UNBIASED ESTIMATORS OF SPECIFIC CONNECTIVITY

Directory of Open Access Journals (Sweden)

Jean-Paul Jernot

2011-05-01

Full Text Available This paper deals with the estimation of the specific connectivity of a stationary random set in IRd. It turns out that the "natural" estimator is only asymptotically unbiased. The example of a boolean model of hypercubes illustrates the amplitude of the bias produced when the measurement field is relatively small with respect to the range of the random set. For that reason unbiased estimators are desired. Such an estimator can be found in the literature in the case where the measurement field is a right parallelotope. In this paper, this estimator is extended to apply to measurement fields of various shapes, and to possess a smaller variance. Finally an example from quantitative metallography (specific connectivity of a population of sintered bronze particles is given.

Unbiased Sampling and Meshing of Isosurfaces

KAUST Repository

Yan, Dongming

2014-05-07

In this paper, we present a new technique to generate unbiased samples on isosurfaces. An isosurface, F(x,y,z) = c , of a function, F , is implicitly defined by trilinear interpolation of background grid points. The key idea of our approach is that of treating the isosurface within a grid cell as a graph (height) function in one of the three coordinate axis directions, restricted to where the slope is not too high, and integrating / sampling from each of these three. We use this unbiased sampling algorithm for applications in Monte Carlo integration, Poisson-disk sampling, and isosurface meshing.

Unbiased Sampling and Meshing of Isosurfaces

KAUST Repository

Yan, Dongming; Wallner, Johannes; Wonka, Peter

2014-01-01

In this paper, we present a new technique to generate unbiased samples on isosurfaces. An isosurface, F(x,y,z) = c , of a function, F , is implicitly defined by trilinear interpolation of background grid points. The key idea of our approach is that of treating the isosurface within a grid cell as a graph (height) function in one of the three coordinate axis directions, restricted to where the slope is not too high, and integrating / sampling from each of these three. We use this unbiased sampling algorithm for applications in Monte Carlo integration, Poisson-disk sampling, and isosurface meshing.

Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix.

Directory of Open Access Journals (Sweden)

Zhe Zhang

2010-09-01

Full Text Available With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest.In the framework of mixed model equations, a new best linear unbiased prediction (BLUP method including a trait-specific relationship matrix (TA was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP and BLUP with realized relationship matrix (GBLUP. It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario.The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

Science.gov (United States)

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Quantum process reconstruction based on mutually unbiased basis

International Nuclear Information System (INIS)

Fernandez-Perez, A.; Saavedra, C.; Klimov, A. B.

2011-01-01

We study a quantum process reconstruction based on the use of mutually unbiased projectors (MUB projectors) as input states for a D-dimensional quantum system, with D being a power of a prime number. This approach connects the results of quantum-state tomography using mutually unbiased bases with the coefficients of a quantum process, expanded in terms of MUB projectors. We also study the performance of the reconstruction scheme against random errors when measuring probabilities at the MUB projectors.

Aggregation-cokriging for highly multivariate spatial data

KAUST Repository

Furrer, R.; Genton, M. G.

2011-01-01

Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.

Aggregation-cokriging for highly multivariate spatial data

KAUST Repository

Furrer, R.

2011-08-26

Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.

Quantifying high dimensional entanglement with two mutually unbiased bases

Directory of Open Access Journals (Sweden)

Paul Erker

2017-07-01

Full Text Available We derive a framework for quantifying entanglement in multipartite and high dimensional systems using only correlations in two unbiased bases. We furthermore develop such bounds in cases where the second basis is not characterized beyond being unbiased, thus enabling entanglement quantification with minimal assumptions. Furthermore, we show that it is feasible to experimentally implement our method with readily available equipment and even conservative estimates of physical parameters.

Unbiased diffusion of Brownian particles on disordered correlated potentials

International Nuclear Information System (INIS)

Salgado-Garcia, Raúl; Maldonado, Cesar

2015-01-01

In this work we study the diffusion of non-interacting overdamped particles, moving on unbiased disordered correlated potentials, subjected to Gaussian white noise. We obtain an exact expression for the diffusion coefficient which allows us to prove that the unbiased diffusion of overdamped particles on a random polymer does not depend on the correlations of the disordered potentials. This universal behavior of the unbiased diffusivity is a direct consequence of the validity of the Einstein relation and the decay of correlations of the random polymer. We test the independence on correlations of the diffusion coefficient for correlated polymers produced by two different stochastic processes, a one-step Markov chain and the expansion-modification system. Within the accuracy of our simulations, we found that the numerically obtained diffusion coefficient for these systems agree with the analytically calculated ones, confirming our predictions. (paper)

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

Science.gov (United States)

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices.

Science.gov (United States)

Zhou, L; Lund, M S; Wang, Y; Su, G

2014-08-01

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G-matrices) across these two breeds. Single-trait genomic best linear unbiased prediction (GBLUP) model and two-trait GBLUP model were used for single-breed and two-breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two-breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two-breed predictions. Weighting the two-breed G-matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two-breed predictions. © 2014 Blackwell Verlag GmbH.

Advanced statistics: linear regression, part I: simple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Minimum variance linear unbiased estimators of loss and inventory

International Nuclear Information System (INIS)

Stewart, K.B.

1977-01-01

The article illustrates a number of approaches for estimating the material balance inventory and a constant loss amount from the accountability data from a sequence of accountability periods. The approaches all lead to linear estimates that have minimum variance. Techniques are shown whereby ordinary least squares, weighted least squares and generalized least squares computer programs can be used. Two approaches are recursive in nature and lend themselves to small specialized computer programs. Another approach is developed that is easy to program; could be used with a desk calculator and can be used in a recursive way from accountability period to accountability period. Some previous results are also reviewed that are very similar in approach to the present ones and vary only in the way net throughput measurements are statistically modeled. 5 refs

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

Science.gov (United States)

Mikulich-Gilbertson, Susan K; Wagner, Brandie D; Grunwald, Gary K; Riggs, Paula D; Zerbe, Gary O

2018-01-01

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes

Mutually Unbiased Maximally Entangled Bases for the Bipartite System Cd⊗ C^{dk}

Science.gov (United States)

Nan, Hua; Tao, Yuan-Hong; Wang, Tian-Jiao; Zhang, Jun

2016-10-01

The construction of maximally entangled bases for the bipartite system Cd⊗ Cd is discussed firstly, and some mutually unbiased bases with maximally entangled bases are given, where 2≤ d≤5. Moreover, we study a systematic way of constructing mutually unbiased maximally entangled bases for the bipartite system Cd⊗ C^{dk}.

Genomic prediction based on data from three layer lines: a comparison between linear methods

NARCIS (Netherlands)

Calus, M.P.L.; Huang, H.; Vereijken, J.; Visscher, J.; Napel, ten J.; Windig, J.J.

2014-01-01

Background The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. Methods Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we

Higher-dimensional orbital-angular-momentum-based quantum key distribution with mutually unbiased bases

CSIR Research Space (South Africa)

Mafu, M

2013-09-01

Full Text Available We present an experimental study of higher-dimensional quantum key distribution protocols based on mutually unbiased bases, implemented by means of photons carrying orbital angular momentum. We perform (d + 1) mutually unbiased measurements in a...

A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters

NARCIS (Netherlands)

Peng, Yijie; Fu, Michael C.; Hu, Jian Qiang; Heidergott, Bernd

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2)

Linear Estimation of Standard Deviation of Logistic Distribution ...

African Journals Online (AJOL)

The paper presents a theoretical method based on order statistics and a FORTRAN program for computing the variance and relative efficiencies of the standard deviation of the logistic population with respect to the Cramer-Rao lower variance bound and the best linear unbiased estimators (BLUE\\'s) when the mean is ...

Linear zonal atmospheric prediction for adaptive optics

Science.gov (United States)

McGuire, Patrick C.; Rhoadarmer, Troy A.; Coy, Hanna A.; Angel, J. Roger P.; Lloyd-Hart, Michael

2000-07-01

We compare linear zonal predictors of atmospheric turbulence for adaptive optics. Zonal prediction has the possible advantage of being able to interpret and utilize wind-velocity information from the wavefront sensor better than modal prediction. For simulated open-loop atmospheric data for a 2- meter 16-subaperture AO telescope with 5 millisecond prediction and a lookback of 4 slope-vectors, we find that Widrow-Hoff Delta-Rule training of linear nets and Back- Propagation training of non-linear multilayer neural networks is quite slow, getting stuck on plateaus or in local minima. Recursive Least Squares training of linear predictors is two orders of magnitude faster and it also converges to the solution with global minimum error. We have successfully implemented Amari's Adaptive Natural Gradient Learning (ANGL) technique for a linear zonal predictor, which premultiplies the Delta-Rule gradients with a matrix that orthogonalizes the parameter space and speeds up the training by two orders of magnitude, like the Recursive Least Squares predictor. This shows that the simple Widrow-Hoff Delta-Rule's slow convergence is not a fluke. In the case of bright guidestars, the ANGL, RLS, and standard matrix-inversion least-squares (MILS) algorithms all converge to the same global minimum linear total phase error (approximately 0.18 rad2), which is only approximately 5% higher than the spatial phase error (approximately 0.17 rad2), and is approximately 33% lower than the total 'naive' phase error without prediction (approximately 0.27 rad2). ANGL can, in principle, also be extended to make non-linear neural network training feasible for these large networks, with the potential to lower the predictor error below the linear predictor error. We will soon scale our linear work to the approximately 108-subaperture MMT AO system, both with simulations and real wavefront sensor data from prime focus.

Unbiased stereologic techniques for practical use in diagnostic histopathology

DEFF Research Database (Denmark)

Sørensen, Flemming Brandt

1995-01-01

by introducing quantitative techniques in the histopathologic discipline of malignancy grading. Unbiased stereologic methods, especially based on measurements of nuclear three-dimensional mean size, have during the last decade proved their value in this regard. In this survey, the methods are reviewed regarding......Grading of malignancy by the examination of morphologic and cytologic details in histologic sections from malignant neoplasms is based exclusively on qualitative features, associated with significant subjectivity, and thus rather poor reproducibility. The traditional way of malignancy grading may...... of solid tumors. This new, unbiased attitude to malignancy grading is associated with excellent virtues, which ultimately may help the clinician in the choice of optimal treatment of the individual patient suffering from cancer. Stereologic methods are not solely applicable to the field of malignancy...

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

Science.gov (United States)

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

KAUST Repository

Camilo, Daniela Castro; Lombardo, Luigi; Mai, Paul Martin; Dou, Jie; Huser, Raphaë l

2017-01-01

Grid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

KAUST Repository

Camilo, Daniela Castro

2017-08-30

Grid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.

An Efficient Test for Gene-Environment Interaction in Generalized Linear Mixed Models with Family Data.

Science.gov (United States)

Mazo Lopera, Mauricio A; Coombes, Brandon J; de Andrade, Mariza

2017-09-27

Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma ( PPARG ) gene associated with diabetes.

About mutually unbiased bases in even and odd prime power dimensions

Science.gov (United States)

Durt, Thomas

2005-06-01

Mutually unbiased bases generalize the X, Y and Z qubit bases. They possess numerous applications in quantum information science. It is well known that in prime power dimensions N = pm (with p prime and m a positive integer), there exists a maximal set of N + 1 mutually unbiased bases. In the present paper, we derive an explicit expression for those bases, in terms of the (operations of the) associated finite field (Galois division ring) of N elements. This expression is shown to be equivalent to the expressions previously obtained by Ivanovic (1981 J. Phys. A: Math. Gen. 14 3241) in odd prime dimensions, and Wootters and Fields (1989 Ann. Phys. 191 363) in odd prime power dimensions. In even prime power dimensions, we derive a new explicit expression for the mutually unbiased bases. The new ingredients of our approach are, basically, the following: we provide a simple expression of the generalized Pauli group in terms of the additive characters of the field, and we derive an exact groupal composition law between the elements of the commuting subsets of the generalized Pauli group, renormalized by a well-chosen phase-factor.

About mutually unbiased bases in even and odd prime power dimensions

International Nuclear Information System (INIS)

Durt, Thomas

2005-01-01

Mutually unbiased bases generalize the X, Y and Z qubit bases. They possess numerous applications in quantum information science. It is well known that in prime power dimensions N = p m (with p prime and m a positive integer), there exists a maximal set of N + 1 mutually unbiased bases. In the present paper, we derive an explicit expression for those bases, in terms of the (operations of the) associated finite field (Galois division ring) of N elements. This expression is shown to be equivalent to the expressions previously obtained by Ivanovic (1981 J. Phys. A: Math. Gen. 14 3241) in odd prime dimensions, and Wootters and Fields (1989 Ann. Phys. 191 363) in odd prime power dimensions. In even prime power dimensions, we derive a new explicit expression for the mutually unbiased bases. The new ingredients of our approach are, basically, the following: we provide a simple expression of the generalized Pauli group in terms of the additive characters of the field, and we derive an exact groupal composition law between the elements of the commuting subsets of the generalized Pauli group, renormalized by a well-chosen phase-factor

Accounting for misclassification in electronic health records-derived exposures using generalized linear finite mixture models.

Science.gov (United States)

Hubbard, Rebecca A; Johnson, Eric; Chubak, Jessica; Wernli, Karen J; Kamineni, Aruna; Bogart, Andy; Rutter, Carolyn M

2017-06-01

Exposures derived from electronic health records (EHR) may be misclassified, leading to biased estimates of their association with outcomes of interest. An example of this problem arises in the context of cancer screening where test indication, the purpose for which a test was performed, is often unavailable. This poses a challenge to understanding the effectiveness of screening tests because estimates of screening test effectiveness are biased if some diagnostic tests are misclassified as screening. Prediction models have been developed for a variety of exposure variables that can be derived from EHR, but no previous research has investigated appropriate methods for obtaining unbiased association estimates using these predicted probabilities. The full likelihood incorporating information on both the predicted probability of exposure-class membership and the association between the exposure and outcome of interest can be expressed using a finite mixture model. When the regression model of interest is a generalized linear model (GLM), the expectation-maximization algorithm can be used to estimate the parameters using standard software for GLMs. Using simulation studies, we compared the bias and efficiency of this mixture model approach to alternative approaches including multiple imputation and dichotomization of the predicted probabilities to create a proxy for the missing predictor. The mixture model was the only approach that was unbiased across all scenarios investigated. Finally, we explored the performance of these alternatives in a study of colorectal cancer screening with colonoscopy. These findings have broad applicability in studies using EHR data where gold-standard exposures are unavailable and prediction models have been developed for estimating proxies.

Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation

Directory of Open Access Journals (Sweden)

Sharad Damodar Gore

2009-10-01

Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.

Characterization of Radiation Hardened Bipolar Linear Devices for High Total Dose Missions

Science.gov (United States)

McClure, Steven S.; Harris, Richard D.; Rax, Bernard G.; Thorbourn, Dennis O.

2012-01-01

Radiation hardened linear devices are characterized for performance in combined total dose and displacement damage environments for a mission scenario with a high radiation level. Performance at low and high dose rate for both biased and unbiased conditions is compared and the impact to hardness assurance methodology is discussed.

Losing the rose tinted glasses: neural substrates of unbiased belief updating in depression

Directory of Open Access Journals (Sweden)

Neil eGarrett

2014-08-01

Full Text Available Recent evidence suggests that a state of good mental health is associated with biased processing of information that supports a positively skewed view of the future. Depression, on the other hand, is associated with unbiased processing of such information. Here, we use brain imaging in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals. Our results reveal that unbiased belief updating in depression is mediated by strong neural coding of estimation errors in response to both good news (in left inferior frontal gyrus and bilateral superior frontal gyrus and bad news (in right inferior parietal lobule and right inferior frontal gyrus regarding the future. In contrast, intact mental health was linked to a relatively attenuated neural coding of bad news about the future. These findings identify a neural substrate mediating the breakdown of biased updating in Major Depression Disorder, which may be essential for mental health.

Assimilating Non-linear Effects of Customized Large-Scale Climate Predictors on Downscaled Precipitation over the Tropical Andes

Science.gov (United States)

Molina, J. M.; Zaitchik, B. F.

2016-12-01

Recent findings considering high CO2 emission scenarios (RCP8.5) suggest that the tropical Andes may experience a massive warming and a significant precipitation increase (decrease) during the wet (dry) seasons by the end of the 21st century. Variations on rainfall-streamflow relationships and seasonal crop yields significantly affect human development in this region and make local communities highly vulnerable to climate change and variability. We developed an expert-informed empirical statistical downscaling (ESD) algorithm to explore and construct robust global climate predictors to perform skillful RCP8.5 projections of in-situ March-May (MAM) precipitation required for impact modeling and adaptation studies. We applied our framework to a topographically-complex region of the Colombian Andes where a number of previous studies have reported El Niño-Southern Oscillation (ENSO) as the main driver of climate variability. Supervised machine learning algorithms were trained with customized and bias-corrected predictors from NCEP reanalysis, and a cross-validation approach was implemented to assess both predictive skill and model selection. We found weak and not significant teleconnections between precipitation and lagged seasonal surface temperatures over El Niño3.4 domain, which suggests that ENSO fails to explain MAM rainfall variability in the study region. In contrast, series of Sea Level Pressure (SLP) over American Samoa -likely associated with the South Pacific Convergence Zone (SPCZ)- explains more than 65% of the precipitation variance. The best prediction skill was obtained with Selected Generalized Additive Models (SGAM) given their ability to capture linear/nonlinear relationships present in the data. While SPCZ-related series exhibited a positive linear effect in the rainfall response, SLP predictors in the north Atlantic and central equatorial Pacific showed nonlinear effects. A multimodel (MIROC, CanESM2 and CCSM) ensemble of ESD projections revealed

Truncated predictor feedback for time-delay systems

CERN Document Server

Zhou, Bin

2014-01-01

This book provides a systematic approach to the design of predictor based controllers for (time-varying) linear systems with either (time-varying) input or state delays. Differently from those traditional predictor based controllers, which are infinite-dimensional static feedback laws and may cause difficulties in their practical implementation, this book develops a truncated predictor feedback (TPF) which involves only finite dimensional static state feedback. Features and topics: A novel approach referred to as truncated predictor feedback for the stabilization of (time-varying) time-delay systems in both the continuous-time setting and the discrete-time setting is built systematically Semi-global and global stabilization problems of linear time-delay systems subject to either magnitude saturation or energy constraints are solved in a systematic manner Both stabilization of a single system and consensus of a group of systems (multi-agent systems) are treated in a unified manner by applying the truncated pre...

Statistics as Unbiased Estimators: Exploring the Teaching of Standard Deviation

Science.gov (United States)

Wasserman, Nicholas H.; Casey, Stephanie; Champion, Joe; Huey, Maryann

2017-01-01

This manuscript presents findings from a study about the knowledge for and planned teaching of standard deviation. We investigate how understanding variance as an unbiased (inferential) estimator--not just a descriptive statistic for the variation (spread) in data--is related to teachers' instruction regarding standard deviation, particularly…

Using small area estimation and Lidar-derived variables for multivariate prediction of forest attributes

Science.gov (United States)

F. Mauro; Vicente Monleon; H. Temesgen

2015-01-01

Small area estimation (SAE) techniques have been successfully applied in forest inventories to provide reliable estimates for domains where the sample size is small (i.e. small areas). Previous studies have explored the use of either Area Level or Unit Level Empirical Best Linear Unbiased Predictors (EBLUPs) in a univariate framework, modeling each variable of interest...

Building unbiased estimators from non-Gaussian likelihoods with application to shear estimation

International Nuclear Information System (INIS)

Madhavacheril, Mathew S.; Sehgal, Neelima; McDonald, Patrick; Slosar, Anže

2015-01-01

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the work of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong's estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g|=0.2

Exploratory regression analysis: a tool for selecting models and determining predictor importance.

Science.gov (United States)

Braun, Michael T; Oswald, Frederick L

2011-06-01

Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.

Prediction error variance and expected response to selection, when selection is based on the best predictor - for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits

DEFF Research Database (Denmark)

Andersen, Anders Holst; Korsgaard, Inge Riis; Jensen, Just

2002-01-01

In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed...... or random effects). In the different models, expressions are given (when these can be found - otherwise unbiased estimates are given) for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non...... Gaussian traits are generalisations of the well-known formulas for Gaussian traits - and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part...

Linear models with R

CERN Document Server

Faraway, Julian J

2014-01-01

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.New to the Second EditionReorganiz

Biased and unbiased perceptual decision-making on vocal emotions.

Science.gov (United States)

Dricu, Mihai; Ceravolo, Leonardo; Grandjean, Didier; Frühholz, Sascha

2017-11-24

Perceptual decision-making on emotions involves gathering sensory information about the affective state of another person and forming a decision on the likelihood of a particular state. These perceptual decisions can be of varying complexity as determined by different contexts. We used functional magnetic resonance imaging and a region of interest approach to investigate the brain activation and functional connectivity behind two forms of perceptual decision-making. More complex unbiased decisions on affective voices recruited an extended bilateral network consisting of the posterior inferior frontal cortex, the orbitofrontal cortex, the amygdala, and voice-sensitive areas in the auditory cortex. Less complex biased decisions on affective voices distinctly recruited the right mid inferior frontal cortex, pointing to a functional distinction in this region following decisional requirements. Furthermore, task-induced neural connectivity revealed stronger connections between these frontal, auditory, and limbic regions during unbiased relative to biased decision-making on affective voices. Together, the data shows that different types of perceptual decision-making on auditory emotions have distinct patterns of activations and functional coupling that follow the decisional strategies and cognitive mechanisms involved during these perceptual decisions.

Nonlinear vs. linear biasing in Trp-cage folding simulations

Energy Technology Data Exchange (ETDEWEB)

Spiwok, Vojtěch, E-mail: spiwokv@vscht.cz; Oborský, Pavel; Králová, Blanka [Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Technická 3, Prague 6 166 28 (Czech Republic); Pazúriková, Jana [Institute of Computer Science, Masaryk University, Botanická 554/68a, 602 00 Brno (Czech Republic); Křenek, Aleš [Institute of Computer Science, Masaryk University, Botanická 554/68a, 602 00 Brno (Czech Republic); Center CERIT-SC, Masaryk Univerzity, Šumavská 416/15, 602 00 Brno (Czech Republic)

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Improved linear least squares estimation using bounded data uncertainty

KAUST Repository

Ballal, Tarig

2015-04-01

This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.

Improved linear least squares estimation using bounded data uncertainty

KAUST Repository

Ballal, Tarig; Al-Naffouri, Tareq Y.

2015-01-01

This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.

PCX, Interior-Point Linear Programming Solver

International Nuclear Information System (INIS)

Czyzyk, J.

2004-01-01

1 - Description of program or function: PCX solves linear programming problems using the Mehrota predictor-corrector interior-point algorithm. PCX can be called as a subroutine or used in stand-alone mode, with data supplied from an MPS file. The software incorporates modules that can be used separately from the linear programming solver, including a pre-solve routine and data structure definitions. 2 - Methods: The Mehrota predictor-corrector method is a primal-dual interior-point method for linear programming. The starting point is determined from a modified least squares heuristic. Linear systems of equations are solved at each interior-point iteration via a sparse Cholesky algorithm native to the code. A pre-solver is incorporated in the code to eliminate inefficiencies in the user's formulation of the problem. 3 - Restriction on the complexity of the problem: There are no size limitations built into the program. The size of problem solved is limited by RAM and swap space on the user's computer

Quantum circuit implementation of cyclic mutually unbiased bases

Energy Technology Data Exchange (ETDEWEB)

Seyfarth, Ulrich; Dittmann, Niklas; Alber, Gernot [Institut fuer Angewandte Physik, Technische Universitaet Darmstadt, 64289 Darmstadt (Germany)

2013-07-01

Complete sets of mutually unbiased bases (MUBs) play an important role in the areas of quantum state tomography and quantum cryptography. Sets which can be generated cyclically may eliminate certain side-channel attacks. To profit from the advantages of these MUBs we propose a method for deriving a quantum circuit that implements the generator of a set into an experimental setup. For some dimensions this circuit is minimal. The presented method is in principle applicable for a larger set of operations and generalizes recently published results.

Characteristic properties of Fibonacci-based mutually unbiased bases

Energy Technology Data Exchange (ETDEWEB)

Seyfarth, Ulrich; Alber, Gernot [Institut fuer Angewandte Physik, Technische Universitaet Darmstadt, 64289 Darmstadt (Germany); Ranade, Kedar [Institut fuer Quantenphysik, Universitaet Ulm, Albert-Einstein-Allee 11, 89069 Ulm (Germany)

2012-07-01

Complete sets of mutually unbiased bases (MUBs) offer interesting applications in quantum information processing ranging from quantum cryptography to quantum state tomography. Different construction schemes provide different perspectives on these bases which are typically also deeply connected to various mathematical research areas. In this talk we discuss characteristic properties resulting from a recently established connection between construction methods for cyclic MUBs and Fibonacci polynomials. As a remarkable fact this connection leads to construction methods which do not involve any relations to mathematical properties of finite fields.

Use of generalized linear models and digital data in a forest inventory of Northern Utah

Science.gov (United States)

Moisen, Gretchen G.; Edwards, Thomas C.

1999-01-01

Forest inventories, like those conducted by the Forest Service's Forest Inventory and Analysis Program (FIA) in the Rocky Mountain Region, are under increased pressure to produce better information at reduced costs. Here we describe our efforts in Utah to merge satellite-based information with forest inventory data for the purposes of reducing the costs of estimates of forest population totals and providing spatial depiction of forest resources. We illustrate how generalized linear models can be used to construct approximately unbiased and efficient estimates of population totals while providing a mechanism for prediction in space for mapping of forest structure. We model forest type and timber volume of five tree species groups as functions of a variety of predictor variables in the northern Utah mountains. Predictor variables include elevation, aspect, slope, geographic coordinates, as well as vegetation cover types based on satellite data from both the Advanced Very High Resolution Radiometer (AVHRR) and Thematic Mapper (TM) platforms. We examine the relative precision of estimates of area by forest type and mean cubic-foot volumes under six different models, including the traditional double sampling for stratification strategy. Only very small gains in precision were realized through the use of expensive photointerpreted or TM-based data for stratification, while models based on topography and spatial coordinates alone were competitive. We also compare the predictive capability of the models through various map accuracy measures. The models including the TM-based vegetation performed best overall, while topography and spatial coordinates alone provided substantial information at very low cost.

Unbiased classification of spatial strategies in the Barnes maze.

Science.gov (United States)

Illouz, Tomer; Madar, Ravit; Clague, Charlotte; Griffioen, Kathleen J; Louzoun, Yoram; Okun, Eitan

2016-11-01

Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: 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.

Triangulation based inclusion probabilities: a design-unbiased sampling approach

OpenAIRE

Fehrmann, Lutz; Gregoire, Timothy; Kleinn, Christoph

2011-01-01

A probabilistic sampling approach for design-unbiased estimation of area-related quantitative characteristics of spatially dispersed population units is proposed. The developed field protocol includes a fixed number of 3 units per sampling location and is based on partial triangulations over their natural neighbors to derive the individual inclusion probabilities. The performance of the proposed design is tested in comparison to fixed area sample plots in a simulation with two forest stands. ...

Simple, efficient estimators of treatment effects in randomized trials using generalized linear models to leverage baseline variables.

Science.gov (United States)

Rosenblum, Michael; van der Laan, Mark J

2010-04-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation.

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

Science.gov (United States)

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

Encoding mutually unbiased bases in orbital angular momentum for quantum key distribution

CSIR Research Space (South Africa)

Dudley, Angela L

2013-07-01

Full Text Available We encode mutually unbiased bases (MUBs) using the higher-dimensional orbital angular momentum (OAM) degree of freedom associated with optical fields. We illustrate how these states are encoded with the use of a spatial light modulator (SLM). We...

Experimental studies of unbiased gluon jets from $e^{+}e^{-}$ annihilations using the jet boost algorithm

CERN Document Server

Abbiendi, G.; Akesson, P.F.; Alexander, G.; Allison, John; Amaral, P.; Anagnostou, G.; Anderson, K.J.; Arcelli, S.; Asai, S.; Axen, D.; Azuelos, G.; Bailey, I.; Barberio, E.; Barlow, R.J.; Batley, R.J.; Bechtle, P.; Behnke, T.; Bell, Kenneth Watson; Bell, P.J.; Bella, G.; Bellerive, A.; Benelli, G.; Bethke, S.; Biebel, O.; Boeriu, O.; Bock, P.; Boutemeur, M.; Braibant, S.; Brigliadori, L.; Brown, Robert M.; Buesser, K.; Burckhart, H.J.; Campana, S.; Carnegie, R.K.; Caron, B.; Carter, A.A.; Carter, J.R.; Chang, C.Y.; Charlton, David G.; Csilling, A.; Cuffiani, M.; Dado, S.; De Roeck, A.; De Wolf, E.A.; Desch, K.; Dienes, B.; Donkers, M.; Dubbert, J.; Duchovni, E.; Duckeck, G.; Duerdoth, I.P.; Etzion, E.; Fabbri, F.; Feld, L.; Ferrari, P.; Fiedler, F.; Fleck, I.; Ford, M.; Frey, A.; Furtjes, A.; Gagnon, P.; Gary, John William; Gaycken, G.; Geich-Gimbel, C.; Giacomelli, G.; Giacomelli, P.; Giunta, Marina; Goldberg, J.; Gross, E.; Grunhaus, J.; Gruwe, M.; Gunther, P.O.; Gupta, A.; Hajdu, C.; Hamann, M.; Hanson, G.G.; Harder, K.; Harel, A.; Harin-Dirac, M.; Hauschild, M.; Hawkes, C.M.; Hawkings, R.; Hemingway, R.J.; Hensel, C.; Herten, G.; Heuer, R.D.; Hill, J.C.; Hoffman, Kara Dion; Horvath, D.; Igo-Kemenes, P.; Ishii, K.; Jeremie, H.; Jovanovic, P.; Junk, T.R.; Kanaya, N.; Kanzaki, J.; Karapetian, G.; Karlen, D.; Kawagoe, K.; Kawamoto, T.; Keeler, R.K.; Kellogg, R.G.; Kennedy, B.W.; Kim, D.H.; Klein, K.; Klier, A.; Kluth, S.; Kobayashi, T.; Kobel, M.; Komamiya, S.; Kormos, Laura L.; Kramer, T.; Krieger, P.; von Krogh, J.; Kruger, K.; Kuhl, T.; Kupper, M.; Lafferty, G.D.; Landsman, H.; Lanske, D.; Layter, J.G.; Leins, A.; Lellouch, D.; Letts, J.; Levinson, L.; Lillich, J.; Lloyd, S.L.; Loebinger, F.K.; Lu, J.; Ludwig, J.; Macpherson, A.; Mader, W.; Marcellini, S.; Martin, A.J.; Masetti, G.; Mashimo, T.; Mattig, Peter; McDonald, W.J.; McKenna, J.; McMahon, T.J.; McPherson, R.A.; Meijers, F.; Menges, W.; Merritt, F.S.; Mes, H.; Michelini, A.; Mihara, S.; Mikenberg, G.; Miller, D.J.; Moed, S.; Mohr, W.; Mori, T.; Mutter, A.; Nagai, K.; Nakamura, I.; Nanjo, H.; Neal, H.A.; Nisius, R.; O'Neale, S.W.; Oh, A.; Okpara, A.; Oreglia, M.J.; Orito, S.; Pahl, C.; Pasztor, G.; Pater, J.R.; Patrick, G.N.; Pilcher, J.E.; Pinfold, J.; Plane, David E.; Poli, B.; Polok, J.; Pooth, O.; Przybycien, M.; Quadt, A.; Rabbertz, K.; Rembser, C.; Renkel, P.; Rick, H.; Roney, J.M.; Rosati, S.; Rozen, Y.; Runge, K.; Sachs, K.; Saeki, T.; Sarkisyan, E.K.G.; Schaile, A.D.; Schaile, O.; Scharff-Hansen, P.; Schieck, J.; Schoerner-Sadenius, Thomas; Schroder, Matthias; Schumacher, M.; Schwick, C.; Scott, W.G.; Seuster, R.; Shears, T.G.; Shen, B.C.; Sherwood, P.; Siroli, G.; Skuja, A.; Smith, A.M.; Sobie, R.; Soldner-Rembold, S.; Spano, F.; Stahl, A.; Stephens, K.; Strom, David M.; Strohmer, R.; Tarem, S.; Tasevsky, M.; Taylor, R.J.; Teuscher, R.; Thomson, M.A.; Torrence, E.; Toya, D.; Tran, P.; Trigger, I.; Trocsanyi, Z.; Tsur, E.; Turner-Watson, M.F.; Ueda, I.; Ujvari, B.; Vollmer, C.F.; Vannerem, P.; Vertesi, R.; Verzocchi, M.; Voss, H.; Vossebeld, J.; Waller, D.; Ward, C.P.; Ward, D.R.; Warsinsky, M.; Watkins, P.M.; Watson, A.T.; Watson, N.K.; Wells, P.S.; Wengler, T.; Wermes, N.; Wetterling, D.; Wilson, G.W.; Wilson, J.A.; Wolf, G.; Wyatt, T.R.; Yamashita, S.; Zer-Zion, D.; Zivkovic, Lidija

2004-01-01

We present the first experimental results based on the jet boost algorithm, a technique to select unbiased samples of gluon jets in e+e- annihilations, i.e. gluon jets free of biases introduced by event selection or jet finding criteria. Our results are derived from hadronic Z0 decays observed with the OPAL detector at the LEP e+e- collider at CERN. First, we test the boost algorithm through studies with Herwig Monte Carlo events and find that it provides accurate measurements of the charged particle multiplicity distributions of unbiased gluon jets for jet energies larger than about 5 GeV, and of the jet particle energy spectra (fragmentation functions) for jet energies larger than about 14 GeV. Second, we apply the boost algorithm to our data to derive unbiased measurements of the gluon jet multiplicity distribution for energies between about 5 and 18 GeV, and of the gluon jet fragmentation function at 14 and 18 GeV. In conjunction with our earlier results at 40 GeV, we then test QCD calculations for the en...

Simulation experiment on total ionization dose effects of linear CCD

International Nuclear Information System (INIS)

Tang Benqi; Zhang Yong; Xiao Zhigang; Wang Zujun; Huang Shaoyan

2004-01-01

We carry out the ionization radiation experiment of linear CCDs operated in unbiased, biased, biased and driven mode respectively by Co-60 γ source with our self-designed test system, and offline test the Dark signal and Saturation voltage and SNR varied with total dose for TCD132D, and get some valuable results. On the basis of above work, we set forth a primary experiment approaches to simulate the total dose radiation effects of charge coupled devices. (authors)

Unbiased water and methanol maser surveys of NGC 1333

Energy Technology Data Exchange (ETDEWEB)

Lyo, A-Ran; Kim, Jongsoo; Byun, Do-Young; Lee, Ho-Gyu, E-mail: arl@kasi.re.kr [Korea Astronomy and Space Science Institute, 776, Daedeokdae-ro Yuseong-gu, Daejeon 305-348 (Korea, Republic of)

2014-11-01

We present the results of unbiased 22 GHz H{sub 2}O water and 44 GHz class I CH{sub 3}OH methanol maser surveys in the central 7' × 10' area of NGC 1333 and two additional mapping observations of a 22 GHz water maser in a ∼3' × 3' area of the IRAS4A region. In the 22 GHz water maser survey of NGC 1333 with a sensitivity of σ ∼ 0.3 Jy, we confirmed the detection of masers toward H{sub 2}O(B) in the region of HH 7-11 and IRAS4B. We also detected new water masers located ∼20'' away in the western direction of IRAS4B or ∼25'' away in the southern direction of IRAS4A. We could not, however, find young stellar objects or molecular outflows associated with them. They showed two different velocity components of ∼0 and ∼16 km s{sup –1}, which are blue- and redshifted relative to the adopted systemic velocity of ∼7 km s{sup –1} for NGC 1333. They also showed time variabilities in both intensity and velocity from multi-epoch observations and an anti-correlation between the intensities of the blue- and redshifted velocity components. We suggest that the unidentified power source of these masers might be found in the earliest evolutionary stage of star formation, before the onset of molecular outflows. Finding this kind of water maser is only possible through an unbiased blind survey. In the 44 GHz methanol maser survey with a sensitivity of σ ∼ 0.5 Jy, we confirmed masers toward IRAS4A2 and the eastern shock region of IRAS2A. Both sources are also detected in 95 and 132 GHz methanol maser lines. In addition, we had new detections of methanol masers at 95 and 132 GHz toward IRAS4B. In terms of the isotropic luminosity, we detected methanol maser sources brighter than ∼5 × 10{sup 25} erg s{sup –1} from our unbiased survey.

Unbiased estimators for spatial distribution functions of classical fluids

Science.gov (United States)

Adib, Artur B.; Jarzynski, Christopher

2005-01-01

We use a statistical-mechanical identity closely related to the familiar virial theorem, to derive unbiased estimators for spatial distribution functions of classical fluids. In particular, we obtain estimators for both the fluid density ρ(r) in the vicinity of a fixed solute and the pair correlation g(r) of a homogeneous classical fluid. We illustrate the utility of our estimators with numerical examples, which reveal advantages over traditional histogram-based methods of computing such distributions.

Unbiased stereological methods used for the quantitative evaluation of guided bone regeneration

DEFF Research Database (Denmark)

Aaboe, Else Merete; Pinholt, E M; Schou, S

1998-01-01

The present study describes the use of unbiased stereological methods for the quantitative evaluation of the amount of regenerated bone. Using the principle of guided bone regeneration the amount of regenerated bone after placement of degradable or non-degradable membranes covering defects...

Mutually unbiased coarse-grained measurements of two or more phase-space variables

Science.gov (United States)

Paul, E. C.; Walborn, S. P.; Tasca, D. S.; Rudnicki, Łukasz

2018-05-01

Mutual unbiasedness of the eigenstates of phase-space operators—such as position and momentum, or their standard coarse-grained versions—exists only in the limiting case of infinite squeezing. In Phys. Rev. Lett. 120, 040403 (2018), 10.1103/PhysRevLett.120.040403, it was shown that mutual unbiasedness can be recovered for periodic coarse graining of these two operators. Here we investigate mutual unbiasedness of coarse-grained measurements for more than two phase-space variables. We show that mutual unbiasedness can be recovered between periodic coarse graining of any two nonparallel phase-space operators. We illustrate these results through optics experiments, using the fractional Fourier transform to prepare and measure mutually unbiased phase-space variables. The differences between two and three mutually unbiased measurements is discussed. Our results contribute to bridging the gap between continuous and discrete quantum mechanics, and they could be useful in quantum-information protocols.

Personalized recommendation based on unbiased consistence

Science.gov (United States)

Zhu, Xuzhen; Tian, Hui; Zhang, Ping; Hu, Zheng; Zhou, Tao

2015-08-01

Recently, in physical dynamics, mass-diffusion-based recommendation algorithms on bipartite network provide an efficient solution by automatically pushing possible relevant items to users according to their past preferences. However, traditional mass-diffusion-based algorithms just focus on unidirectional mass diffusion from objects having been collected to those which should be recommended, resulting in a biased causal similarity estimation and not-so-good performance. In this letter, we argue that in many cases, a user's interests are stable, and thus bidirectional mass diffusion abilities, no matter originated from objects having been collected or from those which should be recommended, should be consistently powerful, showing unbiased consistence. We further propose a consistence-based mass diffusion algorithm via bidirectional diffusion against biased causality, outperforming the state-of-the-art recommendation algorithms in disparate real data sets, including Netflix, MovieLens, Amazon and Rate Your Music.

Black-Box Search by Unbiased Variation

DEFF Research Database (Denmark)

Lehre, Per Kristian; Witt, Carsten

2012-01-01

The complexity theory for black-box algorithms, introduced by Droste, Jansen, and Wegener (Theory Comput. Syst. 39:525–544, 2006), describes common limits on the efficiency of a broad class of randomised search heuristics. There is an obvious trade-off between the generality of the black-box model...... and the strength of the bounds that can be proven in such a model. In particular, the original black-box model provides for well-known benchmark problems relatively small lower bounds, which seem unrealistic in certain cases and are typically not met by popular search heuristics.In this paper, we introduce a more...... restricted black-box model for optimisation of pseudo-Boolean functions which we claim captures the working principles of many randomised search heuristics including simulated annealing, evolutionary algorithms, randomised local search, and others. The key concept worked out is an unbiased variation operator...

Pyroelectric photovoltaic spatial solitons in unbiased photorefractive crystals

International Nuclear Information System (INIS)

Jiang, Qichang; Su, Yanli; Ji, Xuanmang

2012-01-01

A new type of spatial solitons i.e. pyroelectric photovoltaic spatial solitons based on the combination of pyroelectric and photovoltaic effect is predicted theoretically. It shows that bright, dark and grey spatial solitons can exist in unbiased photovoltaic photorefractive crystals with appreciable pyroelectric effect. Especially, the bright soliton can form in self-defocusing photovoltaic crystals if it gives larger self-focusing pyroelectric effect. -- Highlights: ► A new type of spatial soliton i.e. pyroelectric photovoltaic spatial soliton is predicted. ► The bright, dark and grey pyroelectric photovoltaic spatial soliton can form. ► The bright soliton can also exist in self-defocusing photovoltaic crystals.

On the mathematical foundations of mutually unbiased bases

Science.gov (United States)

Thas, Koen

2018-02-01

In order to describe a setting to handle Zauner's conjecture on mutually unbiased bases (MUBs) (stating that in C^d, a set of MUBs of the theoretical maximal size d + 1 exists only if d is a prime power), we pose some fundamental questions which naturally arise. Some of these questions have important consequences for the construction theory of (new) sets of maximal MUBs. Partial answers will be provided in particular cases; more specifically, we will analyze MUBs with associated operator groups that have nilpotence class 2, and consider MUBs of height 1. We will also confirm Zauner's conjecture for MUBs with associated finite nilpotent operator groups.

An asymptotically unbiased minimum density power divergence estimator for the Pareto-tail index

DEFF Research Database (Denmark)

Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle

2013-01-01

We introduce a robust and asymptotically unbiased estimator for the tail index of Pareto-type distributions. The estimator is obtained by fitting the extended Pareto distribution to the relative excesses over a high threshold with the minimum density power divergence criterion. Consistency...

Optimal Trading with Alpha Predictors

OpenAIRE

Filippo Passerini; Samuel E. Vazquez

2015-01-01

We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-trading zone when using market orders, and a corresponding market-making zone when using limit orders. We show that, when combining both market and limit orders, the problem is furthe...

Estimation of group means when adjusting for covariates in generalized linear models.

Science.gov (United States)

Qu, Yongming; Luo, Junxiang

2015-01-01

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

Circulating tumor cell detection: A direct comparison between negative and unbiased enrichment in lung cancer.

Science.gov (United States)

Xu, Yan; Liu, Biao; Ding, Fengan; Zhou, Xiaodie; Tu, Pin; Yu, Bo; He, Yan; Huang, Peilin

2017-06-01

Circulating tumor cells (CTCs), isolated as a 'liquid biopsy', may provide important diagnostic and prognostic information. Therefore, rapid, reliable and unbiased detection of CTCs are required for routine clinical analyses. It was demonstrated that negative enrichment, an epithelial marker-independent technique for isolating CTCs, exhibits a better efficiency in the detection of CTCs compared with positive enrichment techniques that only use specific anti-epithelial cell adhesion molecules. However, negative enrichment techniques incur significant cell loss during the isolation procedure, and as it is a method that uses only one type of antibody, it is inherently biased. The detection procedure and identification of cell types also relies on skilled and experienced technicians. In the present study, the detection sensitivity of using negative enrichment and a previously described unbiased detection method was compared. The results revealed that unbiased detection methods may efficiently detect >90% of cancer cells in blood samples containing CTCs. By contrast, only 40-60% of CTCs were detected by negative enrichment. Additionally, CTCs were identified in >65% of patients with stage I/II lung cancer. This simple yet efficient approach may achieve a high level of sensitivity. It demonstrates a potential for the large-scale clinical implementation of CTC-based diagnostic and prognostic strategies.

Correlation and simple linear regression.

Science.gov (United States)

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Perceived Resources as a Predictor of Satisfaction with Food-Related Life among Chilean Elderly: An Approach with Generalized Linear Models.

Science.gov (United States)

Lobos, G; Schnettler, B; Grunert, K G; Adasme, C

2017-01-01

The main objective of this study is to show why perceived resources are a strong predictor of satisfaction with food-related life in Chilean older adults. Design, sampling and participants: A survey was conducted in rural and urban areas in 30 communes of the Maule Region with 785 participants over 60 years of age who live in their own homes. The Satisfaction with Food-related Life (SWFL) scale was used. Generalized linear models (GLM) were used for the regression analysis. The results led to different considerations: First, older adults' perceived levels of resources are a good reflection of their actual levels of resources. Second, the individuals rated the sum of the perceived resources as 'highly important' to explain older adults' satisfaction with food-related life. Third, SWFL was predicted by satisfaction with economic situation, family importance, quantity of domestic household goods and a relative health indicator. Fourth, older adults who believe they have more resources compared to others are more satisfied with their food-related life. Finally, Poisson and binomial logistic models showed that the sum of perceived resources significantly increased the prediction of SWFL. The main conclusion is that perceived personal resources are a strong predictor of SWFL in Chilean older adults.

Predictors of Relationship Power among Drug-involved Women

OpenAIRE

Campbell, Aimee N. C.; Tross, Susan; Hu, Mei-chen; Pavlicova, Martina; Nunes, Edward V.

2012-01-01

Gender-based relationship power is frequently linked to women’s capacity to reduce sexual risk behaviors. This study offers an exploration of predictors of relationship power, as measured by the multidimensional and theoretically grounded Sexual Relationship Power Scale (SRPS), among women in outpatient substance abuse treatment. Linear models were used to test nine predictors (age, race/ethnicity, education, time in treatment, economic dependence, substance use, sexual concurrency, partner a...

Collision energy alteration during mass spectrometric acquisition is essential to ensure unbiased metabolomic analysis

CSIR Research Space (South Africa)

Madala, NE

2012-08-01

Full Text Available Metabolomics entails identification and quantification of all metabolites within a biological system with a given physiological status; as such, it should be unbiased. A variety of techniques are used to measure the metabolite content of living...

A Comparison of Alternative Estimators of Linearly Aggregated Macro Models

Directory of Open Access Journals (Sweden)

Fikri Akdeniz

2012-07-01

Full Text Available Normal 0 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman","serif"; mso-ansi-language:TR; mso-fareast-language:TR;} This paper deals with the linear aggregation problem. For the true underlying micro relations, which explain the micro behavior of the individuals, no restrictive rank conditions are assumed. Thus the analysis is presented in a framework utilizing generalized inverses of singular matrices. We investigate several estimators for certain linear transformations of the systematic part of the corresponding macro relations. Homogeneity of micro parameters is discussed. Best linear unbiased estimation for micro parameters is described.

Reconstruction of high-dimensional states entangled in orbital angular momentum using mutually unbiased measurements

CSIR Research Space (South Africa)

Giovannini, D

2013-06-01

Full Text Available : QELS_Fundamental Science, San Jose, California United States, 9-14 June 2013 Reconstruction of High-Dimensional States Entangled in Orbital Angular Momentum Using Mutually Unbiased Measurements D. Giovannini1, ⇤, J. Romero1, 2, J. Leach3, A...

Application of Singh et al., unbiased estimator in a dual to ratio-cum ...

African Journals Online (AJOL)

This paper applied an unbiased estimator in a dual to ratio–cum-product estimator in sample surveys to double sampling design. Its efficiency over the conventional biased double sampling design estimator was determined based on the conditions attached to its supremacy. Three different data sets were used to testify to ...

Linear regression and the normality assumption.

Science.gov (United States)

Schmidt, Amand F; Finan, Chris

2017-12-16

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs.

Science.gov (United States)

Xia, Jie; Jin, Hongwei; Liu, Zhenming; Zhang, Liangren; Wang, Xiang Simon

2014-05-27

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the "artificial enrichment" and "analogue bias" of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD.

Automated and unbiased classification of chemical profiles from fungi using high performance liquid chromatography

DEFF Research Database (Denmark)

Hansen, Michael Edberg; Andersen, Birgitte; Smedsgaard, Jørn

2005-01-01

In this paper we present a method for unbiased/unsupervised classification and identification of closely related fungi, using chemical analysis of secondary metabolite profiles created by HPLC with UV diode array detection. For two chromatographic data matrices a vector of locally aligned full sp...

An Unbiased Distance-based Outlier Detection Approach for High-dimensional Data

DEFF Research Database (Denmark)

Nguyen, Hoang Vu; Gopalkrishnan, Vivekanand; Assent, Ira

2011-01-01

than a global property. Different from existing approaches, it is not grid-based and dimensionality unbiased. Thus, its performance is impervious to grid resolution as well as the curse of dimensionality. In addition, our approach ranks the outliers, allowing users to select the number of desired...... outliers, thus mitigating the issue of high false alarm rate. Extensive empirical studies on real datasets show that our approach efficiently and effectively detects outliers, even in high-dimensional spaces....

Monofunctional stealth nanoparticle for unbiased single molecule tracking inside living cells.

Science.gov (United States)

Lisse, Domenik; Richter, Christian P; Drees, Christoph; Birkholz, Oliver; You, Changjiang; Rampazzo, Enrico; Piehler, Jacob

2014-01-01

On the basis of a protein cage scaffold, we have systematically explored intracellular application of nanoparticles for single molecule studies and discovered that recognition by the autophagy machinery plays a key role for rapid metabolism in the cytosol. Intracellular stealth nanoparticles were achieved by heavy surface PEGylation. By combination with a generic approach for nanoparticle monofunctionalization, efficient labeling of intracellular proteins with high fidelity was accomplished, allowing unbiased long-term tracking of proteins in the outer mitochondrial membrane.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

Science.gov (United States)

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

Critical point relascope sampling for unbiased volume estimation of downed coarse woody debris

Science.gov (United States)

Jeffrey H. Gove; Michael S. Williams; Mark J. Ducey; Mark J. Ducey

2005-01-01

Critical point relascope sampling is developed and shown to be design-unbiased for the estimation of log volume when used with point relascope sampling for downed coarse woody debris. The method is closely related to critical height sampling for standing trees when trees are first sampled with a wedge prism. Three alternative protocols for determining the critical...

Predictor feedback for delay systems implementations and approximations

CERN Document Server

Karafyllis, Iasson

2017-01-01

This monograph bridges the gap between the nonlinear predictor as a concept and as a practical tool, presenting a complete theory of the application of predictor feedback to time-invariant, uncertain systems with constant input delays and/or measurement delays. It supplies several methods for generating the necessary real-time solutions to the systems’ nonlinear differential equations, which the authors refer to as approximate predictors. Predictor feedback for linear time-invariant (LTI) systems is presented in Part I to provide a solid foundation on the necessary concepts, as LTI systems pose fewer technical difficulties than nonlinear systems. Part II extends all of the concepts to nonlinear time-invariant systems. Finally, Part III explores extensions of predictor feedback to systems described by integral delay equations and to discrete-time systems. The book’s core is the design of control and observer algorithms with which global stabilization, guaranteed in the previous literature with idealized (b...

Rethinking economy-wide rebound measures: An unbiased proposal

International Nuclear Information System (INIS)

Guerra, Ana-Isabel; Sancho, Ferran

2010-01-01

In spite of having been first introduced in the last half of the ninetieth century, the debate about the possible rebound effects from energy efficiency improvements is still an open question in the economic literature. This paper contributes to the existing research on this issue proposing an unbiased measure for economy-wide rebound effects. The novelty of this economy-wide rebound measure stems from the fact that not only actual energy savings but also potential energy savings are quantified under general equilibrium conditions. Our findings indicate that the use of engineering savings instead of general equilibrium potential savings downward biases economy-wide rebound effects and upward-biases backfire effects. The discrepancies between the traditional indicator and our proposed measure are analysed in the context of the Spanish economy.

Psychosocial predictors of energy underreporting in a large doubly labeled water study.

Science.gov (United States)

Tooze, Janet A; Subar, Amy F; Thompson, Frances E; Troiano, Richard; Schatzkin, Arthur; Kipnis, Victor

2004-05-01

Underreporting of energy intake is associated with self-reported diet measures and appears to be selective according to personal characteristics. Doubly labeled water is an unbiased reference biomarker for energy intake that may be used to assess underreporting. Our objective was to determine which factors are associated with underreporting of energy intake on food-frequency questionnaires (FFQs) and 24-h dietary recalls (24HRs). The study participants were 484 men and women aged 40-69 y who resided in Montgomery County, MD. Using the doubly labeled water method to measure total energy expenditure, we considered numerous psychosocial, lifestyle, and sociodemographic factors in multiple logistic regression models for prediction of the probability of underreporting on the FFQ and 24HR. In the FFQ models, fear of negative evaluation, weight-loss history, and percentage of energy from fat were the best predictors of underreporting in women (R(2) = 0.09); body mass index, comparison of activity level with that of others of the same sex and age, and eating frequency were the best predictors in men (R(2) = 0.10). In the 24HR models, social desirability, fear of negative evaluation, body mass index, percentage of energy from fat, usual activity, and variability in number of meals per day were the best predictors of underreporting in women (R(2) = 0.22); social desirability, dietary restraint, body mass index, eating frequency, dieting history, and education were the best predictors in men (R(2) = 0.25). Although the final models were significantly related to underreporting on both the FFQ and the 24HR, the amount of variation explained by these models was relatively low, especially for the FFQ.

Prediction error variance and expected response to selection, when selection is based on the best predictor – for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits

Directory of Open Access Journals (Sweden)

Jensen Just

2002-05-01

Full Text Available Abstract In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed or random effects. In the different models, expressions are given (when these can be found – otherwise unbiased estimates are given for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non Gaussian traits are generalisations of the well-known formulas for Gaussian traits – and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part of the model (heritability on the normally distributed level of the model or a generalised version of heritability plays a central role in these formulas.

An Unbiased Survey of 500 Nearby Stars for Debris Disks: A JCMT Legacy Program

NARCIS (Netherlands)

Matthews, B.C.; Greaves, J.S.; Holland, W.S.; Wyatt, M.C.; Barlow, M.J.; Bastien, P.; Beichman, C.A.; Biggs, A.; Butner, H.M.; Dent, W.R.F.; Francesco, J. Di; Dominik, C.; Fissel, L.; Friberg, P.; Gibb, A.G.; Halpern, M.; Ivison, R.J.; Jayawardhana, R.; Jenness, T.; Johnstone, D.; Kavelaars, J.J.; Marshall, J.L.; Phillips, N.; Schieven, G.; Snellen, I.A.G.; Walker, H.J.; Ward-Thompson, D.; Weferling, B.; White, G.J.; Yates, J.; Zhu, M.; Craigon, A.

2007-01-01

We present the scientific motivation and observing plan for an upcoming detection survey for debris disks using the James Clerk Maxwell Telescope. The SCUBA-2 Unbiased Nearby Stars (SUNS) survey will observe 500 nearby main-sequence and subgiant stars (100 of each of the A, F, G, K, and M spectral

Automated and unbiased image analyses as tools in phenotypic classification of small-spored Alternaria species

DEFF Research Database (Denmark)

Andersen, Birgitte; Hansen, Michael Edberg; Smedsgaard, Jørn

2005-01-01

often has been broadly applied to various morphologically and chemically distinct groups of isolates from different hosts. The purpose of this study was to develop and evaluate automated and unbiased image analysis systems that will analyze different phenotypic characters and facilitate testing...

Unbiased multi-fidelity estimate of failure probability of a free plane jet

Science.gov (United States)

Marques, Alexandre; Kramer, Boris; Willcox, Karen; Peherstorfer, Benjamin

2017-11-01

Estimating failure probability related to fluid flows is a challenge because it requires a large number of evaluations of expensive models. We address this challenge by leveraging multiple low fidelity models of the flow dynamics to create an optimal unbiased estimator. In particular, we investigate the effects of uncertain inlet conditions in the width of a free plane jet. We classify a condition as failure when the corresponding jet width is below a small threshold, such that failure is a rare event (failure probability is smaller than 0.001). We estimate failure probability by combining the frameworks of multi-fidelity importance sampling and optimal fusion of estimators. Multi-fidelity importance sampling uses a low fidelity model to explore the parameter space and create a biasing distribution. An unbiased estimate is then computed with a relatively small number of evaluations of the high fidelity model. In the presence of multiple low fidelity models, this framework offers multiple competing estimators. Optimal fusion combines all competing estimators into a single estimator with minimal variance. We show that this combined framework can significantly reduce the cost of estimating failure probabilities, and thus can have a large impact in fluid flow applications. This work was funded by DARPA.

Correlations and Non-Linear Probability Models

DEFF Research Database (Denmark)

Breen, Richard; Holm, Anders; Karlson, Kristian Bernt

2014-01-01

the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models....

Comparing linear probability model coefficients across groups

DEFF Research Database (Denmark)

Holm, Anders; Ejrnæs, Mette; Karlson, Kristian Bernt

2015-01-01

of the following three components: outcome truncation, scale parameters and distributional shape of the predictor variable. These results point to limitations in using linear probability model coefficients for group comparisons. We also provide Monte Carlo simulations and real examples to illustrate......This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more...... these limitations, and we suggest a restricted approach to using linear probability model coefficients in group comparisons....

Aldehyde-Selective Wacker-Type Oxidation of Unbiased Alkenes Enabled by a Nitrite Co-Catalyst

KAUST Repository

Wickens, Zachary K.; Morandi, Bill; Grubbs, Robert H.

2013-01-01

Breaking the rules: Reversal of the high Markovnikov selectivity of Wacker-type oxidations was accomplished using a nitrite co-catalyst. Unbiased aliphatic alkenes can be oxidized with high yield and aldehyde selectivity, and several functional groups are tolerated. 18O-labeling experiments indicate that the aldehydic O atom is derived from the nitrite salt.

Aldehyde-Selective Wacker-Type Oxidation of Unbiased Alkenes Enabled by a Nitrite Co-Catalyst

KAUST Repository

Wickens, Zachary K.

2013-09-13

Breaking the rules: Reversal of the high Markovnikov selectivity of Wacker-type oxidations was accomplished using a nitrite co-catalyst. Unbiased aliphatic alkenes can be oxidized with high yield and aldehyde selectivity, and several functional groups are tolerated. 18O-labeling experiments indicate that the aldehydic O atom is derived from the nitrite salt.

An unbiased stereological method for efficiently quantifying the innervation of the heart and other organs based on total length estimations

DEFF Research Database (Denmark)

Mühlfeld, Christian; Papadakis, Tamara; Krasteva, Gabriela

2010-01-01

Quantitative information about the innervation is essential to analyze the structure-function relationships of organs. So far, there has been no unbiased stereological tool for this purpose. This study presents a new unbiased and efficient method to quantify the total length of axons in a given...... reference volume, illustrated on the left ventricle of the mouse heart. The method is based on the following steps: 1) estimation of the reference volume; 2) randomization of location and orientation using appropriate sampling techniques; 3) counting of nerve fiber profiles hit by a defined test area within...

Optimization of piezoelectric cantilever energy harvesters including non-linear effects

International Nuclear Information System (INIS)

Patel, R; McWilliam, S; Popov, A A

2014-01-01

This paper proposes a versatile non-linear model for predicting piezoelectric energy harvester performance. The presented model includes (i) material non-linearity, for both substrate and piezoelectric layers, and (ii) geometric non-linearity incorporated by assuming inextensibility and accurately representing beam curvature. The addition of a sub-model, which utilizes the transfer matrix method to predict eigenfrequencies and eigenvectors for segmented beams, allows for accurate optimization of piezoelectric layer coverage. A validation of the overall theoretical model is performed through experimental testing on both uniform and non-uniform samples manufactured in-house. For the harvester composition used in this work, the magnitude of material non-linearity exhibited by the piezoelectric layer is 35 times greater than that of the substrate layer. It is also observed that material non-linearity, responsible for reductions in resonant frequency with increases in base acceleration, is dominant over geometric non-linearity for standard piezoelectric harvesting devices. Finally, over the tested range, energy loss due to damping is found to increase in a quasi-linear fashion with base acceleration. During an optimization study on piezoelectric layer coverage, results from the developed model were compared with those from a linear model. Unbiased comparisons between harvesters were realized by using devices with identical natural frequencies—created by adjusting the device substrate thickness. Results from three studies, each with a different assumption on mechanical damping variations, are presented. Findings showed that, depending on damping variation, a non-linear model is essential for such optimization studies with each model predicting vastly differing optimum configurations. (paper)

Unbiased stereologic techniques for practical use in diagnostic histopathology

DEFF Research Database (Denmark)

Sørensen, Flemming Brandt

1995-01-01

Grading of malignancy by the examination of morphologic and cytologic details in histologic sections from malignant neoplasms is based exclusively on qualitative features, associated with significant subjectivity, and thus rather poor reproducibility. The traditional way of malignancy grading may...... by introducing quantitative techniques in the histopathologic discipline of malignancy grading. Unbiased stereologic methods, especially based on measurements of nuclear three-dimensional mean size, have during the last decade proved their value in this regard. In this survey, the methods are reviewed regarding...... the basic technique involved, sampling, efficiency, and reproducibility. Various types of cancers, where stereologic grading of malignancy has been used, are reviewed and discussed with regard to the development of a new objective and reproducible basis for carrying out prognosis-related malignancy grading...

An Example of an Improvable Rao-Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator.

Science.gov (United States)

Galili, Tal; Meilijson, Isaac

2016-01-02

The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].

Predictors of relationship power among drug-involved women.

Science.gov (United States)

Campbell, Aimee N C; Tross, Susan; Hu, Mei-chen; Pavlicova, Martina; Nunes, Edward V

2012-08-01

Gender-based relationship power is frequently linked to women's capacity to reduce sexual risk behaviors. This study offers an exploration of predictors of relationship power, as measured by the multidimensional and theoretically grounded sexual relationship power scale, among women in outpatient substance abuse treatment. Linear models were used to test nine predictors (age, race/ethnicity, education, time in treatment, economic dependence, substance use, sexual concurrency, partner abuse, and sex role orientation) of relationship power among 513 women participating in a multi-site HIV risk reduction intervention study. Significant predictors of relationship control included having a non-abusive male partner, only one male partner, and endorsing traditional masculine (or both masculine and feminine) sex role attributes. Predictors of decision-making dominance were interrelated, with substance use × partner abuse and age × sex role orientation interactions. Results contribute to the understanding of factors which may influence relationship power and to their potential role in HIV sexual risk reduction interventions.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

Science.gov (United States)

Meng, Yilin; Roux, Benoît

2015-08-11

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

An Unbiased Unscented Transform Based Kalman Filter for 3D Radar

Institute of Scientific and Technical Information of China (English)

WANGGuohong; XIUJianjuan; HEYou

2004-01-01

As a derivative-free alternative to the Extended Kalman filter (EKF) in the framework of state estimation, the Unscented Kalman filter (UKF) has potential applications in nonlinear filtering. By noting the fact that the unscented transform is generally biased when converting the radar measurements from spherical coordinates into Cartesian coordinates, a new filtering algorithm for 3D radar, called Unbiased unscented Kalman filter (UUKF), is proposed. The new algorithm is validated by Monte Carlo simulation runs. Simulation results show that the UUKF is more effective than the UKF, EKF and the Converted measurement Kalman filter (CMKF).

Multiple Imputation of Predictor Variables Using Generalized Additive Models

NARCIS (Netherlands)

de Jong, Roel; van Buuren, Stef; Spiess, Martin

2016-01-01

The sensitivity of multiple imputation methods to deviations from their distributional assumptions is investigated using simulations, where the parameters of scientific interest are the coefficients of a linear regression model, and values in predictor variables are missing at random. The

Unbiased group-wise image registration: applications in brain fiber tract atlas construction and functional connectivity analysis.

Science.gov (United States)

Geng, Xiujuan; Gu, Hong; Shin, Wanyong; Ross, Thomas J; Yang, Yihong

2011-10-01

We propose an unbiased implicit-reference group-wise (IRG) image registration method and demonstrate its applications in the construction of a brain white matter fiber tract atlas and the analysis of resting-state functional MRI (fMRI) connectivity. Most image registration techniques pair-wise align images to a selected reference image and group analyses are performed in the reference space, which may produce bias. The proposed method jointly estimates transformations, with an elastic deformation model, registering all images to an implicit reference corresponding to the group average. The unbiased registration is applied to build a fiber tract atlas by registering a group of diffusion tensor images. Compared to reference-based registration, the IRG registration improves the fiber track overlap within the group. After applying the method in the fMRI connectivity analysis, results suggest a general improvement in functional connectivity maps at a group level in terms of larger cluster size and higher average t-scores.

Age is no barrier: predictors of academic success in older learners

Science.gov (United States)

Imlach, Abbie-Rose; Ward, David D.; Stuart, Kimberley E.; Summers, Mathew J.; Valenzuela, Michael J.; King, Anna E.; Saunders, Nichole L.; Summers, Jeffrey; Srikanth, Velandai K.; Robinson, Andrew; Vickers, James C.

2017-11-01

Although predictors of academic success have been identified in young adults, such predictors are unlikely to translate directly to an older student population, where such information is scarce. The current study aimed to examine cognitive, psychosocial, lifetime, and genetic predictors of university-level academic performance in older adults (50-79 years old). Participants were mostly female (71%) and had a greater than high school education level (M = 14.06 years, SD = 2.76), on average. Two multiple linear regression analyses were conducted. The first examined all potential predictors of grade point average (GPA) in the subset of participants who had volunteered samples for genetic analysis (N = 181). Significant predictors of GPA were then re-examined in a second multiple linear regression using the full sample (N = 329). Our data show that the cognitive domains of episodic memory and language processing, in conjunction with midlife engagement in cognitively stimulating activities, have a role in predicting academic performance as measured by GPA in the first year of study. In contrast, it was determined that age, IQ, gender, working memory, psychosocial factors, and common brain gene polymorphisms linked to brain function, plasticity and degeneration (APOE, BDNF, COMT, KIBRA, SERT) did not influence academic performance. These findings demonstrate that ageing does not impede academic achievement, and that discrete cognitive skills as well as lifetime engagement in cognitively stimulating activities can promote academic success in older adults.

Identifying predictors of physics item difficulty: A linear regression approach

Science.gov (United States)

Mesic, Vanes; Muratovic, Hasnija

2011-06-01

Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary difficult and very easy items. Knowing the factors that influence physics item difficulty makes it possible to model the item difficulty even before the first pilot study is conducted. Thus, by identifying predictors of physics item difficulty, we can improve the test-design process. Furthermore, we get additional qualitative feedback regarding the basic aspects of student cognitive achievement in physics that are directly responsible for the obtained, quantitative test results. In this study, we conducted a secondary analysis of data that came from two large-scale assessments of student physics achievement at the end of compulsory education in Bosnia and Herzegovina. Foremost, we explored the concept of “physics competence” and performed a content analysis of 123 physics items that were included within the above-mentioned assessments. Thereafter, an item database was created. Items were described by variables which reflect some basic cognitive aspects of physics competence. For each of the assessments, Rasch item difficulties were calculated in separate analyses. In order to make the item difficulties from different assessments comparable, a virtual test equating procedure had to be implemented. Finally, a regression model of physics item difficulty was created. It has been shown that 61.2% of item difficulty variance can be explained by factors which reflect the automaticity, complexity, and modality of the knowledge structure that is relevant for generating the most probable correct solution, as well as by the divergence of required thinking and interference effects between intuitive and formal physics knowledge

Identifying predictors of physics item difficulty: A linear regression approach

Directory of Open Access Journals (Sweden)

Hasnija Muratovic

2011-06-01

Full Text Available Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary difficult and very easy items. Knowing the factors that influence physics item difficulty makes it possible to model the item difficulty even before the first pilot study is conducted. Thus, by identifying predictors of physics item difficulty, we can improve the test-design process. Furthermore, we get additional qualitative feedback regarding the basic aspects of student cognitive achievement in physics that are directly responsible for the obtained, quantitative test results. In this study, we conducted a secondary analysis of data that came from two large-scale assessments of student physics achievement at the end of compulsory education in Bosnia and Herzegovina. Foremost, we explored the concept of “physics competence” and performed a content analysis of 123 physics items that were included within the above-mentioned assessments. Thereafter, an item database was created. Items were described by variables which reflect some basic cognitive aspects of physics competence. For each of the assessments, Rasch item difficulties were calculated in separate analyses. In order to make the item difficulties from different assessments comparable, a virtual test equating procedure had to be implemented. Finally, a regression model of physics item difficulty was created. It has been shown that 61.2% of item difficulty variance can be explained by factors which reflect the automaticity, complexity, and modality of the knowledge structure that is relevant for generating the most probable correct solution, as well as by the divergence of required thinking and interference effects between intuitive and formal

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

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Christophe Coupé

2018-04-01

Full Text Available As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM, which address grouping of observations, and generalized linear mixed-effects models (GLMM, which offer a family of distributions for the dependent variable. Generalized additive models (GAM are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS. We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

Science.gov (United States)

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we

Gender and distance influence performance predictors in young swimmers

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Paulo Victor Mezzaroba

2013-12-01

Full Text Available Predictors of performance in adult swimmers are constantly changing during youth especially because the training routine begins even before puberty in the modality. Therefore this study aimed to determine the group of parameters that best predict short and middle swimming distance performances of young swimmers of both genders. Thirty-three 10-to 16-years-old male and female competitive swimmers participated in the study. Multiple linear regression (MLR was used considering mean speed of maximum 100, 200 and 400 m efforts as dependent variables, and five parameters groups as possible predictors (anthropometry, body composition, physiological and biomechanical parameters, chronological age/pubic hair. The main results revealed explanatory powers of almost 100% for both genders and all performances, but with different predictors entered in MLR models of each parameter group or all variables. Thus, there are considerable differences in short and middle swimming distance, and males and females predictors that should be considered in training programs.

Predictors of transformational leadership of nurse managers.

Science.gov (United States)

Echevarria, Ilia M; Patterson, Barbara J; Krouse, Anne

2017-04-01

The aim of this study was to examine the relationships among education, leadership experience, emotional intelligence and transformational leadership of nurse managers. Nursing leadership research provides limited evidence of predictors of transformational leadership style in nurse managers. A predictive correlational design was used with a sample of nurse managers (n = 148) working in varied health care settings. Data were collected using the Genos Emotional Intelligence Inventory, the Multi-factor Leadership Questionnaire and a demographic questionnaire. Simple linear and multiple regression analyses were used to examine relationships. A statistically significant relationship was found between emotional intelligence and transformational leadership (r = 0.59, P transformational leadership. Nurse managers should be well informed of the predictors of transformational leadership in order to pursue continuing education and development opportunities related to those predictors. The results of this study emphasise the need for emotional intelligence continuing education, leadership development and leader assessment programmes. © 2016 John Wiley & Sons Ltd.

Post-processing through linear regression

Science.gov (United States)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Unbiased minimum variance estimator of a matrix exponential function. Application to Boltzmann/Bateman coupled equations solving

International Nuclear Information System (INIS)

Dumonteil, E.; Diop, C. M.

2009-01-01

This paper derives an unbiased minimum variance estimator (UMVE) of a matrix exponential function of a normal wean. The result is then used to propose a reference scheme to solve Boltzmann/Bateman coupled equations, thanks to Monte Carlo transport codes. The last section will present numerical results on a simple example. (authors)

Unbiased determination of polarized parton distributions and their uncertainties

CERN Document Server

Ball, Richard D.; Guffanti, Alberto; Nocera, Emanuele R.; Ridolfi, Giovanni; Rojo, Juan

2013-01-01

We present a determination of a set of polarized parton distributions (PDFs) of the nucleon, at next-to-leading order, from a global set of longitudinally polarized deep-inelastic scattering data: NNPDFpol1.0. The determination is based on the NNPDF methodology: a Monte Carlo approach, with neural networks used as unbiased interpolants, previously applied to the determination of unpolarized parton distributions, and designed to provide a faithful and statistically sound representation of PDF uncertainties. We present our dataset, its statistical features, and its Monte Carlo representation. We summarize the technique used to solve the polarized evolution equations and its benchmarking, and the method used to compute physical observables. We review the NNPDF methodology for parametrization and fitting of neural networks, the algorithm used to determine the optimal fit, and its adaptation to the polarized case. We finally present our set of polarized parton distributions. We discuss its statistical properties, ...

A note on the use of multiple linear regression in molecular ecology.

Science.gov (United States)

Frasier, Timothy R

2016-03-01

Multiple linear regression analyses (also often referred to as generalized linear models--GLMs, or generalized linear mixed models--GLMMs) are widely used in the analysis of data in molecular ecology, often to assess the relative effects of genetic characteristics on individual fitness or traits, or how environmental characteristics influence patterns of genetic differentiation. However, the coefficients resulting from multiple regression analyses are sometimes misinterpreted, which can lead to incorrect interpretations and conclusions within individual studies, and can propagate to wider-spread errors in the general understanding of a topic. The primary issue revolves around the interpretation of coefficients for independent variables when interaction terms are also included in the analyses. In this scenario, the coefficients associated with each independent variable are often interpreted as the independent effect of each predictor variable on the predicted variable. However, this interpretation is incorrect. The correct interpretation is that these coefficients represent the effect of each predictor variable on the predicted variable when all other predictor variables are zero. This difference may sound subtle, but the ramifications cannot be overstated. Here, my goals are to raise awareness of this issue, to demonstrate and emphasize the problems that can result and to provide alternative approaches for obtaining the desired information. © 2015 John Wiley & Sons Ltd.

Absorption and folding of melittin onto lipid bilayer membranes via unbiased atomic detail microsecond molecular dynamics simulation.

Science.gov (United States)

Chen, Charles H; Wiedman, Gregory; Khan, Ayesha; Ulmschneider, Martin B

2014-09-01

Unbiased molecular simulation is a powerful tool to study the atomic details driving functional structural changes or folding pathways of highly fluid systems, which present great challenges experimentally. Here we apply unbiased long-timescale molecular dynamics simulation to study the ab initio folding and partitioning of melittin, a template amphiphilic membrane active peptide. The simulations reveal that the peptide binds strongly to the lipid bilayer in an unstructured configuration. Interfacial folding results in a localized bilayer deformation. Akin to purely hydrophobic transmembrane segments the surface bound native helical conformer is highly resistant against thermal denaturation. Circular dichroism spectroscopy experiments confirm the strong binding and thermostability of the peptide. The study highlights the utility of molecular dynamics simulations for studying transient mechanisms in fluid lipid bilayer systems. This article is part of a Special Issue entitled: Interfacially Active Peptides and Proteins. Guest Editors: William C. Wimley and Kalina Hristova. Copyright © 2014. Published by Elsevier B.V.

Multivariate covariance generalized linear models

DEFF Research Database (Denmark)

Bonat, W. H.; Jørgensen, Bent

2016-01-01

are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

Unextendible Mutually Unbiased Bases (after Mandayam, Bandyopadhyay, Grassl and Wootters

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Koen Thas

2016-11-01

Full Text Available We consider questions posed in a recent paper of Mandayam et al. (2014 on the nature of “unextendible mutually unbiased bases.” We describe a conceptual framework to study these questions, using a connection proved by the author in Thas (2009 between the set of nonidentity generalized Pauli operators on the Hilbert space of N d-level quantum systems, d a prime, and the geometry of non-degenerate alternating bilinear forms of rank N over finite fields F d . We then supply alternative and short proofs of results obtained in Mandayam et al. (2014, as well as new general bounds for the problems considered in loc. cit. In this setting, we also solve Conjecture 1 of Mandayam et al. (2014 and speculate on variations of this conjecture.

Predictors of nurses' experience of verbal abuse by nurse colleagues.

Science.gov (United States)

Keller, Ronald; Krainovich-Miller, Barbara; Budin, Wendy; Djukic, Maja

Between 45% and 94% of registered nurses (RNs) experience verbal abuse, which is associated with physical and psychological harm. Although several studies examined predictors of RNs' verbal abuse, none examined predictors of RNs' experiences of verbal abuse by RN colleagues. To examine individual, workplace, dispositional, contextual, and interpersonal predictors of RNs' reported experiences of verbal abuse from RN colleagues. In this secondary analysis, a cross-sectional design with multiple linear regression analysis was used to examine the effect of 23 predictors on verbal abuse by RN colleagues in a sample of 1,208 early career RNs. Selected variables in the empirical intragroup conflict model explained 23.8% of variance in RNs' experiences of verbal abuse by RN colleagues. A number of previously unstudied factors were identified that organizational leaders can monitor and develop or modify policies to prevent early career RNs' experiences of verbal abuse by RN colleagues. Copyright © 2017 Elsevier Inc. All rights reserved.

The Herschel/HIFI unbiased spectral survey of the solar-mass protostar IRAS16293

Science.gov (United States)

Bottinelli, S.; Caux, E.; Cecarelli, C.; Kahane, C.

2012-03-01

Unbiased spectral surveys are powerful tools to study the chemistry and the physics of star forming regions, because they can provide a complete census of the molecular content and the observed lines probe the physical structure of the source. While unbiased surveys at the millimeter and sub-millimeter wavelengths observable from ground-based telescopes have previously been performed towards several high-mass protostars, very little data exist on low-mass protostars, with only one such ground-based survey carried out towards this kind of object. However, since low-mass protostars are believed to resemble our own Sun's progenitor, the information provided by spectral surveys is crucial in order to uncover the birth mechanisms of low-mass stars and hence of our Sun. To help fill up this gap in our understanding, we carried out an almost complete spectral survey towards the solar-type protostar IRAS16293-2422 with the HIFI instrument onboard Herschel. The observations covered a range of about 700 GHz, in which a few hundreds lines were detected with more than 3σ confidence interval certainty and identified. All the detected lines which were free from obvious blending effects were fitted with Gaussians to estimate their basic kinematic properties. Contrarily to what is observed in the millimeter range, no lines from complex organic molecules have been observed. In this work, we characterize the different components of IRAS16293-2422 (a known binary at least) by analyzing the numerous emission and absorption lines identified.

Voltage splay modes and enhanced phase locking in a modified linear Josephson array

Science.gov (United States)

Harris, E. B.; Garland, J. C.

1997-02-01

We analyze a modified linear Josephson-junction array in which additional unbiased junctions are used to greatly enhance phase locking. This geometry exhibits strong correlated behavior, with an external magnetic field tuning the voltage splay angle between adjacent Josephson oscillators. The array displays a coherent in-phase mode for f=, where f is the magnetic frustration, while for 0tolerant of critical current disorder approaching 100%. The stability of the array has also been studied by computing Floquet exponents. These exponents are found to be negative for all array lengths, with a 1/N2 dependence, N being the number of series-connected junctions.

Note on an Identity Between Two Unbiased Variance Estimators for the Grand Mean in a Simple Random Effects Model.

Science.gov (United States)

Levin, Bruce; Leu, Cheng-Shiun

2013-01-01

We demonstrate the algebraic equivalence of two unbiased variance estimators for the sample grand mean in a random sample of subjects from an infinite population where subjects provide repeated observations following a homoscedastic random effects model.

Post-processing through linear regression

Directory of Open Access Journals (Sweden)

B. Van Schaeybroeck

2011-03-01

Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.

These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy through conditional analysis: a contribution to the unbiased selection of causal factors

Directory of Open Access Journals (Sweden)

F. Vergari

2011-05-01

Full Text Available In this work the conditional multivariate analysis was applied to evaluate landslide susceptibility in the Upper Orcia River Basin (Tuscany, Italy, where widespread denudation processes and agricultural practices have a mutual impact. We introduced an unbiased procedure for causal factor selection based on some intuitive statistical indices. This procedure is aimed at detecting among different potential factors the most discriminant ones in a given study area. Moreover, this step avoids generating too small and statistically insignificant spatial units by intersecting the factor maps. Finally, a validation procedure was applied based on the partition of the landslide inventory from multi-temporal aerial photo interpretation.

Although encompassing some sources of uncertainties, the applied susceptibility assessment method provided a satisfactory and unbiased prediction for the Upper Orcia Valley. The results confirmed the efficiency of the selection procedure, as an unbiased step of the landslide susceptibility evaluation. Furthermore, we achieved the purpose of presenting a conceptually simple but, at the same time, effective statistical procedure for susceptibility analysis to be used as well by decision makers in land management.

Discrimination, acculturation and other predictors of depression among pregnant Hispanic women.

Science.gov (United States)

Walker, Janiece L; Ruiz, R Jeanne; Chinn, Juanita J; Marti, Nathan; Ricks, Tiffany N

2012-01-01

The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. A prospective observational design was used. Central and Gulf coast areas of Texas in obstetrical offices. A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22-24 weeks gestation was used to collect data. The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, Pdiscrimination was the strongest positive predictor of increased depressive symptoms. It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy.

Effect Displays in R for Generalised Linear Models

Directory of Open Access Journals (Sweden)

John Fox

2003-07-01

Full Text Available This paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by marginality or hierarchy, such as polynomial terms, or main effects and interactions. I call these tables or graphs effect displays. Effect displays are constructed by identifying high-order terms in a generalised linear model. Fitted values under the model are computed for each such term. The lower-order "relatives" of a high-order term (e.g., main effects marginal to an interaction are absorbed into the term, allowing the predictors appearing in the high-order term to range over their values. The values of other predictors are fixed at typical values: for example, a covariate could be fixed at its mean or median, a factor at its proportional distribution in the data, or to equal proportions in its several levels. Variations of effect displays are also described, including representation of terms higher-order to any appearing in the model.

Genomic Prediction of Manganese Efficiency in Winter Barley

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Florian Leplat

2016-07-01

Full Text Available Manganese efficiency is a quantitative abiotic stress trait controlled by several genes each with a small effect. Manganese deficiency leads to yield reduction in winter barley ( L.. Breeding new cultivars for this trait remains difficult because of the lack of visual symptoms and the polygenic features of the trait. Hence, Mn efficiency is a potential suitable trait for a genomic selection (GS approach. A collection of 248 winter barley varieties was screened for Mn efficiency using Chlorophyll (Chl fluorescence in six environments prone to induce Mn deficiency. Two models for genomic prediction were implemented to predict future performance and breeding value of untested varieties. Predictions were obtained using multivariate mixed models: best linear unbiased predictor (BLUP and genomic best linear unbiased predictor (G-BLUP. In the first model, predictions were based on the phenotypic evaluation, whereas both phenotypic and genomic marker data were included in the second model. Accuracy of predicting future phenotype, , and accuracy of predicting true breeding values, , were calculated and compared for both models using six cross-validation (CV schemes; these were designed to mimic plant breeding programs. Overall, the CVs showed that prediction accuracies increased when using the G-BLUP model compared with the prediction accuracies using the BLUP model. Furthermore, the accuracies [] of predicting breeding values were more accurate than accuracy of predicting future phenotypes []. The study confirms that genomic data may enhance the prediction accuracy. Moreover it indicates that GS is a suitable breeding approach for quantitative abiotic stress traits.

Estimating Unbiased Treatment Effects in Education Using a Regression Discontinuity Design

Directory of Open Access Journals (Sweden)

William C. Smith

2014-08-01

Full Text Available The ability of regression discontinuity (RD designs to provide an unbiased treatment effect while overcoming the ethical concerns plagued by Random Control Trials (RCTs make it a valuable and useful approach in education evaluation. RD is the only explicitly recognized quasi-experimental approach identified by the Institute of Education Statistics to meet the prerequisites of a causal relationship. Unfortunately, the statistical complexity of the RD design has limited its application in education research. This article provides a less technical introduction to RD for education researchers and practitioners. Using visual analysis to aide conceptual understanding, the article walks readers through the essential steps of a Sharp RD design using hypothetical, but realistic, district intervention data and provides additional resources for further exploration.

Epistemological Predictors of Prospective Biology Teachers' Nature of Science Understandings

Science.gov (United States)

Köseoglu, Pinar; Köksal, Mustafa Serdar

2015-01-01

The purpose of this study was to investigate epistemological predictors of nature of science understandings of 281 prospective biology teachers surveyed using the Epistemological Beliefs Scale Regarding Science and the Nature of Science Scale. The findings on multiple linear regression showed that understandings about definition of science and…

Predictors of Adolescent Breakfast Consumption: Longitudinal Findings from Project EAT

Science.gov (United States)

Bruening, Meg; Larson, Nicole; Story, Mary; Neumark-Sztainer, Dianne; Hannan, Peter

2011-01-01

Objective: To identify predictors of breakfast consumption among adolescents. Methods: Five-year longitudinal study Project EAT (Eating Among Teens). Baseline surveys were completed in Minneapolis-St. Paul schools and by mail at follow-up by youth (n = 800) transitioning from middle to high school. Linear regression models examined associations…

Unbiased Strain-Typing of Arbovirus Directly from Mosquitoes Using Nanopore Sequencing: A Field-forward Biosurveillance Protocol.

Science.gov (United States)

Russell, Joseph A; Campos, Brittany; Stone, Jennifer; Blosser, Erik M; Burkett-Cadena, Nathan; Jacobs, Jonathan L

2018-04-03

The future of infectious disease surveillance and outbreak response is trending towards smaller hand-held solutions for point-of-need pathogen detection. Here, samples of Culex cedecei mosquitoes collected in Southern Florida, USA were tested for Venezuelan Equine Encephalitis Virus (VEEV), a previously-weaponized arthropod-borne RNA-virus capable of causing acute and fatal encephalitis in animal and human hosts. A single 20-mosquito pool tested positive for VEEV by quantitative reverse transcription polymerase chain reaction (RT-qPCR) on the Biomeme two3. The virus-positive sample was subjected to unbiased metatranscriptome sequencing on the Oxford Nanopore MinION and shown to contain Everglades Virus (EVEV), an alphavirus in the VEEV serocomplex. Our results demonstrate, for the first time, the use of unbiased sequence-based detection and subtyping of a high-consequence biothreat pathogen directly from an environmental sample using field-forward protocols. The development and validation of methods designed for field-based diagnostic metagenomics and pathogen discovery, such as those suitable for use in mobile "pocket laboratories", will address a growing demand for public health teams to carry out their mission where it is most urgent: at the point-of-need.

Predictors of Video Game Console Aggression

OpenAIRE

Bean, Anthony Martin; Ferro, Lauren

2016-01-01

This study was designed to investigate the aggression levels of college students found in the Northeastern part of the United States following exposure to video games. The 59 participants played their assigned game, Mortal Kombat on Nintendo Wii or Halo 2 on the Xbox, for 45 minutes with a partner. The researchers employed twelve t-tests (alpha adjusted to .004) and three multiple linear regressions to explore the difference of aggression levels in gender, violent video game, and predictors o...

Multicollinearity in hierarchical linear models.

Science.gov (United States)

Yu, Han; Jiang, Shanhe; Land, Kenneth C

2015-09-01

This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model. Copyright © 2015 Elsevier Inc. All rights reserved.

Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment

KAUST Repository

Rapakoulia, Trisevgeni

2017-08-09

Motivation: Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations. Results: We here show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome wide scale. We also find that the same linear relationship holds for transcription at enhancer elements. We conclude that the described approach is promising for eliciting the transcriptional response to multidrug treatment at promoters and enhancers in an unbiased genome wide way, which may minimize the need for exhaustive combinatorial screens.

Newborn length predicts early infant linear growth retardation and disproportionately high weight gain in a low-income population.

Science.gov (United States)

Berngard, Samuel Clark; Berngard, Jennifer Bishop; Krebs, Nancy F; Garcés, Ana; Miller, Leland V; Westcott, Jamie; Wright, Linda L; Kindem, Mark; Hambidge, K Michael

2013-12-01

Stunting is prevalent by the age of 6 months in the indigenous population of the Western Highlands of Guatemala. The objective of this study was to determine the time course and predictors of linear growth failure and weight-for-age in early infancy. One hundred and forty eight term newborns had measurements of length and weight in their homes, repeated at 3 and 6 months. Maternal measurements were also obtained. Mean ± SD length-for-age Z-score (LAZ) declined from newborn -1.0 ± 1.01 to -2.20 ± 1.05 and -2.26 ± 1.01 at 3 and 6 months respectively. Stunting rates for newborn, 3 and 6 months were 47%, 53% and 56% respectively. A multiple regression model (R(2) = 0.64) demonstrated that the major predictor of LAZ at 3 months was newborn LAZ with the other predictors being newborn weight-for-age Z-score (WAZ), gender and maternal education∗maternal age interaction. Because WAZ remained essentially constant and LAZ declined during the same period, weight-for-length Z-score (WLZ) increased from -0.44 to +1.28 from birth to 3 months. The more severe the linear growth failure, the greater WAZ was in proportion to the LAZ. The primary conclusion is that impaired fetal linear growth is the major predictor of early infant linear growth failure indicating that prevention needs to start with maternal interventions. © 2013.

A differential-geometric approach to generalized linear models with grouped predictors

NARCIS (Netherlands)

Augugliaro, Luigi; Mineo, Angelo M.; Wit, Ernst C.

We propose an extension of the differential-geometric least angle regression method to perform sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important

Estimating Unbiased Land Cover Change Areas In The Colombian Amazon Using Landsat Time Series And Statistical Inference Methods

Science.gov (United States)

Arevalo, P. A.; Olofsson, P.; Woodcock, C. E.

2017-12-01

Unbiased estimation of the areas of conversion between land categories ("activity data") and their uncertainty is crucial for providing more robust calculations of carbon emissions to the atmosphere, as well as their removals. This is particularly important for the REDD+ mechanism of UNFCCC where an economic compensation is tied to the magnitude and direction of such fluxes. Dense time series of Landsat data and statistical protocols are becoming an integral part of forest monitoring efforts, but there are relatively few studies in the tropics focused on using these methods to advance operational MRV systems (Monitoring, Reporting and Verification). We present the results of a prototype methodology for continuous monitoring and unbiased estimation of activity data that is compliant with the IPCC Approach 3 for representation of land. We used a break detection algorithm (Continuous Change Detection and Classification, CCDC) to fit pixel-level temporal segments to time series of Landsat data in the Colombian Amazon. The segments were classified using a Random Forest classifier to obtain annual maps of land categories between 2001 and 2016. Using these maps, a biannual stratified sampling approach was implemented and unbiased stratified estimators constructed to calculate area estimates with confidence intervals for each of the stable and change classes. Our results provide evidence of a decrease in primary forest as a result of conversion to pastures, as well as increase in secondary forest as pastures are abandoned and the forest allowed to regenerate. Estimating areas of other land transitions proved challenging because of their very small mapped areas compared to stable classes like forest, which corresponds to almost 90% of the study area. Implications on remote sensing data processing, sample allocation and uncertainty reduction are also discussed.

An unbiased estimator of the variance of simple random sampling using mixed random-systematic sampling

OpenAIRE

Padilla, Alberto

2009-01-01

Systematic sampling is a commonly used technique due to its simplicity and ease of implementation. The drawback of this simplicity is that it is not possible to estimate the design variance without bias. There are several ways to circumvent this problem. One method is to suppose that the variable of interest has a random order in the population, so the sample variance of simple random sampling without replacement is used. By means of a mixed random - systematic sample, an unbiased estimator o...

Mutually unbiased bases and trinary operator sets for N qutrits

International Nuclear Information System (INIS)

Lawrence, Jay

2004-01-01

A compete orthonormal basis of N-qutrit unitary operators drawn from the Pauli group consists of the identity and 9 N -1 traceless operators. The traceless ones partition into 3 N +1 maximally commuting subsets (MCS's) of 3 N -1 operators each, whose joint eigenbases are mutually unbiased. We prove that Pauli factor groups of order 3 N are isomorphic to all MCS's and show how this result applies in specific cases. For two qutrits, the 80 traceless operators partition into 10 MCS's. We prove that 4 of the corresponding basis sets must be separable, while 6 must be totally entangled (and Bell-like). For three qutrits, 728 operators partition into 28 MCS's with less rigid structure, allowing for the coexistence of separable, partially entangled, and totally entangled (GHZ-like) bases. However a minimum of 16 GHZ-like bases must occur. Every basis state is described by an N-digit trinary number consisting of the eigenvalues of N observables constructed from the corresponding MCS

LINEAR REGRESSION MODEL ESTİMATİON FOR RIGHT CENSORED DATA

Directory of Open Access Journals (Sweden)

Ersin Yılmaz

2016-05-01

Full Text Available In this study, firstly we will define a right censored data. If we say shortly right-censored data is censoring values that above the exact line. This may be related with scaling device. And then  we will use response variable acquainted from right-censored explanatory variables. Then the linear regression model will be estimated. For censored data’s existence, Kaplan-Meier weights will be used for  the estimation of the model. With the weights regression model  will be consistent and unbiased with that.   And also there is a method for the censored data that is a semi parametric regression and this method also give  useful results  for censored data too. This study also might be useful for the health studies because of the censored data used in medical issues generally.

Towards an unbiased, full-sky clustering search with IceCube in real time

Energy Technology Data Exchange (ETDEWEB)

Bernardini, Elisa; Franckowiak, Anna; Kintscher, Thomas; Kowalski, Marek; Stasik, Alexander [DESY, Zeuthen (Germany); Collaboration: IceCube-Collaboration

2016-07-01

The IceCube neutrino observatory is a 1 km{sup 3} detector for Cherenkov light in the ice at the South Pole. Having observed the presence of a diffuse astrophysical neutrino flux, static point source searches have come up empty handed. Thus, transient and variable objects emerge as promising, detectable source candidates. An unbiased, full-sky clustering search - run in real time - can find neutrino events with close temporal and spatial proximity. The most significant of these clusters serve as alerts to third-party observatories in order to obtain a complete picture of cosmic accelerators. The talk showcases the status and prospects of this project.

A Predictor-Corrector Approach for the Numerical Solution of Fractional Differential Equations

Science.gov (United States)

Diethelm, Kai; Ford, Neville J.; Freed, Alan D.; Gray, Hugh R. (Technical Monitor)

2002-01-01

We discuss an Adams-type predictor-corrector method for the numerical solution of fractional differential equations. The method may be used both for linear and for nonlinear problems, and it may be extended to multi-term equations (involving more than one differential operator) too.

Diagnostics for Linear Models With Functional Responses

OpenAIRE

Xu, Hongquan; Shen, Qing

2005-01-01

Linear models where the response is a function and the predictors are vectors are useful in analyzing data from designed experiments and other situations with functional observations. Residual analysis and diagnostics are considered for such models. Studentized residuals are defined and their properties are studied. Chi-square quantile-quantile plots are proposed to check the assumption of Gaussian error process and outliers. Jackknife residuals and an associated test are proposed to det...

Unbiased free energy estimates in fast nonequilibrium transformations using Gaussian mixtures

International Nuclear Information System (INIS)

Procacci, Piero

2015-01-01

In this paper, we present an improved method for obtaining unbiased estimates of the free energy difference between two thermodynamic states using the work distribution measured in nonequilibrium driven experiments connecting these states. The method is based on the assumption that any observed work distribution is given by a mixture of Gaussian distributions, whose normal components are identical in either direction of the nonequilibrium process, with weights regulated by the Crooks theorem. Using the prototypical example for the driven unfolding/folding of deca-alanine, we show that the predicted behavior of the forward and reverse work distributions, assuming a combination of only two Gaussian components with Crooks derived weights, explains surprisingly well the striking asymmetry in the observed distributions at fast pulling speeds. The proposed methodology opens the way for a perfectly parallel implementation of Jarzynski-based free energy calculations in complex systems

Bipartite entangled stabilizer mutually unbiased bases as maximum cliques of Cayley graphs

Science.gov (United States)

van Dam, Wim; Howard, Mark

2011-07-01

We examine the existence and structure of particular sets of mutually unbiased bases (MUBs) in bipartite qudit systems. In contrast to well-known power-of-prime MUB constructions, we restrict ourselves to using maximally entangled stabilizer states as MUB vectors. Consequently, these bipartite entangled stabilizer MUBs (BES MUBs) provide no local information, but are sufficient and minimal for decomposing a wide variety of interesting operators including (mixtures of) Jamiołkowski states, entanglement witnesses, and more. The problem of finding such BES MUBs can be mapped, in a natural way, to that of finding maximum cliques in a family of Cayley graphs. Some relationships with known power-of-prime MUB constructions are discussed, and observables for BES MUBs are given explicitly in terms of Pauli operators.

Bipartite entangled stabilizer mutually unbiased bases as maximum cliques of Cayley graphs

International Nuclear Information System (INIS)

Dam, Wim van; Howard, Mark

2011-01-01

We examine the existence and structure of particular sets of mutually unbiased bases (MUBs) in bipartite qudit systems. In contrast to well-known power-of-prime MUB constructions, we restrict ourselves to using maximally entangled stabilizer states as MUB vectors. Consequently, these bipartite entangled stabilizer MUBs (BES MUBs) provide no local information, but are sufficient and minimal for decomposing a wide variety of interesting operators including (mixtures of) Jamiolkowski states, entanglement witnesses, and more. The problem of finding such BES MUBs can be mapped, in a natural way, to that of finding maximum cliques in a family of Cayley graphs. Some relationships with known power-of-prime MUB constructions are discussed, and observables for BES MUBs are given explicitly in terms of Pauli operators.

Predictors of outcome for cognitive behaviour therapy in binge eating disorder.

Science.gov (United States)

Lammers, Mirjam W; Vroling, Maartje S; Ouwens, Machteld A; Engels, Rutger C M E; van Strien, Tatjana

2015-05-01

The aim of this naturalistic study was to identify pretreatment predictors of response to cognitive behaviour therapy in treatment-seeking patients with binge eating disorder (BED; N = 304). Furthermore, we examined end-of-treatment factors that predict treatment outcome 6 months later (N = 190). We assessed eating disorder psychopathology, general psychopathology, personality characteristics and demographic variables using self-report questionnaires. Treatment outcome was measured using the bulimia subscale of the Eating Disorder Inventory 1. Predictors were determined using hierarchical linear regression analyses. Several variables significantly predicted outcome, four of which were found to be both baseline predictors of treatment outcome and end-of-treatment predictors of follow-up: Higher levels of drive for thinness, higher levels of interoceptive awareness, lower levels of binge eating pathology and, in women, lower levels of body dissatisfaction predicted better outcome in the short and longer term. Based on these results, several suggestions are made to improve treatment outcome for BED patients. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Estimating unbiased economies of scale of HIV prevention projects: a case study of Avahan.

Science.gov (United States)

Lépine, Aurélia; Vassall, Anna; Chandrashekar, Sudha; Blanc, Elodie; Le Nestour, Alexis

2015-04-01

Governments and donors are investing considerable resources on HIV prevention in order to scale up these services rapidly. Given the current economic climate, providers of HIV prevention services increasingly need to demonstrate that these investments offer good 'value for money'. One of the primary routes to achieve efficiency is to take advantage of economies of scale (a reduction in the average cost of a health service as provision scales-up), yet empirical evidence on economies of scale is scarce. Methodologically, the estimation of economies of scale is hampered by several statistical issues preventing causal inference and thus making the estimation of economies of scale complex. In order to estimate unbiased economies of scale when scaling up HIV prevention services, we apply our analysis to one of the few HIV prevention programmes globally delivered at a large scale: the Indian Avahan initiative. We costed the project by collecting data from the 138 Avahan NGOs and the supporting partners in the first four years of its scale-up, between 2004 and 2007. We develop a parsimonious empirical model and apply a system Generalized Method of Moments (GMM) and fixed-effects Instrumental Variable (IV) estimators to estimate unbiased economies of scale. At the programme level, we find that, after controlling for the endogeneity of scale, the scale-up of Avahan has generated high economies of scale. Our findings suggest that average cost reductions per person reached are achievable when scaling-up HIV prevention in low and middle income countries. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using single-step genomic best linear unbiased predictor to enhance the mitigation of seasonal losses due to heat stress in pigs.

Science.gov (United States)

Fragomeni, B O; Lourenco, D A L; Tsuruta, S; Bradford, H L; Gray, K A; Huang, Y; Misztal, I

2016-12-01

The purposes of this study were to analyze the impact of seasonal losses due to heat stress in pigs from different breeds raised in different environments and to evaluate the accuracy improvement from adding genomic information to genetic evaluations. Data were available for 2 different swine populations: purebred Duroc animals raised in Texas and North Carolina and commercial crosses of Duroc and F females (Landrace × Large White) raised in Missouri and North Carolina; pedigrees provided links for animals from different states. Pedigree information was available for 553,442 animals, of which 8,232 pure breeds were genotyped. Traits were BW at 170 d for purebred animals and HCW for crossbred animals. Analyses were done with an animal model as either single- or 2-trait models using phenotypes measured in different states as separate traits. Additionally, reaction norm models were fitted for 1 or 2 traits using heat load index as a covariable. Heat load was calculated as temperature-humidity index greater than 70 and was averaged over 30 d prior to data collection. Variance components were estimated with average information REML, and EBV and genomic EBV (GEBV) with BLUP or single-step genomic BLUP (ssGBLUP). Validation was assessed for 146 genotyped sires with progeny in the last generation. Accuracy was calculated as a correlation between EBV and GEBV using reduced data (all animals, except the last generation) and using complete data. Heritability estimates for purebred animals were similar across states (varying from 0.23 to 0.26), and reaction norm models did not show evidence of a heat stress effect. Genetic correlations between states for heat loads were always strong (>0.91). For crossbred animals, no differences in heritability were found in single- or 2-trait analysis (from 0.17 to 0.18), and genetic correlations between states were moderate (0.43). In the reaction norm for crossbreeds, heritabilities ranged from 0.15 to 0.30 and genetic correlations between heat loads were as weak as 0.36, with heat load ranging from 0 to 12. Accuracies with ssGBLUP were, on average, 25% greater than with BLUP. Accuracies were greater in 2-trait reaction norm models and at extreme heat load values. Impacts of seasonality are evident only for crossbred animals. Genomic information can help producers mitigate heat stress in swine by identifying superior sires that are more resistant to heat stress.

Predictors of Career Adaptability Skill among Higher Education Students in Nigeria

Science.gov (United States)

Ebenehi, Amos Shaibu; Rashid, Abdullah Mat; Bakar, Ab Rahim

2016-01-01

This paper examined predictors of career adaptability skill among higher education students in Nigeria. A sample of 603 higher education students randomly selected from six colleges of education in Nigeria participated in this study. A set of self-reported questionnaire was used for data collection, and multiple linear regression analysis was used…

Pre-treatment growth and IGF-I deficiency as main predictors of response to growth hormone therapy in neural models

Directory of Open Access Journals (Sweden)

Urszula Smyczyn´ska

2018-01-01

Full Text Available Mathematical models have been applied in prediction of growth hormone treatment effectiveness in children since the end of 1990s. Usually they were multiple linear regression models; however, there are also examples derived by empirical non-linear methods. Proposed solution consists in application of machine learning technique – artificial neural networks – to analyse this problem. This new methodology, contrary to previous ones, allows detection of both linear and non-linear dependencies without assuming their character a priori. The aims of this work included: development of models predicting separately growth during 1st year of treatment and final height as well as identification of important predictors and in-depth analysis of their influence on treatment’s effectiveness. The models were derived on the basis of clinical data of 272 patients treated for at least 1 year, 133 of whom have already attained final height. Starting from models containing 17 and 20 potential predictors, respectively for 1st year and final height model, we were able to reduce their number to 9 and 10. Basing on the final models, IGF-I concentration and earlier growth were indicated as belonging to most important predictors of response to GH therapy, while results of GH secretion tests were automatically excluded as insignificant. Moreover, majority of the dependencies were observed to be non-linear, thus using neural networks seems to be reasonable approach despite it being more complex than previously applied methods.

A predictor-corrector algorithm to estimate the fractional flow in oil-water models

International Nuclear Information System (INIS)

Savioli, Gabriela B; Berdaguer, Elena M Fernandez

2008-01-01

We introduce a predictor-corrector algorithm to estimate parameters in a nonlinear hyperbolic problem. It can be used to estimate the oil-fractional flow function from the Buckley-Leverett equation. The forward model is non-linear: the sought- for parameter is a function of the solution of the equation. Traditionally, the estimation of functions requires the selection of a fitting parametric model. The algorithm that we develop does not require a predetermined parameter model. Therefore, the estimation problem is carried out over a set of parameters which are functions. The algorithm is based on the linearization of the parameter-to-output mapping. This technique is new in the field of nonlinear estimation. It has the advantage of laying aside parametric models. The algorithm is iterative and is of predictor-corrector type. We present theoretical results on the inverse problem. We use synthetic data to test the new algorithm.

Predictors of Longitudinal Quality of Life in Juvenile Localized Scleroderma.

Science.gov (United States)

Ardalan, Kaveh; Zigler, Christina K; Torok, Kathryn S

2017-07-01

Localized scleroderma can negatively affect children's quality of life (QoL), but predictors of impact have not been well described. We sought to identify predictors of QoL impact in juvenile localized scleroderma patients. We analyzed longitudinal data from a single-center cohort of juvenile localized scleroderma patients, using hierarchical generalized linear modeling (HGLM) to identify predictors of QoL impact. HGLM is useful for nested data and allows for evaluation of both time-variant and time-invariant predictors. The number of extracutaneous manifestations (ECMs; e.g., joint contracture and hemifacial atrophy) and female sex predicted negative QoL impact, defined as a Children's Dermatology Life Quality Index score >1 (P = 0.019 for ECMs and P = 0.002 for female sex). As the time since the initial visit increased, the odds of reporting a negative QoL impact decreased (P scleroderma than cutaneous features. Further study is required to determine which ECMs have the most impact on QoL, which factors underlie sex differences in QoL in localized scleroderma, and why increasing the time since the initial visit appears to be protective. An improved understanding of predictors of QoL impact may allow for the identification of patients at risk of poorer outcomes and for the tailoring of treatment and psychosocial support. © 2016, American College of Rheumatology.

Linear Polarimetry with γ→e+e− Conversions

Directory of Open Access Journals (Sweden)

Denis Bernard

2017-11-01

Full Text Available γ -rays are emitted by cosmic sources by non-thermal processes that yield either non-polarized photons, such as those from π 0 decay in hadronic interactions, or linearly polarized photons from synchrotron radiation and the inverse-Compton up-shifting of these on high-energy charged particles. Polarimetry in the MeV energy range would provide a powerful tool to discriminate among “leptonic” and “hadronic” emission models of blazars, for example, but no polarimeter sensitive above 1 MeV has ever been flown into space. Low-Z converter telescopes such as silicon detectors are developed to improve the angular resolution and the point-like sensitivity below 100 MeV. We have shown that in the case of a homogeneous, low-density active target such as a gas time-projection chamber (TPC, the single-track angular resolution is even better and is so good that in addition the linear polarimetry of the incoming radiation can be performed. We actually characterized the performance of a prototype of such a telescope on beam. Track momentum measurement in the tracker would enable calorimeter-free, large effective area telescopes on low-mass space missions. An optimal unbiased momentum estimate can be obtained in the tracker alone based on the momentum dependence of multiple scattering, from a Bayesian analysis of the innovations of Kalman filters applied to the tracks.

Directed transport in a periodic tube driven by asymmetric unbiased forces coexisting with spatially modulated noises

International Nuclear Information System (INIS)

Li Fengguo; Ai Baoquan

2011-01-01

Graphical abstract: The current J as a function of the phase shift φ and ε at a = 1/2π, b = 0.5/2π, k B T = 0.5, α = 0.1, and F 0 = 0.5. Highlights: → Unbiased forces and spatially modulated white noises affect the current. → In the adiabatic limit, the analytical expression of directed current is obtained. → Their competition will induce current reversals. → For negative asymmetric parameters of the force, there exists an optimum parameter. → The current increases monotonously for positive asymmetric parameters. - Abstract: Transport of Brownian particles in a symmetrically periodic tube is investigated in the presence of asymmetric unbiased external forces and spatially modulated Gaussian white noises. In the adiabatic limit, we obtain the analytical expression of the directed current. It is found that the temporal asymmetry can break thermodynamic equilibrium and induce a net current. Their competition between the temporal asymmetry force and the phase shift between the noise modulation and the tube shape will induce some peculiar phenomena, for example, current reversals. The current changes with the phase shift in the form of the sine function. For negative asymmetric parameters of the force, there exists an optimum parameter at which the current takes its maximum value. However, the current increases monotonously for positive asymmetric parameters.

Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials.

Science.gov (United States)

Hua, Xue; Hibar, Derrek P; Ching, Christopher R K; Boyle, Christina P; Rajagopalan, Priya; Gutman, Boris A; Leow, Alex D; Toga, Arthur W; Jack, Clifford R; Harvey, Danielle; Weiner, Michael W; Thompson, Paul M

2013-02-01

Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. Copyright © 2012 Elsevier Inc. All rights reserved.

Nonlinear price impact from linear models

Science.gov (United States)

Patzelt, Felix; Bouchaud, Jean-Philippe

2017-12-01

The impact of trades on asset prices is a crucial aspect of market dynamics for academics, regulators, and practitioners alike. Recently, universal and highly nonlinear master curves were observed for price impacts aggregated on all intra-day scales (Patzelt and Bouchaud 2017 arXiv:1706.04163). Here we investigate how well these curves, their scaling, and the underlying return dynamics are captured by linear ‘propagator’ models. We find that the classification of trades as price-changing versus non-price-changing can explain the price impact nonlinearities and short-term return dynamics to a very high degree. The explanatory power provided by the change indicator in addition to the order sign history increases with increasing tick size. To obtain these results, several long-standing technical issues for model calibration and testing are addressed. We present new spectral estimators for two- and three-point cross-correlations, removing the need for previously used approximations. We also show when calibration is unbiased and how to accurately reveal previously overlooked biases. Therefore, our results contribute significantly to understanding both recent empirical results and the properties of a popular class of impact models.

Introduction to statistical modelling 2: categorical variables and interactions in linear regression.

Science.gov (United States)

Lunt, Mark

2015-07-01

In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading. © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

SU2 nonstandard bases: the case of mutually unbiased bases

International Nuclear Information System (INIS)

Olivier, Albouy; Kibler, Maurice R.

2007-02-01

This paper deals with bases in a finite-dimensional Hilbert space. Such a space can be realized as a subspace of the representation space of SU 2 corresponding to an irreducible representation of SU 2 . The representation theory of SU 2 is reconsidered via the use of two truncated deformed oscillators. This leads to replace the familiar scheme [j 2 , j z ] by a scheme [j 2 , v ra ], where the two-parameter operator v ra is defined in the universal enveloping algebra of the Lie algebra su 2 . The eigenvectors of the commuting set of operators [j 2 , v ra ] are adapted to a tower of chains SO 3 includes C 2j+1 (2j belongs to N * ), where C 2j+1 is the cyclic group of order 2j + 1. In the case where 2j + 1 is prime, the corresponding eigenvectors generate a complete set of mutually unbiased bases. Some useful relations on generalized quadratic Gauss sums are exposed in three appendices. (authors)

PPM-One: a static protein structure based chemical shift predictor

International Nuclear Information System (INIS)

Li, Dawei; Brüschweiler, Rafael

2015-01-01

We mined the most recent editions of the BioMagResDataBank and the protein data bank to parametrize a new empirical knowledge-based chemical shift predictor of protein backbone atoms using either a linear or an artificial neural network model. The resulting chemical shift predictor PPM-One accepts a single static 3D structure as input and emulates the effect of local protein dynamics via interatomic steric contacts. Furthermore, the chemical shift prediction was extended to most side-chain protons and it is found that the prediction accuracy is at a level allowing an independent assessment of stereospecific assignments. For a previously established set of test proteins some overall improvement was achieved over current top-performing chemical shift prediction programs

Genomic prediction based on data from three layer lines using non-linear regression models.

Science.gov (United States)

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional

Gender and distance influence performance predictors in young swimmers

OpenAIRE

Mezzaroba, Paulo Victor; Papoti, Marcelo; Machado, Fabiana Andrade

2013-01-01

Predictors of performance in adult swimmers are constantly changing during youth especially because the training routine begins even before puberty in the modality. Therefore this study aimed to determine the group of parameters that best predict short and middle swimming distance performances of young swimmers of both genders. Thirty-three 10-to 16-years-old male and female competitive swimmers participated in the study. Multiple linear regression (MLR) was used considering mean speed of max...

Voltage splay modes and enhanced phase locking in a modified linear Josephson array

International Nuclear Information System (INIS)

Harris, E.B.; Garland, J.C.

1997-01-01

We analyze a modified linear Josephson-junction array in which additional unbiased junctions are used to greatly enhance phase locking. This geometry exhibits strong correlated behavior, with an external magnetic field tuning the voltage splay angle between adjacent Josephson oscillators. The array displays a coherent in-phase mode for f=(1)/(2), where f is the magnetic frustration, while for 0 p (f)=2aV dc /Φ 0 (1-2f). The locked splay modes are found to be tolerant of critical current disorder approaching 100%. The stability of the array has also been studied by computing Floquet exponents. These exponents are found to be negative for all array lengths, with a 1/N 2 dependence, N being the number of series-connected junctions. copyright 1996 The American Physical Society

Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning.

Science.gov (United States)

Henneghan, Ashley M; Palesh, Oxana; Harrison, Michelle; Kesler, Shelli R

2018-07-15

The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric approach. Cross sectional data collection included completion of a survey, cognitive testing, and non-fasting blood from 66 participants. Data were analyzed using random forest regression to identify the most significant predictors for each of the cognitive test scores. A different cytokine profile predicted each cognitive test. Adjusted R 2 for each model ranged from 0.71-0.77 (p's < 9.50 -10 ). The relationships between all the cytokine predictors and cognitive test scores were non-linear. Our findings are unique to the field of CRCI and suggest non-linear cytokine specificity to neural networks underlying cognitive functions assessed in this study. Copyright © 2018 Elsevier B.V. All rights reserved.

Within-subject template estimation for unbiased longitudinal image analysis.

Science.gov (United States)

Reuter, Martin; Schmansky, Nicholas J; Rosas, H Diana; Fischl, Bruce

2012-07-16

Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects. Copyright © 2012 Elsevier Inc. All rights reserved.

Unbiased determination of polarized parton distributions and their uncertainties

Energy Technology Data Exchange (ETDEWEB)

Ball, Richard D. [Tait Institute, University of Edinburgh, JCMB, KB, Mayfield Rd, Edinburgh EH9 3JZ, Scotland (United Kingdom); Forte, Stefano, E-mail: forte@mi.infn.it [Dipartimento di Fisica, Università di Milano and INFN, Sezione di Milano, Via Celoria 16, I-20133 Milano (Italy); Guffanti, Alberto [The Niels Bohr International Academy and Discovery Center, The Niels Bohr Institute, Blegdamsvej 17, DK-2100 Copenhagen (Denmark); Nocera, Emanuele R. [Dipartimento di Fisica, Università di Milano and INFN, Sezione di Milano, Via Celoria 16, I-20133 Milano (Italy); Ridolfi, Giovanni [Dipartimento di Fisica, Università di Genova and INFN, Sezione di Genova, Genova (Italy); Rojo, Juan [PH Department, TH Unit, CERN, CH-1211 Geneva 23 (Switzerland)

2013-09-01

We present a determination of a set of polarized parton distributions (PDFs) of the nucleon, at next-to-leading order, from a global set of longitudinally polarized deep-inelastic scattering data: NNPDFpol1.0. The determination is based on the NNPDF methodology: a Monte Carlo approach, with neural networks used as unbiased interpolants, previously applied to the determination of unpolarized parton distributions, and designed to provide a faithful and statistically sound representation of PDF uncertainties. We present our dataset, its statistical features, and its Monte Carlo representation. We summarize the technique used to solve the polarized evolution equations and its benchmarking, and the method used to compute physical observables. We review the NNPDF methodology for parametrization and fitting of neural networks, the algorithm used to determine the optimal fit, and its adaptation to the polarized case. We finally present our set of polarized parton distributions. We discuss its statistical properties, test for its stability upon various modifications of the fitting procedure, and compare it to other recent polarized parton sets, and in particular obtain predictions for polarized first moments of PDFs based on it. We find that the uncertainties on the gluon, and to a lesser extent the strange PDF, were substantially underestimated in previous determinations.

Unbiased determination of polarized parton distributions and their uncertainties

International Nuclear Information System (INIS)

Ball, Richard D.; Forte, Stefano; Guffanti, Alberto; Nocera, Emanuele R.; Ridolfi, Giovanni; Rojo, Juan

2013-01-01

We present a determination of a set of polarized parton distributions (PDFs) of the nucleon, at next-to-leading order, from a global set of longitudinally polarized deep-inelastic scattering data: NNPDFpol1.0. The determination is based on the NNPDF methodology: a Monte Carlo approach, with neural networks used as unbiased interpolants, previously applied to the determination of unpolarized parton distributions, and designed to provide a faithful and statistically sound representation of PDF uncertainties. We present our dataset, its statistical features, and its Monte Carlo representation. We summarize the technique used to solve the polarized evolution equations and its benchmarking, and the method used to compute physical observables. We review the NNPDF methodology for parametrization and fitting of neural networks, the algorithm used to determine the optimal fit, and its adaptation to the polarized case. We finally present our set of polarized parton distributions. We discuss its statistical properties, test for its stability upon various modifications of the fitting procedure, and compare it to other recent polarized parton sets, and in particular obtain predictions for polarized first moments of PDFs based on it. We find that the uncertainties on the gluon, and to a lesser extent the strange PDF, were substantially underestimated in previous determinations

AN UNBIASED 1.3 mm EMISSION LINE SURVEY OF THE PROTOPLANETARY DISK ORBITING LkCa 15

Energy Technology Data Exchange (ETDEWEB)

Punzi, K. M.; Kastner, J. H. [Center for Imaging Science, School of Physics and Astronomy, and Laboratory for Multiwavelength Astrophysics, Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623 (United States); Hily-Blant, P.; Forveille, T. [UJF—Grenoble 1/CNRS-INSU, Institut de Planétologie et d’Astrophysique de Grenoble (IPAG) UMR 5274, F-38041, Grenoble (France); Sacco, G. G. [INAF—Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, I-50125, Firenze (Italy)

2015-06-01

The outer (>30 AU) regions of the dusty circumstellar disk orbiting the ∼2–5 Myr old, actively accreting solar analog LkCa 15 are known to be chemically rich, and the inner disk may host a young protoplanet within its central cavity. To obtain a complete census of the brightest molecular line emission emanating from the LkCa 15 disk over the 210–270 GHz (1.4–1.1 mm) range, we have conducted an unbiased radio spectroscopic survey with the Institute de Radioastronomie Millimétrique (IRAM) 30 m telescope. The survey demonstrates that in this spectral region, the most readily detectable lines are those of CO and its isotopologues {sup 13}CO and C{sup 18}O, as well as HCO{sup +}, HCN, CN, C{sub 2}H, CS, and H{sub 2}CO. All of these species had been previously detected in the LkCa 15 disk; however, the present survey includes the first complete coverage of the CN (2–1) and C{sub 2}H (3–2) hyperfine complexes. Modeling of these emission complexes indicates that the CN and C{sub 2}H either reside in the coldest regions of the disk or are subthermally excited, and that their abundances are enhanced relative to molecular clouds and young stellar object environments. These results highlight the value of unbiased single-dish line surveys in guiding future high-resolution interferometric imaging of disks.

Childhood Depression: Relation to Adaptive, Clinical and Predictor Variables

Directory of Open Access Journals (Sweden)

Maite Garaigordobil

2017-05-01

Full Text Available The study had two goals: (1 to explore the relations between self-assessed childhood depression and other adaptive and clinical variables (2 to identify predictor variables of childhood depression. Participants were 420 students aged 7–10 years old (53.3% boys, 46.7% girls. Results revealed: (1 positive correlations between depression and clinical maladjustment, school maladjustment, emotional symptoms, internalizing and externalizing problems, problem behaviors, emotional reactivity, and childhood stress; and (2 negative correlations between depression and personal adaptation, global self-concept, social skills, and resilience (sense of competence and affiliation. Linear regression analysis including the global dimensions revealed 4 predictors of childhood depression that explained 50.6% of the variance: high clinical maladjustment, low global self-concept, high level of stress, and poor social skills. However, upon introducing the sub-dimensions, 9 predictor variables emerged that explained 56.4% of the variance: many internalizing problems, low family self-concept, high anxiety, low responsibility, low personal self-assessment, high social stress, few aggressive behaviors toward peers, many health/psychosomatic problems, and external locus of control. The discussion addresses the importance of implementing prevention programs for childhood depression at early ages.

Identification and characterization of Highlands J virus from a Mississippi sandhill crane using unbiased next-generation sequencing

Science.gov (United States)

Ip, Hon S.; Wiley, Michael R.; Long, Renee; Gustavo, Palacios; Shearn-Bochsler, Valerie; Whitehouse, Chris A.

2014-01-01

Advances in massively parallel DNA sequencing platforms, commonly termed next-generation sequencing (NGS) technologies, have greatly reduced time, labor, and cost associated with DNA sequencing. Thus, NGS has become a routine tool for new viral pathogen discovery and will likely become the standard for routine laboratory diagnostics of infectious diseases in the near future. This study demonstrated the application of NGS for the rapid identification and characterization of a virus isolated from the brain of an endangered Mississippi sandhill crane. This bird was part of a population restoration effort and was found in an emaciated state several days after Hurricane Isaac passed over the refuge in Mississippi in 2012. Post-mortem examination had identified trichostrongyliasis as the possible cause of death, but because a virus with morphology consistent with a togavirus was isolated from the brain of the bird, an arboviral etiology was strongly suspected. Because individual molecular assays for several known arboviruses were negative, unbiased NGS by Illumina MiSeq was used to definitively identify and characterize the causative viral agent. Whole genome sequencing and phylogenetic analysis revealed the viral isolate to be the Highlands J virus, a known avian pathogen. This study demonstrates the use of unbiased NGS for the rapid detection and characterization of an unidentified viral pathogen and the application of this technology to wildlife disease diagnostics and conservation medicine.

Generalized Linear Models in Vehicle Insurance

Directory of Open Access Journals (Sweden)

Silvie Kafková

2014-01-01

Full Text Available Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver. In this paper, an analysis of the portfolio of vehicle insurance data using a generalized linear model (GLM is performed. The main advantage of the approach presented in this article is that the GLMs are not limited by inflexible preconditions. Our aim is to predict the relation of annual claim frequency on given risk factors. Based on a large real-world sample of data from 57 410 vehicles, the present study proposed a classification analysis approach that addresses the selection of predictor variables. The models with different predictor variables are compared by analysis of deviance and Akaike information criterion (AIC. Based on this comparison, the model for the best estimate of annual claim frequency is chosen. All statistical calculations are computed in R environment, which contains stats package with the function for the estimation of parameters of GLM and the function for analysis of deviation.

Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer’s disease clinical trials

Science.gov (United States)

Hua, Xue; Hibar, Derrek P.; Ching, Christopher R.K.; Boyle, Christina P.; Rajagopalan, Priya; Gutman, Boris A.; Leow, Alex D.; Toga, Arthur W.; Jack, Clifford R.; Harvey, Danielle; Weiner, Michael W.; Thompson, Paul M.

2013-01-01

Various neuroimaging measures are being evaluated for tracking Alzheimer’s disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24 months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39 AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. PMID:23153970

Prevalence and predictors of musculoskeletal pain among Danish fishermen

DEFF Research Database (Denmark)

Berg-Beckhoff, Gabriele; Østergaard, Helle; Jepsen, Jørgen Riis

2016-01-01

at sea, age, BMI and education were used as predictors for the overall musculoskeletal pain score (multiple linear regression) and for each single pain site (multinomial logistic regression). RESULTS: The prevalence of pain was high for all musculoskeletal locations. Overall, more than 80...... demanding and impacting their musculoskeletal pain. Potential explanation for this unexpected result like increased work pressure and reduced financial attractiveness in small scale commercial fishery needs to be confirmed in future research....

High levels of absorption in orientation-unbiased, radio-selected 3CR Active Galaxies

Science.gov (United States)

Wilkes, Belinda J.; Haas, Martin; Barthel, Peter; Leipski, Christian; Kuraszkiewicz, Joanna; Worrall, Diana; Birkinshaw, Mark; Willner, Steven P.

2014-08-01

A critical problem in understanding active galaxies (AGN) is the separation of intrinsic physical differences from observed differences that are due to orientation. Obscuration of the active nucleus is anisotropic and strongly frequency dependent leading to complex selection effects for observations in most wavebands. These can only be quantified using a sample that is sufficiently unbiased to test orientation effects. Low-frequency radio emission is one way to select a close-to orientation-unbiased sample, albeit limited to the minority of AGN with strong radio emission.Recent Chandra, Spitzer and Herschel observations combined with multi-wavelength data for a complete sample of high-redshift (1half the sample is significantly obscured with ratios of unobscured: Compton thin (22 24.2) = 2.5:1.4:1 in these high-luminosity (log L(0.3-8keV) ~ 44-46) sources. These ratios are consistent with current expectations based on modelingthe Cosmic X-ray Background. A strong correlation with radio orientation constrains the geometry of the obscuring disk/torus to have a ~60 degree opening angle and ~12 degree Compton-thick cross-section. The deduced ~50% obscured fraction of the population contrasts with typical estimates of ~20% obscured in optically- and X-ray-selected high-luminosity samples. Once the primary nuclear emission is obscured, AGN X-ray spectra are frequently dominated by unobscured non-nuclear or scattered nuclear emission which cannot be distinguished from direct nuclear emission with a lower obscuration level unless high quality data is available. As a result, both the level of obscuration and the estimated instrinsic luminosities of highly-obscured AGN are likely to be significantly (*10-1000) underestimated for 25-50% of the population. This may explain the lower obscured fractions reported for optical and X-ray samples which have no independent measure of the AGN luminosity. Correcting AGN samples for these underestimated luminosities would result in

Verifying mixing in dilution tunnels How to ensure cookstove emissions samples are unbiased

Energy Technology Data Exchange (ETDEWEB)

Wilson, Daniel L. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Rapp, Vi H. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Caubel, Julien J. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chen, Sharon S. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Gadgil, Ashok J. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2017-12-15

A well-mixed diluted sample is essential for unbiased measurement of cookstove emissions. Most cookstove testing labs employ a dilution tunnel, also referred to as a “duct,” to mix clean dilution air with cookstove emissions before sampling. It is important that the emissions be well-mixed and unbiased at the sampling port so that instruments can take representative samples of the emission plume. Some groups have employed mixing baffles to ensure the gaseous and aerosol emissions from cookstoves are well-mixed before reaching the sampling location [2, 4]. The goal of these baffles is to to dilute and mix the emissions stream with the room air entering the fume hood by creating a local zone of high turbulence. However, potential drawbacks of mixing baffles include increased flow resistance (larger blowers needed for the same exhaust flow), nuisance cleaning of baffles as soot collects, and, importantly, the potential for loss of PM2.5 particles on the baffles themselves, thus biasing results. A cookstove emission monitoring system with baffles will collect particles faster than the duct’s walls alone. This is mostly driven by the available surface area for deposition by processes of Brownian diffusion (through the boundary layer) and turbophoresis (i.e. impaction). The greater the surface area available for diffusive and advection-driven deposition to occur, the greater the particle loss will be at the sampling port. As a layer of larger particle “fuzz” builds on the mixing baffles, even greater PM2.5 loss could occur. The micro structure of the deposited aerosol will lead to increased rates of particle loss by interception and a tendency for smaller particles to deposit due to impaction on small features of the micro structure. If the flow stream could be well-mixed without the need for baffles, these drawbacks could be avoided and the cookstove emissions sampling system would be more robust.

Unbiased contaminant removal for 3D galaxy power spectrum measurements

Science.gov (United States)

Kalus, B.; Percival, W. J.; Bacon, D. J.; Samushia, L.

2016-11-01

We assess and develop techniques to remove contaminants when calculating the 3D galaxy power spectrum. We separate the process into three separate stages: (I) removing the contaminant signal, (II) estimating the uncontaminated cosmological power spectrum and (III) debiasing the resulting estimates. For (I), we show that removing the best-fitting contaminant (mode subtraction) and setting the contaminated components of the covariance to be infinite (mode deprojection) are mathematically equivalent. For (II), performing a quadratic maximum likelihood (QML) estimate after mode deprojection gives an optimal unbiased solution, although it requires the manipulation of large N_mode^2 matrices (Nmode being the total number of modes), which is unfeasible for recent 3D galaxy surveys. Measuring a binned average of the modes for (II) as proposed by Feldman, Kaiser & Peacock (FKP) is faster and simpler, but is sub-optimal and gives rise to a biased solution. We present a method to debias the resulting FKP measurements that does not require any large matrix calculations. We argue that the sub-optimality of the FKP estimator compared with the QML estimator, caused by contaminants, is less severe than that commonly ignored due to the survey window.

Unbiased in-depth characterization of CEX fractions from a stressed monoclonal antibody by mass spectrometry.

Science.gov (United States)

Griaud, François; Denefeld, Blandine; Lang, Manuel; Hensinger, Héloïse; Haberl, Peter; Berg, Matthias

2017-07-01

Characterization of charge-based variants by mass spectrometry (MS) is required for the analytical development of a new biologic entity and its marketing approval by health authorities. However, standard peak-based data analysis approaches are time-consuming and biased toward the detection, identification, and quantification of main variants only. The aim of this study was to characterize in-depth acidic and basic species of a stressed IgG1 monoclonal antibody using comprehensive and unbiased MS data evaluation tools. Fractions collected from cation ion exchange (CEX) chromatography were analyzed as intact, after reduction of disulfide bridges, and after proteolytic cleavage using Lys-C. Data of both intact and reduced samples were evaluated consistently using a time-resolved deconvolution algorithm. Peptide mapping data were processed simultaneously, quantified and compared in a systematic manner for all MS signals and fractions. Differences observed between the fractions were then further characterized and assigned. Time-resolved deconvolution enhanced pattern visualization and data interpretation of main and minor modifications in 3-dimensional maps across CEX fractions. Relative quantification of all MS signals across CEX fractions before peptide assignment enabled the detection of fraction-specific chemical modifications at abundances below 1%. Acidic fractions were shown to be heterogeneous, containing antibody fragments, glycated as well as deamidated forms of the heavy and light chains. In contrast, the basic fractions contained mainly modifications of the C-terminus and pyroglutamate formation at the N-terminus of the heavy chain. Systematic data evaluation was performed to investigate multiple data sets and comprehensively extract main and minor differences between each CEX fraction in an unbiased manner.

Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.

Science.gov (United States)

Masuda, Y; Misztal, I; Tsuruta, S; Legarra, A; Aguilar, I; Lourenco, D A L; Fragomeni, B O; Lawlor, T J

2016-03-01

The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix GAPY(-1) based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up GAPY(-1) for 569,404 genotyped animals with 10,000 core animals took 1.3h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predictors of handgrip strength among adults of a rural community in Malaysia.

Science.gov (United States)

Moy, Foong-Ming; Darus, Azlan; Hairi, Noran Naqiah

2015-03-01

Handgrip strength is useful for screening the nutritional status of adult population as it is strongly associated with physical disabilities and mortality. Therefore, we aimed to determine the predictors of handgrip strength among adults of a rural community in Malaysia using a cross-sectional study design with multistage sampling. All adults aged 30 years and older from 1250 households were invited to our study. Structured questionnaire on sociodemographic characteristics, medical history, occupation history, lifestyle practices, and measurements, including anthropometry and handgrip strength were taken. There were 2199 respondents with 55.2% females and majority were of Malay ethnicity. Their mean (standard deviation) age was 53.4 (13.2) years. The response rate for handgrip strength was 94.2%. Females had significantly lower handgrip strength than males (P < .05). In the multiple linear regression models, significant predictors of handgrip strength for males were age, height, job groups, and diabetes, while for females, the significant predictors were age, weight, height, and diabetes. © 2013 APJPH.

Media and Life Dissatisfaction as Predictors of Body Dissatisfaction

Directory of Open Access Journals (Sweden)

Melissa Bittencourt Jaeger

2015-08-01

Full Text Available Body dissatisfaction can contribute to social, occupational and recreational losses, constituting a risk factor to health. This study aimed to evaluate the predictors of body dissatisfaction regarding demographic variables, media and life satisfaction among university students. The sample consisted of 321 participants older than 18 years. Body dissatisfaction, life dissatisfaction and media messages internalization were evaluated by Escala de Silhuetas para Adultos Brasileiros, Subjective Well-Being Scale and Sociocultural Attitudes Towards Appearance Questionnaire-3, respectively. Data were collected by an online survey tool (SurveyMonkey® and were analyzed using multiple linear regression. It was found that body dissatisfaction was positively related to inaccuracy in the perception of body size, Body Mass Index, life dissatisfaction, media messages internalization and television exposure. These findings evidence the importance of these predictors in the dynamics of body dissatisfaction, which support the development of preventive and treatment interventions.

Bagging Weak Predictors

DEFF Research Database (Denmark)

Lukas, Manuel; Hillebrand, Eric

Relations between economic variables can often not be exploited for forecasting, suggesting that predictors are weak in the sense that estimation uncertainty is larger than bias from ignoring the relation. In this paper, we propose a novel bagging predictor designed for such weak predictor variab...

Unbiased stereological estimation of d-dimensional volume in Rn from an isotropic random slice through a fixed point

DEFF Research Database (Denmark)

Jensen, Eva B. Vedel; Kiêu, K

1994-01-01

Unbiased stereological estimators of d-dimensional volume in R(n) are derived, based on information from an isotropic random r-slice through a specified point. The content of the slice can be subsampled by means of a spatial grid. The estimators depend only on spatial distances. As a fundamental ...... lemma, an explicit formula for the probability that an isotropic random r-slice in R(n) through 0 hits a fixed point in R(n) is given....

Motivation for change as a predictor of treatment response for dysthymia.

Science.gov (United States)

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

HERITABILITY AND BREEDING VALUE OF SHEEP FERTILITY ESTIMATED BY MEANS OF THE GIBBS SAMPLING METHOD USING THE LINEAR AND THRESHOLD MODELS

Directory of Open Access Journals (Sweden)

DARIUSZ Piwczynski

2013-03-01

Full Text Available The research was carried out on 4,030 Polish Merino ewes born in the years 1991- 2001, kept in 15 flocks from the Pomorze and Kujawy region. Fertility of ewes in subsequent reproduction seasons was analysed with the use of multiple logistic regression. The research showed that there is a statistical influence of the flock, year of birth, age of dam, flock year interaction of birth on the ewes fertility. In order to estimate the genetic parameters, the Gibbs sampling method was applied, using the univariate animal models, both linear as well as threshold. Estimates of fertility depending on the model equalled 0.067 to 0.104, whereas the estimates of repeatability equalled respectively: 0.076 and 0.139. The obtained genetic parameters were then used to estimate the breeding values of the animals in terms of controlled trait (Best Linear Unbiased Prediction method using linear and threshold models. The obtained animal breeding values rankings in respect of the same trait with the use of linear and threshold models were strongly correlated with each other (rs = 0.972. Negative genetic trends of fertility (0.01-0.08% per year were found.

SU{sub 2} nonstandard bases: the case of mutually unbiased bases

Energy Technology Data Exchange (ETDEWEB)

Olivier, Albouy; Kibler, Maurice R. [Universite de Lyon, Institut de Physique Nucleaire de Lyon, Universite Lyon, CNRS/IN2P3, 43 bd du 11 novembre 1918, F-69622 Villeurbanne Cedex (France)

2007-02-15

This paper deals with bases in a finite-dimensional Hilbert space. Such a space can be realized as a subspace of the representation space of SU{sub 2} corresponding to an irreducible representation of SU{sub 2}. The representation theory of SU{sub 2} is reconsidered via the use of two truncated deformed oscillators. This leads to replace the familiar scheme [j{sub 2}, j{sub z}] by a scheme [j{sup 2}, v{sub ra}], where the two-parameter operator v{sub ra} is defined in the universal enveloping algebra of the Lie algebra su{sub 2}. The eigenvectors of the commuting set of operators [j{sup 2}, v{sub ra}] are adapted to a tower of chains SO{sub 3} includes C{sub 2j+1} (2j belongs to N{sup *}), where C{sub 2j+1} is the cyclic group of order 2j + 1. In the case where 2j + 1 is prime, the corresponding eigenvectors generate a complete set of mutually unbiased bases. Some useful relations on generalized quadratic Gauss sums are exposed in three appendices. (authors)

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

Science.gov (United States)

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

Predictors of outcomes in outpatients with anorexia nervosa - Results from the ANTOP study.

Science.gov (United States)

Wild, Beate; Friederich, Hans-Christoph; Zipfel, Stephan; Resmark, Gaby; Giel, Katrin; Teufel, Martin; Schellberg, Dieter; Löwe, Bernd; de Zwaan, Martina; Zeeck, Almut; Herpertz, Stephan; Burgmer, Markus; von Wietersheim, Jörn; Tagay, Sefik; Dinkel, Andreas; Herzog, Wolfgang

2016-10-30

This study aimed to determine predictors of BMI and recovery for outpatients with anorexia nervosa (AN). Patients were participants of the ANTOP (Anorexia Nervosa Treatment of Out-Patients) trial and randomized to focal psychodynamic therapy (FPT), enhanced cognitive behavior therapy (CBT-E), or optimized treatment as usual (TAU-O). N=169 patients participated in the one-year follow-up (T4). Outcomes were the BMI and global outcome (recovery/partial syndrome/full syndrome) at T4. We examined the following baseline variables as possible predictors: age, BMI, duration of illness, subtype of AN, various axis I diagnoses, quality of life, self-esteem, and psychological characteristics relevant to AN. Linear and logistic regression analyses were conducted to identify the predictors of the BMI and global outcome. The strongest positive predictor for BMI and recovery at T4 was a higher baseline BMI of the patients. Negative predictors for BMI and recovery were a duration of illness >6 years and a lifetime depression diagnosis at baseline. Additionally, higher bodily pain was significantly associated with a lower BMI and self-esteem was a positive predictor for recovery at T4. A higher baseline BMI and shorter illness duration led to a better outcome. Further research is necessary to investigate whether or not AN patients with lifetime depression, higher bodily pain, and lower self-esteem may benefit from specific treatment approaches. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The New Peabody Picture Vocabulary Test-III: An Illusion of Unbiased Assessment?

Science.gov (United States)

Stockman, Ida J

2000-10-01

This article examines whether changes in the ethnic minority composition of the standardization sample for the latest edition of the Peabody Picture Vocabulary Test (PPVT-III, Dunn & Dunn, 1997) can be used as the sole explanation for children's better test scores when compared to an earlier edition, the Peabody Picture Vocabulary Test-Revised (PPVT-R, Dunn & Dunn, 1981). Results from a comparative analysis of these two test editions suggest that other factors may explain improved performances. Among these factors are the number of words and age levels sampled, the types of words and pictures used, and characteristics of the standardization sample other than its ethnic minority composition. This analysis also raises questions regarding the usefulness of converting scores from one edition to the other and the type of criteria that could be used to evaluate whether the PPVT-III is an unbiased test of vocabulary for children from diverse cultural and linguistic backgrounds.

Predictors of Immunosuppressive Regulatory T Lymphocytes in Healthy Women

International Nuclear Information System (INIS)

Hampras, S. S.; Nesline, M.; Davis, W.; Moysich, K. B.; Wallace, P. K.; Odunsi, K.; Furlani, N.

2012-01-01

Immunosuppressive regulatory T (Treg) cells play an important role in antitumor immunity, self-tolerance, transplantation tolerance, and attenuation of allergic response. Higher proportion of Treg cells has been observed in peripheral blood of cancer cases compared to controls. Little is known about potential epidemiological predictors of Treg cell levels in healthy individuals. We conducted a cross-sectional study including 75 healthy women, between 20 and 80 years of age, who participated in the Data Bank and Bio Repository (DBBR) program at Roswell Park Cancer Institute (RPCI), Buffalo, NY, USA. Peripheral blood levels of CD4 + CD25 + FOXP3 + Treg cells were measured using flow cytometric analysis. A range of risk factors was evaluated using Wilcoxon Rank-Sum test, Kruskal-Wallis test, and linear regression. Age, smoking, medications for treatment of osteoporosis, postmenopausal status, body mass index (BMI), and hormone replacement therapy (HRT) were found to be significant positive predictors of Treg cell levels in peripheral blood (π≤0.05 ). Higher education, exercise, age at first birth, oral contraceptives, and use of Ibuprofen were found be significant (π<0.05) negative predictors of Treg levels. Thus, various epidemiological risk factors might explain interindividual variation in immune response to pathological conditions, including cancer.

Interplanetary scintillation observations of an unbiased sample of 90 Ooty occultation radio sources at 326.5 MHz

International Nuclear Information System (INIS)

Banhatti, D.G.; Ananthakrishnan, S.

1989-01-01

We present 327-MHz interplanetary scintillation (IPS) observations of an unbiased sample of 90 extragalactic radio sources selected from the ninth Ooty lunar occultation list. The sources are brighter than 0.75 Jy at 327 MHz and lie outside the galactic plane. We derive values, the fraction of scintillating flux density, and the equivalent Gaussian diameter for the scintillating structure. Various correlations are found between the observed parameters. In particular, the scintillating component weakens and broadens with increasing largest angular size, and stronger scintillators have more compact scintillating components. (author)

THE PREDICTOR FACTORS OF EMERGENCY NURSES' PERFORMANCES TO THE PROFESSIONAL SERVICES EXCELLENCE

Directory of Open Access Journals (Sweden)

Rina Annisa

2017-10-01

Full Text Available Emergency nurses’ performances remains long standing determinates of quality services rendered for patients admitted to get emergency treatments in the hospitals. It has been viewed as a dimension of professional services excellence. The purpose of this study focused on the predictive correlation of five predictors; namely human resources management, transformational leadership, incentives, hospital structure, and job rotation on the emergency nurses’ performance. This descriptive quantitative study used total sampling technique of 100 nurses in the Emergency Department, in four Government Hospital in Banjarmasin, Bajarbaru, and Martapura. All data obtained by administering questionnaires to the participances. The analytical procedure of multiple linear regression was utilized to determine the predicting strength correlation between the dependent and the independent variables. The result of Pearson product‑moment correlation coefficients revealed that positive correlation established between emergency nurses’ performances and human resources management, transformational leadership, incentives, hospital structure, and job rotation, as the independent variables. The summary of multiple linear regression analysis of all independent variables indicated that incentives was the most strongly predictor to the emergency nurses’ performances.

Effect of correlation on covariate selection in linear and nonlinear mixed effect models.

Science.gov (United States)

Bonate, Peter L

2017-01-01

The effect of correlation among covariates on covariate selection was examined with linear and nonlinear mixed effect models. Demographic covariates were extracted from the National Health and Nutrition Examination Survey III database. Concentration-time profiles were Monte Carlo simulated where only one covariate affected apparent oral clearance (CL/F). A series of univariate covariate population pharmacokinetic models was fit to the data and compared with the reduced model without covariate. The "best" covariate was identified using either the likelihood ratio test statistic or AIC. Weight and body surface area (calculated using Gehan and George equation, 1970) were highly correlated (r = 0.98). Body surface area was often selected as a better covariate than weight, sometimes as high as 1 in 5 times, when weight was the covariate used in the data generating mechanism. In a second simulation, parent drug concentration and three metabolites were simulated from a thorough QT study and used as covariates in a series of univariate linear mixed effects models of ddQTc interval prolongation. The covariate with the largest significant LRT statistic was deemed the "best" predictor. When the metabolite was formation-rate limited and only parent concentrations affected ddQTc intervals the metabolite was chosen as a better predictor as often as 1 in 5 times depending on the slope of the relationship between parent concentrations and ddQTc intervals. A correlated covariate can be chosen as being a better predictor than another covariate in a linear or nonlinear population analysis by sheer correlation These results explain why for the same drug different covariates may be identified in different analyses. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Predictors of high out-of-pocket healthcare expenditure: an analysis using Bangladesh household income and expenditure survey, 2010.

Science.gov (United States)

Molla, Azaher Ali; Chi, Chunhuei; Mondaca, Alicia Lorena Núñez

2017-01-31

Predictors of high out-of-pocket household healthcare expenditure are essential for creating effective health system finance policy. In Bangladesh, 63.3% of health expenditure is out-of-pocket and born by households. It is imperative to know what determines household health expenditure. This study aims to investigate the predicting factors of high out-of-pocket household healthcare expenditure targeting to put forward policy recommendations on equity in financial burden. Bangladesh household income and expenditure survey 2010 provides data for this study. Predictors of high out-of-pocket household healthcare expenditure were analyzed using multiple linear regressions. We have modeled non-linear relationship using logarithmic form of linear regression. Heteroscedasticity and multicollinearity were checked using Breusch-Pagan/Cook-Weishberg and VIF tests. Normality of the residuals was checked using Kernel density curve. We applied required adjustment for survey data, so that standard errors and parameters estimation are valid. Presence of chronic disease and household income were found to be the most influential and statistically significant (p financing in Bangladesh to minimize the burden of high OOP healthcare expenditure.

RS-WebPredictor

DEFF Research Database (Denmark)

Zaretzki, J.; Bergeron, C.; Huang, T.-W.

2013-01-01

Regioselectivity-WebPredictor (RS-WebPredictor) is a server that predicts isozyme-specific cytochrome P450 (CYP)-mediated sites of metabolism (SOMs) on drug-like molecules. Predictions may be made for the promiscuous 2C9, 2D6 and 3A4 CYP isozymes, as well as CYPs 1A2, 2A6, 2B6, 2C8, 2C19 and 2E1....... RS-WebPredictor is the first freely accessible server that predicts the regioselectivity of the last six isozymes. Server execution time is fast, taking on average 2s to encode a submitted molecule and 1s to apply a given model, allowing for high-throughput use in lead optimization projects.......Availability: RS-WebPredictor is accessible for free use at http://reccr.chem.rpi.edu/ Software/RS-WebPredictor....

Predictors of Career Adaptability Skill among Higher Education Students in Nigeria

Directory of Open Access Journals (Sweden)

Amos Shaibu Ebenehi

2016-12-01

Full Text Available This paper examined predictors of career adaptability skill among higher  education students in Nigeria. A sample of 603 higher education students randomly selected from six colleges of education in Nigeria participated in this study.  A set of self-reported questionnaire was used for data collection, and multiple linear regression analysis was used to analyze the data.  Results indicated that 33.3% of career adaptability skill was explained by the model.  Four out of the five predictor variables significantly predicted career adaptability skill among higher education students in Nigeria.  Among the four predictors, career self-efficacy sources was the most statistically significant predictor of career adaptability skill among higher education students in Nigeria, followed by personal goal orientation, career future concern, and perceived social support respectively.  Vocational identity did not statistically predict career adaptability skill among higher education students in Nigeria.  The study suggested that similar study should be replicated in other parts of the world in view of the importance of career adaptability skill to the smooth transition of graduates from school to the labor market.  The study concluded by requesting stakeholders of higher institutions in Nigeria to provide career exploration database for the students, and encourage career intervention program in order to enhance career adaptability skill among the students.

Work-home interface stress: an important predictor of emotional exhaustion 15 years into a medical career

Science.gov (United States)

HERTZBERG, Tuva Kolstad; RØ, Karin Isaksson; VAGLUM, Per Jørgen Wiggen; MOUM, Torbjørn; RØVIK, Jan Ole; GUDE, Tore; EKEBERG, Øivind; TYSSEN, Reidar

2015-01-01

The importance of work-home interface stress can vary throughout a medical career and between genders. We studied changes in work-home interface stress over 5 yr, and their prediction of emotional exhaustion (main dimension of burn-out), controlled for other variables. A nationwide doctor cohort (NORDOC; n=293) completed questionnaires at 10 and 15 yr after graduation. Changes over the period were examined and predictors of emotional exhaustion analyzed using linear regression. Levels of work-home interface stress declined, whereas emotional exhaustion stayed on the same level. Lack of reduction in work-home interface stress was an independent predictor of emotional exhaustion in year 15 (β=−0.21, p=0.001). Additional independent predictors were reduction in support from colleagues (β=0.11, p=0.04) and emotional exhaustion at baseline (β=0.62, pseparate analyses, significant adjusted predictors were lack of reduction in work-home interface stress among women, and reduction of collegial support and lack of reduction in working hours among men. Thus, change in work-home interface stress is a key independent predictor of emotional exhaustion among doctors 15 yr after graduation. Some gender differences in predictors of emotional exhaustion were found. PMID:26538002

Nonlinear control of a multicomponent distillation process coupled with a binary distillation model as an EKF predictor.

Science.gov (United States)

Jana, Amiya Kumar; Ganguly, Saibal; Samanta, Amar Nath

2006-10-01

The work is devoted to design the globally linearizing control (GLC) strategy for a multicomponent distillation process. The control system is comprised with a nonlinear transformer, a nonlinear closed-loop state estimator [extended Kalman filter (EKF)], and a linear external controller [conventional proportional integral (PI) controller]. The model of a binary distillation column has been used as a state predictor to avoid huge design complexity of the EKF estimator. The binary components are the light key and the heavy key of the multicomponent system. The proposed GLC-EKF (GLC in conjunction with EKF) control algorithm has been compared with the GLC-ROOLE [GLC coupled with reduced-order open-loop estimator (ROOLE)] and the dual-loop PI controller based on set point tracking and disturbance rejection performance. Despite huge process/predictor mismatch, the superiority of the GLC-EKF has been inspected over the GLC-ROOLE control structure.

Unbiased methods for removing systematics from galaxy clustering measurements

Science.gov (United States)

Elsner, Franz; Leistedt, Boris; Peiris, Hiranya V.

2016-02-01

Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics - template subtraction, basic mode projection, and extended mode projection - all of which make use of externally supplied template maps, designed to characterize and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.

Work-home interface stress: an important predictor of emotional exhaustion 15 years into a medical career.

Science.gov (United States)

Hertzberg, Tuva Kolstad; Rø, Karin Isaksson; Vaglum, Per Jørgen Wiggen; Moum, Torbjørn; Røvik, Jan Ole; Gude, Tore; Ekeberg, Øivind; Tyssen, Reidar

2016-01-01

The importance of work-home interface stress can vary throughout a medical career and between genders. We studied changes in work-home interface stress over 5 yr, and their prediction of emotional exhaustion (main dimension of burn-out), controlled for other variables. A nationwide doctor cohort (NORDOC; n=293) completed questionnaires at 10 and 15 yr after graduation. Changes over the period were examined and predictors of emotional exhaustion analyzed using linear regression. Levels of work-home interface stress declined, whereas emotional exhaustion stayed on the same level. Lack of reduction in work-home interface stress was an independent predictor of emotional exhaustion in year 15 (β=-0.21, p=0.001). Additional independent predictors were reduction in support from colleagues (β=0.11, p=0.04) and emotional exhaustion at baseline (β=0.62, pwork-home interface stress among women, and reduction of collegial support and lack of reduction in working hours among men. Thus, change in work-home interface stress is a key independent predictor of emotional exhaustion among doctors 15 yr after graduation. Some gender differences in predictors of emotional exhaustion were found.

Modelling of diffuse solar fraction with multiple predictors

Energy Technology Data Exchange (ETDEWEB)

Ridley, Barbara; Boland, John [Centre for Industrial and Applied Mathematics, University of South Australia, Mawson Lakes Boulevard, Mawson Lakes, SA 5095 (Australia); Lauret, Philippe [Laboratoire de Physique du Batiment et des Systemes, University of La Reunion, Reunion (France)

2010-02-15

For some locations both global and diffuse solar radiation are measured. However, for many locations, only global radiation is measured, or inferred from satellite data. For modelling solar energy applications, the amount of radiation on a tilted surface is needed. Since only the direct component on a tilted surface can be calculated from direct on some other plane using trigonometry, we need to have diffuse radiation on the horizontal plane available. There are regression relationships for estimating the diffuse on a tilted surface from diffuse on the horizontal. Models for estimating the diffuse on the horizontal from horizontal global that have been developed in Europe or North America have proved to be inadequate for Australia. Boland et al. developed a validated model for Australian conditions. Boland et al. detailed our recent advances in developing the theoretical framework for the use of the logistic function instead of piecewise linear or simple nonlinear functions and was the first step in identifying the means for developing a generic model for estimating diffuse from global and other predictors. We have developed a multiple predictor model, which is much simpler than previous models, and uses hourly clearness index, daily clearness index, solar altitude, apparent solar time and a measure of persistence of global radiation level as predictors. This model performs marginally better than currently used models for locations in the Northern Hemisphere and substantially better for Southern Hemisphere locations. We suggest it can be used as a universal model. (author)

Raman spectroscopy compared against traditional predictors of shear force in lamb m. longissimus lumborum.

Science.gov (United States)

Fowler, Stephanie M; Schmidt, Heinar; van de Ven, Remy; Wynn, Peter; Hopkins, David L

2014-12-01

A Raman spectroscopic hand held device was used to predict shear force (SF) of 80 fresh lamb m. longissimus lumborum (LL) at 1 and 5days post mortem (PM). Traditional predictors of SF including sarcomere length (SL), particle size (PS), cooking loss (CL), percentage myofibrillar breaks and pH were also measured. SF values were regressed against Raman spectra using partial least squares regression and against the traditional predictors using linear regression. The best prediction of shear force values used spectra at 1day PM to predict shear force at 1day which gave a root mean square error of prediction (RMSEP) of 13.6 (Null=14.0) and the R(2) between observed and cross validated predicted values was 0.06 (R(2)cv). Overall, for fresh LL, the predictability SF, by either the Raman hand held probe or traditional predictors was low. Copyright © 2014 Elsevier Ltd. All rights reserved.

H/L transition time estimation in JET using conformal predictors

Energy Technology Data Exchange (ETDEWEB)

Gonzalez, S., E-mail: sergio.gonzalez@ciemat.es [Asociacion EURATOM/CIEMAT para Fusion, Madrid 28040 (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid 28040 (Spain); Murari, A. [Consorzio RFX, Associazione EURATOM/ENEA per la Fusione, Padova 4-25127 (Italy); Pereira, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid 28040 (Spain); Dormido-Canto, S.; Ramirez, J.M. [Departamento de Informatica y Automatica, UNED, Madrid 28040 (Spain)

2012-12-15

Highlights: Black-Right-Pointing-Pointer H/L transitions have been predicted using H/L and L/H models. Black-Right-Pointing-Pointer Models have been built using conformal predictors to hedge the prediction with confidence and credibility measures. Black-Right-Pointing-Pointer Models have been trained using linear and radial basis function kernels. Black-Right-Pointing-Pointer Conformal measures have proven their usefulness to validate data-driven models. - Abstract: Recent advances in data mining allow the automatic recognition of physical phenomena in the databases of fusion devices without human intervention. This is important to create large databases of physical events (thereby increasing the statistical relevance) in an unattended manner. Important examples are the L/H and H/L transitions. In this contribution, a novel technique is introduced to automatically locate H/L transitions in JET by using conformal predictors. The focus is on H/L transitions because typically there is not a clear signature in the time series of the most widely available signals to recognize the change of confinement. Conformal predictors hedge their prediction by means of two parameters: confidence and credibility. The technique has been based on binary supervised classifiers to separate the samples of the respective confinement modes. Results with several underlying classifiers are presented.

Predictors of pathological gambling severity taking gender differences into account.

Science.gov (United States)

González-Ortega, I; Echeburúa, E; Corral, P; Polo-López, R; Alberich, S

2013-01-01

The current study aims to identify predictors of pathological gambling (PG) severity, taking gender differences into account, in an outpatient sample of pathological gamblers seeking treatment. The sample for this study consisted of 103 subjects (51 women and 52 men) meeting current DSM-IV-TR criteria for PG. Linear and logistic regression analyses were used to examine different risk factors (gender, age, impulsivity, sensation seeking, self-esteem) and risk markers (depression, anxiety, gambling-related thoughts, substance abuse) as predictors of PG severity. Impulsivity, maladjustment in everyday life and age at gambling onset were the best predictors in the overall sample. When gender differences were taken into account, duration of gambling disorder in women and depression and impulsivity in men predicted PG severity. In turn, a high degree of severity in the South Oaks Gambling Screen score was related to older age and more familiy support in women and to low self-esteem and alcohol abuse in men. Female gamblers were older than male gamblers and started gambling later in life, but became dependent on gambling more quickly than men. Further research should examine these data to tailor treatment to specific patients' needs according to sex and individual characteristics. Copyright © 2012 S. Karger AG, Basel.

A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models.

Science.gov (United States)

Chen, Qingxia; Ibrahim, Joseph G

2014-07-01

Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.

An EM Algorithm for Double-Pareto-Lognormal Generalized Linear Model Applied to Heavy-Tailed Insurance Claims

Directory of Open Access Journals (Sweden)

Enrique Calderín-Ojeda

2017-11-01

Full Text Available Generalized linear models might not be appropriate when the probability of extreme events is higher than that implied by the normal distribution. Extending the method for estimating the parameters of a double Pareto lognormal distribution (DPLN in Reed and Jorgensen (2004, we develop an EM algorithm for the heavy-tailed Double-Pareto-lognormal generalized linear model. The DPLN distribution is obtained as a mixture of a lognormal distribution with a double Pareto distribution. In this paper the associated generalized linear model has the location parameter equal to a linear predictor which is used to model insurance claim amounts for various data sets. The performance is compared with those of the generalized beta (of the second kind and lognorma distributions.

UNBIASED INCLINATION DISTRIBUTIONS FOR OBJECTS IN THE KUIPER BELT

International Nuclear Information System (INIS)

Gulbis, A. A. S.; Elliot, J. L.; Adams, E. R.; Benecchi, S. D.; Buie, M. W.; Trilling, D. E.; Wasserman, L. H.

2010-01-01

Using data from the Deep Ecliptic Survey (DES), we investigate the inclination distributions of objects in the Kuiper Belt. We present a derivation for observational bias removal and use this procedure to generate unbiased inclination distributions for Kuiper Belt objects (KBOs) of different DES dynamical classes, with respect to the Kuiper Belt plane. Consistent with previous results, we find that the inclination distribution for all DES KBOs is well fit by the sum of two Gaussians, or a Gaussian plus a generalized Lorentzian, multiplied by sin i. Approximately 80% of KBOs are in the high-inclination grouping. We find that Classical object inclinations are well fit by sin i multiplied by the sum of two Gaussians, with roughly even distribution between Gaussians of widths 2.0 +0.6 -0.5 0 and 8.1 +2.6 -2.1 0 . Objects in different resonances exhibit different inclination distributions. The inclinations of Scattered objects are best matched by sin i multiplied by a single Gaussian that is centered at 19.1 +3.9 -3.6 0 with a width of 6.9 +4.1 -2.7 0 . Centaur inclinations peak just below 20 0 , with one exceptionally high-inclination object near 80 0 . The currently observed inclination distribution of the Centaurs is not dissimilar to that of the Scattered Extended KBOs and Jupiter-family comets, but is significantly different from the Classical and Resonant KBOs. While the sample sizes of some dynamical classes are still small, these results should begin to serve as a critical diagnostic for models of solar system evolution.

Generalized linear mixed models modern concepts, methods and applications

CERN Document Server

Stroup, Walter W

2012-01-01

PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data

Law enforcement officer versus non-law enforcement officer status as a longitudinal predictor of traditional and emerging cardiovascular risk factors

NARCIS (Netherlands)

Wright, Bruce R; Barbosa-Leiker, Celestina; Hoekstra, T.

Objective: To determine whether law enforcement officer (LEO) status and perceived stress are longitudinal predictors of traditional and inflammatory cardiovascular (CV) risk factors. Method: Linear hierarchical regression was employed to investigate the longitudinal (more than 7 years) relationship

Direct metagenomic detection of viral pathogens in nasal and fecal specimens using an unbiased high-throughput sequencing approach.

Directory of Open Access Journals (Sweden)

Shota Nakamura

Full Text Available With the severe acute respiratory syndrome epidemic of 2003 and renewed attention on avian influenza viral pandemics, new surveillance systems are needed for the earlier detection of emerging infectious diseases. We applied a "next-generation" parallel sequencing platform for viral detection in nasopharyngeal and fecal samples collected during seasonal influenza virus (Flu infections and norovirus outbreaks from 2005 to 2007 in Osaka, Japan. Random RT-PCR was performed to amplify RNA extracted from 0.1-0.25 ml of nasopharyngeal aspirates (N = 3 and fecal specimens (N = 5, and more than 10 microg of cDNA was synthesized. Unbiased high-throughput sequencing of these 8 samples yielded 15,298-32,335 (average 24,738 reads in a single 7.5 h run. In nasopharyngeal samples, although whole genome analysis was not available because the majority (>90% of reads were host genome-derived, 20-460 Flu-reads were detected, which was sufficient for subtype identification. In fecal samples, bacteria and host cells were removed by centrifugation, resulting in gain of 484-15,260 reads of norovirus sequence (78-98% of the whole genome was covered, except for one specimen that was under-detectable by RT-PCR. These results suggest that our unbiased high-throughput sequencing approach is useful for directly detecting pathogenic viruses without advance genetic information. Although its cost and technological availability make it unlikely that this system will very soon be the diagnostic standard worldwide, this system could be useful for the earlier discovery of novel emerging viruses and bioterrorism, which are difficult to detect with conventional procedures.

Attitudes of prejudice as a predictor of cultural competence among baccalaureate nursing students.

Science.gov (United States)

Dunagan, Pamela B; Kimble, Laura P; Gunby, Susan Sweat; Andrews, Margaret M

2014-06-01

The purpose of this study was to explore the relationship between attitudes of prejudice and cultural competence among nursing students. Using a mixed-methods design, a convenience sample of students (N = 129) currently enrolled in a baccalaureate nursing program was recruited via Web networking. Data regarding attitudes of prejudice, cultural competence, prior cultural experience, and integration of cultural competence were obtained via a Web-based survey. Multiple linear regression was used to predict cultural knowledge, attitudes, and consciousness. Although all three regression models were statistically significant, the significant predictors varied within each model. Greater prejudice was a significant predictor of less culturally competent attitudes toward providing nursing care. Existing prejudice among nursing students needs to be addressed to help promote positive cultural attitudes and, ultimately, cultural competent nursing care.

Unbiased, complete solar charging of a neutral flow battery by a single Si photocathode

DEFF Research Database (Denmark)

Wedege, Kristina; Bae, Dowon; Dražević, Emil

2018-01-01

Solar redox flow batteries have attracted attention as a possible integrated technology for simultaneous conversion and storage of solar energy. In this work, we review current efforts to design aqueous solar flow batteries in terms of battery electrolyte capacity, solar conversion efficiency...... and depth of solar charge. From a materials cost and design perspective, a simple, cost-efficient, aqueous solar redox flow battery will most likely incorporate only one semiconductor, and we demonstrate here a system where a single photocathode is accurately matched to the redox couples to allow...... for a complete solar charge. The single TiO2 protected Si photocathode with a catalytic Pt layer can fully solar charge a neutral TEMPO-sulfate/ferricyanide battery with a cell voltage of 0.35 V. An unbiased solar conversion efficiency of 1.6% is obtained and this system represents a new strategy in solar RFBs...

Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets

KAUST Repository

Castrillon, Julio; Genton, Marc G.; Yokota, Rio

2015-01-01

We develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic

Predicting Longitudinal Change in Language Production and Comprehension in Individuals with Down Syndrome: Hierarchical Linear Modeling.

Science.gov (United States)

Chapman, Robin S.; Hesketh, Linda J.; Kistler, Doris J.

2002-01-01

Longitudinal change in syntax comprehension and production skill, measured over six years, was modeled in 31 individuals (ages 5-20) with Down syndrome. The best fitting Hierarchical Linear Modeling model of comprehension uses age and visual and auditory short-term memory as predictors of initial status, and age for growth trajectory. (Contains…

Recent predictors of Indian summer monsoon based on Indian and Pacific Ocean SST

Science.gov (United States)

Shahi, Namendra Kumar; Rai, Shailendra; Mishra, Nishant

2018-02-01

This study investigates the relationship between sea surface temperature (SST) of various geographical locations of Indian and Pacific Ocean with the Indian summer monsoon rainfall (ISMR) to identify possible predictors of ISMR. We identified eight SST predictors based on spatial patterns of correlation coefficients between ISMR and SST of the regions mentioned above during the time domain 1982-2013. The five multiple linear regression (MLR) models have been developed by these predictors in various combinations. The stability and performance of these MLR models are verified using cross-validation method and other statistical methods. The skill of forecast to predict observed ISMR from these MLR models is found to be substantially better based on various statistical verification measures. It is observed that the MLR models constructed using the combination of SST indices in tropical and extra tropical Indian and Pacific is able to predict ISMR accurately for almost all the years during the time domain of our study. We tried to propose the physical mechanism of the teleconnection through regression analysis with wind over Indian subcontinent and the eight predictors and the results are in the conformity with correlation coefficient analysis. The robustness of these models is seen by predicting the ISMR during recent independent years of 2014-2017 and found the model 5 is able to predict ISMR accurately in these years also.

Revisiting AFLP fingerprinting for an unbiased assessment of genetic structure and differentiation of taurine and zebu cattle

Science.gov (United States)

2014-01-01

Background Descendants from the extinct aurochs (Bos primigenius), taurine (Bos taurus) and zebu cattle (Bos indicus) were domesticated 10,000 years ago in Southwestern and Southern Asia, respectively, and colonized the world undergoing complex events of admixture and selection. Molecular data, in particular genome-wide single nucleotide polymorphism (SNP) markers, can complement historic and archaeological records to elucidate these past events. However, SNP ascertainment in cattle has been optimized for taurine breeds, imposing limitations to the study of diversity in zebu cattle. As amplified fragment length polymorphism (AFLP) markers are discovered and genotyped as the samples are assayed, this type of marker is free of ascertainment bias. In order to obtain unbiased assessments of genetic differentiation and structure in taurine and zebu cattle, we analyzed a dataset of 135 AFLP markers in 1,593 samples from 13 zebu and 58 taurine breeds, representing nine continental areas. Results We found a geographical pattern of expected heterozygosity in European taurine breeds decreasing with the distance from the domestication centre, arguing against a large-scale introgression from European or African aurochs. Zebu cattle were found to be at least as diverse as taurine cattle. Western African zebu cattle were found to have diverged more from Indian zebu than South American zebu. Model-based clustering and ancestry informative markers analyses suggested that this is due to taurine introgression. Although a large part of South American zebu cattle also descend from taurine cows, we did not detect significant levels of taurine ancestry in these breeds, probably because of systematic backcrossing with zebu bulls. Furthermore, limited zebu introgression was found in Podolian taurine breeds in Italy. Conclusions The assessment of cattle diversity reported here contributes an unbiased global view to genetic differentiation and structure of taurine and zebu cattle

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations.

Science.gov (United States)

Bragg, Elise M; Briggs, Farran

2017-02-15

This protocol outlines large-scale reconstructions of neurons combined with the use of independent and unbiased clustering analyses to create a comprehensive survey of the morphological characteristics observed among a selective neuronal population. Combination of these techniques constitutes a novel approach for the collection and analysis of neuroanatomical data. Together, these techniques enable large-scale, and therefore more comprehensive, sampling of selective neuronal populations and establish unbiased quantitative methods for describing morphologically unique neuronal classes within a population. The protocol outlines the use of modified rabies virus to selectively label neurons. G-deleted rabies virus acts like a retrograde tracer following stereotaxic injection into a target brain structure of interest and serves as a vehicle for the delivery and expression of EGFP in neurons. Large numbers of neurons are infected using this technique and express GFP throughout their dendrites, producing "Golgi-like" complete fills of individual neurons. Accordingly, the virus-mediated retrograde tracing method improves upon traditional dye-based retrograde tracing techniques by producing complete intracellular fills. Individual well-isolated neurons spanning all regions of the brain area under study are selected for reconstruction in order to obtain a representative sample of neurons. The protocol outlines procedures to reconstruct cell bodies and complete dendritic arborization patterns of labeled neurons spanning multiple tissue sections. Morphological data, including positions of each neuron within the brain structure, are extracted for further analysis. Standard programming functions were utilized to perform independent cluster analyses and cluster evaluations based on morphological metrics. To verify the utility of these analyses, statistical evaluation of a cluster analysis performed on 160 neurons reconstructed in the thalamic reticular nucleus of the thalamus

Linear engine development for series hybrid electric vehicles

Science.gov (United States)

Toth-Nagy, Csaba

This dissertation argues that diminishing oil reserves, concern over global climate change, and desire to improve ambient air quality all demand the development of environment-friendly personal transportation. In certain applications, series hybrid electric vehicles offer an attractive solution to reducing fuel consumption and emissions. Furthermore, linear engines are emerging as a powerplant suited to series HEV applications. In this dissertation, a linear engine/alternator was considered as the auxiliary power unit of a range extender series hybrid electric vehicle. A prototype linear engine/alternator was developed, constructed and tested at West Virginia University. The engine was a 2-stroke, 2-cylinder, dual piston, direct injection, diesel engine. Experiment on the engine was performed to study its behavior. The study variables included mass of the translator, amount of fuel injected, injection timing, load, and stroke with operating frequency and mechanical efficiency as the basis of comparison. The linear engine was analyzed in detail and a simple simulation model was constructed to compare the trends of simulation with the experimental data and to expand on the area where the experimental data were lacking. The simulation was based on a simple and analytical model, rather than a detailed and intensely numerical one. The experimental and theoretical data showed similar trends. Increasing translator mass decreased the operating frequency and increased compression ratio. Larger mass and increased compression ratio improved the ability of the engine to sustain operation and the engine was able to idle on less fuel injected into the cylinder. Increasing the stroke length caused the operating frequency to drop. Increasing fueling or decreasing the load resulted in increased operating frequency. This projects the possibility of using the operating frequency as an input for feedback control of the engine. Injection timing was varied to investigate two different

Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.

Science.gov (United States)

Li, Yanming; Nan, Bin; Zhu, Ji

2015-06-01

We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.

Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins.

Science.gov (United States)

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2016-12-02

In the postgenomic era, the number of unreviewed protein sequences is remarkably larger and grows tremendously faster than that of reviewed ones. However, existing methods for protein subchloroplast localization often ignore the information from these unlabeled proteins. This paper proposes a multi-label predictor based on ensemble linear neighborhood propagation (LNP), namely, LNP-Chlo, which leverages hybrid sequence-based feature information from both labeled and unlabeled proteins for predicting localization of both single- and multi-label chloroplast proteins. Experimental results on a stringent benchmark dataset and a novel independent dataset suggest that LNP-Chlo performs at least 6% (absolute) better than state-of-the-art predictors. This paper also demonstrates that ensemble LNP significantly outperforms LNP based on individual features. For readers' convenience, the online Web server LNP-Chlo is freely available at http://bioinfo.eie.polyu.edu.hk/LNPChloServer/ .

On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

KAUST Repository

Zollanvari, Amin

2013-05-24

We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

KAUST Repository

Zollanvari, Amin; Genton, Marc G.

2013-01-01

We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

High Efficient THz Emission From Unbiased and Biased Semiconductor Nanowires Fabricated Using Electron Beam Lithography

Energy Technology Data Exchange (ETDEWEB)

Balci, Soner; Czaplewski, David A.; Jung, Il Woong; Kim, Ju-Hyung; Hatami, Fariba; Kung, Patrick; Kim, Seongsin Margaret

2017-07-01

Besides having perfect control on structural features, such as vertical alignment and uniform distribution by fabricating the wires via e-beam lithography and etching process, we also investigated the THz emission from these fabricated nanowires when they are applied DC bias voltage. To be able to apply a voltage bias, an interdigitated gold (Au) electrode was patterned on the high-quality InGaAs epilayer grown on InP substrate bymolecular beam epitaxy. Afterwards, perfect vertically aligned and uniformly distributed nanowires were fabricated in between the electrodes of this interdigitated pattern so that we could apply voltage bias to improve the THz emission. As a result, we achieved enhancement in the emitted THz radiation by ~four times, about 12 dB increase in power ratio at 0.25 THz with a DC biased electric field compared with unbiased NWs.

Exploring Predictors of Information Use to Self-Manage Blood Pressure in Midwestern African American Women with Hypertension.

Science.gov (United States)

Jones, Lenette M; Veinot, Tiffany; Pressler, Susan J; Coleman-Burns, Patricia; McCall, Alecia

2018-06-01

Self-management of hypertension requires patients to find, understand, and use information to lower their blood pressure. Little is known about information use among African American women with hypertension, therefore the purpose of this study was to examine predictors of self-reported information use to self-manage blood pressure. Ninety-four Midwestern African American women (mean age = 59) completed questionnaires about information behaviors (seeking, sharing, use) and personal beliefs (attitude, social norms) related to self-management of blood pressure. Linear regression was used to identify significant predictors of information use. The total variance explained by the model was 36%, F(7, 79) = 6.29, p < .001. Information sharing was the only significant predictor (beta = .46, p < .001). These results provide evidence that information sharing is a potential health behavior to support intervention strategies for African American women with hypertension.

Leaf area estimation of cassava from linear dimensions

Directory of Open Access Journals (Sweden)

SAMARA ZANETTI

2017-08-01

Full Text Available ABSTRACT The objective of this study was to determine predictor models of leaf area of cassava from linear leaf measurements. The experiment was carried out in greenhouse in the municipality of Botucatu, São Paulo state, Brazil. The stem cuttings with 5-7 nodes of the cultivar IAC 576-70 were planted in boxes filled with about 320 liters of soil, keeping soil moisture at field capacity, monitored by puncturing tensiometers. At 80 days after planting, 140 leaves were randomly collected from the top, middle third and base of cassava plants. We evaluated the length and width of the central lobe of leaves, number of lobes and leaf area. The measurements of leaf areas were correlated with the length and width of the central lobe and the number of lobes of the leaves, and adjusted to polynomial and multiple regression models. The linear function that used the length of the central lobe LA = -69.91114 + 15.06462L and linear multiple functions LA = -69.9188 + 15.5102L + 0.0197726K - 0.0768998J or LA = -69.9346 + 15.0106L + 0.188931K - 0.0264323H are suitable models to estimate leaf area of cassava cultivar IAC 576-70.

Unbiased reduced density matrices and electronic properties from full configuration interaction quantum Monte Carlo

International Nuclear Information System (INIS)

Overy, Catherine; Blunt, N. S.; Shepherd, James J.; Booth, George H.; Cleland, Deidre; Alavi, Ali

2014-01-01

Properties that are necessarily formulated within pure (symmetric) expectation values are difficult to calculate for projector quantum Monte Carlo approaches, but are critical in order to compute many of the important observable properties of electronic systems. Here, we investigate an approach for the sampling of unbiased reduced density matrices within the full configuration interaction quantum Monte Carlo dynamic, which requires only small computational overheads. This is achieved via an independent replica population of walkers in the dynamic, sampled alongside the original population. The resulting reduced density matrices are free from systematic error (beyond those present via constraints on the dynamic itself) and can be used to compute a variety of expectation values and properties, with rapid convergence to an exact limit. A quasi-variational energy estimate derived from these density matrices is proposed as an accurate alternative to the projected estimator for multiconfigurational wavefunctions, while its variational property could potentially lend itself to accurate extrapolation approaches in larger systems

Predictors of physical performance and functional ability in people 50+ with and without fibromyalgia.

Science.gov (United States)

Jones, C Jessie; Rutledge, Dana N; Aquino, Jordan

2010-07-01

The purposes of this study were to determine whether people with and without fibromyalgia (FM) age 50 yr and above showed differences in physical performance and perceived functional ability and to determine whether age, gender, depression, and physical activity level altered the impact of FM status on these factors. Dependent variables included perceived function and 6 performance measures (multidimensional balance, aerobic endurance, overall functional mobility, lower body strength, and gait velocity-normal or fast). Independent (predictor) variables were FM status, age, gender, depression, and physical activity level. Results indicated significant differences between adults with and without FM on all physical-performance measures and perceived function. Linear-regression models showed that the contribution of significant predictors was in expected directions. All regression models were significant, accounting for 16-65% of variance in the dependent variables.

LINEAR2007, Linear-Linear Interpolation of ENDF Format Cross-Sections

International Nuclear Information System (INIS)

2007-01-01

1 - Description of program or function: LINEAR converts evaluated cross sections in the ENDF/B format into a tabular form that is subject to linear-linear interpolation in energy and cross section. The code also thins tables of cross sections already in that form. Codes used subsequently need thus to consider only linear-linear data. IAEA1311/15: This version include the updates up to January 30, 2007. Changes in ENDF/B-VII Format and procedures, as well as the evaluations themselves, make it impossible for versions of the ENDF/B pre-processing codes earlier than PREPRO 2007 (2007 Version) to accurately process current ENDF/B-VII evaluations. The present code can handle all existing ENDF/B-VI evaluations through release 8, which will be the last release of ENDF/B-VI. Modifications from previous versions: - Linear VERS. 2007-1 (JAN. 2007): checked against all ENDF/B-VII; increased page size from 60,000 to 600,000 points 2 - Method of solution: Each section of data is considered separately. Each section of File 3, 23, and 27 data consists of a table of cross section versus energy with any of five interpolation laws. LINEAR will replace each section with a new table of energy versus cross section data in which the interpolation law is always linear in energy and cross section. The histogram (constant cross section between two energies) interpolation law is converted to linear-linear by substituting two points for each initial point. The linear-linear is not altered. For the log-linear, linear-log and log- log laws, the cross section data are converted to linear by an interval halving algorithm. Each interval is divided in half until the value at the middle of the interval can be approximated by linear-linear interpolation to within a given accuracy. The LINEAR program uses a multipoint fractional error thinning algorithm to minimize the size of each cross section table

Unbiased metal oxide semiconductor ionising radiation dosemeter

International Nuclear Information System (INIS)

Kumurdjian, N.; Sarrabayrouse, G.J.

1995-01-01

To assess the application of MOS devices as low dose rate dosemeters, the sensitivity is the major factor although little studies have been performed on that subject. It is studied here, as well as thermal stability and linearity of the response curve. Other advantages are specified such as large measurable dose range, low cost, small size, possibility of integration. (D.L.)

Early Predictors of Ten-Year Course in First-Episode Psychosis

DEFF Research Database (Denmark)

Friis, Svein; Melle, Ingrid; Johannessen, Jan Olav

2016-01-01

, five, and ten years (N=186 at ten years). Time in psychosis was defined as time with scores ≥4 on any of the Positive and Negative Syndrome Scale items P1, P3, P5, P6, and G9. Evaluations were retrospective, based on clinical interviews and all available clinical information. During the first two years......, patients were also evaluated by their clinicians at least biweekly. Baseline and early-course predictors of long-term course were identified with linear mixed-model analyses. RESULTS: Four variables provided significant, additive predictions of longer time in psychosis during the ten-year follow...

Talent predictors

Directory of Open Access Journals (Sweden)

Raquel Lorenzo

2007-07-01

Full Text Available The knowledge of talent predictors is the initial point for building diagnosis and encouragement procedures in this field. The meaning of word predictor is to anticipate the future, to divine. Early prediction of high performance is complex problem no resolute by the science yet. There are many discrepancies about what measure and how to do. The article analyze the art state in this problematic because the excellence is determined by the interaction between internal and environmental factors.

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

Science.gov (United States)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger

Unbiased roughness measurements: the key to better etch performance

Science.gov (United States)

Liang, Andrew; Mack, Chris; Sirard, Stephen; Liang, Chen-wei; Yang, Liu; Jiang, Justin; Shamma, Nader; Wise, Rich; Yu, Jengyi; Hymes, Diane

2018-03-01

Edge placement error (EPE) has become an increasingly critical metric to enable Moore's Law scaling. Stochastic variations, as characterized for lines by line width roughness (LWR) and line edge roughness (LER), are dominant factors in EPE and known to increase with the introduction of EUV lithography. However, despite recommendations from ITRS, NIST, and SEMI standards, the industry has not agreed upon a methodology to quantify these properties. Thus, differing methodologies applied to the same image often result in different roughness measurements and conclusions. To standardize LWR and LER measurements, Fractilia has developed an unbiased measurement that uses a raw unfiltered line scan to subtract out image noise and distortions. By using Fractilia's inverse linescan model (FILM) to guide development, we will highlight the key influences of roughness metrology on plasma-based resist smoothing processes. Test wafers were deposited to represent a 5 nm node EUV logic stack. The patterning stack consists of a core Si target layer with spin-on carbon (SOC) as the hardmask and spin-on glass (SOG) as the cap. Next, these wafers were exposed through an ASML NXE 3350B EUV scanner with an advanced chemically amplified resist (CAR). Afterwards, these wafers were etched through a variety of plasma-based resist smoothing techniques using a Lam Kiyo conductor etch system. Dense line and space patterns on the etched samples were imaged through advanced Hitachi CDSEMs and the LER and LWR were measured through both Fractilia and an industry standard roughness measurement software. By employing Fractilia to guide plasma-based etch development, we demonstrate that Fractilia produces accurate roughness measurements on resist in contrast to an industry standard measurement software. These results highlight the importance of subtracting out SEM image noise to obtain quicker developmental cycle times and lower target layer roughness.

Serum Predictors of Percent Lean Mass in Young Adults.

Science.gov (United States)

Lustgarten, Michael S; Price, Lori L; Phillips, Edward M; Kirn, Dylan R; Mills, John; Fielding, Roger A

2016-08-01

Lustgarten, MS, Price, LL, Phillips, EM, Kirn, DR, Mills, J, and Fielding, RA. Serum predictors of percent lean mass in young adults. J Strength Cond Res 30(8): 2194-2201, 2016-Elevated lean (skeletal muscle) mass is associated with increased muscle strength and anaerobic exercise performance, whereas low levels of lean mass are associated with insulin resistance and sarcopenia. Therefore, studies aimed at obtaining an improved understanding of mechanisms related to the quantity of lean mass are of interest. Percent lean mass (total lean mass/body weight × 100) in 77 young subjects (18-35 years) was measured with dual-energy x-ray absorptiometry. Twenty analytes and 296 metabolites were evaluated with the use of the standard chemistry screen and mass spectrometry-based metabolomic profiling, respectively. Sex-adjusted multivariable linear regression was used to determine serum analytes and metabolites significantly (p ≤ 0.05 and q ≤ 0.30) associated with the percent lean mass. Two enzymes (alkaline phosphatase and serum glutamate oxaloacetate aminotransferase) and 29 metabolites were found to be significantly associated with the percent lean mass, including metabolites related to microbial metabolism, uremia, inflammation, oxidative stress, branched-chain amino acid metabolism, insulin sensitivity, glycerolipid metabolism, and xenobiotics. Use of sex-adjusted stepwise regression to obtain a final covariate predictor model identified the combination of 5 analytes and metabolites as overall predictors of the percent lean mass (model R = 82.5%). Collectively, these data suggest that a complex interplay of various metabolic processes underlies the maintenance of lean mass in young healthy adults.

Predictors of specific phobia in children with Williams syndrome.

Science.gov (United States)

Pitts, C H; Klein-Tasman, B P; Osborne, J W; Mervis, C B

2016-10-01

Specific phobia (SP) is the most common anxiety disorder among children with Williams syndrome (WS); prevalence rates derived from Diagnostic and Statistical Manual of Mental Disorders-based diagnostic interviews range from 37% to 56%. We evaluated the effects of gender, age, intellectual abilities and/or behaviour regulation difficulties on the likelihood that a child with WS would be diagnosed with SP. A total of 194 6-17 year-olds with WS were evaluated. To best characterise the relations between the predictors and the probability of a SP diagnosis, we explored not only possible linear effects but also curvilinear effects. No gender differences were detected. As age increased, the likelihood of receiving a SP diagnosis decreased. As IQ increased, the probability of receiving a SP diagnosis also decreased. Behaviour regulation difficulties were the strongest predictor of a positive diagnosis. A quadratic relation was detected: The probability of receiving a SP diagnosis gradually rose as behaviour regulation difficulties increased. However, once behaviour regulation difficulties approached the clinical range, the probability of receiving a SP diagnosis asymptoted at a high level. Children with behaviour regulation difficulties in or just below the clinical range were at the greatest risk of developing SP. These findings highlight the value of large samples and the importance of evaluating for nonlinear effects to provide accurate model specification when characterising relations among a dependent variable and possible predictors. © 2016 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Exploring predictors of change in behavioral problems over a 1-year period in preterm born preschoolers.

Science.gov (United States)

Schappin, Renske; Wijnroks, Lex; Uniken Venema, Monica; Jongmans, Marian

2018-02-01

Although predictors of the prevalence of behavioral problems in preterm-born children have been frequently studied, predictors of behavioral change in these children remain unknown. Therefore, in this study we explore predictors of short-term changes in problem behavior in preterm-born preschoolers, an age period characterized by rapid behavioral change. Two- to 5-year-old children born with a gestational age behavioral problems. Following screening, 59 children with a t-score ≥60 on either the internal, external or total problem scale of the Child Behavior Checklist were included in the study. Linear mixed modeling was used to investigate predictors of change in behavior over a 1-year period. Higher levels of parenting stress, parent perceived child vulnerability, and parental hostility towards the child and lower educational levels of the mother significantly predicted increases in externalizing behavior. The higher the age of the child, the more internalizing problems decreased. Parenting stress, parent perceived child vulnerability and parental hostility towards the child were the only modifiable predictors of increases in externalizing behavior, whilst no modifiable predictors of internalizing behavior were found. There may be a reciprocal interaction between stress in parents and child externalizing problems. Furthermore, stress and worries may directly influence parents' reports on behavioral measures, because it could cause them to be concerned by behavior otherwise perceived as normal. Therefore, future interventions for parents of preterm-born children should primarily address parental stress and concerns regarding their child. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Linearization Method and Linear Complexity

Science.gov (United States)

Tanaka, Hidema

We focus on the relationship between the linearization method and linear complexity and show that the linearization method is another effective technique for calculating linear complexity. We analyze its effectiveness by comparing with the logic circuit method. We compare the relevant conditions and necessary computational cost with those of the Berlekamp-Massey algorithm and the Games-Chan algorithm. The significant property of a linearization method is that it needs no output sequence from a pseudo-random number generator (PRNG) because it calculates linear complexity using the algebraic expression of its algorithm. When a PRNG has n [bit] stages (registers or internal states), the necessary computational cost is smaller than O(2n). On the other hand, the Berlekamp-Massey algorithm needs O(N2) where N(≅2n) denotes period. Since existing methods calculate using the output sequence, an initial value of PRNG influences a resultant value of linear complexity. Therefore, a linear complexity is generally given as an estimate value. On the other hand, a linearization method calculates from an algorithm of PRNG, it can determine the lower bound of linear complexity.

Data assimilation for groundwater flow modelling using Unbiased Ensemble Square Root Filter: Case study in Guantao, North China Plain

Science.gov (United States)

Li, N.; Kinzelbach, W.; Li, H.; Li, W.; Chen, F.; Wang, L.

2017-12-01

Data assimilation techniques are widely used in hydrology to improve the reliability of hydrological models and to reduce model predictive uncertainties. This provides critical information for decision makers in water resources management. This study aims to evaluate a data assimilation system for the Guantao groundwater flow model coupled with a one-dimensional soil column simulation (Hydrus 1D) using an Unbiased Ensemble Square Root Filter (UnEnSRF) originating from the Ensemble Kalman Filter (EnKF) to update parameters and states, separately or simultaneously. To simplify the coupling between unsaturated and saturated zone, a linear relationship obtained from analyzing inputs to and outputs from Hydrus 1D is applied in the data assimilation process. Unlike EnKF, the UnEnSRF updates parameter ensemble mean and ensemble perturbations separately. In order to keep the ensemble filter working well during the data assimilation, two factors are introduced in the study. One is called damping factor to dampen the update amplitude of the posterior ensemble mean to avoid nonrealistic values. The other is called inflation factor to relax the posterior ensemble perturbations close to prior to avoid filter inbreeding problems. The sensitivities of the two factors are studied and their favorable values for the Guantao model are determined. The appropriate observation error and ensemble size were also determined to facilitate the further analysis. This study demonstrated that the data assimilation of both model parameters and states gives a smaller model prediction error but with larger uncertainty while the data assimilation of only model states provides a smaller predictive uncertainty but with a larger model prediction error. Data assimilation in a groundwater flow model will improve model prediction and at the same time make the model converge to the true parameters, which provides a successful base for applications in real time modelling or real time controlling strategies

Predictors of HbA1c levels in patients initiating metformin.

Science.gov (United States)

Martono, Doti P; Hak, Eelko; Lambers Heerspink, Hiddo; Wilffert, Bob; Denig, Petra

2016-12-01

The aim was to assess demographic and clinical factors as predictors of short (6 months) and long term (18 months) HbA1c levels in diabetes patients initiating metformin treatment. We conducted a cohort study including type 2 diabetes patients who received their first metformin prescription between 2007 and 2013 in the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was HbA1c level at follow-up adjusted for baseline HbA1c; the secondary outcome was failing to achieve the target HbA1c level of 53 mmol/mol. Associations were analyzed by linear and logistic regression. Multiple imputation was used for missing data. Additional analyses stratified by dose and adherence level were conducted. The cohort included 6050 patients initiating metformin. Baseline HbA1c at target consistently predicted better HbA1c outcomes. Longer diabetes duration and lower total cholesterol level at baseline were predictors for higher HbA1c levels at 6 months. At 18 months, cholesterol level was not a predictor. Longer diabetes duration was also associated with not achieving the target HbA1c at follow-up. The association for longer diabetes duration was especially seen in patients starting on low dose treatment. No consistent associations were found for comorbidity and comedication. Diabetes duration was a relevant predictor of HbA1c levels after 6 and 18 months of follow-up in patients initiating metformin treatment. Given the study design, no causal inference can be made. Our study suggests that prompt treatment intensification may be needed in patients who have a longer diabetes duration at treatment initiation.

Environmental and social-demographic predictors of the southern house mosquito Culex quinquefasciatus in New Orleans, Louisiana.

Science.gov (United States)

Moise, Imelda K; Riegel, Claudia; Muturi, Ephantus J

2018-04-17

Understanding the major predictors of disease vectors such as mosquitoes can guide the development of effective and timely strategies for mitigating vector-borne disease outbreaks. This study examined the influence of selected environmental, weather and sociodemographic factors on the spatial and temporal distribution of the southern house mosquito Culex quinquefasciatus Say in New Orleans, Louisiana, USA. Adult mosquitoes were collected over a 4-year period (2006, 2008, 2009 and 2010) using CDC gravid traps. Socio-demographic predictors were obtained from the United States Census Bureau, 2005-2009 American Community Survey and the City of New Orleans Department of Code Enforcement. Linear mixed effects models and ERDAS image processing software were used for statistical analysis and image processing. Only two of the 22 predictors examined were significant predictors of Cx. quinquefasciatus abundance. Mean temperature during the week of mosquito collection was positively associated with Cx. quinquefasciatus abundance while developed high intensity areas were negatively associated with Cx. quinquefasciatus abundance. The findings of this study illustrate the power and utility of integrating biophysical and sociodemographic data using GIS analysis to identify the biophysical and sociodemographic processes that increase the risk of vector mosquito abundance. This knowledge can inform development of accurate predictive models that ensure timely implementation of mosquito control interventions.

Predictors of thallium exposure and its relation with preterm birth.

Science.gov (United States)

Jiang, Yangqian; Xia, Wei; Zhang, Bin; Pan, Xinyun; Liu, Wenyu; Jin, Shuna; Huo, Wenqian; Liu, Hongxiu; Peng, Yang; Sun, Xiaojie; Zhang, Hongling; Zhou, Aifen; Xu, Shunqing; Li, Yuanyuan

2018-02-01

Thallium (Tl) is a well-recognized hazardous toxic heavy metal that has been reported to have embryotoxicity and fetotoxicity. However, little is known about its association with preterm birth (PTB) in humans. We aimed to evaluate the predictors of Tl exposure and assessed its relation with PTB. The study population included 7173 mother-infant pairs from a birth cohort in Wuhan, China. Predictors of Tl concentrations were explored using linear regression analyses, and associations of Tl exposure with risk of PTB or gestational age at birth were estimated using logistic regression or generalized linear models. The geometric mean and median values of urinary Tl concentrations were 0.28 μg/L (0.55 μg/g creatinine) and 0.29 μg/L (0.53 μg/g creatinine). We found that maternal urinary Tl concentrations varied by gestational weight gain, educational attainment, multivitamin and iron supplementations. Women with Tl concentrations higher than 0.80 μg/g creatinine were at higher risk of giving birth prematurely versus those with Tl concentrations lower than 0.36 μg/g creatinine [adjusted odds ratio (95% confidence interval (CI)): 1.55 (1.05, 2.27)], and the association was more pronounced in PTB with premature rupture of membranes (PROM) rather than in PTB without PROM. About 3-fold increase in creatinine-corrected Tl concentrations were associated with 0.99-day decrease in gestational length (95% CI: -1.36, -0.63). This is the first report on the associations between maternal Tl exposure and the risk of PTB. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generalized Functional Linear Models With Semiparametric Single-Index Interactions

KAUST Repository

Li, Yehua

2010-06-01

We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.

Generalized Functional Linear Models With Semiparametric Single-Index Interactions

KAUST Repository

Li, Yehua; Wang, Naisyin; Carroll, Raymond J.

2010-01-01

We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.

Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

KAUST Repository

Liang, Faming

2013-06-01

This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.

An unbiased expression screen for synaptogenic proteins identifies the LRRTM protein family as synaptic organizers.

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Linhoff, Michael W; Laurén, Juha; Cassidy, Robert M; Dobie, Frederick A; Takahashi, Hideto; Nygaard, Haakon B; Airaksinen, Matti S; Strittmatter, Stephen M; Craig, Ann Marie

2009-03-12

Delineating the molecular basis of synapse development is crucial for understanding brain function. Cocultures of neurons with transfected fibroblasts have demonstrated the synapse-promoting activity of candidate molecules. Here, we performed an unbiased expression screen for synaptogenic proteins in the coculture assay using custom-made cDNA libraries. Reisolation of NGL-3/LRRC4B and neuroligin-2 accounts for a minority of positive clones, indicating that current understanding of mammalian synaptogenic proteins is incomplete. We identify LRRTM1 as a transmembrane protein that induces presynaptic differentiation in contacting axons. All four LRRTM family members exhibit synaptogenic activity, LRRTMs localize to excitatory synapses, and artificially induced clustering of LRRTMs mediates postsynaptic differentiation. We generate LRRTM1(-/-) mice and reveal altered distribution of the vesicular glutamate transporter VGLUT1, confirming an in vivo synaptic function. These results suggest a prevalence of LRR domain proteins in trans-synaptic signaling and provide a cellular basis for the reported linkage of LRRTM1 to handedness and schizophrenia.

Predictors in Internet-delivered cognitive behavior therapy and behavioral stress management for severe health anxiety.

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Hedman, Erik; Andersson, Erik; Lekander, Mats; Ljótsson, Brjánn

2015-01-01

Severe health anxiety can be effectively treated with exposure-based Internet-delivered cognitive behavior therapy (ICBT), but information about which factors that predict outcome is scarce. Using data from a recently conducted RCT comparing ICBT (n = 79) with Internet-delivered behavioral stress management (IBSM) (n = 79) the presented study investigated predictors of treatment outcome. Analyses were conducted using a two-step linear regression approach and the dependent variable was operationalized both as end state health anxiety at post-treatment and as baseline-to post-treatment improvement. A hypothesis driven approach was used where predictors expected to influence outcome were based on a previous predictor study by our research group. As hypothesized, the results showed that baseline health anxiety and treatment adherence predicted both end state health anxiety and improvement. In addition, anxiety sensitivity, treatment credibility, and working alliance were significant predictors of health anxiety improvement. Demographic variables, i.e. age, gender, marital status, computer skills, educational level, and having children, had no significant predictive value. We conclude that it is possible to predict a substantial proportion of the outcome variance in ICBT and IBSM for severe health anxiety. The findings of the present study can be of high clinical value as they provide information about factors of importance for outcome in the treatment of severe health anxiety. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mutually orthogonal Latin squares from the inner products of vectors in mutually unbiased bases

International Nuclear Information System (INIS)

Hall, Joanne L; Rao, Asha

2010-01-01

Mutually unbiased bases (MUBs) are important in quantum information theory. While constructions of complete sets of d + 1 MUBs in C d are known when d is a prime power, it is unknown if such complete sets exist in non-prime power dimensions. It has been conjectured that complete sets of MUBs only exist in C d if a maximal set of mutually orthogonal Latin squares (MOLS) of side length d also exists. There are several constructions (Roy and Scott 2007 J. Math. Phys. 48 072110; Paterek, Dakic and Brukner 2009 Phys. Rev. A 79 012109) of complete sets of MUBs from specific types of MOLS, which use Galois fields to construct the vectors of the MUBs. In this paper, two known constructions of MUBs (Alltop 1980 IEEE Trans. Inf. Theory 26 350-354; Wootters and Fields 1989 Ann. Phys. 191 363-381), both of which use polynomials over a Galois field, are used to construct complete sets of MOLS in the odd prime case. The MOLS come from the inner products of pairs of vectors in the MUBs.

Linear versus geometric morphometric approaches for the analysis of head shape dimorphism in lizards.

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Fabre, Anne-Claire; Cornette, Raphäel; Huyghe, Katleen; Andrade, Denis V; Herrel, Anthony

2014-09-01

Differences between the sexes may arise because of differences in reproductive strategy, with females investing more in traits related to reproductive output and males investing more in traits related to resource holding capacity and territory defence. Sexual dimorphism is widespread in lizards and in many species males and females also differ in head shape. Males typically have bigger heads than females resulting in intersexual differences in bite force. Whereas most studies documenting differences in head dimensions between sexes use linear dimensions, the use of geometric morphometrics has been advocated as more appropriate to characterize such differences. This method may allow the characterization of local shape differences that may have functional consequences, and provides unbiased indicators of shape. Here, we explore whether the two approaches provide similar results in an analyses of head shape in Tupinambis merianae. The Argentine black and white tegu differs dramatically in body size, head size, and bite force between the sexes. However, whether the intersexual differences in bite force are simply the result of differences in head size or whether more subtle modifications (e.g., in muscle insertion areas) are involved remains currently unknown. Based on the crania and mandibles of 19 lizards with known bite force, we show intersexual differences in the shape of the cranium and mandible using both linear and geometric morphometric approaches. Although both types of analyses showed generally similar results for the mandible, this was not the case for the cranium. Geometric morphometric approaches provided better insights into the underlying functional relationships between the cranium and the jaw musculature, as illustrated by shape differences in muscle insertion areas not detected using linear morphometric data. © 2014 Wiley Periodicals, Inc.

Measures for Predictors of Innovation Adoption

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Chor, Ka Ho Brian; Wisdom, Jennifer P.; Olin, Su-Chin Serene; Hoagwood, Kimberly E.; Horwitz, Sarah M.

2014-01-01

Building on a narrative synthesis of adoption theories by Wisdom et al. (2013), this review identifies 118 measures associated with the 27 adoption predictors in the synthesis. The distribution of measures is uneven across the predictors and predictors vary in modifiability. Multiple dimensions and definitions of predictors further complicate measurement efforts. For state policymakers and researchers, more effective and integrated measurement can advance the adoption of complex innovations such as evidence-based practices. PMID:24740175

Multiple predictor smoothing methods for sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Helton, Jon Craig; Storlie, Curtis B.

2006-08-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.

Multiple predictor smoothing methods for sensitivity analysis

International Nuclear Information System (INIS)

Helton, Jon Craig; Storlie, Curtis B.

2006-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Predictors of Parenting Stress Trajectories in Premature Infant–Mother Dyads

Science.gov (United States)

Spinelli, Maria; Poehlmann, Julie; Bolt, Daniel

2014-01-01

This prospective longitudinal study examined predictors of parenting stress trajectories over time in a sample of 125 mothers and their preterm infants. Infant (multiple birth, gestational age, days hospitalized, and neonatal health risks) and maternal (socioeconomic, education, depressive symptoms, social support, and quality of interaction during infant feeding) characteristics were collected just prior to infant hospital discharge. Parenting stress and maternal interaction quality during play were measured at 4, 24, and 36 months corrected age. Hierarchical linear modeling was used to analyze infant and maternal characteristics as predictors of parenting stress scores and change over time. Results indicated significant variability across individuals in parenting stress at 4 months and in change trajectories. Mothers of multiples and infants with more medical risks and shorter hospitalization, and mothers with lower education and more depressive symptoms, reported more parenting stress at 4 months of age. Parenting stress decreased over time for mothers of multiples and for mothers with lower education more than for mothers of singletons or for mothers with higher educational levels. Changes in parenting stress scores over time were negatively associated with maternal behaviors during mother–infant interactions. Results are interpreted for their implications for preventive interventions. PMID:24188086

Critical thinking skills of undergraduate nursing students: description and demographic predictors.

Science.gov (United States)

Hunter, Sharyn; Pitt, Victoria; Croce, Nic; Roche, Jan

2014-05-01

This study investigated the critical thinking skills among undergraduate nursing students in Australia to obtain a profile and determine demographic predictors of critical thinking. There is universal agreement that being a critical thinker is an outcome requirement for many accreditation and registering nursing bodies. Most studies provide descriptive statistical information about critical thinking skills while some have studied the changes in critical thinking after an intervention. Limited research about factors that predict critical thinking skills is available. A cross-sectional descriptive study was conducted using convenience sampling. Two hundred and sixty-nine students were recruited across three years of an undergraduate programme in 2009. Most students' age ranged from under 20 to 34 years (58%), 87% were female, 91% were Australian and 23% of first and second year students had nursing associated experience external to the university. Data about critical thinking skills were collected via the Health Science Reasoning Test (HSRT). Linear regression analysis investigated the predictors of nursing students' critical thinking skills. The students in third year had a profile of critical thinking skills comparable with HSRT norms. Year of study predicted higher critical thinking scores for all domains (p<0.001) except the subscale, analysis. Nationality predicted higher scores for total CT skill scores (p<0.001) and subscales, inductive (p=0.001) and deductive reasoning (p=0.001). Nursing associated experience predicted higher scores for the subscale, analysis (p<0.001). Age and gender were not predictive. However, these demographic predictors only accounted for a small variance obtained for the domains of CT skills. An understanding of factors that predict nursing students' CT skills is required. Despite this study finding a number of significant predictors of nursing students' CT skills, there are others yet to be understood. Future research is recommended

Baseline Predictors for Success Following Strategy-Based Cognitive Remediation Group Training in Schizophrenia.

Science.gov (United States)

Farreny, Aida; Aguado, Jaume; Corbera, Silvia; Ochoa, Susana; Huerta-Ramos, Elena; Usall, Judith

2016-08-01

Our aim was to examine predictive variables associated with the improvement in cognitive, clinical, and functional outcomes after outpatient participation in REPYFLEC strategy-based Cognitive Remediation (CR) group training. In addition, we investigated which factors might be associated with some long-lasting effects at 6 months' follow-up. Predictors of improvement after CR were studied in a sample of 29 outpatients with schizophrenia. Partial correlations were computed between targeted variables and outcomes of response to explore significant associations. Subsequently, we built linear regression models for each outcome variable and predictors of improvement. The improvement in negative symptoms at posttreatment was linked to faster performance in the Trail Making Test B. Disorganization and cognitive symptoms were related to changes in executive function at follow-up. Lower levels of positive symptoms were related to durable improvements in life skills. Levels of symptoms and cognition were associated with improvements following CR, but the pattern of resulting associations was nonspecific.

Predictors of functional vision changes after cataract surgery: the PROVISION study.

Science.gov (United States)

Chaudhary, Varun; Popovic, Marko; Holmes, Julie; Robinson, Tammy; Mak, Michael; Mohaghegh P, S Mohammad; Eino, Dalia; Mann, Keith; Kobetz, Lawrence; Gusenbauer, Kaela; Barbosa, Joshua

2016-08-01

To ascertain whether time-to-treatment, sex, age, preoperative functional vision scores, education, and ocular comorbidities predict change in functional vision pre- to postoperatively in patients receiving cataract surgery. Prospective cohort study. Three hundred and forty-three cataract patients at the Hamilton Regional Eye Institute. Participants 18 years or older scheduled to undergo cataract surgery completed the Catquest-9SF functional vision questionnaire on the day of their surgery and were mailed a survey 2-3 months postoperatively. Multivariate linear regression was used to determine the ability of predictors to explain variability in functional vision change between questionnaire administrations. One hundred and sixty-six patients completed both baseline and follow-up questionnaires. Mean age of the cohort was 73.8 ± 8.1 years. Most patients were female (59.6%), had cataract surgery performed for the first time (66.9%), and had spent a mean time of 20.3 ± 20.7 weeks waiting for surgery. Functional vision improved in 83.7% of patients. The mean baseline Catquest-9SF score was the only significant predictor of functional vision improvement (adjusted R(2) = 0.47; F1,159 = 144.6; p functional vision improved by 0.74 logits when mean baseline survey score increased by 1 logit. In most patients, functional vision improved after cataract surgery. Mean baseline Catquest-9SF score was a moderate predictor of the observed improvement. Copyright © 2016 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

Two-year predictors of runaway and homeless episodes following shelter services among substance abusing adolescents.

Science.gov (United States)

Slesnick, Natasha; Guo, Xiamei; Brakenhoff, Brittany; Feng, Xin

2013-10-01

Given high levels of health and psychological costs associated with the family disruption of homelessness, identifying predictors of runaway and homeless episodes is an important goal. The current study followed 179 substance abusing, shelter-recruited adolescents who participated in a randomized clinical trial. Predictors of runaway and homeless episodes were examined over a two year period. Results from the hierarchical linear modeling analysis showed that family cohesion and substance use, but not family conflict or depressive symptoms, delinquency, or school enrollment predicted future runaway and homeless episodes. Findings suggest that increasing family support, care and connection and reducing substance use are important targets of intervention efforts in preventing future runaway and homeless episodes amongst a high risk sample of adolescents. Copyright © 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief networks

Science.gov (United States)

Rachmatia, H.; Kusuma, W. A.; Hasibuan, L. S.

2017-05-01

Selection in plant breeding could be more effective and more efficient if it is based on genomic data. Genomic selection (GS) is a new approach for plant-breeding selection that exploits genomic data through a mechanism called genomic prediction (GP). Most of GP models used linear methods that ignore effects of interaction among genes and effects of higher order nonlinearities. Deep belief network (DBN), one of the architectural in deep learning methods, is able to model data in high level of abstraction that involves nonlinearities effects of the data. This study implemented DBN for developing a GP model utilizing whole-genome Single Nucleotide Polymorphisms (SNPs) as data for training and testing. The case study was a set of traits in maize. The maize dataset was acquisitioned from CIMMYT’s (International Maize and Wheat Improvement Center) Global Maize program. Based on Pearson correlation, DBN is outperformed than other methods, kernel Hilbert space (RKHS) regression, Bayesian LASSO (BL), best linear unbiased predictor (BLUP), in case allegedly non-additive traits. DBN achieves correlation of 0.579 within -1 to 1 range.

An R2 statistic for fixed effects in the linear mixed model.

Science.gov (United States)

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

Meta-Analyses of Predictors of Hope in Adolescents.

Science.gov (United States)

Yarcheski, Adela; Mahon, Noreen E

2016-03-01

The purposes of this study were to identify predictors of hope in the literature reviewed, to use meta-analysis to determine the mean effect size (ES) across studies between each predictor and hope, and to examine four moderators on each predictor-hope relationship. Using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for the literature reviewed, 77 published studies or doctoral dissertations completed between 1990 and 2012 met the inclusion criteria. Eleven predictors of hope were identified and each predictor in relation to hope was subjected to meta-analysis. Five predictors (positive affect, life satisfaction, optimism, self-esteem, and social support) of hope had large mean ESs, 1 predictor (depression) had a medium ES, 4 predictors (negative affect, stress, academic achievement, and violence) had small ESs, and 1 predictor (gender) had a trivial ES. Findings are interpreted for the 11 predictors in relation to hope. Limitations and conclusions are addressed; future studies are recommended. © The Author(s) 2014.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

Science.gov (United States)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state

Multifractals embedded in short time series: An unbiased estimation of probability moment

Science.gov (United States)

Qiu, Lu; Yang, Tianguang; Yin, Yanhua; Gu, Changgui; Yang, Huijie

2016-12-01

An exact estimation of probability moments is the base for several essential concepts, such as the multifractals, the Tsallis entropy, and the transfer entropy. By means of approximation theory we propose a new method called factorial-moment-based estimation of probability moments. Theoretical prediction and computational results show that it can provide us an unbiased estimation of the probability moments of continuous order. Calculations on probability redistribution model verify that it can extract exactly multifractal behaviors from several hundred recordings. Its powerfulness in monitoring evolution of scaling behaviors is exemplified by two empirical cases, i.e., the gait time series for fast, normal, and slow trials of a healthy volunteer, and the closing price series for Shanghai stock market. By using short time series with several hundred lengths, a comparison with the well-established tools displays significant advantages of its performance over the other methods. The factorial-moment-based estimation can evaluate correctly the scaling behaviors in a scale range about three generations wider than the multifractal detrended fluctuation analysis and the basic estimation. The estimation of partition function given by the wavelet transform modulus maxima has unacceptable fluctuations. Besides the scaling invariance focused in the present paper, the proposed factorial moment of continuous order can find its various uses, such as finding nonextensive behaviors of a complex system and reconstructing the causality relationship network between elements of a complex system.

Development of an unbiased statistical method for the analysis of unigenic evolution

Directory of Open Access Journals (Sweden)

Shilton Brian H

2006-03-01

Full Text Available Abstract Background Unigenic evolution is a powerful genetic strategy involving random mutagenesis of a single gene product to delineate functionally important domains of a protein. This method involves selection of variants of the protein which retain function, followed by statistical analysis comparing expected and observed mutation frequencies of each residue. Resultant mutability indices for each residue are averaged across a specified window of codons to identify hypomutable regions of the protein. As originally described, the effect of changes to the length of this averaging window was not fully eludicated. In addition, it was unclear when sufficient functional variants had been examined to conclude that residues conserved in all variants have important functional roles. Results We demonstrate that the length of averaging window dramatically affects identification of individual hypomutable regions and delineation of region boundaries. Accordingly, we devised a region-independent chi-square analysis that eliminates loss of information incurred during window averaging and removes the arbitrary assignment of window length. We also present a method to estimate the probability that conserved residues have not been mutated simply by chance. In addition, we describe an improved estimation of the expected mutation frequency. Conclusion Overall, these methods significantly extend the analysis of unigenic evolution data over existing methods to allow comprehensive, unbiased identification of domains and possibly even individual residues that are essential for protein function.

A smart predictor for material property testing

International Nuclear Information System (INIS)

Wang, Wilson; Kanneg, Derek

2008-01-01

A reliable predictor is very useful for real-world industrial applications to forecast the future behavior of dynamic systems. A smart predictor, based on a novel recurrent neural fuzzy (RNF) scheme, is developed in this paper for multi-step-ahead prediction of material properties. A systematic investigation based on two benchmark data sets is conducted in terms of performance and efficiency. Analysis results reveal that, of the data-driven forecasting schemes, predictors based on step input patterns outperform those based on sequential input patterns; the RNF predictor outperforms those based on recurrent neural networks and ANFIS schemes in multi-step-ahead prediction of nonlinear time series. An adaptive Levenberg–Marquardt training technique is adopted to improve the robustness and convergence of the RNF predictor. Furthermore, the proposed smart predictor is implemented for material property testing. Investigation results show that the developed RNF predictor is a reliable forecasting tool for material property testing; it can capture and track the system's dynamic characteristics quickly and accurately. It is also a robust predictor to accommodate different system conditions

Stunted at 10 Years. Linear Growth Trajectories and Stunting from Birth to Pre-Adolescence in a Rural Bangladeshi Cohort.

Directory of Open Access Journals (Sweden)

Pernilla Svefors

Full Text Available Few studies in low-income settings analyse linear growth trajectories from foetal life to pre-adolescence. The aim of this study is to describe linear growth and stunting from birth to 10 years in rural Bangladesh and to analyse whether maternal and environmental determinants at conception are associated with linear growth throughout childhood and stunting at 10 years.Pregnant women participating in the MINIMat trial were identified in early pregnancy and a birth cohort (n = 1054 was followed with 19 growth measurements from birth to 10 years. Analyses of baseline predictors and mean height-for-age Z-scores (HAZ over time were modelled using GLMM. Logistic regression analysis was used to investigate the associations between baseline predictors and stunting (HAZ<-2 at 10 years. HAZ decreased to 2 years, followed by an increase up to 10 years, while the average height-for-age difference in cm (HAD to the WHO reference median continued to increase up to 10 years. Prevalence of stunting was highest at 2 years (50% decreasing to 29% at 10 years. Maternal height, maternal educational level and season of conception were all independent predictors of HAZ from birth to pre-adolescence (p<0.001 and stunting at 10 years. The highest probability to be stunted at 10 years was for children born by short mothers (<147.5 cm (ORadj 2.93, 95% CI: 2.06-4.20, mothers with no education (ORadj 1.74, 95% CI 1.17-2.81 or those conceived in the pre-monsoon season (ORadj 1.94, 95% CI 1.37-2.77.Height growth trajectories and prevalence of stunting in pre-adolescence showed strong intergenerational associations, social differentials, and environmental influence from foetal life. Targeting women before and during pregnancy is needed for the prevention of impaired child growth.

Self-efficacy and Resilience Are Useful Predictors of Transition Readiness Scores in Adolescents with Inflammatory Bowel Diseases

DEFF Research Database (Denmark)

Carlsen, Katrine; Haddad, Nichola; Gordon, Julia

2017-01-01

BACKGROUND: Adolescence is a vulnerable period for those afflicted with inflammatory bowel disease (IBD). There is limited knowledge of factors influencing transition readiness in this population. We sought to determine whether self-efficacy and resilience would be informative predictors of trans......BACKGROUND: Adolescence is a vulnerable period for those afflicted with inflammatory bowel disease (IBD). There is limited knowledge of factors influencing transition readiness in this population. We sought to determine whether self-efficacy and resilience would be informative predictors......-Davidson Resilience Scale. Demographic data and disease-specific information were collected from the medical record and by the provider. General linear modeling and autocorrelation were performed to investigate predictors of transition readiness. RESULTS: Eighty-seven patients (62 Crohn's disease and 25 ulcerative...... colitis) were included, with a median age of 19 years (interquartile range 1-3: 17-20; min-max: 16-23). After controlling for age, the IBD-SES-A predicted TRAQ [F(1) = 11.69, R = 0.16, P = 0.001], accounting for 16% of the variance. The Connor-Davidson Resilience Scale also independently predicted TRAQ...

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

Science.gov (United States)

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Linear Algebra and Smarandache Linear Algebra

OpenAIRE

Vasantha, Kandasamy

2003-01-01

The present book, on Smarandache linear algebra, not only studies the Smarandache analogues of linear algebra and its applications, it also aims to bridge the need for new research topics pertaining to linear algebra, purely in the algebraic sense. We have introduced Smarandache semilinear algebra, Smarandache bilinear algebra and Smarandache anti-linear algebra and their fuzzy equivalents. Moreover, in this book, we have brought out the study of linear algebra and vector spaces over finite p...

Clinical predictors of acute response to transcranial direct current stimulation (tDCS) in major depression.

Science.gov (United States)

D'Urso, Giordano; Dell'Osso, Bernardo; Rossi, Rodolfo; Brunoni, Andre Russowsky; Bortolomasi, Marco; Ferrucci, Roberta; Priori, Alberto; de Bartolomeis, Andrea; Altamura, Alfredo Carlo

2017-09-01

Transcranial direct current stimulation (tDCS) is a promising neuromodulation intervention for poor-responding or refractory depressed patients. However, little is known about predictors of response to this therapy. The present study aimed to analyze clinical predictors of response to tDCS in depressed patients. Clinical data from 3 independent tDCS trials on 171 depressed patients (including unipolar and bipolar depression), were pooled and analyzed to assess predictors of response. Depression severity and the underlying clinical dimensions were measured using the Hamilton Depression Rating Scale (HDRS) at baseline and after the tDCS treatment. Age, gender and diagnosis (bipolar/unipolar depression) were also investigated as predictors of response. Linear mixed models were fitted in order to ascertain which HDRS factors were associated with response to tDCS. Age, gender and diagnosis did not show any association with response to treatment. The reduction in HDRS scores after tDCS was strongly associated with the baseline values of "Cognitive Disturbances" and "Retardation" factors, whilst the "Anxiety/Somatization" factor showed a mild association with the response. Open-label design, the lack of control group, and minor differences in stimulation protocols. No differences in response to tDCS were found between unipolar and bipolar patients, suggesting that tDCS is effective for both conditions. "Cognitive disturbance", "Retardation", and "Anxiety/Somatization", were identified as potential clinical predictors of response to tDCS. These findings point to the pre-selection of the potential responders to tDCS, therefore optimizing the clinical use of this technique and the overall cost-effectiveness of the psychiatric intervention for depressed patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

Directory of Open Access Journals (Sweden)

N. Sriraam

2011-01-01

Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

Individual and mutual predictors of marital satisfaction among prostate cancer patients and their spouses.

Science.gov (United States)

Chien, Ching-Hui; Chuang, Cheng-Keng; Liu, Kuan-Lin; Huang, Xuan-Yi; Pang, See-Tong; Wu, Chun-Te; Chang, Ying-Hsu; Liu, Hsueh-Erh

2017-12-01

To determine the individual and mutual predictors of the marital satisfaction of couples in which the husband experienced prostate cancer. Marital satisfaction of patients with prostate cancer has been insufficiently studied in Asian countries as compared with Western countries. This study used a prospective and repeated-measures design. Seventy Taiwanese couples in which the husband had prostate cancer completed measures at 6 and 12 months post-treatment. Assessments of physical symptoms, marital satisfaction, coping behaviour and psychological distress were made. Multiple linear regression was used to analyse the data. The marital satisfaction of patients with prostate cancer and that of their spouses were significantly correlated. At 6 months, spouses' marital satisfaction, patients' appraisal of prostate cancer as a threat and patients' serum prostate-specific antigen levels were found to be the predictors of patients' marital satisfaction. Furthermore, patients' marital satisfaction and their spouses' psychological distress were predictors of spouses' marital satisfaction. At 12 months, spouses' marital satisfaction and patients' appraisal of prostate cancer as harm were predictors of patients' marital satisfaction. Finally, spouses' marital satisfaction (at 6 months) and appraisal of prostate cancer as a threat were predictors of spouses' marital satisfaction. At 6 months post-treatment, patients' and spouses' marital satisfaction will influence each other. However, at 12 months, patients' marital satisfaction exerts an insignificant effect on spouses' marital satisfaction. Moreover, patients' serum prostate-specific antigen level or the negative appraisal of prostate cancer affects their marital satisfaction. Spouses' marital satisfaction is affected by psychological distress and their negative appraisal of prostate cancer. The results can be used to develop interventions for prostate cancer couples. Such an intervention can be used to modify couples

Robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming.

Science.gov (United States)

Baran, Richard; Northen, Trent R

2013-10-15

Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

Science.gov (United States)

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

Unbiased and non-supervised learning methods for disruption prediction at JET

International Nuclear Information System (INIS)

Murari, A.; Vega, J.; Ratta, G.A.; Vagliasindi, G.; Johnson, M.F.; Hong, S.H.

2009-01-01

The importance of predicting the occurrence of disruptions is going to increase significantly in the next generation of tokamak devices. The expected energy content of ITER plasmas, for example, is such that disruptions could have a significant detrimental impact on various parts of the device, ranging from erosion of plasma facing components to structural damage. Early detection of disruptions is therefore needed with evermore increasing urgency. In this paper, the results of a series of methods to predict disruptions at JET are reported. The main objective of the investigation consists of trying to determine how early before a disruption it is possible to perform acceptable predictions on the basis of the raw data, keeping to a minimum the number of 'ad hoc' hypotheses. Therefore, the chosen learning techniques have the common characteristic of requiring a minimum number of assumptions. Classification and Regression Trees (CART) is a supervised but, on the other hand, a completely unbiased and nonlinear method, since it simply constructs the best classification tree by working directly on the input data. A series of unsupervised techniques, mainly K-means and hierarchical, have also been tested, to investigate to what extent they can autonomously distinguish between disruptive and non-disruptive groups of discharges. All these independent methods indicate that, in general, prediction with a success rate above 80% can be achieved not earlier than 180 ms before the disruption. The agreement between various completely independent methods increases the confidence in the results, which are also confirmed by a visual inspection of the data performed with pseudo Grand Tour algorithms.

Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models

Directory of Open Access Journals (Sweden)

Paccaud Fred

2004-04-01

Full Text Available Abstract Background We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. Methods Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i linear regression; (ii logistic classification; (iii regression trees; (iv classification trees (iii and iv are collectively known as "CART". Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. Results Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60–80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. Conclusions There were no striking differences between either the algebraic (i, ii vs. non-algebraic (iii, iv, or the regression (i, iii vs. classification (ii, iv modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Predictors of depression stigma

Directory of Open Access Journals (Sweden)

Jorm Anthony F

2008-04-01

Full Text Available Abstract Background To investigate and compare the predictors of personal and perceived stigma associated with depression. Method Three samples were surveyed to investigate the predictors: a national sample of 1,001 Australian adults; a local community sample of 5,572 residents of the Australian Capital Territory and Queanbeyan aged 18 to 50 years; and a psychologically distressed subset (n = 487 of the latter sample. Personal and Perceived Stigma were measured using the two subscales of the Depression Stigma Scale. Potential predictors included demographic variables (age, gender, education, country of birth, remoteness of residence, psychological distress, awareness of Australia's national depression initiative beyondblue, depression literacy and level of exposure to depression. Not all predictors were used for all samples. Results Personal stigma was consistently higher among men, those with less education and those born overseas. It was also associated with greater current psychological distress, lower prior contact with depression, not having heard of a national awareness raising initiative, and lower depression literacy. These findings differed from those for perceived stigma except for psychological distress which was associated with both higher personal and higher perceived stigma. Remoteness of residence was not associated with either type of stigma. Conclusion The findings highlight the importance of treating the concepts of personal and perceived stigma separately in designing measures of stigma, in interpreting the pattern of findings in studies of the predictors of stigma, and in designing, interpreting the impact of and disseminating interventions for stigma.

Erectile dysfunction is a strong predictor of poor quality of life in men with Type 2 diabetes mellitus.

Science.gov (United States)

Malavige, L S; Jayaratne, S D; Kathriarachchi, S T; Sivayogan, S; Ranasinghe, P; Levy, J C

2014-06-01

To identify predictors of poor quality of life among men with diabetes from a comprehensive set of sexual, clinical, socio-economic and lifestyle variables. This was a cross-sectional observational-study of 253 men with Type 2 diabetes, randomly selected from a clinic in Colombo, Sri Lanka. Erectile dysfunction was assessed using the five-item International Index of Erectile Function and quality of life was assessed using the Sri Lankan version of the 36-item short form health survey questionnaire and the disease-specific Psychological Impact of Erectile Dysfunction scale. The presence of premature ejaculation, reduced libido, socio-demographic and lifestyle data was obtained using an interviewer-administered questionnaire. Significant predictors of quality of life were identified by stepwise multivariate linear regression models for short form-36 subscales, summary scales and two scales of Psychological Impact of Erectile Dysfunction. Significant predictors on the physical summary scale of the 36-item short form were erectile dysfunction (β = 7.93, 95% CI 3.70-12.17, P 27.5 kg/m(2) (β = 9.12, 95% CI 1.38-17.44, P strong predictor of poor generic and disease-specific quality of life among other sexual and clinical variables in men with diabetes. © 2014 The Authors. Diabetic Medicine © 2014 Diabetes UK.

Predictors of Dietary Energy Density among Preschool Aged Children

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Nilmani N.T. Fernando

2018-02-01

Full Text Available Childhood obesity is a global problem with many contributing factors including dietary energy density (DED. This paper aims to investigate potential predictors of DED among preschool aged children in Victoria, Australia. Secondary analysis of longitudinal data for 209 mother–child pairs from the Melbourne Infant Feeding, Activity and Nutrition Trial was conducted. Data for predictors (maternal child feeding and nutrition knowledge, maternal dietary intake, home food availability, socioeconomic status were obtained through questionnaires completed by first-time mothers when children were aged 4 or 18 months. Three 24-h dietary recalls were completed when children were aged ~3.5 years. DED was calculated utilizing three methods: “food only”, “food and dairy beverages”, and “food and all beverages”. Linear regression analyses were conducted to identify associations between predictors and these three measures of children’s DED. Home availability of fruits (β: −0.82; 95% CI: −1.35, −0.29, p = 0.002 for DEDfood; β: −0.42; 95% CI: −0.82, −0.02, p = 0.041 for DEDfood+dairy beverages and non-core snacks (β: 0.11; 95% CI: 0.02, 0.20, p = 0.016 for DEDfood; β: 0.09; 95% CI: 0.02, 0.15, p = 0.010 for DEDfood+dairy beverages were significantly associated with two of the three DED measures. Providing fruit at home early in a child’s life may encourage the establishment of healthful eating behaviors that could promote a diet that is lower in energy density later in life. Home availability of non-core snacks is likely to increase the energy density of preschool children’s diets, supporting the proposition that non-core snack availability at home should be limited.

A 6-year longitudinal study of predictors for suicide attempts in major depressive disorder.

Science.gov (United States)

Eikelenboom, Merijn; Beekman, Aartjan T F; Penninx, Brenda W J H; Smit, Johannes H

2018-06-13

Major depressive disorder (MDD), represent a major source of risk for suicidality. However, knowledge about risk factors for future suicide attempts (SAs) within MDD is limited. The present longitudinal study examined a wide range of putative non-clinical risk factors (demographic, social, lifestyle, personality) and clinical risk factors (depressive and suicidal indicators) for future SAs among persons with MDD. Furthermore, we examined the relationship between a number of significant predictors and the incidence of a future SA. Data are from 1713 persons (18-65 years) with a lifetime MDD at the baseline measurement of the Netherlands Study of Depression and Anxiety who were subsequently followed up 2, 4 and 6 years. SAs were assessed in the face-to-face measurements. Cox proportional hazard regression analyses were used to examine a wide range of possible non-clinical and clinical predictors for subsequent SAs during 6-year follow-up. Over a period of 6 years, 3.4% of the respondents attempted suicide. Younger age, lower education, unemployment, insomnia, antidepressant use, a previous SA and current suicidal thoughts independently predicted a future SA. The number of significant risk factors (ranging from 0 to 7) linearly predicted the incidence of future SAs: in those with 0 predictors the SA incidence was 0%, which increased to 32% incidence in those with 6+ predictors. Of the non-clinical factors, particularly socio-economic factors predicted a SA independently. Furthermore, preexisting suicidal ideation and insomnia appear to be important clinical risk factors for subsequent SA that are open to preventative intervention.

Unraveling cognitive traits using the Morris water maze unbiased strategy classification (MUST-C) algorithm.

Science.gov (United States)

Illouz, Tomer; Madar, Ravit; Louzon, Yoram; Griffioen, Kathleen J; Okun, Eitan

2016-02-01

The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) - based, automated, MWM unbiased strategy classification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Psycho-cognitive predictors of burnout in healthcare professionals working in emergency departments.

Science.gov (United States)

Masiero, Marianna; Cutica, Ilaria; Russo, Selena; Mazzocco, Ketti; Pravettoni, Gabriella

2018-07-01

Healthcare professionals working in emergency departments commonly experience high work pressure and stress due to witnessing human suffering and the unpredictable nature of the work. Several studies have identified variables that affect burnout syndrome, but poor data are available about the predictors of the different dimensions of burnout (depersonalisation, emotional exhaustion, professional inefficacy and disillusionment). Some research has suggested that alexithymia, coping style and decision-making style may predict burnout. We conducted a noninterventional study to investigate whether and how alexithymia, coping style and decision-making style are associated with the different dimensions of burnout. We recruited a convenience sample of 93 healthcare professionals working in an Italian emergency departments. Participants completed a questionnaire assessing their level of burnout (the Link Burnout Questionnaire), and possible burnout predictors: decision-making style, alexithymia and the coping style. Four bivariate linear regressions were performed to define the predictors that characterised the dimensions of burnout. We found that an avoidant decision-making style and a difficulty to identify and describe feelings (a difficulty close to alexithymia even though not as severe) are strong predictors of some burnout dimensions. Individuals who experience relational depersonalisation are more likely to turn to religion as a way to cope. Our research shows that, to some extent, difficulties in emotion regulation and the attitude to avoid or postpone decisions characterised burnout. These results might be used to develop tailored psycho-educational interventions. This might help healthcare professionals to develop personal skills to cope with the critical conditions that characterise their work and to enable them to recognise potential risk factors that favour burnout. This has pivotal implications for the maintenance of the patient-healthcare professional

Reduction of Linear Programming to Linear Approximation

OpenAIRE

Vaserstein, Leonid N.

2006-01-01

It is well known that every Chebyshev linear approximation problem can be reduced to a linear program. In this paper we show that conversely every linear program can be reduced to a Chebyshev linear approximation problem.

Predictors of psychological resilience amongst medical students following major earthquakes.

Science.gov (United States)

Carter, Frances; Bell, Caroline; Ali, Anthony; McKenzie, Janice; Boden, Joseph M; Wilkinson, Timothy; Bell, Caroline

2016-05-06

To identify predictors of self-reported psychological resilience amongst medical students following major earthquakes in Canterbury in 2010 and 2011. Two hundred and fifty-three medical students from the Christchurch campus, University of Otago, were invited to participate in an electronic survey seven months following the most severe earthquake. Students completed the Connor-Davidson Resilience Scale, the Depression, Anxiety and Stress Scale, the Post-traumatic Disorder Checklist, the Work and Adjustment Scale, and the Eysenck Personality Questionnaire. Likert scales and other questions were also used to assess a range of variables including demographic and historical variables (eg, self-rated resilience prior to the earthquakes), plus the impacts of the earthquakes. The response rate was 78%. Univariate analyses identified multiple variables that were significantly associated with higher resilience. Multiple linear regression analyses produced a fitted model that was able to explain 35% of the variance in resilience scores. The best predictors of higher resilience were: retrospectively-rated personality prior to the earthquakes (higher extroversion and lower neuroticism); higher self-rated resilience prior to the earthquakes; not being exposed to the most severe earthquake; and less psychological distress following the earthquakes. Psychological resilience amongst medical students following major earthquakes was able to be predicted to a moderate extent.

The value of reproductive tract scoring as a predictor of fertility and production outcomes in beef heifers.

Science.gov (United States)

Holm, D E; Thompson, P N; Irons, P C

2009-06-01

In this study, 272 beef heifers were studied from just before their first breeding season (October 15, 2003), through their second breeding season, and until just after they had weaned their first calves in March, 2005. This study was performed concurrently with another study testing the economic effects of an estrous synchronization protocol using PG. Reproductive tract scoring (RTS) by rectal palpation was performed on the group of heifers 1 d before the onset of their first breeding season. The effect of RTS on several fertility and production outcomes was tested, and the association of RTS with the outcomes was compared with that of other input variables such as BW, age, BCS, and Kleiber ratio using multiple or univariable linear, logistic, or Cox regression. Area under the curve for receiver operating characteristic analysis was used to compare the ability of different input variables to predict pregnancy outcome. After adjustment for BW and age, RTS was positively associated with pregnancy rate to the 50-d AI season (P Reproductive tract scoring was a better predictor of fertility than was Kleiber ratio and similar in its prediction of calf weaning weight. It was concluded from this study that RTS is a predictor of heifer fertility, compares well with other traits used as a predictor of production outcomes, and is likely to be a good predictor of lifetime production of the cow.

Integrated analysis of hematopoietic differentiation outcomes and molecular characterization reveals unbiased differentiation capacity and minor transcriptional memory in HPC/HSC-iPSCs.

Science.gov (United States)

Gao, Shuai; Hou, Xinfeng; Jiang, Yonghua; Xu, Zijian; Cai, Tao; Chen, Jiajie; Chang, Gang

2017-01-23

Transcription factor-mediated reprogramming can reset the epigenetics of somatic cells into a pluripotency compatible state. Recent studies show that induced pluripotent stem cells (iPSCs) always inherit starting cell-specific characteristics, called epigenetic memory, which may be advantageous, as directed differentiation into specific cell types is still challenging; however, it also may be unpredictable when uncontrollable differentiation occurs. In consideration of biosafety in disease modeling and personalized medicine, the availability of high-quality iPSCs which lack a biased differentiation capacity and somatic memory could be indispensable. Herein, we evaluate the hematopoietic differentiation capacity and somatic memory state of hematopoietic progenitor and stem cell (HPC/HSC)-derived-iPSCs (HPC/HSC-iPSCs) using a previously established sequential reprogramming system. We found that HPC/HSCs are amenable to being reprogrammed into iPSCs with unbiased differentiation capacity to hematopoietic progenitors and mature hematopoietic cells. Genome-wide analyses revealed that no global epigenetic memory was detectable in HPC/HSC-iPSCs, but only a minor transcriptional memory of HPC/HSCs existed in a specific tetraploid complementation (4 N)-incompetent HPC/HSC-iPSC line. However, the observed minor transcriptional memory had no influence on the hematopoietic differentiation capacity, indicating the reprogramming of the HPC/HSCs was nearly complete. Further analysis revealed the correlation of minor transcriptional memory with the aberrant distribution of H3K27me3. This work provides a comprehensive framework for obtaining high-quality iPSCs from HPC/HSCs with unbiased hematopoietic differentiation capacity and minor transcriptional memory.

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models.

Science.gov (United States)

Wiedermann, Wolfgang; Artner, Richard; von Eye, Alexander

2017-01-01

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.

SIRAH: a structurally unbiased coarse-grained force field for proteins with aqueous solvation and long-range electrostatics.

Science.gov (United States)

Darré, Leonardo; Machado, Matías Rodrigo; Brandner, Astrid Febe; González, Humberto Carlos; Ferreira, Sebastián; Pantano, Sergio

2015-02-10

Modeling of macromolecular structures and interactions represents an important challenge for computational biology, involving different time and length scales. However, this task can be facilitated through the use of coarse-grained (CG) models, which reduce the number of degrees of freedom and allow efficient exploration of complex conformational spaces. This article presents a new CG protein model named SIRAH, developed to work with explicit solvent and to capture sequence, temperature, and ionic strength effects in a topologically unbiased manner. SIRAH is implemented in GROMACS, and interactions are calculated using a standard pairwise Hamiltonian for classical molecular dynamics simulations. We present a set of simulations that test the capability of SIRAH to produce a qualitatively correct solvation on different amino acids, hydrophilic/hydrophobic interactions, and long-range electrostatic recognition leading to spontaneous association of unstructured peptides and stable structures of single polypeptides and protein-protein complexes.

Nonlinear unbiased minimum-variance filter for Mars entry autonomous navigation under large uncertainties and unknown measurement bias.

Science.gov (United States)

Xiao, Mengli; Zhang, Yongbo; Fu, Huimin; Wang, Zhihua

2018-05-01

High-precision navigation algorithm is essential for the future Mars pinpoint landing mission. The unknown inputs caused by large uncertainties of atmospheric density and aerodynamic coefficients as well as unknown measurement biases may cause large estimation errors of conventional Kalman filters. This paper proposes a derivative-free version of nonlinear unbiased minimum variance filter for Mars entry navigation. This filter has been designed to solve this problem by estimating the state and unknown measurement biases simultaneously with derivative-free character, leading to a high-precision algorithm for the Mars entry navigation. IMU/radio beacons integrated navigation is introduced in the simulation, and the result shows that with or without radio blackout, our proposed filter could achieve an accurate state estimation, much better than the conventional unscented Kalman filter, showing the ability of high-precision Mars entry navigation algorithm. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Predictors for half-year outcome of impairment in daily life for back pain patients referred for physiotherapy: a prospective observational study.

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Sven Karstens

Full Text Available BACKGROUND AND OBJECTIVE: From observational studies, there is only sparse information available on the predictors of development of impairment in daily life for patients receiving physiotherapy. Therefore, our aim was to identify factors which predict impairment in daily life for patients with back pain 6 months after receiving physiotherapy. METHODS: We conducted a prospective cohort study with 6-month follow-up. Patients were enrolled for treatment in private physiotherapy practices. Patients with a first physiotherapy referral because of thoracic or low back pain, aged 18 to 65 years were included. Primary outcome impairment was measured utilising the 16-item version of the Musculoskeletal Function Assessment Questionnaire. Therapy was documented on a standardized form. Baseline scores for impairment in daily life, symptom characteristics, sociodemographic and psychosocial factors, physical activity, nicotine consumption, intake of analgesics, comorbidity and delivered primary therapy approach were investigated as possible predictors. Univariate and multiple linear regression analyses were performed. RESULTS: A total of 792 patients participated in the study (59% female, mean age 44.4 (SD 11.4, with 6-month follow-up results available from 391 patients. In univariate analysis 17 variables reached significance. In multiple linear regression identified predictors were: impairment in daily life before therapy, mental disorders, duration of the complaints, self-prognosis on work ability, rheumatoid arthritis, age, form of stress at work and physical activity. The variables explain 34% of variance (adjusted R(2, p<0.001. CONCLUSIONS: With minimal information available from observational studies on the predictors of development of back problems for physiotherapy patients, this study adds new knowledge for forming appropriate referral guidelines. Impairment in daily life before therapy, mental disorder as comorbidity and the duration of the

Predictors of future fasting and 2-h post-OGTT plasma glucose levels in middle-aged men and women-the Inter99 study

DEFF Research Database (Denmark)

Faerch, K; Vaag, A; Witte, D R

2009-01-01

PG levels. Among the anthropometric variables, large waist circumference was the strongest predictor of increased FPG levels in men, whereas high body mass index (BMI) was the strongest predictor of increased FPG levels in women. In both men and women, BMI and waist circumference were equally strong...... elevations of FPG levels were different from those predicting elevations of 2hPG levels in men and women. METHODS: We used baseline and 5-year follow-up data from middle-aged men and women with normal glucose tolerance (NGT) at baseline in the Danish population-based Inter99 study (n = 3164). Anthropometric...... and non-anthropometric baseline predictors of the 5-year FPG and 2hPG levels were estimated in linear regression models stratified by gender. RESULTS: In men, but not in women, smoking and family history of diabetes predicted increased FPG levels, whereas high physical activity predicted a decline in 2h...

Unbiased structural search of small copper clusters within DFT

Energy Technology Data Exchange (ETDEWEB)

Cogollo-Olivo, Beatriz H., E-mail: bcogolloo@unicartagena.edu.co [Maestría en Ciencias Físicas, Universidad de Cartagena, 130001 Cartagena de Indias, Bolívar (Colombia); Seriani, Nicola, E-mail: nseriani@ictp.it [Condensed Matter and Statistical Physics Section, The Abdus Salam ICTP, Strada Costiera 11, 34151 Trieste (Italy); Montoya, Javier A., E-mail: jmontoyam@unicartagena.edu.co [Instituto de Matemáticas Aplicadas, Universidad de Cartagena, 130001 Cartagena de Indias, Bolívar (Colombia); Associates Program, The Abdus Salam ICTP, Strada Costiera 11, 34151 Trieste (Italy)

2015-11-05

Highlights: • We have been able to identify novel metastable structures for small Cu clusters. • We have shown that a linear structure reported for Cu{sub 3} is actually a local maximum. • Some of the structures reported in literature are actually unstable within DFT. • Some of the isomer structures found shows the limits of educated guesses. - Abstract: The atomic structure of small Cu clusters with 3–6 atoms has been investigated by density functional theory and random search algorithm. New metastable structures have been found that lie merely tens of meV/atom above the corresponding ground state, and could therefore be present at thermodynamic equilibrium at room temperature or slightly above. Moreover, we show that the previously proposed linear configuration for Cu{sub 3} is in fact a local maximum of the energy. Finally, we argue that the random search algorithm also provides qualitative information about the attraction basin of each structure in the energy landscape.

Unbiased structural search of small copper clusters within DFT

International Nuclear Information System (INIS)

Cogollo-Olivo, Beatriz H.; Seriani, Nicola; Montoya, Javier A.

2015-01-01

Highlights: • We have been able to identify novel metastable structures for small Cu clusters. • We have shown that a linear structure reported for Cu_3 is actually a local maximum. • Some of the structures reported in literature are actually unstable within DFT. • Some of the isomer structures found shows the limits of educated guesses. - Abstract: The atomic structure of small Cu clusters with 3–6 atoms has been investigated by density functional theory and random search algorithm. New metastable structures have been found that lie merely tens of meV/atom above the corresponding ground state, and could therefore be present at thermodynamic equilibrium at room temperature or slightly above. Moreover, we show that the previously proposed linear configuration for Cu_3 is in fact a local maximum of the energy. Finally, we argue that the random search algorithm also provides qualitative information about the attraction basin of each structure in the energy landscape.

Athletic Departments' Operating Expenses as a Predictor of Their Directors' Cup Standing

Science.gov (United States)

Magner, Amber

2014-01-01

The NACDA Directors' Cup is a competition utilizing an unbiased scoring system that encourages a broad based athletic department as the standard for defining intercollegiate athletic success. Therefore, for NCAA DI athletic administrators the Directors' Cup should be the standard for defining intercollegiate athletic success. The purpose of this…

Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

Directory of Open Access Journals (Sweden)

Eric R. Edelman

2017-06-01

Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related

Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

Science.gov (United States)

Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A

2017-01-01

For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model

Science.gov (United States)

Campbell, Stefan F.; Kaneshige, John T.

2010-01-01

Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).

Sparsity in Linear Predictive Coding of Speech

DEFF Research Database (Denmark)

Giacobello, Daniele

of the effectiveness of their application in audio processing. The second part of the thesis deals with introducing sparsity directly in the linear prediction analysis-by-synthesis (LPAS) speech coding paradigm. We first propose a novel near-optimal method to look for a sparse approximate excitation using a compressed...... one with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter can be modeled as an impulse train, i.e., a sparse sequence. Introducing sparsity in the LP framework will also bring to de- velop the concept...... sensing formulation. Furthermore, we define a novel re-estimation procedure to adapt the predictor coefficients to the given sparse excitation, balancing the two representations in the context of speech coding. Finally, the advantages of the compact parametric representation of a segment of speech, given...

A happiness degree predictor using the conceptual data structure for deep learning architectures.

Science.gov (United States)

Pérez-Benito, Francisco Javier; Villacampa-Fernández, Patricia; Conejero, J Alberto; García-Gómez, Juan M; Navarro-Pardo, Esperanza

2017-11-13

Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires. A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining a HDP in which the network architecture enables the conceptual interpretation of psychological factors associated to happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence. A cross-sectional survey targeting non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors. Our D-SDNN approach provided a better outcome (MSE: 1.46·10 -2 ) than MLR (MSE: 2.30·10 -2 ), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure. We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness

The proportionator: unbiased stereological estimation using biased automatic image analysis and non-uniform probability proportional to size sampling

DEFF Research Database (Denmark)

Gardi, Jonathan Eyal; Nyengaard, Jens Randel; Gundersen, Hans Jørgen Gottlieb

2008-01-01

examined, which in turn leads to any of the known stereological estimates, including size distributions and spatial distributions. The unbiasedness is not a function of the assumed relation between the weight and the structure, which is in practice always a biased relation from a stereological (integral......, the desired number of fields are sampled automatically with probability proportional to the weight and presented to the expert observer. Using any known stereological probe and estimator, the correct count in these fields leads to a simple, unbiased estimate of the total amount of structure in the sections...... geometric) point of view. The efficiency of the proportionator depends, however, directly on this relation to be positive. The sampling and estimation procedure is simulated in sections with characteristics and various kinds of noises in possibly realistic ranges. In all cases examined, the proportionator...

Modified linear predictive coding approach for moving target tracking by Doppler radar

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Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

2016-07-01

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Generalizing a categorization of students’ interpretations of linear kinematics graphs

Directory of Open Access Journals (Sweden)

Laurens Bollen

2016-02-01

Full Text Available We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven and the Basque Country, Spain (University of the Basque Country. We discuss how we adapted the categorization to accommodate a much more diverse student cohort and explain how the prior knowledge of students may account for many differences in the prevalence of approaches and success rates. Although calculus-based physics students make fewer mistakes than algebra-based physics students, they encounter similar difficulties that are often related to incorrectly dividing two coordinates. We verified that a qualitative understanding of kinematics is an important but not sufficient condition for students to determine a correct value for the speed. When comparing responses to questions on linear distance-time graphs with responses to isomorphic questions on linear water level versus time graphs, we observed that the context of a question influences the approach students use. Neither qualitative understanding nor an ability to find the slope of a context-free graph proved to be a reliable predictor for the approach students use when they determine the instantaneous speed.

Generalizing a categorization of students' interpretations of linear kinematics graphs

Science.gov (United States)

Bollen, Laurens; De Cock, Mieke; Zuza, Kristina; Guisasola, Jenaro; van Kampen, Paul

2016-06-01

We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven) and the Basque Country, Spain (University of the Basque Country). We discuss how we adapted the categorization to accommodate a much more diverse student cohort and explain how the prior knowledge of students may account for many differences in the prevalence of approaches and success rates. Although calculus-based physics students make fewer mistakes than algebra-based physics students, they encounter similar difficulties that are often related to incorrectly dividing two coordinates. We verified that a qualitative understanding of kinematics is an important but not sufficient condition for students to determine a correct value for the speed. When comparing responses to questions on linear distance-time graphs with responses to isomorphic questions on linear water level versus time graphs, we observed that the context of a question influences the approach students use. Neither qualitative understanding nor an ability to find the slope of a context-free graph proved to be a reliable predictor for the approach students use when they determine the instantaneous speed.

Predictors of job satisfaction among Academic Faculty: Do instructional and clinical faculty differ?

Science.gov (United States)

Chung, Kevin C.; Song, Jae W.; Kim, H. Myra; Woolliscroft, James O.; Quint, Elisabeth H.; Lukacs, Nicholas W.; Gyetko, Margaret R.

2010-01-01

Objectives To identify and compare predictors of job satisfaction between the instructional and clinical faculty tracks. Method A 61-item faculty job satisfaction survey was distributed to 1,898 academic faculty at the University of Michigan Medical School. The anonymous survey was web-based. Questions covered topics on departmental organization, research, clinical and teaching support, compensation, mentorship, and promotion. Levels of satisfaction were contrasted between the two tracks, and predictors of job satisfaction were identified using linear regression models. Results The response rates for the instructional and clinical tracks were 43.1% and 41.3%, respectively. Clinical faculty reported being less satisfied with how they are mentored, and fewer reported understanding the process for promotion. There was no significant difference in overall job satisfaction between faculty tracks. Surprisingly, clinical faculty with mentors were significantly less satisfied with how they were being mentored, with career advancement and overall job satisfaction, compared to instructional faculty mentees. Additionally, senior-level clinical faculty were significantly less satisfied with their opportunities to mentor junior faculty compared to senior-level instructional faculty. Significant predictors of job satisfaction for both tracks included areas of autonomy, meeting career expectations, work-life balance, and departmental leadership. Unique to the clinical track, compensation and career advancement variables also emerged as significant predictors. Conclusion Greater effort must be placed in the continued attention to faculty well-being both at the institutional level and at the level of departmental leadership. Success in enhancing job satisfaction is more likely if directed by locally designed assessments involving department chairs, specifically in fostering more effective mentoring relationships focused on making available career advancement activities such as

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients.

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Jacobsen, Ramune; Vadstrup, Eva; Røder, Michael; Frølich, Anne

2012-01-01

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

Predictors of Better Self-Care in Patients with Heart Failure after Six Months of Follow-Up Home Visits

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Trojahn, Melina Maria; Ruschel, Karen Brasil; Nogueira de Souza, Emiliane; Mussi, Cláudia Motta; Naomi Hirakata, Vânia; Nogueira Mello Lopes, Alexandra; Rabelo-Silva, Eneida Rejane

2013-01-01

This study aimed to examine the predictors of better self-care behavior in patients with heart failure (HF) in a home visiting program. This is a longitudinal study nested in a randomized controlled trial (ISRCTN01213862) in which the home-based educational intervention consisted of a six-month followup that included four home visits by a nurse, interspersed with four telephone calls. The self-care score was measured at baseline and at six months using the Brazilian version of the European Heart Failure Self-Care Behaviour Scale. The associations included eight variables: age, sex, schooling, having received the intervention, social support, income, comorbidities, and symptom severity. A simple linear regression model was developed using significant variables (P ≤ 0.20), followed by a multivariate model to determine the predictors of better self-care. One hundred eighty-eight patients completed the study. A better self-care behavior was associated with patients who received intervention (P < 0.001), had more years of schooling (P = 0.016), and had more comorbidities (P = 0.008). Having received the intervention (P < 0.001) and having a greater number of comorbidities (P = 0.038) were predictors of better self-care. In the multivariate regression model, being in the intervention group and having more comorbidities were a predictor of better self-care. PMID:24083023

Characteristics and predictors of oral cancer knowledge in a predominantly African American community

Science.gov (United States)

Adjei Boakye, Eric; Hussaini, Adnan S.; Sujijantarat, Nanthiya; Ganesh, Rajan N.; Snider, Matthew; Thompson, Devin; Varvares, Mark A.

2017-01-01

Purpose To characterize smoking and alcohol use, and to describe predictors of oral cancer knowledge among a predominantly African-American population. Methods A cross-sectional study was conducted between September, 2013 among drag racers and fans in East St. Louis. Oral cancer knowledge was derived from combining questionnaire items to form knowledge score. Covariates examined included age, sex, race, marital status, education status, income level, insurance status, tobacco and alcohol use. Adjusted linear regression analysis measured predictors of oral cancer knowledge. Results Three hundred and four participants completed questionnaire; 72.7% were African Americans. Smoking rate was 26.7%, alcohol use was 58.3%, and mean knowledge score was 4.60 ± 2.52 out of 17. In final adjusted regression model, oral cancer knowledge was associated with race and education status. Compared with Caucasians, African Americans were 29% less likely to have high oral cancer knowledge (β = -0.71; 95% CI: -1.35, -0.07); and participants with a high school diploma or less were 124% less likely to have high oral cancer knowledge compared with college graduates (β = -1.24; 95% CI: -2.44, -0.41). Conclusions There was lower oral cancer knowledge among African Americans and those with low education. The prevalence of smoking was also very high. Understanding predictors of oral cancer knowledge is important in future design of educational interventions specifically targeted towards high-risk group for oral cancer. PMID:28545057

Emotional Intelligence and Personality Traits as Predictors of Occupational Therapy students' Practice Education Performance: A Cross-Sectional Study.

Science.gov (United States)

Brown, Ted; Williams, Brett; Etherington, Jamie

2016-12-01

This study investigated whether occupational therapy students' emotional intelligence and personality traits are predictive of specific aspects of their fieldwork performance. A total of 114 second and third year undergraduate occupational therapy students (86.6% response rate) completed the Genos Emotional Intelligence Inventory (Genos EI) and the Ten-Item Personality Inventory (TIPI). Fieldwork performance scores were obtained from the Student Practice Evaluation Form Revised (SPEF-R). Linear regressions were completed with the SPEF-R domains being the dependent variables and the Genos EI and TIPI factors being the independent variables. Regression analysis results revealed that the Genos EI subscales of Emotional Management of Others (EMO), Emotional Awareness of Others (EAO), Emotional Expression (EEX) and Emotional Reasoning (ERE) were significant predictors of various domains of students' fieldwork performance. EAO and ERE were significant predictors of students' Communication Skills accounting for 4.6% of its variance. EMO, EAO, EEX and ERE were significant predictors of students' Documentation Skills explaining 6.8% of its variance. EMO was a significant predictor of students' Professional Behaviour accounting for 3.2% of its variance. No TIPI factors were found to be significant predictors of the SPEF-R domains. Occupational therapy students' emotional intelligence was a significant predictor of components of their fieldwork performance while students' personality traits were not. The convenience sampling approach used, small sample size recruited and potential issue of social desirability of the self-reported Genos EI and TIPI data are acknowledged as study limitations. It is recommended that other studies be completed to investigate if any other relevant constructs or factors are predictive of occupational therapy students' fieldwork performance. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Predictors and overestimation of recalled mobile phone use among children and adolescents.

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Aydin, Denis; Feychting, Maria; Schüz, Joachim; Andersen, Tina Veje; Poulsen, Aslak Harbo; Prochazka, Michaela; Klæboe, Lars; Kuehni, Claudia E; Tynes, Tore; Röösli, Martin

2011-12-01

A growing body of literature addresses possible health effects of mobile phone use in children and adolescents by relying on the study participants' retrospective reconstruction of mobile phone use. In this study, we used data from the international case-control study CEFALO to compare self-reported with objectively operator-recorded mobile phone use. The aim of the study was to assess predictors of level of mobile phone use as well as factors that are associated with overestimating own mobile phone use. For cumulative number and duration of calls as well as for time since first subscription we calculated the ratio of self-reported to operator-recorded mobile phone use. We used multiple linear regression models to assess possible predictors of the average number and duration of calls per day and logistic regression models to assess possible predictors of overestimation. The cumulative number and duration of calls as well as the time since first subscription of mobile phones were overestimated on average by the study participants. Likelihood to overestimate number and duration of calls was not significantly different for controls compared to cases (OR=1.1, 95%-CI: 0.5 to 2.5 and OR=1.9, 95%-CI: 0.85 to 4.3, respectively). However, likelihood to overestimate was associated with other health related factors such as age and sex. As a consequence, such factors act as confounders in studies relying solely on self-reported mobile phone use and have to be considered in the analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predictors of Age of Diagnosis for Children with Autism Spectrum Disorder: The Role of a Consistent Source of Medical Care, Race, and Condition Severity

Science.gov (United States)

Emerson, Natacha D.; Morrell, Holly E. R.; Neece, Cameron

2016-01-01

Having a consistent source of medical care may facilitate diagnosis of autism spectrum disorders (ASD). This study examined predictors of age of ASD diagnosis using data from the 2011-2012 National Survey of Children's Health. Using multiple linear regression analysis, age of diagnosis was predicted by race, ASD severity, having a consistent…

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

Science.gov (United States)

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Linear versus non-linear supersymmetry, in general

Energy Technology Data Exchange (ETDEWEB)

Ferrara, Sergio [Theoretical Physics Department, CERN,CH-1211 Geneva 23 (Switzerland); INFN - Laboratori Nazionali di Frascati,Via Enrico Fermi 40, I-00044 Frascati (Italy); Department of Physics and Astronomy, UniversityC.L.A.,Los Angeles, CA 90095-1547 (United States); Kallosh, Renata [SITP and Department of Physics, Stanford University,Stanford, California 94305 (United States); Proeyen, Antoine Van [Institute for Theoretical Physics, Katholieke Universiteit Leuven,Celestijnenlaan 200D, B-3001 Leuven (Belgium); Wrase, Timm [Institute for Theoretical Physics, Technische Universität Wien,Wiedner Hauptstr. 8-10, A-1040 Vienna (Austria)

2016-04-12

We study superconformal and supergravity models with constrained superfields. The underlying version of such models with all unconstrained superfields and linearly realized supersymmetry is presented here, in addition to the physical multiplets there are Lagrange multiplier (LM) superfields. Once the equations of motion for the LM superfields are solved, some of the physical superfields become constrained. The linear supersymmetry of the original models becomes non-linearly realized, its exact form can be deduced from the original linear supersymmetry. Known examples of constrained superfields are shown to require the following LM’s: chiral superfields, linear superfields, general complex superfields, some of them are multiplets with a spin.

Linear versus non-linear supersymmetry, in general

International Nuclear Information System (INIS)

Ferrara, Sergio; Kallosh, Renata; Proeyen, Antoine Van; Wrase, Timm

2016-01-01

We study superconformal and supergravity models with constrained superfields. The underlying version of such models with all unconstrained superfields and linearly realized supersymmetry is presented here, in addition to the physical multiplets there are Lagrange multiplier (LM) superfields. Once the equations of motion for the LM superfields are solved, some of the physical superfields become constrained. The linear supersymmetry of the original models becomes non-linearly realized, its exact form can be deduced from the original linear supersymmetry. Known examples of constrained superfields are shown to require the following LM’s: chiral superfields, linear superfields, general complex superfields, some of them are multiplets with a spin.

Personal and Social Predictors about Safe Sexual Behavior in Patients with Immune Deficiency Virus in Ahwaz, Iran

Directory of Open Access Journals (Sweden)

Shirin Hasanpoor

2016-12-01

Full Text Available Socio-demographic predictors about safe sex behaviours in individual suffering from immune deficiency virus (HIV had been tried to understand in this cross-sectional study. It was conducted on 120 individuals having immune deficiency virus (HIV. Collection of the data were based on socio-demographic and a safe sex behaviour questionnaire. To determine the socio-demographic the general linear model was used. Result revealed mean (SD of the total score of safe sexual behaviour among men and women was 66.5 (13.1, 62.2 (13.0 respectively and (Score limit: 0-100. Status of sexual partners, unprotected vaginal sex, drugs and alcohols, as well as employment status, were considered as predictors of safe sex behaviours. About 50 percent of the participants pose unsafe sexual practices, thus, it is advisable that the health promotion programs and HIV prevention should implement in various groups of the society.

Estimation of absolute microglial cell numbers in mouse fascia dentata using unbiased and efficient stereological cell counting principles

DEFF Research Database (Denmark)

Wirenfeldt, Martin; Dalmau, Ishar; Finsen, Bente

2003-01-01

Stereology offers a set of unbiased principles to obtain precise estimates of total cell numbers in a defined region. In terms of microglia, which in the traumatized and diseased CNS is an extremely dynamic cell population, the strength of stereology is that the resultant estimate is unaffected...... of microglia, although with this thickness, the intensity of the staining is too high to distinguish single cells. Lectin histochemistry does not visualize microglia throughout the section and, accordingly, is not suited for the optical fractionator. The mean total number of Mac-1+ microglial cells...... in the unilateral dentate gyrus of the normal young adult male C57BL/6 mouse was estimated to be 12,300 (coefficient of variation (CV)=0.13) with a mean coefficient of error (CE) of 0.06. The perspective of estimating microglial cell numbers using stereology is to establish a solid basis for studying the dynamics...

Reliability, reference values and predictor variables of the ulnar sensory nerve in disease free adults.

Science.gov (United States)

Ruediger, T M; Allison, S C; Moore, J M; Wainner, R S

2014-09-01

The purposes of this descriptive and exploratory study were to examine electrophysiological measures of ulnar sensory nerve function in disease free adults to determine reliability, determine reference values computed with appropriate statistical methods, and examine predictive ability of anthropometric variables. Antidromic sensory nerve conduction studies of the ulnar nerve using surface electrodes were performed on 100 volunteers. Reference values were computed from optimally transformed data. Reliability was computed from 30 subjects. Multiple linear regression models were constructed from four predictor variables. Reliability was greater than 0.85 for all paired measures. Responses were elicited in all subjects; reference values for sensory nerve action potential (SNAP) amplitude from above elbow stimulation are 3.3 μV and decrement across-elbow less than 46%. No single predictor variable accounted for more than 15% of the variance in the response. Electrophysiologic measures of the ulnar sensory nerve are reliable. Absent SNAP responses are inconsistent with disease free individuals. Reference values recommended in this report are based on appropriate transformations of non-normally distributed data. No strong statistical model of prediction could be derived from the limited set of predictor variables. Reliability analyses combined with relatively low level of measurement error suggest that ulnar sensory reference values may be used with confidence. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Unbiased estimators of coincidence and correlation in non-analogous Monte Carlo particle transport

International Nuclear Information System (INIS)

Szieberth, M.; Kloosterman, J.L.

2014-01-01

Highlights: • The history splitting method was developed for non-Boltzmann Monte Carlo estimators. • The method allows variance reduction for pulse-height and higher moment estimators. • It works in highly multiplicative problems but Russian roulette has to be replaced. • Estimation of higher moments allows the simulation of neutron noise measurements. • Biased sampling of fission helps the effective simulation of neutron noise methods. - Abstract: The conventional non-analogous Monte Carlo methods are optimized to preserve the mean value of the distributions. Therefore, they are not suited to non-Boltzmann problems such as the estimation of coincidences or correlations. This paper presents a general method called history splitting for the non-analogous estimation of such quantities. The basic principle of the method is that a non-analogous particle history can be interpreted as a collection of analogous histories with different weights according to the probability of their realization. Calculations with a simple Monte Carlo program for a pulse-height-type estimator prove that the method is feasible and provides unbiased estimation. Different variance reduction techniques have been tried with the method and Russian roulette turned out to be ineffective in high multiplicity systems. An alternative history control method is applied instead. Simulation results of an auto-correlation (Rossi-α) measurement show that even the reconstruction of the higher moments is possible with the history splitting method, which makes the simulation of neutron noise measurements feasible

Identifying loci influencing grain number by microsatellite screening in bread wheat (Triticum aestivum L.).

Science.gov (United States)

Zhang, Dongling; Hao, Chenyang; Wang, Lanfen; Zhang, Xueyong

2012-11-01

Grain number (GN) is one of three major yield-related components in wheat. We used the Chinese wheat mini core collection to undertake a genome-wide association analysis of grain number using 531 SSR markers randomly located on all 21 chromosomes. Grain numbers of all accessions were measured in four trials, i.e. two environments in four growing seasons. Association analysis based on a mixed linear model (MLM) revealed that 27 SSR loci were significantly associated with mean GN (MGN) estimated by the best linear unbiased predictor (BLUP) method. These included numerous breeder favorable alleles with strong positive effects at 23 loci. Significant or extremely significant differences were detected on MGN between varieties conveying favored allele and varieties with other alleles. Moreover, statistical simulation showed that the favored alleles have additive genetic effects. Although modern varieties combined larger numbers of favored alleles, the numbers of favored alleles were not significantly different from those in landraces, especially those alleles contributing mostly to the phenotypic variation. These results indicate that there is still considerable genetic potential for use of markers for genome selection of GN for high yield in wheat.

DRREP: deep ridge regressed epitope predictor.

Science.gov (United States)

Sher, Gene; Zhi, Degui; Zhang, Shaojie

2017-10-03

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Diallel analysis for sex-linked and maternal effects.

Science.gov (United States)

Zhu, J; Weir, B S

1996-01-01

Genetic models including sex-linked and maternal effects as well as autosomal gene effects are described. Monte Carlo simulations were conducted to compare efficiencies of estimation by minimum norm quadratic unbiased estimation (MINQUE) and restricted maximum likelihood (REML) methods. MINQUE(1), which has 1 for all prior values, has a similar efficiency to MINQUE(θ), which requires prior estimates of parameter values. MINQUE(1) has the advantage over REML of unbiased estimation and convenient computation. An adjusted unbiased prediction (AUP) method is developed for predicting random genetic effects. AUP is desirable for its easy computation and unbiasedness of both mean and variance of predictors. The jackknife procedure is appropriate for estimating the sampling variances of estimated variances (or covariances) and of predicted genetic effects. A t-test based on jackknife variances is applicable for detecting significance of variation. Worked examples from mice and silkworm data are given in order to demonstrate variance and covariance estimation and genetic effect prediction.

Foundations of linear and generalized linear models

CERN Document Server

Agresti, Alan

2015-01-01

A valuable overview of the most important ideas and results in statistical analysis Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building. The book begins by illustrating the fundamentals of linear models,

On the linear programming bound for linear Lee codes.

Science.gov (United States)

Astola, Helena; Tabus, Ioan

2016-01-01

Based on an invariance-type property of the Lee-compositions of a linear Lee code, additional equality constraints can be introduced to the linear programming problem of linear Lee codes. In this paper, we formulate this property in terms of an action of the multiplicative group of the field [Formula: see text] on the set of Lee-compositions. We show some useful properties of certain sums of Lee-numbers, which are the eigenvalues of the Lee association scheme, appearing in the linear programming problem of linear Lee codes. Using the additional equality constraints, we formulate the linear programming problem of linear Lee codes in a very compact form, leading to a fast execution, which allows to efficiently compute the bounds for large parameter values of the linear codes.

Gender and single nucleotide polymorphisms in MTHFR, BHMT, SPTLC1, CRBP2R, and SCARB1 are significant predictors of plasma homocysteine normalized by RBC folate in healthy adults.

Science.gov (United States)

Using linear regression models, we studied the main and two-way interaction effects of the predictor variables gender, age, BMI, and 64 folate/vitamin B-12/homocysteine/lipid/cholesterol-related single nucleotide polymorphisms (SNP) on log-transformed plasma homocysteine normalized by red blood cell...

Intramolecular Hydroamination of Unbiased and Functionalized Primary Aminoalkenes Catalyzed by a Rhodium Aminophosphine Complex

Science.gov (United States)

Julian, Lisa D.; Hartwig, John F.

2010-01-01

We report a rhodium catalyst that exhibits high reactivity for the hydroamination of primary aminoalkenes that are unbiased toward cyclization and that possess functional groups that would not be tolerated in hydroaminations catalyzed by more electrophilic systems. This catalyst contains an unusual diaminophosphine ligand that binds to rhodium in a κ3-P,O,P mode. The reactions catalyzed by this complex typically proceed at mild temperatures (room temperature to 70 °C), occur with primary aminoalkenes lacking substituents on the alkyl chain that bias the system toward cyclization, occur with primary aminoalkenes containing chloride, ester, ether, enolizable ketone, nitrile, and unprotected alcohol functionality, and occur with primary aminoalkenes containing internal olefins. Mechanistic data imply that these reactions occur with a turnover-limiting step that is different from that of reactions catalyzed by late transition metal complexes of Pd, Pt, and Ir. This change in the turnover-limiting step and resulting high activity of the catalyst stem from favorable relative rates for protonolysis of the M-C bond to release the hydroamination product vs reversion of the aminoalkyl intermediate to regenerate the acyclic precursor. Probes for the origin of the reactivity of the rhodium complex of L1 imply that the aminophosphine groups lead to these favorable rates by effects beyond steric demands and simple electron donation to the metal center. PMID:20839807

Unbiased estimation of the liver volume by the Cavalieri principle using magnetic resonance images

International Nuclear Information System (INIS)

Sahin, Buenyamin; Emirzeoglu, Mehmet; Uzun, Ahmet; Incesu, Luetfi; Bek, Yueksel; Bilgic, Sait; Kaplan, Sueleyman

2003-01-01

Objective: It is often useful to know the exact volume of the liver, such as in monitoring the effects of a disease, treatment, dieting regime, training program or surgical application. Some non-invasive methodologies have been previously described which estimate the volume of the liver. However, these preliminary techniques need special software or skilled performers and they are not ideal for daily use in clinical practice. Here, we describe a simple, accurate and practical technique for estimating liver volume without changing the routine magnetic resonance imaging scanning procedure. Materials and methods: In this study, five normal livers, obtained from cadavers, were scanned by 0.5 T MR machine, in horizontal and sagittal planes. The consecutive sections, in 10 mm thickness, were used to estimate the whole volume of the liver by means of the Cavalieri principle. The volume estimations were done by three different performers to evaluate the reproducibility. Results: There are no statistical differences between the performers and real liver volumes (P>0.05). There is also high correlation between the estimates of performers and the real liver volume (r=0.993). Conclusion: We conclude that the combination of MR imaging with the Cavalieri principle is a non-invasive, direct and unbiased technique that can be safely applied to estimate liver volume with a very moderate workload per individual

Predictors of self-rated health: a 12-month prospective study of IT and media workers.

Science.gov (United States)

Hasson, Dan; Arnetz, Bengt B; Theorell, Töres; Anderberg, Ulla Maria

2006-07-31

The aim of the present study was to determine health-related risk and salutogenic factors and to use these to construct prediction models for future self-rated health (SRH), i.e. find possible characteristics predicting individuals improving or worsening in SRH over time (0-12 months). A prospective study was conducted with measurements (physiological markers and self-ratings) at 0, 6 and 12 months, involving 303 employees (187 men and 116 women, age 23-64) from four information technology and two media companies. There were a multitude of statistically significant cross-sectional correlations (Spearman's Rho) between SRH and other self-ratings as well as physiological markers. Predictors of future SRH were baseline ratings of SRH, self-esteem and social support (logistic regression), and SRH, sleep quality and sense of coherence (linear regression). The results of the present study indicate that baseline SRH and other self-ratings are predictive of future SRH. It is cautiously implied that SRH, self-esteem, social support, sleep quality and sense of coherence might be predictors of future SRH and therefore possibly also of various future health outcomes.

Can we do better than the grid survey: Optimal synoptic surveys in presence of variable uncertainty and decorrelation scales

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Frolov, Sergey; Garau, Bartolame; Bellingham, James

2014-08-01

Regular grid ("lawnmower") survey is a classical strategy for synoptic sampling of the ocean. Is it possible to achieve a more effective use of available resources if one takes into account a priori knowledge about variability in magnitudes of uncertainty and decorrelation scales? In this article, we develop and compare the performance of several path-planning algorithms: optimized "lawnmower," a graph-search algorithm (A*), and a fully nonlinear genetic algorithm. We use the machinery of the best linear unbiased estimator (BLUE) to quantify the ability of a vehicle fleet to synoptically map distribution of phytoplankton off the central California coast. We used satellite and in situ data to specify covariance information required by the BLUE estimator. Computational experiments showed that two types of sampling strategies are possible: a suboptimal space-filling design (produced by the "lawnmower" and the A* algorithms) and an optimal uncertainty-aware design (produced by the genetic algorithm). Unlike the space-filling designs that attempted to cover the entire survey area, the optimal design focused on revisiting areas of high uncertainty. Results of the multivehicle experiments showed that fleet performance predictors, such as cumulative speed or the weight of the fleet, predicted the performance of a homogeneous fleet well; however, these were poor predictors for comparing the performance of different platforms.

Self-consistent predictor/corrector algorithms for stable and efficient integration of the time-dependent Kohn-Sham equation

Science.gov (United States)

Zhu, Ying; Herbert, John M.

2018-01-01

The "real time" formulation of time-dependent density functional theory (TDDFT) involves integration of the time-dependent Kohn-Sham (TDKS) equation in order to describe the time evolution of the electron density following a perturbation. This approach, which is complementary to the more traditional linear-response formulation of TDDFT, is more efficient for computation of broad-band spectra (including core-excited states) and for systems where the density of states is large. Integration of the TDKS equation is complicated by the time-dependent nature of the effective Hamiltonian, and we introduce several predictor/corrector algorithms to propagate the density matrix, one of which can be viewed as a self-consistent extension of the widely used modified-midpoint algorithm. The predictor/corrector algorithms facilitate larger time steps and are shown to be more efficient despite requiring more than one Fock build per time step, and furthermore can be used to detect a divergent simulation on-the-fly, which can then be halted or else the time step modified.

Concurrent sexual partners-A predictor of Chlamydia

DEFF Research Database (Denmark)

Jørgensen, Marianne Johansson; Olesen, Frede; Maindal, Helle Terkildsen

2013-01-01

, but the significance of this compared with other well-known predictors has only been briefly described. Aim: The aim is to examine if concurrent partners isan independent predictor for C. trachomatis infection in young Danes aged 15-29 years. Methods: Detailed sexual behavior data were collected via a web......:These preliminary results suggest that concurrent sexual partners is an important predictor for C.trachomatis infections among young Danes aged 15-29. A more concise conclusion will be presented at the Ph.D day......Background:Chlamydia trachomatis is the most common sexually transmitted bacterial infection among young Danes and the spread is highly dependent on the population’s sexual behavior. Previous studies have found concurrent partnerships to be a possible predictor for C. trachomatis...

The predictors of quality of life in women with polycystic ovarian syndrome.

Science.gov (United States)

Aliasghari, Fatemeh; Mirghafourvand, Mojgan; Charandabi, Sakineh Mohammad-Alizadeh; Lak, Tahereh Behroozi

2017-06-01

Polycystic ovarian syndrome (PCOS) is one of the most common endocrine disorder that may be effective in reducing the quality of life. This study aimed to determine the predictors of quality of life in women with PCOS. This cross-sectional study was conducted on 174 women with PCOS who attended in public and private fertility clinics in Urmia (West Azerbaijan, Iran), 2015. The data were collected through the questionnaires of sociodemographic and obstetrics characteristics, quality of life and Beck depression inventory-II. Multivariate linear regression was used to estimate the effect rate of the independent variables (depression and sociodemographic characteristics) on the dependent variable (quality of life). In this study, the mean (standard deviation) of total score of the quality of life was obtained, 45.8 (11.3) in the range 0-100. The highest and lowest mean scores were in the subdomains of weight and hirsutism. The variables of depression, body mass index, woman's job, menstrual cycle intervals, and sexual satisfaction were predictors of the quality of life in women with PCOS. Because of various effective factors on quality of life in these women such as depression, necessary strategies must be implemented to control these factors and improve the quality of life. © 2017 John Wiley & Sons Australia, Ltd.

Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example

Science.gov (United States)

Helgesson, P.; Sjöstrand, H.

2017-11-01

Fitting a parametrized function to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data are used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.

Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example.

Science.gov (United States)

Helgesson, P; Sjöstrand, H

2017-11-01

Fitting a parametrized function to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r 1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data are used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r 1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r 1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.

Linear algebra

CERN Document Server

Shilov, Georgi E

1977-01-01

Covers determinants, linear spaces, systems of linear equations, linear functions of a vector argument, coordinate transformations, the canonical form of the matrix of a linear operator, bilinear and quadratic forms, Euclidean spaces, unitary spaces, quadratic forms in Euclidean and unitary spaces, finite-dimensional space. Problems with hints and answers.

Biases and statistical errors in Monte Carlo burnup calculations: an unbiased stochastic scheme to solve Boltzmann/Bateman coupled equations

International Nuclear Information System (INIS)

Dumonteil, E.; Diop, C.M.

2011-01-01

External linking scripts between Monte Carlo transport codes and burnup codes, and complete integration of burnup capability into Monte Carlo transport codes, have been or are currently being developed. Monte Carlo linked burnup methodologies may serve as an excellent benchmark for new deterministic burnup codes used for advanced systems; however, there are some instances where deterministic methodologies break down (i.e., heavily angularly biased systems containing exotic materials without proper group structure) and Monte Carlo burn up may serve as an actual design tool. Therefore, researchers are also developing these capabilities in order to examine complex, three-dimensional exotic material systems that do not contain benchmark data. Providing a reference scheme implies being able to associate statistical errors to any neutronic value of interest like k(eff), reaction rates, fluxes, etc. Usually in Monte Carlo, standard deviations are associated with a particular value by performing different independent and identical simulations (also referred to as 'cycles', 'batches', or 'replicas'), but this is only valid if the calculation itself is not biased. And, as will be shown in this paper, there is a bias in the methodology that consists of coupling transport and depletion codes because Bateman equations are not linear functions of the fluxes or of the reaction rates (those quantities being always measured with an uncertainty). Therefore, we have to quantify and correct this bias. This will be achieved by deriving an unbiased minimum variance estimator of a matrix exponential function of a normal mean. The result is then used to propose a reference scheme to solve Boltzmann/Bateman coupled equations, thanks to Monte Carlo transport codes. Numerical tests will be performed with an ad hoc Monte Carlo code on a very simple depletion case and will be compared to the theoretical results obtained with the reference scheme. Finally, the statistical error propagation

A measurement error approach to assess the association between dietary diversity, nutrient intake, and mean probability of adequacy.

Science.gov (United States)

Joseph, Maria L; Carriquiry, Alicia

2010-11-01

Collection of dietary intake information requires time-consuming and expensive methods, making it inaccessible to many resource-poor countries. Quantifying the association between simple measures of usual dietary diversity and usual nutrient intake/adequacy would allow inferences to be made about the adequacy of micronutrient intake at the population level for a fraction of the cost. In this study, we used secondary data from a dietary intake study carried out in Bangladesh to assess the association between 3 food group diversity indicators (FGI) and calcium intake; and the association between these same 3 FGI and a composite measure of nutrient adequacy, mean probability of adequacy (MPA). By implementing Fuller's error-in-the-equation measurement error model (EEM) and simple linear regression (SLR) models, we assessed these associations while accounting for the error in the observed quantities. Significant associations were detected between usual FGI and usual calcium intakes, when the more complex EEM was used. The SLR model detected significant associations between FGI and MPA as well as for variations of these measures, including the best linear unbiased predictor. Through simulation, we support the use of the EEM. In contrast to the EEM, the SLR model does not account for the possible correlation between the measurement errors in the response and predictor. The EEM performs best when the model variables are not complex functions of other variables observed with error (e.g. MPA). When observation days are limited and poor estimates of the within-person variances are obtained, the SLR model tends to be more appropriate.

Competitive state anxiety and self-confidence: intensity and direction as relative predictors of performance on a golf putting task.

Science.gov (United States)

Chamberlain, Sean T; Hale, Bruce D

2007-06-01

This study considered relationships between the intensity and directional aspects of competitive state anxiety as measured by the modified Competitive Sport Anxiety Inventory-2(D) (Jones & Swain, 1992) in a sample of 12 experienced male golfers. Anxiety and performance scores from identical putting tasks performed under three different anxiety-manipulated competitive conditions were used to assess both the predictions of Multidimensional Anxiety Theory (MAT; Martens et al., 1990) and the relative value of intensity and direction in explaining performance variance. A within-subjects regression analysis of the intra-individual data showed partial support for the three MAT hypotheses. Cognitive anxiety intensity demonstrated a negative linear relationship with performance, somatic anxiety intensity showed a curvilinear relationship with performance, and self-confidence intensity revealed a positive linear relation. Cognitive directional anxiety illustrated a positive linear relationship with putting performance. Multiple regression analyses indicated that direction (42% of variance) was a better predictor of performance than intensity (22%).

Differential expression among tissues in morbidly obese individuals using a finite mixture model under BLUP approach

DEFF Research Database (Denmark)

Kogelman, Lisette; Trabzuni, Daniah; Bonder, Marc Jan

effects of the interactions between tissues and probes using BLUP (Best Linear Unbiased Prediction) linear models correcting for gender, which were subsequently used in a finite mixture model to detect DE genes in each tissue. This approach evades the multiple-testing problem and is able to detect...

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

Directory of Open Access Journals (Sweden)

Jo Nishino

2018-04-01

Full Text Available Genome-wide association studies (GWAS suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1. For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.

Predictors of iron levels in 14,737 Danish blood donors

DEFF Research Database (Denmark)

Rigas, Andreas Stribolt; Sørensen, Cecilie Juul; Pedersen, Ole Birger

2014-01-01

BACKGROUND: Dietary studies show a relationship between the intake of iron enhancers and inhibitors and iron stores in the general population. However, the impact of dietary factors on the iron stores of blood donors, whose iron status is affected by blood donations, is incompletely understood....... STUDY DESIGN AND METHODS: In the Danish Blood Donor Study, we assessed the effect of blood donation frequency, physiologic factors, lifestyle and supplemental factors, and dietary factors on ferritin levels. We used multiple linear and logistic regression analyses stratified by sex and menopausal status....... RESULTS: Among high-frequency donors (more than nine donations in the past 3 years), we found iron deficiency (ferritin below 15 ng/mL) in 9, 39, and 22% of men, premenopausal women, and postmenopausal women, respectively. The strongest predictors of iron deficiency were sex, menopausal status, the number...

Selection in sugarcane families with artificial neural networks

Directory of Open Access Journals (Sweden)

Bruno Portela Brasileiro

2015-04-01

Full Text Available The objective of this study was to evaluate Artificial Neural Networks (ANN applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS, demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.

Cokriging model for estimation of water table elevation

International Nuclear Information System (INIS)

Hoeksema, R.J.; Clapp, R.B.; Thomas, A.L.; Hunley, A.E.; Farrow, N.D.; Dearstone, K.C.

1989-01-01

In geological settings where the water table is a subdued replica of the ground surface, cokriging can be used to estimate the water table elevation at unsampled locations on the basis of values of water table elevation and ground surface elevation measured at wells and at points along flowing streams. The ground surface elevation at the estimation point must also be determined. In the proposed method, separate models are generated for the spatial variability of the water table and ground surface elevation and for the dependence between these variables. After the models have been validated, cokriging or minimum variance unbiased estimation is used to obtain the estimated water table elevations and their estimation variances. For the Pits and Trenches area (formerly a liquid radioactive waste disposal facility) near Oak Ridge National Laboratory, water table estimation along a linear section, both with and without the inclusion of ground surface elevation as a statistical predictor, illustrate the advantages of the cokriging model

Using an external surrogate for predictor model training in real-time motion management of lung tumors

Energy Technology Data Exchange (ETDEWEB)

Rottmann, Joerg; Berbeco, Ross [Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 (United States)

2014-12-15

Purpose: Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. Methods: The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable to overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal–external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Results: Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum

Predictors of fatigue and work ability in cancer survivors.

Science.gov (United States)

van Muijen, P; Duijts, S F A; Bonefaas-Groenewoud, K; van der Beek, A J; Anema, J R

2017-12-30

Workers diagnosed with cancer are at risk for job loss or work disability. To determine predictors of fatigue and work ability at 36 months after diagnosis in a population of cancer survivors. Individuals diagnosed with cancer and who applied for work disability benefit at 24 months of sick leave were surveyed at the time of application and again 12 months later. Fatigue was measured using the Functional Assessment of Chronic Illness-Fatigue scale questionnaire and work ability was measured using the work ability index. Linear regression analyses were applied to identify predictors. There were 336 participants. Participants who were divorced or widowed had more physical limitations, more depressive symptoms and were more fatigued at baseline, and who worked in health care demonstrated higher levels of fatigue. Lower fatigue was predicted by having received chemotherapy. A higher level of work ability was predicted by having received chemotherapy, better global health and better work ability at baseline. Lower work ability was predicted by being principal wage earner, insecurity about being free of disease, having more physical limitations and having greater wage loss. Socio-demographic, health- and work-related factors were associated with fatigue and work ability in cancer survivors on long-term sick leave. As fatigue and poor work ability are important risk factors for work disability, addressing the identified predictive factors may assist in mitigation of work disability in cancer survivors. © The Author 2017. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Fixed and dynamic predictors of treatment process in therapeutic communities for substance abusers in Belgium.

Science.gov (United States)

Goethals, Ilse; Vanderplasschen, Wouter; Vandevelde, Stijn; Broekaert, Eric

2012-10-11

Research on substance abuse treatment services in general reflects substantial attention to the notion of treatment process. Despite the growing popularity of process studies, only a few researchers have used instruments specifically tailored to measure the therapeutic community (TC) treatment process, and even fewer have investigated client attributes in relation to early TC treatment process experiences. The aim of the current study is to address this gap by exploring clients' early in-treatment experiences and to determine the predictors that are related to the treatment process, using a TC-specific multidimensional instrument. Data was gathered among 157 adults in five TCs in Flanders (Belgium). Descriptive statistics were used to explore clients' early in-treatment experiences and multiple linear regressions were conducted to determine the fixed and dynamic predictors of Community Environment and Personal Development and Change (two indicators of TC treatment process). Clients reveal a more positive first-month response to TC social processes than to personal-development processes that require self-reflection and insight. The variance in clients' ratings of Community Environment was primarily due to dynamic client factors, while the variance in clients' ratings of Personal Development and Change was only related to fixed client factors. Suitability for treatment was the strongest predictor of Community Environment ratings, whereas a judicial referral more strongly predicted Personal Development and Change scores. Special attention should be devoted to suitability for treatment as part of motivational assessment as this seems to be a very strong predictor of how clients react to the initiation stage of TC treatment. To help improve clients' (meta-)cognitive skills needed to achieve insight and self-reflection and perhaps speed up the process of recovery, the authors suggest the introduction of (meta-)cognitive training strategies in the pre-program and/or the

Greater expectations: using hierarchical linear modeling to examine expectancy for treatment outcome as a predictor of treatment response.

Science.gov (United States)

Price, Matthew; Anderson, Page; Henrich, Christopher C; Rothbaum, Barbara Olasov

2008-12-01

A client's expectation that therapy will be beneficial has long been considered an important factor contributing to therapeutic outcomes, but recent empirical work examining this hypothesis has primarily yielded null findings. The present study examined the contribution of expectancies for treatment outcome to actual treatment outcome from the start of therapy through 12-month follow-up in a clinical sample of individuals (n=72) treated for fear of flying with either in vivo exposure or virtual reality exposure therapy. Using a piecewise hierarchical linear model, outcome expectancy predicted treatment gains made during therapy but not during follow-up. Compared to lower levels, higher expectations for treatment outcome yielded stronger rates of symptom reduction from the beginning to the end of treatment on 2 standardized self-report questionnaires on fear of flying. The analytic approach of the current study is one potential reason that findings contrast with prior literature. The advantages of using hierarchical linear modeling to assess interindividual differences in longitudinal data are discussed.

Linear and non-linear optics of condensed matter

International Nuclear Information System (INIS)

McLean, T.P.

1977-01-01

Part I - Linear optics: 1. General introduction. 2. Frequency dependence of epsilon(ω, k vector). 3. Wave-vector dependence of epsilon(ω, k vector). 4. Tensor character of epsilon(ω, k vector). Part II - Non-linear optics: 5. Introduction. 6. A classical theory of non-linear response in one dimension. 7. The generalization to three dimensions. 8. General properties of the polarizability tensors. 9. The phase-matching condition. 10. Propagation in a non-linear dielectric. 11. Second harmonic generation. 12. Coupling of three waves. 13. Materials and their non-linearities. 14. Processes involving energy exchange with the medium. 15. Two-photon absorption. 16. Stimulated Raman effect. 17. Electro-optic effects. 18. Limitations of the approach presented here. (author)

Predictors of Poststroke Health-Related Quality of Life in Nigerian Stroke Survivors: A 1-Year Follow-Up Study

Directory of Open Access Journals (Sweden)

Ashiru Mohammad Hamza

2014-01-01

Full Text Available This study aims to identify the predictors in the different aspects of the health-related quality of life (HRQoL and to measure the changes of functional status over time in a cohort of Nigerian stroke survivors. A prospective observational study was conducted in three hospitals of Kano state of Nigeria where stroke survivors receive rehabilitation. The linguistic-validated Hausa versions of the stroke impact scale 3.0, modified Rankin scale, Barthel index and Beck depression inventory scales were used. Paired samples t-test was used to calculate the amount of changes that occur over time and the forward stepwise linear regression model was used to identify the predictors. A total of 233 stroke survivors were surveyed at 6 months, and 93% (217/233 were followed at 1 year after stroke. Functional disabilities were significantly reduced during the recovery phase. Motor impairment, disability, and level of depression were independent predictors of HRQoL in the multivariate regression analysis. The involvement of family members as caregivers is the key factor for those survivors with improved functional status. Thus, to enhance the quality of poststroke life, it is proposed that a holistic stroke rehabilitation service and an active involvement of family members are established at every possible level.

Temporal predictors of health-related quality of life in elderly people with diabetes: results of a German cohort study.

Science.gov (United States)

Maatouk, Imad; Wild, Beate; Wesche, Daniela; Herzog, Wolfgang; Raum, Elke; Müller, Heiko; Rothenbacher, Dietrich; Stegmaier, Christa; Schellberg, Dieter; Brenner, Hermann

2012-01-01

The aim of the study was to determine predictors that influence health-related quality of life (HRQOL) in a large cohort of elderly diabetes patients from primary care over a follow-up period of five years. At the baseline measurement of the ESTHER cohort study (2000-2002), 1375 out of 9953 participants suffered from diabetes (13.8%). 1057 of these diabetes patients responded to the second-follow up (2005-2007). HRQOL at baseline and follow-up was measured using the SF-12; mental component scores (MCS) and physical component scores (PCS) were calculated; multiple linear regression models were used to determine predictors of HRQOL at follow-up. As possible predictors for HRQOL, the following baseline variables were examined: treatment with insulin, glycated hemoglobin (HbA1c), number of diabetes related complications, number of comorbid diseases, Body-Mass-Index (BMI), depression and HRQOL. Regression analyses were adjusted for sociodemographic variables and smoking status. 1034 patients (97.8%) responded to the SF-12 both at baseline and after five years and were therefore included in the study. Regression analyses indicated that significant predictors of decreased MCS were a lower HRQOL, a higher number of diabetes related complications and a reported history of depression at baseline. Complications, BMI, smoking and HRQOL at baseline significantly predicted PCS at the five year follow-up. Our findings expand evidence from previous cross-sectional data indicating that in elderly diabetes patients, depression, diabetes related complications, smoking and BMI are temporally predictive for HRQOL.

Predictors of parent-reported quality of life of adolescents with cerebral palsy

DEFF Research Database (Denmark)

Rapp, Marion; Eisemann, Nora; Arnaud, Catherine

2017-01-01

AIM: Parent-reporting is needed to examine Quality of Life (QoL) of children with cerebral palsy (CP) across all severities. This study examines whether QoL changes between childhood and adolescence, and what predicts adolescent QoL. METHOD: SPARCLE is a European cohort study of children with CP...... domain). Associations were assessed using linear regression. RESULTS: Between childhood and adolescence, average QoL reduced in six domains (1.3-3.8 points, pChildhood...... QoL was a strong predictor of all domains of adolescent QoL. Severe impairments of motor function, IQ or communication predicted higher adolescent QoL on some domains; except that severe motor impairment predicted lower adolescent QoL on the Autonomy domain. More psychological problems and higher...

Religiosity and Spirituality as Predictors of Subjectively Perceived Happiness in University Students in Slovakia

Directory of Open Access Journals (Sweden)

Peter Babinčák

2016-03-01

Full Text Available Several research projects discuss the existence of weak to moderately strong positive relation between religiosity/spirituality on the one hand and subjective well-being, life satisfaction or quality of life on the other hand (see Kelley & Miller, 2007. Variables related to religiosity and spirituality of a person may be perceived in two ways: as protective factors of attaining subjective well-being or as barriers limiting its attainment. The objective of this study is verification of mutual relationship between the indicators of religiosity and spirituality with regard to subjectively perceived happiness and verification of predictive strength of these indicators with regard to subjective happiness. The sample of research participants consisted of 194 university students aged 18 to 26. The research used 4 tools: The Expressions of Spirituality Inventory-Revised (MacDonald, 2000, The Salience in Religious Commitment Scale (Roof & Perkins, 1975, Subjective Happiness Scale (Lyubomirsky & Lepper, 1999 and The Oxford Happiness Questionnaire (Hills & Argyle, 2002. Using multiple hierarchical linear regression (stepwise, we obtained 2 dimensions of spirituality as significant predictors of subjective happiness – Existential Well-Being and Experiential/Phenomenological Dimension. Demographic data and confession types were not proved as predictors of happiness.

Self-esteem and insight as predictors of symptom change in schizophrenia: a longitudinal study.

Science.gov (United States)

Erickson, Molly A; Lysaker, Paul H

2012-07-01

Though it is known that symptom profiles in schizophrenia change throughout the course of the illness, it is not yet clear which psychological antecedents predict these changes. The purpose of the present study was to explore "level of insight into mental illness" and "self-esteem" as predictors of positive symptom change in schizophrenia patients. Fifty-seven schizophrenia patients completed assessments of self-esteem, insight into mental illness, positive symptoms and paranoia once every four weeks for a total of eight individual testing sessions. Hierarchical linear regression analysis revealed that changes in self-esteem predicted future changes in paranoia as well as positive symptoms more broadly; decreases in self-esteem at any given time point were associated with an increase in persecutory beliefs and other positive symptoms at the following assessment. On the other hand, decreases in insight were not significantly associated with paranoia or positive symptoms, either as a stable trait of the mental illness or as a predictor of change over time. Taken together, these results suggest that change in self-esteem, but not insight, has a significant and unique association with positive symptoms of schizophrenia, and may be a valuable target for future treatment.

Linearity and Non-linearity of Photorefractive effect in Materials ...

African Journals Online (AJOL)

In this paper we have studied the Linearity and Non-linearity of Photorefractive effect in materials using the band transport model. For low light beam intensities the change in the refractive index is proportional to the electric field for linear optics while for non- linear optics the change in refractive index is directly proportional ...

Mental Health-Ill Health Differences in Disease Severity and Its Sociodemographic Biobehavioral Predictors Among Patients With Knee Osteoarthritis.

Science.gov (United States)

Rezakhani Moghaddam, Hamed; Nadrian, Haidar; Abbagolizadeh, Nategh; Babazadeh, Towhid; Aghemiri, Mehran; Fathipour, Asaad

2018-01-01

Our aim in this cross-sectional study was to investigate mental health-ill health differences in disease severity and its sociodemographic biobehavioral predictors among patients with knee osteoarthritis (OA). Applying convenient sampling, 180 patients with knee OA in Tabriz, Iran, were recruited to participate in completing a three-section questionnaire (SF-12, Lequesne Algofunctional Index and Self-Management Behaviors Scale). Separate hierarchical multiple linear regressions were performed with OA severity as dependent variable: one for OA patients with positive mental health and other for OA patients with mental disorders symptoms. Among the patients with positive mental health, but not those with symptoms of mental disorder, pain management, duration of OA, physical activity management, living alone, and level of education were significant predictors of disease severity. Health care providers with a better understanding on the determinants of disease severity by mental health status may identify vulnerable patients and develop targeted interventions to foster disease management behaviors among OA patients.

Incidence and predictors of coronary stent thrombosis

DEFF Research Database (Denmark)

D'Ascenzo, Fabrizio; Bollati, Mario; Clementi, Fabrizio

2013-01-01

Stent thrombosis remains among the most feared complications of percutaneous coronary intervention (PCI) with stenting. However, data on its incidence and predictors are sparse and conflicting. We thus aimed to perform a collaborative systematic review on incidence and predictors of stent...

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow's Milk.

Science.gov (United States)

Marami Milani, Mohammad Reza; Hense, Andreas; Rahmani, Elham; Ploeger, Angelika

2016-07-23

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new ), and respiratory rate predictor RRP) with three main components of cow's milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p -value < 0.001 and R ² (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation ( p -value < 0.001) with R ² (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Multiple predictor smoothing methods for sensitivity analysis: Example results

International Nuclear Information System (INIS)

Storlie, Curtis B.; Helton, Jon C.

2008-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Special set linear algebra and special set fuzzy linear algebra

OpenAIRE

Kandasamy, W. B. Vasantha; Smarandache, Florentin; Ilanthenral, K.

2009-01-01

The authors in this book introduce the notion of special set linear algebra and special set fuzzy Linear algebra, which is an extension of the notion set linear algebra and set fuzzy linear algebra. These concepts are best suited in the application of multi expert models and cryptology. This book has five chapters. In chapter one the basic concepts about set linear algebra is given in order to make this book a self contained one. The notion of special set linear algebra and their fuzzy analog...

Towards an unbiased comparison of CC, BCC, and FCC lattices in terms of prealiasing

KAUST Repository

Vad, Viktor

2014-06-01

In the literature on optimal regular volume sampling, the Body-Centered Cubic (BCC) lattice has been proven to be optimal for sampling spherically band-limited signals above the Nyquist limit. On the other hand, if the sampling frequency is below the Nyquist limit, the Face-Centered Cubic (FCC) lattice was demonstrated to be optimal in reducing the prealiasing effect. In this paper, we confirm that the FCC lattice is indeed optimal in this sense in a certain interval of the sampling frequency. By theoretically estimating the prealiasing error in a realistic range of the sampling frequency, we show that in other frequency intervals, the BCC lattice and even the traditional Cartesian Cubic (CC) lattice are expected to minimize the prealiasing. The BCC lattice is superior over the FCC lattice if the sampling frequency is not significantly below the Nyquist limit. Interestingly, if the original signal is drastically undersampled, the CC lattice is expected to provide the lowest prealiasing error. Additionally, we give a comprehensible clarification that the sampling efficiency of the FCC lattice is lower than that of the BCC lattice. Although this is a well-known fact, the exact percentage has been erroneously reported in the literature. Furthermore, for the sake of an unbiased comparison, we propose to rotate the Marschner-Lobb test signal such that an undue advantage is not given to either lattice. © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

Towards an unbiased comparison of CC, BCC, and FCC lattices in terms of prealiasing

KAUST Repository

Vad, Viktor; Csé bfalvi, Balá zs; Rautek, Peter; Grö ller, Eduard M.

2014-01-01

In the literature on optimal regular volume sampling, the Body-Centered Cubic (BCC) lattice has been proven to be optimal for sampling spherically band-limited signals above the Nyquist limit. On the other hand, if the sampling frequency is below the Nyquist limit, the Face-Centered Cubic (FCC) lattice was demonstrated to be optimal in reducing the prealiasing effect. In this paper, we confirm that the FCC lattice is indeed optimal in this sense in a certain interval of the sampling frequency. By theoretically estimating the prealiasing error in a realistic range of the sampling frequency, we show that in other frequency intervals, the BCC lattice and even the traditional Cartesian Cubic (CC) lattice are expected to minimize the prealiasing. The BCC lattice is superior over the FCC lattice if the sampling frequency is not significantly below the Nyquist limit. Interestingly, if the original signal is drastically undersampled, the CC lattice is expected to provide the lowest prealiasing error. Additionally, we give a comprehensible clarification that the sampling efficiency of the FCC lattice is lower than that of the BCC lattice. Although this is a well-known fact, the exact percentage has been erroneously reported in the literature. Furthermore, for the sake of an unbiased comparison, we propose to rotate the Marschner-Lobb test signal such that an undue advantage is not given to either lattice. © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

Unbiased total electron content (UTEC), their fluctuations, and correlation with seismic activity over Japan

Science.gov (United States)

Cornely, Pierre-Richard; Hughes, John

2018-02-01

Earthquakes are among the most dangerous events that occur on earth and many scientists have been investigating the underlying processes that take place before earthquakes occur. These investigations are fueling efforts towards developing both single and multiple parameter earthquake forecasting methods based on earthquake precursors. One potential earthquake precursor parameter that has received significant attention within the last few years is the ionospheric total electron content (TEC). Despite its growing popularity as an earthquake precursor, TEC has been under great scrutiny because of the underlying biases associated with the process of acquiring and processing TEC data. Future work in the field will need to demonstrate our ability to acquire TEC data with the least amount of biases possible thereby preserving the integrity of the data. This paper describes a process for removing biases using raw TEC data from the standard Rinex files obtained from any global positioning satellites system. The process is based on developing an unbiased TEC (UTEC) data and model that can be more adaptable to serving as a precursor signal for earthquake forecasting. The model was used during the days and hours leading to the earthquake off the coast of Tohoku, Japan on March 11, 2011 with interesting results. The model takes advantage of the large amount of data available from the GPS Earth Observation Network of Japan to display near real-time UTEC data as the earthquake approaches and for a period of time after the earthquake occurred.

Predictors of reaching a serum uric acid goal in patients with gout and treated with febuxostat

Directory of Open Access Journals (Sweden)

Sheer R

2017-10-01

Full Text Available Richard Sheer,1 Kyle D Null,2 Keith A Szymanski,2 Lavanya Sudharshan,1 Jennifer Banovic,2 Margaret K Pasquale1 1Comprehensive Health Insights, Inc., Louisville, KY, 2Takeda Pharmaceuticals U.S.A., Inc., Deerfield, IL, USA Purpose: Clinical guidelines recommend febuxostat as first-line pharmacologic urate-lowering therapy for patients with gout to achieve a goal serum uric acid (sUA <6 mg/dL; however, little is known about other contributing factors. This study identified clinical characteristics of patients treated with febuxostat to develop and validate a predictive model for achieving a goal sUA.Patients and methods: Patients with Humana Medicare or commercial insurance, diagnosed with gout and newly initiated on febuxostat (index date February 1, 2009 – December 31, 2013, were identified for a retrospective cohort study. Patients were followed for 365 days and the first valid sUA test result ≥120 days after index was retained. A stepwise logistic regression with backward elimination was estimated to model sUA goal attainment, and a linear model was estimated to model the impact of predictor variables on sUA level.Results: The study sample (n=678 was divided into a development (training dataset (n=453 and a validation (holdout dataset (n=225. In the training sample, patients in the sUA <6 mg/dL group were on febuxostat for a longer time, were more adherent, and had a lower average baseline sUA level (all p<0.0001 vs patients in the sUA ≥6 mg/dL group. In the logistic model, febuxostat adherence (odds ratio [OR]=1.03, p<0.0001 and baseline sUA level (OR=0.84, p<0.0001 increased the odds of attaining sUA <6 mg/dL. In the linear regression model, increase in febuxostat adherence (p<0.0001, baseline sUA level (p<0.0001, advanced age (p=0.0021, and not having congestive heart failure (p<0.05 were associated with a reduction of sUA level. Pre-index allopurinol use was a marginally significant predictor of sUA level reduction (p=0

Distribution and Predictors of Pesticides in the Umbilical Cord Blood of Chinese Newborns

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Monica K. Silver

2015-12-01

Full Text Available Rates of pesticide use in Chinese agriculture are five times greater than the global average, leading to high exposure via the diet. Many are neurotoxic, making prenatal pesticide exposure a concern. Previous studies of prenatal exposure in China focused almost entirely on organochlorines. Here the study goals were to characterize the exposure of Chinese newborns to all classes of pesticides and identify predictors of those exposures. Eighty-four pesticides and 12 metabolites were measured in the umbilical cord plasma of 336 infants. Composite variables were created for totals detected overall and by class. Individual pesticides were analyzed as dichotomous or continuous, based on detection rates. Relationships between demographic characteristics and pesticides were evaluated using generalized linear regression. Seventy-five pesticides were detected. The mean number of detects per sample was 15.3. Increased pesticide detects were found in the cord blood of infants born in the summer (β = 2.2, p = 0.01, particularly in July (β = 4.0, p = 0.03. Similar trends were observed for individual insecticide classes. Thus, a summer birth was the strongest predictor of pesticide evidence in cord blood. Associations were more striking for overall pesticide exposure than for individual pesticides, highlighting the importance of considering exposure to mixtures of pesticides, rather than individual agents or classes.

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African Journals Online (AJOL)

relative to generalized least squares estimator in the linear regression model with first-order spatial error process are ... GLS estimator provides the best linear unbiased estimator (BLUE) of 6 in contrast to OLS (see Fomby et al., 1984, p. ..... Applied Matrix Algebra in the Statistical Sciences. Elsevier Science Publishing, New ...

Predictors of Preoperative Tinnitus in Unilateral Sporadic Vestibular Schwannoma

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Georgios Naros

2017-08-01

Full Text Available ObjectiveNearly two-thirds of patients with vestibular schwannoma (VS are reporting a significantly impaired quality of life due to tinnitus. VS-associated tinnitus is attributed to an anatomical and physiological damage of the hearing nerve by displacing growth of the tumor. In contrast, the current pathophysiological concept of non-VS tinnitus hypothesizes a maladaptive neuroplasticity of the central nervous system to a (hidden hearing impairment resulting in a subjective misperception. However, it is unclear whether this concept fits to VS-associated tinnitus. This study aims to determine the clinical predictors of VS-associated tinnitus to ascertain the compatibility of both pathophysiological concepts.MethodsThis retrospective study includes a group of 478 neurosurgical patients with unilateral sporadic VS evaluated preoperatively regarding the occurrence of ipsilateral tinnitus depending on different clinical factors, i.e., age, gender, tumor side, tumor size (T1–T4 according to the Hannover classification, and hearing impairment (Gardner–Robertson classification, GR1–5, using a binary logistic regression.Results61.8% of patients complain about a preoperative tinnitus. The binary logistic regression analysis identified male gender [OR 1.90 (1.25–2.75; p = 0.002] and hearing impairment GR3 [OR 1.90 (1.08–3.35; p = 0.026] and GR4 [OR 8.21 (2.29–29.50; p = 0.001] as positive predictors. In contrast, patients with large T4 tumors [OR 0.33 (0.13–0.86; p = 0.024] and complete hearing loss GR5 [OR 0.36 (0.15–0.84; p = 0.017] were less likely to develop a tinnitus. Yet, 60% of the patients with good clinical hearing (GR1 and 25% of patients with complete hearing loss (GR5 suffered from tinnitus.ConclusionThese data are good accordance with literature about non-VS tinnitus indicating hearing impairment as main risk factor. In contrast, complete hearing loss appears a negative predictor for tinnitus. For the first

Stability analysis of embedded nonlinear predictor neural generalized predictive controller

Directory of Open Access Journals (Sweden)

Hesham F. Abdel Ghaffar

2014-03-01

Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

Multiple predictor smoothing methods for sensitivity analysis: Description of techniques

International Nuclear Information System (INIS)

Storlie, Curtis B.; Helton, Jon C.

2008-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Predictors of life disability in trichotillomania.

Science.gov (United States)

Tung, Esther S; Flessner, Christopher A; Grant, Jon E; Keuthen, Nancy J

2015-01-01

Limited research has investigated disability and functional impairment in trichotillomania (TTM) subjects. This study examined the relationships between hair pulling (HP) style and severity and disability while controlling for mood severity. Disability was measured in individual life areas (work, social, and family/home life) instead of as a total disability score as in previous studies. One hundred fifty three adult hair pullers completed several structured interviews and self-report instruments. HP style and severity, as well as depression, anxiety, and stress were correlated with work, social, and family/home life impairment on the Sheehan Disability Scale (SDS). Multiple regression analyses were performed to determine significant predictors of life impairment. Depressive severity was a significant predictor for all SDS life areas. In addition, interference/avoidance associated with HP was a predictor for work and social life disability. Distress from HP was a significant predictor of social and family/home life disability. Focused HP score and anxiety were significant predictors of family/home life disability. As expected, depression in hair pullers predicted disability across life domains. Avoiding work and social situations can seriously impair functioning in those life domains. Severity of distress and worry about HP may be most elevated in social situations with friends and family and thus predict impairment in those areas. Finally, since HP often occurs at home, time spent in focused hair pulling would have a greater negative impact on family and home responsibilities than social and work life. Copyright © 2014 Elsevier Inc. All rights reserved.

Predictors of quality of life in hemodialysis patients

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Magda Bayoumi

2013-01-01

Full Text Available Quality of Life (QoL is a consistent and powerful predictor that affects the out-come in end-stage renal disease (ESRD patients on dialysis. This study was undertaken to identify the factors that might predict QoL scores among ESRD patients on hemodialysis (HD. The study was conducted at three HD units in Saudi Arabia from January 2007 to January 2008. We studied 100 HD patients (53 males and 47 females and used the SF-36 and KDQoL-SF forms covering six domains of QoL, namely physical, emotional, social, illness impact, medical and financial satisfaction, and overall general health. The mean age of the study patients was 47.5 ± 13.8 years and the mean duration of dialysis was 77.2 ± 75.5 months. The QoL scores were 45.8 ± 17.1 for general health, 53.1 ± 32.0 for physical QoL, 50.5 ± 14.8 for emotional QoL, 54.9 ± 18.1 for social QoL, 46.5 ± 13.7 for illness impact, and 45.9 ± 12.2 for the medical and financial domain. The total QoL score was 49.5 ± 13.7. The male patients had statistically significantly reduced QoL and younger patients had better QoL scores. The QoL scores revealed a decreasing trend with decreasing level of education; they were elevated among employed patients. Multiple linear regression analysis demonstrated that age, dialysis duration, and male sex were negative predictors of QoL score. We conclude from our study that QoL is reduced in all the health domains of HD patients. Older age, male gender, unemployment, and duration of dialysis adversely affected the QoL scores. Adequate management of some of these factors could influence patient outcomes.

Prediction of SO2 pollution incidents near a power station using partially linear models and an historical matrix of predictor-response vectors

International Nuclear Information System (INIS)

Prada-Sanchez, J.M.; Febrero-Bande, M.; Gonzalez-Manteiga, W.; Costos-Yanez, T.; Bermudez-Cela, J.L.; Lucas-Dominguez, T.

2000-01-01

Atmospheric SO 2 concentrations at sampling stations near the fossil fuel fired power station at As Pontes (La Coruna, Spain) were predicted using a model for the corresponding time series consisting of a self-explicative term and a linear combination of exogenous variables. In a supplementary simulation study, models of this kind behaved better than the corresponding pure self-explicative or pure linear regression models. (Author)

Predictors of treatment failure among pulmonary tuberculosis ...

African Journals Online (AJOL)

Introduction: Early identification of Tuberculosis (TB) treatment failure using cost effective means is urgently needed in developing nations. The study set out to describe affordable predictors of TB treatment failure in an African setting. Objective: To determine the predictors of treatment failure among patients with sputum ...

Predictors of Transience among Homeless Emerging Adults

Science.gov (United States)

Ferguson, Kristin M.; Bender, Kimberly; Thompson, Sanna J.

2014-01-01

This study identified predictors of transience among homeless emerging adults in three cities. A total of 601 homeless emerging adults from Los Angeles, Austin, and Denver were recruited using purposive sampling. Ordinary least squares regression results revealed that significant predictors of greater transience include White ethnicity, high…

Predictors of job satisfaction among academic faculty members: do instructional and clinical staff differ?

Science.gov (United States)

Chung, Kevin C; Song, Jae W; Kim, H Myra; Woolliscroft, James O; Quint, Elisabeth H; Lukacs, Nicholas W; Gyetko, Margaret R

2010-10-01

This study aimed to identify and compare predictors of job satisfaction between instructional and clinical faculty members. A 61-item faculty job satisfaction survey was distributed to 1898 academic faculty members at the University of Michigan Medical School. The anonymous survey was web-based. Questions covered topics on departmental organisation, research, clinical and teaching support, compensation, mentorship, and promotion. Levels of satisfaction were contrasted between faculty members on the two tracks, and predictors of job satisfaction were identified using linear regression models. Response rates for the instructional and clinical faculty groups were 43.1% and 46.7%, respectively. Clinical faculty members reported being less satisfied with how they were mentored and fewer reported understanding the process for promotion. There was no significant difference in overall job satisfaction between the two faculty groups. Surprisingly, clinical faculty members with mentors were significantly less satisfied with how they were mentored and with career advancement, and were significantly less likely to choose an academic career if they had to do it all over again compared with instructional faculty mentees. Additionally, senior-level clinical faculty members were significantly less satisfied with their opportunities to mentor junior faculty members compared with senior-level instructional faculty staff. Significant predictors of job satisfaction for both groups included areas of autonomy, meeting career expectations, work-life balance, and departmental leadership. In the clinical track only, compensation and career advancement variables also emerged as significant predictors of overall job satisfaction. Greater emphasis must be placed on faculty members' well-being at both the institutional level and the level of departmental leadership. Efforts to enhance job satisfaction and improve retention are more likely to succeed if they are directed by locally designed

Endotoxin predictors and associated respiratory outcomes differ with climate regions in the U.S.

Science.gov (United States)

Mendy, Angelico; Wilkerson, Jesse; Salo, Pӓivi M; Cohn, Richard D; Zeldin, Darryl C; Thorne, Peter S

2018-03-01

Although endotoxin is a recognized cause of environmental lung disease, how its relationship with respiratory outcomes varies with climate is unknown. To examine the endotoxin predictors as well as endotoxin association with asthma, wheeze, and sensitization to inhalant allergens in various US climate regions. We analyzed data on 6963 participants in the National Health and Nutrition Examination Survey. Endotoxin measurements of house dust from bedroom floor and bedding were performed at the University of Iowa. Linear and logistic regression analyses were used to identify endotoxin predictors and assess endotoxin association with health outcomes. The overall median house dust endotoxin was 16.2 EU/mg; it was higher in mixed-dry/hot-dry regions (19.7 EU/mg) and lower in mixed-humid/marine areas (14.8 EU/mg). Endotoxin predictors and endotoxin association with health outcomes significantly differed across climate regions. In subarctic/very cold/cold regions, log 10 -endotoxin was significantly associated with higher prevalence of wheeze outcomes (OR:1.48, 95% CI:1.19-1.85 for any wheeze, OR:1.48, 95% CI:1.22-1.80 for exercise-induced wheeze, OR:1.50, 95% CI:1.13-1.98 for prescription medication for wheeze, and OR:1.95, 95% CI:1.50-2.54 for doctor/ER visit for wheeze). In hot-humid regions, log 10 -endotoxin was positively associated with any wheeze (OR:1.66, 95% CI:1.04-2.65) and current asthma (OR:1.56, 95% CI:1.11-2.18), but negatively with sensitization to any inhalant allergens (OR:0.83, 95% CI:0.74-0.92). Endotoxin predictors and endotoxin association with asthma and wheeze differ across U.S. climate regions. Endotoxin is associated positively with wheeze or asthma in cold and hot-humid regions, but negatively with sensitization to inhalant allergens in hot-humid climates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Volume fractions of DCE-MRI parameter as early predictor of histologic response in soft tissue sarcoma: A feasibility study.

Science.gov (United States)

Xia, Wei; Yan, Zhuangzhi; Gao, Xin

2017-10-01

To find early predictors of histologic response in soft tissue sarcoma through volume transfer constant (K trans ) analysis based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). 11 Patients with soft tissue sarcoma of the lower extremity that underwent preoperative chemoradiotherapy followed by limb salvage surgery were included in this retrospective study. For each patient, DCE-MRI data sets were collected before and two weeks after therapy initiation, and histologic tumor cell necrosis rate (TCNR) was reported at surgery. The DCE-MRI volumes were aligned by registration. Then, the aligned volumes were used to obtain the K trans variation map. Accordingly, three sub-volumes (with increased, decreased or unchanged K trans ) were defined and identified, and fractions of the sub-volumes, denoted as F + , F - and F 0 , respectively, were calculated. The predictive ability of volume fractions was determined by using area under a receiver operating characteristic curve (AUC). Linear regression analysis was performed to investigate the relationship between TCNR and volume fractions. In addition, the K trans values of the sub-volumes were compared. The AUC for F - (0.896) and F 0 (0.833) were larger than that for change of tumor longest diameter ΔD (0.625) and the change of mean K trans ΔK trans ¯ (0.792). Moreover, the regression results indicated that TCNR was directly proportional to F 0 (R 2 =0.75, P=0.0003), while it was inversely proportional to F - (R 2 =0.77, P=0.0002). However, TCNR had relatively weak linear relationship with ΔK trans ¯ (R 2 =0.64, P=0.0018). Additionally, TCNR did not have linear relationship with DD (R 2 =0.16, P=0.1246). The volume fraction F - and F 0 have potential as early predictors of soft tissue sarcoma histologic response. Copyright © 2017 Elsevier B.V. All rights reserved.

Linear Algebraic Method for Non-Linear Map Analysis

International Nuclear Information System (INIS)

Yu, L.; Nash, B.

2009-01-01

We present a newly developed method to analyze some non-linear dynamics problems such as the Henon map using a matrix analysis method from linear algebra. Choosing the Henon map as an example, we analyze the spectral structure, the tune-amplitude dependence, the variation of tune and amplitude during the particle motion, etc., using the method of Jordan decomposition which is widely used in conventional linear algebra.

Motivational predictors of physical education students' effort, exercise intentions, and leisure-time physical activity: a multilevel linear growth analysis.

Science.gov (United States)

Taylor, Ian M; Ntoumanis, Nikos; Standage, Martyn; Spray, Christopher M

2010-02-01

Grounded in self-determination theory (SDT; Deci & Ryan, 2000), the current study explored whether physical education (PE) students' psychological needs and their motivational regulations toward PE predicted mean differences and changes in effort in PE, exercise intentions, and leisure-time physical activity (LTPA) over the course of one UK school trimester. One hundred and seventy-eight students (69% male) aged between 11 and 16 years completed a multisection questionnaire at the beginning, middle, and end of a school trimester. Multilevel growth models revealed that students' perceived competence and self-determined regulations were the most consistent predictors of the outcome variables at the within- and between-person levels. The results of this work add to the extant SDT-based literature by examining change in PE students' motivational regulations and psychological needs, as well as underscoring the importance of disaggregating within- and between-student effects.

Predictors for half-year outcome of impairment in daily life for back pain patients referred for physiotherapy: a prospective observational study.

Science.gov (United States)

Karstens, Sven; Hermann, Katja; Froböse, Ingo; Weiler, Stephan W

2013-01-01

From observational studies, there is only sparse information available on the predictors of development of impairment in daily life for patients receiving physiotherapy. Therefore, our aim was to identify factors which predict impairment in daily life for patients with back pain 6 months after receiving physiotherapy. We conducted a prospective cohort study with 6-month follow-up. Patients were enrolled for treatment in private physiotherapy practices. Patients with a first physiotherapy referral because of thoracic or low back pain, aged 18 to 65 years were included. Primary outcome impairment was measured utilising the 16-item version of the Musculoskeletal Function Assessment Questionnaire. Therapy was documented on a standardized form. Baseline scores for impairment in daily life, symptom characteristics, sociodemographic and psychosocial factors, physical activity, nicotine consumption, intake of analgesics, comorbidity and delivered primary therapy approach were investigated as possible predictors. Univariate and multiple linear regression analyses were performed. A total of 792 patients participated in the study (59% female, mean age 44.4 (SD 11.4), with 6-month follow-up results available from 391 patients. In univariate analysis 17 variables reached significance. In multiple linear regression identified predictors were: impairment in daily life before therapy, mental disorders, duration of the complaints, self-prognosis on work ability, rheumatoid arthritis, age, form of stress at work and physical activity. The variables explain 34% of variance (adjusted R(2), pphysiotherapy patients, this study adds new knowledge for forming appropriate referral guidelines. Impairment in daily life before therapy, mental disorder as comorbidity and the duration of the complaints can be named as outstanding factors. The results of this study can be used to facilitate comparison of patient therapy goals with the prognosis in everyday practice.

Predictors of self-rated health: a 12-month prospective study of IT and media workers

Directory of Open Access Journals (Sweden)

Arnetz Bengt B

2006-07-01

Full Text Available Abstract Objective The aim of the present study was to determine health-related risk and salutogenic factors and to use these to construct prediction models for future self-rated health (SRH, i.e. find possible characteristics predicting individuals improving or worsening in SRH over time (0–12 months. Methods A prospective study was conducted with measurements (physiological markers and self-ratings at 0, 6 and 12 months, involving 303 employees (187 men and 116 women, age 23–64 from four information technology and two media companies. Results There were a multitude of statistically significant cross-sectional correlations (Spearman's Rho between SRH and other self-ratings as well as physiological markers. Predictors of future SRH were baseline ratings of SRH, self-esteem and social support (logistic regression, and SRH, sleep quality and sense of coherence (linear regression. Conclusion The results of the present study indicate that baseline SRH and other self-ratings are predictive of future SRH. It is cautiously implied that SRH, self-esteem, social support, sleep quality and sense of coherence might be predictors of future SRH and therefore possibly also of various future health outcomes.

Unbiased metabolite profiling by liquid chromatography-quadrupole time-of-flight mass spectrometry and multivariate data analysis for herbal authentication: classification of seven Lonicera species flower buds.

Science.gov (United States)

Gao, Wen; Yang, Hua; Qi, Lian-Wen; Liu, E-Hu; Ren, Mei-Ting; Yan, Yu-Ting; Chen, Jun; Li, Ping

2012-07-06

Plant-based medicines become increasingly popular over the world. Authentication of herbal raw materials is important to ensure their safety and efficacy. Some herbs belonging to closely related species but differing in medicinal properties are difficult to be identified because of similar morphological and microscopic characteristics. Chromatographic fingerprinting is an alternative method to distinguish them. Existing approaches do not allow a comprehensive analysis for herbal authentication. We have now developed a strategy consisting of (1) full metabolic profiling of herbal medicines by rapid resolution liquid chromatography (RRLC) combined with quadrupole time-of-flight mass spectrometry (QTOF MS), (2) global analysis of non-targeted compounds by molecular feature extraction algorithm, (3) multivariate statistical analysis for classification and prediction, and (4) marker compounds characterization. This approach has provided a fast and unbiased comparative multivariate analysis of the metabolite composition of 33-batch samples covering seven Lonicera species. Individual metabolic profiles are performed at the level of molecular fragments without prior structural assignment. In the entire set, the obtained classifier for seven Lonicera species flower buds showed good prediction performance and a total of 82 statistically different components were rapidly obtained by the strategy. The elemental compositions of discriminative metabolites were characterized by the accurate mass measurement of the pseudomolecular ions and their chemical types were assigned by the MS/MS spectra. The high-resolution, comprehensive and unbiased strategy for metabolite data analysis presented here is powerful and opens the new direction of authentication in herbal analysis. Copyright © 2012 Elsevier B.V. All rights reserved.

Temporal predictors of health-related quality of life in elderly people with diabetes: results of a German cohort study.

Directory of Open Access Journals (Sweden)

Imad Maatouk

Full Text Available BACKGROUND: The aim of the study was to determine predictors that influence health-related quality of life (HRQOL in a large cohort of elderly diabetes patients from primary care over a follow-up period of five years. METHODS AND RESULTS: At the baseline measurement of the ESTHER cohort study (2000-2002, 1375 out of 9953 participants suffered from diabetes (13.8%. 1057 of these diabetes patients responded to the second-follow up (2005-2007. HRQOL at baseline and follow-up was measured using the SF-12; mental component scores (MCS and physical component scores (PCS were calculated; multiple linear regression models were used to determine predictors of HRQOL at follow-up. As possible predictors for HRQOL, the following baseline variables were examined: treatment with insulin, glycated hemoglobin (HbA1c, number of diabetes related complications, number of comorbid diseases, Body-Mass-Index (BMI, depression and HRQOL. Regression analyses were adjusted for sociodemographic variables and smoking status. 1034 patients (97.8% responded to the SF-12 both at baseline and after five years and were therefore included in the study. Regression analyses indicated that significant predictors of decreased MCS were a lower HRQOL, a higher number of diabetes related complications and a reported history of depression at baseline. Complications, BMI, smoking and HRQOL at baseline significantly predicted PCS at the five year follow-up. CONCLUSIONS: Our findings expand evidence from previous cross-sectional data indicating that in elderly diabetes patients, depression, diabetes related complications, smoking and BMI are temporally predictive for HRQOL.

Adams Predictor-Corrector Systems for Solving Fuzzy Differential Equations

Directory of Open Access Journals (Sweden)

Dequan Shang

2013-01-01

Full Text Available A predictor-corrector algorithm and an improved predictor-corrector (IPC algorithm based on Adams method are proposed to solve first-order differential equations with fuzzy initial condition. These algorithms are generated by updating the Adams predictor-corrector method and their convergence is also analyzed. Finally, the proposed methods are illustrated by solving an example.

Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Directory of Open Access Journals (Sweden)

Qiutong Jin

2016-06-01

Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

Predictors of change in life skills in schizophrenia after cognitive remediation.

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Kurtz, Matthew M; Seltzer, James C; Fujimoto, Marco; Shagan, Dana S; Wexler, Bruce E

2009-02-01

Few studies have investigated predictors of response to cognitive remediation interventions in patients with schizophrenia. Predictor studies to date have selected treatment outcome measures that were either part of the remediation intervention itself or closely linked to the intervention with few studies investigating factors that predict generalization to measures of everyday life-skills as an index of treatment-related improvement. In the current study we investigated the relationship between four measures of neurocognitive function, crystallized verbal ability, auditory sustained attention and working memory, verbal learning and memory, and problem-solving, two measures of symptoms, total positive and negative symptoms, and the process variables of treatment intensity and duration, to change on a performance-based measure of everyday life-skills after a year of computer-assisted cognitive remediation offered as part of intensive outpatient rehabilitation treatment. Thirty-six patients with schizophrenia or schizoaffective disorder were studied. Results of a linear regression model revealed that auditory attention and working memory predicted a significant amount of the variance in change in performance-based measures of everyday life skills after cognitive remediation, even when variance for all other neurocognitive variables in the model was controlled. Stepwise regression revealed that auditory attention and working memory predicted change in everyday life-skills across the trial even when baseline life-skill scores, symptoms and treatment process variables were controlled. These findings emphasize the importance of sustained auditory attention and working memory for benefiting from extended programs of cognitive remediation.

Predictors of patient dependence in mild-to-moderate Alzheimer's disease.

Science.gov (United States)

Benke, Thomas; Sanin, Günter; Lechner, Anita; Dal-Bianco, Peter; Ransmayr, Gerhard; Uranüs, Margarete; Marksteiner, Josef; Gaudig, Maren; Schmidt, Reinhold

2015-01-01

Patient dependence has rarely been studied in mild-to-moderate Alzheimer's disease (AD). To identify factors which predict patient dependence in mild-to-moderate AD. We studied 398 non-institutionalized AD patients (234 females) of the ongoing Prospective Registry on Dementia (PRODEM) in Austria. The Dependence Scale (DS) was used to assess patient dependence. Patient assessment comprised functional abilities, neuropsychiatric symptoms and cognitive functions. A multiple linear regression analysis was performed to identify predictors of patient dependence. AD patients were mildly-to-moderately impaired (mean scores and SDs were: CDR 0.84 ± 0.43; DAD 74.4 ± 23.3, MMSE = 22.5 ± 3.6). Psychopathology and caregiver burden were in the low range (mean NPI score 13.2, range 0 to 98; mean ZBI score 18, range 0-64). Seventy five percent of patients were classified as having a mild level of patient dependence (DS sum score 0 to 6). Patient dependence correlated significantly and positively with age, functional measures, psychopathology and depression, disease duration, and caregiver burden. Significant negative, but low correlations were found between patient dependence, cognitive variables, and global cognition. Activities of daily living, patient age, and disease severity accounted for 63% of variance in patient dependence, whereas cognitive variables accounted for only 11%. Dependence in this cohort was mainly related to age and functional impairment, and less so to cognitive and neuropsychiatric variables. This differs from studies investigating patients in more advanced disease stages which found abnormal behavior and impairments of cognition as main predictors of patient dependence.

Linear associations between clinically assessed upper motor neuron disease and diffusion tensor imaging metrics in amyotrophic lateral sclerosis.

Science.gov (United States)

Woo, John H; Wang, Sumei; Melhem, Elias R; Gee, James C; Cucchiara, Andrew; McCluskey, Leo; Elman, Lauren

2014-01-01

To assess the relationship between clinically assessed Upper Motor Neuron (UMN) disease in Amyotrophic Lateral Sclerosis (ALS) and local diffusion alterations measured in the brain corticospinal tract (CST) by a tractography-driven template-space region-of-interest (ROI) analysis of Diffusion Tensor Imaging (DTI). This cross-sectional study included 34 patients with ALS, on whom DTI was performed. Clinical measures were separately obtained including the Penn UMN Score, a summary metric based upon standard clinical methods. After normalizing all DTI data to a population-specific template, tractography was performed to determine a region-of-interest (ROI) outlining the CST, in which average Mean Diffusivity (MD) and Fractional Anisotropy (FA) were estimated. Linear regression analyses were used to investigate associations of DTI metrics (MD, FA) with clinical measures (Penn UMN Score, ALSFRS-R, duration-of-disease), along with age, sex, handedness, and El Escorial category as covariates. For MD, the regression model was significant (p = 0.02), and the only significant predictors were the Penn UMN Score (p = 0.005) and age (p = 0.03). The FA regression model was also significant (p = 0.02); the only significant predictor was the Penn UMN Score (p = 0.003). Measured by the template-space ROI method, both MD and FA were linearly associated with the Penn UMN Score, supporting the hypothesis that DTI alterations reflect UMN pathology as assessed by the clinical examination.

Medical Student Attitudes Toward Older Patients: Predictors and Consequences

Science.gov (United States)

1989-12-18

DEC 1989 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Medical Student Attitudes Toward Older Patients : Predictors and... MEDICAL CENTER . Title of Thesis: " Medical Student Attitudes Toward Older Patients : Predictors and Cons.equences" Name of Candidate: Victoria...dissertation manuscript entitled: 11 Medical Student Attitudes Toward Older Patients : Predictors and Consequences 11 beyond brief excerpts is with

Learning and Study Strategies Inventory subtests and factors as predictors of National Board of Chiropractic Examiners Part 1 examination performance.

Science.gov (United States)

Schutz, Christine M; Dalton, Leanne; Tepe, Rodger E

2013-01-01

This study was designed to extend research on the relationship between chiropractic students' learning and study strategies and national board examination performance. Sixty-nine first trimester chiropractic students self-administered the Learning and Study Strategies Inventory (LASSI). Linear trends tests (for continuous variables) and Mantel-Haenszel trend tests (for categorical variables) were utilized to determine if the 10 LASSI subtests and 3 factors predicted low, medium and high levels of National Board of Chiropractic Examiners (NBCE) Part 1 scores. Multiple regression was performed to predict overall mean NBCE examination scores using the 3 LASSI factors as predictor variables. Four LASSI subtests (Anxiety, Concentration, Selecting Main Ideas, Test Strategies) and one factor (Goal Orientation) were significantly associated with NBCE examination levels. One factor (Goal Orientation) was a significant predictor of overall mean NBCE examination performance. Learning and study strategies are predictive of NBCE Part 1 examination performance in chiropractic students. The current study found LASSI subtests Anxiety, Concentration, Selecting Main Ideas, and Test Strategies, and the Goal-Orientation factor to be significant predictors of NBCE scores. The LASSI may be useful to educators in preparing students for academic success. Further research is warranted to explore the effects of learning and study strategies training on GPA and NBCE performance.

Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants.

Science.gov (United States)

González, Mari Feli; Facal, David; Juncos-Rabadán, Onésimo; Yanguas, Javier

2017-10-01

Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition. The total sample, drawn from the "Longitudinal Study Aging in Spain (ELES)" project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance. Education is the variable which best predicts cognitive performance in the 50-59, 60-69, and 70-79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model. Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.

Genetic design of interpolated non-linear controllers for linear plants

International Nuclear Information System (INIS)

Ajlouni, N.

2000-01-01

The techniques of genetic algorithms are proposed as a means of designing non-linear PID control systems. It is shown that the use of genetic algorithms for this purpose results in highly effective non-linear PID control systems. These results are illustrated by using genetic algorithms to design a non-linear PID control system and contrasting the results with an optimally tuned linear PID controller. (author)

Lessons to be learned from a contentious challenge to mainstream radiobiological science (the linear no-threshold theory of genetic mutations)

International Nuclear Information System (INIS)

Beyea, Jan

2017-01-01

found consistent and unbiased. • A 1956 genetics report did not hide estimates and does not need investigation for misconduct. • The scientific record was strong for a no-threshold, linear genetic response to radiation.

Lessons to be learned from a contentious challenge to mainstream radiobiological science (the linear no-threshold theory of genetic mutations)

Energy Technology Data Exchange (ETDEWEB)

Beyea, Jan, E-mail: jbeyea@cipi.com

2017-04-15

found consistent and unbiased. • A 1956 genetics report did not hide estimates and does not need investigation for misconduct. • The scientific record was strong for a no-threshold, linear genetic response to radiation.

Predictors of quality of life and depression in older people living in temporary houses 13 months after the Wenchuan earthquake in western China: A cross-sectional study.

Science.gov (United States)

Xie, Xia; Chen, Yanling; Chen, Hong; Au, Alma; Guo, Hongxia

2017-06-01

In this study, we explored the predictors of quality of life and depressive features in older people living in temporary housing 13 months after the Wenchuan earthquake in western China. Anonymous data were collected via questionnaires in a cross-sectional survey of 189 older people living in temporary housing 13 months after the earthquake. To explore the predictors of the outcomes of interest, Pearson correlation and multiple linear regression analysis were used. The results indicated that interests/hobbies, subjective support, and family function were positive predictors of quality of life, whereas instrumental activities of daily living and depressive symptoms were its negative predictors. In addition, we found that a higher level of instrumental activities of daily living predicted a greater likelihood of depression. These results suggested that developing strategies to decrease the instrumental activities of daily living score of these people helps improve their quality of life and depression. To enhance the quality of life of these individuals, healthcare providers should also focus on developing their interests/hobbies and provide them with adequate social support, especially subjective support. © 2017 John Wiley & Sons Australia, Ltd.

Similarities and Differences Between Warped Linear Prediction and Laguerre Linear Prediction

NARCIS (Netherlands)

Brinker, Albertus C. den; Krishnamoorthi, Harish; Verbitskiy, Evgeny A.

2011-01-01

Linear prediction has been successfully applied in many speech and audio processing systems. This paper presents the similarities and differences between two classes of linear prediction schemes, namely, Warped Linear Prediction (WLP) and Laguerre Linear Prediction (LLP). It is shown that both

Electrical Signs predictors of malignant ventricular arrhythmias

International Nuclear Information System (INIS)

Aleman Fernandez, Ailema Amelia; Dorantes Sanchez, Margarita

2012-01-01

Recurrence of malignant ventricular arrhythmia is frequent in cardioverter-defibrillators related patients. The risk stratification is difficult, there are numerous electrocardiographic predictors but his sensibility and specificity are not absolute. The limit between normal and pathological is not defined, besides the complexity of ventricular arrhythmias. We expose different electrocardiographic predictors that can help to better individual risk stratification

Predictors of physicians' stress related to information systems: a nine-year follow-up survey study.

Science.gov (United States)

Heponiemi, Tarja; Hyppönen, Hannele; Kujala, Sari; Aalto, Anna-Mari; Vehko, Tuulikki; Vänskä, Jukka; Elovainio, Marko

2018-04-13

Among the important stress factors for physicians nowadays are poorly functioning, time consuming and inadequate information systems. The present study examined the predictors of physicians' stress related to information systems (SRIS) among Finnish physicians. The examined predictors were cognitive workload, staffing problems, time pressure, problems in teamwork and job satisfaction, adjusted for baseline levels of SRIS, age, gender and employment sector. The study has a follow-up design with two survey data collection waves, one in 2006 and one in 2015, based on a random sample of Finnish physicians was used. The present study used a sample that included 1109 physicians (61.9% women; mean age in 2015 was 54.5; range 34-72) who provided data on the SRIS in both waves. The effects of a) predictor variable levels in 2006 on SRIS in 2015 and b) the change in the predictor variables from 2006 to 2015 on SRIS in 2015 were analysed with linear regression analyses. Regression analyses showed that the higher level of cognitive workload in 2006 significantly predicted higher level of SRIS in 2015 (β = 0.08). The reciprocity of this association was tested with cross-lagged structural equation model analyses which showed that the direction of the association was from cognitive workload to SRIS, not from SRIS to cognitive workload. Moreover, increases in time pressure (β = 0.16) and problems in teamwork (β = 0.10) were associated with higher levels of SRIS in 2015, whereas job satisfaction increase was associated with lower SRIS (β = - 0.06). According to our results, physicians' cognitive workload may have long-lasting negative ramifications in regard to how stressful physicians experience their health information systems to be. Thus, organisations should pay attention to physicians workload if they wish physicians to master all the systems they need to use. It is also important to provide physicians with enough time and collegial support in their

Finite-dimensional linear algebra

CERN Document Server

Gockenbach, Mark S

2010-01-01

Some Problems Posed on Vector SpacesLinear equationsBest approximationDiagonalizationSummaryFields and Vector SpacesFields Vector spaces Subspaces Linear combinations and spanning sets Linear independence Basis and dimension Properties of bases Polynomial interpolation and the Lagrange basis Continuous piecewise polynomial functionsLinear OperatorsLinear operatorsMore properties of linear operatorsIsomorphic vector spaces Linear operator equations Existence and uniqueness of solutions The fundamental theorem; inverse operatorsGaussian elimination Newton's method Linear ordinary differential eq

Predictors of Indoor Air Concentrations in Smoking and Non-Smoking Residences

Directory of Open Access Journals (Sweden)

Mireille Guay

2010-08-01

Full Text Available Indoor concentrations of air pollutants (benzene, toluene, formaldehyde, acetaldehyde, acrolein, nitrogen dioxide, particulate matter, elemental carbon and ozone were measured in residences in Regina, Saskatchewan, Canada. Data were collected in 106 homes in winter and 111 homes in summer of 2007, with 71 homes participating in both seasons. In addition, data for relative humidity, temperature, air exchange rates, housing characteristics and occupants’ activities during sampling were collected. Multiple linear regression analysis was used to construct season-specific models for the air pollutants. Where smoking was a major contributor to indoor concentrations, separate models were constructed for all homes and for those homes with no cigarette smoke exposure. The housing characteristics and occupants’ activities investigated in this study explained between 11% and 53% of the variability in indoor air pollutant concentrations, with ventilation, age of home and attached garage being important predictors for many pollutants.

Lecturing skills as predictors of tutoring skills in a problem-based medical curriculum.

Science.gov (United States)

Kassab, Salah Eldin; Hassan, Nahla; Abu-Hijleh, Marwan F; Sequeira, Reginald P

2016-01-01

Recruitment of tutors to work in problem-based learning (PBL) programs is challenging, especially in that most of them are graduated from discipline-based programs. Therefore, this study aims at examining whether lecturing skills of faculty could predict their PBL tutoring skills. This study included evaluation of faculty (n=69) who participated in both tutoring and lecturing within particular PBL units at the College of Medicine and Medical Sciences (CMMS), Arabian Gulf University, Bahrain. Each faculty was evaluated by medical students (n=45±8 for lecturing and 8±2 for PBL tutoring) using structured evaluation forms based on a Likert-type scale (poor to excellent). The prediction of tutoring skills using lecturing skills was statistically analyzed using stepwise linear regression. Among the parameters used to judge lecturing skills, the most important predictor for tutoring skills was subject matter mastery in the lecture by explaining difficult concepts and responding effectively to students' questions. Subject matter mastery in the lecture positively predicted five tutoring skills and accounted for 25% of the variance in overall effectiveness of the PBL tutors (F=22.39, P=0.000). Other important predictors for tutoring skills were providing a relaxed class atmosphere and effective use of audiovisual aids in the lecture. Predicting the tutoring skills based on lecturing skills could have implications for recruiting tutors in PBL medical programs and for tutor training initiatives.

Linear algebra

CERN Document Server

Said-Houari, Belkacem

2017-01-01

This self-contained, clearly written textbook on linear algebra is easily accessible for students. It begins with the simple linear equation and generalizes several notions from this equation for the system of linear equations and introduces the main ideas using matrices. It then offers a detailed chapter on determinants and introduces the main ideas with detailed proofs. The third chapter introduces the Euclidean spaces using very simple geometric ideas and discusses various major inequalities and identities. These ideas offer a solid basis for understanding general Hilbert spaces in functional analysis. The following two chapters address general vector spaces, including some rigorous proofs to all the main results, and linear transformation: areas that are ignored or are poorly explained in many textbooks. Chapter 6 introduces the idea of matrices using linear transformation, which is easier to understand than the usual theory of matrices approach. The final two chapters are more advanced, introducing t...

Linear gate

International Nuclear Information System (INIS)

Suwono.

1978-01-01

A linear gate providing a variable gate duration from 0,40μsec to 4μsec was developed. The electronic circuity consists of a linear circuit and an enable circuit. The input signal can be either unipolar or bipolar. If the input signal is bipolar, the negative portion will be filtered. The operation of the linear gate is controlled by the application of a positive enable pulse. (author)

Prediction of SO{sub 2} pollution incidents near a power station using partially linear models and an historical matrix of predictor-response vectors

Energy Technology Data Exchange (ETDEWEB)

Prada-Sanchez, J.M.; Febrero-Bande, M.; Gonzalez-Manteiga, W. [Universidad de Santiago de Compostela, Dept. de Estadistica e Investigacion Operativa, Santiago de Compostela (Spain); Costos-Yanez, T. [Universidad de Vigo, Dept. de Estadistica e Investigacion Operativa, Orense (Spain); Bermudez-Cela, J.L.; Lucas-Dominguez, T. [Laboratorio, Central Termica de As Pontes, La Coruna (Spain)

2000-07-01

Atmospheric SO{sub 2} concentrations at sampling stations near the fossil fuel fired power station at As Pontes (La Coruna, Spain) were predicted using a model for the corresponding time series consisting of a self-explicative term and a linear combination of exogenous variables. In a supplementary simulation study, models of this kind behaved better than the corresponding pure self-explicative or pure linear regression models. (Author)

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies.

Science.gov (United States)

Koerner, Tess K; Zhang, Yang

2017-02-27

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.

The linear programming bound for binary linear codes

NARCIS (Netherlands)

Brouwer, A.E.

1993-01-01

Combining Delsarte's (1973) linear programming bound with the information that certain weights cannot occur, new upper bounds for dmin (n,k), the maximum possible minimum distance of a binary linear code with given word length n and dimension k, are derived.

Predictors of treatment success in smoking cessation with ...

African Journals Online (AJOL)

Background. Identification of the predictors of treatment success in smoking cessation may help healthcare workers to improve the effectiveness of attempts at quitting. Objective. To identify the predictors of success in a randomised controlled trial comparing varenicline alone or in combination with nicotine replacement ...

Prevalence and predictors of compassion fatigue, burnout and compassion satisfaction among oncology nurses: A cross-sectional survey.

Science.gov (United States)

Yu, Hairong; Jiang, Anli; Shen, Jie

2016-05-01

Cancer is a leading cause of death worldwide. Given the complexity of caring work, recent studies have focused on the professional quality of life of oncology nurses. China, the world's largest developing country, faces heavy burdens of care for cancer patients. Chinese oncology nurses may be encountering the negative side of their professional life. However, studies in this field are scarce, and little is known about the prevalence and predictors of oncology nurses' professional quality of life. To describe and explore the prevalence of predictors of professional quality of life (compassion fatigue, burnout and compassion satisfaction) among Chinese oncology nurses under the guidance of two theoretical models. A cross-sectional design with a survey. Ten tertiary hospitals and five secondary hospitals in Shanghai, China. A convenience and cluster sample of 669 oncology nurses was used. All of the nurses worked in oncology departments and had over 1 year of oncology nursing experience. Of the selected nurses, 650 returned valid questionnaires that were used for statistical analyses. The participants completed the demographic and work-related questionnaire, the Chinese version of the Professional Quality of Life Scale for Nurses, the Chinese version of the Jefferson Scales of Empathy, the Simplified Coping Style Questionnaire, the Perceived Social Support Scale, and the Chinese Big Five Personality Inventory brief version. Descriptive statistics, t-tests, one-way analysis of variance, simple and multiple linear regressions were used to determine the predictors of the main research variables. Higher compassion fatigue and burnout were found among oncology nurses who had more years of nursing experience, worked in secondary hospitals and adopted passive coping styles. Cognitive empathy, training and support from organizations were identified as significant protectors, and 'perspective taking' was the strongest predictor of compassion satisfaction, explaining 23.0% of

Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability

Science.gov (United States)

Fu, Guobin; Charles, Stephen P.; Chiew, Francis H. S.; Ekström, Marie; Potter, Nick J.

2018-05-01

The nonhomogeneous hidden Markov model (NHMM) statistical downscaling model, 38 catchments in southeast Australia and 19 general circulation models (GCMs) were used in this study to demonstrate statistical downscaling uncertainties caused by equifinality to and transferability. That is to say, there could be multiple sets of predictors that give similar daily rainfall simulation results for both calibration and validation periods, but project different amounts (or even directions of change) of rainfall changing in the future. Results indicated that two sets of predictors (Set 1 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and specific humidity at 700 hPa and Set 2 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and dewpoint temperature depression at 850 hPa) as inputs to the NHMM produced satisfactory results of seasonal rainfall in comparison with observations. For example, during the model calibration period, the relative errors across the 38 catchments ranged from 0.48 to 1.76% with a mean value of 1.09% for the predictor Set 1, and from 0.22 to 2.24% with a mean value of 1.16% for the predictor Set 2. However, the changes of future rainfall from NHMM projections based on 19 GCMs produced projections with a different sign for these two different sets of predictors: Set 1 predictors project an increase of future rainfall with magnitudes depending on future time periods and emission scenarios, but Set 2 predictors project a decline of future rainfall. Such divergent projections may present a significant challenge for applications of statistical downscaling as well as climate change impact studies, and could potentially imply caveats in many existing studies in the literature.

The role of fire in UK peatland and moorland management: the need for informed, unbiased debate.

Science.gov (United States)

Davies, G Matt; Kettridge, Nicholas; Stoof, Cathelijne R; Gray, Alan; Ascoli, Davide; Fernandes, Paulo M; Marrs, Rob; Allen, Katherine A; Doerr, Stefan H; Clay, Gareth D; McMorrow, Julia; Vandvik, Vigdis

2016-06-05

Fire has been used for centuries to generate and manage some of the UK's cultural landscapes. Despite its complex role in the ecology of UK peatlands and moorlands, there has been a trend of simplifying the narrative around burning to present it as an only ecologically damaging practice. That fire modifies peatland characteristics at a range of scales is clearly understood. Whether these changes are perceived as positive or negative depends upon how trade-offs are made between ecosystem services and the spatial and temporal scales of concern. Here we explore the complex interactions and trade-offs in peatland fire management, evaluating the benefits and costs of managed fire as they are currently understood. We highlight the need for (i) distinguishing between the impacts of fires occurring with differing severity and frequency, and (ii) improved characterization of ecosystem health that incorporates the response and recovery of peatlands to fire. We also explore how recent research has been contextualized within both scientific publications and the wider media and how this can influence non-specialist perceptions. We emphasize the need for an informed, unbiased debate on fire as an ecological management tool that is separated from other aspects of moorland management and from political and economic opinions.This article is part of the themed issue 'The interaction of fire and mankind'. © 2016 The Authors.

The role of fire in UK peatland and moorland management: the need for informed, unbiased debate

Science.gov (United States)

Davies, G. Matt; Kettridge, Nicholas; Stoof, Cathelijne R.; Gray, Alan; Ascoli, Davide; Fernandes, Paulo M.; Marrs, Rob; Clay, Gareth D.; McMorrow, Julia; Vandvik, Vigdis

2016-01-01

Fire has been used for centuries to generate and manage some of the UK's cultural landscapes. Despite its complex role in the ecology of UK peatlands and moorlands, there has been a trend of simplifying the narrative around burning to present it as an only ecologically damaging practice. That fire modifies peatland characteristics at a range of scales is clearly understood. Whether these changes are perceived as positive or negative depends upon how trade-offs are made between ecosystem services and the spatial and temporal scales of concern. Here we explore the complex interactions and trade-offs in peatland fire management, evaluating the benefits and costs of managed fire as they are currently understood. We highlight the need for (i) distinguishing between the impacts of fires occurring with differing severity and frequency, and (ii) improved characterization of ecosystem health that incorporates the response and recovery of peatlands to fire. We also explore how recent research has been contextualized within both scientific publications and the wider media and how this can influence non-specialist perceptions. We emphasize the need for an informed, unbiased debate on fire as an ecological management tool that is separated from other aspects of moorland management and from political and economic opinions. This article is part of the themed issue ‘The interaction of fire and mankind’. PMID:27216512

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

Science.gov (United States)

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Personal And Organisational Variables As Predictors Of Unethical ...

African Journals Online (AJOL)

It was hypothesized that gender, family size, age, reward system, length of service and job security are not viable predictors of unethical behaviour in workplace environments. Results showed that gender, family size, job insecurity and perceived underpayment are viable predictors of unethical behaviour. Male employees ...

Nurses’ Knowledge Regarding Hand Hygiene and Its Individual and Organizational Predictors

Directory of Open Access Journals (Sweden)

Malihe Asadollahi

2015-03-01

Full Text Available Introduction: Based on recommendations from World Health Organization, hand hygiene is the most important way to control the hospital infections. Due to the critical role of nurses in patient care, they should have essential and updated information regarding hand hygiene. So this study aims at determining the knowledge of hand hygiene and its individual and organizational predictors among nurses in neonatal units. Methods: This descriptive and cross-sectional study was conducted in neonatal units in the hospitals affiliated to Tabriz University of Medical Sciences. The participants surveyed in this study were 150 nurses who were invited by census sampling method. A researcher prepared questionnaire that investigated the knowledge of participants about hand hygiene and was used after approving its validity and reliability. The quantitative analysis of this study used Statistical Package for Social Sciences SPSS version 13 by descriptive statistics and pearson correlation test, independent samples t-test, One-way ANOVA. For multivariable explanation of nurses’ knowledge based on independent variables multiple linear regressions was used. Results: Most of participants have an acceptable level of knowledge regarding hand hygiene. The highest score was for infection control domain and the lowest score was for definition of hand hygiene domain. Multivariable analysis showed that work experience and history of previous training were the most important predictors of participants’ knowledge about hand hygiene. Conclusion: It is recommended that infection control committees should revise their educational methods and give more emphasis on update guidelines regarding hand hygiene. Also, more experienced nurses should be employed in neonatal units.

Predictors of agitation in critically ill adults.

Science.gov (United States)

Burk, Ruth S; Grap, Mary Jo; Munro, Cindy L; Schubert, Christine M; Sessler, Curtis N

2014-09-01

Agitation in critically ill adults is a frequent complication of hospitalization and results in multiple adverse outcomes. Potential causes of agitation are numerous; however, data on factors predictive of agitation are limited. To identify predictors of agitation by examining demographic and clinical characteristics of critically ill patients. A medical record review was performed. Documentation of agitation was indicated by scores on the Richmond Agitation-Sedation Scale or the use of an agitation keyword. Records of 200 patients from 1 medical and 1 surgical intensive care unit were used for the study. Risk factors were determined for 2 points in time: admission to the intensive care unit and within 24 hours before the first episode of agitation. Data on baseline demographics, preadmission risk factors, and clinical data were collected and were evaluated by using logistic multivariable regression to determine predictors of agitation. Predictors of agitation on admission to intensive care were history of use of illicit substances, height, respiratory and central nervous system subscores on the Sequential Organ Failure Assessment, and use of restraints. Predictors of agitation within 24 hours before the onset of agitation were history of psychiatric diagnosis, height, score on the Sequential Organ Failure Assessment, ratio of Pao2 to fraction of inspired oxygen less than 200, serum pH, percentage of hours with restraints, percentage of hours of mechanical ventilation, pain, and presence of genitourinary catheters. Predictors of agitation on admission and within 24 hours before the onset of agitation were primarily clinical variables. ©2014 American Association of Critical-Care Nurses.

Predictors of relationship satisfaction for men and women

Directory of Open Access Journals (Sweden)

Gaja Zager Kocjan

2014-06-01

Full Text Available The present study was designed to examine the differences between genders in the perception of romantic relationship as well as in aspects of the relationship that are important for their relationship satisfaction. However, previous studies rarely report significant differences between genders in various predictors of the relationship satisfaction. In our study, similar conclusions were obtained. Relationship satisfaction was predicted with attachment, self-esteem, and partner's social support. The study included 200 participants (63.5% of women who completed the following questionnaires: Experience in Close Relationships – Revised Short ECR-RS, Quality of Relationship Inventory QRI, Relationship Satisfaction Scale RSS, and a single-item self-esteem measure. For both genders, significant positive predictor of their relationship satisfaction was self-esteem, while avoidance, anxiety, and conflict in the relationship were significant negative predictors. There were no significant differences between genders. These findings are consistent with the findings of previous studies, which rarely report significant gender differences in the various predictors.

Handbook on linear motor application

International Nuclear Information System (INIS)

1988-10-01

This book guides the application for Linear motor. It lists classification and speciality of Linear Motor, terms of linear-induction motor, principle of the Motor, types on one-side linear-induction motor, bilateral linear-induction motor, linear-DC Motor on basic of the motor, linear-DC Motor for moving-coil type, linear-DC motor for permanent-magnet moving type, linear-DC motor for electricity non-utility type, linear-pulse motor for variable motor, linear-pulse motor for permanent magneto type, linear-vibration actuator, linear-vibration actuator for moving-coil type, linear synchronous motor, linear electromagnetic motor, linear electromagnetic solenoid, technical organization and magnetic levitation and linear motor and sensor.

Estimation of the simple correlation coefficient.

Science.gov (United States)

Shieh, Gwowen

2010-11-01

This article investigates some unfamiliar properties of the Pearson product-moment correlation coefficient for the estimation of simple correlation coefficient. Although Pearson's r is biased, except for limited situations, and the minimum variance unbiased estimator has been proposed in the literature, researchers routinely employ the sample correlation coefficient in their practical applications, because of its simplicity and popularity. In order to support such practice, this study examines the mean squared errors of r and several prominent formulas. The results reveal specific situations in which the sample correlation coefficient performs better than the unbiased and nearly unbiased estimators, facilitating recommendation of r as an effect size index for the strength of linear association between two variables. In addition, related issues of estimating the squared simple correlation coefficient are also considered.

Solving non-linear Horn clauses using a linear Horn clause solver

DEFF Research Database (Denmark)

Kafle, Bishoksan; Gallagher, John Patrick; Ganty, Pierre

2016-01-01

In this paper we show that checking satisfiability of a set of non-linear Horn clauses (also called a non-linear Horn clause program) can be achieved using a solver for linear Horn clauses. We achieve this by interleaving a program transformation with a satisfiability checker for linear Horn...... clauses (also called a solver for linear Horn clauses). The program transformation is based on the notion of tree dimension, which we apply to a set of non-linear clauses, yielding a set whose derivation trees have bounded dimension. Such a set of clauses can be linearised. The main algorithm...... dimension. We constructed a prototype implementation of this approach and performed some experiments on a set of verification problems, which shows some promise....

Predictors of health-related quality of life in type II diabetic patients in Greece

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Frydas Aristidis

2007-07-01

Full Text Available Abstract Background Diabetes Mellitus (DM is a major cause of morbidity and mortality affecting millions of people worldwide, while placing a noteworthy strain on public health funding. The aim of this study was to assess health-related quality of life (HRQOL of Greek Type II DM patients and to identify significant predictors of the disease in this patient population. Methods The sample (N = 229, 52.8% female, 70.0 years mean age lived in a rural community of Lesvos, an island in the northeast of the Aegean Archipelagos. The generic SF-36 instrument, administered by trainee physicians, was used to measure HRQOL. Scale scores were compared with non-parametric Mann-Whitney and Kruskal-Wallis tests and multivariate stepwise linear regression analyses were used to investigate the effect of sociodemographic and diabetes-related variables on HRQOL. Results The most important predictors of impaired HRQOL were female gender, diabetic complications, non-diabetic comorbidity and years with diabetes. Older age, lower education, being unmarried, obesity, hypertension and hyperlipidaemia were also associated with impaired HRQOL in at least one SF-36 subscale. Multivariate regression analyses produced models explaining significant portions of the variance in SF-36 subscales, especially physical functioning (R2 = 42%, and also showed that diabetes-related indicators were more important disease predictors, compared to sociodemographic variables. Conclusion The findings could have implications for health promotion in rural medical practice in Greece. In order to preserve a good HRQOL, it is obviously important to prevent diabetes complications and properly manage concomitant chronic diseases. Furthermore, the gender difference is interesting and requires further elucidation. Modifying screening methods and medical interventions or formulating educational programs for the local population appear to be steps in the correct direction.

Predictors of self-reported academic performance among undergraduate medical students of Hawassa University, Ethiopia.

Science.gov (United States)

Gedefaw, Abel; Tilahun, Birkneh; Asefa, Anteneh

2015-01-01

This study was conducted to identify predictors of self-reported academic performance in undergraduate medical students at Hawassa University. An analytical cross-sectional study involving 592 undergraduate medical students was conducted in November 2012. The academic performance of the study subjects was measured by self-reported cumulative grade point average (GPA) using a self-administered questionnaire. Data were entered and analyzed using Statistical Package for the Social Sciences version 16 software. Pearson's bivariate correlations, multiple linear regression, and multiple logistic regression were used to identify predictors of academic performance. The self-reported academic performance of students had been decreasing as the academic years progressed, with the highest and lowest performance being in the premedicine (mean GPA 3.47) and clinical I (mean GPA 2.71) years, respectively. One hundred and fifty-eight (26.7%) of the participants had ever been delayed, 37 (6.2%) had ever re-sat for examination, and two (0.3%) had ever been warned due to academic failure. The overall variation in self-reported academic performance of the students was 32.8%. Participant age alone explained 21.9% of the variation. On the other hand, university entrance examination results, substance use at university, and medicine as first choice by students were identified as predictors of variation in self-reported academic performance, accounting for 6.9%, 2.7%, and academic performance was explained by the studied variables. Hence, efficacious mechanisms should be designed to combat the intervenable determinants of self-reported academic performance, like substance use and a low medical school entrance examination result. Further studies should also be undertaken to gain a better understanding of other unstudied determinants, like personality, learning style, cognitive ability, and the system used for academic evaluation.

Predictors of restraint use among child occupants.

Science.gov (United States)

Benedetti, Marco; Klinich, Kathleen D; Manary, Miriam A; Flannagan, Carol A

2017-11-17

The objective of this study was to identify factors that predict restraint use and optimal restraint use among children aged 0 to 13 years. The data set is a national sample of police-reported crashes for years 2010-2014 in which type of child restraint is recorded. The data set was supplemented with demographic census data linked by driver ZIP code, as well as a score for the state child restraint law during the year of the crash relative to best practice recommendations for protecting child occupants. Analysis used linear regression techniques. The main predictor of unrestrained child occupants was the presence of an unrestrained driver. Among restrained children, children had 1.66 (95% confidence interval, 1.27, 2.17) times higher odds of using the recommended type of restraint system if the state law at the time of the crash included requirements based on best practice recommendations. Children are more likely to ride in the recommended type of child restraint when their state's child restraint law includes wording that follows best practice recommendations for child occupant protection. However, state child restraint law requirements do not influence when caregivers fail to use an occupant restraint for their child passengers.

Bayesian modeling of measurement error in predictor variables

NARCIS (Netherlands)

Fox, Gerardus J.A.; Glas, Cornelis A.W.

2003-01-01

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between

Some psychosocial predictors of anxiety disorder in epilepsy ...

African Journals Online (AJOL)

This study was designed to expose the variables or predictors that mediate in anxiety disorders among epileptics in Nigeria. Such variables or predictors are age, level of social support and perceived level of stigmatization were examined with reference to their roles in causing anxiety disorder among epileptics in Nigeria.

Psychosocial predictors of treatment outcome for trauma-affected refugees

Directory of Open Access Journals (Sweden)

Charlotte Sonne

2016-05-01

Full Text Available Background: The effects of treatment in trials with trauma-affected refugees vary considerably not only between studies but also between patients within a single study. However, we know little about why some patients benefit more from treatment, as few studies have analysed predictors of treatment outcome. Objective: The objective of the study was to examine possible psychosocial predictors of treatment outcome for trauma-affected refugees. Method: The participants were 195 adult refugees with posttraumatic stress disorder (PTSD who were enrolled in a 6- to 7-month treatment programme at the Competence Centre for Transcultural Psychiatry (CTP, Denmark. The CTP Predictor Index used in the study included 15 different possible outcome predictors concerning the patients’ past, chronicity of mental health problems, pain, treatment motivation, prerequisites for engaging in psychotherapy, and social situation. The primary outcome measure was PTSD symptoms measured on the Harvard Trauma Questionnaire (HTQ. Other outcome measures included the Hopkins Symptom Check List-25, the WHO-5 Well-being Index, Sheehan Disability Scale, Hamilton Depression and Anxiety Scales, the somatisation scale of the Symptoms Checklist-90, Global Assessment of Functioning scales, and pain rated on visual analogue scales. The relations between treatment outcomes and the total score as well as subscores of the CTP Predictor Index were analysed. Results: Overall, the total score of the CTP Predictor Index was significantly correlated to pre- to post treatment score changes on the majority of the ratings mentioned above. While employment status was the only single item significantly correlated to HTQ-score changes, a number of single items from the CTP Predictor Index correlated significantly with changes in depression and anxiety symptoms, but the size of the correlation coefficients were modest. Conclusions: The total score of the CTP Predictor Index correlated significantly

Linear and non-linear simulation of joints contact surface using ...

African Journals Online (AJOL)

The joint modelling including non-linear effects needs accurate and precise study of their behaviors. When joints are under the dynamic loading, micro, macro- slip happens in contact surface which is non-linear reason of the joint contact surface. The non-linear effects of joint contact surface on total behavior of structure are ...

Linear shaped charge

Energy Technology Data Exchange (ETDEWEB)

Peterson, David; Stofleth, Jerome H.; Saul, Venner W.

2017-07-11

Linear shaped charges are described herein. In a general embodiment, the linear shaped charge has an explosive with an elongated arrowhead-shaped profile. The linear shaped charge also has and an elongated v-shaped liner that is inset into a recess of the explosive. Another linear shaped charge includes an explosive that is shaped as a star-shaped prism. Liners are inset into crevices of the explosive, where the explosive acts as a tamper.

Protein structure refinement using a quantum mechanics-based chemical shielding predictor.

Science.gov (United States)

Bratholm, Lars A; Jensen, Jan H

2017-03-01

change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

Analysis of the Modified Smith Predictor

Directory of Open Access Journals (Sweden)

Jorge A. Herrera-Cuartas

2013-11-01

Full Text Available In this paper an analysis about the modified Smith predictor, is presented. The modified Smith predictor is a scheme used to control stable, unstable and integrative systems. The closed loop equation is developed and analyzed. Additionally, various test are made to verify the behavior of the control scheme. Specify, three test are made. First, it is verify the behavior of the scheme to deal with an uncertainty in the delay model. Second, it is verify the behavior in the face of uncertainties in the parameter of the rational model. 

Linear trend and climate response of five-needle pines in the western United States related to treeline proximity

Energy Technology Data Exchange (ETDEWEB)

Kipfmueller, K.F. [Minnesota Univ., Minneapolis, MN (United States). Dept. of Geography; Salzer, M.W. [Arizona Univ., Tucson, AZ (United States). Laboratory of Tree-Ring Research

2010-01-15

This study investigated sixty-six 5-needle pine growth chronologies from 1896 to their end years in order to identify potential patterns related to linear trends in ring width. Individual chronology responses to climate were also evaluated by comparing the chronologies with seasonal temperature and precipitation data from 1896 to the present date. Chronologies exhibiting similar patterns of climate response were grouped in order to examine the role of treeline proximity on climate-growth relationships. Ring width measurements for pine sites located in the western United States were obtained from the International Tree Ring Data Bank. Growth indices were compared among all sites in order to assess the relative strength of common signals with increasing distance. Pearson correlations were used to calculate linear trends for each chronology. A cluster analysis of climate response patterns indicated that most chronologies positively associated with temperatures were located near upper treeline and contained significant positive linear trends. The study suggested that 5-needle pine treeline chronologies may be used as predictors in temperature reconstructions. However, care must be taken to determine that collection sites have not been impacted by disturbances such as fire or insect outbreaks. 35 refs., 2 tabs., 5 figs.

Predictors and protective factors for adolescent Internet victimization

DEFF Research Database (Denmark)

Helweg-Larsen, Karin; Schütt, Nina; Larsen, Helmer Bøving

2012-01-01

To examine the rate of Internet victimization in a nationally representative sample of adolescents aged 14-17 and to analyze predictors and protective factors for victimization.......To examine the rate of Internet victimization in a nationally representative sample of adolescents aged 14-17 and to analyze predictors and protective factors for victimization....

Predictors of mental health in female teachers.

Science.gov (United States)

Seibt, Reingard; Spitzer, Silvia; Druschke, Diana; Scheuch, Klaus; Hinz, Andreas

2013-12-01

Teaching profession is characterised by an above-average rate of psychosomatic and mental health impairment due to work-related stress. The aim of the study was to identify predictors of mental health in female teachers. A sample of 630 female teachers (average age 47 ± 7 years) participated in a screening diagnostic inventory. Mental health was surveyed with the General Health Questionnaire GHQ-12. The following parameters were measured: specific work conditions (teacher-specific occupational history), scales of the Effort-Reward-Imbalance (ERI) Questionnaire as well as cardiovascular risk factors, physical complaints (BFB) and personal factors such as inability to recover (FABA), sense of coherence (SOC) and health behaviour. First, mentally fit (MH(+)) and mentally impaired teachers (MH(-)) were differentiated based on the GHQ-12 sum score (MH(+): teachers showed evidence of mental impairment. There were no differences concerning work-related and cardiovascular risk factors as well as health behaviour between MH(+) and MH(-). Binary logistic regressions identified 4 predictors that showed a significant effect on mental health. The effort-reward-ratio proved to be the most relevant predictor, while physical complaints as well as inability to recover and sense of coherence were identified as advanced predictors (explanation of variance: 23%). Contrary to the expectations, classic work-related factors can hardly contribute to the explanation of mental health. Additionally, cardiovascular risk factors and health behaviour have no relevant influence. However, effort-reward-ratio, physical complaints and personal factors are of considerable influence on mental health in teachers. These relevant predictors should become a part of preventive arrangements for the conservation of teachers' health in the future.

Predictors of Sexual Intercourse Frequency Among Couples Trying to Conceive.

Science.gov (United States)

Gaskins, Audrey J; Sundaram, Rajeshwari; Buck Louis, Germaine M; Chavarro, Jorge E

2018-04-01

Little is known about the predictors of sexual intercourse frequency (SIF) among couples trying to conceive despite the well-established link between SIF and fecundity. To evaluate men's and women's demographic, occupational, and lifestyle predictors of SIF among couples. 469 Couples without a history of infertility participating in the Longitudinal Investigation of Fertility and the Environment Study (2005-2009) were followed up for ≤1 year while trying to conceive. At enrollment, both partners were interviewed about demographic, occupational, lifestyle, and psychological characteristics using standardized questionnaires. Multivariable generalized linear mixed models with Poisson distribution were used to estimate the adjusted percent difference in SIF across exposure categories. SIF was recorded in daily journals and summarized as average SIF/mo. The median (interquartile range) SIF during follow-up was 6 (4-9) acts/mo. For every year increase in age for women and men, SIF decreased by -0.8% (95% CI -2.5 to 1.0%) and -1.7% (95% CI -3.1 to -0.3%). Women with high school education or less and those of non-white race had 34.4% and 16.0% higher SIF, respectively. A similar trend was seen for men's education and race. Only couples where both partners (but not just 1 partner) worked rotating shifts had -39.1% (95% CI -61.0 to -5.0%) lower SIF compared to couples where neither partner worked rotating shifts. Men's (but not women's) exercise was associated with 13.2% (95% CI 1.7-26.0%) higher SIF. Diagnosis of a mood or anxiety disorder in men (but not women) was associated with a 26.0% (95% CI -42.7 to -4.4%) lower SIF. Household income, smoking status, body mass index, night work, alcohol intake, and psychosocial stress were not associated with SIF. Even among couples trying to conceive, there was substantial variation in SIF. Both partners' age, education, race, and rotating shift work as well as men's exercise and mental health play an important role in determining

Linear algebra

CERN Document Server

Stoll, R R

1968-01-01

Linear Algebra is intended to be used as a text for a one-semester course in linear algebra at the undergraduate level. The treatment of the subject will be both useful to students of mathematics and those interested primarily in applications of the theory. The major prerequisite for mastering the material is the readiness of the student to reason abstractly. Specifically, this calls for an understanding of the fact that axioms are assumptions and that theorems are logical consequences of one or more axioms. Familiarity with calculus and linear differential equations is required for understand

A fresh look at the predictors of naming accuracy and errors in Alzheimer's disease.

Science.gov (United States)

Cuetos, Fernando; Rodríguez-Ferreiro, Javier; Sage, Karen; Ellis, Andrew W

2012-09-01

In recent years, a considerable number of studies have tried to establish which characteristics of objects and their names predict the responses of patients with Alzheimer's disease (AD) in the picture-naming task. The frequency of use of words and their age of acquisition (AoA) have been implicated as two of the most influential variables, with naming being best preserved for objects with high-frequency, early-acquired names. The present study takes a fresh look at the predictors of naming success in Spanish and English AD patients using a range of measures of word frequency and AoA along with visual complexity, imageability, and word length as predictors. Analyses using generalized linear mixed modelling found that naming accuracy was better predicted by AoA ratings taken from older adults than conventional ratings from young adults. Older frequency measures based on written language samples predicted accuracy better than more modern measures based on the frequencies of words in film subtitles. Replacing adult frequency with an estimate of cumulative (lifespan) frequency did not reduce the impact of AoA. Semantic error rates were predicted by both written word frequency and senior AoA while null response errors were only predicted by frequency. Visual complexity, imageability, and word length did not predict naming accuracy or errors. ©2012 The British Psychological Society.

Sex determination using facial linear dimensions and angles among Hausa population of Kano State, Nigeria

Directory of Open Access Journals (Sweden)

Lawan H. Adamu

2016-12-01

Full Text Available The aim of the study was to determine sexual dimorphism as well as to predict sex using facial linear dimensions and angles among Hausas of Kano state Nigeria. A total of 283 subjects comprising 147 males and 136 females age range 18–25 years participated. Photographs methods were used to capture the face. Independent sample t-test was used to test for sex differences in the variables. Binary logistic regression was applied to obtain a predicting equation (BLR model for sex. The predicted probabilities of BLR were analyzed using receiver operating characteristic curve. The results showed that all the facial linear dimensions showed significance sexual dimorphism except interocular distance, upper facial width, philtrum length, lower vermilion width, left and right orbital width. With regards to sex prediction, upper facial height was the single best predictor of sex with an accuracy of 76.2% and 24–33% contribution to the prediction. However, the percentage accuracy increased to 91% when six variables were pooled together in the equations. For facial angles, only nasion and aperture modified angle did not show significant gender differences. However, in the variables with significant sexual dimorphism only nasomental angle showed a significant level of sex prediction with an accuracy of 70.3%. In conclusion, sex discrimination using facial linear dimensions and angles was well established in this study. The sex of an individual of Hausa ethnic group can be determined using facial linear dimensions. Dispite sexual dimorphsm shown by facial angles, only nasomental angle was good discriminator of sex.

A State-Space Approach to Optimal Level-Crossing Prediction for Linear Gaussian Processes

Science.gov (United States)

Martin, Rodney Alexander

2009-01-01

In many complex engineered systems, the ability to give an alarm prior to impending critical events is of great importance. These critical events may have varying degrees of severity, and in fact they may occur during normal system operation. In this article, we investigate approximations to theoretically optimal methods of designing alarm systems for the prediction of level-crossings by a zero-mean stationary linear dynamic system driven by Gaussian noise. An optimal alarm system is designed to elicit the fewest false alarms for a fixed detection probability. This work introduces the use of Kalman filtering in tandem with the optimal level-crossing problem. It is shown that there is a negligible loss in overall accuracy when using approximations to the theoretically optimal predictor, at the advantage of greatly reduced computational complexity. I

Basic linear algebra

CERN Document Server

Blyth, T S

2002-01-01

Basic Linear Algebra is a text for first year students leading from concrete examples to abstract theorems, via tutorial-type exercises. More exercises (of the kind a student may expect in examination papers) are grouped at the end of each section. The book covers the most important basics of any first course on linear algebra, explaining the algebra of matrices with applications to analytic geometry, systems of linear equations, difference equations and complex numbers. Linear equations are treated via Hermite normal forms which provides a successful and concrete explanation of the notion of linear independence. Another important highlight is the connection between linear mappings and matrices leading to the change of basis theorem which opens the door to the notion of similarity. This new and revised edition features additional exercises and coverage of Cramer's rule (omitted from the first edition). However, it is the new, extra chapter on computer assistance that will be of particular interest to readers:...

Software engineering the mixed model for genome-wide association studies on large samples.

Science.gov (United States)

Zhang, Zhiwu; Buckler, Edward S; Casstevens, Terry M; Bradbury, Peter J

2009-11-01

Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software-user interaction. The available software packages are evaluated, and suggestions made for future software development.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

Science.gov (United States)

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε analysis is effective in the evaluation of maize-breeding trials.

Psychological predictors of weight loss after bariatric surgery

DEFF Research Database (Denmark)

Wimmelmann, Cathrine Lawaetz; Dela, Flemming; Mortensen, Erik Lykke

2014-01-01

factors are thought to play animportant role for maintaining the surgical weight loss. However, results from priorresearch examining preoperative psychological predictors of weight loss outcomeare inconsistent. The aim of this article was to review more recent literature onpsychological predictors...... studies published after 2003 were included.Results: 19 eligible studies were identified. Psychological predictors of surgicalweight loss investigated in the reviewed studies include cognitive function, per-sonality, psychiatric disorder, and eating behaviour.......Background: Morbid obesity is the fastest growing BMI group in the U.S. and the prevalence of morbid obesity worldwide has never been higher. Bariatric surgery is the most effective treatment for severe forms of obesity especially with regardto a sustained long-term weight loss. Psychological...

Linear algebra

CERN Document Server

Liesen, Jörg

2015-01-01

This self-contained textbook takes a matrix-oriented approach to linear algebra and presents a complete theory, including all details and proofs, culminating in the Jordan canonical form and its proof. Throughout the development, the applicability of the results is highlighted. Additionally, the book presents special topics from applied linear algebra including matrix functions, the singular value decomposition, the Kronecker product and linear matrix equations. The matrix-oriented approach to linear algebra leads to a better intuition and a deeper understanding of the abstract concepts, and therefore simplifies their use in real world applications. Some of these applications are presented in detailed examples. In several ‘MATLAB-Minutes’ students can comprehend the concepts and results using computational experiments. Necessary basics for the use of MATLAB are presented in a short introduction. Students can also actively work with the material and practice their mathematical skills in more than 300 exerc...

Fasting triglycerides as a predictor of incident diabetes, insulin resistance and β-cell function in a Canadian First Nation.

Science.gov (United States)

Riediger, Natalie D; Clark, Kirsten; Lukianchuk, Virginia; Roulette, Joanne; Bruce, Sharon

2017-01-01

Diabetes prevalence is substantially higher among Canadian First Nations populations than the non-First Nation population. Fasting serum triglycerides have been found to be an important predictor of incident diabetes among non-indigenous populations. However, there is a great need to understand diabetes progression within specific ethnic groups, particularly First Nations populations. The purpose of this study was to test for an association between fasting serum triglycerides and incident diabetes, changes in insulin resistance and changes in β-cell function in a Manitoba First Nation cohort. Study data were from two diabetes screening studies in Sandy Bay First Nation in Manitoba, Canada, collected in 2002/2003 and 2011/2012. The cohort was composed of respondents to both screening studies (n=171). Fasting blood samples and anthropometric, health and demographic data were collected. A generalised linear model with Poisson distribution was used to test for an association between fasting triglycerides and incident diabetes. There were 35 incident cases of diabetes among 128 persons without diabetes at baseline. Participants who developed incident type 2 diabetes were significantly older and had significantly higher body mass index (BMI; p=0.012), total cholesterol (p=0.007), fasting triglycerides (ptriglyceride level was found to be a statistically significant positive predictor of incident diabetes independent of age, sex and waist circumference at baseline. Participants with triglycerides in the highest tertile (≥2.11 mmol/l) had a 4.0-times higher risk of developing incident diabetes compared to those in the lowest tertile (p=0.03). Notably, neither waist circumference nor BMI were significant predictors of incident diabetes independent of age, sex and triglycerides. Fasting triglycerides may be useful as a clinical predictor of insulin resistance and diabetes development among First Nations populations. Unlike other ethnic groups, BMI and waist circumference

Plant Water Content is the Best Predictor of Drought-induced Mortality

Science.gov (United States)

Sapes, G.; Roskilly, B.; Dobrowski, S.; Sala, A.

2017-12-01

Predicting drought-induced forest mortality remains extremely challenging. Recent research has shown that both plant hydraulics and stored non-structural carbohydrates (NSC) interact during drought-induced mortality. The strong interaction between these two variables and the fact that they are both difficult to measure render drought-induced plant mortality extremely difficult to monitor and predict. A variable that is easier to measure and that integrates hydraulic transport and carbohydrate dynamics may, therefore, improve our ability to monitor and predict mortality. Here, we tested whether plant water content is such an integrator variable and, therefore, a better predictor of mortality under drought. We subjected 250 two-year-old ponderosa pine seedlings to drought until they died in a greenhouse experiment. Periodically during the dry down, we measured percent loss of hydraulic conductivity (PLC), NSC concentration (starch and soluble sugars), and tissue volumetric water content (VWC) in roots, stems and leaves. At each measurement time, a separate set of seedlings were re-watered to estimate the probability of mortality at the population level. Linear models were used to explore whether PLC and NSC were linked to VWC and to determine which of the three variables predicted mortality the best. As expected, plants lost hydraulic conductivity in stems and roots during the dry down. Starch concentrations also decreased in all organs as the drought proceeded. In contrast, soluble sugars increased in stems and roots, consistent with the conversion of stored NSCs into osmotically active compounds. Models containing both PLC and NSC concentrations as predictors of VWC were highly significant in all organs and at the whole plant level, indicating that water content is influenced by both PLC and NSCs. PLC, NSC, and VWC explained mortality across organs and at the whole plant level, but VWC was the best predictor (R2 = 0.99). Our results indicate that plant water

Photonic quantum simulator for unbiased phase covariant cloning

Science.gov (United States)

Knoll, Laura T.; López Grande, Ignacio H.; Larotonda, Miguel A.

2018-01-01

We present the results of a linear optics photonic implementation of a quantum circuit that simulates a phase covariant cloner, using two different degrees of freedom of a single photon. We experimentally simulate the action of two mirrored 1→ 2 cloners, each of them biasing the cloned states into opposite regions of the Bloch sphere. We show that by applying a random sequence of these two cloners, an eavesdropper can mitigate the amount of noise added to the original input state and therefore, prepare clones with no bias, but with the same individual fidelity, masking its presence in a quantum key distribution protocol. Input polarization qubit states are cloned into path qubit states of the same photon, which is identified as a potential eavesdropper in a quantum key distribution protocol. The device has the flexibility to produce mirrored versions that optimally clone states on either the northern or southern hemispheres of the Bloch sphere, as well as to simulate optimal and non-optimal cloning machines by tuning the asymmetry on each of the cloning machines.

Linear and Generalized Linear Mixed Models and Their Applications

CERN Document Server

Jiang, Jiming

2007-01-01

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested

Linear and non-linear energy barriers in systems of interacting single-domain ferromagnetic particles

International Nuclear Information System (INIS)

Petrila, Iulian; Bodale, Ilie; Rotarescu, Cristian; Stancu, Alexandru

2011-01-01

A comparative analysis between linear and non-linear energy barriers used for modeling statistical thermally-excited ferromagnetic systems is presented. The linear energy barrier is obtained by new symmetry considerations about the anisotropy energy and the link with the non-linear energy barrier is also presented. For a relevant analysis we compare the effects of linear and non-linear energy barriers implemented in two different models: Preisach-Neel and Ising-Metropolis. The differences between energy barriers which are reflected in different coercive field dependence of the temperature are also presented. -- Highlights: → The linear energy barrier is obtained from symmetry considerations. → The linear and non-linear energy barriers are calibrated and implemented in Preisach-Neel and Ising-Metropolis models. → The temperature and time effects of the linear and non-linear energy barriers are analyzed.

Predicting recovery of cognitive function soon after stroke: differential modeling of logarithmic and linear regression.

Science.gov (United States)

Suzuki, Makoto; Sugimura, Yuko; Yamada, Sumio; Omori, Yoshitsugu; Miyamoto, Masaaki; Yamamoto, Jun-ichi

2013-01-01

Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, PLogarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.

Linearity and Non-linearity of Photorefractive effect in Materials ...

African Journals Online (AJOL)

Linearity and Non-linearity of Photorefractive effect in Materials using the Band transport ... For low light beam intensities the change in the refractive index is ... field is spatially phase shifted by /2 relative to the interference fringe pattern, which ...

Linear integrated circuits

CERN Document Server

Carr, Joseph

1996-01-01

The linear IC market is large and growing, as is the demand for well trained technicians and engineers who understand how these devices work and how to apply them. Linear Integrated Circuits provides in-depth coverage of the devices and their operation, but not at the expense of practical applications in which linear devices figure prominently. This book is written for a wide readership from FE and first degree students, to hobbyists and professionals.Chapter 1 offers a general introduction that will provide students with the foundations of linear IC technology. From chapter 2 onwa

Testing the Pareto against the lognormal distributions with the uniformly most powerful unbiased test applied to the distribution of cities.

Science.gov (United States)

Malevergne, Yannick; Pisarenko, Vladilen; Sornette, Didier

2011-03-01

Fat-tail distributions of sizes abound in natural, physical, economic, and social systems. The lognormal and the power laws have historically competed for recognition with sometimes closely related generating processes and hard-to-distinguish tail properties. This state-of-affair is illustrated with the debate between Eeckhout [Amer. Econ. Rev. 94, 1429 (2004)] and Levy [Amer. Econ. Rev. 99, 1672 (2009)] on the validity of Zipf's law for US city sizes. By using a uniformly most powerful unbiased (UMPU) test between the lognormal and the power-laws, we show that conclusive results can be achieved to end this debate. We advocate the UMPU test as a systematic tool to address similar controversies in the literature of many disciplines involving power laws, scaling, "fat" or "heavy" tails. In order to demonstrate that our procedure works for data sets other than the US city size distribution, we also briefly present the results obtained for the power-law tail of the distribution of personal identity (ID) losses, which constitute one of the major emergent risks at the interface between cyberspace and reality.

Intergroup contact and religiosity as predictor of between group attitudes in conflict environment

Directory of Open Access Journals (Sweden)

Lalić Bojan R.

2013-01-01

Full Text Available The aim of this research was to identify relations between level of religiosity and level of contact on one side and social attitudes towards members of religious out-groups in conflict environment on the other side. This research was conducted on the sample of Christian Orthodox students in Kosovska Mitrovica (which is partially conflict environment and the Muslims were the out-group towards whom attitudes were analyzed. Attitudes measures we used were social distance scale and semantic differential. Likert type scale was used for religiosity measure, quantity and quality of contact. Controlled variables in this research were: gender, age and social status. Results showed that significant amount of variance was explained by independent variables (R2=.270, F(7,779=9.241, p=.000 for semantic differential and R2=.306, F(5,105=9.241, p=.000. However, there is no significant correlation between religiosity and attitude level towards Muslims. Most of the variance for semantic differential was explained by quality of contact (R2=.255, F(1,109=37,285, p=.000 and this was the only significant predictor for this attitude measure. Quantity of contact was significant predictor for social distance attitude measure, with highest incremental value - calculated by hierarchical linear regression (R2change=.216, F(l,109=30,076, p=.000. Following predictor was quality of contact (R2????????=.049, F(l,108=7,269, p=.008 and the last predictor was sex, with the lowest incremental value (R2=.034, F(1,107=5,159, p=.025. These results are interpreted by probable existence of several types of religiosity. There is possibility that general religiosity we measured in this research, was influenced by different types of religiosity, which could be the reason why correlation was not identified. Correlation between quality of contact confirms results published by other authors (Allport, Pettigrew who claimed that contact by itself cannot diminish prejudices and lead to change

SU(2 and SU(1,1 Approaches to Phase Operators and Temporally Stable Phase States: Applications to Mutually Unbiased Bases and Discrete Fourier Transforms

Directory of Open Access Journals (Sweden)

Maurice R. Kibler

2010-07-01

Full Text Available We propose a group-theoretical approach to the generalized oscillator algebra Aκ recently investigated in J. Phys. A: Math. Theor. 2010, 43, 115303. The case κ ≥ 0 corresponds to the noncompact group SU(1,1 (as for the harmonic oscillator and the Pöschl-Teller systems while the case κ < 0 is described by the compact group SU(2 (as for the Morse system. We construct the phase operators and the corresponding temporally stable phase eigenstates for Aκ in this group-theoretical context. The SU(2 case is exploited for deriving families of mutually unbiased bases used in quantum information. Along this vein, we examine some characteristics of a quadratic discrete Fourier transform in connection with generalized quadratic Gauss sums and generalized Hadamard matrices.

Environmental predictors of bovine Eimeria infection in western Kenya.

Science.gov (United States)

Makau, D N; Gitau, G K; Muchemi, G K; Thomas, L F; Cook, E A J; Wardrop, N A; Fèvre, E M; de Glanville, W A

2017-02-01

Eimeriosis is caused by a protozoan infection affecting most domestic animal species. Outbreaks in cattle are associated with various environmental factors in temperate climates but limited work has been done in tropical settings. The objective of this work was to determine the prevalence and environmental factors associated with bovine Eimeria spp. infection in a mixed farming area of western Kenya. A total of 983 cattle were sampled from 226 cattle-keeping households. Faecal samples were collected directly from the rectum via digital extraction and analysed for the presence of Eimeria spp. infection using the MacMaster technique. Individual and household level predictors of infection were explored using mixed effects logistic regression. The prevalence of individual animal Eimeria infection was 32.8% (95% CI 29.9-35.9). A positive linear relationship was found between risk of Eimeria infection and increasing temperature (OR = 1.4, 95% CI 1.06-1.86) and distance to areas at risk of flooding (OR = 1.49, 95% CI 1.17-1.91). There was weak evidence of non-linear relationship between Eimeria infection and the proportion of the area around a household that was classified as swamp (OR = 1.12, 95% CI 0.87-1.44; OR (quadratic term) = 0.85, 95% CI 0.73-1.00), and the sand content of the soil (OR = 1.18, 95% CI 0.91-1.53; OR (quadratic term) = 1.1, 95% CI 0.99-1.23). The risk of animal Eimeria spp. infection is influenced by a number of climatic and soil-associated conditions.

Predictors of Per- and Polyfluoroalkyl Substance (PFAS) Plasma Concentrations in 6-10 Year Old American Children.

Science.gov (United States)

Harris, Maria H; Rifas-Shiman, Sheryl L; Calafat, Antonia M; Ye, Xiaoyun; Mora, Ana Maria; Webster, Thomas F; Oken, Emily; Sagiv, Sharon K

2017-05-02

Certain per- and polyfluoroalkyl substances (PFASs) are suspected developmental toxicants, but data on PFAS concentrations and exposure routes in children are limited. We measured plasma PFASs in children aged 6-10 years from the Boston-area Project Viva prebirth cohort, and used multivariable linear regression to estimate associations with sociodemographic, behavioral, and health-related factors, and maternal PFASs measured during pregnancy. PFAS concentrations in Project Viva children (sampled 2007-2010) were similar to concentrations among youth participants (aged 12-19 years) in the 2007-8 and 2009-10 National Health and Nutrition Examination Survey (NHANES); mean concentrations of most PFASs declined from 2007 to 2010 in Project Viva and NHANES. In mutually adjusted models, predictors of higher PFAS concentrations included older child age, lower adiposity, carpeting or a rug in the child's bedroom, higher maternal education, and higher neighborhood income. Concentrations of perfluorooctanesulfonate (PFOS), perfluorooctanoate (PFOA), perfluorohexanesulfonate (PFHxS), and 2-(N-methyl-perfluorooctane sulfonamido) acetate (Me-PFOSA-AcOH) were 26-36% lower in children of black mothers compared to children of white mothers and increased 12-21% per interquartile range increase in maternal pregnancy PFASs. Breastfeeding duration did not predict childhood PFAS concentrations in adjusted multivariable models. Together, the studied predictors explained the observed variability in PFAS concentrations to only a modest degree.

Predictors of mental health in female teachers

Directory of Open Access Journals (Sweden)

Reingard Seibt

2013-12-01

Full Text Available Objective: Teaching profession is characterised by an above-average rate of psychosomatic and mental health impairment due to work-related stress. The aim of the study was to identify predictors of mental health in female teachers. Material and Methods: A sample of 630 female teachers (average age 47±7 years participated in a screening diagnostic inventory. Mental health was surveyed with the General Health Questionnaire GHQ-12. The following parameters were measured: specific work conditions (teacher-specific occupational history, scales of the Effort-Reward-Imbalance (ERI Questionnaire as well as cardiovascular risk factors, physical complaints (BFB and personal factors such as inability to recover (FABA, sense of coherence (SOC and health behaviour. Results: First, mentally fit (MH+ and mentally impaired teachers (MH- were differentiated based on the GHQ-12 sum score (MH+: < 5; MH-: ≥ 5; 18% of the teachers showed evidence of mental impairment. There were no differences concerning work-related and cardiovascular risk factors as well as health behaviour between MH+ and MH-. Binary logistic regressions identified 4 predictors that showed a significant effect on mental health. The effort-reward-ratio proved to be the most relevant predictor, while physical complaints as well as inability to recover and sense of coherence were identified as advanced predictors (explanation of variance: 23%. Conclusion: Contrary to the expectations, classic work-related factors can hardly contribute to the explanation of mental health. Additionally, cardiovascular risk factors and health behaviour have no relevant influence. However, effort-reward-ratio, physical complaints and personal factors are of considerable influence on mental health in teachers. These relevant predictors should become a part of preventive arrangements for the conservation of teachers' health in the future.

Speed control issues for tunnel-in-the-sky displays with predictor

Science.gov (United States)

Sachs, Gottfried; Sperl, Roman

2001-08-01

Speed control issues are considered for tunnel-in-the-sky displays with a predictor presenting guidance information in a 3-dimensional format for flight path control. Factors driving the predictor design are described. With reference to the resulting predictor control law, it is shown that the pilot-predictor-aircraft system is stable for operation on the frontside of the power-required curve and unstable for operation on the reverse. This instability can be removed by thrust control. It is shown that this control loop is supported by the predictor control law because of favorable coupling effects between the two loops involved. Furthermore, an appropriate speed indication in the tunnel-in-the-sky display is considered an aid in manual speed control. The theoretical findings are supported by experimental results from pilot-in-the-loop simulations.

Waist circumference as a predictor for blood glucose levels in adults

Directory of Open Access Journals (Sweden)

Shinta L Hardiman

2016-02-01

Full Text Available Anthropometric indexes such as body mass index (BMI, waist circumference (WC, hip ciucumference (HC, and waist–hip ratio (WHR, are all useful anthropometric measurements to provide important information on blood glucose concentrations. The aim of this study was to determine different anthropometric measurements, in particular BMI, waist circumference, hip circumference and waist-to-hip ratio, in their ability to predict the blood glucose levels in men and women 40 to 60. A cross-sectional study was conducted on a sample of 44 men and 127 women aged 40 to 50 who lived in Cipete Selatan subdistrict, South Jakarta. Blood glucose levels was assessed and anthropometric measurements comprising BMI, WC, HC, WHR were collected. Multiple linear regression analysis was used to determine the best predictor for blood glucose levels. The study showed that the prevalence of DM type 2 was 25.7% and the prevalence was higher in men (40.9% compared to women (23.5%. The significant predictive variables in the simple regression analysis were age and waist circumference. Multiple linear regression showed that after adjustment for age, WC was positively associated with blood glucose levels. Standardized a value was 0.172 (p=0.026. WC predict blood glucose levels, beyond that explained by traditional diabetic risk factors and BMI. These findings provide support for the recommendation that WC be a routine measure for identification of diabetes mellitus type 2 in men and women aged 40 to 60 years.

Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data.

Science.gov (United States)

Ye, Hao; Luo, Heng; Ng, Hui Wen; Meehan, Joe; Ge, Weigong; Tong, Weida; Hong, Huixiao

2016-01-01

ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals. We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data. We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data. Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct. Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others. Network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals. Published by Elsevier Ltd.

Energy homeostasis and appetite regulating hormones as predictors of weight loss in men and women.

Science.gov (United States)

Williams, Rebecca L; Wood, Lisa G; Collins, Clare E; Morgan, Philip J; Callister, Robin

2016-06-01

Sex differences in weight loss are often seen despite using the same weight loss program. There has been relatively little investigation of physiological influences on weight loss success in males and females, such as energy homeostasis and appetite regulating hormones. The aims were to 1) characterise baseline plasma leptin, ghrelin and adiponectin concentrations in overweight and obese males and females, and 2) determine whether baseline concentrations of these hormones predict weight loss in males and females. Subjects were overweight or obese (BMI 25-40 kg/m(2)) adults aged 18-60 years. Weight was measured at baseline, and after three and six months participation in a weight loss program. Baseline concentrations of leptin, adiponectin and ghrelin were determined by enzyme-linked immunosorbent assay (ELISA). An independent t-test or non-parametric equivalent was used to determine any differences between sex. Linear regression determined whether baseline hormone concentrations were predictors of six-month weight change. Females had significantly higher baseline concentrations of leptin, adiponectin and unacylated ghrelin as well as ratios of leptin:adiponectin and leptin:ghrelin. The ratio of acylated:unacylated ghrelin was significantly higher in males. In males and females, a higher baseline concentration of unacylated ghrelin predicted greater weight loss at six months. Additionally in females, higher baseline total ghrelin predicted greater weight loss and a higher ratio of leptin:ghrelin predicted weight gain at six months. A higher pre-weight-loss plasma concentration of unacylated ghrelin is a modest predictor of weight loss success in males and females, while a higher leptin:ghrelin ratio is a predictor of weight loss failure in females. Further investigation is required into what combinations and concentrations of these hormones are optimal for weight loss success. Copyright © 2016 Elsevier Ltd. All rights reserved.

National trends in occupational injuries before and after 1992 and predictors of workers' compensation costs.

Science.gov (United States)

Bhushan, Abhinav; Leigh, J Paul

2011-01-01

Numbers and costs of occupational injuries and illnesses are significant in terms of morbidity and dollars, yet our understanding of time trends is minimal. We investigated trends and addressed some common hypotheses regarding causes of fluctuations. We pulled data on incidence rates (per 100 full-time employed workers) for injuries and illnesses from the U.S. Bureau of Labor Statistics and on costs and benefits from the National Academy of Social Insurance for 1973 through 2007. Rates reflected all injury and illness cases, lost work-time cases, and cases resulting in at least 31 days away from work. We adjusted dollar costs (premiums) and benefits for inflation and measured them per employed worker. We plotted data in time-trend charts and ran linear regressions. From 1973 to 1991, there was a weak to nonexistent downward trend for injury and illness rates, and rates were strongly and negatively correlated with the unemployment rate. From 1992 to 2007, there were strong, consistent downward trends, but no longer were there statistically significant correlations with unemployment. Significant predictors (and signs) of workers' compensation premiums for 1973-2007 included medical price inflation (positive), number of lost-time injuries (positive), the Dow Jones Industrial Average (negative), and inflation-adjusted interest rate on U.S. Treasury bonds (negative). Dollars of benefits were positively and significantly predicted by medical inflation and number of lost-time cases. For 1992-2007, the Dow Jones variable was the only robust predictor of premiums; the number of injuries was not a significant positive predictor. We had two major conclusions. First, the year 1992 marked a sharp contrast in trends and correlations between unemployment and incidence rates for occupational injuries and illnesses. Second, for the entire time period (1973-2007), insurance carriers' premiums were strongly associated with returns on investments.

Predictors of fibromyalgia: a population-based twin cohort study.

Science.gov (United States)

Markkula, Ritva A; Kalso, Eija A; Kaprio, Jaakko A

2016-01-15

Fibromyalgia (FM) is a pain syndrome, the mechanisms and predictors of which are still unclear. We have earlier validated a set of FM-symptom questions for detecting possible FM in an epidemiological survey and thereby identified a cluster with "possible FM". This study explores prospectively predictors for membership of that FM-symptom cluster. A population-based sample of 8343 subjects of the older Finnish Twin Cohort replied to health questionnaires in 1975, 1981, and 1990. Their answers to the set of FM-symptom questions in 1990 classified them in three latent classes (LC): LC1 with no or few symptoms, LC2 with some symptoms, and LC3 with many FM symptoms. We analysed putative predictors for these symptom classes using baseline (1975 and 1981) data on regional pain, headache, migraine, sleeping, body mass index (BMI), physical activity, smoking, and zygosity, adjusted for age, gender, and education. Those with a high likelihood of having fibromyalgia at baseline were excluded from the analysis. In the final multivariate regression model, regional pain, sleeping problems, and overweight were all predictors for membership in the class with many FM symptoms. The strongest non-genetic predictor was frequent headache (OR 8.6, CI 95% 3.8-19.2), followed by persistent back pain (OR 4.7, CI 95% 3.3-6.7) and persistent neck pain (OR 3.3, CI 95% 1.8-6.0). Regional pain, frequent headache, and persistent back or neck pain, sleeping problems, and overweight are predictors for having a cluster of symptoms consistent with fibromyalgia.

Workplace engagement and workers' compensation claims as predictors for patient safety culture.

Science.gov (United States)

Thorp, Jonathon; Baqai, Waheed; Witters, Dan; Harter, Jim; Agrawal, Sangeeta; Kanitkar, Kirti; Pappas, James

2012-12-01

Demonstrate the relationship between employee engagement and workplace safety for predicting patient safety culture. Patient safety is an issue for the U.S. health-care system, and health care has some of the highest rates of nonfatal workplace injuries. Understanding the types of injuries sustained by health-care employees, the type of safety environment employees of health-care organizations work in, and how employee engagement affects patient safety is vital to improving the safety of both employees and patients. The Gallup Q survey and an approved, abbreviated, and validated subset of questions from the Hospital Survey on Patient Safety Culture were administered to staff at a large tertiary academic medical center in 2007 and 2009. After controlling for demographic variables, researchers conducted a longitudinal, hierarchical linear regression analysis to study the unique contributions of employee engagement, changes in employee engagement, and employee safety in predicting patient safety culture. Teams with higher baseline engagement, more positive change in engagement, fewer workers' compensation claims, and fewer part-time associates in previous years had stronger patient safety cultures in 2009. Baseline engagement and change in engagement were the strongest independent predictors of patient safety culture in 2009. Engagement and compensation claims were additive and complimentary predictors, independent of other variables in the analysis, including the demographic composition of the workgroups in the study. A synergistic effect exists between employee engagement and decreased levels of workers' compensation claims for improving patient safety culture. Organizations can improve engagement and implement safety policies, procedures, and devices for employees with an ultimate effect of improving patient safety culture.

[Psychoneuroimmunological predictors for burden in older caregivers of patients with Alzheimer's disease].

Science.gov (United States)

Corazza, Danilla I; Pedroso, Renata V; Andreatto, Carla A A; Scarpari, Lais; Garuffi, Marcelo; Costa, José L R; Santos-Galduróz, Ruth F

2014-01-01

The responsibility of giving care to patients with Alzheimer's disease (AD) may result in health changes in the older caregiver. It is important to explore the factors which influence the presence of care burden and to create strategies to face this condition. In this context, the aims of present study were to investigate the relationships between psychoneuroimmunological parameters and determine the predictors to burden in older caregivers of patients with AD. A total of 30 AD older caregivers participating in the «Cognitive and Functional Kinesiotherapy Program in Elderly with Alzheimer's disease«(PRO-CDA)», de Rio Claro, SP-Brazil, were submitted to an assessment protocol to evaluate the psychoneuroimmunological parameters. A descriptive statistical analysis, Pearson correlation and multiple linear regressions were performed. The mean age of caregivers was 71.3 (±9.3), and predominantly are first-grade relatives. The caregiver burden was associated with depressive symptoms (r=0.60, P<.001), caregiver distress (r=0.68, P<.001), and neuropsychiatric disorders of AD patients (r=0.53, P<.001). The multiple regression analysis confirmed depressive symptoms and neuropsychiatric disturbances as predictors of caregiver burden. Caregiver burden is associated with, and influenced by parameters related to the caregiver psychological suffering and to characteristics inherent to AD. Thus, it is important to find strategies and implement non-pharmacological programs to provide support to older caregivers, and to assist in the treatment of patients with AD, in order to improve the integral health of this population. Copyright © 2013 SEGG. Published by Elsevier Espana. All rights reserved.

Acute single channel EEG predictors of cognitive function after stroke.

Directory of Open Access Journals (Sweden)

Anna Aminov

Full Text Available Early and accurate identification of factors that predict post-stroke cognitive outcome is important to set realistic targets for rehabilitation and to guide patients and their families accordingly. However, behavioral measures of cognition are difficult to obtain in the acute phase of recovery due to clinical factors (e.g. fatigue and functional barriers (e.g. language deficits. The aim of the current study was to test whether single channel wireless EEG data obtained acutely following stroke could predict longer-term cognitive function.Resting state Relative Power (RP of delta, theta, alpha, beta, delta/alpha ratio (DAR, and delta/theta ratio (DTR were obtained from a single electrode over FP1 in 24 participants within 72 hours of a first-ever stroke. The Montreal Cognitive Assessment (MoCA was administered at 90-days post-stroke. Correlation and regression analyses were completed to identify relationships between 90-day cognitive function and electrophysiological data, neurological status, and demographic characteristics at admission.Four acute qEEG indices demonstrated moderate to high correlations with 90-day MoCA scores: DTR (r = -0.57, p = 0.01, RP theta (r = 0.50, p = 0.01, RP delta (r = -0.47, p = 0.02, and DAR (r = -0.45, p = 0.03. Acute DTR (b = -0.36, p < 0.05 and stroke severity on admission (b = -0.63, p < 0.01 were the best linear combination of predictors of MoCA scores 90-days post-stroke, accounting for 75% of variance.Data generated by a single pre-frontal electrode support the prognostic value of acute DAR, and identify DTR as a potential marker of post-stroke cognitive outcome. Use of single channel recording in an acute clinical setting may provide an efficient and valid predictor of cognitive function after stroke.

Detecting DNA double-stranded breaks in mammalian genomes by linear amplification-mediated high-throughput genome-wide translocation sequencing.

Science.gov (United States)

Hu, Jiazhi; Meyers, Robin M; Dong, Junchao; Panchakshari, Rohit A; Alt, Frederick W; Frock, Richard L

2016-05-01

Unbiased, high-throughput assays for detecting and quantifying DNA double-stranded breaks (DSBs) across the genome in mammalian cells will facilitate basic studies of the mechanisms that generate and repair endogenous DSBs. They will also enable more applied studies, such as those to evaluate the on- and off-target activities of engineered nucleases. Here we describe a linear amplification-mediated high-throughput genome-wide sequencing (LAM-HTGTS) method for the detection of genome-wide 'prey' DSBs via their translocation in cultured mammalian cells to a fixed 'bait' DSB. Bait-prey junctions are cloned directly from isolated genomic DNA using LAM-PCR and unidirectionally ligated to bridge adapters; subsequent PCR steps amplify the single-stranded DNA junction library in preparation for Illumina Miseq paired-end sequencing. A custom bioinformatics pipeline identifies prey sequences that contribute to junctions and maps them across the genome. LAM-HTGTS differs from related approaches because it detects a wide range of broken end structures with nucleotide-level resolution. Familiarity with nucleic acid methods and next-generation sequencing analysis is necessary for library generation and data interpretation. LAM-HTGTS assays are sensitive, reproducible, relatively inexpensive, scalable and straightforward to implement with a turnaround time of <1 week.

Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...

[Predictors of Health-related Quality of Life in Bavarian Preschool Children].

Science.gov (United States)

Weigl, Korbinian; Herr, Caroline Eva Wella; Meyer, Nicole; Otto, Christiane; Stilianakis, Nikolaos; Bolte, Gabriele; Nennstiel-Ratzel, Uta; Kolb, Stefanie

2018-02-01

Little data are available on health-related quality of life (HRQOL) of children in Germany at the age of school enrollment. Aim of this study was to investigate the HRQOL of children during school enrollment and to determine its predictors with special focus on environmental factors. Data from the fifth survey of the Health-Monitoring-Units (GME) conducted in Bavaria (2010/2011) were analyzed. Parent-reported data on HRQOL using the KINDL-R(evised), the Strength and Difficulties Questionnaire (SDQ), socio-demographic characteristics and characteristics of the living environment were assessed. The sample included a total of 3,744 children (45.9% female; mean age: 6.0; SD=0.4). Girls had significantly higher values than boys in total HRQOL (83.7 vs. 82.4, p ≤0.0001) and in all KINDL-R subscales except "psychological well-being" and "physical well-being". For the latter, boys had significantly higher values than girls (84.1 vs. 82.9, p=0.0103). Multiple linear regression analysis showed that parental annoyance with air or noise pollution, possibility for children to safely play outside and the time a child is outside on weekdays in the summertime were significant predictors of total HRQOL measured by the KINDL-R. Obesity was not linked to HRQOL. Children with migration background had significantly higher values in the subscales "family" and "friends". Environmental factors are associated with HRQOL in children at the age of school enrollment but only partially of relevant use. Although they show significant associations, their explanatory power of the variability observed is rather limited. © Georg Thieme Verlag KG Stuttgart · New York.

Predictors of malignancy in patients with pheochromocytomas/paragangliomas: Asian Indian experience

Directory of Open Access Journals (Sweden)

Kranti Khadilkar

2016-12-01

Full Text Available Background and aims: Malignant transformation of pheochromocytomas/paragangliomas (PCC/PGL is a rare occurrence, and predictive factors for the same are not well understood. This study aims to identify the predictors of malignancy in patients with PCC/PGL. Materials and methods: We performed a retrospective analysis of 142 patients with either PCC or PGL registered at our institute between 2000 and 2015. Records were evaluated for clinical parameters like age, gender, familial/syndromic presentation, symptomatic presentation, biochemistry, size, number and location of tumours and presence of metastases and mode of its diagnosis. Results: Twenty patients were found to have metastases; 13 had metastases at diagnosis and seven during follow-up. Metastases were detected by radiology (CT-neck to pelvis in 11/20 patients (5/13 synchronous and 6/7 metachronous, 131I-metaiodobenzylguanidine in five (2/12 synchronous and 3/6 metachronous patients and 18F-flurodeoxyglucose PET/CT in 15 (12/12 synchronous and 3/3 metachronous patients. Malignant tumours were significantly larger than benign tumours (8.3 ± 4.1 cm, range: 3–22 cm vs 5.7 ± 2.3 cm, range: 2–14 cm, P = 0.0001 and less frequently metanephrine secreting. On linear regression analysis, tumour size and lack of metanephrine secretion were the independent predictors of malignancy. Conclusions: Patients with primary tumour size >5.7 cm and lack of metanephrine secretory status should be evaluated for possible malignancy not only at diagnosis but also in the postoperative period. As compared to CT and 131I-MIBG scan, 18F-flurodeoxyglucose PET/CT analyses are better (sensitivity: 100% for the diagnosis of metastases in our study.

Predictors for total medical costs for acute hemorrhagic stroke patients transferred to the rehabilitation ward at a regional hospital in Taiwan.

Science.gov (United States)

Chen, Chien-Min; Ke, Yen-Liang

2016-02-01

One-third of the acute stroke patients in Taiwan receive rehabilitation. It is imperative for clinicians who care for acute stroke patients undergoing inpatient rehabilitation to identify which medical factors could be the predictors of the total medical costs. The aim of this study was to identify the most important predictors of the total medical costs for first-time hemorrhagic stroke patients transferred to inpatient rehabilitation using a retrospective design. All data were retrospectively collected from July 2002 to June 2012 from a regional hospital in Taiwan. A stepwise multivariate linear regression analysis was used to identify the most important predictors for the total medical costs. The medical records of 237 patients (137 males and 100 females) were reviewed. The mean total medical cost per patient was United States dollar (USD) 5939.5 ± 3578.5.The following were the significant predictors for the total medical costs: impaired consciousness [coefficient (B), 1075.7; 95% confidence interval (CI) = 138.5-2012.9], dysphagia [coefficient (B), 1025.8; 95% CI = 193.9-1857.8], number of surgeries [coefficient (B), 796.4; 95% CI = 316.0-1276.7], pneumonia in the neurosurgery ward [coefficient (B), 2330.1; 95% CI = 1339.5-3320.7], symptomatic urinary tract infection (UTI) in the rehabilitation ward [coefficient (B), 1138.7; 95% CI = 221.6-2055.7], and rehabilitation ward stay [coefficient (B), 64.9; 95% CI = 31.2-98.7] (R(2) = 0.387). Our findings could help clinicians to understand that cost reduction may be achieved by minimizing complications (pneumonia and UTI) in these patients.

Application of the Disruption Predictor Feature Developer to developing a machine-portable disruption predictor

Science.gov (United States)

Parsons, Matthew; Tang, William; Feibush, Eliot

2016-10-01

Plasma disruptions pose a major threat to the operation of tokamaks which confine a large amount of stored energy. In order to effectively mitigate this damage it is necessary to predict an oncoming disruption with sufficient warning time to take mitigative action. Machine learning approaches to this problem have shown promise but require further developments to address (1) the need for machine-portable predictors and (2) the availability of multi-dimensional signal inputs. Here we demonstrate progress in these two areas by applying the Disruption Predictor Feature Developer to data from JET and NSTX, and discuss topics of focus for ongoing work in support of ITER. The author is also supported under the Fulbright U.S. Student Program as a graduate student in the department of Nuclear, Plasma and Radiological Engineering at the University of Illinois at Urbana-Champaign.

Levels and predictors of persistent organic pollutants in an adult population from four Spanish regions

International Nuclear Information System (INIS)

Fernández-Rodríguez, M.; Arrebola, J.P.; Artacho-Cordón, F.; Amaya, E.; Aragones, N.; Llorca, J.; Perez-Gomez, B.

2015-01-01

This research aimed to assess serum concentrations of a group of persistent organic pollutants (POPs) in a sample of adults recruited in four different regions from Spain and to assess socio-demographic, dietary, and lifestyle predictors of the exposure. The study population comprised 312 healthy adults selected from among controls recruited in the MCC-Spain multicase-control study. Study variables were collected using standardized questionnaires, and pollutants were analyzed by means of gas chromatography with electron capture detection. Multivariable analyses were performed to identify predictors of log-transformed pollutant concentrations, using combined backward and forward stepwise multiple linear regression models. Detection rates ranged from 89.1% (hexachlorobenzene, HCB) to 93.6% (Polychlorinated biphenyl-153 [PCB-153]); p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE) showed the highest median concentrations (1.04 ng/ml), while HCB showed the lowest (0.24 ng/ml). In the multivariable models, age was positively associated with HCB, p,p′-DDE, and PCB-180. BMI was associated positively with p,p′-DDE but negatively with PCB-138. Total accumulated time residing in an urban area was positively associated with PCB-153 concentrations. The women showed higher HCB and lower p,p′-DDE concentrations versus the men. Notably, POP exposure in our study population was inversely associated with the breastfeeding received by participants and with the number of pregnancies of their mothers but was not related to the participants' history of breastfeeding their children or parity. Smoking was negatively associated with HCB and PCB-153 concentrations. Consumption of fatty foods, including blue fish, was in general positively associated with POP levels. Although POP environmental levels are declining worldwide, there is a need for the continuous monitoring of human exposure in the general population. The results of the present study confirm previous findings and point

Levels and predictors of persistent organic pollutants in an adult population from four Spanish regions

Energy Technology Data Exchange (ETDEWEB)

Fernández-Rodríguez, M., E-mail: mafero@ugr.es [Instituto de Investigación Biosanitaria ibs.Granada, University of Granada, San Cecilio University Hospital, Granada (Spain); Arrebola, J.P., E-mail: jparrebola@ugr.es [Instituto de Investigación Biosanitaria ibs.Granada, University of Granada, San Cecilio University Hospital, Granada (Spain); Oncology Unit, Virgen de las Nieves University Hospital, Granada (Spain); Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid (Spain); Artacho-Cordón, F.; Amaya, E. [Instituto de Investigación Biosanitaria ibs.Granada, University of Granada, San Cecilio University Hospital, Granada (Spain); Aragones, N. [Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid (Spain); Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid (Spain); Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Majadahonda, Madrid (Spain); Llorca, J. [Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid (Spain); Universidad de Cantabria-IDIVAL, Santander (Spain); Perez-Gomez, B. [Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid (Spain); Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid (Spain); Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Majadahonda, Madrid (Spain); and others

2015-12-15

This research aimed to assess serum concentrations of a group of persistent organic pollutants (POPs) in a sample of adults recruited in four different regions from Spain and to assess socio-demographic, dietary, and lifestyle predictors of the exposure. The study population comprised 312 healthy adults selected from among controls recruited in the MCC-Spain multicase-control study. Study variables were collected using standardized questionnaires, and pollutants were analyzed by means of gas chromatography with electron capture detection. Multivariable analyses were performed to identify predictors of log-transformed pollutant concentrations, using combined backward and forward stepwise multiple linear regression models. Detection rates ranged from 89.1% (hexachlorobenzene, HCB) to 93.6% (Polychlorinated biphenyl-153 [PCB-153]); p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE) showed the highest median concentrations (1.04 ng/ml), while HCB showed the lowest (0.24 ng/ml). In the multivariable models, age was positively associated with HCB, p,p′-DDE, and PCB-180. BMI was associated positively with p,p′-DDE but negatively with PCB-138. Total accumulated time residing in an urban area was positively associated with PCB-153 concentrations. The women showed higher HCB and lower p,p′-DDE concentrations versus the men. Notably, POP exposure in our study population was inversely associated with the breastfeeding received by participants and with the number of pregnancies of their mothers but was not related to the participants' history of breastfeeding their children or parity. Smoking was negatively associated with HCB and PCB-153 concentrations. Consumption of fatty foods, including blue fish, was in general positively associated with POP levels. Although POP environmental levels are declining worldwide, there is a need for the continuous monitoring of human exposure in the general population. The results of the present study confirm previous findings and

The linear-non-linear frontier for the Goldstone Higgs

International Nuclear Information System (INIS)

Gavela, M.B.; Saa, S.; Kanshin, K.; Machado, P.A.N.

2016-01-01

The minimal SO(5)/SO(4) σ-model is used as a template for the ultraviolet completion of scenarios in which the Higgs particle is a low-energy remnant of some high-energy dynamics, enjoying a (pseudo) Nambu-Goldstone-boson ancestry. Varying the σ mass allows one to sweep from the perturbative regime to the customary non-linear implementations. The low-energy benchmark effective non-linear Lagrangian for bosons and fermions is obtained, determining as well the operator coefficients including linear corrections. At first order in the latter, three effective bosonic operators emerge which are independent of the explicit soft breaking assumed. The Higgs couplings to vector bosons and fermions turn out to be quite universal: the linear corrections are proportional to the explicit symmetry-breaking parameters. Furthermore, we define an effective Yukawa operator which allows a simple parametrization and comparison of different heavy-fermion ultraviolet completions. In addition, one particular fermionic completion is explored in detail, obtaining the corresponding leading low-energy fermionic operators. (orig.)

The linear-non-linear frontier for the Goldstone Higgs

Energy Technology Data Exchange (ETDEWEB)

Gavela, M.B.; Saa, S. [IFT-UAM/CSIC, Universidad Autonoma de Madrid, Departamento de Fisica Teorica y Instituto de Fisica Teorica, Madrid (Spain); Kanshin, K. [Universita di Padova, Dipartimento di Fisica e Astronomia ' G. Galilei' , Padua (Italy); INFN, Padova (Italy); Machado, P.A.N. [IFT-UAM/CSIC, Universidad Autonoma de Madrid, Departamento de Fisica Teorica y Instituto de Fisica Teorica, Madrid (Spain); Fermi National Accelerator Laboratory, Theoretical Physics Department, Batavia, IL (United States)

2016-12-15

The minimal SO(5)/SO(4) σ-model is used as a template for the ultraviolet completion of scenarios in which the Higgs particle is a low-energy remnant of some high-energy dynamics, enjoying a (pseudo) Nambu-Goldstone-boson ancestry. Varying the σ mass allows one to sweep from the perturbative regime to the customary non-linear implementations. The low-energy benchmark effective non-linear Lagrangian for bosons and fermions is obtained, determining as well the operator coefficients including linear corrections. At first order in the latter, three effective bosonic operators emerge which are independent of the explicit soft breaking assumed. The Higgs couplings to vector bosons and fermions turn out to be quite universal: the linear corrections are proportional to the explicit symmetry-breaking parameters. Furthermore, we define an effective Yukawa operator which allows a simple parametrization and comparison of different heavy-fermion ultraviolet completions. In addition, one particular fermionic completion is explored in detail, obtaining the corresponding leading low-energy fermionic operators. (orig.)

Thermal, Catalytic Conversion of Alkanes to Linear Aldehydes and Linear Amines.

Science.gov (United States)

Tang, Xinxin; Jia, Xiangqing; Huang, Zheng

2018-03-21

Alkanes, the main constituents of petroleum, are attractive feedstocks for producing value-added chemicals. Linear aldehydes and amines are two of the most important building blocks in the chemical industry. To date, there have been no effective methods for directly converting n-alkanes to linear aldehydes and linear amines. Here, we report a molecular dual-catalyst system for production of linear aldehydes via regioselective carbonylation of n-alkanes. The system is comprised of a pincer iridium catalyst for transfer-dehydrogenation of the alkane using t-butylethylene or ethylene as a hydrogen acceptor working sequentially with a rhodium catalyst for olefin isomerization-hydroformylation with syngas. The system exhibits high regioselectivity for linear aldehydes and gives high catalytic turnover numbers when using ethylene as the acceptor. In addition, the direct conversion of light alkanes, n-pentane and n-hexane, to siloxy-terminated alkyl aldehydes through a sequence of Ir/Fe-catalyzed alkane silylation and Ir/Rh-catalyzed alkane carbonylation, is described. Finally, the Ir/Rh dual-catalyst strategy has been successfully applied to regioselective alkane aminomethylation to form linear alkyl amines.

Linear-Algebra Programs

Science.gov (United States)

Lawson, C. L.; Krogh, F. T.; Gold, S. S.; Kincaid, D. R.; Sullivan, J.; Williams, E.; Hanson, R. J.; Haskell, K.; Dongarra, J.; Moler, C. B.

1982-01-01

The Basic Linear Algebra Subprograms (BLAS) library is a collection of 38 FORTRAN-callable routines for performing basic operations of numerical linear algebra. BLAS library is portable and efficient source of basic operations for designers of programs involving linear algebriac computations. BLAS library is supplied in portable FORTRAN and Assembler code versions for IBM 370, UNIVAC 1100 and CDC 6000 series computers.

Prevalence and predictors of smoking in Butajira town, Ethiopia ...

African Journals Online (AJOL)

Conclusions: Socio-demographic predictors of cigarette smoking in Butajira Ethiopia are different to those found in high income countries. The predictors found here suggest that increased taxation may be the most effective tobacco control measure in this low income country setting. Ethiopian Journal of Health Development ...

Satisfaction with retention factors as predictors of the job ...

African Journals Online (AJOL)

Satisfaction with training and development opportunities was the best predictor of organisational fit, while satisfaction with career opportunities was the best predictor of organisational sacrifice. The findings add valuable new knowledge that may be used to inform retention strategies for professional staff with scarce skills in ...

Seasonal variation and predictors of epistaxis.

Science.gov (United States)

Purkey, Matthew R; Seeskin, Zachary; Chandra, Rakesh

2014-09-01

To examine the incidence of epistaxis as a function of season and age and to determine predictors of episodes within the epistaxis patient population presenting to a tertiary hospital system. Retrospective cohort study. Electronic medical record charts of patients presenting to the Northwestern Emergency Department, admitted to an inpatient ward, or seen in an outpatient setting between 2008 and 2012 were reviewed and selected for an International Classifications of Disease-Ninth Revision epistaxis code of 784.7. Season of presentation, demographic factors (age, race, gender, insurance status), medication use (including anticoagulants and topical nasal steroid administration), and several comorbidities were analyzed as potential predictors of episodes. A total of 2,405 patients were identified with a total of 3,666 individual epistaxis episodes over 5 years. Multivariate analysis identified allergic rhinitis (AR), chronic sinusitis (CRS), coagulopathy, hereditary hemorrhagic telangiectasia (HHT), hematologic malignancy, and hypertension (HTN) as predictors of a higher number of cases. Epistaxis occurred more frequently during colder months and in older patients. Epistaxis occurs more commonly during the winter and in older patients. AR, CRS, coagulopathy, HHT, hematologic malignancy, and HTN are associated with increased epistaxis incidence. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

RS-Predictor models augmented with SMARTCyp reactivities

DEFF Research Database (Denmark)

Zaretzki, Jed; Rydberg, Patrik; Bergeron, Charles

2012-01-01

(82.3%) and merged(86.0%). Comprehensive datamining of each substrate set and careful statistical analyses of the predictions made by the different models revealed new insights into molecular features that control metabolic regioselectivity and enable accurate prospective prediction of likely SOMs.......RS-Predictor is a tool for creating pathway-independent, isozyme-specific site of metabolism (SOM) prediction models using any set of known cytochrome P450 substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study we...... report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1 and 3...

Model Predictive Control for Linear Complementarity and Extended Linear Complementarity Systems

Directory of Open Access Journals (Sweden)

Bambang Riyanto

2005-11-01

Full Text Available In this paper, we propose model predictive control method for linear complementarity and extended linear complementarity systems by formulating optimization along prediction horizon as mixed integer quadratic program. Such systems contain interaction between continuous dynamics and discrete event systems, and therefore, can be categorized as hybrid systems. As linear complementarity and extended linear complementarity systems finds applications in different research areas, such as impact mechanical systems, traffic control and process control, this work will contribute to the development of control design method for those areas as well, as shown by three given examples.

Predictors of short term treatment outcome in patients with achalasia following endoscopic or surgical therapy.

Science.gov (United States)

Gheorghe, Cristian; Bancila, Ion; Tutuian, Radu; Iacob, Razvan; Tomulescu, Victor

2012-01-01

Pneumatic balloon dilation and surgical myotomy are the most effective treatments for achalasia. While there is controversy which method is best, the aim of the current study was to identify predictors of symptom recurrence after endoscopic or surgical therapy. Patients undergoing pneumatic balloon dilatation (30mm) or laparoscopic Heller myotomy with Dor fundoplication were included in the study. Analyzed parameters include total symptom score (sum of 0-5 point intensity for dysphagia, regurgitation and chest pain), width and height of esophageal column at 2 and 5 minutes after oral barium ingestion, lower esophageal sphincter (LES) length, resting (LESP) and residual pressure (LESRP) before and 3 months after intervention. Patients with symptoms score surgical group were symptom-free 3 months after intervention. Therapies improved LESP (24.4±8.2mmHg pre- vs. 15.4±10.3mmHg post-therapy; p=0.003) and mean LESRP (7.9±4.3mmHg pre- vs. 5.3±6.7mmHg post-therapy; p=0.03). Univariate linear regression analysis identified barium contrast column width >5cm at 2 minutes (p=0.04), LES length 10mmHg (p=0.02) as predictors for persistent symptoms. While >85% of achalasia patients responded well to 30mm pneumatic balloon dilation, patients with elevated LES pressure, short LES and wide esophagus should be considered as primary surgical candidates.

A cross-sectional examination of psychological distress, positive mental health and their predictors in medical students in their clinical clerkships

Directory of Open Access Journals (Sweden)

Inge van Dijk

2017-11-01

Full Text Available Abstract Background Medical students can experience the transition from theory to clinical clerkships as stressful. Scientific literature on the mental health of clinical clerkship students is scarce and mental health is usually defined as absence of psychological distress without assessing psychological, emotional and social wellbeing, together called ‘positive mental health’. This cross-sectional study examines the prevalence of psychological distress and positive mental health and explores possible predictors in a Dutch sample of clinical clerkship students. Methods Fourth-year medical students in their first year of clinical clerkships were invited to complete an online questionnaire assessing demographics, psychological distress (Brief Symptom Inventory, positive mental health (Mental Health Continuum- SF, dysfunctional cognitions (Irrational Beliefs Inventory and dispositional mindfulness skills (Five Facet Mindfulness Questionnaire. Multiple linear regression analysis was used to explore relationships between psychological distress, positive mental health (dependent variables and demographics, dysfunctional cognitions and dispositional mindfulness skills (predictors. Results Of 454 eligible students, 406 (89% completed the assessment of whom 21% scored in the clinical range of psychological distress and 41% reported a flourishing mental health. These proportions partially overlap each other. Female students reported a significantly higher mean level of psychological distress than males. In the regression analysis the strongest predictors of psychological distress were ‘acting with awareness’ (negative and ‘worrying’ (positive. Strongest predictors of positive mental health were ‘problem avoidance’ (negative and ‘emotional irresponsibility’ (negative. Conclusions The prevalence of psychopathology in our sample of Dutch clinical clerkship students is slightly higher than in the general population. Our results support

A cross-sectional examination of psychological distress, positive mental health and their predictors in medical students in their clinical clerkships.

Science.gov (United States)

van Dijk, Inge; Lucassen, Peter L B J; van Weel, Chris; Speckens, Anne E M

2017-11-17

Medical students can experience the transition from theory to clinical clerkships as stressful. Scientific literature on the mental health of clinical clerkship students is scarce and mental health is usually defined as absence of psychological distress without assessing psychological, emotional and social wellbeing, together called 'positive mental health'. This cross-sectional study examines the prevalence of psychological distress and positive mental health and explores possible predictors in a Dutch sample of clinical clerkship students. Fourth-year medical students in their first year of clinical clerkships were invited to complete an online questionnaire assessing demographics, psychological distress (Brief Symptom Inventory), positive mental health (Mental Health Continuum- SF), dysfunctional cognitions (Irrational Beliefs Inventory) and dispositional mindfulness skills (Five Facet Mindfulness Questionnaire). Multiple linear regression analysis was used to explore relationships between psychological distress, positive mental health (dependent variables) and demographics, dysfunctional cognitions and dispositional mindfulness skills (predictors). Of 454 eligible students, 406 (89%) completed the assessment of whom 21% scored in the clinical range of psychological distress and 41% reported a flourishing mental health. These proportions partially overlap each other. Female students reported a significantly higher mean level of psychological distress than males. In the regression analysis the strongest predictors of psychological distress were 'acting with awareness' (negative) and 'worrying' (positive). Strongest predictors of positive mental health were 'problem avoidance' (negative) and 'emotional irresponsibility' (negative). The prevalence of psychopathology in our sample of Dutch clinical clerkship students is slightly higher than in the general population. Our results support conclusions of previous research that psychological distress and positive mental

Linear and quasi-linear equations of parabolic type

CERN Document Server

Ladyženskaja, O A; Ural′ceva, N N; Uralceva, N N

1968-01-01

Equations of parabolic type are encountered in many areas of mathematics and mathematical physics, and those encountered most frequently are linear and quasi-linear parabolic equations of the second order. In this volume, boundary value problems for such equations are studied from two points of view: solvability, unique or otherwise, and the effect of smoothness properties of the functions entering the initial and boundary conditions on the smoothness of the solutions.

[Application of ordinary Kriging method in entomologic ecology].

Science.gov (United States)

Zhang, Runjie; Zhou, Qiang; Chen, Cuixian; Wang, Shousong

2003-01-01

Geostatistics is a statistic method based on regional variables and using the tool of variogram to analyze the spatial structure and the patterns of organism. In simulating the variogram within a great range, though optimal simulation cannot be obtained, the simulation method of a dialogue between human and computer can be used to optimize the parameters of the spherical models. In this paper, the method mentioned above and the weighted polynomial regression were utilized to simulate the one-step spherical model, the two-step spherical model and linear function model, and the available nearby samples were used to draw on the ordinary Kriging procedure, which provided a best linear unbiased estimate of the constraint of the unbiased estimation. The sum of square deviation between the estimating and measuring values of varying theory models were figured out, and the relative graphs were shown. It was showed that the simulation based on the two-step spherical model was the best simulation, and the one-step spherical model was better than the linear function model.

FEAST: a two-dimensional non-linear finite element code for calculating stresses

International Nuclear Information System (INIS)

Tayal, M.

1986-06-01

The computer code FEAST calculates stresses, strains, and displacements. The code is two-dimensional. That is, either plane or axisymmetric calculations can be done. The code models elastic, plastic, creep, and thermal strains and stresses. Cracking can also be simulated. The finite element method is used to solve equations describing the following fundamental laws of mechanics: equilibrium; compatibility; constitutive relations; yield criterion; and flow rule. FEAST combines several unique features that permit large time-steps in even severely non-linear situations. The features include a special formulation for permitting many finite elements to simultaneously cross the boundary from elastic to plastic behaviour; accomodation of large drops in yield-strength due to changes in local temperature and a three-step predictor-corrector method for plastic analyses. These features reduce computing costs. Comparisons against twenty analytical solutions and against experimental measurements show that predictions of FEAST are generally accurate to ± 5%