum Albumin).

PubMed

Meinerz, Kelsey; Beeman, Scott C; Duan, Chong; Bretthorst, G Larry; Garbow, Joel R; Ackerman, Joseph J H

2018-01-01

Recently, a number of MRI protocols have been reported that seek to exploit the effect of dissolved oxygen (O 2 , paramagnetic) on the longitudinal 1 H relaxation of tissue water, thus providing image contrast related to tissue oxygen content. However, tissue water relaxation is dependent on a number of mechanisms, and this raises the issue of how best to model the relaxation data. This problem, the model selection problem, occurs in many branches of science and is optimally addressed by Bayesian probability theory. High signal-to-noise, densely sampled, longitudinal 1 H relaxation data were acquired from rat brain in vivo and from a cross-linked bovine serum albumin (xBSA) phantom, a sample that recapitulates the relaxation characteristics of tissue water in vivo . Bayesian-based model selection was applied to a cohort of five competing relaxation models: (i) monoexponential, (ii) stretched-exponential, (iii) biexponential, (iv) Gaussian (normal) R 1 -distribution, and (v) gamma R 1 -distribution. Bayesian joint analysis of multiple replicate datasets revealed that water relaxation of both the xBSA phantom and in vivo rat brain was best described by a biexponential model, while xBSA relaxation datasets truncated to remove evidence of the fast relaxation component were best modeled as a stretched exponential. In all cases, estimated model parameters were compared to the commonly used monoexponential model. Reducing the sampling density of the relaxation data and adding Gaussian-distributed noise served to simulate cases in which the data are acquisition-time or signal-to-noise restricted, respectively. As expected, reducing either the number of data points or the signal-to-noise increases the uncertainty in estimated parameters and, ultimately, reduces support for more complex relaxation models.

Characterization of the complete mitochondrial genomes of two whipworms Trichuris ovis and Trichuris discolor (Nematoda: Trichuridae).

PubMed

Liu, Guo-Hua; Wang, Yan; Xu, Min-Jun; Zhou, Dong-Hui; Ye, Yong-Gang; Li, Jia-Yuan; Song, Hui-Qun; Lin, Rui-Qing; Zhu, Xing-Quan

2012-12-01

For many years, whipworms (Trichuris spp.) have been described with a relatively narrow range of both morphological and biometrical features. Moreover, there has been insufficient discrimination between congeners (or closely related species). In the present study, we determined the complete mitochondrial (mt) genomes of two whipworms Trichuris ovis and Trichuris discolor, compared them and then tested the hypothesis that T. ovis and T. discolor are distinct species by phylogenetic analyses using Bayesian inference, maximum likelihood and maximum parsimony) based on the deduced amino acid sequences of the mt protein-coding genes. The complete mt genomes of T. ovis and T. discolor were 13,946 bp and 13,904 bp in size, respectively. Both mt genomes are circular, and consist of 37 genes, including 13 genes coding for proteins, 2 genes for rRNA, and 22 genes for tRNA. The gene content and arrangement are identical to that of human and pig whipworms Trichuris trichiura and Trichuris suis. Taken together, these analyses showed genetic distinctiveness and strongly supported the recent proposal that T. ovis and T. discolor are distinct species using nuclear ribosomal DNA and a portion of the mtDNA sequence dataset. The availability of the complete mtDNA sequences of T. ovis and T. discolor provides novel genetic markers for studying the population genetics, diagnostics and molecular epidemiology of T. ovis and T. discolor. Copyright © 2012 Elsevier B.V. All rights reserved.

First complete female mitochondrial genome in four bivalve species genus Donax and their phylogenetic relationships within the Veneroida order

PubMed Central

Nantón, Ana; Ruiz-Ruano, Francisco J.; Camacho, Juan Pedro M.; Méndez, Josefina

2017-01-01

Background Four species of the genus Donax (D. semistriatus, D. trunculus, D. variegatus and D. vittatus) are common on Iberian Peninsula coasts. Nevertheless, despite their economic importance and overexploitation, scarce genetic resources are available. In this work, we newly determined the complete mitochondrial genomes of these four representatives of the family Donacidae, with the aim of contributing to unveil phylogenetic relationships within the Veneroida order, and of developing genetic markers being useful in wedge clam identification and authentication, and aquaculture stock management. Principal findings The complete female mitochondrial genomes of the four species vary in size from 17,044 to 17,365 bp, and encode 13 protein-coding genes (including the atp8 gene), 2 rRNAs and 22 tRNAs, all located on the same strand. A long non-coding region was identified in each of the four Donax species between cob and cox2 genes, presumably corresponding to the Control Region. The Bayesian and Maximum Likelihood phylogenetic analysis of the Veneroida order indicate that all four species of Donax form a single clade as a sister group of other bivalves within the Tellinoidea superfamily. However, although Tellinoidea is actually monophyletic, none of its families are monophyletic. Conclusions Sequencing of complete mitochondrial genomes provides highly valuable information to establish the phylogenetic relationships within the Veneroida order. Furthermore, we provide here significant genetic resources for further research and conservation of this commercially important fishing resource. PMID:28886105

Radiative neutron capture cross sections on 176Lu at DANCE

NASA Astrophysics Data System (ADS)

Roig, O.; Jandel, M.; Méot, V.; Bond, E. M.; Bredeweg, T. A.; Couture, A. J.; Haight, R. C.; Keksis, A. L.; Rundberg, R. S.; Ullmann, J. L.; Vieira, D. J.

2016-03-01

The cross section of the neutron capture reaction 176Lu(n ,γ ) has been measured for a wide incident neutron energy range with the Detector for Advanced Neutron Capture Experiments at the Los Alamos Neutron Science Center. The thermal neutron capture cross section was determined to be (1912 ±132 ) b for one of the Lu natural isotopes, 176Lu. The resonance part was measured and compared to the Mughabghab's atlas using the R -matrix code, sammy. At higher neutron energies the measured cross sections are compared to ENDF/B-VII.1, JEFF-3.2, and BRC evaluated nuclear data. The Maxwellian averaged cross sections in a stellar plasma for thermal energies between 5 keV and 100 keV were extracted using these data.

VizieR Online Data Catalog: Effective collision strengths of Si VII (Sossah+, 2014)

NASA Astrophysics Data System (ADS)

Sossah, A. M.; Tayal, S. S.

2017-08-01

The purpose of present work is to calculate more accurate data for Si VII by using highly accurate target descriptions and by including a sufficient number of target states in the close-coupling expansion. We also included fine-structure effects in the close-coupling expansions to account for the relativistic effects. We used the B-spline Breit-Pauli R-matrix (BSR) codes (Zatsarinny 2006CoPhC.174..273Z) in our scattering calculations. The present method utilizes the term-dependent non-orthogonal orbital sets for the description of the target wave functions and scattering functions. The collisional and radiative parameters have been calculated for all forbidden and allowed transitions between the lowest 92 LSJ levels of 2s22p4, 2s2p5, 2p6, 2s22p33s, 2s22p33p, 2s22p33d, and 2s2p43s configurations of Si VII. (3 data files).

RELAP-7 Code Assessment Plan and Requirement Traceability Matrix

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

Yoo, Junsoo; Choi, Yong-joon; Smith, Curtis L.

2016-10-01

The RELAP-7, a safety analysis code for nuclear reactor system, is under development at Idaho National Laboratory (INL). Overall, the code development is directed towards leveraging the advancements in computer science technology, numerical solution methods and physical models over the last decades. Recently, INL has also been putting an effort to establish the code assessment plan, which aims to ensure an improved final product quality through the RELAP-7 development process. The ultimate goal of this plan is to propose a suitable way to systematically assess the wide range of software requirements for RELAP-7, including the software design, user interface, andmore » technical requirements, etc. To this end, we first survey the literature (i.e., international/domestic reports, research articles) addressing the desirable features generally required for advanced nuclear system safety analysis codes. In addition, the V&V (verification and validation) efforts as well as the legacy issues of several recently-developed codes (e.g., RELAP5-3D, TRACE V5.0) are investigated. Lastly, this paper outlines the Requirement Traceability Matrix (RTM) for RELAP-7 which can be used to systematically evaluate and identify the code development process and its present capability.« less

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.

Read-Write-Codes: An Erasure Resilient Encoding System for Flexible Reading and Writing in Storage Networks

NASA Astrophysics Data System (ADS)

Mense, Mario; Schindelhauer, Christian

We introduce the Read-Write-Coding-System (RWC) - a very flexible class of linear block codes that generate efficient and flexible erasure codes for storage networks. In particular, given a message x of k symbols and a codeword y of n symbols, an RW code defines additional parameters k ≤ r,w ≤ n that offer enhanced possibilities to adjust the fault-tolerance capability of the code. More precisely, an RWC provides linear left(n,k,dright)-codes that have (a) minimum distance d = n - r + 1 for any two codewords, and (b) for each codeword there exists a codeword for each other message with distance of at most w. Furthermore, depending on the values r,w and the code alphabet, different block codes such as parity codes (e.g. RAID 4/5) or Reed-Solomon (RS) codes (if r = k and thus, w = n) can be generated. In storage networks in which I/O accesses are very costly and redundancy is crucial, this flexibility has considerable advantages as r and w can optimally be adapted to read or write intensive applications; only w symbols must be updated if the message x changes completely, what is different from other codes which always need to rewrite y completely as x changes. In this paper, we first state a tight lower bound and basic conditions for all RW codes. Furthermore, we introduce special RW codes in which all mentioned parameters are adjustable even online, that is, those RW codes are adaptive to changing demands. At last, we point out some useful properties regarding safety and security of the stored data.

High Order Modulation Protograph Codes

NASA Technical Reports Server (NTRS)

Nguyen, Thuy V. (Inventor); Nosratinia, Aria (Inventor); Divsalar, Dariush (Inventor)

2014-01-01

Digital communication coding methods for designing protograph-based bit-interleaved code modulation that is general and applies to any modulation. The general coding framework can support not only multiple rates but also adaptive modulation. The method is a two stage lifting approach. In the first stage, an original protograph is lifted to a slightly larger intermediate protograph. The intermediate protograph is then lifted via a circulant matrix to the expected codeword length to form a protograph-based low-density parity-check code.

Improvement of identification of Capnocytophaga canimorsus by matrix-assisted laser desorption ionization-time of flight mass spectrometry using enriched database.

PubMed

Magnette, Amandine; Huang, Te-Din; Renzi, Francesco; Bogaerts, Pierre; Cornelis, Guy R; Glupczynski, Youri

2016-01-01

Capnocytophaga canimorsus and Capnocytophaga cynodegmi can be transmitted from dogs or cats and cause serious human infections. We aimed to evaluate the ability of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) to identify these two Capnocytophaga species. Ninety-four C. canimorsus and 10 C. cynodegmi isolates identified by 16S rRNA gene sequencing were analyzed. Using the MALDI BioTyper database, correct identification was achieved for only 16 of 94 (17%) C. canimorsus and all 10 C. cynodegmi strains, according to the manufacturer's log score specifications. Following the establishment of a complementary homemade reference database by addition of 51 C. canimorsus and 8 C. cynodegmi mass spectra, MALDI-TOF MS provided reliable identification to the species level for 100% of the 45 blind-coded Capnocytophaga isolates tested. MALDI-TOF MS can accurately identify C. canimorsus and C. cynodegmi using an enriched database and thus constitutes a valuable diagnostic tool in the clinical laboratory. Copyright © 2016 Elsevier Inc. All rights reserved.

Coset Codes Viewed as Terminated Convolutional Codes

NASA Technical Reports Server (NTRS)

Fossorier, Marc P. C.; Lin, Shu

1996-01-01

In this paper, coset codes are considered as terminated convolutional codes. Based on this approach, three new general results are presented. First, it is shown that the iterative squaring construction can equivalently be defined from a convolutional code whose trellis terminates. This convolutional code determines a simple encoder for the coset code considered, and the state and branch labelings of the associated trellis diagram become straightforward. Also, from the generator matrix of the code in its convolutional code form, much information about the trade-off between the state connectivity and complexity at each section, and the parallel structure of the trellis, is directly available. Based on this generator matrix, it is shown that the parallel branches in the trellis diagram of the convolutional code represent the same coset code C(sub 1), of smaller dimension and shorter length. Utilizing this fact, a two-stage optimum trellis decoding method is devised. The first stage decodes C(sub 1), while the second stage decodes the associated convolutional code, using the branch metrics delivered by stage 1. Finally, a bidirectional decoding of each received block starting at both ends is presented. If about the same number of computations is required, this approach remains very attractive from a practical point of view as it roughly doubles the decoding speed. This fact is particularly interesting whenever the second half of the trellis is the mirror image of the first half, since the same decoder can be implemented for both parts.

Comparing models for perfluorooctanoic acid pharmacokinetics using Bayesian analysis

EPA Science Inventory

Selecting the appropriate pharmacokinetic (PK) model given the available data is investigated for perfluorooctanoic acid (PFOA), which has been widely analyzed with an empirical, one-compartment model. This research examined the results of experiments [Kemper R. A., DuPont Haskel...

A Bayesian Model for Highly Accelerated Phase-Contrast MRI

PubMed Central

Rich, Adam; Potter, Lee C.; Jin, Ning; Ash, Joshua; Simonetti, Orlando P.; Ahmad, Rizwan

2015-01-01

Purpose Phase-contrast magnetic resonance imaging (PC-MRI) is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to 4D flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to PC-MRI. Theory and Methods ReVEAL models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers. Results ReVEAL is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R ≤ 10. For SV, Pearson r ≥ 0.996 for phantom imaging (n = 24) and r ≥ 0.956 for prospectively accelerated in vivo imaging (n = 10) for R ≤ 10. Conclusion ReVEAL enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to 4D flow imaging, where higher acceleration may be possible due to additional redundancy. PMID:26444911

GPU acceleration of the Locally Selfconsistent Multiple Scattering code for first principles calculation of the ground state and statistical physics of materials

NASA Astrophysics Data System (ADS)

Eisenbach, Markus; Larkin, Jeff; Lutjens, Justin; Rennich, Steven; Rogers, James H.

2017-02-01

The Locally Self-consistent Multiple Scattering (LSMS) code solves the first principles Density Functional theory Kohn-Sham equation for a wide range of materials with a special focus on metals, alloys and metallic nano-structures. It has traditionally exhibited near perfect scalability on massively parallel high performance computer architectures. We present our efforts to exploit GPUs to accelerate the LSMS code to enable first principles calculations of O(100,000) atoms and statistical physics sampling of finite temperature properties. We reimplement the scattering matrix calculation for GPUs with a block matrix inversion algorithm that only uses accelerator memory. Using the Cray XK7 system Titan at the Oak Ridge Leadership Computing Facility we achieve a sustained performance of 14.5PFlop/s and a speedup of 8.6 compared to the CPU only code.

GPU acceleration of the Locally Selfconsistent Multiple Scattering code for first principles calculation of the ground state and statistical physics of materials

DOE PAGES

Eisenbach, Markus; Larkin, Jeff; Lutjens, Justin; ...

2016-07-12

The Locally Self-consistent Multiple Scattering (LSMS) code solves the first principles Density Functional theory Kohn–Sham equation for a wide range of materials with a special focus on metals, alloys and metallic nano-structures. It has traditionally exhibited near perfect scalability on massively parallel high performance computer architectures. In this paper, we present our efforts to exploit GPUs to accelerate the LSMS code to enable first principles calculations of O(100,000) atoms and statistical physics sampling of finite temperature properties. We reimplement the scattering matrix calculation for GPUs with a block matrix inversion algorithm that only uses accelerator memory. Finally, using the Craymore » XK7 system Titan at the Oak Ridge Leadership Computing Facility we achieve a sustained performance of 14.5PFlop/s and a speedup of 8.6 compared to the CPU only code.« less

Hybrid-coded 3D structured illumination imaging with Bayesian estimation (Conference Presentation)

NASA Astrophysics Data System (ADS)

Chen, Hsi-Hsun; Luo, Yuan; Singh, Vijay R.

2016-03-01

Light induced fluorescent microscopy has long been developed to observe and understand the object at microscale, such as cellular sample. However, the transfer function of lense-based imaging system limits the resolution so that the fine and detailed structure of sample cannot be identified clearly. The techniques of resolution enhancement are fascinated to break the limit of resolution for objective given. In the past decades, the resolution enhancement imaging has been investigated through variety of strategies, including photoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), stimulated emission depletion (STED), and structure illuminated microscopy (SIM). In those methods, only SIM can intrinsically improve the resolution limit for a system without taking the structure properties of object into account. In this paper, we develop a SIM associated with Bayesian estimation, furthermore, with optical sectioning capability rendered from HiLo processing, resulting the high resolution through 3D volume. This 3D SIM can provide the optical sectioning and resolution enhancement performance, and be robust to noise owing to the Data driven Bayesian estimation reconstruction proposed. For validating the 3D SIM, we show our simulation result of algorithm, and the experimental result demonstrating the 3D resolution enhancement.

Bayesian data analysis tools for atomic physics

NASA Astrophysics Data System (ADS)

Trassinelli, Martino

2017-10-01

We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested_fit to calculate the different probability distributions and other related quantities. Nested_fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself.

A cost minimisation and Bayesian inference model predicts startle reflex modulation across species.

PubMed

Bach, Dominik R

2015-04-07

In many species, rapid defensive reflexes are paramount to escaping acute danger. These reflexes are modulated by the state of the environment. This is exemplified in fear-potentiated startle, a more vigorous startle response during conditioned anticipation of an unrelated threatening event. Extant explanations of this phenomenon build on descriptive models of underlying psychological states, or neural processes. Yet, they fail to predict invigorated startle during reward anticipation and instructed attention, and do not explain why startle reflex modulation evolved. Here, we fill this lacuna by developing a normative cost minimisation model based on Bayesian optimality principles. This model predicts the observed pattern of startle modification by rewards, punishments, instructed attention, and several other states. Moreover, the mathematical formalism furnishes predictions that can be tested experimentally. Comparing the model with existing data suggests a specific neural implementation of the underlying computations which yields close approximations to the optimal solution under most circumstances. This analysis puts startle modification into the framework of Bayesian decision theory and predictive coding, and illustrates the importance of an adaptive perspective to interpret defensive behaviour across species. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

Bayesian Atmospheric Radiative Transfer (BART) Code and Application to WASP-43b

NASA Astrophysics Data System (ADS)

Blecic, Jasmina; Harrington, Joseph; Cubillos, Patricio; Bowman, Oliver; Rojo, Patricio; Stemm, Madison; Lust, Nathaniel B.; Challener, Ryan; Foster, Austin James; Foster, Andrew S.; Blumenthal, Sarah D.; Bruce, Dylan

2016-01-01

We present a new open-source Bayesian radiative-transfer framework, Bayesian Atmospheric Radiative Transfer (BART, https://github.com/exosports/BART), and its application to WASP-43b. BART initializes a model for the atmospheric retrieval calculation, generates thousands of theoretical model spectra using parametrized pressure and temperature profiles and line-by-line radiative-transfer calculation, and employs a statistical package to compare the models with the observations. It consists of three self-sufficient modules available to the community under the reproducible-research license, the Thermochemical Equilibrium Abundances module (TEA, https://github.com/dzesmin/TEA, Blecic et al. 2015}, the radiative-transfer module (Transit, https://github.com/exosports/transit), and the Multi-core Markov-chain Monte Carlo statistical module (MCcubed, https://github.com/pcubillos/MCcubed, Cubillos et al. 2015). We applied BART on all available WASP-43b secondary eclipse data from the space- and ground-based observations constraining the temperature-pressure profile and molecular abundances of the dayside atmosphere of WASP-43b. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

Large-scale 3D simulations of ICF and HEDP targets

NASA Astrophysics Data System (ADS)

Marinak, Michael M.

2000-10-01

The radiation hydrodynamics code HYDRA continues to be developed and applied to 3D simulations of a variety of targets for both inertial confinement fusion (ICF) and high energy density physics. Several packages have been added enabling this code to perform ICF target simulations with similar accuracy as two-dimensional codes of long-time historical use. These include a laser ray trace and deposition package, a heavy ion deposition package, implicit Monte Carlo photonics, and non-LTE opacities, derived from XSN or the linearized response matrix approach.(R. More, T. Kato, Phys. Rev. Lett. 81, 814 (1998), S. Libby, F. Graziani, R. More, T. Kato, Proceedings of the 13th International Conference on Laser Interactions and Related Plasma Phenomena, (AIP, New York, 1997).) LTE opacities can also be calculated for arbitrary mixtures online by combining tabular values generated by different opacity codes. Thermonuclear burn, charged particle transport, neutron energy deposition, electron-ion coupling and conduction, and multigroup radiation diffusion packages are also installed. HYDRA can employ ALE hydrodynamics; a number of grid motion algorithms are available. Multi-material flows are resolved using material interface reconstruction. Results from large-scale simulations run on up to 1680 processors, using a combination of massively parallel processing and symmetric multiprocessing, will be described. A large solid angle simulation of Rayleigh-Taylor instability growth in a NIF ignition capsule has resolved simultaneously the full spectrum of the most dangerous modes that grow from surface roughness. Simulations of a NIF hohlraum illuminated with the initial 96 beam configuration have also been performed. The effect of the hohlraum’s 3D intrinsic drive asymmetry on the capsule implosion will be considered. We will also discuss results from a Nova experiment in which a copper sphere is crushed by a planar shock. Several interacting hydrodynamic instabilities, including the Widnall instability, cause breakup of the resulting vortex ring.

Monte Carlo study of a new I‐125 brachytherapy prototype seed with a ceramic radionuclide carrier and radiographic marker

PubMed Central

Paixão, Lucas; Santos, Ana Maria M.; dos Santos, Adriano Márcio; Grynberg, Suely Epsztein

2012-01-01

In prostate cancer treatment, there is an increasing interest in the permanent radioactive seeds implant technique. Currently, in Brazil, the seeds are imported with high prices, which prohibit their use in public hospitals. A ceramic matrix that can be used as a radioisotope carrier and radiographic marker was developed at our institution. The ceramic matrix is distinguished by the characteristic of maintaining the radioactive material uniformly distributed in its surface. In this work, Monte Carlo simulations were performed in order to assess the dose distributions generated by this prototype seed model, with the ceramic matrix encapsulated in titanium, in the same way as the commercial 6711 seed. The obtained data was assessed, as described in the TG‐43U1 report by the American Association of Physicists in Medicine, for two seed models: (1) the most used model 6711 source — for validation and comparison, and (2) for the prototype model with the ceramic matrix. The dosimetric parameters dose rate constant, Λ, radial dose function, gL(r), and anisotropy function, F(r,θ), were derived from simulations by the Monte Carlo method using the MCNP5 code. A Λ 0.992 (±2.33%) cGyh−1U−1 was found for the prototype model. In comparison with the 6711 model, a lower dose fall‐off on transverse axis was found, as well as a lower dose anisotropy for the radius r= 0.25 cm. In general, for all distances, the prototype seed model presents a slightly larger anisotropy between 0° ≤ Θ < 50° and anisotropy similar to the 6711 model for Θ ≥ 50°. The dosimetric characteristics of the prototype model presented in this study suggest that its use is feasible. Because of the model's characteristics, seeds of lower specific activity iodine might be necessary which, on the other hand, would help to reduce costs. However, it has to be emphasized that the proposed source is a prototype, and the required (AAPM prerequisites) experimental study and tolerance manufacturer values are pending for future studies. PACS numbers: 87.53.Jw, 87.55.K PMID:22584172

Calculation of response matrix of CaSO 4:Dy based neutron dosimeter using Monte Carlo code FLUKA and measurement of 241Am-Be spectra

NASA Astrophysics Data System (ADS)

Chatterjee, S.; Bakshi, A. K.; Tripathy, S. P.

2010-09-01

Response matrix for CaSO 4:Dy based neutron dosimeter was generated using Monte Carlo code FLUKA in the energy range thermal to 20 MeV for a set of eight Bonner spheres of diameter 3-12″ including the bare one. Response of the neutron dosimeter was measured for the above set of spheres for 241Am-Be neutron source covered with 2 mm lead. An analytical expression for the response function was devised as a function of sphere mass. Using Frascati Unfolding Iteration Tool (FRUIT) unfolding code, the neutron spectrum of 241Am-Be was unfolded and compared with standard IAEA spectrum for the same.

Raman and Fourier Transform Infrared (FT-IR) Mineral to Matrix Ratios Correlate with Physical Chemical Properties of Model Compounds and Native Bone Tissue.

PubMed

Taylor, Erik A; Lloyd, Ashley A; Salazar-Lara, Carolina; Donnelly, Eve

2017-10-01

Raman and Fourier transform infrared (FT-IR) spectroscopic imaging techniques can be used to characterize bone composition. In this study, our objective was to validate the Raman mineral:matrix ratios (ν 1 PO 4 :amide III, ν 1 PO 4 :amide I, ν 1 PO 4 :Proline + hydroxyproline, ν 1 PO 4 :Phenylalanine, ν 1 PO 4 :δ CH 2 peak area ratios) by correlating them to ash fraction and the IR mineral:matrix ratio (ν 3 PO 4 :amide I peak area ratio) in chemical standards and native bone tissue. Chemical standards consisting of varying ratios of synthetic hydroxyapatite (HA) and collagen, as well as bone tissue from humans, sheep, and mice, were characterized with confocal Raman spectroscopy and FT-IR spectroscopy and gravimetric analysis. Raman and IR mineral:matrix ratio values from chemical standards increased reciprocally with ash fraction (Raman ν 1 PO 4 /Amide III: P < 0.01, R 2  = 0.966; Raman ν 1 PO 4 /Amide I: P < 0.01, R 2  = 0.919; Raman ν 1 PO 4 /Proline + Hydroxyproline: P < 0.01, R 2  = 0.976; Raman ν 1 PO 4 /Phenylalanine: P < 0.01, R 2  = 0.911; Raman ν 1 PO 4 /δ CH 2 : P < 0.01, R 2  = 0.894; IR P < 0.01, R 2  = 0.91). Fourier transform infrared mineral:matrix ratio values from native bone tissue were also similar to theoretical mineral:matrix ratio values for a given ash fraction. Raman and IR mineral:matrix ratio values were strongly correlated ( P < 0.01, R 2  = 0.82). These results were confirmed by calculating the mineral:matrix ratio for theoretical IR spectra, developed by applying the Beer-Lambert law to calculate the relative extinction coefficients of HA and collagen over the same range of wavenumbers (800-1800 cm -1 ). The results confirm that the Raman mineral:matrix bone composition parameter correlates strongly to ash fraction and to its IR counterpart. Finally, the mineral:matrix ratio values of the native bone tissue are similar to those of both chemical standards and theoretical values, confirming the biological relevance of the chemical standards and the characterization techniques.

Development and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims.

PubMed

Bertke, S J; Meyers, A R; Wurzelbacher, S J; Bell, J; Lampl, M L; Robins, D

2012-12-01

Tracking and trending rates of injuries and illnesses classified as musculoskeletal disorders caused by ergonomic risk factors such as overexertion and repetitive motion (MSDs) and slips, trips, or falls (STFs) in different industry sectors is of high interest to many researchers. Unfortunately, identifying the cause of injuries and illnesses in large datasets such as workers' compensation systems often requires reading and coding the free form accident text narrative for potentially millions of records. To alleviate the need for manual coding, this paper describes and evaluates a computer auto-coding algorithm that demonstrated the ability to code millions of claims quickly and accurately by learning from a set of previously manually coded claims. The auto-coding program was able to code claims as a musculoskeletal disorders, STF or other with approximately 90% accuracy. The program developed and discussed in this paper provides an accurate and efficient method for identifying the causation of workers' compensation claims as a STF or MSD in a large database based on the unstructured text narrative and resulting injury diagnoses. The program coded thousands of claims in minutes. The method described in this paper can be used by researchers and practitioners to relieve the manual burden of reading and identifying the causation of claims as a STF or MSD. Furthermore, the method can be easily generalized to code/classify other unstructured text narratives. Published by Elsevier Ltd.

Transport and equilibrium in field-reversed mirrors

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

Boyd, J.K.

Two plasma models relevant to compact torus research have been developed to study transport and equilibrium in field reversed mirrors. In the first model for small Larmor radius and large collision frequency, the plasma is described as an adiabatic hydromagnetic fluid. In the second model for large Larmor radius and small collision frequency, a kinetic theory description has been developed. Various aspects of the two models have been studied in five computer codes ADB, AV, NEO, OHK, RES. The ADB code computes two dimensional equilibrium and one dimensional transport in a flux coordinate. The AV code calculates orbit average integralsmore » in a harmonic oscillator potential. The NEO code follows particle trajectories in a Hill's vortex magnetic field to study stochasticity, invariants of the motion, and orbit average formulas. The OHK code displays analytic psi(r), B/sub Z/(r), phi(r), E/sub r/(r) formulas developed for the kinetic theory description. The RES code calculates resonance curves to consider overlap regions relevant to stochastic orbit behavior.« less

Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks.

PubMed

Zhao, Yubin; Li, Xiaofan; Zhang, Sha; Meng, Tianhui; Zhang, Yiwen

2016-08-23

In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér-Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for the indoor localization systems and serves as an indicator for practical system evaluation.

METal matrix composite ANalyzer (METCAN): Theoretical manual

NASA Technical Reports Server (NTRS)

Murthy, P. L. N.; Chamis, C. C.

1993-01-01

This manuscript is intended to be a companion volume to the 'METCAN User's Manual' and the 'METAN Demonstration Manual.' The primary purpose of the manual is to give details pertaining to micromechanics and macromechanics equations of high temperature metal matrix composites that are programmed in the METCAN computer code. The subroutines which contain the programmed equations are also mentioned in order to facilitate any future changes or modifications that the user may intend to incorporate in the code. Assumptions and derivations leading to the micromechanics equations are briefly mentioned.

Bayesian automated cortical segmentation for neonatal MRI

NASA Astrophysics Data System (ADS)

Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

2017-11-01

Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

a Novel Discrete Optimal Transport Method for Bayesian Inverse Problems

NASA Astrophysics Data System (ADS)

Bui-Thanh, T.; Myers, A.; Wang, K.; Thiery, A.

2017-12-01

We present the Augmented Ensemble Transform (AET) method for generating approximate samples from a high-dimensional posterior distribution as a solution to Bayesian inverse problems. Solving large-scale inverse problems is critical for some of the most relevant and impactful scientific endeavors of our time. Therefore, constructing novel methods for solving the Bayesian inverse problem in more computationally efficient ways can have a profound impact on the science community. This research derives the novel AET method for exploring a posterior by solving a sequence of linear programming problems, resulting in a series of transport maps which map prior samples to posterior samples, allowing for the computation of moments of the posterior. We show both theoretical and numerical results, indicating this method can offer superior computational efficiency when compared to other SMC methods. Most of this efficiency is derived from matrix scaling methods to solve the linear programming problem and derivative-free optimization for particle movement. We use this method to determine inter-well connectivity in a reservoir and the associated uncertainty related to certain parameters. The attached file shows the difference between the true parameter and the AET parameter in an example 3D reservoir problem. The error is within the Morozov discrepancy allowance with lower computational cost than other particle methods.

Assessment of CT image quality using a Bayesian approach

NASA Astrophysics Data System (ADS)

Reginatto, M.; Anton, M.; Elster, C.

2017-08-01

One of the most promising approaches for evaluating CT image quality is task-specific quality assessment. This involves a simplified version of a clinical task, e.g. deciding whether an image belongs to the class of images that contain the signature of a lesion or not. Task-specific quality assessment can be done by model observers, which are mathematical procedures that carry out the classification task. The most widely used figure of merit for CT image quality is the area under the ROC curve (AUC), a quantity which characterizes the performance of a given model observer. In order to estimate AUC from a finite sample of images, different approaches from classical statistics have been suggested. The goal of this paper is to introduce task-specific quality assessment of CT images to metrology and to propose a novel Bayesian estimation of AUC for the channelized Hotelling observer (CHO) applied to the task of detecting a lesion at a known image location. It is assumed that signal-present and signal-absent images follow multivariate normal distributions with the same covariance matrix. The Bayesian approach results in a posterior distribution for the AUC of the CHO which provides in addition a complete characterization of the uncertainty of this figure of merit. The approach is illustrated by its application to both simulated and experimental data.

Comparative Controller Design for a Marine Gas Turbine Propulsion Plant

DTIC Science & Technology

1988-09-01

ADDRESS (City State, and ZIP Code) ,onterey, California 93943-5000 Monterey, California 93943-5000 Sa. NAME OF FUNDING/SPONSORING 8b OFFICE SYMBOL 9 ...155 7. A21 MATRIX COEFFICIENT CORRELATION DATA ----------- 156 8. A22 MATRIX COEFFICIENT CORRELATION DATA ----------- 157 9 . A23 MATRIX...Flowchart of VRD Algorithm ----------------------- 29 8. Steady State Convergence Map --------------------- 34 9 . Smoothed Dynamic Transition Map

EvolQG - An R package for evolutionary quantitative genetics

PubMed Central

Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel

2016-01-01

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352

A Bayesian estimation of a stochastic predator-prey model of economic fluctuations

NASA Astrophysics Data System (ADS)

Dibeh, Ghassan; Luchinsky, Dmitry G.; Luchinskaya, Daria D.; Smelyanskiy, Vadim N.

2007-06-01

In this paper, we develop a Bayesian framework for the empirical estimation of the parameters of one of the best known nonlinear models of the business cycle: The Marx-inspired model of a growth cycle introduced by R. M. Goodwin. The model predicts a series of closed cycles representing the dynamics of labor's share and the employment rate in the capitalist economy. The Bayesian framework is used to empirically estimate a modified Goodwin model. The original model is extended in two ways. First, we allow for exogenous periodic variations of the otherwise steady growth rates of the labor force and productivity per worker. Second, we allow for stochastic variations of those parameters. The resultant modified Goodwin model is a stochastic predator-prey model with periodic forcing. The model is then estimated using a newly developed Bayesian estimation method on data sets representing growth cycles in France and Italy during the years 1960-2005. Results show that inference of the parameters of the stochastic Goodwin model can be achieved. The comparison of the dynamics of the Goodwin model with the inferred values of parameters demonstrates quantitative agreement with the growth cycle empirical data.

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.

Comparison of methods for the implementation of genome-assisted evaluation of Spanish dairy cattle.

PubMed

Jiménez-Montero, J A; González-Recio, O; Alenda, R

2013-01-01

The aim of this study was to evaluate methods for genomic evaluation of the Spanish Holstein population as an initial step toward the implementation of routine genomic evaluations. This study provides a description of the population structure of progeny tested bulls in Spain at the genomic level and compares different genomic evaluation methods with regard to accuracy and bias. Two bayesian linear regression models, Bayes-A and Bayesian-LASSO (B-LASSO), as well as a machine learning algorithm, Random-Boosting (R-Boost), and BLUP using a realized genomic relationship matrix (G-BLUP), were compared. Five traits that are currently under selection in the Spanish Holstein population were used: milk yield, fat yield, protein yield, fat percentage, and udder depth. In total, genotypes from 1859 progeny tested bulls were used. The training sets were composed of bulls born before 2005; including 1601 bulls for production and 1574 bulls for type, whereas the testing sets contained 258 and 235 bulls born in 2005 or later for production and type, respectively. Deregressed proofs (DRP) from January 2009 Interbull (Uppsala, Sweden) evaluation were used as the dependent variables for bulls in the training sets, whereas DRP from the December 2011 DRPs Interbull evaluation were used to compare genomic predictions with progeny test results for bulls in the testing set. Genomic predictions were more accurate than traditional pedigree indices for predicting future progeny test results of young bulls. The gain in accuracy, due to inclusion of genomic data varied by trait and ranged from 0.04 to 0.42 Pearson correlation units. Results averaged across traits showed that B-LASSO had the highest accuracy with an advantage of 0.01, 0.03 and 0.03 points in Pearson correlation compared with R-Boost, Bayes-A, and G-BLUP, respectively. The B-LASSO predictions also showed the least bias (0.02, 0.03 and 0.10 SD units less than Bayes-A, R-Boost and G-BLUP, respectively) as measured by mean difference between genomic predictions and progeny test results. The R-Boosting algorithm provided genomic predictions with regression coefficients closer to unity, which is an alternative measure of bias, for 4 out of 5 traits and also resulted in mean squared errors estimates that were 2%, 10%, and 12% smaller than B-LASSO, Bayes-A, and G-BLUP, respectively. The observed prediction accuracy obtained with these methods was within the range of values expected for a population of similar size, suggesting that the prediction method and reference population described herein are appropriate for implementation of routine genome-assisted evaluations in Spanish dairy cattle. R-Boost is a competitive marker regression methodology in terms of predictive ability that can accommodate large data sets. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Bayesian model for matching the radiometric measurements of aerospace and field ocean color sensors.

PubMed

Salama, Mhd Suhyb; Su, Zhongbo

2010-01-01

A Bayesian model is developed to match aerospace ocean color observation to field measurements and derive the spatial variability of match-up sites. The performance of the model is tested against populations of synthesized spectra and full and reduced resolutions of MERIS data. The model derived the scale difference between synthesized satellite pixel and point measurements with R(2) > 0.88 and relative error < 21% in the spectral range from 400 nm to 695 nm. The sub-pixel variabilities of reduced resolution MERIS image are derived with less than 12% of relative errors in heterogeneous region. The method is generic and applicable to different sensors.

A Bayesian Framework for Estimating the Concordance Correlation Coefficient Using Skew-elliptical Distributions.

PubMed

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2018-04-05

The concordance correlation coefficient (CCC) is a widely used scaled index in the study of agreement. In this article, we propose estimating the CCC by a unified Bayesian framework that can (1) accommodate symmetric or asymmetric and light- or heavy-tailed data; (2) select model from several candidates; and (3) address other issues frequently encountered in practice such as confounding covariates and missing data. The performance of the proposal was studied and demonstrated using simulated as well as real-life biomarker data from a clinical study of an insomnia drug. The implementation of the proposal is accessible through a package in the Comprehensive R Archive Network.

Context-aware and locality-constrained coding for image categorization.

PubMed

Xiao, Wenhua; Wang, Bin; Liu, Yu; Bao, Weidong; Zhang, Maojun

2014-01-01

Improving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Coding (CALC) approach with context information for describing objects in a discriminative way. It is generally achieved by learning a word-to-word cooccurrence prior to imposing context information over locality-constrained coding. Firstly, the local context of each category is evaluated by learning a word-to-word cooccurrence matrix representing the spatial distribution of local features in neighbor region. Then, the learned cooccurrence matrix is used for measuring the context distance between local features and code words. Finally, a coding strategy simultaneously considers locality in feature space and context space, while introducing the weight of feature is proposed. This novel coding strategy not only semantically preserves the information in coding, but also has the ability to alleviate the noise distortion of each class. Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. Experimental results show that our method significantly improves the performance of baselines and achieves comparable and even better performance with the state of the arts.

NAS Experiences of Porting CM Fortran Codes to HPF on IBM SP2 and SGI Power Challenge

NASA Technical Reports Server (NTRS)

Saini, Subhash

1995-01-01

Current Connection Machine (CM) Fortran codes developed for the CM-2 and the CM-5 represent an important class of parallel applications. Several users have employed CM Fortran codes in production mode on the CM-2 and the CM-5 for the last five to six years, constituting a heavy investment in terms of cost and time. With Thinking Machines Corporation's decision to withdraw from the hardware business and with the decommissioning of many CM-2 and CM-5 machines, the best way to protect the substantial investment in CM Fortran codes is to port the codes to High Performance Fortran (HPF) on highly parallel systems. HPF is very similar to CM Fortran and thus represents a natural transition. Conversion issues involved in porting CM Fortran codes on the CM-5 to HPF are presented. In particular, the differences between data distribution directives and the CM Fortran Utility Routines Library, as well as the equivalent functionality in the HPF Library are discussed. Several CM Fortran codes (Cannon algorithm for matrix-matrix multiplication, Linear solver Ax=b, 1-D convolution for 2-D datasets, Laplace's Equation solver, and Direct Simulation Monte Carlo (DSMC) codes have been ported to Subset HPF on the IBM SP2 and the SGI Power Challenge. Speedup ratios versus number of processors for the Linear solver and DSMC code are presented.

Electron impact excitation of Kr XXXII

NASA Astrophysics Data System (ADS)

Aggarwal, K. M.; Keenan, F. P.; Lawson, K. D.

2009-09-01

Collision strengths (Ω) have been calculated for all 7750 transitions among the lowest 125 levels belonging to the 2s2p,2s2p,2p,2s3ℓ,2s2p3ℓ, and 2p3ℓ configurations of boron-like krypton, Kr XXXII, for which the Dirac Atomic R-matrix Code has been adopted. All partial waves with angular momentum J⩽40 have been included, sufficient for the convergence of Ω for forbidden transitions. For allowed transitions, a top-up has been included in order to obtain converged values of Ω up to an energy of 500 Ryd. Resonances in the thresholds region have been resolved in a narrow energy mesh, and results for effective collision strengths (ϒ) have been obtained after averaging the values of Ω over a Maxwellian distribution of electron velocities. Values of ϒ are reported over a wide temperature range below 107.3K, and the accuracy of the results is assessed. Values of ϒ are also listed in the temperature range 7.3⩽logTe(K)⩽9.0, obtained from the nonresonant collision strengths from the Flexible Atomic Code.

Double ionization in R -matrix theory using a two-electron outer region

NASA Astrophysics Data System (ADS)

Wragg, Jack; Parker, J. S.; van der Hart, H. W.

2015-08-01

We have developed a two-electron outer region for use within R -matrix theory to describe double ionization processes. The capability of this method is demonstrated for single-photon double ionization of He in the photon energy region between 80 and 180 eV. The cross sections are in agreement with established data. The extended R -matrix with time dependence method also provides information on higher-order processes, as demonstrated by the identification of signatures for sequential double ionization processes involving an intermediate He+ state with n =2 .

Ab initio R-matrix calculations of e+-molecule scattering

NASA Technical Reports Server (NTRS)

Danby, Grahame; Tennyson, Jonathan

1990-01-01

The adaptation of the molecular R-matrix method, originally developed for electron-molecule collision studies, to positron scattering is discussed. Ab initio R-matrix calculations are presented for collisions of low energy positrons with a number of diatomic systems including H2, HF and N2. Differential elastic cross sections for positron-H2 show a minimum at about 45 deg for collision energies between 0.3 and 0.5 Ryd. The calculations predict a bound state of positronHF. Calculations on inelastic processes in N2 and O2 are also discussed.

A collagen-based scaffold delivering exogenous microrna-29B to modulate extracellular matrix remodeling.

PubMed

Monaghan, Michael; Browne, Shane; Schenke-Layland, Katja; Pandit, Abhay

2014-04-01

Directing appropriate extracellular matrix remodeling is a key aim of regenerative medicine strategies. Thus, antifibrotic interfering RNA (RNAi) therapy with exogenous microRNA (miR)-29B was proposed as a method to modulate extracellular matrix remodeling following cutaneous injury. It was hypothesized that delivery of miR-29B from a collagen scaffold will efficiently modulate the extracellular matrix remodeling response and reduce maladaptive remodeling such as aggressive deposition of collagen type I after injury. The release of RNA from the scaffold was assessed and its ability to silence collagen type I and collagen type III expression was evaluated in vitro. When primary fibroblasts were cultured with scaffolds doped with miR-29B, reduced levels of collagen type I and collagen type III mRNA expression were observed for up to 2 weeks of culture. When the scaffolds were applied to full thickness wounds in vivo, reduced wound contraction, improved collagen type III/I ratios and a significantly higher matrix metalloproteinase (MMP)-8: tissue inhibitor of metalloproteinase (TIMP)-1 ratio were detected when the scaffolds were functionalized with miR-29B. Furthermore, these effects were significantly influenced by the dose of miR-29B in the collagen scaffold (0.5 versus 5 μg). This study shows a potential of combining exogenous miRs with collagen scaffolds to improve extracellular matrix remodeling following injury.

Loss of Endothelial Barrier in Marfan Mice (mgR/mgR) Results in Severe Inflammation after Adenoviral Gene Therapy

PubMed Central

Weymann, Alexander; Arif, Rawa; Weber, Antje; Zaradzki, Marcin; Richter, Karsten; Ensminger, Stephan; Robinson, Peter Nicholas; Wagner, Andreas H.; Karck, Matthias; Kallenbach, Klaus

2016-01-01

Objectives Marfan syndrome is an autosomal dominant inherited disorder of connective tissue. The vascular complications of Marfan syndrome have the biggest impact on life expectancy. The aorta of Marfan patients reveals degradation of elastin layers caused by increased proteolytic activity of matrix metalloproteinases (MMPs). In this study we performed adenoviral gene transfer of human tissue inhibitor of matrix metalloproteinases-1 (hTIMP-1) in aortic grafts of fibrillin-1 deficient Marfan mice (mgR/mgR) in order to reduce elastolysis. Methods We performed heterotopic infrarenal transplantation of the thoracic aorta in female mice (n = 7 per group). Before implantation, mgR/mgR and wild-type aortas (WT, C57BL/6) were transduced ex vivo with an adenoviral vector coding for human TIMP-1 (Ad.hTIMP-1) or β-galactosidase (Ad.β-Gal). As control mgR/mgR and wild-type aortas received no gene therapy. Thirty days after surgery, overexpression of the transgene was assessed by immunohistochemistry (IHC) and collagen in situ zymography. Histologic staining was performed to investigate inflammation, the neointimal index (NI), and elastin breaks. Endothelial barrier function of native not virus-exposed aortas was evaluated by perfusion of fluorescent albumin and examinations of virus-exposed tissue were performed by transmission electron microscopy (TEM). Results IHC and ISZ revealed sufficient expression of the transgene. Severe cellular inflammation and intima hyperplasia were seen only in adenovirus treated mgR/mgR aortas (Ad.β-Gal, Ad.hTIMP-1 NI: 0.23; 0.43), but not in native and Ad.hTIMP-1 treated WT (NI: 0.01; 0.00). Compared to native mgR/mgR and Ad.hTIMP-1 treated WT aorta, the NI is highly significant greater in Ad.hTIMP-1 transduced mgR/mgR aorta (p = 0.001; p = 0.001). As expected, untreated Marfan grafts showed significant more elastolysis compared to WT (p = 0.001). However, elastolysis in Marfan aortas was not reduced by adenoviral overexpression of hTIMP-1 (compared to untreated Marfan aorta: Ad.hTIMP-1 p = 0.902; control Ad.β-Gal. p = 0.165). The virus-untreated and not transplanted mgR/mgR aorta revealed a significant increase of albumin diffusion through the endothelial barrier (p = 0.037). TEM analysis of adenovirus-exposed mgR/mgR aortas displayed disruption of the basement membrane and basolateral space. Conclusions Murine Marfan aortic grafts developed severe inflammation after adenoviral contact. We demonstrated that fibrillin-1 deficiency is associated with relevant dysfunction of the endothelial barrier that enables adenovirus to induce vessel-harming inflammation. Endothelial dysfunction may play a pivotal role in the development of the vascular phenotype of Marfan syndrome. PMID:26840980

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

PubMed

Duan, Chong; Kallehauge, Jesper F; Pérez-Torres, Carlos J; Bretthorst, G Larry; Beeman, Scott C; Tanderup, Kari; Ackerman, Joseph J H; Garbow, Joel R

2018-02-01

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

Combined group ECC protection and subgroup parity protection

DOEpatents

Gara, Alan G.; Chen, Dong; Heidelberger, Philip; Ohmacht, Martin

2013-06-18

A method and system are disclosed for providing combined error code protection and subgroup parity protection for a given group of n bits. The method comprises the steps of identifying a number, m, of redundant bits for said error protection; and constructing a matrix P, wherein multiplying said given group of n bits with P produces m redundant error correction code (ECC) protection bits, and two columns of P provide parity protection for subgroups of said given group of n bits. In the preferred embodiment of the invention, the matrix P is constructed by generating permutations of m bit wide vectors with three or more, but an odd number of, elements with value one and the other elements with value zero; and assigning said vectors to rows of the matrix P.

Bayesian ionospheric multi-instrument 3D tomography

NASA Astrophysics Data System (ADS)

Norberg, Johannes; Vierinen, Juha; Roininen, Lassi

2017-04-01

The tomographic reconstruction of ionospheric electron densities is an inverse problem that cannot be solved without relatively strong regularising additional information. % Especially the vertical electron density profile is determined predominantly by the regularisation. % %Often utilised regularisations in ionospheric tomography include smoothness constraints and iterative methods with initial ionospheric models. % Despite its crucial role, the regularisation is often hidden in the algorithm as a numerical procedure without physical understanding. % % The Bayesian methodology provides an interpretative approach for the problem, as the regularisation can be given in a physically meaningful and quantifiable prior probability distribution. % The prior distribution can be based on ionospheric physics, other available ionospheric measurements and their statistics. % Updating the prior with measurements results as the posterior distribution that carries all the available information combined. % From the posterior distribution, the most probable state of the ionosphere can then be solved with the corresponding probability intervals. % Altogether, the Bayesian methodology provides understanding on how strong the given regularisation is, what is the information gained with the measurements and how reliable the final result is. % In addition, the combination of different measurements and temporal development can be taken into account in a very intuitive way. However, a direct implementation of the Bayesian approach requires inversion of large covariance matrices resulting in computational infeasibility. % In the presented method, Gaussian Markov random fields are used to form a sparse matrix approximations for the covariances. % The approach makes the problem computationally feasible while retaining the probabilistic and physical interpretation. Here, the Bayesian method with Gaussian Markov random fields is applied for ionospheric 3D tomography over Northern Europe. % Multi-instrument measurements are utilised from TomoScand receiver network for Low Earth orbit beacon satellite signals, GNSS receiver networks, as well as from EISCAT ionosondes and incoherent scatter radars. % %The performance is demonstrated in three-dimensional spatial domain with temporal development also taken into account.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

PubMed Central

2010-01-01

Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data. PMID:21062443

Estimating the extent and distribution of new-onset adult asthma in British Columbia using frequentist and Bayesian approaches.

PubMed

Beach, Jeremy; Burstyn, Igor; Cherry, Nicola

2012-07-01

We previously described a method to identify the incidence of new-onset adult asthma (NOAA) in Alberta by industry and occupation, utilizing Workers' Compensation Board (WCB) and physician billing data. The aim of this study was to extend this method to data from British Columbia (BC) so as to compare the two provinces and to incorporate Bayesian methodology into estimates of risk. WCB claims for any reason 1995-2004 were linked to physician billing data. NOAA was defined as a billing for asthma (ICD-9 493) in the 12 months before a WCB claim without asthma in the previous 3 years. Incidence was calculated by occupation and industry. In a matched case-referent analysis, associations with exposures were examined using an asthma-specific job exposure matrix (JEM). Posterior distributions from the Alberta analysis and estimated misclassification parameters were used as priors in the Bayesian analysis of the BC data. Among 1 118 239 eligible WCB claims the incidence of NOAA was 1.4%. Sixteen occupations and 44 industries had a significantly increased risk; six industries had a decreased risk. The JEM identified wood dust [odds ratio (OR) 1.55, 95% confidence interval (CI) 1.08-2.24] and animal antigens (OR 1.66, 95% CI 1.17-2.36) as related to an increased risk of NOAA. Exposure to isocyanates was associated with decreased risk (OR 0.57, 95% CI 0.39-0.85). Bayesian analyses taking account of exposure misclassification and informative priors resulted in posterior distributions of ORs with lower boundary of 95% credible intervals >1.00 for almost all exposures. The distribution of NOAA in BC appeared somewhat similar to that in Alberta, except for isocyanates. Bayesian analyses allowed incorporation of prior evidence into risk estimates, permitting reconsideration of the apparently protective effect of isocyanate exposure.