Penalized variable selection in competing risks regression.

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Fu, Zhixuan; Parikh, Chirag R; Zhou, Bingqing

2017-07-01

Penalized variable selection methods have been extensively studied for standard time-to-event data. Such methods cannot be directly applied when subjects are at risk of multiple mutually exclusive events, known as competing risks. The proportional subdistribution hazard (PSH) model proposed by Fine and Gray (J Am Stat Assoc 94:496-509, 1999) has become a popular semi-parametric model for time-to-event data with competing risks. It allows for direct assessment of covariate effects on the cumulative incidence function. In this paper, we propose a general penalized variable selection strategy that simultaneously handles variable selection and parameter estimation in the PSH model. We rigorously establish the asymptotic properties of the proposed penalized estimators and modify the coordinate descent algorithm for implementation. Simulation studies are conducted to demonstrate the good performance of the proposed method. Data from deceased donor kidney transplants from the United Network of Organ Sharing illustrate the utility of the proposed method.

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

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Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao

2014-10-07

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

A numeric comparison of variable selection algorithms for supervised learning

International Nuclear Information System (INIS)

Palombo, G.; Narsky, I.

2009-01-01

Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

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Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable

Variable selection and estimation for longitudinal survey data

KAUST Repository

Wang, Li

2014-09-01

There is wide interest in studying longitudinal surveys where sample subjects are observed successively over time. Longitudinal surveys have been used in many areas today, for example, in the health and social sciences, to explore relationships or to identify significant variables in regression settings. This paper develops a general strategy for the model selection problem in longitudinal sample surveys. A survey weighted penalized estimating equation approach is proposed to select significant variables and estimate the coefficients simultaneously. The proposed estimators are design consistent and perform as well as the oracle procedure when the correct submodel was known. The estimating function bootstrap is applied to obtain the standard errors of the estimated parameters with good accuracy. A fast and efficient variable selection algorithm is developed to identify significant variables for complex longitudinal survey data. Simulated examples are illustrated to show the usefulness of the proposed methodology under various model settings and sampling designs. © 2014 Elsevier Inc.

Exhaustive Search for Sparse Variable Selection in Linear Regression

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Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

Ensembling Variable Selectors by Stability Selection for the Cox Model

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Qing-Yan Yin

2017-01-01

Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

Variable selection methods in PLS regression - a comparison study on metabolomics data

DEFF Research Database (Denmark)

Karaman, İbrahim; Hedemann, Mette Skou; Knudsen, Knud Erik Bach

. The aim of the metabolomics study was to investigate the metabolic profile in pigs fed various cereal fractions with special attention to the metabolism of lignans using LC-MS based metabolomic approach. References 1. Lê Cao KA, Rossouw D, Robert-Granié C, Besse P: A Sparse PLS for Variable Selection when...... integrated approach. Due to the high number of variables in data sets (both raw data and after peak picking) the selection of important variables in an explorative analysis is difficult, especially when different data sets of metabolomics data need to be related. Variable selection (or removal of irrelevant...... different strategies for variable selection on PLSR method were considered and compared with respect to selected subset of variables and the possibility for biological validation. Sparse PLSR [1] as well as PLSR with Jack-knifing [2] was applied to data in order to achieve variable selection prior...

Combining epidemiologic and biostatistical tools to enhance variable selection in HIV cohort analyses.

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Christopher Rentsch

Full Text Available BACKGROUND: Variable selection is an important step in building a multivariate regression model for which several methods and statistical packages are available. A comprehensive approach for variable selection in complex multivariate regression analyses within HIV cohorts is explored by utilizing both epidemiological and biostatistical procedures. METHODS: Three different methods for variable selection were illustrated in a study comparing survival time between subjects in the Department of Defense's National History Study and the Atlanta Veterans Affairs Medical Center's HIV Atlanta VA Cohort Study. The first two methods were stepwise selection procedures, based either on significance tests (Score test, or on information theory (Akaike Information Criterion, while the third method employed a Bayesian argument (Bayesian Model Averaging. RESULTS: All three methods resulted in a similar parsimonious survival model. Three of the covariates previously used in the multivariate model were not included in the final model suggested by the three approaches. When comparing the parsimonious model to the previously published model, there was evidence of less variance in the main survival estimates. CONCLUSIONS: The variable selection approaches considered in this study allowed building a model based on significance tests, on an information criterion, and on averaging models using their posterior probabilities. A parsimonious model that balanced these three approaches was found to provide a better fit than the previously reported model.

Variability-based active galactic nucleus selection using image subtraction in the SDSS and LSST era

Energy Technology Data Exchange (ETDEWEB)

Choi, Yumi; Gibson, Robert R.; Becker, Andrew C.; Ivezić, Željko; Connolly, Andrew J.; Ruan, John J.; Anderson, Scott F. [Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195 (United States); MacLeod, Chelsea L., E-mail: ymchoi@astro.washington.edu [Physics Department, U.S. Naval Academy, 572 Holloway Road, Annapolis, MD 21402 (United States)

2014-02-10

With upcoming all-sky surveys such as LSST poised to generate a deep digital movie of the optical sky, variability-based active galactic nucleus (AGN) selection will enable the construction of highly complete catalogs with minimum contamination. In this study, we generate g-band difference images and construct light curves (LCs) for QSO/AGN candidates listed in Sloan Digital Sky Survey Stripe 82 public catalogs compiled from different methods, including spectroscopy, optical colors, variability, and X-ray detection. Image differencing excels at identifying variable sources embedded in complex or blended emission regions such as Type II AGNs and other low-luminosity AGNs that may be omitted from traditional photometric or spectroscopic catalogs. To separate QSOs/AGNs from other sources using our difference image LCs, we explore several LC statistics and parameterize optical variability by the characteristic damping timescale (τ) and variability amplitude. By virtue of distinguishable variability parameters of AGNs, we are able to select them with high completeness of 93.4% and efficiency (i.e., purity) of 71.3%. Based on optical variability, we also select highly variable blazar candidates, whose infrared colors are consistent with known blazars. One-third of them are also radio detected. With the X-ray selected AGN candidates, we probe the optical variability of X-ray detected optically extended sources using their difference image LCs for the first time. A combination of optical variability and X-ray detection enables us to select various types of host-dominated AGNs. Contrary to the AGN unification model prediction, two Type II AGN candidates (out of six) show detectable variability on long-term timescales like typical Type I AGNs. This study will provide a baseline for future optical variability studies of extended sources.

Machine learning techniques to select variable stars

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García-Varela Alejandro

2017-01-01

Full Text Available In order to perform a supervised classification of variable stars, we propose and evaluate a set of six features extracted from the magnitude density of the light curves. They are used to train automatic classification systems using state-of-the-art classifiers implemented in the R statistical computing environment. We find that random forests is the most successful method to select variables.

A Variable-Selection Heuristic for K-Means Clustering.

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Brusco, Michael J.; Cradit, J. Dennis

2001-01-01

Presents a variable selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. Subjected the heuristic to Monte Carlo testing across more than 2,200 datasets. Results indicate that the heuristic is extremely effective at eliminating masking variables. (SLD)

A survey of variable selection methods in two Chinese epidemiology journals

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Lynn Henry S

2010-09-01

Full Text Available Abstract Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163 via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44% used stepwise procedures, 89 (41% tested individual regression coefficients, but 33 (15% did not mention how variables were selected. Sixty percent (58/97 of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals.

ENSEMBLE VARIABILITY OF NEAR-INFRARED-SELECTED ACTIVE GALACTIC NUCLEI

International Nuclear Information System (INIS)

Kouzuma, S.; Yamaoka, H.

2012-01-01

We present the properties of the ensemble variability V for nearly 5000 near-infrared active galactic nuclei (AGNs) selected from the catalog of Quasars and Active Galactic Nuclei (13th Edition) and the SDSS-DR7 quasar catalog. From three near-infrared point source catalogs, namely, Two Micron All Sky Survey (2MASS), Deep Near Infrared Survey (DENIS), and UKIDSS/LAS catalogs, we extract 2MASS-DENIS and 2MASS-UKIDSS counterparts for cataloged AGNs by cross-identification between catalogs. We further select variable AGNs based on an optimal criterion for selecting the variable sources. The sample objects are divided into subsets according to whether near-infrared light originates by optical emission or by near-infrared emission in the rest frame; and we examine the correlations of the ensemble variability with the rest-frame wavelength, redshift, luminosity, and rest-frame time lag. In addition, we also examine the correlations of variability amplitude with optical variability, radio intensity, and radio-to-optical flux ratio. The rest-frame optical variability of our samples shows negative correlations with luminosity and positive correlations with rest-frame time lag (i.e., the structure function, SF), and this result is consistent with previous analyses. However, no well-known negative correlation exists between the rest-frame wavelength and optical variability. This inconsistency might be due to a biased sampling of high-redshift AGNs. Near-infrared variability in the rest frame is anticorrelated with the rest-frame wavelength, which is consistent with previous suggestions. However, correlations of near-infrared variability with luminosity and rest-frame time lag are the opposite of these correlations of the optical variability; that is, the near-infrared variability is positively correlated with luminosity but negatively correlated with the rest-frame time lag. Because these trends are qualitatively consistent with the properties of radio-loud quasars reported

Variable selection and model choice in geoadditive regression models.

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Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS

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Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-10-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection

KAUST Repository

Chen, Lisha

2012-12-01

The reduced-rank regression is an effective method in predicting multiple response variables from the same set of predictor variables. It reduces the number of model parameters and takes advantage of interrelations between the response variables and hence improves predictive accuracy. We propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty. We apply a group-lasso type penalty that treats each row of the matrix of the regression coefficients as a group and show that this penalty satisfies certain desirable invariance properties. We develop two numerical algorithms to solve the penalized regression problem and establish the asymptotic consistency of the proposed method. In particular, the manifold structure of the reduced-rank regression coefficient matrix is considered and studied in our theoretical analysis. In our simulation study and real data analysis, the new method is compared with several existing variable selection methods for multivariate regression and exhibits competitive performance in prediction and variable selection. © 2012 American Statistical Association.

Selected Macroeconomic Variables and Stock Market Movements: Empirical evidence from Thailand

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Joseph Ato Forson

2014-06-01

Full Text Available This paper investigates and analyzes the long-run equilibrium relationship between the Thai stock Exchange Index (SETI and selected macroeconomic variables using monthly time series data that cover a 20-year period from January 1990 to December 2009. The following macroeconomic variables are included in our analysis: money supply (MS, the consumer price index (CPI, interest rate (IR and the industrial production index (IP (as a proxy for GDP. Our findings prove that the SET Index and the selected macroeconomic variables are cointegrated at I (1 and have a significant equilibrium relationship over the long run. Money supply demonstrates a strong positive relationship with the SET Index over the long run, whereas the industrial production index and consumer price index show negative long-run relationships with the SET Index. Furthermore, in non-equilibrium situations, the error correction mechanism suggests that the consumer price index, industrial production index and money supply each contribute in some way to restore equilibrium. In addition, using Toda and Yamamoto’s augmented Granger causality test, we identify a bi-causal relationship between industrial production and money supply and unilateral causal relationships between CPI and IR, IP and CPI, MS and CPI, and IP and SETI, indicating that all of these variables are sensitive to Thai stock market movements. The policy implications of these findings are also discussed.

The Selection, Use, and Reporting of Control Variables in International Business Research

DEFF Research Database (Denmark)

Nielsen, Bo Bernhard; Raswant, Arpit

2018-01-01

This study explores the selection, use, and reporting of control variables in studies published in the leading international business (IB) research journals. We review a sample of 246 empirical studies published in the top five IB journals over the period 2012–2015 with particular emphasis...... on selection, use, and reporting of controls. Approximately 83% of studies included only half of what we consider Minimum Standard of Practice with regards to controls, whereas only 38% of the studies met the 75% threshold. We provide recommendations on how to effectively identify, use and report controls...

Characterizing the Optical Variability of Bright Blazars: Variability-based Selection of Fermi Active Galactic Nuclei

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Ruan, John J.; Anderson, Scott F.; MacLeod, Chelsea L.; Becker, Andrew C.; Burnett, T. H.; Davenport, James R. A.; Ivezić, Željko; Kochanek, Christopher S.; Plotkin, Richard M.; Sesar, Branimir; Stuart, J. Scott

2012-11-01

We investigate the use of optical photometric variability to select and identify blazars in large-scale time-domain surveys, in part to aid in the identification of blazar counterparts to the ~30% of γ-ray sources in the Fermi 2FGL catalog still lacking reliable associations. Using data from the optical LINEAR asteroid survey, we characterize the optical variability of blazars by fitting a damped random walk model to individual light curves with two main model parameters, the characteristic timescales of variability τ, and driving amplitudes on short timescales \\hat{\\sigma }. Imposing cuts on minimum τ and \\hat{\\sigma } allows for blazar selection with high efficiency E and completeness C. To test the efficacy of this approach, we apply this method to optically variable LINEAR objects that fall within the several-arcminute error ellipses of γ-ray sources in the Fermi 2FGL catalog. Despite the extreme stellar contamination at the shallow depth of the LINEAR survey, we are able to recover previously associated optical counterparts to Fermi active galactic nuclei with E >= 88% and C = 88% in Fermi 95% confidence error ellipses having semimajor axis r beaming. After correcting for beaming, we estimate that the characteristic timescale of blazar variability is ~3 years in the rest frame of the jet, in contrast with the ~320 day disk flux timescale observed in quasars. The variability-based selection method presented will be useful for blazar identification in time-domain optical surveys and is also a probe of jet physics.

The Properties of Model Selection when Retaining Theory Variables

DEFF Research Database (Denmark)

Hendry, David F.; Johansen, Søren

Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....

CHARACTERIZING THE OPTICAL VARIABILITY OF BRIGHT BLAZARS: VARIABILITY-BASED SELECTION OF FERMI ACTIVE GALACTIC NUCLEI

International Nuclear Information System (INIS)

Ruan, John J.; Anderson, Scott F.; MacLeod, Chelsea L.; Becker, Andrew C.; Davenport, James R. A.; Ivezić, Željko; Burnett, T. H.; Kochanek, Christopher S.; Plotkin, Richard M.; Sesar, Branimir; Stuart, J. Scott

2012-01-01

We investigate the use of optical photometric variability to select and identify blazars in large-scale time-domain surveys, in part to aid in the identification of blazar counterparts to the ∼30% of γ-ray sources in the Fermi 2FGL catalog still lacking reliable associations. Using data from the optical LINEAR asteroid survey, we characterize the optical variability of blazars by fitting a damped random walk model to individual light curves with two main model parameters, the characteristic timescales of variability τ, and driving amplitudes on short timescales σ-circumflex. Imposing cuts on minimum τ and σ-circumflex allows for blazar selection with high efficiency E and completeness C. To test the efficacy of this approach, we apply this method to optically variable LINEAR objects that fall within the several-arcminute error ellipses of γ-ray sources in the Fermi 2FGL catalog. Despite the extreme stellar contamination at the shallow depth of the LINEAR survey, we are able to recover previously associated optical counterparts to Fermi active galactic nuclei with E ≥ 88% and C = 88% in Fermi 95% confidence error ellipses having semimajor axis r < 8'. We find that the suggested radio counterpart to Fermi source 2FGL J1649.6+5238 has optical variability consistent with other γ-ray blazars and is likely to be the γ-ray source. Our results suggest that the variability of the non-thermal jet emission in blazars is stochastic in nature, with unique variability properties due to the effects of relativistic beaming. After correcting for beaming, we estimate that the characteristic timescale of blazar variability is ∼3 years in the rest frame of the jet, in contrast with the ∼320 day disk flux timescale observed in quasars. The variability-based selection method presented will be useful for blazar identification in time-domain optical surveys and is also a probe of jet physics.

Variable selection for mixture and promotion time cure rate models.

Science.gov (United States)

Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng

2016-11-16

Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.

Regional regression models of percentile flows for the contiguous United States: Expert versus data-driven independent variable selection

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Geoffrey Fouad

2018-06-01

New hydrological insights for the region: A set of three variables selected based on an expert assessment of factors that influence percentile flows performed similarly to larger sets of variables selected using a data-driven method. Expert assessment variables included mean annual precipitation, potential evapotranspiration, and baseflow index. Larger sets of up to 37 variables contributed little, if any, additional predictive information. Variables used to describe the distribution of basin data (e.g. standard deviation were not useful, and average values were sufficient to characterize physical and climatic basin conditions. Effectiveness of the expert assessment variables may be due to the high degree of multicollinearity (i.e. cross-correlation among additional variables. A tool is provided in the Supplementary material to predict percentile flows based on the three expert assessment variables. Future work should develop new variables with a strong understanding of the processes related to percentile flows.

[Application of characteristic NIR variables selection in portable detection of soluble solids content of apple by near infrared spectroscopy].

Science.gov (United States)

Fan, Shu-Xiang; Huang, Wen-Qian; Li, Jiang-Bo; Guo, Zhi-Ming; Zhaq, Chun-Jiang

2014-10-01

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including unin- formative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were pro- posed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS- SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.

Meta-Statistics for Variable Selection: The R Package BioMark

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Ron Wehrens

2012-11-01

Full Text Available Biomarker identification is an ever more important topic in the life sciences. With the advent of measurement methodologies based on microarrays and mass spectrometry, thousands of variables are routinely being measured on complex biological samples. Often, the question is what makes two groups of samples different. Classical hypothesis testing suffers from the multiple testing problem; however, correcting for this often leads to a lack of power. In addition, choosing α cutoff levels remains somewhat arbitrary. Also in a regression context, a model depending on few but relevant variables will be more accurate and precise, and easier to interpret biologically.We propose an R package, BioMark, implementing two meta-statistics for variable selection. The first, higher criticism, presents a data-dependent selection threshold for significance, instead of a cookbook value of α = 0.05. It is applicable in all cases where two groups are compared. The second, stability selection, is more general, and can also be applied in a regression context. This approach uses repeated subsampling of the data in order to assess the variability of the model coefficients and selects those that remain consistently important. It is shown using experimental spike-in data from the field of metabolomics that both approaches work well with real data. BioMark also contains functionality for simulating data with specific characteristics for algorithm development and testing.

QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE

International Nuclear Information System (INIS)

Kim, Dae-Won; Protopapas, Pavlos; Alcock, Charles; Trichas, Markos; Byun, Yong-Ik; Khardon, Roni

2011-01-01

We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ∼80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million light curves, and found 1620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

A modification of the successive projections algorithm for spectral variable selection in the presence of unknown interferents.

Science.gov (United States)

Soares, Sófacles Figueredo Carreiro; Galvão, Roberto Kawakami Harrop; Araújo, Mário César Ugulino; da Silva, Edvan Cirino; Pereira, Claudete Fernandes; de Andrade, Stéfani Iury Evangelista; Leite, Flaviano Carvalho

2011-03-09

This work proposes a modification to the successive projections algorithm (SPA) aimed at selecting spectral variables for multiple linear regression (MLR) in the presence of unknown interferents not included in the calibration data set. The modified algorithm favours the selection of variables in which the effect of the interferent is less pronounced. The proposed procedure can be regarded as an adaptive modelling technique, because the spectral features of the samples to be analyzed are considered in the variable selection process. The advantages of this new approach are demonstrated in two analytical problems, namely (1) ultraviolet-visible spectrometric determination of tartrazine, allure red and sunset yellow in aqueous solutions under the interference of erythrosine, and (2) near-infrared spectrometric determination of ethanol in gasoline under the interference of toluene. In these case studies, the performance of conventional MLR-SPA models is substantially degraded by the presence of the interferent. This problem is circumvented by applying the proposed Adaptive MLR-SPA approach, which results in prediction errors smaller than those obtained by three other multivariate calibration techniques, namely stepwise regression, full-spectrum partial-least-squares (PLS) and PLS with variables selected by a genetic algorithm. An inspection of the variable selection results reveals that the Adaptive approach successfully avoids spectral regions in which the interference is more intense. Copyright © 2011 Elsevier B.V. All rights reserved.

Not accounting for interindividual variability can mask habitat selection patterns: a case study on black bears.

Science.gov (United States)

Lesmerises, Rémi; St-Laurent, Martin-Hugues

2017-11-01

Habitat selection studies conducted at the population scale commonly aim to describe general patterns that could improve our understanding of the limiting factors in species-habitat relationships. Researchers often consider interindividual variation in selection patterns to control for its effects and avoid pseudoreplication by using mixed-effect models that include individuals as random factors. Here, we highlight common pitfalls and possible misinterpretations of this strategy by describing habitat selection of 21 black bears Ursus americanus. We used Bayesian mixed-effect models and compared results obtained when using random intercept (i.e., population level) versus calculating individual coefficients for each independent variable (i.e., individual level). We then related interindividual variability to individual characteristics (i.e., age, sex, reproductive status, body condition) in a multivariate analysis. The assumption of comparable behavior among individuals was verified only in 40% of the cases in our seasonal best models. Indeed, we found strong and opposite responses among sampled bears and individual coefficients were linked to individual characteristics. For some covariates, contrasted responses canceled each other out at the population level. In other cases, interindividual variability was concealed by the composition of our sample, with the majority of the bears (e.g., old individuals and bears in good physical condition) driving the population response (e.g., selection of young forest cuts). Our results stress the need to consider interindividual variability to avoid misinterpretation and uninformative results, especially for a flexible and opportunistic species. This study helps to identify some ecological drivers of interindividual variability in bear habitat selection patterns.

Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis

Science.gov (United States)

Pérez, Noel; Guevara, Miguel A.; Silva, Augusto

2013-02-01

This work addresses the issue of variable selection within the context of breast cancer classification with mammography. A comprehensive repository of feature vectors was used including a hybrid subset gathering image-based and clinical features. It aimed to gather experimental evidence of variable selection in terms of cardinality, type and find a classification scheme that provides the best performance over the Area Under Receiver Operating Characteristics Curve (AUC) scores using the ranked features subset. We evaluated and classified a total of 300 subsets of features formed by the application of Chi-Square Discretization, Information-Gain, One-Rule and RELIEF methods in association with Feed-Forward Backpropagation Neural Network (FFBP), Support Vector Machine (SVM) and Decision Tree J48 (DTJ48) Machine Learning Algorithms (MLA) for a comparative performance evaluation based on AUC scores. A variable selection analysis was performed for Single-View Ranking and Multi-View Ranking groups of features. Features subsets representing Microcalcifications (MCs), Masses and both MCs and Masses lesions achieved AUC scores of 0.91, 0.954 and 0.934 respectively. Experimental evidence demonstrated that classification performance was improved by combining image-based and clinical features. The most important clinical and image-based features were StromaDistortion and Circularity respectively. Other less important but worth to use due to its consistency were Contrast, Perimeter, Microcalcification, Correlation and Elongation.

A New Variable Selection Method Based on Mutual Information Maximization by Replacing Collinear Variables for Nonlinear Quantitative Structure-Property Relationship Models

Energy Technology Data Exchange (ETDEWEB)

Ghasemi, Jahan B.; Zolfonoun, Ehsan [Toosi University of Technology, Tehran (Korea, Republic of)

2012-05-15

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

A New Variable Selection Method Based on Mutual Information Maximization by Replacing Collinear Variables for Nonlinear Quantitative Structure-Property Relationship Models

International Nuclear Information System (INIS)

Ghasemi, Jahan B.; Zolfonoun, Ehsan

2012-01-01

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms

Effects of musical tempo on physiological, affective, and perceptual variables and performance of self-selected walking pace.

Science.gov (United States)

Almeida, Flávia Angélica Martins; Nunes, Renan Felipe Hartmann; Ferreira, Sandro Dos Santos; Krinski, Kleverton; Elsangedy, Hassan Mohamed; Buzzachera, Cosme Franklin; Alves, Ragami Chaves; Gregorio da Silva, Sergio

2015-06-01

[Purpose] This study investigated the effects of musical tempo on physiological, affective, and perceptual responses as well as the performance of self-selected walking pace. [Subjects] The study included 28 adult women between 29 and 51 years old. [Methods] The subjects were divided into three groups: no musical stimulation group (control), and 90 and 140 beats per minute musical tempo groups. Each subject underwent three experimental sessions: involved familiarization with the equipment, an incremental test to exhaustion, and a 30-min walk on a treadmill at a self-selected pace, respectively. During the self-selected walking session, physiological, perceptual, and affective variables were evaluated, and walking performance was evaluated at the end. [Results] There were no significant differences in physiological variables or affective response among groups. However, there were significant differences in perceptual response and walking performance among groups. [Conclusion] Fast music (140 beats per minute) promotes a higher rating of perceived exertion and greater performance in self-selected walking pace without significantly altering physiological variables or affective response.

Portfolio Selection Based on Distance between Fuzzy Variables

Directory of Open Access Journals (Sweden)

Weiyi Qian

2014-01-01

Full Text Available This paper researches portfolio selection problem in fuzzy environment. We introduce a new simple method in which the distance between fuzzy variables is used to measure the divergence of fuzzy investment return from a prior one. Firstly, two new mathematical models are proposed by expressing divergence as distance, investment return as expected value, and risk as variance and semivariance, respectively. Secondly, the crisp forms of the new models are also provided for different types of fuzzy variables. Finally, several numerical examples are given to illustrate the effectiveness of the proposed approach.

Genome-wide prediction of traits with different genetic architecture through efficient variable selection.

Science.gov (United States)

Wimmer, Valentin; Lehermeier, Christina; Albrecht, Theresa; Auinger, Hans-Jürgen; Wang, Yu; Schön, Chris-Carolin

2013-10-01

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.

Selecting minimum dataset soil variables using PLSR as a regressive multivariate method

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Stellacci, Anna Maria; Armenise, Elena; Castellini, Mirko; Rossi, Roberta; Vitti, Carolina; Leogrande, Rita; De Benedetto, Daniela; Ferrara, Rossana M.; Vivaldi, Gaetano A.

2017-04-01

Long-term field experiments and science-based tools that characterize soil status (namely the soil quality indices, SQIs) assume a strategic role in assessing the effect of agronomic techniques and thus in improving soil management especially in marginal environments. Selecting key soil variables able to best represent soil status is a critical step for the calculation of SQIs. Current studies show the effectiveness of statistical methods for variable selection to extract relevant information deriving from multivariate datasets. Principal component analysis (PCA) has been mainly used, however supervised multivariate methods and regressive techniques are progressively being evaluated (Armenise et al., 2013; de Paul Obade et al., 2016; Pulido Moncada et al., 2014). The present study explores the effectiveness of partial least square regression (PLSR) in selecting critical soil variables, using a dataset comparing conventional tillage and sod-seeding on durum wheat. The results were compared to those obtained using PCA and stepwise discriminant analysis (SDA). The soil data derived from a long-term field experiment in Southern Italy. On samples collected in April 2015, the following set of variables was quantified: (i) chemical: total organic carbon and nitrogen (TOC and TN), alkali-extractable C (TEC and humic substances - HA-FA), water extractable N and organic C (WEN and WEOC), Olsen extractable P, exchangeable cations, pH and EC; (ii) physical: texture, dry bulk density (BD), macroporosity (Pmac), air capacity (AC), and relative field capacity (RFC); (iii) biological: carbon of the microbial biomass quantified with the fumigation-extraction method. PCA and SDA were previously applied to the multivariate dataset (Stellacci et al., 2016). PLSR was carried out on mean centered and variance scaled data of predictors (soil variables) and response (wheat yield) variables using the PLS procedure of SAS/STAT. In addition, variable importance for projection (VIP

A New Variable Weighting and Selection Procedure for K-Means Cluster Analysis

Science.gov (United States)

Steinley, Douglas; Brusco, Michael J.

2008-01-01

A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these…

Punishment induced behavioural and neurophysiological variability reveals dopamine-dependent selection of kinematic movement parameters

Science.gov (United States)

Galea, Joseph M.; Ruge, Diane; Buijink, Arthur; Bestmann, Sven; Rothwell, John C.

2013-01-01

Action selection describes the high-level process which selects between competing movements. In animals, behavioural variability is critical for the motor exploration required to select the action which optimizes reward and minimizes cost/punishment, and is guided by dopamine (DA). The aim of this study was to test in humans whether low-level movement parameters are affected by punishment and reward in ways similar to high-level action selection. Moreover, we addressed the proposed dependence of behavioural and neurophysiological variability on DA, and whether this may underpin the exploration of kinematic parameters. Participants performed an out-and-back index finger movement and were instructed that monetary reward and punishment were based on its maximal acceleration (MA). In fact, the feedback was not contingent on the participant’s behaviour but pre-determined. Blocks highly-biased towards punishment were associated with increased MA variability relative to blocks with either reward or without feedback. This increase in behavioural variability was positively correlated with neurophysiological variability, as measured by changes in cortico-spinal excitability with transcranial magnetic stimulation over the primary motor cortex. Following the administration of a DA-antagonist, the variability associated with punishment diminished and the correlation between behavioural and neurophysiological variability no longer existed. Similar changes in variability were not observed when participants executed a pre-determined MA, nor did DA influence resting neurophysiological variability. Thus, under conditions of punishment, DA-dependent processes influence the selection of low-level movement parameters. We propose that the enhanced behavioural variability reflects the exploration of kinematic parameters for less punishing, or conversely more rewarding, outcomes. PMID:23447607

Curve fitting and modeling with splines using statistical variable selection techniques

Science.gov (United States)

Smith, P. L.

1982-01-01

The successful application of statistical variable selection techniques to fit splines is demonstrated. Major emphasis is given to knot selection, but order determination is also discussed. Two FORTRAN backward elimination programs, using the B-spline basis, were developed. The program for knot elimination is compared in detail with two other spline-fitting methods and several statistical software packages. An example is also given for the two-variable case using a tensor product basis, with a theoretical discussion of the difficulties of their use.

The use of vector bootstrapping to improve variable selection precision in Lasso models

NARCIS (Netherlands)

Laurin, C.; Boomsma, D.I.; Lubke, G.H.

2016-01-01

The Lasso is a shrinkage regression method that is widely used for variable selection in statistical genetics. Commonly, K-fold cross-validation is used to fit a Lasso model. This is sometimes followed by using bootstrap confidence intervals to improve precision in the resulting variable selections.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

Science.gov (United States)

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

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Hui Wang

2017-10-01

Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

Ethnic variability in adiposity and cardiovascular risk: the variable disease selection hypothesis.

Science.gov (United States)

Wells, Jonathan C K

2009-02-01

Evidence increasingly suggests that ethnic differences in cardiovascular risk are partly mediated by adipose tissue biology, which refers to the regional distribution of adipose tissue and its differential metabolic activity. This paper proposes a novel evolutionary hypothesis for ethnic genetic variability in adipose tissue biology. Whereas medical interest focuses on the harmful effect of excess fat, the value of adipose tissue is greatest during chronic energy insufficiency. Following Neel's influential paper on the thrifty genotype, proposed to have been favoured by exposure to cycles of feast and famine, much effort has been devoted to searching for genetic markers of 'thrifty metabolism'. However, whether famine-induced starvation was the primary selective pressure on adipose tissue biology has been questioned, while the notion that fat primarily represents a buffer against starvation appears inconsistent with historical records of mortality during famines. This paper reviews evidence for the role played by adipose tissue in immune function and proposes that adipose tissue biology responds to selective pressures acting through infectious disease. Different diseases activate the immune system in different ways and induce different metabolic costs. It is hypothesized that exposure to different infectious disease burdens has favoured ethnic genetic variability in the anatomical location of, and metabolic profile of, adipose tissue depots.

Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

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Thandi Kapwata

2016-11-01

Full Text Available Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

Energy Technology Data Exchange (ETDEWEB)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brink, Henrik [Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O (Denmark); Long, James P.; Rice, John, E-mail: jwrichar@stat.berkeley.edu [Statistics Department, University of California, Berkeley, CA 94720-7450 (United States)

2012-01-10

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

International Nuclear Information System (INIS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Science.gov (United States)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Uninformative variable elimination assisted by Gram-Schmidt Orthogonalization/successive projection algorithm for descriptor selection in QSAR

DEFF Research Database (Denmark)

Omidikia, Nematollah; Kompany-Zareh, Mohsen

2013-01-01

Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as colline......Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well...... as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram-Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination...

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Directory of Open Access Journals (Sweden)

Jun-He Yang

2017-01-01

Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model

Directory of Open Access Journals (Sweden)

Ge-Jin Chu

2014-01-01

Full Text Available Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq  (1/2select key risk factors in the Cox’s proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL, the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

Science.gov (United States)

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

Effects of selected design variables on three ramp, external compression inlet performance. [boundary layer control bypasses, and mass flow rate

Science.gov (United States)

Kamman, J. H.; Hall, C. L.

1975-01-01

Two inlet performance tests and one inlet/airframe drag test were conducted in 1969 at the NASA-Ames Research Center. The basic inlet system was two-dimensional, three ramp (overhead), external compression, with variable capture area. The data from these tests were analyzed to show the effects of selected design variables on the performance of this type of inlet system. The inlet design variables investigated include inlet bleed, bypass, operating mass flow ratio, inlet geometry, and variable capture area.

Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.

Science.gov (United States)

Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H

2014-12-30

For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

Directory of Open Access Journals (Sweden)

Mabaso Musawenkosi LH

2007-09-01

produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.

A QSAR Study of Environmental Estrogens Based on a Novel Variable Selection Method

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Aiqian Zhang

2012-05-01

Full Text Available A large number of descriptors were employed to characterize the molecular structure of 53 natural, synthetic, and environmental chemicals which are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones and may thus pose a serious threat to the health of humans and wildlife. In this work, a robust quantitative structure-activity relationship (QSAR model with a novel variable selection method has been proposed for the effective estrogens. The variable selection method is based on variable interaction (VSMVI with leave-multiple-out cross validation (LMOCV to select the best subset. During variable selection, model construction and assessment, the Organization for Economic Co-operation and Development (OECD principles for regulation of QSAR acceptability were fully considered, such as using an unambiguous multiple-linear regression (MLR algorithm to build the model, using several validation methods to assessment the performance of the model, giving the define of applicability domain and analyzing the outliers with the results of molecular docking. The performance of the QSAR model indicates that the VSMVI is an effective, feasible and practical tool for rapid screening of the best subset from large molecular descriptors.

Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

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Quilty, J.; Adamowski, J. F.

2015-12-01

Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

Social variables exert selective pressures in the evolution and form of primate mimetic musculature.

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Burrows, Anne M; Li, Ly; Waller, Bridget M; Micheletta, Jerome

2016-04-01

Mammals use their faces in social interactions more so than any other vertebrates. Primates are an extreme among most mammals in their complex, direct, lifelong social interactions and their frequent use of facial displays is a means of proximate visual communication with conspecifics. The available repertoire of facial displays is primarily controlled by mimetic musculature, the muscles that move the face. The form of these muscles is, in turn, limited by and influenced by phylogenetic inertia but here we use examples, both morphological and physiological, to illustrate the influence that social variables may exert on the evolution and form of mimetic musculature among primates. Ecomorphology is concerned with the adaptive responses of morphology to various ecological variables such as diet, foliage density, predation pressures, and time of day activity. We present evidence that social variables also exert selective pressures on morphology, specifically using mimetic muscles among primates as an example. Social variables include group size, dominance 'style', and mating systems. We present two case studies to illustrate the potential influence of social behavior on adaptive morphology of mimetic musculature in primates: (1) gross morphology of the mimetic muscles around the external ear in closely related species of macaque (Macaca mulatta and Macaca nigra) characterized by varying dominance styles and (2) comparative physiology of the orbicularis oris muscle among select ape species. This muscle is used in both facial displays/expressions and in vocalizations/human speech. We present qualitative observations of myosin fiber-type distribution in this muscle of siamang (Symphalangus syndactylus), chimpanzee (Pan troglodytes), and human to demonstrate the potential influence of visual and auditory communication on muscle physiology. In sum, ecomorphologists should be aware of social selective pressures as well as ecological ones, and that observed morphology might

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

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Ueki Masao

2012-05-01

Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

Two-step variable selection in quantile regression models

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FAN Yali

2015-06-01

Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.

Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.

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Storlie, Curtis B; Bondell, Howard D; Reich, Brian J; Zhang, Hao Helen

2011-04-01

Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.

Protein construct storage: Bayesian variable selection and prediction with mixtures.

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Clyde, M A; Parmigiani, G

1998-07-01

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

Taylor Series Trajectory Calculations Including Oblateness Effects and Variable Atmospheric Density

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Scott, James R.

2011-01-01

Taylor series integration is implemented in NASA Glenn's Spacecraft N-body Analysis Program, and compared head-to-head with the code's existing 8th- order Runge-Kutta Fehlberg time integration scheme. This paper focuses on trajectory problems that include oblateness and/or variable atmospheric density. Taylor series is shown to be significantly faster and more accurate for oblateness problems up through a 4x4 field, with speedups ranging from a factor of 2 to 13. For problems with variable atmospheric density, speedups average 24 for atmospheric density alone, and average 1.6 to 8.2 when density and oblateness are combined.

The XRF spectrometer and the selection of analysis conditions (instrumental variables)

International Nuclear Information System (INIS)

Willis, J.P.

2002-01-01

Full text: This presentation will begin with a brief discussion of EDXRF and flat- and curved-crystal WDXRF spectrometers, contrasting the major differences between the three types. The remainder of the presentation will contain a detailed overview of the choice and settings of the many instrumental variables contained in a modern WDXRF spectrometer, and will discuss critically the choices facing the analyst in setting up a WDXRF spectrometer for different elements and applications. In particular it will discuss the choice of tube target (when a choice is possible), the kV and mA settings, tube filters, collimator masks, collimators, analyzing crystals, secondary collimators, detectors, pulse height selection, X-ray path medium (air, nitrogen, vacuum or helium), counting times for peak and background positions and their effect on counting statistics and lower limit of detection (LLD). The use of Figure of Merit (FOM) calculations to objectively choose the best combination of instrumental variables also will be discussed. This presentation will be followed by a shorter session on a subsequent day entitled - A Selection of XRF Conditions - Practical Session, where participants will be given the opportunity to discuss in groups the selection of the best instrumental variables for three very diverse applications. Copyright (2002) Australian X-ray Analytical Association Inc

Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy

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Duan, Fajie; Fu, Xiao; Jiang, Jiajia; Huang, Tingting; Ma, Ling; Zhang, Cong

2018-05-01

In this work, an automatic variable selection method for quantitative analysis of soil samples using laser-induced breakdown spectroscopy (LIBS) is proposed, which is based on full spectrum correction (FSC) and modified iterative predictor weighting-partial least squares (mIPW-PLS). The method features automatic selection without artificial processes. To illustrate the feasibility and effectiveness of the method, a comparison with genetic algorithm (GA) and successive projections algorithm (SPA) for different elements (copper, barium and chromium) detection in soil was implemented. The experimental results showed that all the three methods could accomplish variable selection effectively, among which FSC-mIPW-PLS required significantly shorter computation time (12 s approximately for 40,000 initial variables) than the others. Moreover, improved quantification models were got with variable selection approaches. The root mean square errors of prediction (RMSEP) of models utilizing the new method were 27.47 (copper), 37.15 (barium) and 39.70 (chromium) mg/kg, which showed comparable prediction effect with GA and SPA.

A Robust Supervised Variable Selection for Noisy High-Dimensional Data

Czech Academy of Sciences Publication Activity Database

Kalina, Jan; Schlenker, Anna

2015-01-01

Roč. 2015, Article 320385 (2015), s. 1-10 ISSN 2314-6133 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : dimensionality reduction * variable selection * robustness Subject RIV: BA - General Mathematics Impact factor: 2.134, year: 2015

Variable selection in the explorative analysis of several data blocks in metabolomics

DEFF Research Database (Denmark)

Karaman, İbrahim; Nørskov, Natalja; Yde, Christian Clement

highly correlated data sets in one integrated approach. Due to the high number of variables in data sets from metabolomics (both raw data and after peak picking) the selection of important variables in an explorative analysis is difficult, especially when different data sets of metabolomics data need...... to be related. Tools for the handling of mental overflow minimising false discovery rates both by using statistical and biological validation in an integrative approach are needed. In this paper different strategies for variable selection were considered with respect to false discovery and the possibility...... for biological validation. The data set used in this study is metabolomics data from an animal intervention study. The aim of the metabolomics study was to investigate the metabolic profile in pigs fed various cereal fractions with special attention to the metabolism of lignans using NMR and LC-MS based...

EFFECT OF CORE TRAINING ON SELECTED HEMATOLOGICAL VARIABLES AMONG BASKETBALL PLAYERS

OpenAIRE

K. Rejinadevi; Dr. C. Ramesh

2017-01-01

The purpose of the study was to find out the effect of core training on selected haematological variables among basketball players. For the purpose of the study forty men basketball players were selected as subjects from S.V.N College and Arul Anandar College, Madurai, Tamilnadu at random and their age ranged from 18 to 25 years. The selected subjects are divided in to two groups of twenty subjects each. Group I acted as core training group and Group II acted as control group. The experimenta...

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention.

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Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L

2013-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. We have hypothesized that the changes in neural activity observed during increased cholinergic function reflect an increase in neural efficiency that leads to improved task performance. The current study tested this hypothesis by assessing neural efficiency based on cholinergically-mediated effects on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover fMRI study. Following an infusion of physostigmine (1 mg/h) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Physostigmine administration also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus

STEPWISE SELECTION OF VARIABLES IN DEA USING CONTRIBUTION LOADS

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Fernando Fernandez-Palacin

Full Text Available ABSTRACT In this paper, we propose a new methodology for variable selection in Data Envelopment Analysis (DEA. The methodology is based on an internal measure which evaluates the contribution of each variable in the calculation of the efficiency scores of DMUs. In order to apply the proposed method, an algorithm, known as “ADEA”, was developed and implemented in R. Step by step, the algorithm maximizes the load of the variable (input or output which contribute least to the calculation of the efficiency scores, redistributing the weights of the variables without altering the efficiency scores of the DMUs. Once the weights have been redistributed, if the lower contribution does not reach a previously given critical value, a variable with minimum contribution will be removed from the model and, as a result, the DEA will be solved again. The algorithm will stop when all variables reach a given contribution load to the DEA or until no more variables can be removed. In this way and contrary to what is usual, the algorithm provides a clear stop rule. In both cases, the efficiencies obtained from the DEA will be considered suitable and rightly interpreted in terms of the remaining variables, indicating the load themselves; moreover, the algorithm will provide a sequence of alternative nested models - potential solutions - that could be evaluated according to external criterion. To illustrate the procedure, we have applied the methodology proposed to obtain a research ranking of Spanish public universities. In this case, at each step of the algorithm, the critical value is obtained based on a simulation study.

Variable selection in PLSR and extensions to a multi-block setting for metabolomics data

DEFF Research Database (Denmark)

Karaman, İbrahim; Hedemann, Mette Skou; Knudsen, Knud Erik Bach

When applying LC-MS or NMR spectroscopy in metabolomics studies, high-dimensional data are generated and effective tools for variable selection are needed in order to detect the important metabolites. Methods based on sparsity combined with PLSR have recently attracted attention in the field...... of genomics [1]. They became quickly well established in the field of statistics because a close relationship to elastic net has been established. In sparse variable selection combined with PLSR, a soft thresholding is applied on each loading weight separately. In the field of chemometrics Jack-knifing has...... been introduced for variable selection in PLSR [2]. Jack-knifing has been frequently applied in the field of spectroscopy and is implemented in software tools like The Unscrambler. In Jack-knifing uncertainty estimates of regression coefficients are estimated and a t-test is applied on these estimates...

Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection

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Lianqing Zhu

2018-01-01

Full Text Available In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection.

The Effects of Variability and Risk in Selection Utility Analysis: An Empirical Comparison.

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Rich, Joseph R.; Boudreau, John W.

1987-01-01

Investigated utility estimate variability for the selection utility of using the Programmer Aptitude Test to select computer programmers. Comparison of Monte Carlo results to other risk assessment approaches (sensitivity analysis, break-even analysis, algebraic derivation of the distribtion) suggests that distribution information provided by Monte…

Mahalanobis distance and variable selection to optimize dose response

International Nuclear Information System (INIS)

Moore, D.H. II; Bennett, D.E.; Wyrobek, A.J.; Kranzler, D.

1979-01-01

A battery of statistical techniques are combined to improve detection of low-level dose response. First, Mahalanobis distances are used to classify objects as normal or abnormal. Then the proportion classified abnormal is regressed on dose. Finally, a subset of regressor variables is selected which maximizes the slope of the dose response line. Use of the techniques is illustrated by application to mouse sperm damaged by low doses of x-rays

r2VIM: A new variable selection method for random forests in genome-wide association studies.

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Szymczak, Silke; Holzinger, Emily; Dasgupta, Abhijit; Malley, James D; Molloy, Anne M; Mills, James L; Brody, Lawrence C; Stambolian, Dwight; Bailey-Wilson, Joan E

2016-01-01

Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses. We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS. The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.

Isoenzymatic variability in tropical maize populations under reciprocal recurrent selection

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Pinto Luciana Rossini

2003-01-01

Full Text Available Maize (Zea mays L. is one of the crops in which the genetic variability has been extensively studied at isoenzymatic loci. The genetic variability of the maize populations BR-105 and BR-106, and the synthetics IG-3 and IG-4, obtained after one cycle of a high-intensity reciprocal recurrent selection (RRS, was investigated at seven isoenzymatic loci. A total of twenty alleles were identified, and most of the private alleles were found in the BR-106 population. One cycle of reciprocal recurrent selection (RRS caused reductions of 12% in the number of alleles in both populations. Changes in allele frequencies were also observed between populations and synthetics, mainly for the Est 2 locus. Populations presented similar values for the number of alleles per locus, percentage of polymorphic loci, and observed and expected heterozygosities. A decrease of the genetic variation values was observed for the synthetics as a consequence of genetic drift effects and reduction of the effective population sizes. The distribution of the genetic diversity within and between populations revealed that most of the diversity was maintained within them, i.e. BR-105 x BR-106 (G ST = 3.5% and IG-3 x IG-4 (G ST = 4.0%. The genetic distances between populations and synthetics increased approximately 21%. An increase in the genetic divergence between the populations occurred without limiting new selection procedures.

Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection

KAUST Repository

Chen, Lisha; Huang, Jianhua Z.

2012-01-01

and hence improves predictive accuracy. We propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty. We apply a group-lasso type penalty that treats each row of the matrix of the regression coefficients as a group

Penalized regression procedures for variable selection in the potential outcomes framework.

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Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L

2015-05-10

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple 'impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data, and imputation are drawn. A difference least absolute shrinkage and selection operator algorithm is defined, along with its multiple imputation analogs. The procedures are illustrated using a well-known right-heart catheterization dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Impact of including surface currents on simulation of Indian Ocean variability with the POAMA coupled model

Energy Technology Data Exchange (ETDEWEB)

Zhao, Mei; Wang, Guomin; Hendon, Harry H.; Alves, Oscar [Bureau of Meteorology, Centre for Australian Weather and Climate Research, Melbourne (Australia)

2011-04-15

Impacts on the coupled variability of the Indo-Pacific by including the effects of surface currents on surface stress are explored in four extended integrations of an experimental version of the Bureau of Meteorology's coupled seasonal forecast model POAMA. The first pair of simulations differs only in their treatment of momentum coupling: one version includes the effects of surface currents on the surface stress computation and the other does not. The version that includes the effect of surface currents has less mean-state bias in the equatorial Pacific cold tongue but produces relatively weak coupled variability in the Tropics, especially that related to the Indian Ocean dipole (IOD) and El Nino/Southern Oscillation (ENSO). The version without the effects of surface currents has greater bias in the Pacific cold tongue but stronger IOD and ENSO variability. In order to diagnose the role of changes in local coupling from changes in remote forcing by ENSO for causing changes in IOD variability, a second set of simulations is conducted where effects of surface currents are included only in the Indian Ocean and only in the Pacific Ocean. IOD variability is found to be equally reduced by inclusion of the local effects of surface currents in the Indian Ocean and by the reduction of ENSO variability as a result of including effects of surface currents in the Pacific. Some implications of these results for predictability of the IOD and its dependence on ENSO, and for ocean subsurface data assimilation are discussed. (orig.)

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

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Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

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Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

2015-01-01

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of

Data re-arranging techniques leading to proper variable selections in high energy physics

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Kůs, Václav; Bouř, Petr

2017-12-01

We introduce a new data based approach to homogeneity testing and variable selection carried out in high energy physics experiments, where one of the basic tasks is to test the homogeneity of weighted samples, mainly the Monte Carlo simulations (weighted) and real data measurements (unweighted). This technique is called ’data re-arranging’ and it enables variable selection performed by means of the classical statistical homogeneity tests such as Kolmogorov-Smirnov, Anderson-Darling, or Pearson’s chi-square divergence test. P-values of our variants of homogeneity tests are investigated and the empirical verification through 46 dimensional high energy particle physics data sets is accomplished under newly proposed (equiprobable) quantile binning. Particularly, the procedure of homogeneity testing is applied to re-arranged Monte Carlo samples and real DATA sets measured at the particle accelerator Tevatron in Fermilab at DØ experiment originating from top-antitop quark pair production in two decay channels (electron, muon) with 2, 3, or 4+ jets detected. Finally, the variable selections in the electron and muon channels induced by the re-arranging procedure for homogeneity testing are provided for Tevatron top-antitop quark data sets.

Variable mechanical ventilation.

Science.gov (United States)

Fontela, Paula Caitano; Prestes, Renata Bernardy; Forgiarini, Luiz Alberto; Friedman, Gilberto

2017-01-01

To review the literature on the use of variable mechanical ventilation and the main outcomes of this technique. Search, selection, and analysis of all original articles on variable ventilation, without restriction on the period of publication and language, available in the electronic databases LILACS, MEDLINE®, and PubMed, by searching the terms "variable ventilation" OR "noisy ventilation" OR "biologically variable ventilation". A total of 36 studies were selected. Of these, 24 were original studies, including 21 experimental studies and three clinical studies. Several experimental studies reported the beneficial effects of distinct variable ventilation strategies on lung function using different models of lung injury and healthy lungs. Variable ventilation seems to be a viable strategy for improving gas exchange and respiratory mechanics and preventing lung injury associated with mechanical ventilation. However, further clinical studies are necessary to assess the potential of variable ventilation strategies for the clinical improvement of patients undergoing mechanical ventilation.

How to include the variability of TMS responses in simulations: a speech mapping case study

Science.gov (United States)

De Geeter, N.; Lioumis, P.; Laakso, A.; Crevecoeur, G.; Dupré, L.

2016-11-01

When delivered over a specific cortical site, TMS can temporarily disrupt the ongoing process in that area. This allows mapping of speech-related areas for preoperative evaluation purposes. We numerically explore the observed variability of TMS responses during a speech mapping experiment performed with a neuronavigation system. We selected four cases with very small perturbations in coil position and orientation. In one case (E) a naming error occurred, while in the other cases (NEA, B, C) the subject appointed the images as smoothly as without TMS. A realistic anisotropic head model was constructed of the subject from T1-weighted and diffusion-weighted MRI. The induced electric field distributions were computed, associated to the coil parameters retrieved from the neuronavigation system. Finally, the membrane potentials along relevant white matter fibre tracts, extracted from DTI-based tractography, were computed using a compartmental cable equation. While only minor differences could be noticed between the induced electric field distributions of the four cases, computing the corresponding membrane potentials revealed different subsets of tracts were activated. A single tract was activated for all coil positions. Another tract was only triggered for case E. NEA induced action potentials in 13 tracts, while NEB stimulated 11 tracts and NEC one. The calculated results are certainly sensitive to the coil specifications, demonstrating the observed variability in this study. However, even though a tract connecting Broca’s with Wernicke’s area is only triggered for the error case, further research is needed on other study cases and on refining the neural model with synapses and network connections. Case- and subject-specific modelling that includes both electromagnetic fields and neuronal activity enables demonstration of the variability in TMS experiments and can capture the interaction with complex neural networks.

Seleção de variáveis em QSAR Variable selection in QSAR

Directory of Open Access Journals (Sweden)

Márcia Miguel Castro Ferreira

2002-05-01

Full Text Available The process of building mathematical models in quantitative structure-activity relationship (QSAR studies is generally limited by the size of the dataset used to select variables from. For huge datasets, the task of selecting a given number of variables that produces the best linear model can be enormous, if not unfeasible. In this case, some methods can be used to separate good parameter combinations from the bad ones. In this paper three methodologies are analyzed: systematic search, genetic algorithm and chemometric methods. These methods have been exposed and discussed through practical examples.

IRAS variables as galactic structure tracers - Classification of the bright variables

Science.gov (United States)

Allen, L. E.; Kleinmann, S. G.; Weinberg, M. D.

1993-01-01

The characteristics of the 'bright infrared variables' (BIRVs), a sample consisting of the 300 brightest stars in the IRAS Point Source Catalog with IRAS variability index VAR of 98 or greater, are investigated with the purpose of establishing which of IRAS variables are AGB stars (e.g., oxygen-rich Miras and carbon stars, as was assumed by Weinberg (1992)). Results of the analysis of optical, infrared, and microwave spectroscopy of these stars indicate that, out of 88 stars in the BIRV sample identified with cataloged variables, 86 can be classified as Miras. Results of a similar analysis performed for a color-selected sample of stars, using the color limits employed by Habing (1988) to select AGB stars, showed that, out of 52 percent of classified stars, 38 percent are non-AGB stars, including H II regions, planetary nebulae, supergiants, and young stellar objects, indicating that studies using color-selected samples are subject to misinterpretation.

Selection of controlled variables in bioprocesses. Application to a SHARON-Anammox process for autotrophic nitrogen removal

DEFF Research Database (Denmark)

Mauricio Iglesias, Miguel; Valverde Perez, Borja; Sin, Gürkan

Selecting the right controlled variables in a bioprocess is challenging since the objectives of the process (yields, product or substrate concentration) are difficult to relate with a given actuator. We apply here process control tools that can be used to assist in the selection of controlled var...... variables to the case of the SHARON-Anammox process for autotrophic nitrogen removal....

Comparison of Sparse and Jack-knife partial least squares regression methods for variable selection

DEFF Research Database (Denmark)

Karaman, Ibrahim; Qannari, El Mostafa; Martens, Harald

2013-01-01

The objective of this study was to compare two different techniques of variable selection, Sparse PLSR and Jack-knife PLSR, with respect to their predictive ability and their ability to identify relevant variables. Sparse PLSR is a method that is frequently used in genomics, whereas Jack-knife PL...

HEART RATE VARIABILITY CLASSIFICATION USING SADE-ELM CLASSIFIER WITH BAT FEATURE SELECTION

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R Kavitha

2017-07-01

Full Text Available The electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient’s cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient’s body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self- Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN to be able to increase the accuracy of the effect for HRV transmission classification. Hence, it is observed that the experiment carried out unveils that the precision is improved by the SADE-ELM method and concurrently optimizes the computation time.

Selection Component Analysis of Natural Polymorphisms using Population Samples Including Mother-Offspring Combinations, II

DEFF Research Database (Denmark)

Jarmer, Hanne Østergaard; Christiansen, Freddy Bugge

1981-01-01

Population samples including mother-offspring combinations provide information on the selection components: zygotic selection, sexual selection, gametic seletion and fecundity selection, on the mating pattern, and on the deviation from linkage equilibrium among the loci studied. The theory...

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

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Benjamin Hofner

2016-10-01

Full Text Available Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from the Comprehensive R Archive Network (CRAN at https://CRAN.R-project.org/package=gamboostLSS.

Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts

Science.gov (United States)

Muller, Benjamin J.; Cade, Brian S.; Schwarzkoph, Lin

2018-01-01

Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

PLS-based and regularization-based methods for the selection of relevant variables in non-targeted metabolomics data

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Renata Bujak

2016-07-01

Full Text Available Non-targeted metabolomics constitutes a part of systems biology and aims to determine many metabolites in complex biological samples. Datasets obtained in non-targeted metabolomics studies are multivariate and high-dimensional due to the sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA without and with multiple testing correction as well as least absolute shrinkage and selection operator (LASSO were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction, selected 46 and 218 variables based on VIP criteria using Pareto and UV scaling, respectively. In the case of the PH study, 217 and 320 variables were selected based on VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built with multiple testing correction, selected 4 and 19 variables as statistically significant in terms of Pareto and UV scaling, respectively. For PH study, 14 and 18 variables were selected based on VIP criteria in terms of Pareto and UV scaling, respectively. Additionally, the concept and fundaments of the least absolute shrinkage and selection operator (LASSO with bootstrap procedure evaluating reproducibility of results, was demonstrated. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3% and 100%. However, apart from the popularity of PLS-DA and OPLS-DA methods in metabolomics, it should be highlighted that they do not control type I or type II error, but only arbitrarily establish a cut-off value for PLS-DA loadings

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

DEFF Research Database (Denmark)

Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line H.

2017-01-01

Principal component analysis (PCA) is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA...

How novice, skilled and advanced clinical researchers include variables in a case report form for clinical research: a qualitative study.

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Chu, Hongling; Zeng, Lin; Fetters, Micheal D; Li, Nan; Tao, Liyuan; Shi, Yanyan; Zhang, Hua; Wang, Xiaoxiao; Li, Fengwei; Zhao, Yiming

2017-09-18

Despite varying degrees in research training, most academic clinicians are expected to conduct clinical research. The objective of this research was to understand how clinical researchers of different skill levels include variables in a case report form for their clinical research. The setting for this research was a major academic institution in Beijing, China. The target population was clinical researchers with three levels of experience, namely, limited clinical research experience, clinicians with rich clinical research experience and clinical research experts. Using a qualitative approach, we conducted 13 individual interviews (face to face) and one group interview (n=4) with clinical researchers from June to September 2016. Based on maximum variation sampling to identify researchers with three levels of research experience: eight clinicians with limited clinical research experience, five clinicians with rich clinical research experience and four clinical research experts. These 17 researchers had diverse hospital-based medical specialties and or specialisation in clinical research. Our analysis yields a typology of three processes developing a case report form that varies according to research experience level. Novice clinician researchers often have an incomplete protocol or none at all, and conduct data collection and publication based on a general framework. Experienced clinician researchers include variables in the case report form based on previous experience with attention to including domains or items at risk for omission and by eliminating unnecessary variables. Expert researchers consider comprehensively in advance data collection and implementation needs and plan accordingly. These results illustrate increasing levels of sophistication in research planning that increase sophistication in selection for variables in the case report form. These findings suggest that novice and intermediate-level researchers could benefit by emulating the comprehensive

Current Debates on Variability in Child Welfare Decision-Making: A Selected Literature Review

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Emily Keddell

2014-11-01

Full Text Available This article considers selected drivers of decision variability in child welfare decision-making and explores current debates in relation to these drivers. Covering the related influences of national orientation, risk and responsibility, inequality and poverty, evidence-based practice, constructions of abuse and its causes, domestic violence and cognitive processes, it discusses the literature in regards to how each of these influences decision variability. It situates these debates in relation to the ethical issue of variability and the equity issues that variability raises. I propose that despite the ecological complexity that drives decision variability, that improving internal (within-country decision consistency is still a valid goal. It may be that the use of annotated case examples, kind learning systems, and continued commitments to the social justice issues of inequality and individualisation can contribute to this goal.

A flow system for generation of concentration perturbation in two-dimensional correlation near-infrared spectroscopy: application to variable selection in multivariate calibration.

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Pereira, Claudete Fernandes; Pasquini, Celio

2010-05-01

A flow system is proposed to produce a concentration perturbation in liquid samples, aiming at the generation of two-dimensional correlation near-infrared spectra. The system presents advantages in relation to batch systems employed for the same purpose: the experiments are accomplished in a closed system; application of perturbation is rapid and easy; and the experiments can be carried out with micro-scale volumes. The perturbation system has been evaluated in the investigation and selection of relevant variables for multivariate calibration models for the determination of quality parameters of gasoline, including ethanol content, MON (motor octane number), and RON (research octane number). The main advantage of this variable selection approach is the direct association between spectral features and chemical composition, allowing easy interpretation of the regression models.

Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten

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Lü, Chengxu; Jiang, Xunpeng; Zhou, Xingfan; Zhang, Yinqiao; Zhang, Naiqian; Wei, Chongfeng; Mao, Wenhua

2017-10-01

Wet gluten is a useful quality indicator for wheat, and short wave near infrared spectroscopy (NIRS) is a high performance technique with the advantage of economic rapid and nondestructive test. To study the feasibility of short wave NIRS analyzing wet gluten directly from wheat seed, 54 representative wheat seed samples were collected and scanned by spectrometer. 8 spectral pretreatment method and genetic algorithm (GA) variable selection method were used to optimize analysis. Both quantitative and qualitative model of wet gluten were built by partial least squares regression and discriminate analysis. For quantitative analysis, normalization is the optimized pretreatment method, 17 wet gluten sensitive variables are selected by GA, and GA model performs a better result than that of all variable model, with R2V=0.88, and RMSEV=1.47. For qualitative analysis, automatic weighted least squares baseline is the optimized pretreatment method, all variable models perform better results than those of GA models. The correct classification rates of 3 class of 30% wet gluten content are 95.45, 84.52, and 90.00%, respectively. The short wave NIRS technique shows potential for both quantitative and qualitative analysis of wet gluten for wheat seed.

Bayesian inference for the genetic control of water deficit tolerance in spring wheat by stochastic search variable selection.

Science.gov (United States)

Safari, Parviz; Danyali, Syyedeh Fatemeh; Rahimi, Mehdi

2018-06-02

Drought is the main abiotic stress seriously influencing wheat production. Information about the inheritance of drought tolerance is necessary to determine the most appropriate strategy to develop tolerant cultivars and populations. In this study, generation means analysis to identify the genetic effects controlling grain yield inheritance in water deficit and normal conditions was considered as a model selection problem in a Bayesian framework. Stochastic search variable selection (SSVS) was applied to identify the most important genetic effects and the best fitted models using different generations obtained from two crosses applying two water regimes in two growing seasons. The SSVS is used to evaluate the effect of each variable on the dependent variable via posterior variable inclusion probabilities. The model with the highest posterior probability is selected as the best model. In this study, the grain yield was controlled by the main effects (additive and non-additive effects) and epistatic. The results demonstrate that breeding methods such as recurrent selection and subsequent pedigree method and hybrid production can be useful to improve grain yield.

Joint Bayesian variable and graph selection for regression models with network-structured predictors

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Peterson, C. B.; Stingo, F. C.; Vannucci, M.

2015-01-01

In this work, we develop a Bayesian approach to perform selection of predictors that are linked within a network. We achieve this by combining a sparse regression model relating the predictors to a response variable with a graphical model describing conditional dependencies among the predictors. The proposed method is well-suited for genomic applications since it allows the identification of pathways of functionally related genes or proteins which impact an outcome of interest. In contrast to previous approaches for network-guided variable selection, we infer the network among predictors using a Gaussian graphical model and do not assume that network information is available a priori. We demonstrate that our method outperforms existing methods in identifying network-structured predictors in simulation settings, and illustrate our proposed model with an application to inference of proteins relevant to glioblastoma survival. PMID:26514925

Variable selection for confounder control, flexible modeling and Collaborative Targeted Minimum Loss-based Estimation in causal inference

Science.gov (United States)

Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan

2015-01-01

This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129

Selection of variables for neural network analysis. Comparisons of several methods with high energy physics data

International Nuclear Information System (INIS)

Proriol, J.

1994-01-01

Five different methods are compared for selecting the most important variables with a view to classifying high energy physics events with neural networks. The different methods are: the F-test, Principal Component Analysis (PCA), a decision tree method: CART, weight evaluation, and Optimal Cell Damage (OCD). The neural networks use the variables selected with the different methods. We compare the percentages of events properly classified by each neural network. The learning set and the test set are the same for all the neural networks. (author)

Demographic Variables and Selective, Sustained Attention and Planning through Cognitive Tasks among Healthy Adults

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Afsaneh Zarghi

2011-04-01

Full Text Available Introduction: Cognitive tasks are considered to be applicable and appropriate in assessing cognitive domains. The purpose of our study is to determine the relationship existence between variables of age, sex and education with selective, sustained attention and planning abilities by means of computerized cognitive tasks among healthy adults. Methods: A cross-sectional study was implemented during 6 months from June to November, 2010 on 84 healthy adults (42 male and 42 female. The whole participants performed computerized CPT, STROOP and TOL tests after being content and trained. Results: The obtained data indicate that there is a significant correlation coefficient between age, sex and education variables (p<0.05. Discussion: The above-mentioned tests can be used to assess selective, sustained attention and planning.

Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies.

Science.gov (United States)

Wang, Haohan; Aragam, Bryon; Xing, Eric P

2018-04-26

A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method. Copyright © 2018. Published by Elsevier Inc.

Chaotic Dynamical State Variables Selection Procedure Based Image Encryption Scheme

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Zia Bashir

2017-12-01

Full Text Available Nowadays, in the modern digital era, the use of computer technologies such as smartphones, tablets and the Internet, as well as the enormous quantity of confidential information being converted into digital form have resulted in raised security issues. This, in turn, has led to rapid developments in cryptography, due to the imminent need for system security. Low-dimensional chaotic systems have low complexity and key space, yet they achieve high encryption speed. An image encryption scheme is proposed that, without compromising the security, uses reasonable resources. We introduced a chaotic dynamic state variables selection procedure (CDSVSP to use all state variables of a hyper-chaotic four-dimensional dynamical system. As a result, less iterations of the dynamical system are required, and resources are saved, thus making the algorithm fast and suitable for practical use. The simulation results of security and other miscellaneous tests demonstrate that the suggested algorithm excels at robustness, security and high speed encryption.

Cataclysmic variables from a ROSAT/2MASS selection - I. Four new intermediate polars

NARCIS (Netherlands)

Gänsicke, B.T.; Marsh, T.R.; Edge, A.; Rodríguez-Gil, P.; Steeghs, D.; Araujo-Betancor, S.; Harlaftis, E.; Giannakis, O.; Pyrzas, S.; Morales-Rueda, L.; Aungwerojwit, A.

2005-01-01

We report the first results from a new search for cataclysmic variables (CVs) using a combined X-ray (ROSAT)/infrared (2MASS) target selection that discriminates against background active galactic nuclei. Identification spectra were obtained at the Isaac Newton Telescope for a total of 174 targets,

Direct-phase-variable model of a synchronous reluctance motor including all slot and winding harmonics

International Nuclear Information System (INIS)

Obe, Emeka S.; Binder, A.

2011-01-01

A detailed model in direct-phase variables of a synchronous reluctance motor operating at mains voltage and frequency is presented. The model includes the stator and rotor slot openings, the actual winding layout and the reluctance rotor geometry. Hence, all mmf and permeance harmonics are taken into account. It is seen that non-negligible harmonics introduced by slots are present in the inductances computed by the winding function procedure. These harmonics are usually ignored in d-q models. The machine performance is simulated in the stator reference frame to depict the difference between this new direct-phase model including all harmonics and the conventional rotor reference frame d-q model. Saturation is included by using a polynomial fitting the variation of d-axis inductance with stator current obtained by finite-element software FEMAG DC (registered) . The detailed phase-variable model can yield torque pulsations comparable to those obtained from finite elements while the d-q model cannot.

A Case for Including Atmospheric Thermodynamic Variables in Wind Turbine Fatigue Loading Parameter Identification

International Nuclear Information System (INIS)

Kelley, Neil D.

1999-01-01

This paper makes the case for establishing efficient predictor variables for atmospheric thermodynamics that can be used to statistically correlate the fatigue accumulation seen on wind turbines. Recently, two approaches to this issue have been reported. One uses multiple linear-regression analysis to establish the relative causality between a number of predictors related to the turbulent inflow and turbine loads. The other approach, using many of the same predictors, applies the technique of principal component analysis. An examination of the ensemble of predictor variables revealed that they were all kinematic in nature; i.e., they were only related to the description of the velocity field. Boundary-layer turbulence dynamics depends upon a description of the thermal field and its interaction with the velocity distribution. We used a series of measurements taken within a multi-row wind farm to demonstrate the need to include atmospheric thermodynamic variables as well as velocity-related ones in the search for efficient turbulence loading predictors in various turbine-operating environments. Our results show that a combination of vertical stability and hub-height mean shearing stress variables meet this need over a period of 10 minutes

Effects of carprofen or meloxicam on selected haemostatic variables in miniature pigs after orthopaedic surgery

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Petr Raušer

2011-01-01

Full Text Available The aim of the study was to detect and compare the haemostatic variables and bleeding after 7‑days administration of carprofen or meloxicam in clinically healthy miniature pigs. Twenty-one clinically healthy Göttingen miniature pigs were divided into 3 groups. Selected haemostatic variables such as platelet count, prothrombin time, activated partial thromboplastin time, thrombin time, fibrinogen, serum biochemical variables such as total protein, bilirubin, urea, creatinine, alkaline phosphatase, alanine aminotransferase and gamma-glutamyltransferase and haemoglobin, haematocrit, red blood cells, white blood cells and buccal mucosal bleeding time were assessed before and 7 days after daily intramuscular administration of saline (1.5 ml per animal, control group, carprofen (2 mg·kg-1 or meloxicam (0.1 mg·kg-1. In pigs receiving carprofen or meloxicam, the thrombin time was significantly increased (p p p p < 0.05 compared to the control group. Significant differences were not detected in other haemostatic, biochemical variables or bleeding time compared to other groups or to the pretreatment values. Intramuscular administration of carprofen or meloxicam in healthy miniature pigs for 7 days causes sporadic, but not clinically important changes of selected haemostatic variables. Therefore, we can recommend them for perioperative use, e.g. for their analgesic effects, in orthopaedic or other surgical procedures without increased bleeding.

Locating disease genes using Bayesian variable selection with the Haseman-Elston method

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He Qimei

2003-12-01

Full Text Available Abstract Background We applied stochastic search variable selection (SSVS, a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models. Results In applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results. Conclusions We showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.

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

Science.gov (United States)

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

2017-06-01

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

Multivariate modeling of complications with data driven variable selection: Guarding against overfitting and effects of data set size

International Nuclear Information System (INIS)

Schaaf, Arjen van der; Xu Chengjian; Luijk, Peter van; Veld, Aart A. van’t; Langendijk, Johannes A.; Schilstra, Cornelis

2012-01-01

Purpose: Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. Materials and methods: To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. Results: For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small

FCERI AND HISTAMINE METABOLISM GENE VARIABILITY IN SELECTIVE RESPONDERS TO NSAIDS

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Gemma Amo

2016-09-01

Full Text Available The high-affinity IgE receptor (Fcε RI is a heterotetramer of three subunits: Fcε RIα, Fcε RIβ and Fcε RIγ (αβγ2 encoded by three genes designated as FCER1A, FCER1B (MS4A2 and FCER1G, respectively. Recent evidence points to FCERI gene variability as a relevant factor in the risk of developing allergic diseases. Because Fcε RI plays a key role in the events downstream of the triggering factors in immunological response, we hypothesized that FCERI gene variants might be related with the risk of, or with the clinical response to, selective (IgE mediated non-steroidal anti-inflammatory (NSAID hypersensitivity.From a cohort of 314 patients suffering from selective hypersensitivity to metamizole, ibuprofen, diclofenac, paracetamol, acetylsalicylic acid (ASA, propifenazone, naproxen, ketoprofen, dexketoprofen, etofenamate, aceclofenac, etoricoxib, dexibuprofen, indomethacin, oxyphenylbutazone or piroxicam, and 585 unrelated healthy controls that tolerated these NSAIDs, we analyzed the putative effects of the FCERI SNPs FCER1A rs2494262, rs2427837 and rs2251746; FCER1B rs1441586, rs569108 and rs512555; FCER1G rs11587213, rs2070901 and rs11421. Furthermore, in order to identify additional genetic markers which might be associated with the risk of developing selective NSAID hypersensitivity, or which may modify the putative association of FCERI gene variations with risk, we analyzed polymorphisms known to affect histamine synthesis or metabolism, such as rs17740607, rs2073440, rs1801105, rs2052129, rs10156191, rs1049742 and rs1049793 in the HDC, HNMT and DAO genes.No major genetic associations with risk or with clinical presentation, and no gene-gene interactions, or gene-phenotype interactions (including age, gender, IgE concentration, antecedents of atopy, culprit drug or clinical presentation were identified in patients. However, logistic regression analyses indicated that the presence of antecedents of atopy and the DAO SNP rs2052129 (GG

Chromospheric activity of periodic variable stars (including eclipsing binaries) observed in DR2 LAMOST stellar spectral survey

Science.gov (United States)

Zhang, Liyun; Lu, Hongpeng; Han, Xianming L.; Jiang, Linyan; Li, Zhongmu; Zhang, Yong; Hou, Yonghui; Wang, Yuefei; Cao, Zihuang

2018-05-01

The LAMOST spectral survey provides a rich databases for studying stellar spectroscopic properties and chromospheric activity. We cross-matched a total of 105,287 periodic variable stars from several photometric surveys and databases (CSS, LINEAR, Kepler, a recently updated eclipsing star catalogue, ASAS, NSVS, some part of SuperWASP survey, variable stars from the Tsinghua University-NAOC Transient Survey, and other objects from some new references) with four million stellar spectra published in the LAMOST data release 2 (DR2). We found 15,955 spectra for 11,469 stars (including 5398 eclipsing binaries). We calculated their equivalent widths (EWs) of their Hα, Hβ, Hγ, Hδ and Caii H lines. Using the Hα line EW, we found 447 spectra with emission above continuum for a total of 316 stars (178 eclipsing binaries). We identified 86 active stars (including 44 eclipsing binaries) with repeated LAMOST spectra. A total of 68 stars (including 34 eclipsing binaries) show chromospheric activity variability. We also found LAMOST spectra of 12 cataclysmic variables, five of which show chromospheric activity variability. We also made photometric follow-up studies of three short period targets (DY CVn, HAT-192-0001481, and LAMOST J164933.24+141255.0) using the Xinglong 60-cm telescope and the SARA 90-cm and 1-m telescopes, and obtained new BVRI CCD light curves. We analyzed these light curves and obtained orbital and starspot parameters. We detected the first flare event with a huge brightness increase of more than about 1.5 magnitudes in R filter in LAMOST J164933.24+141255.0.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

Science.gov (United States)

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Statistical model selection with “Big Data”

Directory of Open Access Journals (Sweden)

Jurgen A. Doornik

2015-12-01

Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

Calibration Variable Selection and Natural Zero Determination for Semispan and Canard Balances

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Ulbrich, Norbert M.

2013-01-01

Independent calibration variables for the characterization of semispan and canard wind tunnel balances are discussed. It is shown that the variable selection for a semispan balance is determined by the location of the resultant normal and axial forces that act on the balance. These two forces are the first and second calibration variable. The pitching moment becomes the third calibration variable after the normal and axial forces are shifted to the pitch axis of the balance. Two geometric distances, i.e., the rolling and yawing moment arms, are the fourth and fifth calibration variable. They are traditionally substituted by corresponding moments to simplify the use of calibration data during a wind tunnel test. A canard balance is related to a semispan balance. It also only measures loads on one half of a lifting surface. However, the axial force and yawing moment are of no interest to users of a canard balance. Therefore, its calibration variable set is reduced to the normal force, pitching moment, and rolling moment. The combined load diagrams of the rolling and yawing moment for a semispan balance are discussed. They may be used to illustrate connections between the wind tunnel model geometry, the test section size, and the calibration load schedule. Then, methods are reviewed that may be used to obtain the natural zeros of a semispan or canard balance. In addition, characteristics of three semispan balance calibration rigs are discussed. Finally, basic requirements for a full characterization of a semispan balance are reviewed.

Estimation and variable selection for generalized additive partial linear models

KAUST Repository

Wang, Li

2011-08-01

We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.

Selection for altruism through random drift in variable size populations

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Houchmandzadeh Bahram

2012-05-01

Full Text Available Abstract Background Altruistic behavior is defined as helping others at a cost to oneself and a lowered fitness. The lower fitness implies that altruists should be selected against, which is in contradiction with their widespread presence is nature. Present models of selection for altruism (kin or multilevel show that altruistic behaviors can have ‘hidden’ advantages if the ‘common good’ produced by altruists is restricted to some related or unrelated groups. These models are mostly deterministic, or assume a frequency dependent fitness. Results Evolutionary dynamics is a competition between deterministic selection pressure and stochastic events due to random sampling from one generation to the next. We show here that an altruistic allele extending the carrying capacity of the habitat can win by increasing the random drift of “selfish” alleles. In other terms, the fixation probability of altruistic genes can be higher than those of a selfish ones, even though altruists have a smaller fitness. Moreover when populations are geographically structured, the altruists advantage can be highly amplified and the fixation probability of selfish genes can tend toward zero. The above results are obtained both by numerical and analytical calculations. Analytical results are obtained in the limit of large populations. Conclusions The theory we present does not involve kin or multilevel selection, but is based on the existence of random drift in variable size populations. The model is a generalization of the original Fisher-Wright and Moran models where the carrying capacity depends on the number of altruists.

Bias in random forest variable importance measures: Illustrations, sources and a solution

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Hothorn Torsten

2007-01-01

Full Text Available Abstract Background Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. Results Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. Conclusion We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and

Impact of menstruation on select hematology and clinical chemistry variables in cynomolgus macaques.

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Perigard, Christopher J; Parrula, M Cecilia M; Larkin, Matthew H; Gleason, Carol R

2016-06-01

In preclinical studies with cynomolgus macaques, it is common to have one or more females presenting with menses. Published literature indicates that the blood lost during menses causes decreases in red blood cell mass variables (RBC, HGB, and HCT), which would be a confounding factor in the interpretation of drug-related effects on clinical pathology data, but no scientific data have been published to support this claim. This investigation was conducted to determine if the amount of blood lost during menses in cynomolgus macaques has an effect on routine hematology and serum chemistry variables. Ten female cynomolgus macaques (Macaca fascicularis), 5 to 6.5 years old, were observed daily during approximately 3 months (97 days) for the presence of menses. Hematology and serum chemistry variables were evaluated twice weekly. The results indicated that menstruation affects the erythrogram including RBC, HGB, HCT, MCHC, MCV, reticulocyte count, RDW, the leukogram including neutrophil, lymphocyte, and monocyte counts, and chemistry variables, including GGT activity, and the concentrations of total proteins, albumin, globulins, and calcium. The magnitude of the effect of menstruation on susceptible variables is dependent on the duration of the menstrual phase. Macaques with menstrual phases lasting ≥ 7 days are more likely to develop changes in variables related to chronic blood loss. In preclinical toxicology studies with cynomolgus macaques, interpretation of changes in several commonly evaluated hematology and serum chemistry variables requires adequate clinical observation and documentation concerning presence and duration of menses. There is a concern that macaques with long menstrual cycles can develop iron deficiency anemia due to chronic menstrual blood loss. © 2016 American Society for Veterinary Clinical Pathology.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

Science.gov (United States)

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between

Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares

Science.gov (United States)

Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

2018-01-01

Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods

Science.gov (United States)

Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; Mello, Paola de Azevedo; Ferrão, Marco Flores; dos Santos, Maria de Fátima Pereira; Guimarães, Regina Célia Lourenço; Müller, Edson Irineu; Flores, Erico Marlon Moraes

2012-04-01

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm-1). This model produced a RMSECV of 400 mg kg-1 S and RMSEP of 420 mg kg-1 S, showing a correlation coefficient of 0.990.

Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations

DEFF Research Database (Denmark)

Ödman, Peter; Johansen, C.L.; Olsson, L.

2010-01-01

of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable......Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction...

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

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Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

Fuzzy target selection using RFM variables

NARCIS (Netherlands)

Kaymak, U.

2001-01-01

An important data mining problem from the world of direct marketing is target selection. The main task in target selection is the determination of potential customers for a product from a client database. Target selection algorithms identify the profiles of customer groups for a particular product,

Diagnostic Value of Selected Echocardiographic Variables to Identify Pulmonary Hypertension in Dogs with Myxomatous Mitral Valve Disease.

Science.gov (United States)

Tidholm, A; Höglund, K; Häggström, J; Ljungvall, I

2015-01-01

Pulmonary hypertension (PH) is commonly associated with myxomatous mitral valve disease (MMVD). Because dogs with PH present without measureable tricuspid regurgitation (TR), it would be useful to investigate echocardiographic variables that can identify PH. To investigate associations between estimated systolic TR pressure gradient (TRPG) and dog characteristics and selected echocardiographic variables. 156 privately owned dogs. Prospective observational study comparing the estimations of TRPG with dog characteristics and selected echocardiographic variables in dogs with MMVD and measureable TR. Tricuspid regurgitation pressure gradient was significantly (P modeled as linear variables LA/Ao (P modeled as second order polynomial variables: AT/DT (P = .0039) and LVIDDn (P value for the final model was 0.45 and receiver operating characteristic curve analysis suggested the model's performance to predict PH, defined as 36, 45, and 55 mmHg as fair (area under the curve [AUC] = 0.80), good (AUC = 0.86), and excellent (AUC = 0.92), respectively. In dogs with MMVD, the presence of PH might be suspected with the combination of decreased PA AT/DT, increased RVIDDn and LA/Ao, and a small or great LVIDDn. Copyright © 2015 The Authors Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments

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Cain, James W.; Gedir, Jay V.; Marshal, Jason P.; Krausman, Paul R.; Allen, Jamison D.; Duff, Glenn C.; Jansen, Brian; Morgart, John R.

2017-01-01

Nutritional ecology forms the interface between environmental variability and large herbivore behaviour, life history characteristics, and population dynamics. Forage conditions in arid and semi-arid regions are driven by unpredictable spatial and temporal patterns in rainfall. Diet selection by herbivores should be directed towards overcoming the most pressing nutritional limitation (i.e. energy, protein [nitrogen, N], moisture) within the constraints imposed by temporal and spatial variability in forage conditions. We investigated the influence of precipitation-induced shifts in forage nutritional quality and subsequent large herbivore responses across widely varying precipitation conditions in an arid environment. Specifically, we assessed seasonal changes in diet breadth and forage selection of adult female desert bighorn sheep Ovis canadensis mexicana in relation to potential nutritional limitations in forage N, moisture and energy content (as proxied by dry matter digestibility, DMD). Succulents were consistently high in moisture but low in N and grasses were low in N and moisture until the wet period. Nitrogen and moisture content of shrubs and forbs varied among seasons and climatic periods, whereas trees had consistently high N and moderate moisture levels. Shrubs, trees and succulents composed most of the seasonal sheep diets but had little variation in DMD. Across all seasons during drought and during summer with average precipitation, forages selected by sheep were higher in N and moisture than that of available forage. Differences in DMD between sheep diets and available forage were minor. Diet breadth was lowest during drought and increased with precipitation, reflecting a reliance on few key forage species during drought. Overall, forage selection was more strongly associated with N and moisture content than energy content. Our study demonstrates that unlike north-temperate ungulates which are generally reported to be energy-limited, N and moisture

Impact of strong selection for the PrP major gene on genetic variability of four French sheep breeds (Open Access publication

Directory of Open Access Journals (Sweden)

Pantano Thais

2008-11-01

Full Text Available Abstract Effective selection on the PrP gene has been implemented since October 2001 in all French sheep breeds. After four years, the ARR "resistant" allele frequency increased by about 35% in young males. The aim of this study was to evaluate the impact of this strong selection on genetic variability. It is focussed on four French sheep breeds and based on the comparison of two groups of 94 animals within each breed: the first group of animals was born before the selection began, and the second, 3–4 years later. Genetic variability was assessed using genealogical and molecular data (29 microsatellite markers. The expected loss of genetic variability on the PrP gene was confirmed. Moreover, among the five markers located in the PrP region, only the three closest ones were affected. The evolution of the number of alleles, heterozygote deficiency within population, expected heterozygosity and the Reynolds distances agreed with the criteria from pedigree and pointed out that neutral genetic variability was not much affected. This trend depended on breed, i.e. on their initial states (population size, PrP frequencies and on the selection strategies for improving scrapie resistance while carrying out selection for production traits.

A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

Science.gov (United States)

Thomas, D.L.; Johnson, D.; Griffith, B.

2006-01-01

Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a

Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models

DEFF Research Database (Denmark)

Kock, Anders Bredahl

This paper generalizes the results for the Bridge estimator of Huang et al. (2008) to linear random and fixed effects panel data models which are allowed to grow in both dimensions. In particular we show that the Bridge estimator is oracle efficient. It can correctly distinguish between relevant...... and irrelevant variables and the asymptotic distribution of the estimators of the coefficients of the relevant variables is the same as if only these had been included in the model, i.e. as if an oracle had revealed the true model prior to estimation. In the case of more explanatory variables than observations......, we prove that the Marginal Bridge estimator can asymptotically correctly distinguish between relevant and irrelevant explanatory variables. We do this without restricting the dependence between covariates and without assuming sub Gaussianity of the error terms thereby generalizing the results...

A fast chaos-based image encryption scheme with a dynamic state variables selection mechanism

Science.gov (United States)

Chen, Jun-xin; Zhu, Zhi-liang; Fu, Chong; Yu, Hai; Zhang, Li-bo

2015-03-01

In recent years, a variety of chaos-based image cryptosystems have been investigated to meet the increasing demand for real-time secure image transmission. Most of them are based on permutation-diffusion architecture, in which permutation and diffusion are two independent procedures with fixed control parameters. This property results in two flaws. (1) At least two chaotic state variables are required for encrypting one plain pixel, in permutation and diffusion stages respectively. Chaotic state variables produced with high computation complexity are not sufficiently used. (2) The key stream solely depends on the secret key, and hence the cryptosystem is vulnerable against known/chosen-plaintext attacks. In this paper, a fast chaos-based image encryption scheme with a dynamic state variables selection mechanism is proposed to enhance the security and promote the efficiency of chaos-based image cryptosystems. Experimental simulations and extensive cryptanalysis have been carried out and the results prove the superior security and high efficiency of the scheme.

Genotype-by-environment interactions leads to variable selection on life-history strategy in Common Evening Primrose (Oenothera biennis).

Science.gov (United States)

Johnson, M T J

2007-01-01

Monocarpic plant species, where reproduction is fatal, frequently exhibit variation in the length of their prereproductive period prior to flowering. If this life-history variation in flowering strategy has a genetic basis, genotype-by-environment interactions (G x E) may maintain phenotypic diversity in flowering strategy. The native monocarpic plant Common Evening Primrose (Oenothera biennis L., Onagraceae) exhibits phenotypic variation for annual vs. biennial flowering strategies. I tested whether there was a genetic basis to variation in flowering strategy in O. biennis, and whether environmental variation causes G x E that imposes variable selection on flowering strategy. In a field experiment, I randomized more than 900 plants from 14 clonal families (genotypes) into five distinct habitats that represented a natural productivity gradient. G x E strongly affected the lifetime fruit production of O. biennis, with the rank-order in relative fitness of genotypes changing substantially between habitats. I detected genetic variation in annual vs. biennial strategies in most habitats, as well as a G x E effect on flowering strategy. This variation in flowering strategy was correlated with genetic variation in relative fitness, and phenotypic and genotypic selection analyses revealed that environmental variation resulted in variable directional selection on annual vs. biennial strategies. Specifically, a biennial strategy was favoured in moderately productive environments, whereas an annual strategy was favoured in low-productivity environments. These results highlight the importance of variable selection for the maintenance of genetic variation in the life-history strategy of a monocarpic plant.

Online Monitoring of Copper Damascene Electroplating Bath by Voltammetry: Selection of Variables for Multiblock and Hierarchical Chemometric Analysis of Voltammetric Data

Directory of Open Access Journals (Sweden)

Aleksander Jaworski

2017-01-01

Full Text Available The Real Time Analyzer (RTA utilizing DC- and AC-voltammetric techniques is an in situ, online monitoring system that provides a complete chemical analysis of different electrochemical deposition solutions. The RTA employs multivariate calibration when predicting concentration parameters from a multivariate data set. Although the hierarchical and multiblock Principal Component Regression- (PCR- and Partial Least Squares- (PLS- based methods can handle data sets even when the number of variables significantly exceeds the number of samples, it can be advantageous to reduce the number of variables to obtain improvement of the model predictions and better interpretation. This presentation focuses on the introduction of a multistep, rigorous method of data-selection-based Least Squares Regression, Simple Modeling of Class Analogy modeling power, and, as a novel application in electroanalysis, Uninformative Variable Elimination by PLS and by PCR, Variable Importance in the Projection coupled with PLS, Interval PLS, Interval PCR, and Moving Window PLS. Selection criteria of the optimum decomposition technique for the specific data are also demonstrated. The chief goal of this paper is to introduce to the community of electroanalytical chemists numerous variable selection methods which are well established in spectroscopy and can be successfully applied to voltammetric data analysis.

INDUCED GENETIC VARIABILITY AND SELECTION FOR HIGH YIELDING MUTANTS IN BREAD WHEAT(TRITICUM AESTIVUM L.)

International Nuclear Information System (INIS)

SOBIEH, S.EL-S.S.

2007-01-01

This study was conducted during the two winter seasons of 2004/2005 and 2005/2006 at the experimental farm belonging to Plant Research Department, Nuclear Research Centre, AEA, Egypt.The aim of this study is to determine the effect of gamma rays(150, 200 and 250 Gy) on means of yield and its attributes for exotic wheat variety (vir-25) and induction of genetic variability that permits to perform visual selection through the irradiated populations, as well as to determine difference in seed protein patterns between vir-25 parent variety and some selectants in M2 generation.The results showed that the different doses of gamma rays had non-significant effect on mean value of yield/plant and significant effect on mean values of it's attributes. 0n the other hand, the considered genetic variability was generated as result of applying gamma irradiation. The highest amount of induced genetic variability was detected for number of grains/ spike, spike length and number of spikes/plant. Additionally, these three traits exhibited strong association with grain yield/plant, hence, they were used as a criterion for selection.Some variant plants were selected from radiation treatment 250 Gy, with 2-10 spikes per plant.These variant plants exhibited increasing in spike length and number of gains/spike.The results also revealed that protein electrophoresis were varied in the number and position of bands from genotype to another and various genotypes share bands with molecular weights 31.4 and 3.2 KD.Many bands were found to be specific for the genotype and the nine wheat mutants were characterized by the presence of bands of molecular weights: 151.9, 125.7, 14.1 and 5.7 KD at M-167.4, 21.7 and 8.2 at M-299.7 KD at M-3136.1, 97.6, 49.8, 27.9 and 20.6 KD at M-4 135.2, 95.3 and 28.1 KD at M-5 135.5, 67.7, 47.1, 32.3, 21.9 and 9.6 KD at M-6 126.1, 112.1, 103.3, 58.8, 20.9 and 12.1 KD at M-7 127.7, 116.6, 93.9, 55.0 and 47.4 KD at M-8 141.7, 96.1, 79.8, 68.9, 42.1, 32.7, 22.0 and 13

Simulated selection responses for breeding programs including resistance and resilience to parasites in Creole goats.

Science.gov (United States)

Gunia, M; Phocas, F; Gourdine, J-L; Bijma, P; Mandonnet, N

2013-02-01

The Creole goat is a local breed used for meat production in Guadeloupe (French West Indies). As in other tropical countries, improvement of parasite resistance is needed. In this study, we compared predicted selection responses for alternative breeding programs with or without parasite resistance and resilience traits. The overall breeding goal included traits for production, reproduction, and parasite resilience and resistance to ensure a balanced selection outcome. The production traits were BW and dressing percentage (DP). The reproduction trait was fertility (FER), which was the number of doe kiddings per mating. The resistance trait was worm fecal egg count (FEC), which is a measurement of the number of gastro-intestinal parasite eggs found in the feces. The resilience trait was the packed cell volume (PCV), which is a measurement of the volume of red blood cells in the blood. Dressing percentage, BW, and FEC were measured at 11 mo of age, which is the mating or selling age. Fertility and PCV were measured on females at each kidding period. The breeding program accounting for the overall breeding goal and a selection index including all traits gave annual selection responses of 800 g for BW, 3.75% for FER, 0.08% for DP, -0.005 ln(eggs/g) for FEC, and 0.28% for PCV. The expected selection responses for BW and DP in this breeding program were reduced by 2% and 6%, respectively, compared with a breeding program not accounting for FEC and PCV. The overall breeding program, proposed for the Creole breed, offers the best breeding strategy in terms of expected selection responses, making it possible to improve all traits together. It offers a good balance between production and adaptation traits and may present some interest for the selection of other goat breeds in the tropics.

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

Science.gov (United States)

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier

Diagnostic for two-mode variable valve activation device

Science.gov (United States)

Fedewa, Andrew M

2014-01-07

A method is provided for diagnosing a multi-mode valve train device which selectively provides high lift and low lift to a combustion valve of an internal combustion engine having a camshaft phaser actuated by an electric motor. The method includes applying a variable electric current to the electric motor to achieve a desired camshaft phaser operational mode and commanding the multi-mode valve train device to a desired valve train device operational mode selected from a high lift mode and a low lift mode. The method also includes monitoring the variable electric current and calculating a first characteristic of the parameter. The method also includes comparing the calculated first characteristic against a predetermined value of the first characteristic measured when the multi-mode valve train device is known to be in the desired valve train device operational mode.

Energy-efficient relay selection and optimal power allocation for performance-constrained dual-hop variable-gain AF relaying

KAUST Repository

Zafar, Ammar; Radaydeh, Redha Mahmoud Mesleh; Chen, Yunfei; Alouini, Mohamed-Slim

2013-01-01

This paper investigates the energy-efficiency enhancement of a variable-gain dual-hop amplify-and-forward (AF) relay network utilizing selective relaying. The objective is to minimize the total consumed power while keeping the end-to-end signal

A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

Science.gov (United States)

Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

2015-01-01

Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

Science.gov (United States)

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for

NUMBER OF SUCCESSIVE CYCLES NECESSARY TO ACHIEVE STABILITY OF SELECTED GROUND REACTION FORCE VARIABLES DURING CONTINUOUS JUMPING

Directory of Open Access Journals (Sweden)

Jasmes M.W. Brownjohn

2009-12-01

Full Text Available Because of inherent variability in all human cyclical movements, such as walking, running and jumping, data collected across a single cycle might be atypical and potentially unable to represent an individual's generalized performance. The study described here was designed to determine the number of successive cycles due to continuous, repetitive countermovement jumping which a test subject should perform in a single experimental session to achieve stability of the mean of the corresponding continuously measured ground reaction force (GRF variables. Seven vertical GRF variables (period of jumping cycle, duration of contact phase, peak force amplitude and its timing, average rate of force development, average rate of force relaxation and impulse were extracted on the cycle-by-cycle basis from vertical jumping force time histories generated by twelve participants who were jumping in response to regular electronic metronome beats in the range 2-2.8 Hz. Stability of the selected GRF variables across successive jumping cycles was examined for three jumping rates (2, 2.4 and 2.8 Hz using two statistical methods: intra-class correlation (ICC analysis and segmental averaging technique (SAT. Results of the ICC analysis indicated that an average of four successive cycles (mean 4.5 ± 2.7 for 2 Hz; 3.9 ± 2.6 for 2.4 Hz; 3.3 ± 2.7 for 2.8 Hz were necessary to achieve maximum ICC values. Except for jumping period, maximum ICC values took values from 0.592 to 0.991 and all were significantly (p < 0.05 different from zero. Results of the SAT revealed that an average of ten successive cycles (mean 10.5 ± 3.5 for 2 Hz; 9.2 ± 3.8 for 2.4 Hz; 9.0 ± 3.9 for 2.8 Hz were necessary to achieve stability of the selected parameters using criteria previously reported in the literature. Using 10 reference trials, the SAT required standard deviation criterion values of 0.49, 0.41 and 0.55 for 2 Hz, 2.4 Hz and 2.8 Hz jumping rates, respectively, in order to approximate

Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils.

Science.gov (United States)

Terra, Luciana A; Filgueiras, Paulo R; Tose, Lílian V; Romão, Wanderson; de Souza, Douglas D; de Castro, Eustáquio V R; de Oliveira, Mirela S L; Dias, Júlio C M; Poppi, Ronei J

2014-10-07

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

Demographic Variables and Selective, Sustained Attention and Planning through Cognitive Tasks among Healthy Adults

OpenAIRE

Afsaneh Zarghi; Zali; A; Tehranidost; M; Mohammad Reza Zarindast; Ashrafi; F; Doroodgar; Khodadadi

2011-01-01

Introduction: Cognitive tasks are considered to be applicable and appropriate in assessing cognitive domains. The purpose of our study is to determine the relationship existence between variables of age, sex and education with selective, sustained attention and planning abilities by means of computerized cognitive tasks among healthy adults. Methods: A cross-sectional study was implemented during 6 months from June to November, 2010 on 84 healthy adults (42 male and 42 female). The whole part...

The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-SWATH-MS Acquisition.

Science.gov (United States)

Zhang, Ying; Bilbao, Aivett; Bruderer, Tobias; Luban, Jeremy; Strambio-De-Castillia, Caterina; Lisacek, Frédérique; Hopfgartner, Gérard; Varesio, Emmanuel

2015-10-02

As tryptic peptides and metabolites are not equally distributed along the mass range, the probability of cross fragment ion interference is higher in certain windows when fixed Q1 SWATH windows are applied. We evaluated the benefits of utilizing variable Q1 SWATH windows with regards to selectivity improvement. Variable windows based on equalizing the distribution of either the precursor ion population (PIP) or the total ion current (TIC) within each window were generated by an in-house software, swathTUNER. These two variable Q1 SWATH window strategies outperformed, with respect to quantification and identification, the basic approach using a fixed window width (FIX) for proteomic profiling of human monocyte-derived dendritic cells (MDDCs). Thus, 13.8 and 8.4% additional peptide precursors, which resulted in 13.1 and 10.0% more proteins, were confidently identified by SWATH using the strategy PIP and TIC, respectively, in the MDDC proteomic sample. On the basis of the spectral library purity score, some improvement warranted by variable Q1 windows was also observed, albeit to a lesser extent, in the metabolomic profiling of human urine. We show that the novel concept of "scheduled SWATH" proposed here, which incorporates (i) variable isolation windows and (ii) precursor retention time segmentation further improves both peptide and metabolite identifications.

Disruption of Brewers' yeast by hydrodynamic cavitation: Process variables and their influence on selective release.

Science.gov (United States)

Balasundaram, B; Harrison, S T L

2006-06-05

Intracellular products, not secreted from the microbial cell, are released by breaking the cell envelope consisting of cytoplasmic membrane and an outer cell wall. Hydrodynamic cavitation has been reported to cause microbial cell disruption. By manipulating the operating variables involved, a wide range of intensity of cavitation can be achieved resulting in a varying extent of disruption. The effect of the process variables including cavitation number, initial cell concentration of the suspension and the number of passes across the cavitation zone on the release of enzymes from various locations of the Brewers' yeast was studied. The release profile of the enzymes studied include alpha-glucosidase (periplasmic), invertase (cell wall bound), alcohol dehydrogenase (ADH; cytoplasmic) and glucose-6-phosphate dehydrogenase (G6PDH; cytoplasmic). An optimum cavitation number Cv of 0.13 for maximum disruption was observed across the range Cv 0.09-0.99. The optimum cell concentration was found to be 0.5% (w/v, wet wt) when varying over the range 0.1%-5%. The sustained effect of cavitation on the yeast cell wall when re-circulating the suspension across the cavitation zone was found to release the cell wall bound enzyme invertase (86%) to a greater extent than the enzymes from other locations of the cell (e.g. periplasmic alpha-glucosidase at 17%). Localised damage to the cell wall could be observed using transmission electron microscopy (TEM) of cells subjected to less intense cavitation conditions. Absence of the release of cytoplasmic enzymes to a significant extent, absence of micronisation as observed by TEM and presence of a lower number of proteins bands in the culture supernatant on SDS-PAGE analysis following hydrodynamic cavitation compared to disruption by high-pressure homogenisation confirmed the selective release offered by hydrodynamic cavitation. Copyright 2006 Wiley Periodicals, Inc.

Is DAS28-CRP with three and four variables interchangeable in individual patients selected for biological treatment in daily clinical practice?

DEFF Research Database (Denmark)

Madsen, Ole Rintek

2011-01-01

DAS28 is a widely used composite score for the assessment of disease activity in patients with rheumatoid arthritis (RA) and is often used as a treatment decision tool in the daily clinic. Different versions of DAS28 are available. DAS28-CRP(3) is calculated based on three variables: swollen...... and tender joint counts and CRP. DAS28-CRP(4) also includes patient global assessment. Thresholds for low and high disease activity are the same for the two scores. Based on the Bland-Altman method, the interchangeability between DAS28-CRP with three and four variables was examined in 319 RA patients...... selected for initiating biological treatment. Data were extracted from the Danish registry for biological treatment in rheumatology (DANBIO). Multiple regression analysis was used to assess the predictability of the DAS28 scores by several measures of disease activity. The overall mean DAS28-CRP was 4...

Spatial and temporal variability of winds in the Northern European Seas

DEFF Research Database (Denmark)

Karagali, Ioanna; Badger, Merete; Hahmann, Andrea N.

2013-01-01

the spatial and temporal variability of the near-surface wind field, including the inter- and intra-annual variability for resource assessment purposes. This study demonstrates the applicability of satellite observations as the means to provide information useful for selecting areas to perform higher...

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

OpenAIRE

Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...

Variable selection for modelling effects of eutrophication on stream and river ecosystems

NARCIS (Netherlands)

Nijboer, R.C.; Verdonschot, P.F.M.

2004-01-01

Models are needed for forecasting the effects of eutrophication on stream and river ecosystems. Most of the current models do not include differences in local stream characteristics and effects on the biota. To define the most important variables that should be used in a stream eutrophication model,

Variable Cycle Intake for Reverse Core Engine

Science.gov (United States)

Suciu, Gabriel L (Inventor); Chandler, Jesse M (Inventor); Staubach, Joseph B (Inventor)

2016-01-01

A gas generator for a reverse core engine propulsion system has a variable cycle intake for the gas generator, which variable cycle intake includes a duct system. The duct system is configured for being selectively disposed in a first position and a second position, wherein free stream air is fed to the gas generator when in the first position, and fan stream air is fed to the gas generator when in the second position.

Relation between sick leave and selected exposure variables among women semiconductor workers in Malaysia

Science.gov (United States)

Chee, H; Rampal, K

2003-01-01

Aims: To determine the relation between sick leave and selected exposure variables among women semiconductor workers. Methods: This was a cross sectional survey of production workers from 18 semiconductor factories. Those selected had to be women, direct production operators up to the level of line leader, and Malaysian citizens. Sick leave and exposure to physical and chemical hazards were determined by self reporting. Three sick leave variables were used; number of sick leave days taken in the past year was the variable of interest in logistic regression models where the effects of age, marital status, work task, work schedule, work section, and duration of work in factory and work section were also explored. Results: Marital status was strongly linked to the taking of sick leave. Age, work schedule, and duration of work in the factory were significant confounders only in certain cases. After adjusting for these confounders, chemical and physical exposures, with the exception of poor ventilation and smelling chemicals, showed no significant relation to the taking of sick leave within the past year. Work section was a good predictor for taking sick leave, as wafer polishing workers faced higher odds of taking sick leave for each of the three cut off points of seven days, three days, and not at all, while parts assembly workers also faced significantly higher odds of taking sick leave. Conclusion: In Malaysia, the wafer fabrication factories only carry out a limited portion of the work processes, in particular, wafer polishing and the processes immediately prior to and following it. This study, in showing higher illness rates for workers in wafer polishing compared to semiconductor assembly, has implications for the governmental policy of encouraging the setting up of wafer fabrication plants with the full range of work processes. PMID:12660374

The Relationship between Macroeconomic Variables and ISE Industry Index

Directory of Open Access Journals (Sweden)

Ahmet Ozcan

2012-01-01

Full Text Available In this study, the relationship between macroeconomic variables and Istanbul Stock Exchange (ISE industry index is examined. Over the past years, numerous studies have analyzed these relationships and the different results obtained from these studies have motivated further research. The relationship between stock exchange index and macroeconomic variables has been well documented for the developed markets. However, there are few studies regarding the relationship between macroeconomic variables and stock exchange index for the developing markets. Thus, this paper seeks to address the question of whether macroeconomic variables have a significant relationship with ISE industry index using monthly data for the period from 2003 to 2010. The selected macroeconomic variables for the study include interest rates, consumer price index, money supply, exchange rate, gold prices, oil prices, current account deficit and export volume. The Johansen’s cointegration test is utilized to determine the impact of selected macroeconomic variables on ISE industry index. The result of the Johansen’s cointegration shows that macroeconomic variables exhibit a long run equilibrium relationship with the ISE industry index.

Resiliency and subjective health assessment. Moderating role of selected psychosocial variables

Directory of Open Access Journals (Sweden)

Michalina Sołtys

2015-12-01

Full Text Available Background Resiliency is defined as a relatively permanent personality trait, which may be assigned to the category of health resources. The aim of this study was to determine conditions in which resiliency poses a significant health resource (moderation, thereby broadening knowledge of the specifics of the relationship between resiliency and subjective health assessment. Participants and procedure The study included 142 individuals. In order to examine the level of resiliency, the Assessment Resiliency Scale (SPP-25 by N. Ogińska-Bulik and Z. Juczyński was used. Participants evaluated subjective health state by means of an analogue-visual scale. Additionally, in the research the following moderating variables were controlled: sex, objective health status, having a partner, professional activity and age. These data were obtained by personal survey. Results The results confirmed the relationship between resiliency and subjective health assessment. Multiple regression analysis revealed that sex, having a partner and professional activity are significant moderators of associations between level of resiliency and subjective health evaluation. However, statistically significant interaction effects for health status and age as a moderator were not observed. Conclusions Resiliency is associated with subjective health assessment among adults, and selected socio-demographic features (such as sex, having a partner, professional activity moderate this relationship. This confirms the significant role of resiliency as a health resource and a reason to emphasize the benefits of enhancing the potential of individuals for their psychophysical wellbeing. However, the research requires replication in a more homogeneous sample.

Input variable selection for interpolating high-resolution climate ...

African Journals Online (AJOL)

Although the primary input data of climate interpolations are usually meteorological data, other related (independent) variables are frequently incorporated in the interpolation process. One such variable is elevation, which is known to have a strong influence on climate. This research investigates the potential of 4 additional ...

Energy-efficient relay selection and optimal power allocation for performance-constrained dual-hop variable-gain AF relaying

KAUST Repository

Zafar, Ammar

2013-12-01

This paper investigates the energy-efficiency enhancement of a variable-gain dual-hop amplify-and-forward (AF) relay network utilizing selective relaying. The objective is to minimize the total consumed power while keeping the end-to-end signal-to-noise-ratio (SNR) above a certain peak value and satisfying the peak power constraints at the source and relay nodes. To achieve this objective, an optimal relay selection and power allocation strategy is derived by solving the power minimization problem. Numerical results show that the derived optimal strategy enhances the energy-efficiency as compared to a benchmark scheme in which both the source and the selected relay transmit at peak power. © 2013 IEEE.

Sex-specific selection for MHC variability in Alpine chamois

Directory of Open Access Journals (Sweden)

Schaschl Helmut

2012-02-01

Full Text Available Abstract Background In mammals, males typically have shorter lives than females. This difference is thought to be due to behavioural traits which enhance competitive abilities, and hence male reproductive success, but impair survival. Furthermore, in many species males usually show higher parasite burden than females. Consequently, the intensity of selection for genetic factors which reduce susceptibility to pathogens may differ between sexes. High variability at the major histocompatibility complex (MHC genes is believed to be advantageous for detecting and combating the range of infectious agents present in the environment. Increased heterozygosity at these immune genes is expected to be important for individual longevity. However, whether males in natural populations benefit more from MHC heterozygosity than females has rarely been investigated. We investigated this question in a long-term study of free-living Alpine chamois (Rupicapra rupicapra, a polygynous mountain ungulate. Results Here we show that male chamois survive significantly (P = 0.022 longer if heterozygous at the MHC class II DRB locus, whereas females do not. Improved survival of males was not a result of heterozygote advantage per se, as background heterozygosity (estimated across twelve microsatellite loci did not change significantly with age. Furthermore, reproductively active males depleted their body fat reserves earlier than females leading to significantly impaired survival rates in this sex (P Conclusions Increased MHC class II DRB heterozygosity with age in males, suggests that MHC heterozygous males survive longer than homozygotes. Reproductively active males appear to be less likely to survive than females most likely because of the energetic challenge of the winter rut, accompanied by earlier depletion of their body fat stores, and a generally higher parasite burden. This scenario renders the MHC-mediated immune response more important for males than for females

Repeat what after whom? Exploring variable selectivity in a cross-dialectal shadowing task.

Directory of Open Access Journals (Sweden)

Abby eWalker

2015-05-01

Full Text Available Twenty women from Christchurch, New Zealand and sixteen from Columbus Ohio (dialect region U.S. Midland participated in a bimodal lexical naming task where they repeated monosyllabic words after four speakers from four regional dialects: New Zealand, Australia, U.S. Inland North and U.S. Midland. The resulting utterances were acoustically analyzed, and presented to listeners on Amazon Mechanical Turk in an AXB task. Convergence is observed, but differs depending on the dialect of the speaker, the dialect of the model, the particular word class being shadowed, and the order in which dialects are presented to participants. We argue that these patterns are generally consistent with findings that convergence is promoted by a large phonetic distance between shadower and model (Babel, 2010, contra Kim, Horton & Bradlow, 2011, and greater existing variability in a vowel class (Babel, 2012. The results also suggest that more comparisons of accommodation towards different dialects are warranted, and that the investigation of the socio-indexical meaning of specific linguistic forms in context is a promising avenue for understanding variable selectivity in convergence.

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

OpenAIRE

Hofner, Benjamin; Mayr, Andreas; Schmid, Matthias

2014-01-01

Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we...

Selective dopamine D3 receptor antagonism by SB-277011A attenuates cocaine reinforcement as assessed by progressive-ratio and variable-cost–variable-payoff fixed-ratio cocaine self-administration in rats

OpenAIRE

Xi, Zheng-Xiong; Gilbert, Jeremy G.; Pak, Arlene C.; Ashby, Charles R.; Heidbreder, Christian A.; Gardner, Eliot L.

2005-01-01

In rats, acute administration of SB-277011A, a highly selective dopamine (DA) D3 receptor antagonist, blocks cocaine-enhanced brain stimulation reward, cocaine-seeking behaviour and reinstatement of cocaine-seeking behaviour. Here, we investigated whether SB-277011A attenuates cocaine reinforcement as assessed by cocaine self-administration under variable-cost–variable-payoff fixed-ratio (FR) and progressive-ratio (PR) reinforcement schedules. Acute i.p. administration of SB-277011A (3–24 mg/...

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting

Directory of Open Access Journals (Sweden)

Robert Suchting

2018-05-01

Full Text Available Rationale: Given datasets with a large or diverse set of predictors of aggression, machine learning (ML provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior.Objectives: The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5 polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults.Methods: The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a select variables from an initial set of 20 to build a model of trait aggression; and then (b reduce that model to maximize parsimony and generalizability.Results: From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ total score, with R2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect, childhood trauma (physical abuse and neglect, and the FKBP5_13 gene (rs1360780. The six-factor model approximated the initial eight-factor model at 99.4% of R2.Conclusions: Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for

Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study

Science.gov (United States)

Lamb, David W.; Mengersen, Kerrie

2016-01-01

Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment. PMID:27603135

Birth order and selected work-related personality variables.

Science.gov (United States)

Phillips, A S; Bedeian, A G; Mossholder, K W; Touliatos, J

1988-12-01

A possible link between birth order and various individual characteristics (e. g., intelligence, potential eminence, need for achievement, sociability) has been suggested by personality theorists such as Adler for over a century. The present study examines whether birth order is associated with selected personality variables that may be related to various work outcomes. 3 of 7 hypotheses were supported and the effect sizes for these were small. Firstborns scored significantly higher than later borns on measures of dominance, good impression, and achievement via conformity. No differences between firstborns and later borns were found in managerial potential, work orientation, achievement via independence, and sociability. The study's sample consisted of 835 public, government, and industrial accountants responding to a national US survey of accounting professionals. The nature of the sample may have been partially responsible for the results obtained. Its homogeneity may have caused any birth order effects to wash out. It can be argued that successful membership in the accountancy profession requires internalization of a set of prescribed rules and standards. It may be that accountants as a group are locked in to a behavioral framework. Any differentiation would result from spurious interpersonal differences, not from predictable birth-order related characteristics. A final interpretation is that birth order effects are nonexistent or statistical artifacts. Given the present data and particularistic sample, however, the authors have insufficient information from which to draw such a conclusion.

The effect of aquatic plyometric training with and without resistance on selected physical fitness variables among volleyball players

Directory of Open Access Journals (Sweden)

K. KAMALAKKANNAN

2011-06-01

Full Text Available The purpose of this study is to analyze the effect of aquatic plyometric training with and without the use ofweights on selected physical fitness variables among volleyball players. To achieve the purpose of these study 36physically active undergraduate volleyball players between 18 and 20 years of age volunteered as participants.The participants were randomly categorized into three groups of 12 each: a control group (CG, an aquaticPlyometric training with weight group (APTWG, and an aquatic Plyometric training without weight group(APTWOG. The subjects of the control group were not exposed to any training. Both experimental groupsunderwent their respective experimental treatment for 12 weeks, 3 days per week and a single session on eachday. Speed, endurance, and explosive power were measured as the dependent variables for this study. 36 days ofexperimental treatment was conducted for all the groups and pre and post data was collected. The collected datawere analyzed using an analysis of covariance (ANCOVA and followed by a Scheffé’s post hoc test. The resultsrevealed significant differences between groups on all the selected dependent variables. This study demonstratedthat aquatic plyometric training can be one effective means for improving speed, endurance, and explosivepower in volley ball players

Impact of Psychological Variables on Playing Ability of University Level Soccer Players

Directory of Open Access Journals (Sweden)

Ertan Tufekcioglu

2014-10-01

Full Text Available The purpose of the study was to find out the relationship between psychological variables and soccer playing ability among the university level male players. 42 soccer players representing different universities who participated in inter university competitions were selected as the subjects of the study. The dependent variable was soccer playing ability and independent variables were the selected psychological variables. Soccer playing ability was determined through a 10 point scale at the time of competitions. Psychological variables included achievement motivation, anxiety, self-concept and aggression. The data was statistically analyzed using Karl Pearson’s correlation coefficient and multiple regression analysis using SPSS. It was concluded that soccer playing ability has a positive correlation with achievement motivation and self-concept whereas anxiety and aggression have a negative correlation with soccer playing ability.

Prediction of Placental Barrier Permeability: A Model Based on Partial Least Squares Variable Selection Procedure

Directory of Open Access Journals (Sweden)

Yong-Hong Zhang

2015-05-01

Full Text Available Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure–activity relationship (QSAR method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares (PLS variable selection and modeling procedure was used to pick out optimal descriptors from a pool of 620 descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices (CI. The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. It was shown that the model developed is robust and has a good predictive potential (r2 = 0.9064, RMSE = 0.09, q2 = 0.7323, rp2 = 0.7656, RMSP = 0.14. The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability. This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs.

Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis

KAUST Repository

Bhadra, Anindya

2013-04-22

We describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. To search the high-dimensional model space, where both the number of predictors and the number of possibly correlated responses can be larger than the sample size, we demonstrate that a marginalization-based collapsed Gibbs sampler, in combination with spike and slab type of priors, offers a computationally feasible and efficient solution. As an example, we apply our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets of SNPs and possibly correlated genetic transcripts. Our method also allows for inference on the sparse interaction network of the transcripts (response variables) after accounting for the effect of the SNPs (predictor variables). We exploit properties of Gaussian graphical models to make statements concerning conditional independence of the responses. Our method compares favorably to existing Bayesian approaches developed for this purpose. © 2013, The International Biometric Society.

Angular scanning and variable wavelength surface plasmon resonance allowing free sensor surface selection for optimum material- and bio-sensing

NARCIS (Netherlands)

Lakayan, Dina; Tuppurainen, Jussipekka; Albers, Martin; van Lint, Matthijs J.; van Iperen, Dick J.; Weda, Jelmer J.A.; Kuncova-Kallio, Johana; Somsen, Govert W.; Kool, Jeroen

2018-01-01

A variable-wavelength Kretschmann configuration surface plasmon resonance (SPR) apparatus with angle scanning is presented. The setup provides the possibility of selecting the optimum wavelength with respect to the properties of the metal layer of the sensorchip, sample matrix, and biomolecular

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.

An experiment on selecting most informative variables in socio-economic data

Directory of Open Access Journals (Sweden)

L. Jenkins

2014-01-01

Full Text Available In many studies where data are collected on several variables, there is a motivation to find if fewer variables would provide almost as much information. Variance of a variable about its mean is the common statistical measure of information content, and that is used here. We are interested whether the variability in one variable is sufficiently correlated with that in one or more of the other variables that the first variable is redundant. We wish to find one or more ‘principal variables’ that sufficiently reflect the information content in all the original variables. The paper explains the method of principal variables and reports experiments using the technique to see if just a few variables are sufficient to reflect the information in 11 socioeconomic variables on 130 countries from a World Bank (WB database. While the method of principal variables is highly successful in a statistical sense, the WB data varies greatly from year to year, demonstrating that fewer variables wo uld be inadequate for this data.

Variably insulating portable heater/cooler

Science.gov (United States)

Potter, T.F.

1998-09-29

A compact vacuum insulation panel is described comprising a chamber enclosed by two sheets of metal, glass-like spaces disposed in the chamber between the sidewalls, and a high-grade vacuum in the chamber includes apparatus and methods for enabling and disabling, or turning ``on`` and ``off`` the thermal insulating capability of the panel. One type of enabling and disabling apparatus and method includes a metal hydride for releasing hydrogen gas into the chamber in response to heat, and a hydrogen grate between the metal hydride and the chamber for selectively preventing and allowing return of the hydrogen gas to the metal hydride. Another type of enabling and disabling apparatus and method includes a variable emissivity coating on the sheets of metal in which the emissivity is controllably variable by heat or electricity. Still another type of enabling and disabling apparatus and method includes metal-to-metal contact devices that can be actuated to establish or break metal-to-metal heat paths or thermal short circuits between the metal sidewalls. 25 figs.

Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies.

Science.gov (United States)

Savitsky, Terrance; Vannucci, Marina; Sha, Naijun

2011-02-01

This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data. Our specific interest is in the analysis of data sets with predictors that have an a priori unknown form of possibly nonlinear associations to the response. The modeling approach we describe incorporates Gaussian processes in a generalized linear model framework to obtain a class of nonparametric regression models where the covariance matrix depends on the predictors. We consider, in particular, continuous, categorical and count responses. We also look into models that account for survival outcomes. We explore alternative covariance formulations for the Gaussian process prior and demonstrate the flexibility of the construction. Next, we focus on the important problem of selecting variables from the set of possible predictors and describe a general framework that employs mixture priors. We compare alternative MCMC strategies for posterior inference and achieve a computationally efficient and practical approach. We demonstrate performances on simulated and benchmark data sets.

Managing anthelmintic resistance-Variability in the dose of drug reaching the target worms influences selection for resistance?

Science.gov (United States)

Leathwick, Dave M; Luo, Dongwen

2017-08-30

The concentration profile of anthelmintic reaching the target worms in the host can vary between animals even when administered doses are tailored to individual liveweight at the manufacturer's recommended rate. Factors contributing to variation in drug concentration include weather, breed of animal, formulation and the route by which drugs are administered. The implications of this variability for the development of anthelmintic resistance was investigated using Monte-Carlo simulation. A model framework was established where 100 animals each received a single drug treatment. The 'dose' of drug allocated to each animal (i.e. the concentration-time profile of drug reaching the target worms) was sampled at random from a distribution of doses with mean m and standard deviation s. For each animal the dose of drug was used in conjunction with pre-determined dose-response relationships, representing single and poly-genetic inheritance, to calculate efficacy against susceptible and resistant genotypes. These data were then used to calculate the overall change in resistance gene frequency for the worm population as a result of the treatment. Values for m and s were varied to reflect differences in both mean dose and the variability in dose, and for each combination of these 100,000 simulations were run. The resistance gene frequency in the population after treatment increased as m decreased and as s increased. This occurred for both single and poly-gene models and for different levels of dominance (survival under treatment) of the heterozygote genotype(s). The results indicate that factors which result in lower and/or more variable concentrations of active reaching the target worms are more likely to select for resistance. The potential of different routes of anthelmintic administration to play a role in the development of anthelmintic resistance is discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

Science.gov (United States)

Haller, Bernhard; Ulm, Kurt

2018-02-20

To individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomarker-treatment interactions in randomized clinical trials with time-to-event outcomes. A simulation study was performed to assess how prognostic factors affect the estimate of the biomarker-treatment interaction for a time-to-event outcome, when different approaches, like ignoring other prognostic factors, including all available covariates or using variable selection strategies, are applied. Different scenarios regarding the proportion of censored observations, the correlation structure between the covariate of interest and further potential prognostic variables, and the strength of the interaction were considered. The simulation study revealed that in a regression model for estimating a biomarker-treatment interaction, the probability of detecting a biomarker-treatment interaction can be increased by including prognostic variables that are associated with the outcome, and that the interaction estimate is biased when relevant prognostic variables are not considered. However, the probability of a false-positive finding increases if too many potential predictors are included or if variable selection is performed inadequately. We recommend undertaking an adequate literature search before data analysis to derive information about potential prognostic variables and to gain power for detecting true interaction effects and pre-specifying analyses to avoid selective reporting and increased false-positive rates.

VARIABILITY OF AMYLOSE AND AMYLOPECTIN IN WINTER WHEAT AND SELECTION FOR SPECIAL PURPOSES

Directory of Open Access Journals (Sweden)

Nikolina Weg Krstičević

2015-06-01

Full Text Available The aim of this study was to investigate the variability of amylose and amylopectin in 24 Croatian and six foreign winter wheat varieties and to detect the potential of these varieties for special purposes. Starch composition analysis was based on the separation of amylose and amylopectin and the determination of their amounts and ratios. Analysis of the amount of amylose and amylopectin determined statistically highly significant differences between the varieties. The tested varieties are mostly bread wheat of different quality which have the usual content of amylose and amylopectin. Some varieties were identified among them with high amylopectin and low amylose content and one variety with high amylose content. They have the potential in future breeding programs and selection for special purposes.

Why Include Impacts on Biodiversity from Land Use in LCIA and How to Select Useful Indicators?

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Ottar Michelsen

2015-05-01

Full Text Available Loss of biodiversity is one of the most severe threats to sustainability, and land use and land use changes are still the single most important factor. Still, there is no sign of any consensus on how to include impacts on biodiversity from land use and land use changes in LCIA. In this paper, different characteristics of biodiversity are discussed and related to proposals on how to include land use and land use changes in LCIA. We identify the question of why we should care about biodiversity as a key question, since different motivations will result in different choices for the indicators, and we call for more openness in the motivation for indicator selection. We find a promising trend in combining pressure indicators with geographic weighting and regard this as a promising way ahead. More knowledge on the consequences of different choices, such as the selection of a reference state, is still needed.

Habitat Heterogeneity Variably Influences Habitat Selection by Wild Herbivores in a Semi-Arid Tropical Savanna Ecosystem.

Directory of Open Access Journals (Sweden)

Victor K Muposhi

Full Text Available An understanding of the habitat selection patterns by wild herbivores is critical for adaptive management, particularly towards ecosystem management and wildlife conservation in semi arid savanna ecosystems. We tested the following predictions: (i surface water availability, habitat quality and human presence have a strong influence on the spatial distribution of wild herbivores in the dry season, (ii habitat suitability for large herbivores would be higher compared to medium-sized herbivores in the dry season, and (iii spatial extent of suitable habitats for wild herbivores will be different between years, i.e., 2006 and 2010, in Matetsi Safari Area, Zimbabwe. MaxEnt modeling was done to determine the habitat suitability of large herbivores and medium-sized herbivores. MaxEnt modeling of habitat suitability for large herbivores using the environmental variables was successful for the selected species in 2006 and 2010, except for elephant (Loxodonta africana for the year 2010. Overall, large herbivores probability of occurrence was mostly influenced by distance from rivers. Distance from roads influenced much of the variability in the probability of occurrence of medium-sized herbivores. The overall predicted area for large and medium-sized herbivores was not different. Large herbivores may not necessarily utilize larger habitat patches over medium-sized herbivores due to the habitat homogenizing effect of water provisioning. Effect of surface water availability, proximity to riverine ecosystems and roads on habitat suitability of large and medium-sized herbivores in the dry season was highly variable thus could change from one year to another. We recommend adaptive management initiatives aimed at ensuring dynamic water supply in protected areas through temporal closure and or opening of water points to promote heterogeneity of wildlife habitats.

A selective overview of feature screening for ultrahigh-dimensional data.

Science.gov (United States)

JingYuan, Liu; Wei, Zhong; RunZe, L I

2015-10-01

High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of high-dimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many high-dimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

Directory of Open Access Journals (Sweden)

C. Fernandez-Lozano

2013-01-01

Full Text Available Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM. Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA, the most representative variables for a specific classification problem can be selected.

Understanding morphological variability in a taxonomic context in Chilean diplomystids (Teleostei: Siluriformes, including the description of a new species

Directory of Open Access Journals (Sweden)

Gloria Arratia

2017-02-01

Full Text Available Following study of the external morphology and its unmatched variability throughout ontogeny and a re-examination of selected morphological characters based on many specimens of diplomystids from Central and South Chile, we revised and emended previous specific diagnoses and consider Diplomystes chilensis, D. nahuelbutaensis, D. camposensis, and Olivaichthys viedmensis (Baker River to be valid species. Another group, previously identified as Diplomystes sp., D. spec., D. aff. chilensis, and D. cf. chilensis inhabiting rivers between Rapel and Itata Basins is given a new specific name (Diplomystes incognitus and is diagnosed. An identification key to the Chilean species, including the new species, is presented. All specific diagnoses are based on external morphological characters, such as aspects of the skin, neuromast lines, and main lateral line, and position of the anus and urogenital pore, as well as certain osteological characters to facilitate the identification of these species that previously was based on many internal characters. Diplomystids below 150 mm standard length (SL share a similar external morphology and body proportions that make identification difficult; however, specimens over 150 mm SL can be diagnosed by the position of the urogenital pore and anus, and a combination of external and internal morphological characters. According to current knowledge, diplomystid species have an allopatric distribution with each species apparently endemic to particular basins in continental Chile and one species (O. viedmensis known only from one river in the Chilean Patagonia, but distributed extensively in southern Argentina.

Neuronal Intra-Individual Variability Masks Response Selection Differences between ADHD Subtypes—A Need to Change Perspectives

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Annet Bluschke

2017-06-01

Full Text Available Due to the high intra-individual variability in attention deficit/hyperactivity disorder (ADHD, there may be considerable bias in knowledge about altered neurophysiological processes underlying executive dysfunctions in patients with different ADHD subtypes. When aiming to establish dimensional cognitive-neurophysiological constructs representing symptoms of ADHD as suggested by the initiative for Research Domain Criteria, it is crucial to consider such processes independent of variability. We examined patients with the predominantly inattentive subtype (attention deficit disorder, ADD and the combined subtype of ADHD (ADHD-C in a flanker task measuring conflict control. Groups were matched for task performance. Besides using classic event-related potential (ERP techniques and source localization, neurophysiological data was also analyzed using residue iteration decomposition (RIDE to statistically account for intra-individual variability and S-LORETA to estimate the sources of the activations. The analysis of classic ERPs related to conflict monitoring revealed no differences between patients with ADD and ADHD-C. When individual variability was accounted for, clear differences became apparent in the RIDE C-cluster (analog to the P3 ERP-component. While patients with ADD distinguished between compatible and incompatible flanker trials early on, patients with ADHD-C seemed to employ more cognitive resources overall. These differences are reflected in inferior parietal areas. The study demonstrates differences in neuronal mechanisms related to response selection processes between ADD and ADHD-C which, according to source localization, arise from the inferior parietal cortex. Importantly, these differences could only be detected when accounting for intra-individual variability. The results imply that it is very likely that differences in neurophysiological processes between ADHD subtypes are underestimated and have not been recognized because intra

A selective review of the first 20 years of instrumental variables models in health-services research and medicine.

Science.gov (United States)

Cawley, John

2015-01-01

The method of instrumental variables (IV) is useful for estimating causal effects. Intuitively, it exploits exogenous variation in the treatment, sometimes called natural experiments or instruments. This study reviews the literature in health-services research and medical research that applies the method of instrumental variables, documents trends in its use, and offers examples of various types of instruments. A literature search of the PubMed and EconLit research databases for English-language journal articles published after 1990 yielded a total of 522 original research articles. Citations counts for each article were derived from the Web of Science. A selective review was conducted, with articles prioritized based on number of citations, validity and power of the instrument, and type of instrument. The average annual number of papers in health services research and medical research that apply the method of instrumental variables rose from 1.2 in 1991-1995 to 41.8 in 2006-2010. Commonly-used instruments (natural experiments) in health and medicine are relative distance to a medical care provider offering the treatment and the medical care provider's historic tendency to administer the treatment. Less common but still noteworthy instruments include randomization of treatment for reasons other than research, randomized encouragement to undertake the treatment, day of week of admission as an instrument for waiting time for surgery, and genes as an instrument for whether the respondent has a heritable condition. The use of the method of IV has increased dramatically in the past 20 years, and a wide range of instruments have been used. Applications of the method of IV have in several cases upended conventional wisdom that was based on correlations and led to important insights about health and healthcare. Future research should pursue new applications of existing instruments and search for new instruments that are powerful and valid.

Assessment of acute pesticide toxicity with selected biochemical variables in suicide attempting subjects

International Nuclear Information System (INIS)

Soomro, A.M.; Seehar, G.M.; Bhanger, M.I.

2003-01-01

Pesticide induced changes were assessed in thirty two subjects of attempted suicide cases. Among all, the farmers and their families were recorded as most frequently suicide attempting. The values obtained from seven biochemical variables of 29 years old (average age) hospitalized subjects were compared to the same number and age matched normal volunteers. The results revealed major differences in the mean values of the selected parameters. The mean difference calculate; alkaline phosphatase (178.7 mu/l), Bilirubin (7.5 mg/dl), GPT (59.2 mu/l) and glucose (38.6 mg/dl) were higher than the controls, which indicate the hepatotoxicity induced by the pesticides in suicide attempting individuals. Increase in serum creatinine and urea indicated renal malfunction that could be linked with pesticide induced nephrotoxicity among them. (author)

Impact of perennial energy crops income variability on the crop selection of risk averse farmers

International Nuclear Information System (INIS)

Alexander, Peter; Moran, Dominic

2013-01-01

The UK Government policy is for the area of perennial energy crops in the UK to expand significantly. Farmers need to choose these crops in preference to conventional rotations for this to be achievable. This paper looks at the potential level and variability of perennial energy crop incomes and the relation to incomes from conventional arable crops. Assuming energy crop prices are correlated to oil prices the results suggests that incomes from them are not well correlated to conventional arable crop incomes. A farm scale mathematical programming model is then used to attempt to understand the affect on risk averse farmers crop selection. The inclusion of risk reduces the energy crop price required for the selection of these crops. However yields towards the highest of those predicted in the UK are still required to make them an optimal choice, suggesting only a small area of energy crops within the UK would be expected to be chosen to be grown. This must be regarded as a tentative conclusion, primarily due to high sensitivity found to crop yields, resulting in the proposal for further work to apply the model using spatially disaggregated data. - Highlights: ► Energy crop and conventional crop incomes suggested as uncorrelated. ► Diversification effect of energy crops investigated for a risk averse farmer. ► Energy crops indicated as optimal selection only on highest yielding UK sites. ► Large establishment grant rates to substantially alter crop selections.

Relation of desert pupfish abundance to selected environmental variables in natural and manmade habitats in the Salton Sea basin

Science.gov (United States)

Martin, B.A.; Saiki, M.K.

2005-01-01

We assessed the relation between abundance of desert pupfish, Cyprinodon macularius, and selected biological and physicochemical variables in natural and manmade habitats within the Salton Sea Basin. Field sampling in a natural tributary, Salt Creek, and three agricultural drains captured eight species including pupfish (1.1% of the total catch), the only native species encountered. According to Bray-Curtis resemblance functions, fish species assemblages differed mostly between Salt Creek and the drains (i.e., the three drains had relatively similar species assemblages). Pupfish numbers and environmental variables varied among sites and sample periods. Canonical correlation showed that pupfish abundance was positively correlated with abundance of western mosquitofish, Gambusia affinis, and negatively correlated with abundance of porthole livebearers, Poeciliopsis gracilis, tilapias (Sarotherodon mossambica and Tilapia zillii), longjaw mudsuckers, Gillichthys mirabilis, and mollies (Poecilia latipinnaandPoecilia mexicana). In addition, pupfish abundance was positively correlated with cover, pH, and salinity, and negatively correlated with sediment factor (a measure of sediment grain size) and dissolved oxygen. Pupfish abundance was generally highest in habitats where water quality extremes (especially high pH and salinity, and low dissolved oxygen) seemingly limited the occurrence of nonnative fishes. This study also documented evidence of predation by mudsuckers on pupfish. These findings support the contention of many resource managers that pupfish populations are adversely influenced by ecological interactions with nonnative fishes. ?? Springer 2005.

A Study on Site Selecting for National Project including High Level Radioactive Waste Disposal

Energy Technology Data Exchange (ETDEWEB)

Kim, Kilyoo [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

Many national projects are stopped since sites for the projects are not determined. The sites selections are hold by NIMBY for unpleasant facilities or by PYMFY for preferable facilities among local governments. The followings are the typical ones; NIMBY projects: high level radioactive waste disposal, THAAD, Nuclear power plant(NPP), etc. PIMFY projects: South-east new airport, KTX station, Research center for NPP decommission, etc. The site selection for high level radioactive waste disposal is more difficult problem, and thus government did not decide and postpone to a dead end street. Since it seems that there is no solution for site selection for high level radioactive waste disposal due to NIMBY among local governments, a solution method is proposed in this paper. To decide a high level radioactive waste disposal, the first step is to invite a bid by suggesting a package deal including PIMFY projects such as Research Center for NPP decommission. Maybe potential host local governments are asked to submit sealed bids indicating the minimum compensation sum that they would accept the high level radioactive waste disposal site. If there are more than one local government put in a bid, then decide an adequate site by considering both the accumulated PESS point and technical evaluation results. By considering how fairly preferable national projects and unpleasant national projects are distributed among local government, sites selection for NIMBY or PIMFY facilities is suggested. For NIMBY national projects, risk, cost benefit analysis is useful and required since it generates cost value to be used in the PESS. For many cases, the suggested method may be not adequate. However, similar one should be prepared, and be basis to decide sites for NIMBY or PIMFY national projects.

Internet interventions for chronic pain including headache: A systematic review

Directory of Open Access Journals (Sweden)

Monica Buhrman

2016-05-01

Full Text Available Chronic pain is a major health problem and behavioral based treatments have been shown to be effective. However, the availability of these kinds of treatments is scarce and internet-based treatments have been shown to be promising in this area. The objective of the present systematic review is to evaluate internet-based interventions for persons with chronic pain. The specific aims are to do an updated review with a broad inclusion of different chronic pain diagnoses and to assess disability and pain and also measures of catastrophizing, depression and anxiety. A systematic search identified 891 studies and 22 trials were selected as eligible for review. Two of the selected trials included children/youth and five included individuals with chronic headache and/or migraine. The most frequently measured domain reflected in the primary outcomes was interference/disability, followed by catastrophizing. Result across the studies showed a number of beneficial effects. Twelve trials reported significant effects on disability/interference outcomes and pain intensity. Positive effects were also found on psychological variable such as catastrophizing, depression and anxiety. Several studies (n = 12 were assessed to have an unclear level of risk bias. The attrition levels ranged from 4% to 54% where the headache trials had the highest drop-out levels. However, findings suggest that internet-based treatments based on cognitive behavioural therapy (CBT are efficacious measured with different outcome variables. Results are in line with trials in clinical settings. Meta-analytic statistics were calculated for interference/disability, pain intensity, catastrophizing and mood ratings. Results showed that the effect size for interference/disability was Hedge's g = −0.39, for pain intensity Hedge's g = −0.33, for catastrophizing Hedge's g = −0.49 and for mood variables (depression Hedge's g = −0.26.

Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

Science.gov (United States)

Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng

2016-03-10

Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. Copyright © 2016 Elsevier B.V. All rights reserved.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

Science.gov (United States)

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-04-01

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Variable Selection Strategies for Small-area Estimation Using FIA Plots and Remotely Sensed Data

Science.gov (United States)

Andrew Lister; Rachel Riemann; James Westfall; Mike Hoppus

2005-01-01

The USDA Forest Service's Forest Inventory and Analysis (FIA) unit maintains a network of tens of thousands of georeferenced forest inventory plots distributed across the United States. Data collected on these plots include direct measurements of tree diameter and height and other variables. We present a technique by which FIA plot data and coregistered...

ROTSE All-Sky Surveys for Variable Stars. I. Test Fields

International Nuclear Information System (INIS)

Akerlof, C.; Amrose, S.; Balsano, R.; Bloch, J.; Casperson, D.; Fletcher, S.; Gisler, G.; Hills, J.; Kehoe, R.; Lee, B.

2000-01-01

The Robotic Optical Transient Search Experiment I (ROTSE-I) experiment has generated CCD photometry for the entire northern sky in two epochs nightly since 1998 March. These sky patrol data are a powerful resource for studies of astrophysical transients. As a demonstration project, we present first results of a search for periodic variable stars derived from ROTSE-I observations. Variable identification, period determination, and type classification are conducted via automatic algorithms. In a set of nine ROTSE-I sky patrol fields covering roughly 2000 deg2, we identify 1781 periodic variable stars with mean magnitudes between m v = 10.0 and m v = 15.5. About 90% of these objects are newly identified as variable. Examples of many familiar types are presented. All classifications for this study have been manually confirmed. The selection criteria for this analysis have been conservatively defined and are known to be biased against some variable classes. This preliminary study includes only 5.6% of the total ROTSE-I sky coverage, suggesting that the full ROTSE-I variable catalog will include more than 32,000 periodic variable stars. (c) (c) 2000. The American Astronomical Society

Cleanup and treatment of radioactively contaminated land including areas near nuclear facilities. A selected bibliography

International Nuclear Information System (INIS)

Fore, C.S.; Faust, R.A.; Brewster, R.H.

1982-09-01

This annotated bibliography of 337 references summarizes the literature published on the cleanup and treatment of radioactively contaminated land. Specifically, this bibliography focuses on literature concerned with the methods of cleanup and treatment being applied - chemical, physical, or vegetative stabilization; the types of equipment being used; and the influence of climatic conditions on the method selected for use. The emphasis in such literature is placed on hazardous site cleanup efforts that have been completed as well as those that are in progress and are being planned. Appendix A includes 135 additional references to literature identified but not included in the bibliography because of time and funding constraints. Appendix B consists of a table that identifies the cleanup and treatment research conducted at specific sites. All of the information included in this bibliography is stored in a computerized form that is readily available upon request

Selective attrition and intraindividual variability in response time moderate cognitive change.

Science.gov (United States)

Yao, Christie; Stawski, Robert S; Hultsch, David F; MacDonald, Stuart W S

2016-01-01

Selection of a developmental time metric is useful for understanding causal processes that underlie aging-related cognitive change and for the identification of potential moderators of cognitive decline. Building on research suggesting that time to attrition is a metric sensitive to non-normative influences of aging (e.g., subclinical health conditions), we examined reason for attrition and intraindividual variability (IIV) in reaction time as predictors of cognitive performance. Three hundred and four community dwelling older adults (64-92 years) completed annual assessments in a longitudinal study. IIV was calculated from baseline performance on reaction time tasks. Multilevel models were fit to examine patterns and predictors of cognitive change. We show that time to attrition was associated with cognitive decline. Greater IIV was associated with declines on executive functioning and episodic memory measures. Attrition due to personal health reasons was also associated with decreased executive functioning compared to that of individuals who remained in the study. These findings suggest that time to attrition is a useful metric for representing cognitive change, and reason for attrition and IIV are predictive of non-normative influences that may underlie instances of cognitive loss in older adults.

THE TIME-DOMAIN SPECTROSCOPIC SURVEY: UNDERSTANDING THE OPTICALLY VARIABLE SKY WITH SEQUELS IN SDSS-III

International Nuclear Information System (INIS)

Ruan, John J.; Anderson, Scott F.; Davenport, James R. A.; Green, Paul J.; Morganson, Eric; Eracleous, Michael; Brandt, William N.; Myers, Adam D.; Badenes, Carles; Bershady, Matthew A.; Chambers, Kenneth C.; Flewelling, Heather; Kaiser, Nick; Dawson, Kyle S.; Heckman, Timothy M.; Isler, Jedidah C.; Kneib, Jean-Paul; MacLeod, Chelsea L.; Ross, Nicholas P.; Paris, Isabelle

2016-01-01

The Time-Domain Spectroscopic Survey (TDSS) is an SDSS-IV eBOSS subproject primarily aimed at obtaining identification spectra of ∼220,000 optically variable objects systematically selected from SDSS/Pan-STARRS1 multi-epoch imaging. We present a preview of the science enabled by TDSS, based on TDSS spectra taken over ∼320 deg 2 of sky as part of the SEQUELS survey in SDSS-III, which is in part a pilot survey for eBOSS in SDSS-IV. Using the 15,746 TDSS-selected single-epoch spectra of photometrically variable objects in SEQUELS, we determine the demographics of our variability-selected sample and investigate the unique spectral characteristics inherent in samples selected by variability. We show that variability-based selection of quasars complements color-based selection by selecting additional redder quasars and mitigates redshift biases to produce a smooth quasar redshift distribution over a wide range of redshifts. The resulting quasar sample contains systematically higher fractions of blazars and broad absorption line quasars than from color-selected samples. Similarly, we show that M dwarfs in the TDSS-selected stellar sample have systematically higher chromospheric active fractions than the underlying M-dwarf population based on their H α emission. TDSS also contains a large number of RR Lyrae and eclipsing binary stars with main-sequence colors, including a few composite-spectrum binaries. Finally, our visual inspection of TDSS spectra uncovers a significant number of peculiar spectra, and we highlight a few cases of these interesting objects. With a factor of ∼15 more spectra, the main TDSS survey in SDSS-IV will leverage the lessons learned from these early results for a variety of time-domain science applications.

Including investment risk in large-scale power market models

DEFF Research Database (Denmark)

Lemming, Jørgen Kjærgaard; Meibom, P.

2003-01-01

Long-term energy market models can be used to examine investments in production technologies, however, with market liberalisation it is crucial that such models include investment risks and investor behaviour. This paper analyses how the effect of investment risk on production technology selection...... can be included in large-scale partial equilibrium models of the power market. The analyses are divided into a part about risk measures appropriate for power market investors and a more technical part about the combination of a risk-adjustment model and a partial-equilibrium model. To illustrate...... the analyses quantitatively, a framework based on an iterative interaction between the equilibrium model and a separate risk-adjustment module was constructed. To illustrate the features of the proposed modelling approach we examined how uncertainty in demand and variable costs affects the optimal choice...

Estimation of Genetic Variance Components Including Mutation and Epistasis using Bayesian Approach in a Selection Experiment on Body Weight in Mice

DEFF Research Database (Denmark)

Widyas, Nuzul; Jensen, Just; Nielsen, Vivi Hunnicke

Selection experiment was performed for weight gain in 13 generations of outbred mice. A total of 18 lines were included in the experiment. Nine lines were allotted to each of the two treatment diets (19.3 and 5.1 % protein). Within each diet three lines were selected upwards, three lines were...... selected downwards and three lines were kept as controls. Bayesian statistical methods are used to estimate the genetic variance components. Mixed model analysis is modified including mutation effect following the methods by Wray (1990). DIC was used to compare the model. Models including mutation effect...... have better fit compared to the model with only additive effect. Mutation as direct effect contributes 3.18% of the total phenotypic variance. While in the model with interactions between additive and mutation, it contributes 1.43% as direct effect and 1.36% as interaction effect of the total variance...

Including Children with Selective Mutism in Mainstream Schools and Kindergartens: Problems and Possibilities

Science.gov (United States)

Omdal, Heidi

2008-01-01

There is little research on inclusion of children with selective mutism in school/kindergarten. Moreover, few studies have tried to understand selectively mute children's interactions in the natural surroundings of their home and school/kindergarten. Five children meeting the DSM-IV criteria for selective mutism were video-observed in social…

A novel peak-hopping stepwise feature selection method with application to Raman spectroscopy

International Nuclear Information System (INIS)

McShane, M.J.; Cameron, B.D.; Cote, G.L.; Motamedi, M.; Spiegelman, C.H.

1999-01-01

A new stepwise approach to variable selection for spectroscopy that includes chemical information and attempts to test several spectral regions producing high ranking coefficients has been developed to improve on currently available methods. Existing selection techniques can, in general, be placed into two groups: the first, time-consuming optimization approaches that ignore available information about sample chemistry and require considerable expertise to arrive at appropriate solutions (e.g. genetic algorithms), and the second, stepwise procedures that tend to select many variables in the same area containing redundant information. The algorithm described here is a fast stepwise procedure that uses multiple ranking chains to identify several spectral regions correlated with known sample properties. The multiple-chain approach allows the generation of a final ranking vector that moves quickly away from the initial selection point, testing several areas exhibiting correlation between spectra and composition early in the stepping procedure. Quantitative evidence of the success of this approach as applied to Raman spectroscopy is given in terms of processing speed, number of selected variables, and prediction error in comparison with other selection methods. In this respect, the procedure described here may be considered as a significant evolutionary step in variable selection algorithms. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

Variable selectivity and the role of nutritional quality in food selection by a planktonic rotifer

International Nuclear Information System (INIS)

Sierszen, M.E.

1990-01-01

To investigate the potential for selective feeding to enhance fitness, I test the hypothesis that an herbivorous zooplankter selects those food items that best support its reproduction. Under this hypothesis, growth and reproduction on selected food items should be higher than on less preferred items. The hypothesis is not supported. In situ selectivity by the rotifer Keratella taurocephala for Cryptomonas relative to Chlamydomonas goes through a seasonal cycle, in apparent response to fluctuating Cryptomonas populations. However, reproduction on a unialgal diet of Cryptomonas is consistently high and similar to that on Chlamydomonas. Oocystis, which also supports reproduction equivalent to that supported by Chlamydomonas, is sometimes rejected by K. taurocephala. In addition, K. taurocephala does not discriminate between Merismopedia and Chlamydomonas even though Merismopedia supports virtually no reproduction by the rotifer. Selection by K. taurocephala does not simply maximize the intake of food items that yield high reproduction. Selectivity is a complex, dynamic process, one function of which may be the exploitation of locally or seasonally abundant foods. (author)

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

Science.gov (United States)

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

Synthesis, Characterization, and Variable-Temperature NMR Studies of Silver(I) Complexes for Selective Nitrene Transfer.

Science.gov (United States)

Huang, Minxue; Corbin, Joshua R; Dolan, Nicholas S; Fry, Charles G; Vinokur, Anastasiya I; Guzei, Ilia A; Schomaker, Jennifer M

2017-06-05

An array of silver complexes supported by nitrogen-donor ligands catalyze the transformation of C═C and C-H bonds to valuable C-N bonds via nitrene transfer. The ability to achieve high chemoselectivity and site selectivity in an amination event requires an understanding of both the solid- and solution-state behavior of these catalysts. X-ray structural characterizations were helpful in determining ligand features that promote the formation of monomeric versus dimeric complexes. Variable-temperature 1 H and DOSY NMR experiments were especially useful for understanding how the ligand identity influences the nuclearity, coordination number, and fluxional behavior of silver(I) complexes in solution. These insights are valuable for developing improved ligand designs.

Variable frequency microwave heating apparatus

Energy Technology Data Exchange (ETDEWEB)

Bible, D.W.; Lauf, R.J.; Johnson, A.C.; Thigpen, L.T.

1999-10-05

A variable frequency microwave heating apparatus (10) designed to allow modulation of the frequency of the microwaves introduced into a multi-mode microwave cavity (34) for testing or other selected applications. The variable frequency microwave heating apparatus (10) includes a microwave signal generator (12) and a high-power microwave amplifier (20) or a high-power microwave oscillator (14). A power supply (22) is provided for operation of the high-power microwave oscillator (14) or microwave amplifier (20). A directional coupler (24) is provided for detecting the direction and amplitude of signals incident upon and reflected from the microwave cavity (34). A first power meter (30) is provided for measuring the power delivered to the microwave furnace (32). A second power meter (26) detects the magnitude of reflected power. Reflected power is dissipated in the reflected power load (28).

Pathogen-mediated selection for MHC variability in wild zebrafish

Czech Academy of Sciences Publication Activity Database

Smith, C.; Ondračková, Markéta; Spence, R.; Adams, S.; Betts, D. S.; Mallon, E.

2011-01-01

Roč. 13, č. 6 (2011), s. 589-605 ISSN 1522-0613 Institutional support: RVO:68081766 Keywords : digenean * frequency-dependent selection * heterozygote advantage * major histocompatibility complex * metazoan parasite * pathogen-driven selection Subject RIV: EG - Zoology Impact factor: 1.029, year: 2011

THE TIME-DOMAIN SPECTROSCOPIC SURVEY: UNDERSTANDING THE OPTICALLY VARIABLE SKY WITH SEQUELS IN SDSS-III

Energy Technology Data Exchange (ETDEWEB)

Ruan, John J.; Anderson, Scott F.; Davenport, James R. A. [Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195 (United States); Green, Paul J.; Morganson, Eric [Harvard Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Eracleous, Michael; Brandt, William N. [Department of Astronomy and Astrophysics, 525 Davey Lab, The Pennsylvania State University, University Park, PA 16802 (United States); Myers, Adam D. [Department of Physics and Astronomy 3905, University of Wyoming, 1000 E. University, Laramie, WY 82071 (United States); Badenes, Carles [Department of Physics and Astronomy and Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT-PACC), University of Pittsburgh (United States); Bershady, Matthew A. [Department of Astronomy, University of Wisconsin-Madison, 475 N. Charter Street, Madison, WI 53706 (United States); Chambers, Kenneth C.; Flewelling, Heather; Kaiser, Nick [Institute for Astronomy, University of Hawaii at Manoa, Honolulu, HI 96822 (United States); Dawson, Kyle S. [Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112 (United States); Heckman, Timothy M. [Center for Astrophysical Sciences, Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218 (United States); Isler, Jedidah C. [Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37235 (United States); Kneib, Jean-Paul [Laboratoire d’astrophysique, Ecole Polytechnique Fédérale de Lausanne Observatoire de Sauverny, 1290 Versoix (Switzerland); MacLeod, Chelsea L.; Ross, Nicholas P. [Institute for Astronomy, University of Edinburgh, Royal Observatory, Edinburgh, EH9 3HJ (United Kingdom); Paris, Isabelle, E-mail: jruan@astro.washington.edu [INAF—Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, I-34131 Trieste (Italy); and others

2016-07-10

The Time-Domain Spectroscopic Survey (TDSS) is an SDSS-IV eBOSS subproject primarily aimed at obtaining identification spectra of ∼220,000 optically variable objects systematically selected from SDSS/Pan-STARRS1 multi-epoch imaging. We present a preview of the science enabled by TDSS, based on TDSS spectra taken over ∼320 deg{sup 2} of sky as part of the SEQUELS survey in SDSS-III, which is in part a pilot survey for eBOSS in SDSS-IV. Using the 15,746 TDSS-selected single-epoch spectra of photometrically variable objects in SEQUELS, we determine the demographics of our variability-selected sample and investigate the unique spectral characteristics inherent in samples selected by variability. We show that variability-based selection of quasars complements color-based selection by selecting additional redder quasars and mitigates redshift biases to produce a smooth quasar redshift distribution over a wide range of redshifts. The resulting quasar sample contains systematically higher fractions of blazars and broad absorption line quasars than from color-selected samples. Similarly, we show that M dwarfs in the TDSS-selected stellar sample have systematically higher chromospheric active fractions than the underlying M-dwarf population based on their H α emission. TDSS also contains a large number of RR Lyrae and eclipsing binary stars with main-sequence colors, including a few composite-spectrum binaries. Finally, our visual inspection of TDSS spectra uncovers a significant number of peculiar spectra, and we highlight a few cases of these interesting objects. With a factor of ∼15 more spectra, the main TDSS survey in SDSS-IV will leverage the lessons learned from these early results for a variety of time-domain science applications.

Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2017-08-22

The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large. In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first local minimum is retained. In this paper, a modification of UVE-PLS is proposed and investigated, in which UVE is repeated until no further reduction in variables is possible, followed by a search for the global RMSECV minimum. The method is called Global-Minimum Error Uninformative-Variable Elimination for PLS, denoted as GME-UVE-PLS or simply GME-UVE. After each iteration, the predictive ability of the PLS model, built with the remaining variable set, is assessed by RMSECV. The variable set with the global RMSECV minimum is then finally selected. The goal is to obtain smaller sets of variables with similar or improved predictability than those from the classical UVE-PLS method. The performance of the GME-UVE-PLS method is investigated using four data sets, i.e. a simulated set, NIR and NMR spectra, and a theoretical molecular descriptors set, resulting in twelve profile-response (X-y) calibrations. The selective and predictive performances of the models resulting from GME-UVE-PLS are statistically compared to those from UVE-PLS and 1-step UVE, one-sided paired t-tests. The results demonstrate that variable reduction with the proposed GME-UVE-PLS method, usually eliminates significantly more variables than the classical UVE-PLS, while the predictive abilities of the resulting models are better. With GME-UVE-PLS, a lower number of uninformative variables, without a chemical meaning for the response, may be retained than with UVE-PLS. The selectivity of the classical UVE method

The impact of selected organizational variables and managerial leadership on radiation therapists' organizational commitment

International Nuclear Information System (INIS)

Akroyd, Duane; Legg, Jeff; Jackowski, Melissa B.; Adams, Robert D.

2009-01-01

The purpose of this study was to examine the impact of selected organizational factors and the leadership behavior of supervisors on radiation therapists' commitment to their organizations. The population for this study consists of all full time clinical radiation therapists registered by the American Registry of Radiologic Technologists (ARRT) in the United States. A random sample of 800 radiation therapists was obtained from the ARRT for this study. Questionnaires were mailed to all participants and measured organizational variables; managerial leadership variable and three components of organizational commitment (affective, continuance and normative). It was determined that organizational support, and leadership behavior of supervisors each had a significant and positive affect on normative and affective commitment of radiation therapists and each of the models predicted over 40% of the variance in radiation therapists organizational commitment. This study examined radiation therapists' commitment to their organizations and found that affective (emotional attachment to the organization) and normative (feelings of obligation to the organization) commitments were more important than continuance commitment (awareness of the costs of leaving the organization). This study can help radiation oncology administrators and physicians to understand the values their radiation therapy employees hold that are predictive of their commitment to the organization. A crucial result of the study is the importance of the perceived support of the organization and the leadership skills of managers/supervisors on radiation therapists' commitment to the organization.

Statistical variability of hydro-meteorological variables as indicators ...

African Journals Online (AJOL)

Statistical variability of hydro-meteorological variables as indicators of climate change in north-east Sokoto-Rima basin, Nigeria. ... water resources development including water supply project, agriculture and tourism in the study area. Key word: Climate change, Climatic variability, Actual evapotranspiration, Global warming ...

Variable selection in multiple linear regression: The influence of ...

African Journals Online (AJOL)

provide an indication of whether the fit of the selected model improves or ... and calculate M(−i); quantify the influence of case i in terms of a function, f(•), of M and ..... [21] Venter JH & Snyman JLJ, 1997, Linear model selection based on risk ...

The Performance of Variable Annuities

OpenAIRE

Michael J. McNamara; Henry R. Oppenheimer

1991-01-01

Variable annuities have become increasingly important in retirement plans. This paper provides an examination of the investment performance of variable annuities for the period year-end 1973 to year-end 1988. Returns, risk, and selectivity measures are analyzed for the sample of annuities, for individual variable annuities, and for subsamples of annuities with similar portfolio size and turnover. While the investment returns of variable annuities were greater than inflation over the period, t...

Exploratory Spectroscopy of Magnetic Cataclysmic Variables Candidates and Other Variable Objects

Energy Technology Data Exchange (ETDEWEB)

Oliveira, A. S.; Palhares, M. S. [IP and D, Universidade do Vale do Paraíba, 12244-000, São José dos Campos, SP (Brazil); Rodrigues, C. V.; Cieslinski, D.; Jablonski, F. J. [Divisão de Astrofísica, Instituto Nacional de Pesquisas Espaciais, 12227-010, São José dos Campos, SP (Brazil); Silva, K. M. G. [Gemini Observatory, Casilla 603, La Serena (Chile); Almeida, L. A. [Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, 05508-900, São Paulo, SP (Brazil); Rodríguez-Ardila, A., E-mail: alexandre@univap.br [Laboratório Nacional de Astrofísica LNA/MCTI, 37504-364, Itajubá MG (Brazil)

2017-04-01

The increasing number of synoptic surveys made by small robotic telescopes, such as the photometric Catalina Real-Time Transient Survey (CRTS), provides a unique opportunity to discover variable sources and improves the statistical samples of such classes of objects. Our goal is the discovery of magnetic Cataclysmic Variables (mCVs). These are rare objects that probe interesting accretion scenarios controlled by the white-dwarf magnetic field. In particular, improved statistics of mCVs would help to address open questions on their formation and evolution. We performed an optical spectroscopy survey to search for signatures of magnetic accretion in 45 variable objects selected mostly from the CRTS. In this sample, we found 32 CVs, 22 being mCV candidates, 13 of which were previously unreported as such. If the proposed classifications are confirmed, it would represent an increase of 4% in the number of known polars and 12% in the number of known IPs. A fraction of our initial sample was classified as extragalactic sources or other types of variable stars by the inspection of the identification spectra. Despite the inherent complexity in identifying a source as an mCV, variability-based selection, followed by spectroscopic snapshot observations, has proved to be an efficient strategy for their discoveries, being a relatively inexpensive approach in terms of telescope time.

Resolving the Conflict Between Associative Overdominance and Background Selection

Science.gov (United States)

Zhao, Lei; Charlesworth, Brian

2016-01-01

In small populations, genetic linkage between a polymorphic neutral locus and loci subject to selection, either against partially recessive mutations or in favor of heterozygotes, may result in an apparent selective advantage to heterozygotes at the neutral locus (associative overdominance) and a retardation of the rate of loss of variability by genetic drift at this locus. In large populations, selection against deleterious mutations has previously been shown to reduce variability at linked neutral loci (background selection). We describe analytical, numerical, and simulation studies that shed light on the conditions under which retardation vs. acceleration of loss of variability occurs at a neutral locus linked to a locus under selection. We consider a finite, randomly mating population initiated from an infinite population in equilibrium at a locus under selection. With mutation and selection, retardation occurs only when S, the product of twice the effective population size and the selection coefficient, is of order 1. With S >> 1, background selection always causes an acceleration of loss of variability. Apparent heterozygote advantage at the neutral locus is, however, always observed when mutations are partially recessive, even if there is an accelerated rate of loss of variability. With heterozygote advantage at the selected locus, loss of variability is nearly always retarded. The results shed light on experiments on the loss of variability at marker loci in laboratory populations and on the results of computer simulations of the effects of multiple selected loci on neutral variability. PMID:27182952

Genetic gain and economic values of selection strategies including semen traits in three- and four-way crossbreeding systems for swine production.

Science.gov (United States)

González-Peña, D; Knox, R V; MacNeil, M D; Rodriguez-Zas, S L

2015-03-01

Four semen traits: volume (VOL), concentration (CON), progressive motility of spermatozoa (MOT), and abnormal spermatozoa (ABN) provide complementary information on boar fertility. Assessment of the impact of selection for semen traits is hindered by limited information on economic parameters. Objectives of this study were to estimate economic values for semen traits and to evaluate the genetic gain when these traits are incorporated into traditional selection strategies in a 3-tier system of swine production. Three-way (maternal nucleus lines A and B and paternal nucleus line C) and 4-way (additional paternal nucleus line D) crossbreeding schemes were compared. A novel population structure that accommodated selection for semen traits was developed. Three selection strategies were simulated. Selection Strategy I (baseline) encompassed selection for maternal traits: number of pigs born alive (NBA), litter birth weight (LBW), adjusted 21-d litter weight (A21), and number of pigs at 21 d (N21); and paternal traits: number of days to 113.5 kg (D113), backfat (BF), ADG, feed efficiency (FE), and carcass lean % (LEAN). Selection Strategy II included Strategy I and the number of usable semen doses per collection (DOSES), a function of the 4 semen traits. Selection Strategy III included Strategy I and the 4 semen traits individually. The estimated economic values of VOL, CON, MOT, ABN, and DOSES for 7 to 1 collections/wk ranged from $0.21 to $1.44/mL, $0.12 to $0.83/10 spermatozoa/mm, $0.61 to $12.66/%, -$0.53 to -$10.88/%, and $2.01 to $41.43/%, respectively. The decrease in the relative economic values of semen traits and DOSES with higher number of collections per wk was sharper between 1 and 2.33 collections/wk than between 2.33 and 7 collections/wk. The higher economic value of MOT and ABN relative to VOL and CON could be linked to the genetic variances and covariances of these traits. Average genetic gains for the maternal traits were comparable across strategies

Joint Variable Selection and Classification with Immunohistochemical Data

Directory of Open Access Journals (Sweden)

Debashis Ghosh

2009-01-01

Full Text Available To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature of the staining profiles. In this article, we consider modelling such data using recently developed ideas from the machine learning community. In particular, we consider the joint goals of feature selection and classification. We develop estimation procedures for the analysis of immunohistochemical profiles using the least absolute selection and shrinkage operator. These lead to novel and flexible models and algorithms for the analysis of compositional data. The techniques are illustrated using data from a cancer biomarker study.

THE CHANDRA VARIABLE GUIDE STAR CATALOG

International Nuclear Information System (INIS)

Nichols, Joy S.; Lauer, Jennifer L.; Morgan, Douglas L.; Sundheim, Beth A.; Henden, Arne A.; Huenemoerder, David P.; Martin, Eric

2010-01-01

Variable stars have been identified among the optical-wavelength light curves of guide stars used for pointing control of the Chandra X-ray Observatory. We present a catalog of these variable stars along with their light curves and ancillary data. Variability was detected to a lower limit of 0.02 mag amplitude in the 4000-10000 A range using the photometrically stable Aspect Camera on board the Chandra spacecraft. The Chandra Variable Guide Star Catalog (VGUIDE) contains 827 stars, of which 586 are classified as definitely variable and 241 are identified as possibly variable. Of the 586 definite variable stars, we believe 319 are new variable star identifications. Types of variables in the catalog include eclipsing binaries, pulsating stars, and rotating stars. The variability was detected during the course of normal verification of each Chandra pointing and results from analysis of over 75,000 guide star light curves from the Chandra mission. The VGUIDE catalog represents data from only about 9 years of the Chandra mission. Future releases of VGUIDE will include newly identified variable guide stars as the mission proceeds. An important advantage of the use of space data to identify and analyze variable stars is the relatively long observations that are available. The Chandra orbit allows for observations up to 2 days in length. Also, guide stars were often used multiple times for Chandra observations, so many of the stars in the VGUIDE catalog have multiple light curves available from various times in the mission. The catalog is presented as both online data associated with this paper and as a public Web interface. Light curves with data at the instrumental time resolution of about 2 s, overplotted with the data binned at 1 ks, can be viewed on the public Web interface and downloaded for further analysis. VGUIDE is a unique project using data collected during the mission that would otherwise be ignored. The stars available for use as Chandra guide stars are

Detecting correlation between allele frequencies and environmental variables as a signature of selection. A fast computational approach for genome-wide studies

DEFF Research Database (Denmark)

Guillot, Gilles; Vitalis, Renaud; Rouzic, Arnaud le

2014-01-01

to disentangle the potential effect of environmental variables from the confounding effect of population history. For the routine analysis of genome-wide datasets, one also needs fast inference and model selection algorithms. We propose a method based on an explicit spatial model which is an instance of spatial...... for the most common types of genetic markers, obtained either at the individual or at the population level. Analyzing the simulated data produced under a geostatistical model then under an explicit model of selection, we show that the method is efficient. We also re-analyze a dataset relative to nineteen pine...

Selection of entropy-measure parameters for knowledge discovery in heart rate variability data.

Science.gov (United States)

Mayer, Christopher C; Bachler, Martin; Hörtenhuber, Matthias; Stocker, Christof; Holzinger, Andreas; Wassertheurer, Siegfried

2014-01-01

Heart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multiple parameters, the selection of which is controversial and depends on the intended purpose. This study describes the results of tests conducted to support parameter selection, towards the goal of enabling further biomarker discovery. This study deals with approximate, sample, fuzzy, and fuzzy measure entropies. All data were obtained from PhysioNet, a free-access, on-line archive of physiological signals, and represent various medical conditions. Five tests were defined and conducted to examine the influence of: varying the threshold value r (as multiples of the sample standard deviation σ, or the entropy-maximizing rChon), the data length N, the weighting factors n for fuzzy and fuzzy measure entropies, and the thresholds rF and rL for fuzzy measure entropy. The results were tested for normality using Lilliefors' composite goodness-of-fit test. Consequently, the p-value was calculated with either a two sample t-test or a Wilcoxon rank sum test. The first test shows a cross-over of entropy values with regard to a change of r. Thus, a clear statement that a higher entropy corresponds to a high irregularity is not possible, but is rather an indicator of differences in regularity. N should be at least 200 data points for r = 0.2 σ and should even exceed a length of 1000 for r = rChon. The results for the weighting parameters n for the fuzzy membership function show different behavior when coupled with different r values, therefore the weighting parameters have been chosen independently for the different threshold values. The tests concerning rF and rL showed that there is no optimal choice, but r = rF = rL is reasonable with r = rChon or r = 0.2σ. Some of the tests showed a dependency of the test significance on the data at hand. Nevertheless, as the medical

Genetic variability, partial regression, Co-heritability studies and their implication in selection of high yielding potato gen

International Nuclear Information System (INIS)

Iqbal, Z.M.; Khan, S.A.

2003-01-01

Partial regression coefficient, genotypic and phenotypic variabilities, heritability co-heritability and genetic advance were studied in 15 Potato varieties of exotic and local origin. Both genotypic and phenotypic coefficients of variations were high for scab and rhizoctonia incidence percentage. Significant partial regression coefficient for emergence percentage indicated its relative importance in tuber yield. High heritability (broadsense) estimates coupled with high genetic advance for plant height, number of stems per plant and scab percentage revealed substantial contribution of additive genetic variance in the expression of these traits. Hence, the selection based on these characters could play a significant role in their improvement the dominance and epistatic variance was more important for character expression of yield ha/sup -1/, emergence and rhizoctonia percentage. This phenomenon is mainly due to the accumulative effects of low heritability and low to moderate genetic advance. The high co-heritability coupled with negative genotypic and phenotypic covariance revealed that selection of varieties having low scab and rhizoctonia percentage resulted in more potato yield. (author)

Sample size estimation and sampling techniques for selecting a representative sample

Directory of Open Access Journals (Sweden)

Aamir Omair

2014-01-01

Full Text Available Introduction: The purpose of this article is to provide a general understanding of the concepts of sampling as applied to health-related research. Sample Size Estimation: It is important to select a representative sample in quantitative research in order to be able to generalize the results to the target population. The sample should be of the required sample size and must be selected using an appropriate probability sampling technique. There are many hidden biases which can adversely affect the outcome of the study. Important factors to consider for estimating the sample size include the size of the study population, confidence level, expected proportion of the outcome variable (for categorical variables/standard deviation of the outcome variable (for numerical variables, and the required precision (margin of accuracy from the study. The more the precision required, the greater is the required sample size. Sampling Techniques: The probability sampling techniques applied for health related research include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. These are more recommended than the nonprobability sampling techniques, because the results of the study can be generalized to the target population.

Modeling of the selective pertraction of carboxylic acids obtained by citric fermentation

Directory of Open Access Journals (Sweden)

Cascaval Dan

2004-01-01

Full Text Available Facilitated pertraction was applied for the selective separation of citric, maleic and succinic acids from a mixture obtained by citric fermentation. The pertraction equipment included a U-shaped cell containing 1,2-dichloro-ethane as the liquid membrane and Amberlite LA-2 as the carrier. The experimental data indicated that maleic and succinic acids can be initially selectively separated from citric acid, followed by the selectively separation of maleic acid from succinic acid. Using statistical analysis and a second order factorial experiment, two mathematical correlations describing the influence of the main process variables on pertraction selectivity were established. For both extraction systems, the considered variables controlled the extraction process to an extent of 92.9-99.9%, the carrier concentration inside the liquid membrane exhibiting the most important influence.

The role of protozoa-driven selection in shaping human genetic variability.

Science.gov (United States)

Pozzoli, Uberto; Fumagalli, Matteo; Cagliani, Rachele; Comi, Giacomo P; Bresolin, Nereo; Clerici, Mario; Sironi, Manuela

2010-03-01

Protozoa exert a strong selective pressure in humans. The selection signatures left by these pathogens can be exploited to identify genetic modulators of infection susceptibility. We show that protozoa diversity in different geographic locations is a good measure of protozoa-driven selective pressure; protozoa diversity captured selection signatures at known malaria resistance loci and identified several selected single nucleotide polymorphisms in immune and hemolytic anemia genes. A genome-wide search enabled us to identify 5180 variants mapping to 1145 genes that are subjected to protozoa-driven selective pressure. We provide a genome-wide estimate of protozoa-driven selective pressure and identify candidate susceptibility genes for protozoa-borne diseases. Copyright 2010 Elsevier Ltd. All rights reserved.

Process variables in organizational stress management intervention evaluation research: a systematic review.

Science.gov (United States)

Havermans, Bo M; Schlevis, Roosmarijn Mc; Boot, Cécile Rl; Brouwers, Evelien Pm; Anema, Johannes; van der Beek, Allard J

2016-09-01

This systematic review aimed to explore which process variables are used in stress management intervention (SMI) evaluation research. A systematic review was conducted using seven electronic databases. Studies were included if they reported on an SMI aimed at primary or secondary stress prevention, were directed at paid employees, and reported process data. Two independent researchers checked all records and selected the articles for inclusion. Nielsen and Randall's model for process evaluation was used to cluster the process variables. The three main clusters were context, intervention, and mental models. In the 44 articles included, 47 process variables were found, clustered into three main categories: context (two variables), intervention (31 variables), and mental models (14 variables). Half of the articles contained no reference to process evaluation literature. The collection of process evaluation data mostly took place after the intervention and at the level of the employee. The findings suggest that there is great heterogeneity in methods and process variables used in process evaluations of SMI. This, together with the lack of use of a standardized framework for evaluation, hinders the advancement of process evaluation theory development.

Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination.

Science.gov (United States)

Yau, Christopher; Holmes, Chris

2011-07-01

We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. This allows us to perform full Gibbs sampling to obtain posterior distributions over parameters of interest including an explicit measure of each covariate's relevance and a distribution over the number of potential clusters present in the data. This also allows for individual cluster specific variable selection. We demonstrate improved inference on a number of canonical problems.

THE HOST GALAXY PROPERTIES OF VARIABILITY SELECTED AGN IN THE PAN-STARRS1 MEDIUM DEEP SURVEY

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Heinis, S.; Gezari, S.; Kumar, S. [Department of Astronomy, University of Maryland, College Park, MD (United States); Burgett, W. S.; Flewelling, H.; Huber, M. E.; Kaiser, N.; Wainscoat, R. J.; Waters, C. [Institute for Astronomy, University of Hawaii at Manoa, Honolulu, HI 96822 (United States)

2016-07-20

We study the properties of 975 active galactic nuclei (AGNs) selected by variability in the Pan-STARRS1 Medium deep Survey. Using complementary multi-wavelength data from the ultraviolet to the far-infrared, we use spectral energy distribution fitting to determine the AGN and host properties at z < 1 and compare to a well-matched control sample. We confirm the trend previously observed: that the variability amplitude decreases with AGN luminosity, but we also observe that the slope of this relation steepens with wavelength, resulting in a “redder when brighter” trend at low luminosities. Our results show that AGNs are hosted by more massive hosts than control sample galaxies, while the rest frame dust-corrected NUV r color distribution of AGN hosts is similar to control galaxies. We find a positive correlation between the AGN luminosity and star formation rate (SFR), independent of redshift. AGN hosts populate the entire range of SFRs within and outside of the Main Sequence of star-forming galaxies. Comparing the distribution of AGN hosts and control galaxies, we show that AGN hosts are less likely to be hosted by quiescent galaxies and more likely to be hosted by Main Sequence or starburst galaxies.

Dealing with selection bias in educational transition models

DEFF Research Database (Denmark)

Holm, Anders; Jæger, Mads Meier

2011-01-01

This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational tr...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models.......This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...

Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.

Science.gov (United States)

Shen, Chung-Wei; Chen, Yi-Hau

2018-03-13

We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within-cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly. © 2018, The International Biometric Society.

Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data.

Science.gov (United States)

Ultsch, Alfred; Lötsch, Jörn

2015-01-01

Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets. The algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits implement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the "important few", is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized. As a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological interpretation of the results and increased the fraction of valid information that was obtained from the experimental data. The method is applicable to many further biomedical problems including the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data.

Analysis of general and specific combining abilities of popcorn populations, including selfed parents

Directory of Open Access Journals (Sweden)

José Marcelo Soriano Viana

2003-12-01

Full Text Available Estimation of general and specific combining ability effects in a diallel analysis of cross-pollinating populations, including the selfed parents, is presented in this work. The restrictions considered satisfy the parametric values of the GCA and SCA effects. The method is extended to self-pollinating populations (suitable for other species, without the selfed parents. The analysis of changes in population means due to inbreeding (sensitivity to inbreeding also permits to assess the predominant direction of dominance deviations and the relative genetic variability in each parent population. The methodology was used to select popcorn populations for intra- and inter-population breeding programs and for hybrid production, developed at the Federal University of Viçosa, MG, Brazil. Two yellow pearl grain popcorn populations were selected.

Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration.

Science.gov (United States)

Jović, Ozren; Smrečki, Neven; Popović, Zora

2016-04-01

A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for poil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEPoil (R(2)>0.99). Copyright © 2015 Elsevier B.V. All rights reserved.

Selecting candidate predictor variables for the modelling of post ...

African Journals Online (AJOL)

Objectives: The objective of this project was to determine the variables most likely to be associated with post- .... (as defined subjectively by the research team) in global .... ed on their lack of knowledge of wealth scoring tools. ... HIV serology.

Variable cycle engine

Energy Technology Data Exchange (ETDEWEB)

Adamson, A.P.; Sprunger, E.V.

1980-09-16

A variable cycle turboshaft engine includes a remote fan system and respective high and low pressure systems for selectively driving the fan system in such a manner as to provide VTOL takeoff capability and minimum specific fuel consumption (SFC) at cruise and loiter conditions. For takeoff the fan system is primarily driven by the relatively large low pressure system whose combustor receives the motive fluid from a core bypass duct and, for cruise and loiter conditions, the fan system is driven by both a relatively small high pressure core and the low pressure system with its combustor inoperative. A mixer is disposed downstream of the high pressure system for mixing the relatively cold air from the bypass duct and the relatively hot air from the core prior to its flow to the low pressure turbine.

Physical attraction to reliable, low variability nervous systems: Reaction time variability predicts attractiveness.

Science.gov (United States)

Butler, Emily E; Saville, Christopher W N; Ward, Robert; Ramsey, Richard

2017-01-01

The human face cues a range of important fitness information, which guides mate selection towards desirable others. Given humans' high investment in the central nervous system (CNS), cues to CNS function should be especially important in social selection. We tested if facial attractiveness preferences are sensitive to the reliability of human nervous system function. Several decades of research suggest an operational measure for CNS reliability is reaction time variability, which is measured by standard deviation of reaction times across trials. Across two experiments, we show that low reaction time variability is associated with facial attractiveness. Moreover, variability in performance made a unique contribution to attractiveness judgements above and beyond both physical health and sex-typicality judgements, which have previously been associated with perceptions of attractiveness. In a third experiment, we empirically estimated the distribution of attractiveness preferences expected by chance and show that the size and direction of our results in Experiments 1 and 2 are statistically unlikely without reference to reaction time variability. We conclude that an operating characteristic of the human nervous system, reliability of information processing, is signalled to others through facial appearance. Copyright © 2016 Elsevier B.V. All rights reserved.

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

Gaia DR1 documentation Chapter 6: Variability

Science.gov (United States)

Eyer, L.; Rimoldini, L.; Guy, L.; Holl, B.; Clementini, G.; Cuypers, J.; Mowlavi, N.; Lecoeur-Taïbi, I.; De Ridder, J.; Charnas, J.; Nienartowicz, K.

2017-12-01

This chapter describes the photometric variability processing of the Gaia DR1 data. Coordination Unit 7 is responsible for the variability analysis of over a billion celestial sources. In particular the definition, design, development, validation and provision of a software package for the data processing of photometrically variable objects. Data Processing Centre Geneva (DPCG) responsibilities cover all issues related to the computational part of the CU7 analysis. These span: hardware provisioning, including selection, deployment and optimisation of suitable hardware, choosing and developing software architecture, defining data and scientific workflows as well as operational activities such as configuration management, data import, time series reconstruction, storage and processing handling, visualisation and data export. CU7/DPCG is also responsible for interaction with other DPCs and CUs, software and programming training for the CU7 members, scientific software quality control and management of software and data lifecycle. Details about the specific data treatment steps of the Gaia DR1 data products are found in Eyer et al. (2017) and are not repeated here. The variability content of the Gaia DR1 focusses on a subsample of Cepheids and RR Lyrae stars around the South ecliptic pole, showcasing the performance of the Gaia photometry with respect to variable objects.

Machine learning search for variable stars

Science.gov (United States)

Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

2018-04-01

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

The quasar luminosity function from a variability-selected sample

Science.gov (United States)

Hawkins, M. R. S.; Veron, P.

1993-01-01

A sample of quasars is selected from a 10-yr sequence of 30 UK Schmidt plates. Luminosity functions are derived in several redshift intervals, which in each case show a featureless power-law rise towards low luminosities. There is no sign of the 'break' found in the recent UVX sample of Boyle et al. It is suggested that reasons for the disagreement are connected with biases in the selection of the UVX sample. The question of the nature of quasar evolution appears to be still unresolved.

Closed-form solutions for linear regulator-design of mechanical systems including optimal weighting matrix selection

Science.gov (United States)

Hanks, Brantley R.; Skelton, Robert E.

1991-01-01

This paper addresses the restriction of Linear Quadratic Regulator (LQR) solutions to the algebraic Riccati Equation to design spaces which can be implemented as passive structural members and/or dampers. A general closed-form solution to the optimal free-decay control problem is presented which is tailored for structural-mechanical systems. The solution includes, as subsets, special cases such as the Rayleigh Dissipation Function and total energy. Weighting matrix selection is a constrained choice among several parameters to obtain desired physical relationships. The closed-form solution is also applicable to active control design for systems where perfect, collocated actuator-sensor pairs exist. Some examples of simple spring mass systems are shown to illustrate key points.

The Criteria and Variables Affecting the Selection of Quality Book Ideally Suited for Translation: The Perspectives of King Saud University Staff

Directory of Open Access Journals (Sweden)

Abdulaziz Abdulrahman Abanomey

2015-04-01

Full Text Available This study investigated the ideal definition of QB, that is Quality Book- one that is ideally suited for translation- and the variables affecting its selection criteria among 136 members of King Saud University (KSU academic staff. A workshop was held to elicit the ideal definition of QB to answer the first question, and a 19-item electronic questionnaire with four domains was designed to help collect the data necessary to answer the other two questions of the study. The results revealed that all four domains came low; “Authorship and Publication” came the highest with a mean score of 2.28 and “Titling and Contents” came the lowest with a mean score of 1.76. 5-way ANOVA (without interaction was applied in accordance with the variables of the study at α≤ 0.05 among the mean scores. The analysis revealed significance of the variables of gender, those who translated a book or more before, and those who participated in a conference devoted for translation whereas the variables of qualification and revising a translated book did not reveal any statistical significance. Key words: Quality Book, KSU, Authorship, Translation, Titling

Statistical methodology for discrete fracture model - including fracture size, orientation uncertainty together with intensity uncertainty and variability

Energy Technology Data Exchange (ETDEWEB)

Darcel, C. (Itasca Consultants SAS (France)); Davy, P.; Le Goc, R.; Dreuzy, J.R. de; Bour, O. (Geosciences Rennes, UMR 6118 CNRS, Univ. def Rennes, Rennes (France))

2009-11-15

the lineament scale (k{sub t} = 2) on the other, addresses the issue of the nature of the transition. We develop a new 'mechanistic' model that could help in modeling why and where this transition can occur. The transition between both regimes would occur for a fracture length of 1-10 m and even at a smaller scale for the few outcrops that follow the self-similar density model. A consequence for the disposal issue is that the model that is likely to apply in the 'blind' scale window between 10-100 m is the self-similar model as it is defined for large-scale lineaments. The self-similar model, as it is measured for some outcrops and most lineament maps, is definitely worth being investigated as a reference for scales above 1-10 m. In the rest of the report, we develop a methodology for incorporating uncertainty and variability into the DFN modeling. Fracturing properties arise from complex processes which produce an intrinsic variability; characterizing this variability as an admissible variation of model parameter or as the division of the site into subdomains with distinct DFN models is a critical point of the modeling effort. Moreover, the DFN model encompasses a part of uncertainty, due to data inherent uncertainties and sampling limits. Both effects must be quantified and incorporated into the DFN site model definition process. In that context, all available borehole data including recording of fracture intercept positions, pole orientation and relative uncertainties are used as the basis for the methodological development and further site model assessment. An elementary dataset contains a set of discrete fracture intercepts from which a parent orientation/density distribution can be computed. The elementary bricks of the site, from which these initial parent density distributions are computed, rely on the former Single Hole Interpretation division of the boreholes into sections whose local boundaries are expected to reflect - locally - geology

A PHOTOMETRIC VARIABILITY SURVEY OF FIELD K AND M DWARF STARS WITH HATNet

International Nuclear Information System (INIS)

Hartman, J. D.; Bakos, G. A.; Noyes, R. W.; Sipocz, B.; Pal, A.; Kovacs, G.; Mazeh, T.; Shporer, A.

2011-01-01

Using light curves from the HATNet survey for transiting extrasolar planets we investigate the optical broadband photometric variability of a sample of 27, 560 field K and M dwarfs selected by color and proper motion (V - K ∼> 3.0, μ > 30 mas yr -1 , plus additional cuts in J - H versus H - K S and on the reduced proper motion). We search the light curves for periodic variations and for large-amplitude, long-duration flare events. A total of 2120 stars exhibit potential variability, including 95 stars with eclipses and 60 stars with flares. Based on a visual inspection of these light curves and an automated blending classification, we select 1568 stars, including 78 eclipsing binaries (EBs), as secure variable star detections that are not obvious blends. We estimate that a further ∼26% of these stars may be blends with fainter variables, though most of these blends are likely to be among the hotter stars in our sample. We find that only 38 of the 1568 stars, including five of the EBs, have previously been identified as variables or are blended with previously identified variables. One of the newly identified EBs is 1RXS J154727.5+450803, a known P = 3.55 day, late M-dwarf SB2 system, for which we derive preliminary estimates for the component masses and radii of M 1 = M 2 = 0.258 ± 0.008 M sun and R 1 = R 2 = 0.289 ± 0.007 R sun . The radii of the component stars are larger than theoretical expectations if the system is older than ∼200 Myr. The majority of the variables are heavily spotted BY Dra-type stars for which we determine rotation periods. Using this sample, we investigate the relations between period, color, age, and activity measures, including optical flaring, for K and M dwarfs, finding that many of the well-established relations for F, G, and K dwarfs continue into the M dwarf regime. We find that the fraction of stars that is variable with peak-to-peak amplitudes greater than 0.01 mag increases exponentially with the V - K S color such that

Answers to selected problems in multivariable calculus with linear algebra and series

CERN Document Server

Trench, William F

1972-01-01

Answers to Selected Problems in Multivariable Calculus with Linear Algebra and Series contains the answers to selected problems in linear algebra, the calculus of several variables, and series. Topics covered range from vectors and vector spaces to linear matrices and analytic geometry, as well as differential calculus of real-valued functions. Theorems and definitions are included, most of which are followed by worked-out illustrative examples.The problems and corresponding solutions deal with linear equations and matrices, including determinants; vector spaces and linear transformations; eig

Advanced supersonic propulsion study, phases 3 and 4. [variable cycle engines

Science.gov (United States)

Allan, R. D.; Joy, W.

1977-01-01

An evaluation of various advanced propulsion concepts for supersonic cruise aircraft resulted in the identification of the double-bypass variable cycle engine as the most promising concept. This engine design utilizes special variable geometry components and an annular exhaust nozzle to provide high take-off thrust and low jet noise. The engine also provides good performance at both supersonic cruise and subsonic cruise. Emission characteristics are excellent. The advanced technology double-bypass variable cycle engine offers an improvement in aircraft range performance relative to earlier supersonic jet engine designs and yet at a lower level of engine noise. Research and technology programs required in certain design areas for this engine concept to realize its potential benefits include refined parametric analysis of selected variable cycle engines, screening of additional unconventional concepts, and engine preliminary design studies. Required critical technology programs are summarized.

Natural selection of mitochondria during somatic lifetime promotes healthy aging

DEFF Research Database (Denmark)

Rodell, Anders; Rasmussen, Lene J; Bergersen, Linda H

2013-01-01

Stimulation of mitochondrial biogenesis during life-time challenges both eliminates disadvantageous properties and drives adaptive selection of advantageous phenotypic variations. Intermittent fission and fusion of mitochondria provide specific targets for health promotion by brief temporal...... stressors, interspersed with periods of recovery and biogenesis. For mitochondria, the mechanisms of selection, variability, and heritability, are complicated by interaction of two independent genomes, including the multiple copies of DNA in each mitochondrion, as well as the shared nuclear genome of each...

The impact of selected organizational variables and managerial leadership on radiation therapists' organizational commitment

Energy Technology Data Exchange (ETDEWEB)

Akroyd, Duane [Department of Adult and Community College Education, College of Education, Campus Box 7801, North Carolina State University, Raleigh, NC 27695 (United States)], E-mail: duane_akroyd@ncsu.edu; Legg, Jeff [Department of Radiologic Sciences, Virginia Commonwealth University, Richmond, VA 23284 (United States); Jackowski, Melissa B. [Division of Radiologic Sciences, University of North Carolina School of Medicine 27599 (United States); Adams, Robert D. [Department of Radiation Oncology, University of North Carolina School of Medicine 27599 (United States)

2009-05-15

The purpose of this study was to examine the impact of selected organizational factors and the leadership behavior of supervisors on radiation therapists' commitment to their organizations. The population for this study consists of all full time clinical radiation therapists registered by the American Registry of Radiologic Technologists (ARRT) in the United States. A random sample of 800 radiation therapists was obtained from the ARRT for this study. Questionnaires were mailed to all participants and measured organizational variables; managerial leadership variable and three components of organizational commitment (affective, continuance and normative). It was determined that organizational support, and leadership behavior of supervisors each had a significant and positive affect on normative and affective commitment of radiation therapists and each of the models predicted over 40% of the variance in radiation therapists organizational commitment. This study examined radiation therapists' commitment to their organizations and found that affective (emotional attachment to the organization) and normative (feelings of obligation to the organization) commitments were more important than continuance commitment (awareness of the costs of leaving the organization). This study can help radiation oncology administrators and physicians to understand the values their radiation therapy employees hold that are predictive of their commitment to the organization. A crucial result of the study is the importance of the perceived support of the organization and the leadership skills of managers/supervisors on radiation therapists' commitment to the organization.

Climate variability from isotope records in precipitation

International Nuclear Information System (INIS)

Grassl, H.; Latif, M.; Schotterer, U.; Gourcy, L.

2002-01-01

Selected time series from the Global Network for Isotopes in Precipitation (GNIP) revealed a close relationship to climate variability phenomena like El Nino - Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO) although the precipitation anomaly in the case studies of Manaus (Brazil) and Groningen (The Netherlands) is rather weak. For a sound understanding of this relationship especially in the case of Manaus, the data should include major events like the 1997/98 El Nino, however, the time series are interrupted frequently or important stations are even closed. Improvements are only possible if existing key stations and new ones (placed at 'hot spots' derived from model experiments) are supported continuously. A close link of GNIP to important scientific programmes like CLIVAR, the Climate Variability and Predictability Programme seems to be indispensable for a successful continuation. (author)

Evaluating the underlying factors behind variable rate debt.

Science.gov (United States)

McCue, Michael J; Kim, Tae Hyun Tanny

2007-01-01

Recent trends show a greater usage of variable rate debt among health care bond issues. In 2004, 63.4% of the total health care bonds issued were variable rate compared with 30.6% in 1995 (Fitch Ratings, 2005). The purpose of this study is to gain a better understanding of the underlying factors, credit spread, issue characteristics, and issuer factors behind why hospitals and health system borrowers select variable rate debt compared with fixed rate debt. From 2000 to 2004, this study sampled 230 newly issued tax-exempt bonds issued by acute care hospitals and health care systems that included both variable and fixed rate debt issues. Using a logistic regression model, hospitals with variable rate debt issues were assigned a value of 1, whereas hospitals with fixed rate debt issues were assigned a value of 0. This study found a positive association between bond insurance and variable rate debt and a negative association between callable feature and variable rate debt. Facilities located in certificate-of-need states that possessed higher case mix acuity, earned higher profit margins, generated higher debt service coverage, and held less debt were more likely to issue variable rate debt. Overall, hospital managers and board members of hospitals possessing a strong financial performance have an interest in utilizing variable rate debt to lower their cost of capital. In addition, this outcome may also reflect that investment bankers are doing a better job in educating senior hospital management about the interest rate savings benefit of variable rate compared with fixed rate debt.

Variability in dose estimates associated with the food-chain transport and ingestion of selected radionuclides

International Nuclear Information System (INIS)

Hoffman, F.O.; Gardner, R.H.; Eckerman, K.F.

1982-06-01

Dose predictions for the ingestion of 90 Sr and 137 Cs, using aquatic and terrestrial food chain transport models similar to those in the Nuclear Regulatory Commission's Regulatory Guide 1.109, are evaluated through estimating the variability of model parameters and determining the effect of this variability on model output. The variability in the predicted dose equivalent is determined using analytical and numerical procedures. In addition, a detailed discussion is included on 90 Sr dosimetry. The overall estimates of uncertainty are most relevant to conditions where site-specific data is unavailable and when model structure and parameter estimates are unbiased. Based on the comparisons performed in this report, it is concluded that the use of the generic default parameters in Regulatory Guide 1.109 will usually produce conservative dose estimates that exceed the 90th percentile of the predicted distribution of dose equivalents. An exception is the meat pathway for 137 Cs, in which use of generic default values results in a dose estimate at the 24th percentile. Among the terrestrial pathways of exposure, the non-leafy vegetable pathway is the most important for 90 Sr. For 90 Sr, the parameters for soil retention, soil-to-plant transfer, and internal dosimetry contribute most significantly to the variability in the predicted dose for the combined exposure to all terrestrial pathways. For 137 Cs, the meat transfer coefficient the mass interception factor for pasture forage, and the ingestion dose factor are the most important parameters. The freshwater finfish bioaccumulation factor is the most important parameter for the dose prediction of 90 Sr and 137 Cs transported over the water-fish-man pathway

Fatigue Behavior under Multiaxial Stress States Including Notch Effects and Variable Amplitude Loading

Science.gov (United States)

Gates, Nicholas R.

The central objective of the research performed in this study was to be able to better understand and predict fatigue crack initiation and growth from stress concentrations subjected to complex service loading histories. As such, major areas of focus were related to the understanding and modeling of material deformation behavior, fatigue damage quantification, notch effects, cycle counting, damage accumulation, and crack growth behavior under multiaxial nominal loading conditions. To support the analytical work, a wide variety of deformation and fatigue tests were also performed using tubular and plate specimens made from 2024-T3 aluminum alloy, with and without the inclusion of a circular through-thickness hole. However, the analysis procedures implemented were meant to be general in nature, and applicable to a wide variety of materials and component geometries. As a result, experimental data from literature were also used, when appropriate, to supplement the findings of various analyses. Popular approaches currently used for multiaxial fatigue life analysis are based on the idea of computing an equivalent stress/strain quantity through the extension of static yield criteria. This equivalent stress/strain is then considered to be equal, in terms of fatigue damage, to a uniaxial loading of the same magnitude. However, it has often been shown, and was shown again in this study, that although equivalent stress- and strain-based analysis approaches may work well in certain situations, they lack a general robustness and offer little room for improvement. More advanced analysis techniques, on the other hand, provide an opportunity to more accurately account for various aspects of the fatigue failure process under both constant and variable amplitude loading conditions. As a result, such techniques were of primary interest in the investigations performed. By implementing more advanced life prediction methodologies, both the overall accuracy and the correlation of fatigue

Using ANFIS for selection of more relevant parameters to predict dew point temperature

International Nuclear Information System (INIS)

Mohammadi, Kasra; Shamshirband, Shahaboddin; Petković, Dalibor; Yee, Por Lip; Mansor, Zulkefli

2016-01-01

Highlights: • ANFIS is used to select the most relevant variables for dew point temperature prediction. • Two cities from the central and south central parts of Iran are selected as case studies. • Influence of 5 parameters on dew point temperature is evaluated. • Appropriate selection of input variables has a notable effect on prediction. • Considering the most relevant combination of 2 parameters would be more suitable. - Abstract: In this research work, for the first time, the adaptive neuro fuzzy inference system (ANFIS) is employed to propose an approach for identifying the most significant parameters for prediction of daily dew point temperature (T_d_e_w). The ANFIS process for variable selection is implemented, which includes a number of ways to recognize the parameters offering favorable predictions. According to the physical factors influencing the dew formation, 8 variables of daily minimum, maximum and average air temperatures (T_m_i_n, T_m_a_x and T_a_v_g), relative humidity (R_h), atmospheric pressure (P), water vapor pressure (V_P), sunshine hour (n) and horizontal global solar radiation (H) are considered to investigate their effects on T_d_e_w. The used data include 7 years daily measured data of two Iranian cities located in the central and south central parts of the country. The results indicate that despite climate difference between the considered case studies, for both stations, V_P is the most influential variable while R_h is the least relevant element. Furthermore, the combination of T_m_i_n and V_P is recognized as the most influential set to predict T_d_e_w. The conducted examinations show that there is a remarkable difference between the errors achieved for most and less relevant input parameters, which highlights the importance of appropriate selection of input parameters. The use of more than two inputs may not be advisable and appropriate; thus, considering the most relevant combination of 2 parameters would be more suitable

Dynamic variable selection in SNP genotype autocalling from APEX microarray data

Directory of Open Access Journals (Sweden)

Zamar Ruben H

2006-11-01

Full Text Available Abstract Background Single nucleotide polymorphisms (SNPs are DNA sequence variations, occurring when a single nucleotide – adenine (A, thymine (T, cytosine (C or guanine (G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Our laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX. This mini-sequencing method is a powerful combination of a highly parallel microarray with distinctive Sanger-based dideoxy terminator sequencing chemistry. Using this microarray platform, our current genotype calling system (known as SNP Chart is capable of calling single SNP genotypes by manual inspection of the APEX data, which is time-consuming and exposed to user subjectivity bias. Results Using a set of 32 Coriell DNA samples plus three negative PCR controls as a training data set, we have developed a fully-automated genotyping algorithm based on simple linear discriminant analysis (LDA using dynamic variable selection. The algorithm combines separate analyses based on the multiple probe sets to give a final posterior probability for each candidate genotype. We have tested our algorithm on a completely independent data set of 270 DNA samples, with validated genotypes, from patients admitted to the intensive care unit (ICU of St. Paul's Hospital (plus one negative PCR control sample. Our method achieves a concordance rate of 98.9% with a 99.6% call rate for a set of 96 SNPs. By adjusting the threshold value for the final posterior probability of the called genotype, the call rate reduces to 94.9% with a higher concordance rate of 99.6%. We also reversed the two independent data sets in their training and testing roles, achieving a concordance rate up to 99.8%. Conclusion The strength of this APEX chemistry-based platform is its unique redundancy having multiple probes for a single SNP. Our

Spatially variable natural selection and the divergence between parapatric subspecies of lodgepole pine (Pinus contorta, Pinaceae).

Science.gov (United States)

Eckert, Andrew J; Shahi, Hurshbir; Datwyler, Shannon L; Neale, David B

2012-08-01

Plant populations arrayed across sharp environmental gradients are ideal systems for identifying the genetic basis of ecologically relevant phenotypes. A series of five uplifted marine terraces along the northern coast of California represents one such system where morphologically distinct populations of lodgepole pine (Pinus contorta) are distributed across sharp soil gradients ranging from fertile soils near the coast to podzolic soils ca. 5 km inland. A total of 92 trees was sampled across four coastal marine terraces (N = 10-46 trees/terrace) located in Mendocino County, California and sequenced for a set of 24 candidate genes for growth and responses to various soil chemistry variables. Statistical analyses relying on patterns of nucleotide diversity were employed to identify genes whose diversity patterns were inconsistent with three null models. Most genes displayed patterns of nucleotide diversity that were consistent with null models (N = 19) or with the presence of paralogs (N = 3). Two genes, however, were exceptional: an aluminum responsive ABC-transporter with F(ST) = 0.664 and an inorganic phosphate transporter characterized by divergent haplotypes segregating at intermediate frequencies in most populations. Spatially variable natural selection along gradients of aluminum and phosphate ion concentrations likely accounted for both outliers. These results shed light on some of the genetic components comprising the extended phenotype of this ecosystem, as well as highlight ecotones as fruitful study systems for the detection of adaptive genetic variants.

Harmonize input selection for sediment transport prediction

Science.gov (United States)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.

Do birds of a feather flock together? The variable bases for African American, Asian American, and European American adolescents' selection of similar friends.

Science.gov (United States)

Hamm, J V

2000-03-01

Variability in adolescent-friend similarity is documented in a diverse sample of African American, Asian American, and European American adolescents. Similarity was greatest for substance use, modest for academic orientations, and low for ethnic identity. Compared with Asian American and European American adolescents, African American adolescents chose friends who were less similar with respect to academic orientation or substance use but more similar with respect to ethnic identity. For all three ethnic groups, personal endorsement of the dimension in question and selection of cross-ethnic-group friends heightened similarity. Similarity was a relative rather than an absolute selection criterion: Adolescents did not choose friends with identical orientations. These findings call for a comprehensive theory of friendship selection sensitive to diversity in adolescents' experiences. Implications for peer influence and self-development are discussed.

Probing contextuality with pre- and post-selection

International Nuclear Information System (INIS)

Tollaksen, Jeff

2007-01-01

By analyzing the concept of contextuality (Bell-Kochen-Specker) in terms of pre-and-post-selection (PPS), it is possible to assign definite values to observables in a new way. Physical reasons are presented for restrictions on these assignments. When measurements are performed which do not disturb the pre- and post-selection (i.e. weak measurements), then novel experimental aspects of contextuality can be demonstrated including a proof that every PPS-paradox with definite predictions implies contextuality. Certain results of these measurements (eccentric weak values with e.g. negative values outside the spectrum), however, cannot be explained by a 'classical-like' hidden variable theory. Surprising theoretical implications are discussed

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators.

Science.gov (United States)

Vansteelandt, Stijn; Walter, Stefan; Tchetgen Tchetgen, Eric

2018-07-01

Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis of such studies sensitive to survivor bias, a type of selection bias. A particular concern is that the instrumental variable conditions, even when valid for the source population, may be violated for the selective population of individuals who survive the onset of the study. This is potentially very damaging because Mendelian randomization studies are known to be sensitive to bias due to even minor violations of the instrumental variable conditions. Interestingly, the instrumental variable conditions continue to hold within certain risk sets of individuals who are still alive at a given age when the instrument and unmeasured confounders exert additive effects on the exposure, and moreover, the exposure and unmeasured confounders exert additive effects on the hazard of death. In this article, we will exploit this property to derive a two-stage instrumental variable estimator for the effect of exposure on mortality, which is insulated against the above described selection bias under these additivity assumptions.

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

Science.gov (United States)

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

Energy Technology Data Exchange (ETDEWEB)

Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife

2001-07-01

Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.

Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2013-01-14

The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative variables. Many variable-reduction methods are based on so-called predictor-variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the variable-reduction process. Recently, a new predictive-property-ranked variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward variable elimination method applied on the predictive-property-ranked variables. The variable number is first reduced, with constant PLS1 model complexity A, until A variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of variables. In this study for three data sets the utility and effectiveness of six individual and nine combined predictor-variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a predictor variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are

Exploratory Spectroscopy of Magnetic Cataclysmic Variables Candidates and Other Variable Objects

Science.gov (United States)

Oliveira, A. S.; Rodrigues, C. V.; Cieslinski, D.; Jablonski, F. J.; Silva, K. M. G.; Almeida, L. A.; Rodríguez-Ardila, A.; Palhares, M. S.

2017-04-01

The increasing number of synoptic surveys made by small robotic telescopes, such as the photometric Catalina Real-Time Transient Survey (CRTS), provides a unique opportunity to discover variable sources and improves the statistical samples of such classes of objects. Our goal is the discovery of magnetic Cataclysmic Variables (mCVs). These are rare objects that probe interesting accretion scenarios controlled by the white-dwarf magnetic field. In particular, improved statistics of mCVs would help to address open questions on their formation and evolution. We performed an optical spectroscopy survey to search for signatures of magnetic accretion in 45 variable objects selected mostly from the CRTS. In this sample, we found 32 CVs, 22 being mCV candidates, 13 of which were previously unreported as such. If the proposed classifications are confirmed, it would represent an increase of 4% in the number of known polars and 12% in the number of known IPs. A fraction of our initial sample was classified as extragalactic sources or other types of variable stars by the inspection of the identification spectra. Despite the inherent complexity in identifying a source as an mCV, variability-based selection, followed by spectroscopic snapshot observations, has proved to be an efficient strategy for their discoveries, being a relatively inexpensive approach in terms of telescope time. Based on observations obtained at the Observatório do Pico dos Dias/LNA, and at the Southern Astrophysical Research (SOAR) telescope, which is a joint project of the Ministério da Ciência, Tecnologia, e Inovação (MCTI) da República Federativa do Brasil, the U.S. National Optical Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill (UNC), and Michigan State University (MSU).

The Taiwanese-American occultation survey project stellar variability. III. Detection of 58 new variable stars

Energy Technology Data Exchange (ETDEWEB)

Ishioka, R.; Wang, S.-Y.; Zhang, Z.-W.; Lehner, M. J.; Cook, K. H.; King, S.-K.; Lee, T.; Marshall, S. L.; Schwamb, M. E.; Wang, J.-H.; Wen, C.-Y. [Institute of Astronomy and Astrophysics, Academia Sinica, 11F of Astronomy-Mathematics Building, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan (China); Alcock, C.; Protopapas, P. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Axelrod, T. [Steward Observatory, 933 North Cherry Avenue, Room N204, Tucson, AZ 85721 (United States); Bianco, F. B. [Center for Cosmology and Particle Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Byun, Y.-I. [Department of Astronomy and University Observatory, Yonsei University, 134 Shinchon, Seoul 120-749 (Korea, Republic of); Chen, W. P.; Ngeow, C.-C. [Institute of Astronomy, National Central University, No. 300, Jhongda Road, Jhongli City, Taoyuan County 320, Taiwan (China); Kim, D.-W. [Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg (Germany); Rice, J. A., E-mail: ishioka@asiaa.sinica.edu.tw [Department of Statistics, University of California Berkeley, 367 Evans Hall, Berkeley, CA 94720 (United States)

2014-04-01

The Taiwanese-American Occultation Survey project is designed for the detection of stellar occultations by small-size Kuiper Belt Objects, and it has monitored selected fields along the ecliptic plane by using four telescopes with a 3 deg{sup 2} field of view on the sky since 2005. We have analyzed data accumulated during 2005-2012 to detect variable stars. Sixteen fields with observations of more than 100 epochs were examined. We recovered 85 variables among a total of 158 known variable stars in these 16 fields. Most of the unrecovered variables are located in the fields observed less frequently. We also detected 58 variable stars which are not listed in the International Variable Star Index of the American Association of Variable Star Observers. These variable stars are classified as 3 RR Lyrae, 4 Cepheid, 1 δ Scuti, 5 Mira, 15 semi-regular, and 27 eclipsing binaries based on the periodicity and the profile of the light curves.

Relationship of Powder Feedstock Variability to Microstructure and Defects in Selective Laser Melted Alloy 718

Science.gov (United States)

Smith, T. M.; Kloesel, M. F.; Sudbrack, C. K.

2017-01-01

Powder-bed additive manufacturing processes use fine powders to build parts layer by layer. For selective laser melted (SLM) Alloy 718, the powders that are available off-the-shelf are in the 10-45 or 15-45 micron size range. A comprehensive investigation of sixteen powders from these typical ranges and two off-nominal-sized powders is underway to gain insight into the impact of feedstock on processing, durability and performance of 718 SLM space-flight hardware. This talk emphasizes an aspect of this work: the impact of powder variability on the microstructure and defects observed in the as-fabricated and full heated material, where lab-scale components were built using vendor recommended parameters. These typical powders exhibit variation in composition, percentage of fines, roughness, morphology and particle size distribution. How these differences relate to the melt-pool size, porosity, grain structure, precipitate distributions, and inclusion content will be presented and discussed in context of build quality and powder acceptance.

Auxiliary variables in multiple imputation in regression with missing X: a warning against including too many in small sample research

Directory of Open Access Journals (Sweden)

Hardt Jochen

2012-12-01

Full Text Available Abstract Background Multiple imputation is becoming increasingly popular. Theoretical considerations as well as simulation studies have shown that the inclusion of auxiliary variables is generally of benefit. Methods A simulation study of a linear regression with a response Y and two predictors X1 and X2 was performed on data with n = 50, 100 and 200 using complete cases or multiple imputation with 0, 10, 20, 40 and 80 auxiliary variables. Mechanisms of missingness were either 100% MCAR or 50% MAR + 50% MCAR. Auxiliary variables had low (r=.10 vs. moderate correlations (r=.50 with X’s and Y. Results The inclusion of auxiliary variables can improve a multiple imputation model. However, inclusion of too many variables leads to downward bias of regression coefficients and decreases precision. When the correlations are low, inclusion of auxiliary variables is not useful. Conclusion More research on auxiliary variables in multiple imputation should be performed. A preliminary rule of thumb could be that the ratio of variables to cases with complete data should not go below 1 : 3.

BLAZAR OPTICAL VARIABILITY IN THE PALOMAR-QUEST SURVEY

International Nuclear Information System (INIS)

Bauer, Anne; Baltay, Charles; Coppi, Paolo; Ellman, Nancy; Jerke, Jonathan; Rabinowitz, David; Scalzo, Richard

2009-01-01

We study the ensemble optical variability of 276 flat-spectrum radio quasars (FSRQs) and 86 BL Lacs in the Palomar-QUEST Survey with the goal of searching for common fluctuation properties, examining the range of behavior across the sample, and characterizing the appearance of blazars in such a survey so that future work can more easily identify such objects. The survey, which covers 15,000 deg 2 multiple times over 3.5 years, allows for the first ensemble blazar study of this scale. Variability amplitude distributions are shown for the FSRQ and BL Lac samples for numerous time lags, and also studied through structure function analyses. Individual blazars show a wide range of variability amplitudes, timescales, and duty cycles. Of the best-sampled objects, 35% are seen to vary by more than 0.4 mag; for these, the fraction of measurements contributing to the high-amplitude variability ranges constantly from about 5% to 80%. Blazar variability has some similarities to that of type I quasi-stellar objects (QSOs) but includes larger amplitude fluctuations on all timescales. FSRQ variability amplitudes are particularly similar to those of QSOs on timescales of several months, suggesting significant contributions from the accretion disk to the variable flux at these timescales. Optical variability amplitudes are correlated with the maximum apparent velocities of the radio jet for the subset of FSRQs with MOJAVE Very Long Baseline Array measurements, implying that the optically variable flux's strength is typically related to that of the radio emission. We also study CRATES radio-selected FSRQ candidates, which show similar variability characteristics to known FSRQs; this suggests a high purity for the CRATES sample.

Using exogenous variables in testing for monotonic trends in hydrologic time series

Science.gov (United States)

Alley, William M.

1988-01-01

One approach that has been used in performing a nonparametric test for monotonic trend in a hydrologic time series consists of a two-stage analysis. First, a regression equation is estimated for the variable being tested as a function of an exogenous variable. A nonparametric trend test such as the Kendall test is then performed on the residuals from the equation. By analogy to stagewise regression and through Monte Carlo experiments, it is demonstrated that this approach will tend to underestimate the magnitude of the trend and to result in some loss in power as a result of ignoring the interaction between the exogenous variable and time. An alternative approach, referred to as the adjusted variable Kendall test, is demonstrated to generally have increased statistical power and to provide more reliable estimates of the trend slope. In addition, the utility of including an exogenous variable in a trend test is examined under selected conditions.

Individual treatment selection for patients with posttraumatic stress disorder.

Science.gov (United States)

Deisenhofer, Anne-Katharina; Delgadillo, Jaime; Rubel, Julian A; Böhnke, Jan R; Zimmermann, Dirk; Schwartz, Brian; Lutz, Wolfgang

2018-04-16

Trauma-focused cognitive behavioral therapy (Tf-CBT) and eye movement desensitization and reprocessing (EMDR) are two highly effective treatment options for posttraumatic stress disorder (PTSD). Yet, on an individual level, PTSD patients vary substantially in treatment response. The aim of the paper is to test the application of a treatment selection method based on a personalized advantage index (PAI). The study used clinical data for patients accessing treatment for PTSD in a primary care mental health service in the north of England. PTSD patients received either EMDR (N = 75) or Tf-CBT (N = 242). The Patient Health Questionnaire (PHQ-9) was used as an outcome measure for depressive symptoms associated with PTSD. Variables predicting differential treatment response were identified using an automated variable selection approach (genetic algorithm) and afterwards included in regression models, allowing the calculation of each patient's PAI. Age, employment status, gender, and functional impairment were identified as relevant variables for Tf-CBT. For EMDR, baseline depressive symptoms as well as prescribed antidepressant medication were selected as predictor variables. Fifty-six percent of the patients (n = 125) had a PAI equal or higher than one standard deviation. From those patients, 62 (50%) did not receive their model-predicted treatment and could have benefited from a treatment assignment based on the PAI. Using a PAI-based algorithm has the potential to improve clinical decision making and to enhance individual patient outcomes, although further replication is necessary before such an approach can be implemented in prospective studies. © 2018 Wiley Periodicals, Inc.

Rainfall trends and variability in selected areas of Ethiopian Somali ...

African Journals Online (AJOL)

Moreover, proper spatial distribution of meteorological stations together with early warning system are required to further support local adaptive and coping strategies that the community designed towards rainfall variability in particular and climate change/disaster and risk at large. Keywords: Ethiopian Somali Region, Gode, ...

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

Science.gov (United States)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Inlet-engine matching for SCAR including application of a bicone variable geometry inlet

Science.gov (United States)

Wasserbauer, J. F.; Gerstenmaier, W. H.

1978-01-01

Airflow characteristics of variable cycle engines (VCE) designed for Mach 2.32 can have transonic airflow requirements as high as 1.6 times the cruise airflow. This is a formidable requirement for conventional, high performance, axisymmetric, translating centerbody mixed compression inlets. An alternate inlet is defined, where the second cone of a two cone center body collapses to the initial cone angle to provide a large off-design airflow capability, and incorporates modest centerbody translation to minimize spillage drag. Estimates of transonic spillage drag are competitive with those of conventional translating centerbody inlets. The inlet's cruise performance exhibits very low bleed requirements with good recovery and high angle of attack capability.

Influence of olfactory and visual cover on nest site selection and nest success for grassland-nesting birds.

Science.gov (United States)

Fogarty, Dillon T; Elmore, R Dwayne; Fuhlendorf, Samuel D; Loss, Scott R

2017-08-01

Habitat selection by animals is influenced by and mitigates the effects of predation and environmental extremes. For birds, nest site selection is crucial to offspring production because nests are exposed to extreme weather and predation pressure. Predators that forage using olfaction often dominate nest predator communities; therefore, factors that influence olfactory detection (e.g., airflow and weather variables, including turbulence and moisture) should influence nest site selection and survival. However, few studies have assessed the importance of olfactory cover for habitat selection and survival. We assessed whether ground-nesting birds select nest sites based on visual and/or olfactory cover. Additionally, we assessed the importance of visual cover and airflow and weather variables associated with olfactory cover in influencing nest survival. In managed grasslands in Oklahoma, USA, we monitored nests of Northern Bobwhite ( Colinus virginianus ), Eastern Meadowlark ( Sturnella magna ), and Grasshopper Sparrow ( Ammodramus savannarum ) during 2015 and 2016. To assess nest site selection, we compared cover variables between nests and random points. To assess factors influencing nest survival, we used visual cover and olfactory-related measurements (i.e., airflow and weather variables) to model daily nest survival. For nest site selection, nest sites had greater overhead visual cover than random points, but no other significant differences were found. Weather variables hypothesized to influence olfactory detection, specifically precipitation and relative humidity, were the best predictors of and were positively related to daily nest survival. Selection for overhead cover likely contributed to mitigation of thermal extremes and possibly reduced detectability of nests. For daily nest survival, we hypothesize that major nest predators focused on prey other than the monitored species' nests during high moisture conditions, thus increasing nest survival on these

Statistical methodology for discrete fracture model - including fracture size, orientation uncertainty together with intensity uncertainty and variability

International Nuclear Information System (INIS)

Darcel, C.; Davy, P.; Le Goc, R.; Dreuzy, J.R. de; Bour, O.

2009-11-01

the other, addresses the issue of the nature of the transition. We develop a new 'mechanistic' model that could help in modeling why and where this transition can occur. The transition between both regimes would occur for a fracture length of 1-10 m and even at a smaller scale for the few outcrops that follow the self-similar density model. A consequence for the disposal issue is that the model that is likely to apply in the 'blind' scale window between 10-100 m is the self-similar model as it is defined for large-scale lineaments. The self-similar model, as it is measured for some outcrops and most lineament maps, is definitely worth being investigated as a reference for scales above 1-10 m. In the rest of the report, we develop a methodology for incorporating uncertainty and variability into the DFN modeling. Fracturing properties arise from complex processes which produce an intrinsic variability; characterizing this variability as an admissible variation of model parameter or as the division of the site into subdomains with distinct DFN models is a critical point of the modeling effort. Moreover, the DFN model encompasses a part of uncertainty, due to data inherent uncertainties and sampling limits. Both effects must be quantified and incorporated into the DFN site model definition process. In that context, all available borehole data including recording of fracture intercept positions, pole orientation and relative uncertainties are used as the basis for the methodological development and further site model assessment. An elementary dataset contains a set of discrete fracture intercepts from which a parent orientation/density distribution can be computed. The elementary bricks of the site, from which these initial parent density distributions are computed, rely on the former Single Hole Interpretation division of the boreholes into sections whose local boundaries are expected to reflect - locally - geology and fracturing properties main characteristics. From that

Conflict Management Styles of Selected Managers and Their Relationship With Management and Organization Variables

Directory of Open Access Journals (Sweden)

Concepcion Martires

1990-12-01

Full Text Available This study sought to determine the relationship between the conflict management styles of managers and certain management and organization factors. A total of 462 top, middle, and lower managers from 72 companies participated in the study which utilized the Thomas-Killman Conflict Mode Instrument. To facilitate the computation of the statistical data, a microcomputer and a software package was used.The majority of the managers of the 17 types of organization included in the study use collaborative mode of managing conflict. This finding is congruent with the findings of past studies conducted on managers of commercial banks, service, manufacturing, trading advertising, appliance, investment houses, and overseas recruitment industries showing their high degree of objectivity and assertiveness of their own personal goals and of other people's concerns. The second dominant style, which is compromising, indicates their desire in sharing and searching for solutions that result in satisfaction among conflicting parties. This finding is highly consistent with the strong Filipino value of smooth interpersonal relationships (SIR as reflected and discussed in the numerous researches on Filipino values.The chi-square tests generated by the computer package in statistics showed independence between the manager's conflict management styles and each of the variables of sex, civil status, position level at work, work experience, type of corporation, and number of subordinates. This result is again congruent with those of past studies conducted in the Philippines. The past and present findings may imply that conflict management mode may be a highly personal style that is not dependent on any of these variables included in the study. However, the chi-square tests show that management style is dependent on the manager's age and educational attainment.

The influence of some selected variables from accounting system on profit or loss of agricultural companies in the Slovak republic

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Alexandra Ferenczi Vaňová

2017-01-01

Full Text Available 1024x768 The article presents the influence assessment of significance of some selected variables from the entrepreneurs' accounting system on the achieved profit or loss of the agricultural companies in the Slovak Republic. Accounting information serves as an active tool for internal users for operational as well as strategic company management, and for external users the information is determined as legally binding output information which is a subject to disclosure. Individual financial statements of assessed agricultural companies are considered to be the relevant source of information. Agricultural companies are represented by commercial companies and agricultural cooperatives. Profit or loss after income tax presents the final complex effect of economic company's performance. The existence and development of companies is conditioned by assets which amount and structure depend on focus and the range of subject activity but as well as on specific factors set by the production process in the agricultural primary production. The increase in liabilities is notable by the influence of unsufficient amount of own company funding sources, mainly the increase in trade payables. The continuance of company reproduction process is secured by a bank loan drawdown. The income situation of companies of agricultural primary production is favourably influenced by the subsidies of non-investment character. During the observed period of years 2004 - 2014 the examined variables were assessed by means of statistical methods. The obtained results of rate determination of statistical correlation between selected variables by means of classical canonical analysis and non-parametric correlation analysis secured that in the assessed group of companies all analysed variables influenced statistically significantly profit or loss after income tax, mainly the total value of assets and non-investment subsidies, except for years 2010, 2012 a 2013, when the statistically

Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables

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Adams, Matthew D.; Arain, Altaf; Papatheodorou, Stefania; Koutrakis, Petros; Mahmoud, Moataz

2018-01-01

Background. Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors. Objectives. We systematically reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality. Search Methods. We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016. Selection Criteria. We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM2.5 or PM10); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender. Data Collection and Analysis. We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies. Main Results. We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM2.5 and

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

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Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-01

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

The effect of virtual reality on gait variability.

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Katsavelis, Dimitrios; Mukherjee, Mukul; Decker, Leslie; Stergiou, Nicholas

2010-07-01

Optic Flow (OF) plays an important role in human locomotion and manipulation of OF characteristics can cause changes in locomotion patterns. The purpose of the study was to investigate the effect of the velocity of optic flow on the amount and structure of gait variability. Each subject underwent four conditions of treadmill walking at their self-selected pace. In three conditions the subjects walked in an endless virtual corridor, while a fourth control condition was also included. The three virtual conditions differed in the speed of the optic flow displayed as follows--same speed (OFn), faster (OFf), and slower (OFs) than that of the treadmill. Gait kinematics were tracked with an optical motion capture system. Gait variability measures of the hip, knee and ankle range of motion and stride interval were analyzed. Amount of variability was evaluated with linear measures of variability--coefficient of variation, while structure of variability i.e., its organization over time, were measured with nonlinear measures--approximate entropy and detrended fluctuation analysis. The linear measures of variability, CV, did not show significant differences between Non-VR and VR conditions while nonlinear measures of variability identified significant differences at the hip, ankle, and in stride interval. In response to manipulation of the optic flow, significant differences were observed between the three virtual conditions in the following order: OFn greater than OFf greater than OFs. Measures of structure of variability are more sensitive to changes in gait due to manipulation of visual cues, whereas measures of the amount of variability may be concealed by adaptive mechanisms. Visual cues increase the complexity of gait variability and may increase the degrees of freedom available to the subject. Further exploration of the effects of optic flow manipulation on locomotion may provide us with an effective tool for rehabilitation of subjects with sensorimotor issues.

Using Copulas in the Estimation of the Economic Project Value in the Mining Industry, Including Geological Variability

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Krysa, Zbigniew; Pactwa, Katarzyna; Wozniak, Justyna; Dudek, Michal

2017-12-01

Geological variability is one of the main factors that has an influence on the viability of mining investment projects and on the technical risk of geology projects. In the current scenario, analyses of economic viability of new extraction fields have been performed for the KGHM Polska Miedź S.A. underground copper mine at Fore Sudetic Monocline with the assumption of constant averaged content of useful elements. Research presented in this article is aimed at verifying the value of production from copper and silver ore for the same economic background with the use of variable cash flows resulting from the local variability of useful elements. Furthermore, the ore economic model is investigated for a significant difference in model value estimated with the use of linear correlation between useful elements content and the height of mine face, and the approach in which model parameters correlation is based upon the copula best matched information capacity criterion. The use of copula allows the simulation to take into account the multi variable dependencies at the same time, thereby giving a better reflection of the dependency structure, which linear correlation does not take into account. Calculation results of the economic model used for deposit value estimation indicate that the correlation between copper and silver estimated with the use of copula generates higher variation of possible project value, as compared to modelling correlation based upon linear correlation. Average deposit value remains unchanged.

Methodological development for selection of significant predictors explaining fatal road accidents.

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Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

The importance of immune gene variability (MHC in evolutionary ecology and conservation

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Sommer Simone

2005-10-01

Full Text Available Abstract Genetic studies have typically inferred the effects of human impact by documenting patterns of genetic differentiation and levels of genetic diversity among potentially isolated populations using selective neutral markers such as mitochondrial control region sequences, microsatellites or single nucleotide polymorphism (SNPs. However, evolutionary relevant and adaptive processes within and between populations can only be reflected by coding genes. In vertebrates, growing evidence suggests that genetic diversity is particularly important at the level of the major histocompatibility complex (MHC. MHC variants influence many important biological traits, including immune recognition, susceptibility to infectious and autoimmune diseases, individual odours, mating preferences, kin recognition, cooperation and pregnancy outcome. These diverse functions and characteristics place genes of the MHC among the best candidates for studies of mechanisms and significance of molecular adaptation in vertebrates. MHC variability is believed to be maintained by pathogen-driven selection, mediated either through heterozygote advantage or frequency-dependent selection. Up to now, most of our knowledge has derived from studies in humans or from model organisms under experimental, laboratory conditions. Empirical support for selective mechanisms in free-ranging animal populations in their natural environment is rare. In this review, I first introduce general information about the structure and function of MHC genes, as well as current hypotheses and concepts concerning the role of selection in the maintenance of MHC polymorphism. The evolutionary forces acting on the genetic diversity in coding and non-coding markers are compared. Then, I summarise empirical support for the functional importance of MHC variability in parasite resistance with emphasis on the evidence derived from free-ranging animal populations investigated in their natural habitat. Finally, I

Genetic variability and natural selection at the ligand domain of the Duffy binding protein in brazilian Plasmodium vivax populations

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Gil Luiz HS

2010-11-01

Full Text Available Abstract Background Plasmodium vivax malaria is a major public health challenge in Latin America, Asia and Oceania, with 130-435 million clinical cases per year worldwide. Invasion of host blood cells by P. vivax mainly depends on a type I membrane protein called Duffy binding protein (PvDBP. The erythrocyte-binding motif of PvDBP is a 170 amino-acid stretch located in its cysteine-rich region II (PvDBPII, which is the most variable segment of the protein. Methods To test whether diversifying natural selection has shaped the nucleotide diversity of PvDBPII in Brazilian populations, this region was sequenced in 122 isolates from six different geographic areas. A Bayesian method was applied to test for the action of natural selection under a population genetic model that incorporates recombination. The analysis was integrated with a structural model of PvDBPII, and T- and B-cell epitopes were localized on the 3-D structure. Results The results suggest that: (i recombination plays an important role in determining the haplotype structure of PvDBPII, and (ii PvDBPII appears to contain neutrally evolving codons as well as codons evolving under natural selection. Diversifying selection preferentially acts on sites identified as epitopes, particularly on amino acid residues 417, 419, and 424, which show strong linkage disequilibrium. Conclusions This study shows that some polymorphisms of PvDBPII are present near the erythrocyte-binding domain and might serve to elude antibodies that inhibit cell invasion. Therefore, these polymorphisms should be taken into account when designing vaccines aimed at eliciting antibodies to inhibit erythrocyte invasion.

Selection and adaptation in irradiated plant and animal populations: a review

International Nuclear Information System (INIS)

Hart, D.R.

1981-03-01

Available literature on the effects of ionizing radiation on mutation rates, variability and adaptive responses to selection in exposed plant and animal populations is reviewed. Accumulated variability, and hence potential selection differentials, may be increased by many times due to induced mutation. The radiation dose that maximizes induced mutation varies greatly among species, strains and genetic systems. Induced variability tends to enhance the respose to selection, but this effect may be delayed or prevented by an initial reduction in the heritability of induced variation. Significantly, the detrimental effects of harmful mutations in irradiated populations may exceed the beneficial effects of selection for adaptive characteristics. Selection for radioresistance may occur at lethal or sub-lethal radiation doses but dose relationships are highly variable. (author)

Evolution of dispersal in spatially and temporally variable environments: The importance of life cycles.

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Massol, François; Débarre, Florence

2015-07-01

Spatiotemporal variability of the environment is bound to affect the evolution of dispersal, and yet model predictions strongly differ on this particular effect. Recent studies on the evolution of local adaptation have shown that the life cycle chosen to model the selective effects of spatiotemporal variability of the environment is a critical factor determining evolutionary outcomes. Here, we investigate the effect of the order of events in the life cycle on the evolution of unconditional dispersal in a spatially heterogeneous, temporally varying landscape. Our results show that the occurrence of intermediate singular strategies and disruptive selection are conditioned by the temporal autocorrelation of the environment and by the life cycle. Life cycles with dispersal of adults versus dispersal of juveniles, local versus global density regulation, give radically different evolutionary outcomes that include selection for total philopatry, evolutionary bistability, selection for intermediate stable states, and evolutionary branching points. Our results highlight the importance of accounting for life-cycle specifics when predicting the effects of the environment on evolutionarily selected trait values, such as dispersal, as well as the need to check the robustness of model conclusions against modifications of the life cycle. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Selection harvests in Amazonian rainforests: long-term impacts on soil properties

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K.L. McNabb; M.S. Miller; B.G. Lockaby; B.J. Stokes; R.G. Clawson; John A. Stanturf; J.N.M. Silva

1997-01-01

Surface soil properties were compared among disturbance classes associated with a single-tree selection harvest study installed in 1979 in the Brazilian Amazon. Response variables included pH, total N, total organic C, extractable P, exchangeable K, Ca, Mg, and bulk density. In general, concentrations of all elements displayed residual effects 16 years after harvests...

The study of variability and strain selection in Streptomyces atroolivaceus. III

International Nuclear Information System (INIS)

Blumauerova, M.; Lipavska, H.; Stajner, K.; Vanek, Z.

1976-01-01

Mutants of Streptomyces atroolivaceus blocked in the biosynthesis of mithramycin were isolated both by natural selection and after treatment with mutagenic factors (UV and gamma rays, nitrous acid). Both physical factors were more effective than nitrous acid. The selection was complicated by the high instability of isolates, out of which 20 to 80%=. (depending on their origin) reversed spontaneously to the parent type. Primary screening (selection of morphological variants and determination of their activity using the method of agar blocks) made it possible to detect only potentially non-productive strains; however, the final selection always had to be made under submerged conditions. Fifty-four stable non-productive mutants were divided, according to results of the chromatographic analysis, into five groups differing in the production of the six biologically inactive metabolites. The mutants did not accumulate chromomycinone, chromocyclomycin and chromocyclin. On mixed cultivation none of the pairs of mutants was capable of the cosynthesis of mithramycin or of new compounds differing from standard metabolites. Possible causes of the above results are discussed. (author)

On the mid-infrared variability of candidate eruptive variables (exors): A comparison between Spitzer and WISE data

Energy Technology Data Exchange (ETDEWEB)

Antoniucci, S.; Giannini, T.; Li Causi, G.; Lorenzetti, D., E-mail: simone.antoniucci@oa-roma.inaf.it, E-mail: teresa.giannini@oa-roma.inaf.it, E-mail: gianluca.licausi@oa-roma.inaf.it, E-mail: dario.lorenzetti@oa-roma.inaf.it [INAF-Osservatorio Astronomico di Roma, via Frascati 33, I-00040 Monte Porzio (Italy)

2014-02-10

Aiming to statistically study the variability in the mid-IR of young stellar objects, we have compared the 3.6, 4.5, and 24 μm Spitzer fluxes of 1478 sources belonging to the C2D (Cores to Disks) legacy program with the WISE fluxes at 3.4, 4.6, and 22 μm. From this comparison, we have selected a robust sample of 34 variable sources. Their variations were classified per spectral Class (according to the widely accepted scheme of Class I/flat/II/III protostars), and per star forming region. On average, the number of variable sources decreases with increasing Class and is definitely higher in Perseus and Ophiuchus than in Chamaeleon and Lupus. According to the paradigm Class ≡ Evolution, the photometric variability can be considered to be a feature more pronounced in less evolved protostars, and, as such, related to accretion processes. Moreover, our statistical findings agree with the current knowledge of star formation activity in different regions. The 34 selected variables were further investigated for similarities with known young eruptive variables, namely the EXors. In particular, we analyzed (1) the shape of the spectral energy distribution, (2) the IR excess over the stellar photosphere, (3) magnitude versus color variations, and (4) output parameters of model fitting. This first systematic search for EXors ends up with 11 bona fide candidates that can be considered as suitable targets for monitoring or future investigations.

The nebular variables

CERN Document Server

Glasby, John S

1974-01-01

The Nebular Variables focuses on the nebular variables and their characteristics. Discussions are organized by type of nebular variable, namely, RW Aurigae stars, T Orionis stars, T Tauri stars, and peculiar nebular objects. Topics range from light variations of the stars to their spectroscopic and physical characteristics, spatial distribution, interaction with nebulosity, and evolutionary features. This volume is divided into four sections and consists of 25 chapters, the first of which provides general information on nebular variables, including their stellar associations and their classifi

A Study of Quasar Selection in the Supernova Fields of the Dark Energy Survey

International Nuclear Information System (INIS)

Tie, S. S.; Martini, P.; Mudd, D.; Ostrovski, F.; Reed, S. L.

2017-01-01

In this paper, we present a study of quasar selection using the supernova fields of the Dark Energy Survey (DES). We used a quasar catalog from an overlapping portion of the SDSS Stripe 82 region to quantify the completeness and efficiency of selection methods involving color, probabilistic modeling, variability, and combinations of color/probabilistic modeling with variability. In all cases, we considered only objects that appear as point sources in the DES images. We examine color selection methods based on the Wide-field Infrared Survey Explorer (WISE) mid-IR W1-W2 color, a mixture of WISE and DES colors (g - i and i-W1), and a mixture of Vista Hemisphere Survey and DES colors (g - i and i - K). For probabilistic quasar selection, we used XDQSO, an algorithm that employs an empirical multi-wavelength flux model of quasars to assign quasar probabilities. Our variability selection uses the multi-band χ"2-probability that sources are constant in the DES Year 1 griz-band light curves. The completeness and efficiency are calculated relative to an underlying sample of point sources that are detected in the required selection bands and pass our data quality and photometric error cuts. We conduct our analyses at two magnitude limits, i 85% for both i-band magnitude limits and efficiencies of >80% to the bright limit and >60% to the faint limit; however, the giW1 and giW1+variability methods give the highest quasar surface densities. The XDQSOz method and combinations of W1W2/giW1/XDQSOz with variability are among the better selection methods when both high completeness and high efficiency are desired. We also present the OzDES Quasar Catalog of 1263 spectroscopically confirmed quasars from three years of OzDES observation in the 30 deg"2 of the DES supernova fields. Finally, the catalog includes quasars with redshifts up to z ~ 4 and brighter than i = 22 mag, although the catalog is not complete up to this magnitude limit.

Modeling Short-Range Soil Variability and its Potential Use in Variable-Rate Treatment of Experimental Plots

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A Moameni

2011-02-01

Full Text Available Abstract In Iran, the experimental plots under fertilizer trials are managed in such a way that the whole plot area uniformly receives agricultural inputs. This could lead to biased research results and hence to suppressing of the efforts made by the researchers. This research was conducted in a selected site belonging to the Gonbad Agricultural Research Station, located in the semiarid region, northeastern Iran. The aim was to characterize the short-range spatial variability of the inherent and management-depended soil properties and to determine if this variation is large and can be managed at practical scales. The soils were sampled using a grid 55 m apart. In total, 100 composite soil samples were collected from topsoil (0-30 cm and were analyzed for calcium carbonate equivalent, organic carbon, clay, available phosphorus, available potassium, iron, copper, zinc and manganese. Descriptive statistics were applied to check data trends. Geostatistical analysis was applied to variography, model fitting and contour mapping. Sampling at 55 m made it possible to split the area of the selected experimental plot into relatively uniform areas that allow application of agricultural inputs with variable rates. Keywords: Short-range soil variability, Within-field soil variability, Interpolation, Precision agriculture, Geostatistics

Global variables and the dynamics or relativistic nucleus-nucleus collisions

International Nuclear Information System (INIS)

Cugnon, J.; L'Hote, D.

1983-01-01

Various global variables providing a simple description of high multiplicity events are reviewed. Many of them are calculated in the framework of an intra-nuclear cascade model, which describes the collision process as a series of binary on-shell relativistic baryon-baryon collisions and which includes inelasticity through the production of δ-resonances. The calculations are first made for the Ar+KCl system at 0.8 GeV/A, with global variables including either all the nucleons or only the participant nucleons. The shape and the orientation of the ellipsoid of sphericity are particularly investigated. For both cases, on the average, the large axis of the ellipsoid is found to point in the beam direction. This result is discussed in comparison with hydrodynamics predictions and in relation with the mean free path. A kind of small 'bounce-off effect' is detected for intermediate impact parameters. The possibility of extracting the value of the impact parameter b from the value of a global variable is shown to depend upon the variation of this variable with b and upon the fluctuation of the global variable for a given impact parameter. A quality factor is defined to quantify this possibility. No current global variable seems to be more appropriate than the number of participant nucleons for the impact parameter selection. The physical origin of the fluctuations inside the intranuclear cascade model is discussed and the possibility of extracting useful information on the dynamics of the system from the fluctuations is pointed out. The energy dependence of our results is discussed. Some results of the calculations at 250 and 400 MeV/A are also presented for the same system Ar+KCl. (orig.)

Noncontextuality with Marginal Selectivity in Reconstructing Mental Architectures

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Ru eZhang

2015-06-01

Full Text Available We present a general theory of series-parallel mental architectures with selectively influenced stochastically non-independent components. A mental architecture is a hypothetical network of processes aimed at performing a task, of which we only observe the overall time it takes under variable parameters of the task. It is usually assumed that the network contains several processes selectively influenced by different experimental factors, and then the question is asked as to how these processes are arranged within the network, e.g., whether they are concurrent or sequential. One way of doing this is to consider the distribution functions for the overall processing time and compute certain linear combinations thereof (interaction contrasts. The theory of selective influences in psychology can be viewed as a special application of the interdisciplinary theory of (noncontextuality having its origins and main applications in quantum theory. In particular, lack of contextuality is equivalent to the existence of a hidden random entity of which all the random variables in play are functions. Consequently, for any given value of this common random entity, the processing times and their compositions (minima, maxima, or sums become deterministic quantities. These quantities, in turn, can be treated as random variables with (shifted Heaviside distribution functions, for which one can easily compute various linear combinations across different treatments, including interaction contrasts. This mathematical fact leads to a simple method, more general than the previously used ones, to investigate and characterize the interaction contrast for different types of series-parallel architectures.

Conspicuous plumage colours are highly variable

OpenAIRE

Delhey, Kaspar; Szecsenyi, Beatrice; Nakagawa, Shinichi; Peters, Anne

2017-01-01

Elaborate ornamental traits are often under directional selection for greater elaboration, which in theory should deplete underlying genetic variation. Despite this, many ornamental traits appear to remain highly variable and how this essential variation is maintained is a key question in evolutionary biology. One way to address this question is to compare differences in intraspecific variability across different types of traits to determine whether high levels of variation are associated wit...

Development from childhood to adulthood increases morphological and functional inter-individual variability in the right superior temporal cortex.

Science.gov (United States)

Bonte, Milene; Frost, Martin A; Rutten, Sanne; Ley, Anke; Formisano, Elia; Goebel, Rainer

2013-12-01

We study the developmental trajectory of morphology and function of the superior temporal cortex (STC) in children (8-9 years), adolescents (14-15 years) and young adults. We analyze cortical surface landmarks and functional MRI (fMRI) responses to voices, other natural categories and tones and examine how hemispheric asymmetry and inter-subject variability change across age. Our results show stable morphological asymmetries across age groups, including a larger left planum temporale and a deeper right superior temporal sulcus. fMRI analyses show that a rightward lateralization for voice-selective responses is present in all groups but decreases with age. Furthermore, STC responses to voices change from being less selective and more spatially diffuse in children to highly selective and focal in adults. Interestingly, the analysis of morphological landmarks reveals that inter-subject variability increases during development in the right--but not in the left--STC. Similarly, inter-subject variability of cortically-realigned functional responses to voices, other categories and tones increases with age in the right STC. Our findings reveal asymmetric developmental changes in brain regions crucial for auditory and voice perception. The age-related increase of inter-subject variability in right STC suggests that anatomy and function of this region are shaped by unique individual developmental experiences. © 2013.

Prediction of university student’s addictability based on some demographic variables, academic procrastination, and interpersonal variables

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Mohammad Ali Tavakoli

2014-02-01

Full Text Available Objectives: This study aimed to predict addictability among the students, based on demographic variables, academic procrastination, and interpersonal variables, and also to study the prevalence of addictability among these students. Method: The participants were 500 students (260 females, 240 males selected through a stratified random sampling among the students in Islamic Azad University Branch Abadan. The participants were assessed through Individual specification inventory, addiction potential scale and Aitken procrastination Inventory. Findings: The findings showed %23/6 of students’ readiness for addiction. Men showed higher addictability than women, but age wasn’t an issue. Also variables such as economic status, age, major, and academic procrastination predicted %13, and among interpersonal variables, the variables of having friends who use drugs and dissociated family predicted %13/2 of the variance in addictability. Conclusion: This study contains applied implications for addiction prevention.

Imaging Variable Stars with HST

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Karovska, M.

2012-06-01

(Abstract only) The Hubble Space Telescope (HST) observations of astronomical sources, ranging from objects in our solar system to objects in the early Universe, have revolutionized our knowledge of the Universe its origins and contents. I highlight results from HST observations of variable stars obtained during the past twenty or so years. Multiwavelength observations of numerous variable stars and stellar systems were obtained using the superb HST imaging capabilities and its unprecedented angular resolution, especially in the UV and optical. The HST provided the first detailed images probing the structure of variable stars including their atmospheres and circumstellar environments. AAVSO observations and light curves have been critical for scheduling of many of these observations and provided important information and context for understanding of the imaging results of many variable sources. I describe the scientific results from the imaging observations of variable stars including AGBs, Miras, Cepheids, semiregular variables (including supergiants and giants), YSOs and interacting stellar systems with a variable stellar components. These results have led to an unprecedented understanding of the spatial and temporal characteristics of these objects and their place in the stellar evolutionary chains, and in the larger context of the dynamic evolving Universe.

Iwamoto-Harada coalescence/pickup model for cluster emission: state density approach including angular momentum variables

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Běták Emil

2014-04-01

Full Text Available For low-energy nuclear reactions well above the resonance region, but still below the pion threshold, statistical pre-equilibrium models (e.g., the exciton and the hybrid ones are a frequent tool for analysis of energy spectra and the cross sections of cluster emission. For α’s, two essentially distinct approaches are popular, namely the preformed one and the different versions of coalescence approaches, whereas only the latter group of models can be used for other types of cluster ejectiles. The original Iwamoto-Harada model of pre-equilibrium cluster emission was formulated using the overlap of the cluster and its constituent nucleons in momentum space. Transforming it into level or state densities is not a straigthforward task; however, physically the same model was presented at a conference on reaction models five years earlier. At that time, only the densities without spin were used. The introduction of spin variables into the exciton model enabled detailed calculation of the γ emission and its competition with nucleon channels, and – at the same time – it stimulated further developments of the model. However – to the best of our knowledge – no spin formulation has been presented for cluster emission till recently, when the first attempts have been reported, but restricted to the first emission only. We have updated this effort now and we are able to handle (using the same simplifications as in our previous work pre-equilibrium cluster emission with spin including all nuclei in the reaction chain.

Enhancing the efficacy of treatment for temporomandibular patients with muscular diagnosis through cognitive-behavioral intervention, including hypnosis: a randomized study.

Science.gov (United States)

Ferrando, Maite; Galdón, María José; Durá, Estrella; Andreu, Yolanda; Jiménez, Yolanda; Poveda, Rafael

2012-01-01

This study evaluated the efficacy of a cognitive-behavioral therapy (CBT), including hypnosis, in patients with temporomandibular disorders (TMDs) with muscular diagnosis. Seventy-two patients (65 women and 7 men with an average age of 39 years) were selected according to the Research Diagnostic Criteria for TMD, and assigned to the experimental group (n = 41), receiving the 6-session CBT program, and the control group (n = 31). All patients received conservative standard treatment for TMD. The assessment included pain variables and psychologic distress. There were significant differences between the groups, the experimental group showing a higher improvement in the variables evaluated. Specifically, 90% of the patients under CBT reported a significant reduction in frequency of pain and 70% in emotional distress. The improvement was stable over time, with no significant differences between posttreatment and 9-month follow-up. CBT, including hypnosis, significantly improved conservative standard treatment outcome in TMD patients. Copyright © 2012 Elsevier Inc. All rights reserved.

Estimation of genetic variability and selection response for clutch length in dwarf brown-egg layers carrying or not the naked neck gene

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Tixier-Boichard Michèle

2003-03-01

Full Text Available Abstract In order to investigate the possibility of using the dwarf gene for egg production, two dwarf brown-egg laying lines were selected for 16 generations on average clutch length; one line (L1 was normally feathered and the other (L2 was homozygous for the naked neck gene NA. A control line from the same base population, dwarf and segregating for the NA gene, was maintained during the selection experiment under random mating. The average clutch length was normalized using a Box-Cox transformation. Genetic variability and selection response were estimated either with the mixed model methodology, or with the classical methods for calculating genetic gain, as the deviation from the control line, and the realized heritability, as the ratio of the selection response on cumulative selection differentials. Heritability of average clutch length was estimated to be 0.42 ± 0.02, with a multiple trait animal model, whereas the estimates of the realized heritability were lower, being 0.28 and 0.22 in lines L1 and L2, respectively. REML estimates of heritability were found to decline with generations of selection, suggesting a departure from the infinitesimal model, either because a limited number of genes was involved, or their frequencies were changed. The yearly genetic gains in average clutch length, after normalization, were estimated to be 0.37 ± 0.02 and 0.33 ± 0.04 with the classical methods, 0.46 ± 0.02 and 0.43 ± 0.01 with animal model methodology, for lines L1 and L2 respectively, which represented about 30% of the genetic standard deviation on the transformed scale. Selection response appeared to be faster in line L2, homozygous for the NA gene, but the final cumulated selection response for clutch length was not different between the L1 and L2 lines at generation 16.

Selecting sagebrush seed sources for restoration in a variable climate: ecophysiological variation among genotypes

Science.gov (United States)

Germino, Matthew J.

2012-01-01

Big sagebrush (Artemisia tridentata) communities dominate a large fraction of the United States and provide critical habitat for a number of wildlife species of concern. Loss of big sagebrush due to fire followed by poor restoration success continues to reduce ecological potential of this ecosystem type, particularly in the Great Basin. Choice of appropriate seed sources for restoration efforts is currently unguided due to knowledge gaps on genetic variation and local adaptation as they relate to a changing landscape. We are assessing ecophysiological responses of big sagebrush to climate variation, comparing plants that germinated from ~20 geographically distinct populations of each of the three subspecies of big sagebrush. Seedlings were previously planted into common gardens by US Forest Service collaborators Drs. B. Richardson and N. Shaw, (USFS Rocky Mountain Research Station, Provo, Utah and Boise, Idaho) as part of the Great Basin Native Plant Selection and Increase Project. Seed sources spanned all states in the conterminous Western United States. Germination, establishment, growth and ecophysiological responses are being linked to genomics and foliar palatability. New information is being produced to aid choice of appropriate seed sources by Bureau of Land Management and USFS field offices when they are planning seed acquisitions for emergency post-fire rehabilitation projects while considering climate variability and wildlife needs.

Associations between period 3 gene polymorphisms and sleep- /chronotype-related variables in patients with late-life insomnia.

Science.gov (United States)

Mansour, Hader A; Wood, Joel; Chowdari, Kodavali V; Tumuluru, Divya; Bamne, Mikhil; Monk, Timothy H; Hall, Martica H; Buysse, Daniel J; Nimgaonkar, Vishwajit L

2017-01-01

A variable number tandem repeat polymorphism (VNTR) in the period 3 (PER3) gene has been associated with heritable sleep and circadian variables, including self-rated chronotypes, polysomnographic (PSG) variables, insomnia and circadian sleep-wake disorders. This report describes novel molecular and clinical analyses of PER3 VNTR polymorphisms to better define their functional consequences. As the PER3 VNTR is located in the exonic (protein coding) region of PER3, we initially investigated whether both alleles (variants) are transcribed into messenger RNA in human fibroblasts. The VNTR showed bi-allelic gene expression. We next investigated genetic associations in relation to clinical variables in 274 older adult Caucasian individuals. Independent variables included genotypes for the PER3 VNTR as well as a representative set of single nucleotide polymorphisms (SNPs) that tag common variants at the PER3 locus (linkage disequilibrium (LD) between genetic variants sleep time and sleep latency, self-rated chronotype, estimated with the Composite Scale (CS), and lifestyle regularity, estimated using the social rhythm metric (SRM). Initially, genetic polymorphisms were individually analyzed in relation to each outcome variable using analysis of variance (ANOVA). Nominally significant associations were further tested using regression analyses that incorporated individual ANOVA-associated DNA variants as potential predictors and each of the selected sleep/circadian variables as outcomes. The covariates included age, gender, body mass index and an index of medical co-morbidity. Significant genetic associations with the VNTR were not detected with the sleep or circadian variables. Nominally significant associations were detected between SNP rs1012477 and CS scores (p = 0.003) and between rs10462021 and SRM (p = 0.047); rs11579477 and average delta power (p = 0.043) (analyses uncorrected for multiple comparisons). In conclusion, alleles of the VNTR are expressed at the

Age-related change in renal corticomedullary differentiation: evaluation with noncontrast-enhanced steady-state free precession (SSFP) MRI with spatially selective inversion pulse using variable inversion time.

Science.gov (United States)

Noda, Yasufumi; Kanki, Akihiko; Yamamoto, Akira; Higashi, Hiroki; Tanimoto, Daigo; Sato, Tomohiro; Higaki, Atsushi; Tamada, Tsutomu; Ito, Katsuyoshi

2014-07-01

To evaluate age-related change in renal corticomedullary differentiation and renal cortical thickness by means of noncontrast-enhanced steady-state free precession (SSFP) magnetic resonance imaging (MRI) with spatially selective inversion recovery (IR) pulse. The Institutional Review Board of our hospital approved this retrospective study and patient informed consent was waived. This study included 48 patients without renal diseases who underwent noncontrast-enhanced SSFP MRI with spatially selective IR pulse using variable inversion times (TIs) (700-1500 msec). The signal intensity of renal cortex and medulla were measured to calculate renal corticomedullary contrast ratio. Additionally, renal cortical thickness was measured. The renal corticomedullary junction was clearly depicted in all patients. The mean cortical thickness was 3.9 ± 0.83 mm. The mean corticomedullary contrast ratio was 4.7 ± 1.4. There was a negative correlation between optimal TI for the best visualization of renal corticomedullary differentiation and age (r = -0.378; P = 0.001). However, there was no significant correlation between renal corticomedullary contrast ratio and age (r = 0.187; P = 0.20). Similarly, no significant correlation was observed between renal cortical thickness and age (r = 0.054; P = 0.712). In the normal kidney, noncontrast-enhanced SSFP MRI with spatially selective IR pulse can be used to assess renal corticomedullary differentiation and cortical thickness without the influence of aging, although optimal TI values for the best visualization of renal corticomedullary junction were shortened with aging. © 2013 Wiley Periodicals, Inc.

Selection Bias in Educational Transition Models: Theory and Empirical Evidence

DEFF Research Database (Denmark)

Holm, Anders; Jæger, Mads

variables. This paper, first, explains theoretically how selection on unobserved variables leads to waning coefficients and, second, illustrates empirically how selection leads to biased estimates of the effect of family background on educational transitions. Our empirical analysis using data from...

Talent identification and selection in elite youth football: An Australian context.

Science.gov (United States)

O'Connor, Donna; Larkin, Paul; Mark Williams, A

2016-10-01

We identified the perceptual-cognitive skills and player history variables that differentiate players selected or not selected into an elite youth football (i.e. soccer) programme in Australia. A sample of elite youth male football players (n = 127) completed an adapted participation history questionnaire and video-based assessments of perceptual-cognitive skills. Following data collection, 22 of these players were offered a full-time scholarship for enrolment at an elite player residential programme. Participants selected for the scholarship programme recorded superior performance on the combined perceptual-cognitive skills tests compared to the non-selected group. There were no significant between group differences on the player history variables. Stepwise discriminant function analysis identified four predictor variables that resulted in the best categorization of selected and non-selected players (i.e. recent match-play performance, region, number of other sports participated, combined perceptual-cognitive performance). The effectiveness of the discriminant function is reflected by 93.7% of players being correctly classified, with the four variables accounting for 57.6% of the variance. Our discriminating model for selection may provide a greater understanding of the factors that influence elite youth talent selection and identification.

AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

Science.gov (United States)

Sun, Lei; Wang, Jun; Wei, Jinmao

2017-03-14

The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

[Selective mutism].

Science.gov (United States)

Ytzhak, A; Doron, Y; Lahat, E; Livne, A

2012-10-01

Selective mutism is an uncommon disorder in young children, in which they selectively don't speak in certain social situations, while being capable of speaking easily in other social situations. Many etiologies were proposed for selective mutism including psychodynamic, behavioral and familial etc. A developmental etiology that includes insights from all the above is gaining support. Accordingly, mild language impairment in a child with an anxiety trait may be at the root of developing selective mutism. The behavior will be reinforced by an avoidant pattern in the family. Early treatment and followup for children with selective mutism is important. The treatment includes non-pharmacological therapy (psychodynamic, behavioral and familial) and pharmacologic therapy--mainly selective serotonin reuptake inhibitors (SSRI).

Variables As Currency: Linking Meta-Analysis Research and Data Paths in Sciences

Directory of Open Access Journals (Sweden)

Hua Qin

2014-11-01

Full Text Available Meta-analyses are studies that bring together data or results from multiple independent studies to produce new and over-arching findings. Current data curation systems only partially support meta-analytic research. Some important meta-analytic tasks, such as the selection of relevant studies for review and the integration of research datasets or findings, are not well supported in current data curation systems. To design tools and services that more fully support meta-analyses, we need a better understanding of meta-analytic research. This includes an understanding of both the practices of researchers who perform the analyses and the characteristics of the individual studies that are brought together. In this study, we make an initial contribution to filling this gap by developing a conceptual framework linking meta-analyses with data paths represented in published articles selected for the analysis. The framework focuses on key variables that represent primary/secondary datasets or derived socio-ecological data, contexts of use, and the data transformations that are applied. We introduce the notion of using variables and their relevant information (e.g., metadata and variable relationships as a type of currency to facilitate synthesis of findings across individual studies and leverage larger bodies of relevant source data produced in small science research. Handling variables in this manner provides an equalizing factor between data from otherwise disparate data-producing communities. We conclude with implications for exploring data integration and synthesis issues as well as system development.

Variable volume combustor

Science.gov (United States)

Ostebee, Heath Michael; Ziminsky, Willy Steve; Johnson, Thomas Edward; Keener, Christopher Paul

2017-01-17

The present application provides a variable volume combustor for use with a gas turbine engine. The variable volume combustor may include a liner, a number of micro-mixer fuel nozzles positioned within the liner, and a linear actuator so as to maneuver the micro-mixer fuel nozzles axially along the liner.

Variable Cycle Engine Technology Program Planning and Definition Study

Science.gov (United States)

Westmoreland, J. S.; Stern, A. M.

1978-01-01

The variable stream control engine, VSCE-502B, was selected as the base engine, with the inverted flow engine concept selected as a backup. Critical component technologies were identified, and technology programs were formulated. Several engine configurations were defined on a preliminary basis to serve as demonstration vehicles for the various technologies. The different configurations present compromises in cost, technical risk, and technology return. Plans for possible variably cycle engine technology programs were formulated by synthesizing the technology requirements with the different demonstrator configurations.

Closed-form solutions for linear regulator design of mechanical systems including optimal weighting matrix selection

Science.gov (United States)

Hanks, Brantley R.; Skelton, Robert E.

1991-01-01

Vibration in modern structural and mechanical systems can be reduced in amplitude by increasing stiffness, redistributing stiffness and mass, and/or adding damping if design techniques are available to do so. Linear Quadratic Regulator (LQR) theory in modern multivariable control design, attacks the general dissipative elastic system design problem in a global formulation. The optimal design, however, allows electronic connections and phase relations which are not physically practical or possible in passive structural-mechanical devices. The restriction of LQR solutions (to the Algebraic Riccati Equation) to design spaces which can be implemented as passive structural members and/or dampers is addressed. A general closed-form solution to the optimal free-decay control problem is presented which is tailored for structural-mechanical system. The solution includes, as subsets, special cases such as the Rayleigh Dissipation Function and total energy. Weighting matrix selection is a constrained choice among several parameters to obtain desired physical relationships. The closed-form solution is also applicable to active control design for systems where perfect, collocated actuator-sensor pairs exist.

Selecting groups of covariates in the elastic net

DEFF Research Database (Denmark)

Clemmensen, Line Katrine Harder

This paper introduces a novel method to select groups of variables in sparse regression and classication settings. The groups are formed based on the correlations between covariates and ensure that for example spatial or spectral relations are preserved without explicitly coding for these....... The preservation of relations gives increased interpretability. The method is based on the elastic net and adaptively selects highly correlated groups of variables and does therefore not waste time in grouping irrelevant variables for the problem at hand. The method is illustrated on a simulated data set...

Improving observational study estimates of treatment effects using joint modeling of selection effects and outcomes: the case of AAA repair.

Science.gov (United States)

O'Malley, A James; Cotterill, Philip; Schermerhorn, Marc L; Landon, Bruce E

2011-12-01

When 2 treatment approaches are available, there are likely to be unmeasured confounders that influence choice of procedure, which complicates estimation of the causal effect of treatment on outcomes using observational data. To estimate the effect of endovascular (endo) versus open surgical (open) repair, including possible modification by institutional volume, on survival after treatment for abdominal aortic aneurysm, accounting for observed and unobserved confounding variables. Observational study of data from the Medicare program using a joint model of treatment selection and survival given treatment to estimate the effects of type of surgery and institutional volume on survival. We studied 61,414 eligible repairs of intact abdominal aortic aneurysms during 2001 to 2004. The outcome, perioperative death, is defined as in-hospital death or death within 30 days of operation. The key predictors are use of endo, transformed endo and open volume, and endo-volume interactions. There is strong evidence of nonrandom selection of treatment with potential confounding variables including institutional volume and procedure date, variables not typically adjusted for in clinical trials. The best fitting model included heterogeneous transformations of endo volume for endo cases and open volume for open cases as predictors. Consistent with our hypothesis, accounting for unmeasured selection reduced the mortality benefit of endo. The effect of endo versus open surgery varies nonlinearly with endo and open volume. Accounting for institutional experience and unmeasured selection enables better decision-making by physicians making treatment referrals, investigators evaluating treatments, and policy makers.

Continuous-variable quantum teleportation in bosonic structured environments

Energy Technology Data Exchange (ETDEWEB)

He Guangqiang; Zhang Jingtao; Zhu Jun; Zeng Guihua [State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

2011-09-15

The effects of dynamics of continuous-variable entanglement under the various kinds of environments on quantum teleportation are quantitatively investigated. Only under assumption of the weak system-reservoir interaction, the evolution of teleportation fidelity is analytically derived and is numerically plotted in terms of environment parameters including reservoir temperature and its spectral density, without Markovian and rotating wave approximations. We find that the fidelity of teleportation is a monotonically decreasing function for Markovian interaction in Ohmic-like environments, while it oscillates for non-Markovian ones. According to the dynamical laws of teleportation, teleportation with better performances can be implemented by selecting the appropriate time.

Biological variables for the site survey of surface ecosystems - existing data and survey methods

International Nuclear Information System (INIS)

Kylaekorpi, Lasse; Berggren, Jens; Larsson, Mats; Liberg, Maria; Rydgren, Bernt

2000-06-01

In the process of selecting a safe and environmentally acceptable location for the deep level repository of nuclear waste, site surveys will be carried out. These site surveys will also include studies of the biota at the site, in order to assure that the chosen site will not conflict with important ecological interests, and to establish a thorough baseline for future impact assessments and monitoring programmes. As a preparation to the site survey programme, a review of the variables that need to be surveyed is conducted. This report contains the review for some of those variables. For each variable, existing data sources and their characteristics are listed. For those variables for which existing data sources are inadequate, suggestions are made for appropriate methods that will enable the establishment of an acceptable baseline. In this report the following variables are reviewed: Fishery, Landscape, Vegetation types, Key biotopes, Species (flora and fauna), Red-listed species (flora and fauna), Biomass (flora and fauna), Water level, water retention time (incl. water body and flow), Nutrients/toxins, Oxygen concentration, Layering, stratification, Light conditions/transparency, Temperature, Sediment transport, (Marine environments are excluded from this review). For a major part of the variables, the existing data coverage is most likely insufficient. Both the temporal and/or the geographical resolution is often limited, which means that complementary surveys must be performed during (or before) the site surveys. It is, however, in general difficult to make exact judgements on the extent of existing data, and also to give suggestions for relevant methods to use in the site surveys. This can be finally decided only when the locations for the sites are decided upon. The relevance of the different variables also depends on the environmental characteristics of the sites. Therefore, we suggest that when the survey sites are selected, an additional review is

Biological variables for the site survey of surface ecosystems - existing data and survey methods

Energy Technology Data Exchange (ETDEWEB)

Kylaekorpi, Lasse; Berggren, Jens; Larsson, Mats; Liberg, Maria; Rydgren, Bernt [SwedPower AB, Stockholm (Sweden)

2000-06-01

In the process of selecting a safe and environmentally acceptable location for the deep level repository of nuclear waste, site surveys will be carried out. These site surveys will also include studies of the biota at the site, in order to assure that the chosen site will not conflict with important ecological interests, and to establish a thorough baseline for future impact assessments and monitoring programmes. As a preparation to the site survey programme, a review of the variables that need to be surveyed is conducted. This report contains the review for some of those variables. For each variable, existing data sources and their characteristics are listed. For those variables for which existing data sources are inadequate, suggestions are made for appropriate methods that will enable the establishment of an acceptable baseline. In this report the following variables are reviewed: Fishery, Landscape, Vegetation types, Key biotopes, Species (flora and fauna), Red-listed species (flora and fauna), Biomass (flora and fauna), Water level, water retention time (incl. water body and flow), Nutrients/toxins, Oxygen concentration, Layering, stratification, Light conditions/transparency, Temperature, Sediment transport, (Marine environments are excluded from this review). For a major part of the variables, the existing data coverage is most likely insufficient. Both the temporal and/or the geographical resolution is often limited, which means that complementary surveys must be performed during (or before) the site surveys. It is, however, in general difficult to make exact judgements on the extent of existing data, and also to give suggestions for relevant methods to use in the site surveys. This can be finally decided only when the locations for the sites are decided upon. The relevance of the different variables also depends on the environmental characteristics of the sites. Therefore, we suggest that when the survey sites are selected, an additional review is

Applicability of bioanalysis of multiple analytes in drug discovery and development: review of select case studies including assay development considerations.

Science.gov (United States)

Srinivas, Nuggehally R

2006-05-01

The development of sound bioanalytical method(s) is of paramount importance during the process of drug discovery and development culminating in a marketing approval. Although the bioanalytical procedure(s) originally developed during the discovery stage may not necessarily be fit to support the drug development scenario, they may be suitably modified and validated, as deemed necessary. Several reviews have appeared over the years describing analytical approaches including various techniques, detection systems, automation tools that are available for an effective separation, enhanced selectivity and sensitivity for quantitation of many analytes. The intention of this review is to cover various key areas where analytical method development becomes necessary during different stages of drug discovery research and development process. The key areas covered in this article with relevant case studies include: (a) simultaneous assay for parent compound and metabolites that are purported to display pharmacological activity; (b) bioanalytical procedures for determination of multiple drugs in combating a disease; (c) analytical measurement of chirality aspects in the pharmacokinetics, metabolism and biotransformation investigations; (d) drug monitoring for therapeutic benefits and/or occupational hazard; (e) analysis of drugs from complex and/or less frequently used matrices; (f) analytical determination during in vitro experiments (metabolism and permeability related) and in situ intestinal perfusion experiments; (g) determination of a major metabolite as a surrogate for the parent molecule; (h) analytical approaches for universal determination of CYP450 probe substrates and metabolites; (i) analytical applicability to prodrug evaluations-simultaneous determination of prodrug, parent and metabolites; (j) quantitative determination of parent compound and/or phase II metabolite(s) via direct or indirect approaches; (k) applicability in analysis of multiple compounds in select

Restricted cross-scale habitat selection by American beavers.

Science.gov (United States)

Francis, Robert A; Taylor, Jimmy D; Dibble, Eric; Strickland, Bronson; Petro, Vanessa M; Easterwood, Christine; Wang, Guiming

2017-12-01

Animal habitat selection, among other ecological phenomena, is spatially scale dependent. Habitat selection by American beavers Castor canadensis (hereafter, beaver) has been studied at singular spatial scales, but to date no research addresses multi-scale selection. Our objectives were to determine if beaver habitat selection was specialized to semiaquatic habitats and if variables explaining habitat selection are consistent between landscape and fine spatial scales. We built maximum entropy (MaxEnt) models to relate landscape-scale presence-only data to landscape variables, and used generalized linear mixed models to evaluate fine spatial scale habitat selection using global positioning system (GPS) relocation data. Explanatory variables between the landscape and fine spatial scale were compared for consistency. Our findings suggested that beaver habitat selection at coarse (study area) and fine (within home range) scales was congruent, and was influenced by increasing amounts of woody wetland edge density and shrub edge density, and decreasing amounts of open water edge density. Habitat suitability at the landscape scale also increased with decreasing amounts of grass frequency. As territorial, central-place foragers, beavers likely trade-off open water edge density (i.e., smaller non-forested wetlands or lodges closer to banks) for defense and shorter distances to forage and obtain construction material. Woody plants along edges and expanses of open water for predator avoidance may limit beaver fitness and subsequently determine beaver habitat selection.

Cephalometric variables predicting the long-term success or failure of combined rapid maxillary expansion and facial mask therapy.

Science.gov (United States)

Baccetti, Tiziano; Franchi, Lorenzo; McNamara, James A

2004-07-01

The aim of this study was to select a model of cephalometric variables to predict the results of early treatment of Class III malocclusion with rapid maxillary expansion and facemask therapy followed by comprehensive treatment with fixed appliances. Lateral cephalograms of 42 patients (20 boys, 22 girls) with Class III malocclusion were analyzed at the start of treatment (mean age 8 years 6 months +/- 2 years, at stage I in cervical vertebral maturation). All patients were reevaluated after a mean period of 6 years 6 months (at stage IV or V in cervical vertebral maturation) that included active treatment plus retention. At this time, the sample was divided into 2 groups according to occlusal criteria: a successful group (30 patients) and an unsuccessful group (12 patients). Discriminant analysis was applied to select pretreatment predictive variables of long-term treatment outcome. Stepwise variable selection of the cephalometric measurements at the first observation identified 3 predictive variables. Orthopedic treatment of Class III malocclusion might be unfavorable over the long term when a patient's pretreatment cephalometric records exhibit a long mandibular ramus (ie, increased posterior facial height), an acute cranial base angle, and a steep mandibular plane angle. On the basis of the equation generated by the multivariate statistical method, the outcome of interceptive orthopedic treatment for each new patient with Class III malocclusion can be predicted with a probability error of 16.7%.

Development of high yielding mutants of Brassica campestris L. cv. Toria selection through gamma rays irradiation

International Nuclear Information System (INIS)

Javed, M.A.; Siddiqui, M.A.; Khan, M.K.R.; Khatri, A.; Khan, I.A.; Dahar, N.A.; Khanzada, M.H.; Khan, R.

2003-01-01

Homogeneous seeds of Brassica campestris L. cv. Toria selection were treated with different doses of gamma rays (750, 1000 and 1250 Gy) to induce genetic variability for the selection of new genotypes with improved agronomic traits. After passing through different stages of selection, two promising mutants were selected for further studies. Two selected mutants along with 5 other entries including parent variety were evaluated for yield and yield components in yield trials for two consecutive years. The mutant TS96-752 was significantly (P less than or equal to 0.05) superior to all other entries in grain yield but at par with FSD 86028-3

The Impact of Variability of Selected Geological and Mining Parameters on the Value and Risks of Projects in the Hard Coal Mining Industry

Science.gov (United States)

Kopacz, Michał

2017-09-01

The paper attempts to assess the impact of variability of selected geological (deposit) parameters on the value and risks of projects in the hard coal mining industry. The study was based on simulated discounted cash flow analysis, while the results were verified for three existing bituminous coal seams. The Monte Carlo simulation was based on nonparametric bootstrap method, while correlations between individual deposit parameters were replicated with use of an empirical copula. The calculations take into account the uncertainty towards the parameters of empirical distributions of the deposit variables. The Net Present Value (NPV) and the Internal Rate of Return (IRR) were selected as the main measures of value and risk, respectively. The impact of volatility and correlation of deposit parameters were analyzed in two aspects, by identifying the overall effect of the correlated variability of the parameters and the indywidual impact of the correlation on the NPV and IRR. For this purpose, a differential approach, allowing determining the value of the possible errors in calculation of these measures in numerical terms, has been used. Based on the study it can be concluded that the mean value of the overall effect of the variability does not exceed 11.8% of NPV and 2.4 percentage points of IRR. Neglecting the correlations results in overestimating the NPV and the IRR by up to 4.4%, and 0.4 percentage point respectively. It should be noted, however, that the differences in NPV and IRR values can vary significantly, while their interpretation depends on the likelihood of implementation. Generalizing the obtained results, based on the average values, the maximum value of the risk premium in the given calculation conditions of the "X" deposit, and the correspondingly large datasets (greater than 2500), should not be higher than 2.4 percentage points. The impact of the analyzed geological parameters on the NPV and IRR depends primarily on their co-existence, which can be

A SEARCH FOR L/T TRANSITION DWARFS WITH Pan-STARRS1 AND WISE: DISCOVERY OF SEVEN NEARBY OBJECTS INCLUDING TWO CANDIDATE SPECTROSCOPIC VARIABLES

International Nuclear Information System (INIS)

Best, William M. J.; Liu, Michael C.; Magnier, Eugene A.; Aller, Kimberly M.; Burgett, W. S.; Chambers, K. C.; Hodapp, K. W.; Kaiser, N.; Kudritzki, R.-P.; Morgan, J. S.; Tonry, J. L.; Wainscoat, R. J.; Deacon, Niall R.; Dupuy, Trent J.; Redstone, Joshua; Price, P. A.

2013-01-01

We present initial results from a wide-field (30,000 deg 2 ) search for L/T transition brown dwarfs within 25 pc using the Pan-STARRS1 and Wide-field Infrared Survey Explorer (WISE) surveys. Previous large-area searches have been incomplete for L/T transition dwarfs, because these objects are faint in optical bands and have near-infrared (near-IR) colors that are difficult to distinguish from background stars. To overcome these obstacles, we have cross-matched the Pan-STARRS1 (optical) and WISE (mid-IR) catalogs to produce a unique multi-wavelength database for finding ultracool dwarfs. As part of our initial discoveries, we have identified seven brown dwarfs in the L/T transition within 9-15 pc of the Sun. The L9.5 dwarf PSO J140.2308+45.6487 and the T1.5 dwarf PSO J307.6784+07.8263 (both independently discovered by Mace et al.) show possible spectroscopic variability at the Y and J bands. Two more objects in our sample show evidence of photometric J-band variability, and two others are candidate unresolved binaries based on their spectra. We expect our full search to yield a well-defined, volume-limited sample of L/T transition dwarfs that will include many new targets for study of this complex regime. PSO J307.6784+07.8263 in particular may be an excellent candidate for in-depth study of variability, given its brightness (J = 14.2 mag) and proximity (11 pc)

Inference for feature selection using the Lasso with high-dimensional data

DEFF Research Database (Denmark)

Brink-Jensen, Kasper; Ekstrøm, Claus Thorn

2014-01-01

Penalized regression models such as the Lasso have proved useful for variable selection in many fields - especially for situations with high-dimensional data where the numbers of predictors far exceeds the number of observations. These methods identify and rank variables of importance but do...... not generally provide any inference of the selected variables. Thus, the variables selected might be the "most important" but need not be significant. We propose a significance test for the selection found by the Lasso. We introduce a procedure that computes inference and p-values for features chosen...... by the Lasso. This method rephrases the null hypothesis and uses a randomization approach which ensures that the error rate is controlled even for small samples. We demonstrate the ability of the algorithm to compute $p$-values of the expected magnitude with simulated data using a multitude of scenarios...

The Impact of Crude Oil Price on Macroeconomic Variables: New Evidence from Malaysia

OpenAIRE

Abdullah, Ahmad Monir; Masih, Abul Mansur M.

2014-01-01

An understanding of how volatilities of and correlations between crude oil and macroeconomic variables change over time including their directions and size is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is a humble attempt to add value to the existing literature by empirically testing for the ‘time-varying’ and ‘scale dependent’ correlations between selected commodities an...

Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms

Directory of Open Access Journals (Sweden)

Fernando A. Auat Cheein

2013-01-01

Full Text Available Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter, the EKF (Extended Kalman Filter and the UKF (Unscented Kalman Filter. Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.

Risk estimates for hip fracture from clinical and densitometric variables and impact of database selection in Lebanese subjects.

Science.gov (United States)

Badra, Mohammad; Mehio-Sibai, Abla; Zeki Al-Hazzouri, Adina; Abou Naja, Hala; Baliki, Ghassan; Salamoun, Mariana; Afeiche, Nadim; Baddoura, Omar; Bulos, Suhayl; Haidar, Rachid; Lakkis, Suhayl; Musharrafieh, Ramzi; Nsouli, Afif; Taha, Assaad; Tayim, Ahmad; El-Hajj Fuleihan, Ghada

2009-01-01

Bone mineral density (BMD) and fracture incidence vary greatly worldwide. The data, if any, on clinical and densitometric characteristics of patients with hip fractures from the Middle East are scarce. The objective of the study was to define risk estimates from clinical and densitometric variables and the impact of database selection on such estimates. Clinical and densitometric information were obtained in 60 hip fracture patients and 90 controls. Hip fracture subjects were 74 yr (9.4) old, were significantly taller, lighter, and more likely to be taking anxiolytics and sleeping pills than controls. National Health and Nutrition Examination Survey (NHANES) database selection resulted in a higher sensitivity and almost equal specificity in identifying patients with a hip fracture compared with the Lebanese database. The odds ratio (OR) and its confidence interval (CI) for hip fracture per standard deviation (SD) decrease in total hip BMD was 2.1 (1.45-3.05) with the NHANES database, and 2.11 (1.36-2.37) when adjusted for age and body mass index (BMI). Risk estimates were higher in male compared with female subjects. In Lebanese subjects, BMD- and BMI-derived hip fracture risk estimates are comparable to western standards. The study validates the universal use of the NHANES database, and the applicability of BMD- and BMI-derived risk fracture estimates in the World Health Organization (WHO) global fracture risk model, to the Lebanese.

Network-based group variable selection for detecting expression quantitative trait loci (eQTL

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Zhang Xuegong

2011-06-01

Full Text Available Abstract Background Analysis of expression quantitative trait loci (eQTL aims to identify the genetic loci associated with the expression level of genes. Penalized regression with a proper penalty is suitable for the high-dimensional biological data. Its performance should be enhanced when we incorporate biological knowledge of gene expression network and linkage disequilibrium (LD structure between loci in high-noise background. Results We propose a network-based group variable selection (NGVS method for QTL detection. Our method simultaneously maps highly correlated expression traits sharing the same biological function to marker sets formed by LD. By grouping markers, complex joint activity of multiple SNPs can be considered and the dimensionality of eQTL problem is reduced dramatically. In order to demonstrate the power and flexibility of our method, we used it to analyze two simulations and a mouse obesity and diabetes dataset. We considered the gene co-expression network, grouped markers into marker sets and treated the additive and dominant effect of each locus as a group: as a consequence, we were able to replicate results previously obtained on the mouse linkage dataset. Furthermore, we observed several possible sex-dependent loci and interactions of multiple SNPs. Conclusions The proposed NGVS method is appropriate for problems with high-dimensional data and high-noise background. On eQTL problem it outperforms the classical Lasso method, which does not consider biological knowledge. Introduction of proper gene expression and loci correlation information makes detecting causal markers more accurate. With reasonable model settings, NGVS can lead to novel biological findings.

Genetic variability and heritability studies of some reproductive traits ...

African Journals Online (AJOL)

GRACE

2006-07-03

Jul 3, 2006 ... The success of most crop improvement programs largely depends upon the genetic variability and the heritability of desirable traits. The magnitude and type of genetic variability help the breeder to determine the selection criteria and breeding schemes to be used for improvement purposes. A screen.

Cortical Response Variability as a Developmental Index of Selective Auditory Attention

Science.gov (United States)

Strait, Dana L.; Slater, Jessica; Abecassis, Victor; Kraus, Nina

2014-01-01

Attention induces synchronicity in neuronal firing for the encoding of a given stimulus at the exclusion of others. Recently, we reported decreased variability in scalp-recorded cortical evoked potentials to attended compared with ignored speech in adults. Here we aimed to determine the developmental time course for this neural index of auditory…

Genome-wide single-generation signatures of local selection in the panmictic European eel

DEFF Research Database (Denmark)

Pujolar, J. M.; Jacobsen, M. W.; Als, Thomas Damm

2014-01-01

Next-generation sequencing and the collection of genome-wide data allow identifying adaptive variation and footprints of directional selection. Using a large SNP data set from 259 RAD-sequenced European eel individuals (glass eels) from eight locations between 34 and 64oN, we examined the patterns...... of genome-wide genetic diversity across locations. We tested for local selection by searching for increased population differentiation using FST-based outlier tests and by testing for significant associations between allele frequencies and environmental variables. The overall low genetic differentiation...... with single-generation signatures of spatially varying selection acting on glass eels. After screening 50 354 SNPs, a total of 754 potentially locally selected SNPs were identified. Candidate genes for local selection constituted a wide array of functions, including calcium signalling, neuroactive ligand...

On a Robust MaxEnt Process Regression Model with Sample-Selection

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Hea-Jung Kim

2018-04-01

Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

Selective dopamine D3 receptor antagonism by SB-277011A attenuates cocaine reinforcement as assessed by progressive-ratio and variable-cost–variable-payoff fixed-ratio cocaine self-administration in rats

Science.gov (United States)

Xi, Zheng-Xiong; Gilbert, Jeremy G.; Pak, Arlene C.; Ashby, Charles R.; Heidbreder, Christian A.; Gardner, Eliot L.

2013-01-01

In rats, acute administration of SB-277011A, a highly selective dopamine (DA) D3 receptor antagonist, blocks cocaine-enhanced brain stimulation reward, cocaine-seeking behaviour and reinstatement of cocaine-seeking behaviour. Here, we investigated whether SB-277011A attenuates cocaine reinforcement as assessed by cocaine self-administration under variable-cost–variable-payoff fixed-ratio (FR) and progressive-ratio (PR) reinforcement schedules. Acute i.p. administration of SB-277011A (3–24 mg/kg) did not significantly alter cocaine (0.75 mg/kg/infusion) self-administration reinforced under FR1 (one lever press for one cocaine infusion) conditions. However, acute administration of SB-277011A (24 mg/kg, i.p.) progressively attenuated cocaine self-administration when: (a) the unit dose of self-administered cocaine was lowered from 0.75 to 0.125–0.5 mg/kg, and (b) the work demand for cocaine reinforcement was increased from FR1 to FR10. Under PR (increasing number of lever presses for each successive cocaine infusion) cocaine reinforcement, acute administration of SB-277011A (6–24 mg/kg i.p.) lowered the PR break point for cocaine self-administration in a dose-dependent manner. The reduction in the cocaine (0.25–1.0 mg/kg) dose–response break-point curve produced by 24 mg/kg SB-277011A is consistent with a reduction in cocaine’s reinforcing efficacy. When substituted for cocaine, SB-277011A alone did not sustain self-administration behaviour. In contrast with the mixed DA D2/D3 receptor antagonist haloperidol (1 mg/kg), SB-277011A (3, 12 or 24 mg/kg) failed to impede locomotor activity, failed to impair rearing behaviour, failed to produce catalepsy and failed to impair rotarod performance. These results show that SB-277011A significantly inhibits acute cocaine-induced reinforcement except at high cocaine doses and low work requirement for cocaine. If these results extrapolate to humans, SB-277011A or similar selective DA D3 receptor antagonists may be

Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

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Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2011-10-31

The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain

The Salience of Selected Variables on Choice for Movie Attendance among High School Students.

Science.gov (United States)

Austin, Bruce A.

A questionnaire was designed for a study assessing both the importance of 28 variables in movie attendance and the importance of movie-going as a leisure-time activity. Respondents were 130 ninth and twelfth grade students. The 28 variables were broadly organized into eight categories: movie production personnel, production elements, advertising,…

Selective oxidation

International Nuclear Information System (INIS)

Cortes Henao, Luis F.; Castro F, Carlos A.

2000-01-01

It is presented a revision and discussion about the characteristics and factors that relate activity and selectivity in the catalytic and not catalytic partial oxidation of methane and the effect of variables as the temperature, pressure and others in the methane conversion to methanol. It thinks about the zeolites use modified for the catalytic oxidation of natural gas

Lecture Hall and Learning Design: A Survey of Variables, Parameters, Criteria and Interrelationships for Audio-Visual Presentation Systems and Audience Reception.

Science.gov (United States)

Justin, J. Karl

Variables and parameters affecting architectural planning and audiovisual systems selection for lecture halls and other learning spaces are surveyed. Interrelationships of factors are discussed, including--(1) design requirements for modern educational techniques as differentiated from cinema, theater or auditorium design, (2) general hall…

Natural climate variability in a coupled model

International Nuclear Information System (INIS)

Zebiak, S.E.; Cane, M.A.

1990-01-01

Multi-century simulations with a simplified coupled ocean-atmosphere model are described. These simulations reveal an impressive range of variability on decadal and longer time scales, in addition to the dominant interannual el Nino/Southern Oscillation signal that the model originally was designed to simulate. Based on a very large sample of century-long simulations, it is nonetheless possible to identify distinct model parameter sensitivities that are described here in terms of selected indices. Preliminary experiments motivated by general circulation model results for increasing greenhouse gases suggest a definite sensitivity to model global warming. While these results are not definitive, they strongly suggest that coupled air-sea dynamics figure prominently in global change and must be included in models for reliable predictions

Integrated Multiscale Latent Variable Regression and Application to Distillation Columns

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Muddu Madakyaru

2013-01-01

Full Text Available Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions, which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR techniques, such as principal component regression (PCR, partial least squares (PLS, and regularized canonical correlation analysis (RCCA. Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.

Strategy for selection of soybean genotypes tolerant to drought during germination.

Science.gov (United States)

Dantas, S A G; Silva, F C S; Silva, L J; Silva, F L

2017-05-10

Water deficit is the main reason for instability in the context of soybean culture. The development of strategies for the selection of more tolerant genotypes is necessary. These strategies include the use of polyethylene glycol 6000 solutions (PEG-6000) for conducting the germination test under conditions of water restriction. Thus, the objective of this study was to determine the osmotic potential and the main characteristics that promote the discrimination of soybean genotypes with regard to water stress tolerance during germination and the vigor test. Thirteen soybean cultivars were used. The seeds were allowed to germinate on sheets of germitest paper moistened in solution with PEG-6000, simulating different levels of water availability, which is expressed as osmotic potential (0.0, -0.2, -0.4, and -0.6 MPa). We assessed germination, length, and dry mass for seedlings and seeds, as well as reserve dynamics. Germination and variables related to the dynamics of reservation have great influence on the expression of variability in environments under stress. Among the different osmotic potentials, the -0.2 MPa was the most efficient for the expression of genetic variability among the cultivars. Conducting the germination test with PEG-6000 solution to -0.2 MPa was efficient for selecting soybean cultivars tolerant to water stress. This was accomplished by evaluating the percentage of germination, along with variables related to the dynamics of reservation.

Quantitative sacroiliac scintigraphy. The effect of method of selection of region of interest

International Nuclear Information System (INIS)

Davis, M.C.; Turner, D.A.; Charters, J.R.; Golden, H.E.; Ali, A.; Fordham, E.W.

1984-01-01

Various authors have advocated quantitative methods of evaluating bone scintigrams to detect sacroiliitis, while others have not found them useful. Many explanations for this disagreement have been offered, including differences in the method of case selection, ethnicity, gender, and previous drug therapy. It would appear that one of the most important impediments to consistent results is the variability of selecting sacroiliac joint and reference regions of interest (ROIs). The effect of ROI selection would seem particularly important because of the normal variability of radioactivity within the reference regions that have been used (sacrum, spine, iliac wing) and the inhomogeneity of activity in the SI joints. We have investigated the effect of ROI selection, using five different methods representative of, though not necessarily identical to, those found in the literature. Each method produced unique mean indices that were different for patients with ankylosing spondylitis (AS) and controls. The method of Ayres (19) proved superior (largest mean difference, smallest variance), but none worked well as a diagnostic tool because of substantial overlap of the distributions of indices of patient and control groups. We conclude that ROI selection is important in determining results, and quantitative scintigraphic methods in general are not effective tools for diagnosing AS. Among the possible factors limiting success, difficulty in selecting a stable reference area seems of particular importance

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.

Life-history strategies associated with local population variability confer regional stability.

Science.gov (United States)

Pribil, Stanislav; Houlahan, Jeff E

2003-07-07

A widely held ecological tenet is that, at the local scale, populations of K-selected species (i.e. low fecundity, long lifespan and large body size) will be less variable than populations of r-selected species (i.e. high fecundity, short lifespan and small body size). We examined the relationship between long-term population trends and life-history attributes for 185 bird species in the Czech Republic and found that, at regional spatial scales and over moderate temporal scales (100-120 years), K-selected bird species were more likely to show both large increases and decreases in population size than r-selected species. We conclude that life-history attributes commonly associated with variable populations at the local scale, confer stability at the regional scale.

Problems of selectivity in liquid-phase oxidation

Energy Technology Data Exchange (ETDEWEB)

Emanuel, N M

1978-07-01

Based on a kinetic analysis of a generalized scheme for radical-chain process and on published experimental results, factors determining the selectivities of various liquid-phase oxidations of organic compounds are examined, including the kinetic chain length, molecular and chain decomposition of products, and competing routes in the initiated oxidation or autoxidation of hydrocarbons to peroxides. Also discussed are selective inhibition of undesirable routes in chain reactions, e.g., styrene and acetaldehyde co-oxidation; activation of molecular oxygen by variable-valence metal compounds used as homogeneous catalysts; modeling of fermentative processes by oxidation of hydrocarbons in complex catalytic systems, e.g., hydroxylation of alkanes, epoxidation or carbonylation of olefins, or oxidation of alcohols and ketones to acids; and the mechanisms of heterogeneous catalysis in liquid-phase reactions, e.g., oxidation of alkylaromatic hydrocarbons to peroxides and co-oxidation of propylene and acetaldehyde.

The Relationship between Attitudes toward Censorship and Selected Academic Variables.

Science.gov (United States)

Dwyer, Edward J.; Summy, Mary K.

1989-01-01

To examine characteristics of subjects relative to their attitudes toward censorship, a study surveyed 98 college students selected from students in a public university in the southeastern United States. A 24-item Likert-style censorship scale was used to measure attitudes toward censorship. Strong agreement with affirmative items would suggest…

Variability of macrofauna distribution along a dissipative log-spiral sandy beach in Rio de Janeiro, Southeastern Brazil

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Carlos A.M. Barboza

2017-03-01

Full Text Available Log-spiral beaches display defined physical gradients alongshore. However, the majority of studies focus on the variability of a single population of macrofauna species. We aimed to investigate the variation in species distribution and in community structure along ten transects on a log-spiral beach. Principal component analysis indicated a clear physical gradient alongshore. Redundancy analysis showed that the sheltered end was related to smaller particle sizes, higher organic matter content and high densities of polychaetes. The exposed end was characterized by coarser sand, lower organic matter content and a high presence of crustaceans. Model selection indicated that the “best fit” to explain the variability in the number of individuals included grain size and beach slope. Variability of the polychaete Scolelepis squamata was best explained by grain size, slope and sediment sorting. The best model for the cirolanid Excirolana armata only included sediment sorting. The physical gradient in sediment texture and the beach slope explained more than one-third of the variability in community structure. The physical variables were also correlated with the distribution of the individual species. We showed that the physical gradient on log-spiral coasts may be an important driver of macrofauna variability, even at mesoscales and in dissipative conditions.

A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses.

Science.gov (United States)

Zhang, Linlin; Guindani, Michele; Versace, Francesco; Vannucci, Marina

2014-07-15

In this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis-Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data. Copyright © 2014 Elsevier Inc. All rights reserved.

Perspectives on Current Issues Is ``Anthropic Selection'' Science?

Science.gov (United States)

Larson, Ronald G.

2007-01-01

I argue that there are strong reasons for resisting as a principle of science the concept of “anthropic selection.” This concept asserts that the existence of “observers” in a universe can be used as a condition that selects physical laws and constants necessary for intelligent life from different laws or physical constants prevailing in a vast number of other universes, to thereby explain why the properties of our universe are conducive to intelligent life. My reasons for limiting “anthropic selection” to the realm of speculation rather than permitting it to creep into mainstream science include our inability to estimate the probabilities of emergence of “observers” in a universe, the lack of testability through direct observation of the assumed high variability of the constants of nature, the lack of a clear definition of an “observer,” and the arbitrariness in how and to what questions anthropic selection is applied.

Diversified models for portfolio selection based on uncertain semivariance

Science.gov (United States)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

A SEARCH FOR L/T TRANSITION DWARFS WITH Pan-STARRS1 AND WISE: DISCOVERY OF SEVEN NEARBY OBJECTS INCLUDING TWO CANDIDATE SPECTROSCOPIC VARIABLES

Energy Technology Data Exchange (ETDEWEB)

Best, William M. J.; Liu, Michael C.; Magnier, Eugene A.; Aller, Kimberly M.; Burgett, W. S.; Chambers, K. C.; Hodapp, K. W.; Kaiser, N.; Kudritzki, R.-P.; Morgan, J. S.; Tonry, J. L.; Wainscoat, R. J. [Institute for Astronomy, University of Hawaii at Manoa, Honolulu, HI 96822 (United States); Deacon, Niall R. [Max Planck Institute for Astronomy, Koenigstuhl 17, D-69117 Heidelberg (Germany); Dupuy, Trent J. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Redstone, Joshua [Facebook, 335 Madison Ave, New York, NY 10017-4677 (United States); Price, P. A., E-mail: wbest@ifa.hawaii.edu [Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States)

2013-11-10

We present initial results from a wide-field (30,000 deg{sup 2}) search for L/T transition brown dwarfs within 25 pc using the Pan-STARRS1 and Wide-field Infrared Survey Explorer (WISE) surveys. Previous large-area searches have been incomplete for L/T transition dwarfs, because these objects are faint in optical bands and have near-infrared (near-IR) colors that are difficult to distinguish from background stars. To overcome these obstacles, we have cross-matched the Pan-STARRS1 (optical) and WISE (mid-IR) catalogs to produce a unique multi-wavelength database for finding ultracool dwarfs. As part of our initial discoveries, we have identified seven brown dwarfs in the L/T transition within 9-15 pc of the Sun. The L9.5 dwarf PSO J140.2308+45.6487 and the T1.5 dwarf PSO J307.6784+07.8263 (both independently discovered by Mace et al.) show possible spectroscopic variability at the Y and J bands. Two more objects in our sample show evidence of photometric J-band variability, and two others are candidate unresolved binaries based on their spectra. We expect our full search to yield a well-defined, volume-limited sample of L/T transition dwarfs that will include many new targets for study of this complex regime. PSO J307.6784+07.8263 in particular may be an excellent candidate for in-depth study of variability, given its brightness (J = 14.2 mag) and proximity (11 pc)

ASSESSMENT OF THE CHANGES IN BLOOD PRESSURE CIRCADIAN PROFILE AND VARIABILITY IN PATIENTS WITH CHRONIC HEART FAILURE AND ARTERIAL HYPERTENSION DURING COMBINED THERAPY INCLUDING IVABRADINE

Directory of Open Access Journals (Sweden)

M. V. Surovtseva

2012-01-01

Full Text Available Aim. To assess the changes in blood pressure (BP circadian profile and variability in patients with chronic heart failure (CHF of ischemic etiology and arterial hypertension (HT due to the complex therapy including ivabradine. Material and methods. Patients (n=90 with CHF class II–III NYHA associated with stable angina II-III class and HT were examined. The patients were randomized into 3 groups depending on received drugs: perindopril and ivabradine - group 1; perindopril, bisoprolol and ivabradine - group 2; perindopril and bisoprolol - group 3. The duration of therapy was 6 months. Ambulatory BP monitoring (ABPM was assessed at baseline and after treatment. Results. More significant reduction in average 24-hours systolic BP was found in groups 1 and 2 compared to group 3 (Δ%: -19.4±0,4; -21.1±0.4 and -11.8±0.6, respectively as well as diastolic BP (Δ%: -10.6±0.6; -12.9±0.4 and -4,3±0.3, respectively and other ABPM indicators. Improvement of BP circadian rhythm was found due to increase in the number of «Dipper» patients (p=0.016. More significant reduction in average daily and night systolic and diastolic BP (p=0.001, as well as daily and night BP variability (p=0.001 was also found in patients of group 2 compared to these of group 1. Conclusion. Moderate antihypertensive effect (in respect of both diastolic and systolic BP was shown when ivabradine was included into the complex therapy of patients with ischemic CHF and HT. The effect was more pronounced when ivabradine was combined with perindopril and bisoprolol. This was accompanied by reduction in high BP daily variability and improvement of the BP circadian rhythm. 

ASSESSMENT OF THE CHANGES IN BLOOD PRESSURE CIRCADIAN PROFILE AND VARIABILITY IN PATIENTS WITH CHRONIC HEART FAILURE AND ARTERIAL HYPERTENSION DURING COMBINED THERAPY INCLUDING IVABRADINE

Directory of Open Access Journals (Sweden)

M. V. Surovtseva

2015-12-01

Full Text Available Aim. To assess the changes in blood pressure (BP circadian profile and variability in patients with chronic heart failure (CHF of ischemic etiology and arterial hypertension (HT due to the complex therapy including ivabradine. Material and methods. Patients (n=90 with CHF class II–III NYHA associated with stable angina II-III class and HT were examined. The patients were randomized into 3 groups depending on received drugs: perindopril and ivabradine - group 1; perindopril, bisoprolol and ivabradine - group 2; perindopril and bisoprolol - group 3. The duration of therapy was 6 months. Ambulatory BP monitoring (ABPM was assessed at baseline and after treatment. Results. More significant reduction in average 24-hours systolic BP was found in groups 1 and 2 compared to group 3 (Δ%: -19.4±0,4; -21.1±0.4 and -11.8±0.6, respectively as well as diastolic BP (Δ%: -10.6±0.6; -12.9±0.4 and -4,3±0.3, respectively and other ABPM indicators. Improvement of BP circadian rhythm was found due to increase in the number of «Dipper» patients (p=0.016. More significant reduction in average daily and night systolic and diastolic BP (p=0.001, as well as daily and night BP variability (p=0.001 was also found in patients of group 2 compared to these of group 1. Conclusion. Moderate antihypertensive effect (in respect of both diastolic and systolic BP was shown when ivabradine was included into the complex therapy of patients with ischemic CHF and HT. The effect was more pronounced when ivabradine was combined with perindopril and bisoprolol. This was accompanied by reduction in high BP daily variability and improvement of the BP circadian rhythm. 

Distribution and predictors of wing shape and size variability in three sister species of solitary bees.

Directory of Open Access Journals (Sweden)

Simon Dellicour

Full Text Available Morphological traits can be highly variable over time in a particular geographical area. Different selective pressures shape those traits, which is crucial in evolutionary biology. Among these traits, insect wing morphometry has already been widely used to describe phenotypic variability at the inter-specific level. On the contrary, fewer studies have focused on intra-specific wing morphometric variability. Yet, such investigations are relevant to study potential convergences of variation that could highlight micro-evolutionary processes. The recent sampling and sequencing of three solitary bees of the genus Melitta across their entire species range provides an excellent opportunity to jointly analyse genetic and morphometric variability. In the present study, we first aim to analyse the spatial distribution of the wing shape and centroid size (used as a proxy for body size variability. Secondly, we aim to test different potential predictors of this variability at both the intra- and inter-population levels, which includes genetic variability, but also geographic locations and distances, elevation, annual mean temperature and precipitation. The comparison of spatial distribution of intra-population morphometric diversity does not reveal any convergent pattern between species, thus undermining the assumption of a potential local and selective adaptation at the population level. Regarding intra-specific wing shape differentiation, our results reveal that some tested predictors, such as geographic and genetic distances, are associated with a significant correlation for some species. However, none of these predictors are systematically identified for the three species as an important factor that could explain the intra-specific morphometric variability. As a conclusion, for the three solitary bee species and at the scale of this study, our results clearly tend to discard the assumption of the existence of a common pattern of intra-specific signal

Distribution and predictors of wing shape and size variability in three sister species of solitary bees.

Science.gov (United States)

Dellicour, Simon; Gerard, Maxence; Prunier, Jérôme G; Dewulf, Alexandre; Kuhlmann, Michael; Michez, Denis

2017-01-01

Morphological traits can be highly variable over time in a particular geographical area. Different selective pressures shape those traits, which is crucial in evolutionary biology. Among these traits, insect wing morphometry has already been widely used to describe phenotypic variability at the inter-specific level. On the contrary, fewer studies have focused on intra-specific wing morphometric variability. Yet, such investigations are relevant to study potential convergences of variation that could highlight micro-evolutionary processes. The recent sampling and sequencing of three solitary bees of the genus Melitta across their entire species range provides an excellent opportunity to jointly analyse genetic and morphometric variability. In the present study, we first aim to analyse the spatial distribution of the wing shape and centroid size (used as a proxy for body size) variability. Secondly, we aim to test different potential predictors of this variability at both the intra- and inter-population levels, which includes genetic variability, but also geographic locations and distances, elevation, annual mean temperature and precipitation. The comparison of spatial distribution of intra-population morphometric diversity does not reveal any convergent pattern between species, thus undermining the assumption of a potential local and selective adaptation at the population level. Regarding intra-specific wing shape differentiation, our results reveal that some tested predictors, such as geographic and genetic distances, are associated with a significant correlation for some species. However, none of these predictors are systematically identified for the three species as an important factor that could explain the intra-specific morphometric variability. As a conclusion, for the three solitary bee species and at the scale of this study, our results clearly tend to discard the assumption of the existence of a common pattern of intra-specific signal/structure within the

The double tragedy of agriculture vulnerability to climate variability in Africa: How vulnerable is smallholder agriculture to rainfall variability in Ghana?

Directory of Open Access Journals (Sweden)

Emmanuel K. Derbile

2016-04-01

Full Text Available This article analysed vulnerability of smallholder agriculture to climate variability, particularly the alternating incidences of drought and heavy precipitation events in Ghana. Although there is an unmet need for understanding the linkages between climate change and livelihoods, the urgent need for climate change adaptation planning (CCAP in response to climate change makes vulnerability assessment even more compelling in development research. The data for analysis were collected from two complementary studies. These included a regional survey in the Upper West Region and an in-depth study in three selected communities in the Sissala East District. The results showed that smallholder agriculture is significantly vulnerable to climate variability in the region and that three layers of vulnerability can be identified in a ladder of vulnerability. Firstly, farmers are confronted with the double tragedy of droughts and heavy precipitation events, which adversely affect both crops and livestock. Secondly, farmers have to decide on crops for adaptation, but each option – whether indigenous crops, new early-maturing crops or genetically modified crops – predisposes farmers to a different set of risks. Finally, the overall impact is a higher-level vulnerability, namely the risk of total livelihood failure and food insecurity. The article recommended CCAP and an endogenous development (ED approach to addressing agriculture vulnerability to climate variability within the framework of decentralisation and local governance in Ghana. Keywords: Climate variability; agriculture; vulnerability; endogenous development; Ghana

A remark on Carathéodory type selections

Directory of Open Access Journals (Sweden)

Julian Janus

1986-11-01

Full Text Available We prove existence of Carathéodory type selections for multifunctions of two variables which are weakly lower semicontinuous with respect to one variable and measurable with respect to the other.

Effect of balance exercise on selected kinematic gait variables in ...

African Journals Online (AJOL)

The purpose of this study was to investigate the effect of balance exercise on some selected kinematic gait parameters in patients with knee joint osteoarthritis. Forty subjects (18 men and 22 women) participated in the study.They were divided into two groups: Group 1 (experimental) that was treated with balance exercises, ...

Choice of the thermodynamic variables

International Nuclear Information System (INIS)

Balian, R.

1985-09-01

Some basic ideas of thermodynamics and statistical mechanics, both at equilibrium and off equilibrium, are recalled. In particular, the selection of relevant variables which underlies any macroscopic description is discussed, together with the meaning of the various thermodynamic quantities, in order to set the thermodynamic approaches used in nuclear physics in a general prospect [fr

Norepinephrine genes predict response time variability and methylphenidate-induced changes in neuropsychological function in attention deficit hyperactivity disorder.

Science.gov (United States)

Kim, Bung-Nyun; Kim, Jae-Won; Cummins, Tarrant D R; Bellgrove, Mark A; Hawi, Ziarih; Hong, Soon-Beom; Yang, Young-Hui; Kim, Hyo-Jin; Shin, Min-Sup; Cho, Soo-Churl; Kim, Ji-Hoon; Son, Jung-Woo; Shin, Yun-Mi; Chung, Un-Sun; Han, Doug-Hyun

2013-06-01

Noradrenergic dysfunction may be associated with cognitive impairments in attention-deficit/hyperactivity disorder (ADHD), including increased response time variability, which has been proposed as a leading endophenotype for ADHD. The aim of this study was to examine the relationship between polymorphisms in the α-2A-adrenergic receptor (ADRA2A) and norepinephrine transporter (SLC6A2) genes and attentional performance in ADHD children before and after pharmacological treatment.One hundred one medication-naive ADHD children were included. All subjects were administered methylphenidate (MPH)-OROS for 12 weeks. The subjects underwent a computerized comprehensive attention test to measure the response time variability at baseline before MPH treatment and after 12 weeks. Additive regression analyses controlling for ADHD symptom severity, age, sex, IQ, and final dose of MPH examined the association between response time variability on the comprehensive attention test measures and allelic variations in single-nucleotide polymorphisms of the ADRA2A and SLC6A2 before and after MPH treatment.Increasing possession of an A allele at the G1287A polymorphism of SLC6A2 was significantly related to heightened response time variability at baseline in the sustained (P = 2.0 × 10) and auditory selective attention (P = 1.0 × 10) tasks. Response time variability at baseline increased additively with possession of the T allele at the DraI polymorphism of the ADRA2A gene in the auditory selective attention task (P = 2.0 × 10). After medication, increasing possession of a G allele at the MspI polymorphism of the ADRA2A gene was associated with increased MPH-related change in response time variability in the flanker task (P = 1.0 × 10).Our study suggested an association between norepinephrine gene variants and response time variability measured at baseline and after MPH treatment in children with ADHD. Our results add to a growing body of evidence, suggesting that response time

Climate variability slows evolutionary responses of Colias butterflies to recent climate change.

Science.gov (United States)

Kingsolver, Joel G; Buckley, Lauren B

2015-03-07

How does recent climate warming and climate variability alter fitness, phenotypic selection and evolution in natural populations? We combine biophysical, demographic and evolutionary models with recent climate data to address this question for the subalpine and alpine butterfly, Colias meadii, in the southern Rocky Mountains. We focus on predicting patterns of selection and evolution for a key thermoregulatory trait, melanin (solar absorptivity) on the posterior ventral hindwings, which affects patterns of body temperature, flight activity, adult and egg survival, and reproductive success in Colias. Both mean annual summer temperatures and thermal variability within summers have increased during the past 60 years at subalpine and alpine sites. At the subalpine site, predicted directional selection on wing absorptivity has shifted from generally positive (favouring increased wing melanin) to generally negative during the past 60 years, but there is substantial variation among years in the predicted magnitude and direction of selection and the optimal absorptivity. The predicted magnitude of directional selection at the alpine site declined during the past 60 years and varies substantially among years, but selection has generally been positive at this site. Predicted evolutionary responses to mean climate warming at the subalpine site since 1980 is small, because of the variability in selection and asymmetry of the fitness function. At both sites, the predicted effects of adaptive evolution on mean population fitness are much smaller than the fluctuations in mean fitness due to climate variability among years. Our analyses suggest that variation in climate within and among years may strongly limit evolutionary responses of ectotherms to mean climate warming in these habitats. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Influence of Flat-Panel Fluoroscopic Equipment Variables on Cardiac Radiation Doses

International Nuclear Information System (INIS)

Nickoloff, Edward L.; Lu Zhengfeng; Dutta, Ajoy; So, James; Balter, Stephen; Moses, Jeffrey

2007-01-01

Purpose. To assess the influence of physician-selectable equipment variables on the potential radiation dose reductions during cardiac catheterization examinations using modern imaging equipment. Materials. A modern bi-plane angiography unit with flat-panel image receptors was used. Patients were simulated with 15-30 cm of acrylic plastic. The variables studied were: patient thickness, fluoroscopy pulse rates, record mode frame rates, image receptor field-of-view (FoV), automatic dose control (ADC) mode, SID/SSD geometry setting, automatic collimation, automatic positioning, and others. Results. Patient radiation doses double for every additional 3.5-4.5 cm of soft tissue. The dose is directly related to the imaging frame rate; a decrease from 30 pps to 15 pps reduces the dose by about 50%. The dose is related to [(FoV) -N ] where 2.0 < N < 3.0. Suboptimal positioning of the patient can nearly double the dose. The ADC system provides three selections that can vary the radiation level by 50%. For pediatric studies (2-5 years old), the selection of equipment variables can result in entrance radiation doses that range between 6 and 60 cGy for diagnostic cases and between 15 and 140 cGy for interventional cases. For adult studies, the equipment variables can produce entrance radiation doses that range between 13 and 130 cGy for diagnostic cases and between 30 and 400 cGy for interventional cases. Conclusions. Overall dose reductions of 70-90% can be achieved with pediatric patients and about 90% with adult patients solely through optimal selection of equipment variables

Bayesian nonparametric variable selection as an exploratory tool for discovering differentially expressed genes.

Science.gov (United States)

Shahbaba, Babak; Johnson, Wesley O

2013-05-30

High-throughput scientific studies involving no clear a priori hypothesis are common. For example, a large-scale genomic study of a disease may examine thousands of genes without hypothesizing that any specific gene is responsible for the disease. In these studies, the objective is to explore a large number of possible factors (e.g., genes) in order to identify a small number that will be considered in follow-up studies that tend to be more thorough and on smaller scales. A simple, hierarchical, linear regression model with random coefficients is assumed for case-control data that correspond to each gene. The specific model used will be seen to be related to a standard Bayesian variable selection model. Relatively large regression coefficients correspond to potential differences in responses for cases versus controls and thus to genes that might 'matter'. For large-scale studies, and using a Dirichlet process mixture model for the regression coefficients, we are able to find clusters of regression effects of genes with increasing potential effect or 'relevance', in relation to the outcome of interest. One cluster will always correspond to genes whose coefficients are in a neighborhood that is relatively close to zero and will be deemed least relevant. Other clusters will correspond to increasing magnitudes of the random/latent regression coefficients. Using simulated data, we demonstrate that our approach could be quite effective in finding relevant genes compared with several alternative methods. We apply our model to two large-scale studies. The first study involves transcriptome analysis of infection by human cytomegalovirus. The second study's objective is to identify differentially expressed genes between two types of leukemia. Copyright © 2012 John Wiley & Sons, Ltd.

A Conceptual Characterization of Online Videos Explaining Natural Selection

Science.gov (United States)

Bohlin, Gustav; Göransson, Andreas; Höst, Gunnar E.; Tibell, Lena A. E.

2017-11-01

Educational videos on the Internet comprise a vast and highly diverse source of information. Online search engines facilitate access to numerous videos claiming to explain natural selection, but little is known about the degree to which the video content match key evolutionary content identified as important in evolution education research. In this study, we therefore analyzed the content of 60 videos accessed through the Internet, using a criteria catalog with 38 operationalized variables derived from research literature. The variables were sorted into four categories: (a) key concepts (e.g. limited resources and inherited variation), (b) threshold concepts (abstract concepts with a transforming and integrative function), (c) misconceptions (e.g. that evolution is driven by need), and (d) organismal context (e.g. animal or plant). The results indicate that some concepts are frequently communicated, and certain taxa are commonly used to illustrate concepts, while others are seldom included. In addition, evolutionary phenomena at small temporal and spatial scales, such as subcellular processes, are rarely covered. Rather, the focus is on population-level events over time scales spanning years or longer. This is consistent with an observed lack of explanations regarding how randomly occurring mutations provide the basis for variation (and thus natural selection). The findings imply, among other things, that some components of natural selection warrant far more attention in biology teaching and science education research.

Methods for model selection in applied science and engineering.

Energy Technology Data Exchange (ETDEWEB)

Field, Richard V., Jr.

2004-10-01

Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be

The productivity of mental health care: an instrumental variable approach.

Science.gov (United States)

Lu, Mingshan

1999-06-01

BACKGROUND: Like many other medical technologies and treatments, there is a lack of reliable evidence on treatment effectiveness of mental health care. Increasingly, data from non-experimental settings are being used to study the effect of treatment. However, as in a number of studies using non-experimental data, a simple regression of outcome on treatment shows a puzzling negative and significant impact of mental health care on the improvement of mental health status, even after including a large number of potential control variables. The central problem in interpreting evidence from real-world or non-experimental settings is, therefore, the potential "selection bias" problem in observational data set. In other words, the choice/quantity of mental health care may be correlated with other variables, particularly unobserved variables, that influence outcome and this may lead to a bias in the estimate of the effect of care in conventional models. AIMS OF THE STUDY: This paper addresses the issue of estimating treatment effects using an observational data set. The information in a mental health data set obtained from two waves of data in Puerto Rico is explored. The results using conventional models - in which the potential selection bias is not controlled - and that from instrumental variable (IV) models - which is what was proposed in this study to correct for the contaminated estimation from conventional models - are compared. METHODS: Treatment effectiveness is estimated in a production function framework. Effectiveness is measured as the improvement in mental health status. To control for the potential selection bias problem, IV approaches are employed. The essence of the IV method is to use one or more instruments, which are observable factors that influence treatment but do not directly affect patient outcomes, to isolate the effect of treatment variation that is independent of unobserved patient characteristics. The data used in this study are the first (1992

Statistical identification of effective input variables

International Nuclear Information System (INIS)

Vaurio, J.K.

1982-09-01

A statistical sensitivity analysis procedure has been developed for ranking the input data of large computer codes in the order of sensitivity-importance. The method is economical for large codes with many input variables, since it uses a relatively small number of computer runs. No prior judgemental elimination of input variables is needed. The sceening method is based on stagewise correlation and extensive regression analysis of output values calculated with selected input value combinations. The regression process deals with multivariate nonlinear functions, and statistical tests are also available for identifying input variables that contribute to threshold effects, i.e., discontinuities in the output variables. A computer code SCREEN has been developed for implementing the screening techniques. The efficiency has been demonstrated by several examples and applied to a fast reactor safety analysis code (Venus-II). However, the methods and the coding are general and not limited to such applications

An adaptive technique for multiscale approximate entropy (MAEbin) threshold (r) selection: application to heart rate variability (HRV) and systolic blood pressure variability (SBPV) under postural stress.

Science.gov (United States)

Singh, Amritpal; Saini, Barjinder Singh; Singh, Dilbag

2016-06-01

Multiscale approximate entropy (MAE) is used to quantify the complexity of a time series as a function of time scale τ. Approximate entropy (ApEn) tolerance threshold selection 'r' is based on either: (1) arbitrary selection in the recommended range (0.1-0.25) times standard deviation of time series (2) or finding maximum ApEn (ApEnmax) i.e., the point where self-matches start to prevail over other matches and choosing the corresponding 'r' (rmax) as threshold (3) or computing rchon by empirically finding the relation between rmax, SD1/SD2 ratio and N using curve fitting, where, SD1 and SD2 are short-term and long-term variability of a time series respectively. None of these methods is gold standard for selection of 'r'. In our previous study [1], an adaptive procedure for selection of 'r' is proposed for approximate entropy (ApEn). In this paper, this is extended to multiple time scales using MAEbin and multiscale cross-MAEbin (XMAEbin). We applied this to simulations i.e. 50 realizations (n = 50) of random number series, fractional Brownian motion (fBm) and MIX (P) [1] series of data length of N = 300 and short term recordings of HRV and SBPV performed under postural stress from supine to standing. MAEbin and XMAEbin analysis was performed on laboratory recorded data of 50 healthy young subjects experiencing postural stress from supine to upright. The study showed that (i) ApEnbin of HRV is more than SBPV in supine position but is lower than SBPV in upright position (ii) ApEnbin of HRV decreases from supine i.e. 1.7324 ± 0.112 (mean ± SD) to upright 1.4916 ± 0.108 due to vagal inhibition (iii) ApEnbin of SBPV increases from supine i.e. 1.5535 ± 0.098 to upright i.e. 1.6241 ± 0.101 due sympathetic activation (iv) individual and cross complexities of RRi and systolic blood pressure (SBP) series depend on time scale under consideration (v) XMAEbin calculated using ApEnmax is correlated with cross-MAE calculated using ApEn (0.1-0.26) in steps of 0

Fundamental Aspects of Selective Melting Additive Manufacturing Processes

Energy Technology Data Exchange (ETDEWEB)

van Swol, Frank B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Miller, James E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-12-01

Certain details of the additive manufacturing process known as selective laser melting (SLM) affect the performance of the final metal part. To unleash the full potential of SLM it is crucial that the process engineer in the field receives guidance about how to select values for a multitude of process variables employed in the building process. These include, for example, the type of powder (e.g., size distribution, shape, type of alloy), orientation of the build axis, the beam scan rate, the beam power density, the scan pattern and scan rate. The science-based selection of these settings con- stitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy, reactive, dynamic wetting followed by re-solidification. In addition, inherent to the process is its considerable variability that stems from the powder packing. Each time a limited number of powder particles are placed, the stacking is intrinsically different from the previous, possessing a different geometry, and having a different set of contact areas with the surrounding particles. As a result, even if all other process parameters (scan rate, etc) are exactly the same, the shape and contact geometry and area of the final melt pool will be unique to that particular configuration. This report identifies the most important issues facing SLM, discusses the fundamental physics associated with it and points out how modeling can support the additive manufacturing efforts.

What variables influence the ability of an AFO to improve function and when are they indicated?

Science.gov (United States)

Malas, Bryan S

2011-05-01

Children with spina bifida often present with functional deficits of the lower limb associated with neurosegmental lesion levels and require orthotic management. The most used orthosis for children with spina bifida is the ankle-foot orthosis (AFO). The AFO improves ambulation and reduces energy cost while walking. Despite the apparent benefits of using an AFO, limited evidence documents the influence of factors predicting the ability of an AFO to improve function and when they are indicated. These variables include AFO design, footwear, AFO-footwear combination, and data acquisition. When these variables are not adequately considered in clinical decision-making, there is a risk the AFO will be abandoned prematurely or the patient's stability, function, and safety compromised. The purposes of this study are to (1) describe the functional deficits based on lesion levels; (2) identify and describe variables that influence the ability of an AFO to control deformities; and (3) describe what variables are indicated for the AFO to control knee flexion during stance, hyperpronation, and valgus stress at the knee. A selective literature review was undertaken searching MEDLINE and Cochrane databases using terms related to "orthosis" and "spina bifida." Based on previous studies and gait analysis data, suggestions can be made regarding material selection/geometric configuration, sagittal alignment, footplate length, and trim lines of an AFO for reducing knee flexion, hyperpronation, and valgus stress at the knee. Further research is required to determine what variables allow an AFO to improve function.

A decision tool for selecting trench cap designs

Energy Technology Data Exchange (ETDEWEB)

Paige, G.B.; Stone, J.J.; Lane, L.J. [USDA-ARS, Tucson, AZ (United States)] [and others

1995-12-31

A computer based prototype decision support system (PDSS) is being developed to assist the risk manager in selecting an appropriate trench cap design for waste disposal sites. The selection of the {open_quote}best{close_quote} design among feasible alternatives requires consideration of multiple and often conflicting objectives. The methodology used in the selection process consists of: selecting and parameterizing decision variables using data, simulation models, or expert opinion; selecting feasible trench cap design alternatives; ordering the decision variables and ranking the design alternatives. The decision model is based on multi-objective decision theory and uses a unique approach to order the decision variables and rank the design alternatives. Trench cap designs are evaluated based on federal regulations, hydrologic performance, cover stability and cost. Four trench cap designs, which were monitored for a four year period at Hill Air Force Base in Utah, are used to demonstrate the application of the PDSS and evaluate the results of the decision model. The results of the PDSS, using both data and simulations, illustrate the relative advantages of each of the cap designs and which cap is the {open_quotes}best{close_quotes} alternative for a given set of criteria and a particular importance order of those decision criteria.

A composite model including visfatin, tissue polypeptide-specific antigen, hyaluronic acid, and hematological variables for the diagnosis of moderate-to-severe fibrosis in nonalcoholic fatty liver disease: a preliminary study.

Science.gov (United States)

Chwist, Alina; Hartleb, Marek; Lekstan, Andrzej; Kukla, Michał; Gutkowski, Krzysztof; Kajor, Maciej

2014-01-01

Histopathological risk factors for end-stage liver failure in patients with nonalcoholic fatty liver disease (NAFLD) include nonalcoholic steatohepatitis (NASH) and advanced liver fibrosis. There is a need for noninvasive diagnostic methods for these 2 conditions. The aim of this study was to investigate new laboratory variables with a predictive potential to detect advanced fibrosis (stages 2 and 3) in NAFLD. The study involved 70 patients with histologically proven NAFLD of varied severity. Additional laboratory variables included zonulin, haptoglobin, visfatin, adiponectin, leptin, tissue polypeptide-specific antigen (TPSA), hyaluronic acid, and interleukin 6. Patients with NASH (NAFLD activity score of ≥5) had significantly higher HOMA-IR values and serum levels of visfatin, haptoglobin, and zonulin as compared with those without NASH on histological examination. Advanced fibrosis was found in 16 patients (22.9%) and the risk factors associated with its prevalence were age, the ratio of erythrocyte count to red blood cell distribution width, platelet count, and serum levels of visfatin and TPSA. Based on these variables, we constructed a scoring system that differentiated between NAFLD patients with and without advanced fibrosis with a sensitivity of 75% and specificity of 100% (area under the receiver operating characteristic curve, 0.93). The scoring system based on the above variables allows to predict advanced fibrosis with high sensitivity and specificity. However, its clinical utility should be verified in further studies involving a larger number of patients.

Searching for evidence of selection in avian DNA barcodes.

Science.gov (United States)

Kerr, Kevin C R

2011-11-01

The barcode of life project has assembled a tremendous number of mitochondrial cytochrome c oxidase I (COI) sequences. Although these sequences were gathered to develop a DNA-based system for species identification, it has been suggested that further biological inferences may also be derived from this wealth of data. Recurrent selective sweeps have been invoked as an evolutionary mechanism to explain limited intraspecific COI diversity, particularly in birds, but this hypothesis has not been formally tested. In this study, I collated COI sequences from previous barcoding studies on birds and tested them for evidence of selection. Using this expanded data set, I re-examined the relationships between intraspecific diversity and interspecific divergence and sampling effort, respectively. I employed the McDonald-Kreitman test to test for neutrality in sequence evolution between closely related pairs of species. Because amino acid sequences were generally constrained between closely related pairs, I also included broader intra-order comparisons to quantify patterns of protein variation in avian COI sequences. Lastly, using 22 published whole mitochondrial genomes, I compared the evolutionary rate of COI against the other 12 protein-coding mitochondrial genes to assess intragenomic variability. I found no conclusive evidence of selective sweeps. Most evidence pointed to an overall trend of strong purifying selection and functional constraint. The COI protein did vary across the class Aves, but to a very limited extent. COI was the least variable gene in the mitochondrial genome, suggesting that other genes might be more informative for probing factors constraining mitochondrial variation within species. © 2011 Blackwell Publishing Ltd.

Application of random effects to the study of resource selection by animals.

Science.gov (United States)

Gillies, Cameron S; Hebblewhite, Mark; Nielsen, Scott E; Krawchuk, Meg A; Aldridge, Cameron L; Frair, Jacqueline L; Saher, D Joanne; Stevens, Cameron E; Jerde, Christopher L

2006-07-01

1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions

Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments

Science.gov (United States)

Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.

2015-12-01

The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide

Distributional and efficiency results for subset selection

NARCIS (Netherlands)

Laan, van der P.

1996-01-01

Assume k (??k \\geq 2) populations are given. The associated independent random variables have continuous distribution functions with an unknown location parameter. The statistical selec??tion goal is to select a non??empty subset which contains the best population,?? that is the pop??ulation with

DIFFERENCES BETWEEN YOUNG (13-14 YEARS OF AGE WATER POLO PLAYERS SELECTED AND NOT SELECTED TO THE NATIONAL TEAM

Directory of Open Access Journals (Sweden)

Igor Štirn

2010-09-01

Full Text Available Young water polo players at age 13 to 14 years were examined once a year in a four- year period using three morphological and eight specific skill tests: body height and mass, vital capacity, swimming at distances 5, 25 and 200 meters, swimming 4x5 meters with changing directions, ball dribbling, vertical jump and reach, vertical eggbeater kick and velocity of a throw at the goal. From the sum of 139 players tested, a group of 73 non-selected and of 66 selected players to the national team (U16, wider selection were formed and checked for differences. Differences in all observed variables (except body mass were found between the groups (P<0.05. One significant discriminant function was revealed (canonical R = 0.52 and the accounted variance was 100 %, P = 0.000. The variables that most differentiated the groups were swimming tests at distances of 25 and 200 meters, followed by vertical-egg beater kick and throwing velocity, while morphological variables differentiated the groups least.

Deriving estimates of individual variability in genetic potentials of performance traits for 3 dairy breeds, using a model of lifetime nutrient partitioning

DEFF Research Database (Denmark)

Phuong, H N; Martin, O; de Boer, I J M

2015-01-01

, body reserve usage, and growth for different genotypes of cow. Moreover, it can be used to separate genetic variability in performance between individual cows from environmental noise. The model enables simulation of the effects of a genetic selection strategy on lifetime efficiency of individual cows......, which has a main advantage of including the rearing costs, and thus, can be used to explore the impact of future selection on animal performance and efficiency....

Multi-diameter pigging: factors affecting the design and selection of pigging tools for multi-diameter pipelines

Energy Technology Data Exchange (ETDEWEB)

Dawson, Karl [Pipeline Engineering and Supply Co. Ltd., Richmond, NY (United States)

2009-07-01

This paper will consider the process involved in pigging tool selection for pipelines with two or more significant internal diameters which require pigging tools capable of negotiating the different internal diameters whilst also carrying out the necessary pipeline cleaning operation. The paper will include an analysis of pipeline features that affect pigging tool selection and then go on to look at other variables that determine the pigging tool design; this will include a step by step guide outlining how the tool is designed, the development of prototype pigs and the importance of testing and validation prior to final deployment in operational pigging programmes. (author)

Selected correlates of white nursing students' attitudes toward black American patients.

Science.gov (United States)

Morgan, B S

1983-01-01

Multivariate analyses were used to examine the relationships between white nursing students' attitudes toward black American patients and variables selected within a theoretical framework of prejudice which included socialization factors and personality-based factors. The variables selected were: authoritarianism and self-esteem (personality-based factors), parents' attitudes toward black Americans, peer attitudes toward black Americans, interracial contact and socioeconomic status (socialization factors). The study also examined the differences in the relationship among white nursing students enrolled in baccalaureate degree, associate degree and diploma nursing programs. Data were collected from 201 senior nursing students enrolled in the three types of nursing programs in Rhode Island during the late fall and winter of 1979-1980. Although baccalaureate degree, associate degree and diploma students were similar in terms of peer attitudes toward black Americans, fathers' attitudes toward black Americans, self-esteem and attitudes toward black American patients, they were significantly different in terms of age, socioeconomic status, mothers' attitudes toward black Americans, interracial contact and authoritarianism. The major findings of this study indicate that the socialization explanation of prejudice is more significant than the personality-based explanation. The variables socioeconomic status, interracial contact and peer attitudes toward black Americans (all socialization variables) accounted for 22.0% of the total variance in attitudes toward black American patients for the total sample of nursing students. However, this relationship was not generalizable across the three different types of nursing programs.

The selection of a mode of urban transportation: Integrating psychological variables to discrete choice models

International Nuclear Information System (INIS)

Cordoba Maquilon, Jorge E; Gonzalez Calderon, Carlos A; Posada Henao, John J

2011-01-01

A study using revealed preference surveys and psychological tests was conducted. Key psychological variables of behavior involved in the choice of transportation mode in a population sample of the Metropolitan Area of the Valle de Aburra were detected. The experiment used the random utility theory for discrete choice models and reasoned action in order to assess beliefs. This was used as a tool for analysis of the psychological variables using the sixteen personality factor questionnaire (16PF test). In addition to the revealed preference surveys, two other surveys were carried out: one with socio-economic characteristics and the other with latent indicators. This methodology allows for an integration of discrete choice models and latent variables. The integration makes the model operational and quantifies the unobservable psychological variables. The most relevant result obtained was that anxiety affects the choice of urban transportation mode and shows that physiological alterations, as well as problems in perception and beliefs, can affect the decision-making process.

Modelling the co-evolution of indirect genetic effects and inherited variability.

Science.gov (United States)

Marjanovic, Jovana; Mulder, Han A; Rönnegård, Lars; Bijma, Piter

2018-03-28

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of

Pollen parameters estimates of genetic variability among newly ...

African Journals Online (AJOL)

Pollen parameters estimates of genetic variability among newly selected Nigerian roselle (Hibiscus sabdariffa L.) genotypes. ... Estimates of some pollen parameters where used to assess the genetic diversity among ... HOW TO USE AJOL.

Reduced variability in motor behaviour : An indicator of impaired cerebral connectivity?

NARCIS (Netherlands)

Hadders-Algra, Mijna

2008-01-01

Evidence is accumulating that abundance in cerebral connectivity is the neural basis of human behavioural variability, i.e., the ability to select adaptive solutions from a large repertoire of behavioural options. Recently it was demonstrated that variability in motor behaviour - the hallmark of

Identifying market segments in consumer markets: variable selection and data interpretation

OpenAIRE

Tonks, D G

2004-01-01

Market segmentation is often articulated as being a process which displays the recognised features of classical rationalism but in part; convention, convenience, prior experience and the overarching impact of rhetoric will influence if not determine the outcomes of a segmentation exercise. Particular examples of this process are addressed critically in this paper which concentrates on the issues of variable choice for multivariate approaches to market segmentation and also the methods used fo...

Does plant trait diversity reduce the ability of herbivores to defend against predators? The plant variability-gut acclimation hypothesis.

Science.gov (United States)

Wetzel, William C; Thaler, Jennifer S

2016-04-01

Variability in plant chemistry has long been believed to suppress populations of insect herbivores by constraining herbivore resource selection behavior in ways that make herbivores more vulnerable to predation. The focus on behavior, however, overlooks the pervasive physiological effects of plant variability on herbivores. Here we propose the plant variability-gut acclimation hypothesis, which posits that plant chemical variability constrains herbivore anti-predator defenses by frequently requiring herbivores to acclimate their guts to changing plant defenses and nutrients. Gut acclimation, including changes to morphology and detoxification enzymes, requires time and nutrients, and we argue these costs will constrain how and when herbivores can mount anti-predator defenses. A consequence of this hypothesis is stronger top-down control of herbivores in heterogeneous plant populations. Copyright © 2016 Elsevier Inc. All rights reserved.

The variability is in the sex chromosomes.

Science.gov (United States)

Reinhold, Klaus; Engqvist, Leif

2013-12-01

Sex differences in the mean trait expression are well documented, not only for traits that are directly associated with reproduction. Less is known about how the variability of traits differs between males and females. In species with sex chromosomes and dosage compensation, the heterogametic sex is expected to show larger trait variability ("sex-chromosome hypothesis"), yet this central prediction, based on fundamental genetic principles, has never been evaluated in detail. Here we show that in species with heterogametic males, male variability in body size is significantly larger than in females, whereas the opposite can be shown for species with heterogametic females. These results support the prediction of the sex-chromosome hypothesis that individuals of the heterogametic sex should be more variable. We argue that the pattern demonstrated here for sex-specific body size variability is likely to apply to any trait and needs to be considered when testing predictions about sex-specific variability and sexual selection. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

The relationship of document and quantitative literacy with learning styles and selected personal variables for aerospace technology students at Indiana State University

Science.gov (United States)

Martin, Royce Ann

The purpose of this study was to determine the extent that student scores on a researcher-constructed quantitative and document literacy test, the Aviation Documents Delineator (ADD), were associated with (a) learning styles (imaginative, analytic, common sense, dynamic, and undetermined), as identified by the Learning Type Measure, (b) program curriculum (aerospace administration, professional pilot, both aerospace administration and professional pilot, other, or undeclared), (c) overall cumulative grade point average at Indiana State University, and (d) year in school (freshman, sophomore, junior, or senior). The Aviation Documents Delineator (ADD) was a three-part, 35 question survey that required students to interpret graphs, tables, and maps. Tasks assessed in the ADD included (a) locating, interpreting, and describing specific data displayed in the document, (b) determining data for a specified point on the table through interpolation, (c) comparing data for a string of variables representing one aspect of aircraft performance to another string of variables representing a different aspect of aircraft performance, (d) interpreting the documents to make decisions regarding emergency situations, and (e) performing single and/or sequential mathematical operations on a specified set of data. The Learning Type Measure (LTM) was a 15 item self-report survey developed by Bernice McCarthy (1995) to profile an individual's processing and perception tendencies in order to reveal different individual approaches to learning. The sample used in this study included 143 students enrolled in Aerospace Technology Department courses at Indiana State University in the fall of 1996. The ADD and the LTM were administered to each subject. Data collected in this investigation were analyzed using a stepwise multiple regression analysis technique. Results of the study revealed that the variables, year in school and GPA, were significant predictors of the criterion variables, document

High-dimensional model estimation and model selection

CERN Multimedia

CERN. Geneva

2015-01-01

I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.

Microscopic age determination of human skeletons including an unknown but calculable variable

DEFF Research Database (Denmark)

Wallin, Johan Albert; Tkocz, Izabella; Kristensen, Gustav

1994-01-01

estimation, which includes the covariance matrix of four single equation residuals, improves the accuracy of age determination. The standard deviation, however, of age prediction remains 12.58 years. An experimental split of the data was made in order to demonstrate that the use of subgroups gives a false...

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

Directory of Open Access Journals (Sweden)

José Carlos Ortiz-Bayliss

2018-01-01

Full Text Available When solving constraint satisfaction problems (CSPs, it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems.

Science.gov (United States)

Ortiz-Bayliss, José Carlos; Amaya, Ivan; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.

A variational conformational dynamics approach to the selection of collective variables in metadynamics

Science.gov (United States)

McCarty, James; Parrinello, Michele

2017-11-01

In this paper, we combine two powerful computational techniques, well-tempered metadynamics and time-lagged independent component analysis. The aim is to develop a new tool for studying rare events and exploring complex free energy landscapes. Metadynamics is a well-established and widely used enhanced sampling method whose efficiency depends on an appropriate choice of collective variables. Often the initial choice is not optimal leading to slow convergence. However by analyzing the dynamics generated in one such run with a time-lagged independent component analysis and the techniques recently developed in the area of conformational dynamics, we obtain much more efficient collective variables that are also better capable of illuminating the physics of the system. We demonstrate the power of this approach in two paradigmatic examples.

IMPROVED VARIABLE STAR SEARCH IN LARGE PHOTOMETRIC DATA SETS: NEW VARIABLES IN CoRoT FIELD LRa02 DETECTED BY BEST II

International Nuclear Information System (INIS)

Fruth, T.; Cabrera, J.; Csizmadia, Sz.; Eigmüller, P.; Erikson, A.; Kirste, S.; Pasternacki, T.; Rauer, H.; Titz-Weider, R.; Kabath, P.; Chini, R.; Lemke, R.; Murphy, M.

2012-01-01

The CoRoT field LRa02 has been observed with the Berlin Exoplanet Search Telescope II (BEST II) during the southern summer 2007/2008. A first analysis of stellar variability led to the publication of 345 newly discovered variable stars. Now, a deeper analysis of this data set was used to optimize the variability search procedure. Several methods and parameters have been tested in order to improve the selection process compared to the widely used J index for variability ranking. This paper describes an empirical approach to treat systematic trends in photometric data based upon the analysis of variance statistics that can significantly decrease the rate of false detections. Finally, the process of reanalysis and method improvement has virtually doubled the number of variable stars compared to the first analysis by Kabath et al. A supplementary catalog of 272 previously unknown periodic variables plus 52 stars with suspected variability is presented. Improved ephemerides are given for 19 known variables in the field. In addition, the BEST II results are compared with CoRoT data and its automatic variability classification.