Sample records for regression correlation coefficients
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Modified Regression Correlation Coefficient for Poisson Regression Model
NASA Astrophysics Data System (ADS)
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
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Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi
2018-04-01
Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.
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Biostatistics Series Module 6: Correlation and Linear Regression.
Hazra, Avijit; Gogtay, Nithya
2016-01-01
Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.
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Biostatistics Series Module 6: Correlation and Linear Regression
Hazra, Avijit; Gogtay, Nithya
2016-01-01
Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175
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Ridge: a computer program for calculating ridge regression estimates
Donald E. Hilt; Donald W. Seegrist
1977-01-01
Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.
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Zhao, Yang; Zhang, Xue Qing; Bian, Xiao Dong
2018-01-01
To investigate the early supplementary processes of fishre sources in the Bohai Sea, the geographically weighted regression (GWR) was introduced to the habitat suitability index (HSI) model. The Bohai Sea larval Japanese Halfbeak HSI GWR model was established with four environmental variables, including sea surface temperature (SST), sea surface salinity (SSS), water depth (DEP), and chlorophyll a concentration (Chl a). Results of the simulation showed that the four variables had different performances in August 2015. SST and Chl a were global variables, and had little impacts on HSI, with the regression coefficients of -0.027 and 0.006, respectively. SSS and DEP were local variables, and had larger impacts on HSI, while the average values of absolute values of their regression coefficients were 0.075 and 0.129, respectively. In the central Bohai Sea, SSS showed a negative correlation with HSI, and the most negative correlation coefficient was -0.3. In contrast, SSS was correlated positively but weakly with HSI in the three bays of Bohai Sea, and the largest correlation coefficient was 0.1. In particular, DEP and HSI were negatively correlated in the entire Bohai Sea, while they were more negatively correlated in the three bays of Bohai than in the central Bohai Sea, and the most negative correlation coefficient was -0.16 in the three bays. The Poisson regression coefficient of the HSI GWR model was 0.705, consistent with field measurements. Therefore, it could provide a new method for the research on fish habitats in the future.
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Ecotoxicology of phenylphosphonothioates.
Francis, B M; Hansen, L G; Fukuto, T R; Lu, P Y; Metcalf, R L
1980-01-01
The phenylphosphonothioate insecticides EPN and leptophos, and several analogs, were evaluated with respect to their delayed neurotoxic effects in hens and their environmental behavior in a terrestrial-aquatic model ecosystem. Acute toxicity to insects was highly correlated with sigma sigma of the substituted phenyl group (regression coefficient r = -0.91) while acute toxicity to mammals was slightly less well correlated (regression coefficient r = -0.71), and neurotoxicity was poorly correlated with sigma sigma (regression coefficient r = -0.35). Both EPN and leptophos were markedly more persistent and bioaccumulative in the model ecosystem than parathion. Desbromoleptophos, a contaminant and metabolite of leptophos, was seen to be a highly stable and persistent terminal residue of leptophos. PMID:6159210
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SCI model structure determination program (OSR) user's guide. [optimal subset regression
NASA Technical Reports Server (NTRS)
1979-01-01
The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.
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Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
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Confidence Intervals for Squared Semipartial Correlation Coefficients: The Effect of Nonnormality
ERIC Educational Resources Information Center
Algina, James; Keselman, H. J.; Penfield, Randall D.
2010-01-01
The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…
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Investigating bias in squared regression structure coefficients
Nimon, Kim F.; Zientek, Linda R.; Thompson, Bruce
2015-01-01
The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients. PMID:26217273
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40 CFR 53.34 - Test procedure for methods for PM10 and Class I methods for PM2.5.
Code of Federal Regulations, 2011 CFR
2011-07-01
... linear regression parameters (slope, intercept, and correlation coefficient) describing the relationship... correlation coefficient. (2) To pass the test for comparability, the slope, intercept, and correlation...
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Yamazaki, Takeshi; Takeda, Hisato; Hagiya, Koichi; Yamaguchi, Satoshi; Sasaki, Osamu
2018-03-13
Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a random regression model. We analyzed test-day milk records from 85690 Holstein cows in their first lactations and 131727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. The first-order Legendre polynomials were practical covariates of random regression for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.
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QSAR modeling of flotation collectors using principal components extracted from topological indices.
Natarajan, R; Nirdosh, Inderjit; Basak, Subhash C; Mills, Denise R
2002-01-01
Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.
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Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.
ERIC Educational Resources Information Center
Algina, James; Olejnik, Stephen
2000-01-01
Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)
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NASA Astrophysics Data System (ADS)
Zhai, Mengting; Chen, Yan; Li, Jing; Zhou, Jun
2017-12-01
The molecular electrongativity distance vector (MEDV-13) was used to describe the molecular structure of benzyl ether diamidine derivatives in this paper, Based on MEDV-13, The three-parameter (M 3, M 15, M 47) QSAR model of insecticidal activity (pIC 50) for 60 benzyl ether diamidine derivatives was constructed by leaps-and-bounds regression (LBR) . The traditional correlation coefficient (R) and the cross-validation correlation coefficient (R CV ) were 0.975 and 0.971, respectively. The robustness of the regression model was validated by Jackknife method, the correlation coefficient R were between 0.971 and 0.983. Meanwhile, the independent variables in the model were tested to be no autocorrelation. The regression results indicate that the model has good robust and predictive capabilities. The research would provide theoretical guidance for the development of new generation of anti African trypanosomiasis drugs with efficiency and low toxicity.
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Multicollinearity and Regression Analysis
NASA Astrophysics Data System (ADS)
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
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NASA Astrophysics Data System (ADS)
Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi
2017-03-01
Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.
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Waller, Niels G
2016-01-01
For a fixed set of standardized regression coefficients and a fixed coefficient of determination (R-squared), an infinite number of predictor correlation matrices will satisfy the implied quadratic form. I call such matrices fungible correlation matrices. In this article, I describe an algorithm for generating positive definite (PD), positive semidefinite (PSD), or indefinite (ID) fungible correlation matrices that have a random or fixed smallest eigenvalue. The underlying equations of this algorithm are reviewed from both algebraic and geometric perspectives. Two simulation studies illustrate that fungible correlation matrices can be profitably used in Monte Carlo research. The first study uses PD fungible correlation matrices to compare penalized regression algorithms. The second study uses ID fungible correlation matrices to compare matrix-smoothing algorithms. R code for generating fungible correlation matrices is presented in the supplemental materials.
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NASA Astrophysics Data System (ADS)
Hammud, Hassan H.; Ghannoum, Amer; Masoud, Mamdouh S.
2006-02-01
Sixteen Schiff bases obtained from the condensation of benzaldehyde or salicylaldehyde with various amines (aniline, 4-carboxyaniline, phenylhydrazine, 2,4-dinitrophenylhydrazine, ethylenediamine, hydrazine, o-phenylenediamine and 2,6-pyridinediamine) are studied with UV-vis spectroscopy to observe the effect of solvents, substituents and other structural factors on the spectra. The bands involving different electronic transitions are interpreted. Computerized analysis and multiple regression techniques were applied to calculate the regression and correlation coefficients based on the equation that relates peak position λmax to the solvent parameters that depend on the H-bonding ability, refractive index and dielectric constant of solvents.
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An improved multiple linear regression and data analysis computer program package
NASA Technical Reports Server (NTRS)
Sidik, S. M.
1972-01-01
NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.
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Enhance-Synergism and Suppression Effects in Multiple Regression
ERIC Educational Resources Information Center
Lipovetsky, Stan; Conklin, W. Michael
2004-01-01
Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…
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SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients.
Weaver, Bruce; Wuensch, Karl L
2013-09-01
Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 - α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.
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Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity
Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.
2012-01-01
While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655
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Tools to support interpreting multiple regression in the face of multicollinearity.
Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K
2012-01-01
While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.
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Statistical methods for astronomical data with upper limits. II - Correlation and regression
NASA Technical Reports Server (NTRS)
Isobe, T.; Feigelson, E. D.; Nelson, P. I.
1986-01-01
Statistical methods for calculating correlations and regressions in bivariate censored data where the dependent variable can have upper or lower limits are presented. Cox's regression and the generalization of Kendall's rank correlation coefficient provide significant levels of correlations, and the EM algorithm, under the assumption of normally distributed errors, and its nonparametric analog using the Kaplan-Meier estimator, give estimates for the slope of a regression line. Monte Carlo simulations demonstrate that survival analysis is reliable in determining correlations between luminosities at different bands. Survival analysis is applied to CO emission in infrared galaxies, X-ray emission in radio galaxies, H-alpha emission in cooling cluster cores, and radio emission in Seyfert galaxies.
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Kitagawa, Yasuhisa; Teramoto, Tamio; Daida, Hiroyuki
2012-01-01
We evaluated the impact of adherence to preferable behavior on serum lipid control assessed by a self-reported questionnaire in high-risk patients taking pravastatin for primary prevention of coronary artery disease. High-risk patients taking pravastatin were followed for 2 years. Questionnaire surveys comprising 21 questions, including 18 questions concerning awareness of health, and current status of diet, exercise, and drug therapy, were conducted at baseline and after 1 year. Potential domains were established by factor analysis from the results of questionnaires, and adherence scores were calculated in each domain. The relationship between adherence scores and lipid values during the 1-year treatment period was analyzed by each domain using multiple regression analysis. A total of 5,792 patients taking pravastatin were included in the analysis. Multiple regression analysis showed a significant correlation in terms of "Intake of high fat/cholesterol/sugar foods" (regression coefficient -0.58, p=0.0105) and "Adherence to instructions for drug therapy" (regression coefficient -6.61, p<0.0001). Low-density lipoprotein cholesterol (LDL-C) values were significantly lower in patients who had an increase in the adherence score in the "Awareness of health" domain compared with those with a decreased score. There was a significant correlation between high-density lipoprotein (HDL-C) values and "Awareness of health" (regression coefficient 0.26; p= 0.0037), "Preferable dietary behaviors" (regression coefficient 0.75; p<0.0001), and "Exercise" (regression coefficient 0.73; p= 0.0002). Similar relations were seen with triglycerides. In patients who have a high awareness of their health, a positive attitude toward lipid-lowering treatment including diet, exercise, and high adherence to drug therapy, is related with favorable overall lipid control even in patients under treatment with pravastatin.
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Suppressor Variables: The Difference between "Is" versus "Acting As"
ERIC Educational Resources Information Center
Ludlow, Larry; Klein, Kelsey
2014-01-01
Correlated predictors in regression models are a fact of life in applied social science research. The extent to which they are correlated will influence the estimates and statistics associated with the other variables they are modeled along with. These effects, for example, may include enhanced regression coefficients for the other variables--a…
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NASA Astrophysics Data System (ADS)
Cambra-López, María; Winkel, Albert; Mosquera, Julio; Ogink, Nico W. M.; Aarnink, André J. A.
2015-06-01
The objective of this study was to compare co-located real-time light scattering devices and equivalent gravimetric samplers in poultry and pig houses for PM10 mass concentration, and to develop animal-specific calibration factors for light scattering samplers. These results will contribute to evaluate the comparability of different sampling instruments for PM10 concentrations. Paired DustTrak light scattering device (DustTrak aerosol monitor, TSI, U.S.) and PM10 gravimetric cyclone sampler were used for measuring PM10 mass concentrations during 24 h periods (from noon to noon) inside animal houses. Sampling was conducted in 32 animal houses in the Netherlands, including broilers, broiler breeders, layers in floor and in aviary system, turkeys, piglets, growing-finishing pigs in traditional and low emission housing with dry and liquid feed, and sows in individual and group housing. A total of 119 pairs of 24 h measurements (55 for poultry and 64 for pigs) were recorded and analyzed using linear regression analysis. Deviations between samplers were calculated and discussed. In poultry, cyclone sampler and DustTrak data fitted well to a linear regression, with a regression coefficient equal to 0.41, an intercept of 0.16 mg m-3 and a correlation coefficient of 0.91 (excluding turkeys). Results in turkeys showed a regression coefficient equal to 1.1 (P = 0.49), an intercept of 0.06 mg m-3 (P < 0.0001) and a correlation coefficient of 0.98. In pigs, we found a regression coefficient equal to 0.61, an intercept of 0.05 mg m-3 and a correlation coefficient of 0.84. Measured PM10 concentrations using DustTraks were clearly underestimated (approx. by a factor 2) in both poultry and pig housing systems compared with cyclone pre-separators. Absolute, relative, and random deviations increased with concentration. DustTrak light scattering devices should be self-calibrated to investigate PM10 mass concentrations accurately in animal houses. We recommend linear regression equations as animal-specific calibration factors for DustTraks instead of manufacturer calibration factors, especially in heavily dusty environments such as animal houses.
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NASA Technical Reports Server (NTRS)
Stolzer, Alan J.; Halford, Carl
2007-01-01
In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.
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NASA Astrophysics Data System (ADS)
Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya
2013-03-01
This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.
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Shrinkage regression-based methods for microarray missing value imputation.
Wang, Hsiuying; Chiu, Chia-Chun; Wu, Yi-Ching; Wu, Wei-Sheng
2013-01-01
Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets. To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do. Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.
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Testing a single regression coefficient in high dimensional linear models
Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling
2017-01-01
In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively. PMID:28663668
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Testing a single regression coefficient in high dimensional linear models.
Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling
2016-11-01
In linear regression models with high dimensional data, the classical z -test (or t -test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z -test to assess the significance of each covariate. Based on the p -value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.
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Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.
Xu, Pengpeng; Huang, Helai; Dong, Ni; Wong, S C
2017-01-01
This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Li, Zhenghua; Cheng, Fansheng; Xia, Zhining
2011-01-01
The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.
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ERIC Educational Resources Information Center
Kane, Michael T.; Mroch, Andrew A.
2010-01-01
In evaluating the relationship between two measures across different groups (i.e., in evaluating "differential validity") it is necessary to examine differences in correlation coefficients and in regression lines. Ordinary least squares (OLS) regression is the standard method for fitting lines to data, but its criterion for optimal fit…
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Sabetghadam, Samaneh; Ahmadi-Givi, Farhang
2014-01-01
Light extinction, which is the extent of attenuation of light signal for every distance traveled by light in the absence of special weather conditions (e.g., fog and rain), can be expressed as the sum of scattering and absorption effects of aerosols. In this paper, diurnal and seasonal variations of the extinction coefficient are investigated for the urban areas of Tehran from 2007 to 2009. Cases of visibility impairment that were concurrent with reports of fog, mist, precipitation, or relative humidity above 90% are filtered. The mean value and standard deviation of daily extinction are 0.49 and 0.39 km(-1), respectively. The average is much higher than that in many other large cities in the world, indicating the rather poor air quality over Tehran. The extinction coefficient shows obvious diurnal variations in each season, with a peak in the morning that is more pronounced in the wintertime. Also, there is a very slight increasing trend in the annual variations of atmospheric extinction coefficient, which suggests that air quality has regressed since 2007. The horizontal extinction coefficient decreased from January to July in each year and then increased between July and December, with the maximum value in the winter. Diurnal variation of extinction is often associated with small values for low relative humidity (RH), but increases significantly at higher RH. Annual correlation analysis shows that there is a positive correlation between the extinction coefficient and RH, CO, PM10, SO2, and NO2 concentration, while negative correlation exists between the extinction and T, WS, and O3, implying their unfavorable impact on extinction variation. The extinction budget was derived from multiple regression equations using the regression coefficients. On average, 44% of the extinction is from suspended particles, 3% is from air molecules, about 5% is from NO2 absorption, 0.35% is from RH, and approximately 48% is unaccounted for, which may represent errors in the data as well as contribution of other atmospheric constituents omitted from the analysis. Stronger regression equation is achieved in the summer, meaning that the extinction is more predictable in this season using pollutant concentrations.
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Li, Cun-Yu; Wu, Xin; Gu, Jia-Mei; Li, Hong-Yang; Peng, Guo-Ping
2018-04-01
Based on the molecular sieving and solution-diffusion effect in nanofiltration separation, the correlation between initial concentration and mass transfer coefficient of three typical phenolic acids from Salvia miltiorrhiza was fitted to analyze the relationship among mass transfer coefficient, molecular weight and concentration. The experiment showed a linear relationship between operation pressure and membrane flux. Meanwhile, the membrane flux was gradually decayed with the increase of solute concentration. On the basis of the molecular sieving and solution-diffusion effect, the mass transfer coefficient and initial concentration of three phenolic acids showed a power function relationship, and the regression coefficients were all greater than 0.9. The mass transfer coefficient and molecular weight of three phenolic acids were negatively correlated with each other, and the order from high to low is protocatechualdehyde >rosmarinic acid> salvianolic acid B. The separation mechanism of nanofiltration for phenolic acids was further clarified through the analysis of the correlation of molecular weight and nanofiltration mass transfer coefficient. The findings provide references for nanofiltration separation, especially for traditional Chinese medicine with phenolic acids. Copyright© by the Chinese Pharmaceutical Association.
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Income inequality, disinvestment in health care and use of dental services.
Bhandari, Bishal; Newton, Jonathan T; Bernabé, Eduardo
2015-01-01
To explore the interrelationships between income inequality, disinvestment in health care, and use of dental services at country level. This study pooled national estimates for use of dental services among adults aged 18 years or older from the 70 countries that participated in the World Health Survey from 2002 to 2004, together with aggregate data on national income (GDP per capita), income inequality (Gini coefficient), and disinvestment in health care (total health expenditure and dentist-to-population ratio) from various international sources. Use of dental services was defined as having had dental problems in the last 12 months and having received any treatment to address those needs. Associations between variables were explored using Pearson correlation coefficients and linear regression. Data from 63 countries representing the six WHO regions were analyzed. Use of dental services was negatively correlated with Gini coefficient (Pearson correlation coefficient -0.48, P < 0.001) and positively correlated with GDP per capita (0.40, P < 0.05), total health expenditure (0.45, P < 0.001), and dentist-to-population ratio (0.67, P < 0.001). The association between Gini coefficient and use of dental services was attenuated but remained significant after adjustments for GDP per capita, total health expenditure, and dentist-to-population ratio (regression coefficient -0.36; 95% CI -0.57, -0.15). This study shows an inverse relationship between income inequality and use of dental services. Of the two indicators of disinvestment in health care assessed, only dentist-to-population ratio was associated with income inequality and use of dental services. © 2014 American Association of Public Health Dentistry.
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NASA Astrophysics Data System (ADS)
Rock, N. M. S.; Duffy, T. R.
REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.
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Genetic parameters for stayability to consecutive calvings in Zebu cattle.
Silva, D O; Santana, M L; Ayres, D R; Menezes, G R O; Silva, L O C; Nobre, P R C; Pereira, R J
2017-12-22
Longer-lived cows tend to be more profitable and the stayability trait is a selection criterion correlated to longevity. An alternative to the traditional approach to evaluate stayability is its definition based on consecutive calvings, whose main advantage is the more accurate evaluation of young bulls. However, no study using this alternative approach has been conducted for Zebu breeds. Therefore, the objective of this study was to compare linear random regression models to fit stayability to consecutive calvings of Guzerá, Nelore and Tabapuã cows and to estimate genetic parameters for this trait in the respective breeds. Data up to the eighth calving were used. The models included the fixed effects of age at first calving and year-season of birth of the cow and the random effects of contemporary group, additive genetic, permanent environmental and residual. Random regressions were modeled by orthogonal Legendre polynomials of order 1 to 4 (2 to 5 coefficients) for contemporary group, additive genetic and permanent environmental effects. Using Deviance Information Criterion as the selection criterion, the model with 4 regression coefficients for each effect was the most adequate for the Nelore and Tabapuã breeds and the model with 5 coefficients is recommended for the Guzerá breed. For Guzerá, heritabilities ranged from 0.05 to 0.08, showing a quadratic trend with a peak between the fourth and sixth calving. For the Nelore and Tabapuã breeds, the estimates ranged from 0.03 to 0.07 and from 0.03 to 0.08, respectively, and increased with increasing calving number. The additive genetic correlations exhibited a similar trend among breeds and were higher for stayability between closer calvings. Even between more distant calvings (second v. eighth), stayability showed a moderate to high genetic correlation, which was 0.77, 0.57 and 0.79 for the Guzerá, Nelore and Tabapuã breeds, respectively. For Guzerá, when the models with 4 or 5 regression coefficients were compared, the rank correlations between predicted breeding values for the intercept were always higher than 0.99, indicating the possibility of practical application of the least parameterized model. In conclusion, the model with 4 random regression coefficients is recommended for the genetic evaluation of stayability to consecutive calvings in Zebu cattle.
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Overcoming multicollinearity in multiple regression using correlation coefficient
NASA Astrophysics Data System (ADS)
Zainodin, H. J.; Yap, S. J.
2013-09-01
Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.
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NASA Astrophysics Data System (ADS)
Aheyeva, Viktoryia; Gruzdev, Aleksandr; Grishaev, Mikhail
Data of ground-based measurements of NO2 column contents are analyzed to study winter-spring NO2 anomalies associated with negative anomalies in column ozone and stratospheric temperature. Episodes of significant decrease in column NO2 contents in the winter-spring period of 2011 in the northern hemisphere (NH) were detected at European and Siberian stations of Zvenigorod (55.7°N, Moscow Region) and Tomsk (56.5°N, West Siberia) in the middle latitudes, Harestua (60.2°N), Sodankyla (67.4°N, both in North Europe), and Zhigansk (66.8°N, East Siberia) in the high latitudes, and at the Arctic station of Scoresbysund (70.5°N, Greenland). All the stations, except Tomsk, are a part of the Network of the Detection of Atmospheric Composition Change (NDACC), and the data are accesses at http://ndacc.org. The decrease in NO2 is generally accompanied by total ozone and stratospheric temperature decrease and is shown to be caused by the transport of stratospheric air from the region of the ozone hole observed that season in the Arctic. Overpass total ozone data from Giovanni service and radiosonde data were used for the analysis. Although negative NO2 anomalies due to the transport from the Arctic were also observed in some other years, the anomalies in 2011 reached record magnitudes. A significant positive correlation has been found between variations in NO2 and ozone columns as well as NO2 column and stratospheric temperature during the winter-spring period of 2011, whereas the correlation is much weaker in years without Arctic ozone depletion. The correlation becomes even stronger if only episodes with significant NO2 decrease are considered. For example the correlation coefficients between NO2 and ozone columns deviations are about 0.9 for Zvenigorod and Scoresbysund. Correlation coefficients between variations in column NO2 and total ozone and stratospheric temperature as well as coefficients of regression of NO2 on ozone and temperature in the winter-spring period of 2011 for the Siberian stations are less than those for European stations. For comparison analysis, data of column NO2, total ozone and stratospheric temperature at the southern hemisphere (SH) stations of Dumont D’Urville (66.7°S, the Antarctic), Macquarie Island (54.5°S) and Kerguelen Island (49.3°S) (all stations are NDACC stations) were used. Correlation and regression coefficients between variations in column NO2 and total ozone as well as in column NO2 and stratospheric temperature for the winter-spring periods at the SH stations depend on the phase of the quasi-biennial oscillation (QBO) in the 30 hPa equatorial wind velocity. The correlation coefficients and the coefficients of regression of NO2 on ozone and temperature for the west QBO phase are large compared to those for the east phase. The 2011 Arctic ozone hole was observed during the west phase of the 30 hPa QBO. The calculated correlation coefficients at the NH stations for the winter-spring period of 2011 associated with the Arctic ozone hole are close to similar coefficients at the SH stations in winter-spring periods for the west QBO phase. The regression coefficients at the NH stations are less than those at the SH stations for the west QBO phase but greater than similar coefficients for the east phase. We can conclude that physico-chemical processes specific for ozone hole conditions cause spatial correlation between distribution of stratospheric NO2 and distributions of total ozone and temperature in polar and adjacent regions, which is generally stronger for stronger ozone deficit in a polar region. This results in significant time correlation between NO2, ozone and temperature at observation sites due to transport processes.
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ERIC Educational Resources Information Center
Carter, David S.
1979-01-01
There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)
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Osinga, Rik; Babst, Doris; Bodmer, Elvira S; Link, Bjoern C; Fritsche, Elmar; Hug, Urs
2017-12-01
This work assessed both subjective and objective postoperative parameters after breast reduction surgery and compared between patients and plastic surgeons. After an average postoperative observation period of 6.7 ± 2.7 (2 - 13) years, 159 out of 259 patients (61 %) were examined. The mean age at the time of surgery was 37 ± 14 (15 - 74) years. The postoperative anatomy of the breast and other anthropometric parameters were measured in cm with the patient in an upright position. The visual analogue scale (VAS) values for symmetry, size, shape, type of scar and overall satisfaction both from the patient's and from four plastic surgeons' perspectives were assessed and compared. Patients rated the postoperative result significantly better than surgeons. Good subjective ratings by patients for shape, symmetry and sensitivity correlated with high scores for overall assessment. Shape had the strongest influence on overall satisfaction (regression coefficient 0.357; p < 0.001), followed by symmetry (regression coefficient 0.239; p < 0.001) and sensitivity (regression coefficient 0.109; p = 0.040) of the breast. The better the subjective rating for symmetry by the patient, the smaller the measured difference of the jugulum-mamillary distance between left and right (regression coefficient -0.773; p = 0.002) and the smaller the difference in height of the lowest part of the breast between left and right (regression coefficient -0.465; p = 0.035). There was no significant correlation between age, weight, height, BMI, resected weight of the breast, postoperative breast size or type of scar with overall satisfaction. After breast reduction surgery, long-term outcome is rated significantly better by patients than by plastic surgeons. Good subjective ratings by patients for shape, symmetry and sensitivity correlated with high scores for overall assessment. Shape had the strongest influence on overall satisfaction, followed by symmetry and sensitivity of the breast. Postoperative size of the breast, resection weight, type of scar, age or BMI was not of significant influence. Symmetry was the only assessed subjective parameter of this study that could be objectified by postoperative measurements. Georg Thieme Verlag KG Stuttgart · New York.
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NASA Technical Reports Server (NTRS)
Rogers, R. H. (Principal Investigator)
1976-01-01
The author has identified the following significant results. Computer techniques were developed for mapping water quality parameters from LANDSAT data, using surface samples collected in an ongoing survey of water quality in Saginaw Bay. Chemical and biological parameters were measured on 31 July 1975 at 16 bay stations in concert with the LANDSAT overflight. Application of stepwise linear regression bands to nine of these parameters and corresponding LANDSAT measurements for bands 4 and 5 only resulted in regression correlation coefficients that varied from 0.94 for temperature to 0.73 for Secchi depth. Regression equations expressed with the pair of bands 4 and 5, rather than the ratio band 4/band 5, provided higher correlation coefficients for all the water quality parameters studied (temperature, Secchi depth, chloride, conductivity, total kjeldahl nitrogen, total phosphorus, chlorophyll a, total solids, and suspended solids).
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Solar energy distribution over Egypt using cloudiness from Meteosat photos
DOE Office of Scientific and Technical Information (OSTI.GOV)
Mosalam Shaltout, M.A.; Hassen, A.H.
1990-01-01
In Egypt, there are 10 ground stations for measuring the global solar radiation, and five stations for measuring the diffuse solar radiation. Every day at noon, the Meteorological Authority in Cairo receives three photographs of cloudiness over Egypt from the Meteosat satellite, one in the visible, and two in the infra-red bands (10.5-12.5 {mu}m) and (5.7-7.1 {mu}m). The monthly average cloudiness for 24 sites over Egypt are measured and calculated from Meteosat observations during the period 1985-1986. Correlation analysis between the cloudiness observed by Meteosat and global solar radiation measured from the ground stations is carried out. It is foundmore » that, the correlation coefficients are about 0.90 for the simple linear regression, and increase for the second and third degree regressions. Also, the correlation coefficients for the cloudiness with the diffuse solar radiation are about 0.80 for the simple linear regression, and increase for the second and third degree regression. Models and empirical relations for estimating the global and diffuse solar radiation from Meteosat cloudiness data over Egypt are deduced and tested. Seasonal maps for the global and diffuse radiation over Egypt are carried out.« less
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Use of Empirical Estimates of Shrinkage in Multiple Regression: A Caution.
ERIC Educational Resources Information Center
Kromrey, Jeffrey D.; Hines, Constance V.
1995-01-01
The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…
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NASA Technical Reports Server (NTRS)
Lee, C. M.; Addy, H. E.; Bond, T. H.; Chun, K. S.; Lu, C. Y.
1987-01-01
The main objective of this report was to derive equations to estimate heat transfer coefficients in both the combustion chamber and coolant pasage of a rotary engine. This was accomplished by making detailed temperature and pressure measurements in a direct-injection stratified-charge rotary engine under a range of conditions. For each sppecific measurement point, the local physical properties of the fluids were calculated. Then an empirical correlation of the coefficients was derived by using a multiple regression program. This correlation expresses the Nusselt number as a function of the Prandtl number and Reynolds number.
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Qing, Si-han; Chang, Yun-feng; Dong, Xiao-ai; Li, Yuan; Chen, Xiao-gang; Shu, Yong-kang; Deng, Zhen-hua
2013-10-01
To establish the mathematical models of stature estimation for Sichuan Han female with measurement of lumbar vertebrae by X-ray to provide essential data for forensic anthropology research. The samples, 206 Sichuan Han females, were divided into three groups including group A, B and C according to the ages. Group A (206 samples) consisted of all ages, group B (116 samples) were 20-45 years old and 90 samples over 45 years old were group C. All the samples were examined lumbar vertebrae through CR technology, including the parameters of five centrums (L1-L5) as anterior border, posterior border and central heights (x1-x15), total central height of lumbar spine (x16), and the real height of every sample. The linear regression analysis was produced using the parameters to establish the mathematical models of stature estimation. Sixty-two trained subjects were tested to verify the accuracy of the mathematical models. The established mathematical models by hypothesis test of linear regression equation model were statistically significant (P<0.05). The standard errors of the equation were 2.982-5.004 cm, while correlation coefficients were 0.370-0.779 and multiple correlation coefficients were 0.533-0.834. The return tests of the highest correlation coefficient and multiple correlation coefficient of each group showed that the highest accuracy of the multiple regression equation, y = 100.33 + 1.489 x3 - 0.548 x6 + 0.772 x9 + 0.058 x12 + 0.645 x15, in group A were 80.6% (+/- lSE) and 100% (+/- 2SE). The established mathematical models in this study could be applied for the stature estimation for Sichuan Han females.
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The Geometry of Enhancement in Multiple Regression
ERIC Educational Resources Information Center
Waller, Niels G.
2011-01-01
In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…
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Determination of suitable drying curve model for bread moisture loss during baking
NASA Astrophysics Data System (ADS)
Soleimani Pour-Damanab, A. R.; Jafary, A.; Rafiee, S.
2013-03-01
This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.
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NASA Astrophysics Data System (ADS)
Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.
2013-10-01
In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.
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Raman spectroscopy-based screening of hepatitis C and associated molecular changes
NASA Astrophysics Data System (ADS)
Bilal, Maria; Bilal, M.; Saleem, M.; Khan, Saranjam; Ullah, Rahat; Fatima, Kiran; Ahmed, M.; Hayat, Abbas; Shahzada, Shaista; Ullah Khan, Ehsan
2017-09-01
This study presents the optical screening of hepatitis C and its associated molecular changes in human blood sera using a partial least-squares regression model based on their Raman spectra. In total, 152 samples were tested through enzyme-linked immunosorbent assay for confirmation. This model utilizes minor spectral variations in the Raman spectra of the positive and control groups. Regression coefficients of this model were analyzed with reference to the variations in concentration of associated molecules in these two groups. It was found that trehalose, chitin, ammonia, and cytokines are positively correlated while lipids, beta structures of proteins, and carbohydrate-binding proteins are negatively correlated with hepatitis C. The regression vector yielded by this model is utilized to predict hepatitis C in unknown samples. This model has been evaluated by a cross-validation method, which yielded a correlation coefficient of 0.91. Moreover, 30 unknown samples were screened for hepatitis C infection using this model to test its performance. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve from these predictions were found to be 93.3%, 100%, 96.7%, and 1, respectively.
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Reimus, Paul W; Callahan, Timothy J; Ware, S Doug; Haga, Marc J; Counce, Dale A
2007-08-15
Diffusion cell experiments were conducted to measure nonsorbing solute matrix diffusion coefficients in forty-seven different volcanic rock matrix samples from eight different locations (with multiple depth intervals represented at several locations) at the Nevada Test Site. The solutes used in the experiments included bromide, iodide, pentafluorobenzoate (PFBA), and tritiated water ((3)HHO). The porosity and saturated permeability of most of the diffusion cell samples were measured to evaluate the correlation of these two variables with tracer matrix diffusion coefficients divided by the free-water diffusion coefficient (D(m)/D*). To investigate the influence of fracture coating minerals on matrix diffusion, ten of the diffusion cells represented paired samples from the same depth interval in which one sample contained a fracture surface with mineral coatings and the other sample consisted of only pure matrix. The log of (D(m)/D*) was found to be positively correlated with both the matrix porosity and the log of matrix permeability. A multiple linear regression analysis indicated that both parameters contributed significantly to the regression at the 95% confidence level. However, the log of the matrix diffusion coefficient was more highly-correlated with the log of matrix permeability than with matrix porosity, which suggests that matrix diffusion coefficients, like matrix permeabilities, have a greater dependence on the interconnectedness of matrix porosity than on the matrix porosity itself. The regression equation for the volcanic rocks was found to provide satisfactory predictions of log(D(m)/D*) for other types of rocks with similar ranges of matrix porosity and permeability as the volcanic rocks, but it did a poorer job predicting log(D(m)/D*) for rocks with lower porosities and/or permeabilities. The presence of mineral coatings on fracture walls did not appear to have a significant effect on matrix diffusion in the ten paired diffusion cell experiments.
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NASA Astrophysics Data System (ADS)
Reimus, Paul W.; Callahan, Timothy J.; Ware, S. Doug; Haga, Marc J.; Counce, Dale A.
2007-08-01
Diffusion cell experiments were conducted to measure nonsorbing solute matrix diffusion coefficients in forty-seven different volcanic rock matrix samples from eight different locations (with multiple depth intervals represented at several locations) at the Nevada Test Site. The solutes used in the experiments included bromide, iodide, pentafluorobenzoate (PFBA), and tritiated water ( 3HHO). The porosity and saturated permeability of most of the diffusion cell samples were measured to evaluate the correlation of these two variables with tracer matrix diffusion coefficients divided by the free-water diffusion coefficient ( Dm/ D*). To investigate the influence of fracture coating minerals on matrix diffusion, ten of the diffusion cells represented paired samples from the same depth interval in which one sample contained a fracture surface with mineral coatings and the other sample consisted of only pure matrix. The log of ( Dm/ D*) was found to be positively correlated with both the matrix porosity and the log of matrix permeability. A multiple linear regression analysis indicated that both parameters contributed significantly to the regression at the 95% confidence level. However, the log of the matrix diffusion coefficient was more highly-correlated with the log of matrix permeability than with matrix porosity, which suggests that matrix diffusion coefficients, like matrix permeabilities, have a greater dependence on the interconnectedness of matrix porosity than on the matrix porosity itself. The regression equation for the volcanic rocks was found to provide satisfactory predictions of log( Dm/ D*) for other types of rocks with similar ranges of matrix porosity and permeability as the volcanic rocks, but it did a poorer job predicting log( Dm/ D*) for rocks with lower porosities and/or permeabilities. The presence of mineral coatings on fracture walls did not appear to have a significant effect on matrix diffusion in the ten paired diffusion cell experiments.
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Advanced Statistical Analyses to Reduce Inconsistency of Bond Strength Data.
Minamino, T; Mine, A; Shintani, A; Higashi, M; Kawaguchi-Uemura, A; Kabetani, T; Hagino, R; Imai, D; Tajiri, Y; Matsumoto, M; Yatani, H
2017-11-01
This study was designed to clarify the interrelationship of factors that affect the value of microtensile bond strength (µTBS), focusing on nondestructive testing by which information of the specimens can be stored and quantified. µTBS test specimens were prepared from 10 noncarious human molars. Six factors of µTBS test specimens were evaluated: presence of voids at the interface, X-ray absorption coefficient of resin, X-ray absorption coefficient of dentin, length of dentin part, size of adhesion area, and individual differences of teeth. All specimens were observed nondestructively by optical coherence tomography and micro-computed tomography before µTBS testing. After µTBS testing, the effect of these factors on µTBS data was analyzed by the general linear model, linear mixed effects regression model, and nonlinear regression model with 95% confidence intervals. By the general linear model, a significant difference in individual differences of teeth was observed ( P < 0.001). A significantly positive correlation was shown between µTBS and length of dentin part ( P < 0.001); however, there was no significant nonlinearity ( P = 0.157). Moreover, a significantly negative correlation was observed between µTBS and size of adhesion area ( P = 0.001), with significant nonlinearity ( P = 0.014). No correlation was observed between µTBS and X-ray absorption coefficient of resin ( P = 0.147), and there was no significant nonlinearity ( P = 0.089). Additionally, a significantly positive correlation was observed between µTBS and X-ray absorption coefficient of dentin ( P = 0.022), with significant nonlinearity ( P = 0.036). A significant difference was also observed between the presence and absence of voids by linear mixed effects regression analysis. Our results showed correlations between various parameters of tooth specimens and µTBS data. To evaluate the performance of the adhesive more precisely, the effect of tooth variability and a method to reduce variation in bond strength values should also be considered.
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Daily magnesium intake and serum magnesium concentration among Japanese people.
Akizawa, Yoriko; Koizumi, Sadayuki; Itokawa, Yoshinori; Ojima, Toshiyuki; Nakamura, Yosikazu; Tamura, Tarou; Kusaka, Yukinori
2008-01-01
The vitamins and minerals that are deficient in the daily diet of a normal adult remain unknown. To answer this question, we conducted a population survey focusing on the relationship between dietary magnesium intake and serum magnesium level. The subjects were 62 individuals from Fukui Prefecture who participated in the 1998 National Nutrition Survey. The survey investigated the physical status, nutritional status, and dietary data of the subjects. Holidays and special occasions were avoided, and a day when people are most likely to be on an ordinary diet was selected as the survey date. The mean (+/-standard deviation) daily magnesium intake was 322 (+/-132), 323 (+/-163), and 322 (+/-147) mg/day for men, women, and the entire group, respectively. The mean (+/-standard deviation) serum magnesium concentration was 20.69 (+/-2.83), 20.69 (+/-2.88), and 20.69 (+/-2.83) ppm for men, women, and the entire group, respectively. The distribution of serum magnesium concentration was normal. Dietary magnesium intake showed a log-normal distribution, which was then transformed by logarithmic conversion for examining the regression coefficients. The slope of the regression line between the serum magnesium concentration (Y ppm) and daily magnesium intake (X mg) was determined using the formula Y = 4.93 (log(10)X) + 8.49. The coefficient of correlation (r) was 0.29. A regression line (Y = 14.65X + 19.31) was observed between the daily intake of magnesium (Y mg) and serum magnesium concentration (X ppm). The coefficient of correlation was 0.28. The daily magnesium intake correlated with serum magnesium concentration, and a linear regression model between them was proposed.
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Wang, W; Ma, C Y; Chen, W; Ma, H Y; Zhang, H; Meng, Y Y; Ni, Y; Ma, L B
2016-08-19
Determining correlations between certain traits of economic importance constitutes an essential component of selective activities. In this study, our aim was to provide effective indicators for breeding programs of Lateolabrax maculatus, an important aquaculture species in China. We analyzed correlations between 20 morphometric traits and body weight, using correlation and path analyses. The results indicated that the correlations among all 21 traits were highly significant, with the highest correlation coefficient identified between total length and body weight. The path analysis indicated that total length (X 1 ), body width (X 5 ), distance from first dorsal fin origin to anal fin origin (X 10 ), snout length (X 16 ), eye diameter (X 17 ), eye cross (X 18 ), and slanting distance from snout tip to first dorsal fin origin (X 19 ) significantly affected body weight (Y) directly. The following multiple-regression equation was obtained using stepwise multiple-regression analysis: Y = -472.108 + 1.065X 1 + 7.728X 5 + 1.973X 10 - 7.024X 16 - 4.400X 17 - 3.338X 18 + 2.138X 19 , with an adjusted multiple-correlation coefficient of 0.947. Body width had the largest determinant coefficient, as well as the highest positive direct correlation with body weight. At the same time, high indirect effects with six other morphometric traits on L. maculatus body weight, through body width, were identified. Hence, body width could be a key factor that efficiently indicates significant effects on body weight in L. maculatus.
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Relationship between photoreceptor outer segment length and visual acuity in diabetic macular edema.
Forooghian, Farzin; Stetson, Paul F; Meyer, Scott A; Chew, Emily Y; Wong, Wai T; Cukras, Catherine; Meyerle, Catherine B; Ferris, Frederick L
2010-01-01
The purpose of this study was to quantify photoreceptor outer segment (PROS) length in 27 consecutive patients (30 eyes) with diabetic macular edema using spectral domain optical coherence tomography and to describe the correlation between PROS length and visual acuity. Three spectral domain-optical coherence tomography scans were performed on all eyes during each session using Cirrus HD-OCT. A prototype algorithm was developed for quantitative assessment of PROS length. Retinal thicknesses and PROS lengths were calculated for 3 parameters: macular grid (6 x 6 mm), central subfield (1 mm), and center foveal point (0.33 mm). Intrasession repeatability was assessed using coefficient of variation and intraclass correlation coefficient. The association between retinal thickness and PROS length with visual acuity was assessed using linear regression and Pearson correlation analyses. The main outcome measures include intrasession repeatability of macular parameters and correlation of these parameters with visual acuity. Mean retinal thickness and PROS length were 298 mum to 381 microm and 30 microm to 32 mum, respectively, for macular parameters assessed in this study. Coefficient of variation values were 0.75% to 4.13% for retinal thickness and 1.97% to 14.01% for PROS length. Intraclass correlation coefficient values were 0.96 to 0.99 and 0.73 to 0.98 for retinal thickness and PROS length, respectively. Slopes from linear regression analyses assessing the association of retinal thickness and visual acuity were not significantly different from 0 (P > 0.20), whereas the slopes of PROS length and visual acuity were significantly different from 0 (P < 0.0005). Correlation coefficients for macular thickness and visual acuity ranged from 0.13 to 0.22, whereas coefficients for PROS length and visual acuity ranged from -0.61 to -0.81. Photoreceptor outer segment length can be quantitatively assessed using Cirrus HD-OCT. Although the intrasession repeatability of PROS measurements was less than that of macular thickness measurements, the stronger correlation of PROS length with visual acuity suggests that the PROS measures may be more directly related to visual function. Photoreceptor outer segment length may be a useful physiologic outcome measure, both clinically and as a direct assessment of treatment effects.
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Jaman, Ajmery; Latif, Mahbub A H M; Bari, Wasimul; Wahed, Abdus S
2016-05-20
In generalized estimating equations (GEE), the correlation between the repeated observations on a subject is specified with a working correlation matrix. Correct specification of the working correlation structure ensures efficient estimators of the regression coefficients. Among the criteria used, in practice, for selecting working correlation structure, Rotnitzky-Jewell, Quasi Information Criterion (QIC) and Correlation Information Criterion (CIC) are based on the fact that if the assumed working correlation structure is correct then the model-based (naive) and the sandwich (robust) covariance estimators of the regression coefficient estimators should be close to each other. The sandwich covariance estimator, used in defining the Rotnitzky-Jewell, QIC and CIC criteria, is biased downward and has a larger variability than the corresponding model-based covariance estimator. Motivated by this fact, a new criterion is proposed in this paper based on the bias-corrected sandwich covariance estimator for selecting an appropriate working correlation structure in GEE. A comparison of the proposed and the competing criteria is shown using simulation studies with correlated binary responses. The results revealed that the proposed criterion generally performs better than the competing criteria. An example of selecting the appropriate working correlation structure has also been shown using the data from Madras Schizophrenia Study. Copyright © 2015 John Wiley & Sons, Ltd.
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Nakatsuka, Haruo; Chiba, Keiko; Watanabe, Takao; Sawatari, Hideyuki; Seki, Takako
2016-11-01
Iodine intake by adults in farming districts in Northeastern Japan was evaluated by two methods: (1) government-approved food composition tables based calculation and (2) instrumental measurement. The correlation between these two values and a regression model for the calibration of calculated values was presented. Iodine intake was calculated, using the values in the Japan Standard Tables of Food Composition (FCT), through the analysis of duplicate samples of complete 24-h food consumption for 90 adult subjects. In cases where the value for iodine content was not available in the FCT, it was assumed to be zero for that food item (calculated values). Iodine content was also measured by ICP-MS (measured values). Calculated and measured values rendered geometric means (GM) of 336 and 279 μg/day, respectively. There was no statistically significant (p > 0.05) difference between calculated and measured values. The correlation coefficient was 0.646 (p < 0.05). With this high correlation coefficient, a simple regression line can be applied to estimate measured value from calculated value. A survey of the literature suggests that the values in this study were similar to values that have been reported to date for Japan, and higher than those for other countries in Asia. Iodine intake of Japanese adults was 336 μg/day (GM, calculated) and 279 μg/day (GM, measured). Both values correlated so well, with a correlation coefficient of 0.646, that a regression model (Y = 130.8 + 1.9479X, where X and Y are measured and calculated values, respectively) could be used to calibrate calculated values.
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Richter, Jörg
2015-04-01
Methods to assess intervention progress and outcome for frequent use are needed. To provide preliminary information about psychometric properties for the Norwegian version of the Brief Problems Monitor. Cronbach's alpha scores and intra-class correlation coefficients as indicators for internal consistency (reliability) and Pearson correlation coefficients between corresponding subscales of the long and short ASEBA form versions as well as multiple regression coefficients to explore the predictive power of the reduced item-set related to the corresponding scale-scores of the long version were calculated in large, representative data sets of Norwegian children and adolescents. Cronbach's alpha scores of the Norwegian version of the BPM subscales varied between 0.67 (attention BPM-youth) and 0.88 (attention BPM-teacher) and between 0.90 (BPM-youth) and 0.96 (BPM-teacher) for its total problem score. Corresponding subscales from the long versions and the BPM as well as the total problems scores were closely correlated with coefficients of high effect size (all r > 0.80). The variance of the items of the BPM explained about three-quarters or more of the variance in the corresponding subscales of the long version. The Norwegian BPM has good psychometric properties in terms of 1) being acceptable to good internal consistency and in terms of 2) regression coefficients of high effect size from the BPM items to the problem-scale scores of the long versions as validity indicators. Its use in clinical practice and research can be recommended.
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Aisbett, B; Le Rossignol, P
2003-09-01
The VO2-power regression and estimated total energy demand for a 6-minute supra-maximal exercise test was predicted from a continuous incremental exercise test. Sub-maximal VO2-power co-ordinates were established from the last 40 seconds (s) of 150-second exercise stages. The precision of the estimated total energy demand was determined using the 95% confidence interval (95% CI) of the estimated total energy demand. The linearity of the individual VO2-power regression equations was determined using Pearson's correlation coefficient. The mean 95% CI of the estimated total energy demand was 5.9 +/- 2.5 mL O2 Eq x kg(-1) x min(-1), and the mean correlation coefficient was 0.9942 +/- 0.0042. The current study contends that the sub-maximal VO2-power co-ordinates from a continuous incremental exercise test can be used to estimate supra-maximal energy demand without compromising the precision of the accumulated oxygen deficit (AOD) method.
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NASA Astrophysics Data System (ADS)
Zhou, Yu; Chen, Shi
2016-02-01
In this paper, we investigate the high-frequency cross-correlation relationship between Chinese treasury futures contracts and treasury ETF. We analyze the logarithmic return of these two price series, from which we can conclude that both return series are not normally distributed and the futures markets have greater volatility. We find significant cross-correlation between these two series. We further confirm the relationship using the DCCA coefficient and the DMCA coefficient. We quantify the long-range cross-correlation with DCCA method, and we further show that the relationship is multifractal. An arbitrage algorithm based on DFA regression with stable return is proposed in the last part.
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ERIC Educational Resources Information Center
Vasu, Ellen Storey
1978-01-01
The effects of the violation of the assumption of normality in the conditional distributions of the dependent variable, coupled with the condition of multicollinearity upon the outcome of testing the hypothesis that the regression coefficient equals zero, are investigated via a Monte Carlo study. (Author/JKS)
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Pal, Shyamali
2017-12-01
The presence of Macro prolactin is a significant cause of elevated prolactin resulting in misdiagnosis in all automated systems. Poly ethylene glycol (PEG) pretreatment is the preventive process but such process includes the probability of loss of a fraction of bioactive prolactin. Surprisingly, PEG treated EQAS & IQAS samples in Cobas e 411 are found out to be correlating with direct results of at least 3 immunoassay systems and treated and untreated Cobas e 411 results are comparable by a correlation coefficient. Comparison of EQAS, IQAS and patient samples were done to find out the trueness of such correlation factor. Study with patient's results have established the correlation coefficient is valid for very small concentration of prolactin also. EQAS, IQAS and 150 patient samples were treated with PEG and prolactin results of treated and untreated samples obtained from Roche Cobas e 411. 25 patient's results (treated) were compared with direct results in Advia Centaur, Architect I & Access2 systems. Correlation coefficient was obtained from trend line of the treated and untreated results. Two tailed p-value obtained from regression coefficient(r) and sample size. The correlation coefficient is in the range (0.761-0.771). Reverse correlation range is (1.289-1.301). r value of two sets of calculated results were 0.995. Two tailed p- value is zero approving dismissal of null hypothesis. The z-score of EQAS does not always assure authenticity of resultsPEG precipitation is correlated by the factor 0.761 even in very small concentrationsAbbreviationsGFCgel filtration chromatographyPEGpolyethylene glycolEQASexternal quality assurance systemM-PRLmacro prolactinPRLprolactinECLIAelectro-chemiluminescence immunoassayCLIAclinical laboratory improvement amendmentsIQASinternal quality assurance systemrregression coefficient.
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[Quantitative determination of glass content in monazite glass-ceramics by IR technique].
He, Yong; Zhang, Bao-min
2003-04-01
Monazite glass-ceramics consist of both monazite and metaphoshate glass phases. The absorption bands of both phases do not overlap each other, and the absorption intensities of bands 1,275 and 616 cm-1 vary with the glass contents. The correlation coefficient between logarithmic absorbance ratio of the two bands and glass contents was r = 0.9975 and its regression equation was y = 48.356 + 25.93x. The absorbance ratio of bands 952 and 616 cm-1 also varied with different ratios of Ce2O3/La2O3 in synthetic monazites, with r = 0.9917 and a regression equation y = 0.2211 exp (0.0221x). High correlation coefficients show that the IR technique could find new application in the quantitative analysis of glass content in phosphate glass-ceramics.
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van Mierlo, Trevor; Hyatt, Douglas; Ching, Andrew T
2016-01-01
Digital Health Social Networks (DHSNs) are common; however, there are few metrics that can be used to identify participation inequality. The objective of this study was to investigate whether the Gini coefficient, an economic measure of statistical dispersion traditionally used to measure income inequality, could be employed to measure DHSN inequality. Quarterly Gini coefficients were derived from four long-standing DHSNs. The combined data set included 625,736 posts that were generated from 15,181 actors over 18,671 days. The range of actors (8-2323), posts (29-28,684), and Gini coefficients (0.15-0.37) varied. Pearson correlations indicated statistically significant associations between number of actors and number of posts (0.527-0.835, p < .001), and Gini coefficients and number of posts (0.342-0.725, p < .001). However, the association between Gini coefficient and number of actors was only statistically significant for the addiction networks (0.619 and 0.276, p < .036). Linear regression models had positive but mixed R 2 results (0.333-0.527). In all four regression models, the association between Gini coefficient and posts was statistically significant ( t = 3.346-7.381, p < .002). However, unlike the Pearson correlations, the association between Gini coefficient and number of actors was only statistically significant in the two mental health networks ( t = -4.305 and -5.934, p < .000). The Gini coefficient is helpful in measuring shifts in DHSN inequality. However, as a standalone metric, the Gini coefficient does not indicate optimal numbers or ratios of actors to posts, or effective network engagement. Further, mixed-methods research investigating quantitative performance metrics is required.
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Validation of MODIS Aerosol Optical Depth Retrievals over a Tropical Urban Site, Pune, India
NASA Technical Reports Server (NTRS)
More, Sanjay; Kuman, P. Pradeep; Gupta, Pawan; Devara, P. C. S.; Aher, G. R.
2011-01-01
In the present paper, MODIS (Terra and Aqua; level 2, collection 5) derived aerosoloptical depths (AODs) are compared with the ground-based measurements obtained from AERONET (level 2.0) and Microtops - II sun-photometer over a tropical urban station, Pune (18 deg 32'N; 73 deg 49'E, 559 m amsl). This is the first ever systematic validation of the MODIS aerosol products over Pune. Analysis of the data indicates that the Terra and Aqua MODIS AOD retrievals at 550 nm have good correlations with the AERONET and Microtops - II sun-photometer AOD measurements. During winter the linear regression correlation coefficients for MODIS products against AERONET measurements are 0.79 for Terra and 0.62 for Aqua; however for premonsoon, the corresponding coefficients are 0.78 and 0.74. Similarly, the linear regression correlation coefficients for Microtops measurements against MODIS products are 0.72 and 0.93 for Terra and Aqua data respectively during winter and are 0.78 and 0.75 during pre-monsoon. On yearly basis in 2008-2009, correlation coefficients for MODIS products against AERONET measurements are 0.80 and 0.78 for Terra and Aqua respectively while the corresponding coefficients are 0.70 and 0.73 during 2009-2010. The regressed intercepts with MODIS vs. AERONET are 0.09 for Terra and 0.05 for Aqua during winter whereas their values are 0.04 and 0.07 during pre-monsoon. However, MODIS AODs are found to underestimate during winter and overestimate during pre-monsoon with respect to AERONET and Microtops measurements having slopes 0.63 (Terra) and 0.74 (Aqua) during winter and 0.97 (Terra) and 0.94 (Aqua) during pre-monsoon. Wavelength dependency of Single Scattering Albedo (SSA) shows presence of absorbing and scattering aerosol particles. For winter, SSA decreases with wavelength with the values 0.86 +/- 0.03 at 440 nm and 0.82 +/- 0.04 at 1020nm. In pre-monsoon, it increases with wavelength (SSA is 0.87 +/- 0.02 at 440nm; and 0.88 +/-0.04 at 1020 nm).
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Daily Magnesium Intake and Serum Magnesium Concentration among Japanese People
Akizawa, Yoriko; Koizumi, Sadayuki; Itokawa, Yoshinori; Ojima, Toshiyuki; Nakamura, Yosikazu; Tamura, Tarou; Kusaka, Yukinori
2008-01-01
Background The vitamins and minerals that are deficient in the daily diet of a normal adult remain unknown. To answer this question, we conducted a population survey focusing on the relationship between dietary magnesium intake and serum magnesium level. Methods The subjects were 62 individuals from Fukui Prefecture who participated in the 1998 National Nutrition Survey. The survey investigated the physical status, nutritional status, and dietary data of the subjects. Holidays and special occasions were avoided, and a day when people are most likely to be on an ordinary diet was selected as the survey date. Results The mean (±standard deviation) daily magnesium intake was 322 (±132), 323 (±163), and 322 (±147) mg/day for men, women, and the entire group, respectively. The mean (±standard deviation) serum magnesium concentration was 20.69 (±2.83), 20.69 (±2.88), and 20.69 (±2.83) ppm for men, women, and the entire group, respectively. The distribution of serum magnesium concentration was normal. Dietary magnesium intake showed a log-normal distribution, which was then transformed by logarithmic conversion for examining the regression coefficients. The slope of the regression line between the serum magnesium concentration (Y ppm) and daily magnesium intake (X mg) was determined using the formula Y = 4.93 (log10X) + 8.49. The coefficient of correlation (r) was 0.29. A regression line (Y = 14.65X + 19.31) was observed between the daily intake of magnesium (Y mg) and serum magnesium concentration (X ppm). The coefficient of correlation was 0.28. Conclusion The daily magnesium intake correlated with serum magnesium concentration, and a linear regression model between them was proposed. PMID:18635902
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Pulmonary Catherization Data Correlate Poorly with Renal Function in Heart Failure.
Masha, Luke; Stone, James; Stone, Danielle; Zhang, Jun; Sheng, Luo
2018-04-10
The mechanisms of renal dysfunction in heart failure are poorly understood. We chose to explore the relationship of cardiac filling pressures and cardiac index (CI) in relation to renal dysfunction in advanced heart failure. To determine the relationship between renal function and cardiac filling pressures using the United Network of Organ Sharing (UNOS) pulmonary artery catherization registry. Patients over the age of 18 years who were listed for single-organ heart transplantation were included. Exclusion criteria included a history of mechanical circulatory support, previous transplantation, any use of renal replacement therapy, prior history of malignancy, and cardiac surgery, amongst others. Correlations between serum creatinine (SCr) and CI, pulmonary capillary wedge pressure (PCWP), pulmonary artery systolic pressure (PASP), and pulmonary artery diastolic pressure (PADP) were assessed by Pearson correlation coefficients and simple linear regression coefficients. Pearson correlation coefficients between SCr and PCWP, PASP, and PADP were near zero with values of 0.1, 0.07, and 0.08, respectively (p < 0.0001). A weak negative correlation coefficient between SCr and CI was found (correlation coefficient, -0.045, p = 0.027). In a subgroup of young patients unlikely to have noncardiac etiologies, no significant correlations between these values were identified. These findings suggest that, as assessed by pulmonary artery catherization, none of the factors - PCWP, PASP, PADP, or CI - play a prominent role in cardiorenal syndromes. © 2018 S. Karger AG, Basel.
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NASA Astrophysics Data System (ADS)
Das Bhowmik, R.; Arumugam, S.
2015-12-01
Multivariate downscaling techniques exhibited superiority over univariate regression schemes in terms of preserving cross-correlations between multiple variables- precipitation and temperature - from GCMs. This study focuses on two aspects: (a) develop an analytical solutions on estimating biases in cross-correlations from univariate downscaling approaches and (b) quantify the uncertainty in land-surface states and fluxes due to biases in cross-correlations in downscaled climate forcings. Both these aspects are evaluated using climate forcings available from both historical climate simulations and CMIP5 hindcasts over the entire US. The analytical solution basically relates the univariate regression parameters, co-efficient of determination of regression and the co-variance ratio between GCM and downscaled values. The analytical solutions are compared with the downscaled univariate forcings by choosing the desired p-value (Type-1 error) in preserving the observed cross-correlation. . For quantifying the impacts of biases on cross-correlation on estimating streamflow and groundwater, we corrupt the downscaled climate forcings with different cross-correlation structure.
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ERIC Educational Resources Information Center
Shieh, Gwowen
2006-01-01
This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…
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Inequality in Maternal Mortality in Iran: An Ecologic Study
Tajik, Parvin; Nedjat, Saharnaz; Afshar, Nozhat Emami; Changizi, Nasrin; Yazdizadeh, Bahareh; Azemikhah, Arash; Aamrolalaei, Sima; Majdzadeh, Reza
2012-01-01
Background: Maternal mortality (MM) is an avoidable death and there is national, international and political commitment to reduce it. The objective of this study is to examine the relation of MM to socioeconomic factors and its inequality in Iran's provinces at an ecologic level. Methods: The overall MM from each province was considered for 3 years from 2004 to 2006. The five independent variables whose relations were studied included the literacy rate among men and women in each province, mean annual household income per capita, Gini coefficients in each province, and Human Development Index (HDI). The correlation of Maternal Mortality Ratio (MMR) to the above five variables was evaluated through Pearson's correlation coefficient (simple and weighted for each province's population) and linear regression – by considering MMR as the dependent variable and the Gini coefficient, HDI, and difference in literacy rate among men and women as the independent variables. Results: The mean MMR in the years 2004–2006 was 24.7 in 100,000 live births. The correlation coefficients between MMR and literacy rate among women, literacy rate among men, the mean annual household income per capita, Gini coefficient and HDI were 0.82, 0.90, –0.61, 0.52 and –0.77, respectively. Based on multivariate regression, MMR was significantly associated with HDI (standardized B=–0.93) and difference in literacy rate among men and women (standardized B=–0.47). However, MMR was not significantly associated with the Gini coefficient. Conclusion: This study shows the association between socioeconomic variables and their inequalities with MMR in Iran's provinces at an ecologic level. In addition to the other direct interventions performed to reduce MM, it seems essential to especially focus on more distal factors influencing MMR. PMID:22347608
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Muradian, Kh K; Utko, N O; Mozzhukhina, T H; Pishel', I M; Litoshenko, O Ia; Bezrukov, V V; Fraĭfel'd, V E
2002-01-01
Correlative and regressive relations between the gaseous exchange, thermoregulation and mitochondrial protein content were analyzed by two- and three-dimensional statistics in mice. It has been shown that the pair wise linear methods of analysis did not reveal any significant correlation between the parameters under exploration. However, it became evident at three-dimensional and non-linear plotting for which the coefficients of multivariable correlation reached and even exceeded 0.7-0.8. The calculations based on partial differentiation of the multivariable regression equations allow to conclude that at certain values of VO2, VCO2 and body temperature negative relations between the systems of gaseous exchange and thermoregulation become dominating.
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The dynamic correlation between policy uncertainty and stock market returns in China
NASA Astrophysics Data System (ADS)
Yang, Miao; Jiang, Zhi-Qiang
2016-11-01
The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.
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Yoneoka, Daisuke; Henmi, Masayuki
2017-06-01
Recently, the number of regression models has dramatically increased in several academic fields. However, within the context of meta-analysis, synthesis methods for such models have not been developed in a commensurate trend. One of the difficulties hindering the development is the disparity in sets of covariates among literature models. If the sets of covariates differ across models, interpretation of coefficients will differ, thereby making it difficult to synthesize them. Moreover, previous synthesis methods for regression models, such as multivariate meta-analysis, often have problems because covariance matrix of coefficients (i.e. within-study correlations) or individual patient data are not necessarily available. This study, therefore, proposes a brief explanation regarding a method to synthesize linear regression models under different covariate sets by using a generalized least squares method involving bias correction terms. Especially, we also propose an approach to recover (at most) threecorrelations of covariates, which is required for the calculation of the bias term without individual patient data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
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NASA Astrophysics Data System (ADS)
Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng
2014-11-01
Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.
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How well do clinical pain assessment tools reflect pain in infants?
Slater, Rebeccah; Cantarella, Anne; Franck, Linda; Meek, Judith; Fitzgerald, Maria
2008-06-24
Pain in infancy is poorly understood, and medical staff often have difficulty assessing whether an infant is in pain. Current pain assessment tools rely on behavioural and physiological measures, such as change in facial expression, which may not accurately reflect pain experience. Our ability to measure cortical pain responses in young infants gives us the first opportunity to evaluate pain assessment tools with respect to the sensory input and establish whether the resultant pain scores reflect cortical pain processing. Cortical haemodynamic activity was measured in infants, aged 25-43 wk postmenstrual, using near-infrared spectroscopy following a clinically required heel lance and compared to the magnitude of the premature infant pain profile (PIPP) score in the same infant to the same stimulus (n = 12, 33 test occasions). Overall, there was good correlation between the PIPP score and the level of cortical activity (regression coefficient = 0.72, 95% confidence interval [CI] limits 0.32-1.11, p = 0.001; correlation coefficient = 0.57). Of the different PIPP components, facial expression correlated best with cortical activity (regression coefficient = 1.26, 95% CI limits 0.84-1.67, p < 0.0001; correlation coefficient = 0.74) (n = 12, 33 test occasions). Cortical pain responses were still recorded in some infants who did not display a change in facial expression. While painful stimulation generally evokes parallel cortical and behavioural responses in infants, pain may be processed at the cortical level without producing detectable behavioural changes. As a result, an infant with a low pain score based on behavioural assessment tools alone may not be pain free.
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Measuring Productivity of Depot-Level Aircraft Maintenance in the Air Force Logistics Command.
1985-09-01
of Figures...... . . . . . . . . . . . . vi List of Tables . . . . . . . . . ............ vii Abstract . . . ...................... viii I...59 6. DEA Efficiency Values (Third DEA Model) . .... 62 7. DMU 5 Input Efficiencies ................ 64 vi F "-’ List of Tables Table Page I. DEA...Regression Results for 20 Months . . . ..... 68 V. Regression Results for 7 Quarters . . ..... 70 VI . Coefficients of Correlation (Using Quarterly Data
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Michienzi, Alissa; Kron, Tomas; Callahan, Jason; Plumridge, Nikki; Ball, David; Everitt, Sarah
2017-04-01
Cone-beam computed tomography (CBCT) is a valuable image-guidance tool in radiation therapy (RT). This study was initiated to assess the accuracy of CBCT for quantifying non-small cell lung cancer (NSCLC) tumour volumes compared to the anatomical 'gold standard', CT. Tumour regression or progression on CBCT was also analysed. Patients with Stage I-III NSCLC, prescribed 60 Gy in 30 fractions RT with concurrent platinum-based chemotherapy, routine CBCT and enrolled in a prospective study of serial PET/CT (baseline, weeks two and four) were eligible. Time-matched CBCT and CT gross tumour volumes (GTVs) were manually delineated by a single observer on MIM software, and were analysed descriptively and using Pearson's correlation coefficient (r) and linear regression (R 2 ). Of 94 CT/CBCT pairs, 30 patients were eligible for inclusion. The mean (± SD) CT GTV vs CBCT GTV on the four time-matched pairs were 95 (±182) vs 98.8 (±160.3), 73.6 (±132.4) vs 70.7 (±96.6), 54.7 (±92.9) vs 61.0 (±98.8) and 61.3 (±53.3) vs 62.1 (±47.9) respectively. Pearson's correlation coefficient (r) was 0.98 (95% CI 0.97-0.99, ρ < 0.001). The mean (±SD) CT/CBCT Dice's similarity coefficient was 0.66 (±0.16). Of 289 CBCT scans, tumours in 27 (90%) patients regressed by a mean (±SD) rate of 1.5% (±0.75) per fraction. The mean (±SD) GTV regression was 43.1% (±23.1) from the first to final CBCT. Primary lung tumour volumes observed on CBCT and time-matched CT are highly correlated (although not identical), thereby validating observations of GTV regression on CBCT in NSCLC. © 2016 The Royal Australian and New Zealand College of Radiologists.
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NASA Astrophysics Data System (ADS)
Lee, Kang Il
2012-08-01
The present study aims to provide insight into the relationships of the phase velocity with the microarchitectural parameters in bovine trabecular bone in vitro. The frequency-dependent phase velocity was measured in 22 bovine femoral trabecular bone samples by using a pair of transducers with a diameter of 25.4 mm and a center frequency of 0.5 MHz. The phase velocity exhibited positive correlation coefficients of 0.48 and 0.32 with the ratio of bone volume to total volume and the trabecular thickness, respectively, but a negative correlation coefficient of -0.62 with the trabecular separation. The best univariate predictor of the phase velocity was the trabecular separation, yielding an adjusted squared correlation coefficient of 0.36. The multivariate regression models yielded adjusted squared correlation coefficients of 0.21-0.36. The theoretical phase velocity predicted by using a stratified model for wave propagation in periodically stratified media consisting of alternating parallel solid-fluid layers showed reasonable agreements with the experimental measurements.
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Shui, Wei; DU, Yong; Chen, Yi Ping; Jian, Xiao Mei; Fan, Bing Xiong
2017-04-18
Anxi County, specializing in tea cultivation, was taken as a case in this research. Pearson correlation analysis, ordinary least squares model (OLS) and geographically weighted regression model (GWR) were used to select four primary influence factors of specialization in tea cultivation (i.e., the average elevation, net income per capita, proportion of agricultural population, and the distance from roads) by analyzing the specialization degree of each town of Anxi County. Meanwhile, the spatial patterns of specialization in tea cultivation of Anxi County were evaluated. The results indicated that specialization in tea cultivation of Anxi County showed an obvious spatial auto-correlation, and a spatial pattern with "low-middle-high" circle structure, which was similar to Von Thünen's circle structure model, appeared from the county town to its surrounding region. Meanwhile, GWR (0.624) had a better fitting degree than OLS (0.595), and GWR could reasonably expound the spatial data. Contrary to the agricultural location theory of Von Thünen's model, which indicated that distance from market was a determination factor, the specialization degree of tea cultivation in Anxi was mainly decided by natural conditions of mountain area, instead of the social factors. Specialization degree of tea cultivation was positively correlated with the average elevation, net income per capita and the proportion of agricultural population, while a negative correlation was found between the distance from roads and specialization degree of tea cultivation. Coefficients of regression between the specialization degree of tea cultivation and two factors (i.e., the average elevation and net income per capita) showed a spatial pattern of higher level in the north direction and lower level in the south direction. On the contrary, the regression coefficients for the proportion of agricultural population increased from south to north of Anxi County. Furthermore, regression coefficient for the distance from roads showed a spatial pattern of higher level in the northeast direction and lower level in the southwest direction of Anxi County.
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Preliminary Survey on TRY Forest Traits and Growth Index Relations - New Challenges
NASA Astrophysics Data System (ADS)
Lyubenova, Mariyana; Kattge, Jens; van Bodegom, Peter; Chikalanov, Alexandre; Popova, Silvia; Zlateva, Plamena; Peteva, Simona
2016-04-01
Forest ecosystems provide critical ecosystem goods and services, including food, fodder, water, shelter, nutrient cycling, and cultural and recreational value. Forests also store carbon, provide habitat for a wide range of species and help alleviate land degradation and desertification. Thus they have a potentially significant role to play in climate change adaptation planning through maintaining ecosystem services and providing livelihood options. Therefore the study of forest traits is such an important issue not just for individual countries but for the planet as a whole. We need to know what functional relations between forest traits exactly can express TRY data base and haw it will be significant for the global modeling and IPBES. The study of the biodiversity characteristics at all levels and functional links between them is extremely important for the selection of key indicators for assessing biodiversity and ecosystem services for sustainable natural capital control. By comparing the available information in tree data bases: TRY, ITR (International Tree Ring) and SP-PAM the 42 tree species are selected for the traits analyses. The dependence between location characteristics (latitude, longitude, altitude, annual precipitation, annual temperature and soil type) and forest traits (specific leaf area, leaf weight ratio, wood density and growth index) is studied by by multiply regression analyses (RDA) using the statistical software package Canoco 4.5. The Pearson correlation coefficient (measure of linear correlation), Kendal rank correlation coefficient (non parametric measure of statistical dependence) and Spearman correlation coefficient (monotonic function relationship between two variables) are calculated for each pair of variables (indexes) and species. After analysis of above mentioned correlation coefficients the dimensional linear regression models, multidimensional linear and nonlinear regression models and multidimensional neural networks models are built. The strongest dependence between It and WD was obtained. The research will support the work on: Strategic Plan for Biodiversity 2011-2020, modelling and implementation of ecosystem-based approaches to climate change adaptation and disaster risk reduction. Key words: Specific leaf area (SLA), Leaf weight ratio (LWR), Wood density (WD), Growth index (It)
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Microbial Transformation of Esters of Chlorinated Carboxylic Acids
Paris, D. F.; Wolfe, N. L.; Steen, W. C.
1984-01-01
Two groups of compounds were selected for microbial transformation studies. In the first group were carboxylic acid esters having a fixed aromatic moiety and an increasing length of the alkyl component. Ethyl esters of chlorine-substituted carboxylic acids were in the second group. Microorganisms from environmental waters and a pure culture of Pseudomonas putida U were used. The bacterial populations were monitored by plate counts, and disappearance of the parent compound was followed by gas-liquid chromatography as a function of time. The products of microbial hydrolysis were the respective carboxylic acids. Octanol-water partition coefficients (Kow) for the compounds were measured. These values spanned three orders of magnitude, whereas microbial transformation rate constants (kb) varied only 50-fold. The microbial rate constants of the carboxylic acid esters with a fixed aromatic moiety increased with an increasing length of alkyl substituents. The regression coefficient for the linear relationships between log kb and log Kow was high for group 1 compounds, indicating that these parameters correlated well. The regression coefficient for the linear relationships for group 2 compounds, however, was low, indicating that these parameters correlated poorly. PMID:16346459
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Estimation of PM2.5 and PM10 using ground-based AOD measurements during KORUS-AQ campaign
NASA Astrophysics Data System (ADS)
Koo, J. H.; Kim, J.; Kim, S.; Go, S.; Lee, S.; Lee, H.; Mok, J.; Hong, J.; Lee, J.; Eck, T. F.; Holben, B. N.
2017-12-01
During the KORUS-AQ campaign (2 May - 12 June, 2016), aerosol optical depth (AOD) was obtained at multiple channels using various ground-based instruments at Yonsei University, Seoul: AERONET sunphotometer, SKYNET skyradiometer, Brewer spectrophotometer, and multi-filter rotating shadowband radiometer (MFRSR). At the same location, planetary boundary layer (PBL) height and vertical profile of backscattering coefficients also can be obtained based on the celiometer measurements. Using celiometer products and various AODs, we try to estimate the amount of particular matter (PM2.5 and PM10) and validate with in-situ surface PM2.5 and PM10 measurements from AIRKOREA network. Direct comparison between PM2.5 and AOD reveals that the ultraviolet(UV) channel AOD has better correlations, due to the higher sensitivity of short wavelength to the fine-mode particle. In contrast, PM10 shows the highest correlation with the near-infrared(NIR) AOD. Next, we extract the boundary-layer portion of AOD using either PBL height or vertical profile of backscattering coefficients to compare with PM2.5 and PM10. Both results enhance the correlation, but consideration of weighting factor calculated from backscattering coefficients shows larger contribution to the correlation increase. Finally, we performed the multiple linear regression to estimate PM2.5 and PM10 using AODs. Consideration of meteorology (temperature, wind speed, and relative humidity) can enhance the correlation and also O3 and NO2 consideration highly contributes to the high correlation. This finding implies the importance to consider the ambient condition of secondary aerosol formation related to the PM2.5 variation. Multiple regression model finally finds the correlation 0.7-0.8, and diminishes the wavelength-dependent correlation patterns.
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Hasegawa, Daisuke; Onishi, Hideo; Matsutomo, Norikazu
2016-02-01
This study aimed to evaluate the novel index of hepatic receptor (IHR) on the regression analysis derived from time activity curve of the liver for hepatic functional reserve. Sixty patients had undergone (99m)Tc-galactosyl serum albumin ((99m)Tc-GSA) scintigraphy in the retrospective clinical study. Time activity curves for liver were obtained by region of interest (ROI) on the whole liver. A novel hepatic functional predictor was calculated with multiple regression analysis of time activity curves. In the multiple regression function, the objective variables were the indocyanine green (ICG) retention rate at 15 min, and the explanatory variables were the liver counts in 3-min intervals until end from beginning. Then, this result was defined by IHR, and we analyzed the correlation between IHR and ICG, uptake ratio of the heart at 15 minutes to that at 3 minutes (HH15), uptake ratio of the liver to the liver plus heart at 15 minutes (LHL15), and index of convexity (IOC). Regression function of IHR was derived as follows: IHR=0.025×L(6)-0.052×L(12)+0.027×L(27). The multiple regression analysis indicated that liver counts at 6 min, 12 min, and 27 min were significantly related to objective variables. The correlation coefficient between IHR and ICG was 0.774, and the correlation coefficient between ICG and conventional indices (HH15, LHL15, and IOC) were 0.837, 0.773, and 0.793, respectively. IHR had good correlation with HH15, LHL15, and IOC. The finding results suggested that IHR would provide clinical benefit for hepatic functional assessment in the (99m)Tc-GSA scintigraphy.
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Relationship between parent–infant attachment and parental satisfaction with supportive nursing care
Ghadery-Sefat, Akram; Abdeyazdan, Zahra; Badiee, Zohreh; Zargham-Boroujeni, Ali
2016-01-01
Background: Parent–infant attachment is an important factor in accepting parenting role, accelerating infant survival, and adjusting to the environment outside the uterus. Since family supportive interventions can strengthen the parent–infant caring relationship, this study sought to investigate the relationship between mother–infant attachment and satisfaction of the mothers with the supportive nursing care received in the neonatal intensive care unit (NICU). Materials and Methods: In this descriptive–correlational study, 210 mothers with premature infants who were hospitalized in the NICUs affiliated to Isfahan Medical University hospitals took part. The data were collected via Maternal Postnatal Attachment Scale and researcher's self-tailored questionnaire based on Nurse Parent Support Tool. Pearson correlation coefficient and multiple linear regressions were used to analyze the collected data. Results: The results showed that the overall score of mother–infant attachment and the overall score of maternal satisfaction correlated with a correlation coefficient of r = 0.195. Also, the overall score of mother–infant attachment and mothers’ satisfaction scores in the emotional, communicative-informative, and self-confidence domains correlated with correlation coefficients of r = 0.182, r = 0.0.189, and r = 0.0.304, respectively. The results of multiple regression analysis revealed that about 15% of changes in the dependent variable (mother–infant attachment) could be explained by different dimensions of mothers’ satisfaction. Conclusions: The results of the study showed that mother–infant attachment improved by increasing mothers’ satisfaction of supportive nursing care. Therefore, it seems necessary to increase maternal satisfaction through given nursing care support, in order to promote mother–infant attachment. PMID:26985225
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Ghadery-Sefat, Akram; Abdeyazdan, Zahra; Badiee, Zohreh; Zargham-Boroujeni, Ali
2016-01-01
Parent-infant attachment is an important factor in accepting parenting role, accelerating infant survival, and adjusting to the environment outside the uterus. Since family supportive interventions can strengthen the parent-infant caring relationship, this study sought to investigate the relationship between mother-infant attachment and satisfaction of the mothers with the supportive nursing care received in the neonatal intensive care unit (NICU). In this descriptive-correlational study, 210 mothers with premature infants who were hospitalized in the NICUs affiliated to Isfahan Medical University hospitals took part. The data were collected via Maternal Postnatal Attachment Scale and researcher's self-tailored questionnaire based on Nurse Parent Support Tool. Pearson correlation coefficient and multiple linear regressions were used to analyze the collected data. The results showed that the overall score of mother-infant attachment and the overall score of maternal satisfaction correlated with a correlation coefficient of r = 0.195. Also, the overall score of mother-infant attachment and mothers' satisfaction scores in the emotional, communicative-informative, and self-confidence domains correlated with correlation coefficients of r = 0.182, r = 0.0.189, and r = 0.0.304, respectively. The results of multiple regression analysis revealed that about 15% of changes in the dependent variable (mother-infant attachment) could be explained by different dimensions of mothers' satisfaction. The results of the study showed that mother-infant attachment improved by increasing mothers' satisfaction of supportive nursing care. Therefore, it seems necessary to increase maternal satisfaction through given nursing care support, in order to promote mother-infant attachment.
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Prediction of kinase-inhibitor binding affinity using energetic parameters
Usha, Singaravelu; Selvaraj, Samuel
2016-01-01
The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log (IC50) values of 25 different kinase-inhibitors using multiple regressions which gave a correlation coefficient of 0.93. The regression equation obtained was tested on 93 kinase-inhibitor complexes and an average deviation of 0.92 from the experimental log IC50 values was shown. The same set of descriptors was used to predict binding affinities for a test set of five individual kinase families, with correlation values > 0.9. We show that the protein-ligand interaction energies and partition coefficient values form the major deterministic factors for binding affinity of the ligand for its receptor. PMID:28149052
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Three-parameter modeling of the soil sorption of acetanilide and triazine herbicide derivatives.
Freitas, Mirlaine R; Matias, Stella V B G; Macedo, Renato L G; Freitas, Matheus P; Venturin, Nelson
2014-02-01
Herbicides have widely variable toxicity and many of them are persistent soil contaminants. Acetanilide and triazine family of herbicides have widespread use, but increasing interest for the development of new herbicides has been rising to increase their effectiveness and to diminish environmental hazard. The environmental risk of new herbicides can be accessed by estimating their soil sorption (logKoc), which is usually correlated to the octanol/water partition coefficient (logKow). However, earlier findings have shown that this correlation is not valid for some acetanilide and triazine herbicides. Thus, easily accessible quantitative structure-property relationship models are required to predict logKoc of analogues of the these compounds. Octanol/water partition coefficient, molecular weight and volume were calculated and then regressed against logKoc for two series of acetanilide and triazine herbicides using multiple linear regression, resulting in predictive and validated models.
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Study on Hyperspectral Estimation Model of Total Nitrogen Content in Soil of Shaanxi Province
NASA Astrophysics Data System (ADS)
Liu, Jinbao; Dong, Zhenyu; Chen, Xi
2018-01-01
The development of hyperspectral remote sensing technology has been widely used in soil nutrient prediction. The soil is the representative soil type in Shaanxi Province. In this study, the soil total nitrogen content in Shaanxi soil was used as the research target, and the soil samples were measured by reflectance spectroscopy using ASD method. Pre-treatment, the first order differential, second order differential and reflectance logarithmic transformation of the reflected spectrum after pre-treatment, and the hyperspectral estimation model is established by using the least squares regression method and the principal component regression method. The results show that the correlation between the reflectance spectrum and the total nitrogen content of the soil is significantly improved. The correlation coefficient between the original reflectance and soil total nitrogen content is in the range of 350 ~ 2500nm. The correlation coefficient of soil total nitrogen content and first deviation of reflectance is more than 0.5 at 142nm, 1963nm, 2204nm and 2307nm, the second deviation has a significant positive correlation at 1114nm, 1470nm, 1967nm, 2372nm and 2402nm, respectively. After the reciprocal logarithmic transformation of the reflectance with the total nitrogen content of the correlation analysis found that the effect is not obvious. Rc2 = 0.7102, RMSEC = 0.0788; Rv2 = 0.8480, RMSEP = 0.0663, which can achieve the rapid prediction of the total nitrogen content in the region. The results show that the principal component regression model is the best.
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Turpin, Anthony; Paget-Bailly, Sophie; Ploquin, Anne; Hollebecque, Antoine; Peugniez, Charlotte; El-Hajbi, Farid; Bonnetain, Franck; Hebbar, Mohamed
2018-03-01
We studied the relationship between intermediate criteria and overall survival (OS) in metastatic colorectal cancer (mCRC) patients who received first-line chemotherapy with bevacizumab. We assessed OS, progression-free survival (PFS), duration of disease control (DDC), the sum of the periods in which the disease did not progress, and the time to failure of strategy (TFS), which was defined as the entire period before the introduction of a second-line treatment. Linear correlation and regression models were used, and Prentice criteria were investigated. With a median follow-up of 57.6 months for 216 patients, the median OS was 24.5 months (95% confidence interval [CI], 21.3-29.7). The median PFS, DDC, and TFS were 8.9 (95% CI, 8.4-9.7), 11.0 (95% CI, 9.8-12.4), and 11.1 (95% CI, 10.0-13.0) months, respectively. The correlations between OS and DDC (Pearson coefficient, 0.79 [95% CI, 0.73-0.83], determination coefficient, 0.62) and OS and TFS (Pearson coefficient, 0.79 [95% CI, 0.73-0.84], determination coefficient, 0.63) were satisfactory. Linear regression analysis showed a significant association between OS and DDC, and between OS and TFS. Prentice criteria were verified for TFS as well as DDC. DDC and TFS correlated with OS and are relevant as intermediate criteria in the setting of patients with mCRC treated with a first-line bevacizumab-based regimen. Copyright © 2017 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Gholizadeh, H.; Robeson, S. M.
2015-12-01
Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.
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Rogers, Paul; Stoner, Julie
2016-01-01
Regression models for correlated binary outcomes are commonly fit using a Generalized Estimating Equations (GEE) methodology. GEE uses the Liang and Zeger sandwich estimator to produce unbiased standard error estimators for regression coefficients in large sample settings even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large, and there are few repeated measurements. The sandwich estimator is not without drawbacks; its asymptotic properties do not hold in small sample settings. In these situations, the sandwich estimator is biased downwards, underestimating the variances. In this project, a modified form for the sandwich estimator is proposed to correct this deficiency. The performance of this new sandwich estimator is compared to the traditional Liang and Zeger estimator as well as alternative forms proposed by Morel, Pan and Mancl and DeRouen. The performance of each estimator was assessed with 95% coverage probabilities for the regression coefficient estimators using simulated data under various combinations of sample sizes and outcome prevalence values with an Independence (IND), Autoregressive (AR) and Compound Symmetry (CS) correlation structure. This research is motivated by investigations involving rare-event outcomes in aviation data. PMID:26998504
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Oziminski, Wojciech P; Krygowski, Tadeusz M
2011-03-01
Electronic structure of 22 monosubstituted derivatives of benzene and exocyclically substituted fulvene with substituents: B(OH)(2), BH(2), CCH, CF(3), CH(3), CHCH(2), CHO, Cl, CMe(3), CN, COCH(3), CONH(2), COOH, F, NH(2), NMe(2), NO, NO(2), OCH(3), OH, SiH(3), SiMe(3) were studied theoretically by means of Natural Bond Orbital analysis. It is shown, that sum of π-electron population of carbon atoms of the fulvene and benzene rings, pEDA(F) and pEDA(B), respectively correlate well with Hammett substituent constants [Formula in text] and aromaticity index NICS. The substituent effect acting on pi-electron occupation at carbon atoms of the fulvene ring is significantly stronger than in the case of benzene. Electron occupations of ring carbon atoms (except C1) in fulvene plotted against each other give linear regressions with high correlation coefficients. The same is true for ortho- and para-carbon atoms in benzene. Positive slopes of the regressions indicate similar for fulvene and benzene kind of substituent effect - mostly resonance in nature. Only the regressions of occupation at the carbon atom in meta- position of benzene against ortho- and para-positions gives negative slopes and low correlation coefficients.
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Applicability of Cameriere's and Drusini's age estimation methods to a sample of Turkish adults.
Hatice, Boyacioglu Dogru; Nihal, Avcu; Nursel, Akkaya; Humeyra Ozge, Yilanci; Goksuluk, Dincer
2017-10-01
The aim of this study was to investigate the applicability of Drusini's and Cameriere's methods to a sample of Turkish people. Panoramic images of 200 individuals were allocated into two groups as study and test groups and examined by two observers. Tooth coronal indexes (TCI), which is the ratio between coronal pulp cavity height and crown height, were calculated in the mandibular first and second premolars and molars. Pulp/tooth area ratios (ARs) were calculated in the maxillary and mandibular canine teeth. Study group measurements were used to derive a regression model. Test group measurements were used to evaluate the accuracy of the regression model. Pearson's correlation coefficients and regression analysis were used. The correlations between TCIs and age were -0.230, -0.301, -0.344 and -0.257 for mandibular first premolar, second premolar, first molar and second molar, respectively. Those for the maxillary canine (MX) and mandibular canine (MN) ARs were -0.716 and -0.514, respectively. The MX ARs were used to build the linear regression model that explained 51.2% of the total variation, with a standard error of 9.23 years. The mean error of the estimates in test group was 8 years and age of 64% of the individuals were estimated with an error of <±10 years which is acceptable in forensic age prediction. The low correlation coefficients between age and TCI indicate that Drusini's method was not applicable to the estimation of age in a Turkish population. Using Cameriere's method, we derived a regression model.
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Neonates with Bartter syndrome have enormous fluid and sodium requirements.
Azzi, Antonio; Chehade, Hassib; Deschênes, Georges
2015-07-01
Managing neonatal Bartter syndrome by achieving adequate weight gain is challenging. We assessed the correlation between weight gain in neonatal Bartter syndrome and the introduction of fluid and sodium supplementations and indomethacin during the first 4 weeks of life. Daily fluid and electrolytes requirements were analysed using linear regression and Spearman correlation coefficients. The weight gain coefficient was calculated as daily weight gain after physiological neonatal weight loss. We studied seven infants. The highest weight gain coefficients occurred between weeks two and four in the five neonates who either received prompt amounts of fluid (maximum 810 mL/kg/day) and sodium (maximum 70 mmol/kg/day) or were treated with indomethacin. For the two patients with the highest weight gain coefficient, water and sodium supplementations were decreased in weeks two to four leading to a significant negative Spearman correlation between weight gain and fluid supplements (r = -0.55 and -0.68) and weight gain and sodium supplementations (r = -0.96 and -0.72). The two patients with the lowest weight gain coefficient had positive Spearman correlation coefficients between weight gain and fluid and sodium supplementations. Infants with neonatal Bartter syndrome required rapid and enormous fluid and sodium supplementations or the early introduction of indomethacin treatment to achieve adequate weight gain during the early postnatal period. ©2015 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.
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Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan
2016-08-25
Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Innovating patient care delivery: DSRIP's interrupted time series analysis paradigm.
Shenoy, Amrita G; Begley, Charles E; Revere, Lee; Linder, Stephen H; Daiger, Stephen P
2017-12-08
Adoption of Medicaid Section 1115 waiver is one of the many ways of innovating healthcare delivery system. The Delivery System Reform Incentive Payment (DSRIP) pool, one of the two funding pools of the waiver has four categories viz. infrastructure development, program innovation and redesign, quality improvement reporting and lastly, bringing about population health improvement. A metric of the fourth category, preventable hospitalization (PH) rate was analyzed in the context of eight conditions for two time periods, pre-reporting years (2010-2012) and post-reporting years (2013-2015) for two hospital cohorts, DSRIP participating and non-participating hospitals. The study explains how DSRIP impacted Preventable Hospitalization (PH) rates of eight conditions for both hospital cohorts within two time periods. Eight PH rates were regressed as the dependent variable with time, intervention and post-DSRIP Intervention as independent variables. PH rates of eight conditions were then consolidated into one rate for regressing with the above independent variables to evaluate overall impact of DSRIP. An interrupted time series regression was performed after accounting for auto-correlation, stationarity and seasonality in the dataset. In the individual regression model, PH rates showed statistically significant coefficients for seven out of eight conditions in DSRIP participating hospitals. In the combined regression model, the coefficient of the PH rate showed a statistically significant decrease with negative p-values for regression coefficients in DSRIP participating hospitals compared to positive/increased p-values for regression coefficients in DSRIP non-participating hospitals. Several macro- and micro-level factors may have likely contributed DSRIP hospitals outperforming DSRIP non-participating hospitals. Healthcare organization/provider collaboration, support from healthcare professionals, DSRIP's design, state reimbursement and coordination in care delivery methods may have led to likely success of DSRIP. IV, a retrospective cohort study based on longitudinal data. Copyright © 2017 Elsevier Inc. All rights reserved.
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Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan.
Tsai, Pui-Jen; Teng, Hwa-Jen
2016-11-09
Aedes mosquitoes in Taiwan mainly comprise Aedes albopictus and Ae. aegypti. However, the species contributing to autochthonous dengue spread and the extent at which it occurs remain unclear. Thus, in this study, we spatially analyzed real data to determine spatial features related to local dengue incidence and mosquito density, particularly that of Ae. albopictus and Ae. aegypti. We used bivariate Moran's I statistic and geographically weighted regression (GWR) spatial methods to analyze the globally spatial dependence and locally regressed relationship between (1) imported dengue incidences and Breteau indices (BIs) of Ae. albopictus, (2) imported dengue incidences and BI of Ae. aegypti, (3) autochthonous dengue incidences and BI of Ae. albopictus, (4) autochthonous dengue incidences and BI of Ae. aegypti, (5) all dengue incidences and BI of Ae. albopictus, (6) all dengue incidences and BI of Ae. aegypti, (7) BI of Ae. albopictus and human population density, and (8) BI of Ae. aegypti and human population density in 348 townships in Taiwan. In the GWR models, regression coefficients of spatially regressed relationships between the incidence of autochthonous dengue and vector density of Ae. aegypti were significant and positive in most townships in Taiwan. However, Ae. albopictus had significant but negative regression coefficients in clusters of dengue epidemics. In the global bivariate Moran's index, spatial dependence between the incidence of autochthonous dengue and vector density of Ae. aegypti was significant and exhibited positive correlation in Taiwan (bivariate Moran's index = 0.51). However, Ae. albopictus exhibited positively significant but low correlation (bivariate Moran's index = 0.06). Similar results were observed in the two spatial methods between all dengue incidences and Aedes mosquitoes (Ae. aegypti and Ae. albopictus). The regression coefficients of spatially regressed relationships between imported dengue cases and Aedes mosquitoes (Ae. aegypti and Ae. albopictus) were significant in 348 townships in Taiwan. The results indicated that local Aedes mosquitoes do not contribute to the dengue incidence of imported cases. The density of Ae. aegypti positively correlated with the density of human population. By contrast, the density of Ae. albopictus negatively correlated with the density of human population in the areas of southern Taiwan. The results indicated that Ae. aegypti has more opportunities for human-mosquito contact in dengue endemic areas in southern Taiwan. Ae. aegypti, but not Ae. albopictus, and human population density in southern Taiwan are closely associated with an increased risk of autochthonous dengue incidence.
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Structural features that predict real-value fluctuations of globular proteins
Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke
2012-01-01
It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics trajectories of non-homologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real-value of residue fluctuations using the support vector regression. It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in molecular dynamics trajectories. Moreover, support vector regression that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson’s correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed for the prediction by the Gaussian network model. An advantage of the developed method over the Gaussian network models is that the former predicts the real-value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. PMID:22328193
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Correlation and prediction of dynamic human isolated joint strength from lean body mass
NASA Technical Reports Server (NTRS)
Pandya, Abhilash K.; Hasson, Scott M.; Aldridge, Ann M.; Maida, James C.; Woolford, Barbara J.
1992-01-01
A relationship between a person's lean body mass and the amount of maximum torque that can be produced with each isolated joint of the upper extremity was investigated. The maximum dynamic isolated joint torque (upper extremity) on 14 subjects was collected using a dynamometer multi-joint testing unit. These data were reduced to a table of coefficients of second degree polynomials, computed using a least squares regression method. All the coefficients were then organized into look-up tables, a compact and convenient storage/retrieval mechanism for the data set. Data from each joint, direction and velocity, were normalized with respect to that joint's average and merged into files (one for each curve for a particular joint). Regression was performed on each one of these files to derive a table of normalized population curve coefficients for each joint axis, direction, and velocity. In addition, a regression table which included all upper extremity joints was built which related average torque to lean body mass for an individual. These two tables are the basis of the regression model which allows the prediction of dynamic isolated joint torques from an individual's lean body mass.
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Hidaka, Nobuhiro; Murata, Masaharu; Sasahara, Jun; Ishii, Keisuke; Mitsuda, Nobuaki
2015-05-01
Observed/expected lung area to head circumference ratio (o/e LHR) and lung to thorax transverse area ratio (LTR) are the sonographic indicators of postnatal outcome in fetuses with congenital diaphragmatic hernia (CDH), and they are not influenced by gestational age. We aimed to evaluate the relationship between these two parameters in the same subjects with fetal left-sided CDH. Fetuses with left-sided CDH managed between 2005 and 2012 were included. Data of LTR and o/e LHR values measured on the same day prior to 33 weeks' gestation in target fetuses were retrospectively collected. The correlation between the two parameters was estimated using the Spearman's rank-correlation coefficient, and linear regression analysis was used to assess the relationship between them. Data on 61 measurements from 36 CDH fetuses were analyzed to obtain a Spearman's rank-correlation coefficient of 0.74 with the following linear equation: LTR = 0.002 × (o/e LHR) + 0.005. The determination coefficient of this linear equation was sufficiently high at 0.712, and the prediction accuracy obtained with this regression formula was considered satisfactory. A good linear correlation between the LTR and the o/e LHR was obtained, suggesting that we can translate the predictive parameters for each other. This information is expected to be useful to improve our understanding of different investigations focusing on LTR or o/e LHR as a predictor of postnatal outcome in CDH. © 2014 Japanese Teratology Society.
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NASA Technical Reports Server (NTRS)
Ryu, J. Y.; Wada, M.
1985-01-01
In order to examine the stability of neutron monitor observation, each of the monthly average counting rates of a neutron monitors is correlated to those of Kiel neutron monitor. The regression coefficients thus obtained are compared with the coupling coefficients of isotropic intensity radiation. The results of the comparisons for five year periods during 1963 to 1982, and for whole period are given. The variation spectrum with a single power law with an exponent of -0.75 up to 50 GV is not so unsatisfactory one. More than one half of the stations show correlations with the coefficient greater than 0.9. Some stations have shifted the level of mean counting rates by changing the instrumental characteristics which can be adjusted.
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2011-01-01
Background In accord with new European university reforms initiated by the Bologna Process, our objectives were to assess psychological quality of life (QoL) and to analyse its associations with academic employability skills (AES) among students from the Faculty of Language, Literature, Humanities, Arts and Education, Walferdange Luxembourg (F1, mostly vocational/applied courses); the Faculty of Social and Human Sciences, Liege, Belgium (F2, mainly general courses); and the Faculty of Social Work, Iasi, Romania (F3, mainly vocational/professional courses). Method Students who redoubled or who had studied at other universities were excluded. 355 newly-registered first-year students (145 from F1, 125 from F2, and 85 from F3) were invited to complete an online questionnaire (in French, German, English or Romanian) covering socioeconomic data, the AES scale and the QoL-psychological, QoL-social relationships and QoL-environment subscales as measured with the World Health Organisation Quality of Life short-form (WHOQoL-BREF) questionnaire. Analyses included multiple regressions with interactions. Results QoL-psychological, QoL-social relationships and QoL-environment' scores were highest in F1 (Luxembourg), and the QoL-psychological score in F2 (Belgium) was the lower. AES score was higher in F1 than in F3 (Romania). A positive link was found between QoL-psychological and AES for F1 (correlation coefficient 0.29, p < 0.01) and F3 (correlation coefficient 0.30, p < 0.05), but the association was negative for F2 (correlation coefficient -0.25, p < 0.01). QoL-psychological correlated positively with QoL-social relationships (regression coefficient 0.31, p < 0.001) and QoL-environment (regression coefficient 0.35, p < 0.001). Conclusions Psychological quality of life is associated with acquisition of skills that increase employability from the faculties offering vocational/applied/professional courses in Luxembourg and Romania, but not their academically orientated Belgian counterparts. In the context of developing a European Higher Educational Area, these measurements are major indicators that can be used as a guide to promoting programs geared towards counseling, improvement of the social environment, and services to assist with university work and facilitate achievement of future professional projects. PMID:21501507
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Baumann, Michèle; Ionescu, Ion; Chau, Nearkasen
2011-04-18
In accord with new European university reforms initiated by the Bologna Process, our objectives were to assess psychological quality of life (QoL) and to analyse its associations with academic employability skills (AES) among students from the Faculty of Language, Literature, Humanities, Arts and Education, Walferdange Luxembourg (F1, mostly vocational/applied courses); the Faculty of Social and Human Sciences, Liege, Belgium (F2, mainly general courses); and the Faculty of Social Work, Iasi, Romania (F3, mainly vocational/professional courses). Students who redoubled or who had studied at other universities were excluded. 355 newly-registered first-year students (145 from F1, 125 from F2, and 85 from F3) were invited to complete an online questionnaire (in French, German, English or Romanian) covering socioeconomic data, the AES scale and the QoL-psychological, QoL-social relationships and QoL-environment subscales as measured with the World Health Organisation Quality of Life short-form (WHOQoL-BREF) questionnaire. Analyses included multiple regressions with interactions. QoL-psychological, QoL-social relationships and QoL-environment' scores were highest in F1 (Luxembourg), and the QoL-psychological score in F2 (Belgium) was the lower. AES score was higher in F1 than in F3 (Romania). A positive link was found between QoL-psychological and AES for F1 (correlation coefficient 0.29, p<0.01) and F3 (correlation coefficient 0.30, p<0.05), but the association was negative for F2 (correlation coefficient -0.25, p<0.01). QoL-psychological correlated positively with QoL-social relationships (regression coefficient 0.31, p<0.001) and QoL-environment (regression coefficient 0.35, p<0.001). Psychological quality of life is associated with acquisition of skills that increase employability from the faculties offering vocational/applied/professional courses in Luxembourg and Romania, but not their academically orientated Belgian counterparts. In the context of developing a European Higher Educational Area, these measurements are major indicators that can be used as a guide to promoting programs geared towards counseling, improvement of the social environment, and services to assist with university work and facilitate achievement of future professional projects.
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Satellite remote sensing of fine particulate air pollutants over Indian mega cities
NASA Astrophysics Data System (ADS)
Sreekanth, V.; Mahesh, B.; Niranjan, K.
2017-11-01
In the backdrop of the need for high spatio-temporal resolution data on PM2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM2.5 data (from US Consulate and Embassy's air monitoring program) and MODIS AOD, spanning for almost four years. PM2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship.
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Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.
Chen, Yanguang
2016-01-01
In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.
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Cui, Yang; Wang, Silong; Yan, Shaokui
2016-01-01
Phi coefficient directly depends on the frequencies of occurrence of organisms and has been widely used in vegetation ecology to analyse the associations of organisms with site groups, providing a characterization of ecological preference, but its application in soil ecology remains rare. Based on a single field experiment, this study assessed the applicability of phi coefficient in indicating the habitat preferences of soil fauna, through comparing phi coefficient-induced results with those of ordination methods in charactering soil fauna-habitat(factors) relationships. Eight different habitats of soil fauna were implemented by reciprocal transfer of defaunated soil cores between two types of subtropical forests. Canonical correlation analysis (CCorA) showed that ecological patterns of fauna-habitat relationships and inter-fauna taxa relationships expressed, respectively, by phi coefficients and predicted abundances calculated from partial redundancy analysis (RDA), were extremely similar, and a highly significant relationship between the two datasets was observed (Pillai's trace statistic = 1.998, P = 0.007). In addition, highly positive correlations between phi coefficients and predicted abundances for Acari, Collembola, Nematode and Hemiptera were observed using linear regression analysis. Quantitative relationships between habitat preferences and soil chemical variables were also obtained by linear regression, which were analogous to the results displayed in a partial RDA biplot. Our results suggest that phi coefficient could be applicable on a local scale in evaluating habitat preferences of soil fauna at coarse taxonomic levels, and that the phi coefficient-induced information, such as ecological preferences and the associated quantitative relationships with habitat factors, will be largely complementary to the results of ordination methods. The application of phi coefficient in soil ecology may extend our knowledge about habitat preferences and distribution-abundance relationships, which will benefit the understanding of biodistributions and variations in community compositions in the soil. Similar studies in other places and scales apart from our local site will be need for further evaluation of phi coefficient.
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Cui, Yang; Wang, Silong; Yan, Shaokui
2016-01-01
Phi coefficient directly depends on the frequencies of occurrence of organisms and has been widely used in vegetation ecology to analyse the associations of organisms with site groups, providing a characterization of ecological preference, but its application in soil ecology remains rare. Based on a single field experiment, this study assessed the applicability of phi coefficient in indicating the habitat preferences of soil fauna, through comparing phi coefficient-induced results with those of ordination methods in charactering soil fauna-habitat(factors) relationships. Eight different habitats of soil fauna were implemented by reciprocal transfer of defaunated soil cores between two types of subtropical forests. Canonical correlation analysis (CCorA) showed that ecological patterns of fauna-habitat relationships and inter-fauna taxa relationships expressed, respectively, by phi coefficients and predicted abundances calculated from partial redundancy analysis (RDA), were extremely similar, and a highly significant relationship between the two datasets was observed (Pillai's trace statistic = 1.998, P = 0.007). In addition, highly positive correlations between phi coefficients and predicted abundances for Acari, Collembola, Nematode and Hemiptera were observed using linear regression analysis. Quantitative relationships between habitat preferences and soil chemical variables were also obtained by linear regression, which were analogous to the results displayed in a partial RDA biplot. Our results suggest that phi coefficient could be applicable on a local scale in evaluating habitat preferences of soil fauna at coarse taxonomic levels, and that the phi coefficient-induced information, such as ecological preferences and the associated quantitative relationships with habitat factors, will be largely complementary to the results of ordination methods. The application of phi coefficient in soil ecology may extend our knowledge about habitat preferences and distribution-abundance relationships, which will benefit the understanding of biodistributions and variations in community compositions in the soil. Similar studies in other places and scales apart from our local site will be need for further evaluation of phi coefficient. PMID:26930593
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Yoo, Kyung Hee
2007-06-01
This study was conducted to investigate the correlation among uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers. Self report questionnaires were used to measure the variables. Variables were uncertainty, mastery and appraisal of uncertainty. In data analysis, the SPSSWIN 12.0 program was utilized for descriptive statistics, Pearson's correlation coefficients, and regression analysis. Reliability of the instruments was cronbach's alpha=.84~.94. Mastery negatively correlated with uncertainty(r=-.444, p=.000) and danger appraisal of uncertainty(r=-.514, p=.000). In regression of danger appraisal of uncertainty, uncertainty and mastery were significant predictors explaining 39.9%. Mastery was a significant mediating factor between uncertainty and danger appraisal of uncertainty in hospitalized children's mothers. Therefore, nursing interventions which improve mastery must be developed for hospitalized children's mothers.
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Dong, Yingbo; Lin, Hai; He, Yinhai
2017-03-01
The physicochemical properties of the 24 modified clinoptilolite samples and their ammonia-nitrogen removal rates were measured to investigate the correlation between them. The modified clinoptilolites obtained by acid modification, alkali modification, salt modification, and thermal modification were used to adsorb ammonia-nitrogen. The surface area, average pore width, macropore volume, mecropore volume, micropore volume, cation exchange capacity (CEC), zeta potential, silicon-aluminum ratios, and ammonia-nitrogen removal rate of the 24 modified clinoptilolite samples were measured. Subsequently, the linear regression analysis method was used to research the correlation between the physicochemical property of the different modified clinoptilolite samples and the ammonia-nitrogen removal rate. Results showed that the CEC was the major physicochemical property affecting the ammonia-nitrogen removal performance. According to the impacts from strong to weak, the order was CEC > silicon-aluminum ratios > mesopore volume > micropore volume > surface area. On the contrary, the macropore volume, average pore width, and zeta potential had a negligible effect on the ammonia-nitrogen removal rate. The relational model of physicochemical property and ammonia-nitrogen removal rate of the modified clinoptilolite was established, which was ammonia-nitrogen removal rate = 1.415[CEC] + 173.533 [macropore volume] + 0.683 [surface area] + 4.789[Si/Al] - 201.248. The correlation coefficient of this model was 0.982, which passed the validation of regression equation and regression coefficients. The results of the significance test showed a good fit to the correlation model.
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The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress.
de Galarreta, Sergio Ruiz; Cazón, Aitor; Antón, Raúl; Finol, Ender A
2017-08-01
The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.
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[Health expenditures, income inequality, and the marginalization index in Mexico's health system].
Pinzón Florez, Carlos Eduardo; Reveiz, Ludovic; Idrovo, Alvaro J; Reyes Morales, Hortensia
2014-01-01
Evaluate the effect of the relationship among public health expenditures, income inequality, and the marginalization index on maternal and child mortality in Mexico, to determine the effect of these factors on health system performance from a technical efficiency perspective. An ecological study of 32 Mexican states. Correlations were estimated between maternal and infant mortality and public health expenditures in total per capita, federal per capita, and state per capita for the years 2000, 2005, and 2010 (Gini coefficient and marginalization index). Linear regressions were used to explore the association of these variables with health indicators in the state systems. Negative correlations were observed for the marginalization index and Gini coefficient with regard to life expectancy at birth (-0.62 and -0.28 respectively). Furthermore, there was a positive correlation of 0.59 between the marginalization index and infant mortality (P <0.05). Multiple linear regression models revealed a negative effect of the marginalization index and Gini coefficient on health out-comes. Federal funding had a positive effect on system performance in terms of health indicators. Health system reform in Mexico has had a positive impact on the country's health indicators; federal financial investment seems to be effective in this regard. Social determinants have an important effect on health system performance, and analysis using multisectoral and multidisciplinary approaches are needed in addressing them.
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Sung, Sheng-Feng; Hsieh, Cheng-Yang; Kao Yang, Yea-Huei; Lin, Huey-Juan; Chen, Chih-Hung; Chen, Yu-Wei; Hu, Ya-Han
2015-11-01
Case-mix adjustment is difficult for stroke outcome studies using administrative data. However, relevant prescription, laboratory, procedure, and service claims might be surrogates for stroke severity. This study proposes a method for developing a stroke severity index (SSI) by using administrative data. We identified 3,577 patients with acute ischemic stroke from a hospital-based registry and analyzed claims data with plenty of features. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). We used two data mining methods and conventional multiple linear regression (MLR) to develop prediction models, comparing the model performance according to the Pearson correlation coefficient between the SSI and the NIHSS. We validated these models in four independent cohorts by using hospital-based registry data linked to a nationwide administrative database. We identified seven predictive features and developed three models. The k-nearest neighbor model (correlation coefficient, 0.743; 95% confidence interval: 0.737, 0.749) performed slightly better than the MLR model (0.742; 0.736, 0.747), followed by the regression tree model (0.737; 0.731, 0.742). In the validation cohorts, the correlation coefficients were between 0.677 and 0.725 for all three models. The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data. Copyright © 2015 Elsevier Inc. All rights reserved.
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Kamali, M A; Ghorbani, S H; Sharbabak, M Moradi; Zamiri, M J
2007-08-01
1. Genetic parameters were estimated in a base population of a closed experimental strain of fowl. Data were obtained on 21 245 Iranian native hens (breeding centre for Fars province) subject to 8 successive generations of selection. This population had been selected for body weight at 12 weeks of age (BW12) and egg number during the first 12 weeks of the laying period (EN), mean egg weight (EW) at weeks 28, 30 and 32, and age at sexual maturity (ASM). 2. The method of multi-traits restricted maximum likelihood with an animal model was used to estimate genetic parameters. Resulting heritabilities for BW12, EN, EW and ASM were 0.68 +/- 0.02, 0.40 +/- 0.02, 0.64 +/- 0.02 and 0.49 +/- 0.02, respectively. 3. Genetic correlations between BW12 and EN, EW and ASM were 0.11 +/- 0.33, 0.54 +/- 0.21 and -0.12 +/- 0.03, respectively. Genetic correlations between EN and EW and ASM were -0.09 +/- 0.03 and -0.85 +/- 0.01, respectively, while between EW and ASM, it was 0.05 +/- 0.03. 4. The overall predicted genetic gains, after 7 generations of selection, estimated by the regression coefficients of the breeding value on generation number were equal to 22.7, 0.17, 0.04 and -1.38, for BW12, EN, EW and ASM, respectively. 5. A pedigree file of 21 245 female and male birds was used to calculate inbreeding coefficients and their influence on production and reproduction traits. Average inbreeding coefficients for all birds, inbred birds, female birds and male birds were 0.048, 0.673, 0.055 and 0.047%, respectively. Regression coefficients of BW12, ASM, EN and EW on inbreeding coefficient for all birds were equal to 0.51 +/- 0.001, 0.31 +/- 0.003, -0.51 +/- 0.003 and 0.03 +/- 0.001, respectively.
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NASA Astrophysics Data System (ADS)
Chen, Hua-cai; Chen, Xing-dan; Lu, Yong-jun; Cao, Zhi-qiang
2006-01-01
Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.
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Meili, Marc; Kutz, Alexander; Briel, Matthias; Christ-Crain, Mirjam; Bucher, Heiner C; Mueller, Beat; Schuetz, Philipp
2016-03-24
There is a lack of studies comparing the utility of C-reactive protein (CRP) with Procalcitonin (PCT) for the management of patients with acute respiratory tract infections (ARI) in primary care. Our aim was to study the correlation between these markers and to compare their predictive accuracy in regard to clinical outcome prediction. This is a secondary analysis using clinical and biomarker data of 458 primary care patients with pneumonic and non-pneumonic ARI. We used correlation statistics (spearman's rank test) and multivariable regression models to assess association of markers with adverse outcome, namely days with restricted activities and persistence of discomfort from infection at day 14. At baseline, CRP and PCT did not correlate well in the overall population (r(2) = 0.16) and particularly in the subgroup of patients with non-pneumonic ARI (r(2) = 0.08). Low correlation of biomarkers were also found when comparing cut-off ranges, day seven levels or changes from baseline to day seven. High baseline levels of CRP (>100 mg/dL, regression coefficient 1.6, 95 % CI 0.5 to 2.6, sociodemographic-adjusted model) as well as PCT (>0.5ug/L regression coefficient 2.0, 95 % CI 0.0 to 4.0, sociodemographic-adjusted model) were significantly associated with larger number of days with restricted activities. There were no associations of either biomarker with persistence of discomfort at day 14. CRP and PCT levels do not well correlate, but both have moderate prognostic accuracy in primary care patients with ARI to predict clinical outcomes. The low correlation between the two biomarkers calls for interventional research comparing these markers head to head in regard to their ability to guide antibiotic decisions. Current Controlled Trials, ISRCTN73182671.
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NASA Astrophysics Data System (ADS)
Cheymol, Anne; de Backer, Hugo; Josefsson, Weine; Stübi, René
2006-08-01
The Aerosol Optical Depths (AODs) retrieved from Brewer Ozone Spectrophotometer measurements with a method previously developed (Cheymol and De Backer, 2003) are now validated by comparisons between AODs from six Brewer spectrophotometers and two CSEM SPM2000 sunphotometers: two Brewer spectrophotometers 016 and 178 at Uccle in Belgium; one Brewer spectrophotometer 128 and one sunphotometer CSEM SPM2000 at Norrköping in Sweden; and three Brewer instruments 040, 072, 156 at Arosa and one CSEM SPM2000 sunphotometer at Davos in Switzerland. The comparison between AODs from Brewer spectrophotometer 128 at 320.1 nm and sunphotometer SPM2000 at 368 nm at Norrköping shows that the AODs obtained from the Brewer measurements with the Langley Plot Method (LPM) are very accurate if the neutral density filter spectral transmittances are well known: with the measured values of these filters, the correlation coefficient, the slope, and the intercept of the regression line are 0.98, 0.85 ± 0.004, and 0.02 ± 0.0014, respectively. The bias observed is mainly owing to the wavelength difference between the two instruments. The comparison between AODs from different Brewer spectrophotometers confirm that AODs will be in very good agreement if they are measured with several Brewer instruments at the same place: At Uccle, the correlation coefficient, slope, and intercept of the regression line are 0.98, 1.02 ± 0.003, and 0.06 ± 0.001, respectively; at Arosa, the comparisons between the AODs from three Brewer spectrophotometers 040, 072, and 156 give a correlation coefficient, a slope, and an intercept of the regression line above 0.94, 0.98 and below 0.04, respectively.
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1990-05-01
THUMBBR THUMB BREADTH NO. VARIABLE CONSTANT REGRESS. COEF. ST.ERROR ADJUQTED (.ERR OF ESTIMATE E4 58 HANDBRTH 7.623 0.183 ( 0.006) 1.124 .319 59 HANDCIRC...945, 956, 965 TRAGION TO TOP OF HEAD (TRAGT, 255) 39,51, 718, 851, 899, 945, 956, 965 Trapezius Post 23 Trochanter 23 TROCHArNTERION HEIGHT (TROQINT...THGHCLR, 105) 33, 40, 673, M08 881,927, 955, 964 Thigh Poia 23 THUMB BREADTH (THUMBBR, 106) 34, 49, 674,88 882,94955, 964 ThumlAip 23 THUMBTIP REACH
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Brink, Carsten, E-mail: carsten.brink@rsyd.dk; Laboratory of Radiation Physics, Odense University Hospital; Bernchou, Uffe
2014-07-15
Purpose: Large interindividual variations in volume regression of non-small cell lung cancer (NSCLC) are observable on standard cone beam computed tomography (CBCT) during fractionated radiation therapy. Here, a method for automated assessment of tumor volume regression is presented and its potential use in response adapted personalized radiation therapy is evaluated empirically. Methods and Materials: Automated deformable registration with calculation of the Jacobian determinant was applied to serial CBCT scans in a series of 99 patients with NSCLC. Tumor volume at the end of treatment was estimated on the basis of the first one third and two thirds of the scans.more » The concordance between estimated and actual relative volume at the end of radiation therapy was quantified by Pearson's correlation coefficient. On the basis of the estimated relative volume, the patients were stratified into 2 groups having volume regressions below or above the population median value. Kaplan-Meier plots of locoregional disease-free rate and overall survival in the 2 groups were used to evaluate the predictive value of tumor regression during treatment. Cox proportional hazards model was used to adjust for other clinical characteristics. Results: Automatic measurement of the tumor regression from standard CBCT images was feasible. Pearson's correlation coefficient between manual and automatic measurement was 0.86 in a sample of 9 patients. Most patients experienced tumor volume regression, and this could be quantified early into the treatment course. Interestingly, patients with pronounced volume regression had worse locoregional tumor control and overall survival. This was significant on patient with non-adenocarcinoma histology. Conclusions: Evaluation of routinely acquired CBCT images during radiation therapy provides biological information on the specific tumor. This could potentially form the basis for personalized response adaptive therapy.« less
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NASA Astrophysics Data System (ADS)
Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.
2018-03-01
Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.
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NASA Technical Reports Server (NTRS)
Clark, P. E.; Andre, C. G.; Adler, I.; Weidner, J.; Podwysocki, M.
1976-01-01
The positive correlation between Al/Si X-ray fluorescence intensity ratios determined during the Apollo 15 lunar mission and a broad-spectrum visible albedo of the moon is quantitatively established. Linear regression analysis performed on 246 1 degree geographic cells of X-ray fluorescence intensity and visible albedo data points produced a statistically significant correlation coefficient of .78. Three distinct distributions of data were identified as (1) within one standard deviation of the regression line, (2) greater than one standard deviation below the line, and (3) greater than one standard deviation above the line. The latter two distributions of data were found to occupy distinct geographic areas in the Palus Somni region.
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Srivastava, Nishi; Srivastava, Amit; Srivastava, Sharad; Rawat, Ajay Kumar Singh; Khan, Abdul Rahman
2016-03-01
A rapid, sensitive, selective and robust quantitative densitometric high-performance thin-layer chromatographic method was developed and validated for separation and quantification of syringic acid (SYA) and kaempferol (KML) in the hydrolyzed extracts of Bergenia ciliata and Bergenia stracheyi. The separation was performed on silica gel 60F254 high-performance thin-layer chromatography plates using toluene : ethyl acetate : formic acid (5 : 4: 1, v/v/v) as the mobile phase. The quantification of SYA and KML was carried out using a densitometric reflection/absorption mode at 290 nm. A dense spot of SYA and KML appeared on the developed plate at a retention factor value of 0.61 ± 0.02 and 0.70 ± 0.01. A precise and accurate quantification was performed using linear regression analysis by plotting the peak area vs concentration 100-600 ng/band (correlation coefficient: r = 0.997, regression coefficient: R(2) = 0.996) for SYA and 100-600 ng/band (correlation coefficient: r = 0.995, regression coefficient: R(2) = 0.991) for KML. The developed method was validated in terms of accuracy, recovery and inter- and intraday study as per International Conference on Harmonisation guidelines. The limit of detection and limit of quantification of SYA and KML were determined, respectively, as 91.63, 142.26 and 277.67, 431.09 ng. The statistical data analysis showed that the method is reproducible and selective for the estimation of SYA and KML in extracts of B. ciliata and B. stracheyi. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Simple, simultaneous gravimetric determination of calcite and dolomite in calcareous soils
USDA-ARS?s Scientific Manuscript database
Literature pertaining to determination of calcite and dolomite is not modern and describes slow methods that require expensive specialized apparatus. The objective of this paper was to describe a new method that requires no specialized equipment. Linear regressions and correlation coefficients for...
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Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping
2015-01-01
Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China.
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Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping
2015-01-01
Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China. PMID:26340555
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To Identify the Important Soil Properties Affecting Dinoseb Adsorption with Statistical Analysis
Guan, Yiqing; Wei, Jianhui; Zhang, Danrong; Zu, Mingjuan; Zhang, Liru
2013-01-01
Investigating the influences of soil characteristic factors on dinoseb adsorption parameter with different statistical methods would be valuable to explicitly figure out the extent of these influences. The correlation coefficients and the direct, indirect effects of soil characteristic factors on dinoseb adsorption parameter were analyzed through bivariate correlation analysis, and path analysis. With stepwise regression analysis the factors which had little influence on the adsorption parameter were excluded. Results indicate that pH and CEC had moderate relationship and lower direct effect on dinoseb adsorption parameter due to the multicollinearity with other soil factors, and organic carbon and clay contents were found to be the most significant soil factors which affect the dinoseb adsorption process. A regression is thereby set up to explore the relationship between the dinoseb adsorption parameter and the two soil factors: the soil organic carbon and clay contents. A 92% of the variation of dinoseb sorption coefficient could be attributed to the variation of the soil organic carbon and clay contents. PMID:23737715
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[Culture and quality of life assessment in Chinese populations].
Xia, Ping; Li, Ning-Xiu; Liu, Chao-Jie; Lü, Yu-Bo; Zhang, Qiang; Ou, Ai-Hua
2010-07-01
To investigate the impact of cultural factors on quality of life (QOL) and to identify appropriate ways of dividing sub-populations for population norm-based quality of life assessment. The WHOQOL-BREF was used as a QOL instrument. Another questionnaire was developed to assess cultural values. A cross-sectional survey was undertaken in 1090 Guangzhou residents, which included 635 respondents from communities and 455 patients who visited outpatient departments of hospitals. Cronbach's a coefficients and item-domain correlation coefficients were calculated to test the reliability and validity of the WHOQOL-BREF, respectively. Student t test, ANOVA and stepwise multiple linear regression analysis were performed to identify the variables that might have an impact on the QOL. Two regression models with and without including cultural variables were constructed, and the extent of impact exerted by the cultural factors was assessed through a comparison of the change of adjusted R square values. A total of 1052 (96%) valid questionnaire were returned. The Cronbach's alpha coefficients of the WHOQOL-BREF ranged from 0.67 to 0.78. Age, education, occupation and family income were correlated with all of the domains of the WHOQOL-BREF. Chronic condition was correlated with physical, psychological, and social relationship domains of the WHOQOL-BREF. Gender was correlated with physical and psychological domains of the WHOQOL-BREF. The multiple regression analysis showed that social and demographic factors contributed to 6.3%, 13.6%, 10.4% and 8.7% of the predicted variances for the physical, psychological, social relationship, and environment domains, respectively. Social support, horizontal collectivism, vertical individualism, escape acceptance, fear of death, health value, supernatural belief had a significant impact on QOL. However, social support was the only one factor that had an impact on all of the four QOL domains. It is necessary to divide sub-cultural populations for population norm-based QOL assessment. Further research is needed to develop a practical approach to the sub-cultural population division.
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The observation-based relationships between PM2.5 and AOD over China
NASA Astrophysics Data System (ADS)
Xin, Jinyuan; Gong, Chongshui; Liu, Zirui; Cong, Zhiyuan; Gao, Wenkang; Song, Tao; Pan, Yuepeng; Sun, Yang; Ji, Dongsheng; Wang, Lili; Tang, Guiqian; Wang, Yuesi
2016-09-01
This is the first investigation of the generalized linear regressions of PM2.5 and aerosol optical depth (AOD) with the Campaign on atmospheric Aerosol Research-China network over the large high-concentration aerosol region during the period from 2012 to 2013. The map of the PM2.5 and AOD levels showed large spatial differences in the aerosol concentrations and aerosol optical properties over China. The ranges of the annual mean PM2.5 and AOD were 10-117 µg/m3 and 0.12-1.11 from the clean regions to seriously polluted regions, from the almost "arctic" and the Tibetan Plateau to tropical environments. There were significant spatial agreements and correlations between the PM2.5 and AOD. However, the linear regression functions (PM2.5 = A*AOD + B) exhibited large differences in different regions and seasons. The slopes (A) were from 13 to 90, the intercepts (B) were from 0.8 to 33.3, and the correlation coefficients (R2) ranged from 0.06 to 0.75. The slopes (A) were much higher in the north (41-99) than in the south (13-64) because the extinction efficiency of hygroscopic aerosol was rapidly increasing with the increasing humidity from the dry north to the humid south. Meanwhile, the intercepts (B) were generally lower, and the correlation coefficients (R2) were much higher in the dry north than in the humid south. There was high consistency of AOD versus PM2.5 for all sites in three ranges of the atmospheric column precipitable water vapor (PWV). The segmented linear regression functions were y = 84.66x + 9.85 (PWV < 1.0), y = 69.47x + 11.87 (1.0 < PWV < 2.5), and y = 52.37x + 8.59 (PWV > 2.5). The correlation coefficients (R2) were high from 0.64 to 0.70 across China.
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Retrieval Algorithm for Broadband Albedo at the Top of the Atmosphere
NASA Astrophysics Data System (ADS)
Lee, Sang-Ho; Lee, Kyu-Tae; Kim, Bu-Yo; Zo, ll-Sung; Jung, Hyun-Seok; Rim, Se-Hun
2018-05-01
The objective of this study is to develop an algorithm that retrieves the broadband albedo at the top of the atmosphere (TOA albedo) for radiation budget and climate analysis of Earth's atmosphere using Geostationary Korea Multi-Purse Satellite/Advanced Meteorological Imager (GK-2A/AMI) data. Because the GK-2A satellite will launch in 2018, we used data from the Japanese weather satellite Himawari-8 and onboard sensor Advanced Himawari Imager (AHI), which has similar sensor properties and observation area to those of GK-2A. TOA albedo was retrieved based on reflectance and regression coefficients of shortwave channels 1 to 6 of AHI. The regression coefficient was calculated using the results of the radiative transfer model (SBDART) and ridge regression. The SBDART used simulations of the correlation between TOA albedo and reflectance of each channel according to each atmospheric conditions (solar zenith angle, viewing zenith angle, relative azimuth angle, surface type, and absence/presence of clouds). The TOA albedo from Himawari-8/AHI were compared to that from the National Aeronautics and Space Administration (NASA) satellite Terra with onboard sensor Clouds and the Earth's Radiant Energy System (CERES). The correlation coefficients between the two datasets from the week containing the first day of every month between 1st August 2015 and 1st July 2016 were high, ranging between 0.934 and 0.955, with the root mean square error in the 0.053-0.068 range.
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Haiduc, Adrian Marius; van Duynhoven, John
2005-02-01
The porous properties of food materials are known to determine important macroscopic parameters such as water-holding capacity and texture. In conventional approaches, understanding is built from a long process of establishing macrostructure-property relations in a rational manner. Only recently, multivariate approaches were introduced for the same purpose. The model systems used here are oil-in-water emulsions, stabilised by protein, and form complex structures, consisting of fat droplets dispersed in a porous protein phase. NMR time-domain decay curves were recorded for emulsions with varied levels of fat, protein and water. Hardness, dry matter content and water drainage were determined by classical means and analysed for correlation with the NMR data with multivariate techniques. Partial least squares can calibrate and predict these properties directly from the continuous NMR exponential decays and yields regression coefficients higher than 82%. However, the calibration coefficients themselves belong to the continuous exponential domain and do little to explain the connection between NMR data and emulsion properties. Transformation of the NMR decays into a discreet domain with non-negative least squares permits the use of multilinear regression (MLR) on the resulting amplitudes as predictors and hardness or water drainage as responses. The MLR coefficients show that hardness is highly correlated with the components that have T2 distributions of about 20 and 200 ms whereas water drainage is correlated with components that have T2 distributions around 400 and 1800 ms. These T2 distributions very likely correlate with water populations present in pores with different sizes and/or wall mobility. The results for the emulsions studied demonstrate that NMR time-domain decays can be employed to predict properties and to provide insight in the underlying microstructural features.
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Chen, Gang; Wu, Yulian; Wang, Tao; Liang, Jixing; Lin, Wei; Li, Liantao; Wen, Junping; Lin, Lixiang; Huang, Huibin
2012-10-01
The role of the endogenous secretory receptor for advanced glycation end products (esRAGE) in depression of diabetes patients and its clinical significance are unclear. This study investigated the role of serum esRAGE in patients with type 2 diabetes mellitus with depression in the Chinese population. One hundred nineteen hospitalized patients with type 2 diabetes were recruited at Fujian Provincial Hospital (Fuzhou, China) from February 2010 to January 2011. All selected subjects were assessed with the Hamilton Rating Scale for Depression (HAMD). Among them, 71 patients with both type 2 diabetes and depression were included. All selected subjects were examined for the following: esRAGE concentration, glycosylated hemoglobin (HbA1c), blood lipids, C-reactive protein, trace of albumin in urine, and carotid artery intima-media thickness (IMT). Association between serum esRAGE levels and risk of type 2 diabetes mellitus with depression was also analyzed. There were statistically significant differences in gender, age, body mass index, waist circumference, and treatment methods between the group with depression and the group without depression (P<0.05). Multiple linear regression analysis showed that HAMD scores were negatively correlated with esRAGE levels (standard regression coefficient -0.270, P<0.01). HAMD-17 scores were positively correlated with IMT (standard regression coefficient 0.183, P<0.05) and with HbA1c (standard regression coefficient 0.314, P<0.01). Female gender, younger age, obesity, poor glycemic control, complications, and insulin therapy are all risk factors of type 2 diabetes mellitus with combined depression in the Chinese population. Inflammation and atherosclerosis play an important role in the pathogenesis of depression. esRAGE is a protective factor of depression among patients who have type 2 diabetes.
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Zhou, Yunyi; Tao, Chenyang; Lu, Wenlian; Feng, Jianfeng
2018-04-20
Functional connectivity is among the most important tools to study brain. The correlation coefficient, between time series of different brain areas, is the most popular method to quantify functional connectivity. Correlation coefficient in practical use assumes the data to be temporally independent. However, the time series data of brain can manifest significant temporal auto-correlation. A widely applicable method is proposed for correcting temporal auto-correlation. We considered two types of time series models: (1) auto-regressive-moving-average model, (2) nonlinear dynamical system model with noisy fluctuations, and derived their respective asymptotic distributions of correlation coefficient. These two types of models are most commonly used in neuroscience studies. We show the respective asymptotic distributions share a unified expression. We have verified the validity of our method, and shown our method exhibited sufficient statistical power for detecting true correlation on numerical experiments. Employing our method on real dataset yields more robust functional network and higher classification accuracy than conventional methods. Our method robustly controls the type I error while maintaining sufficient statistical power for detecting true correlation in numerical experiments, where existing methods measuring association (linear and nonlinear) fail. In this work, we proposed a widely applicable approach for correcting the effect of temporal auto-correlation on functional connectivity. Empirical results favor the use of our method in functional network analysis. Copyright © 2018. Published by Elsevier B.V.
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Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D
2014-09-01
The Neck Disability Index (NDI) and numeric rating scales (0 to 10) for neck pain and arm pain are widely used cervical spine disease-specific measures. Recent studies have shown that there is a strong relationship between the SF-6D and the NDI such that using a simple linear regression allows for the estimation of an SF-6D value from the NDI alone. Due to ease of administration and scoring, the EQ-5D is increasingly being used as a measure of utility in the clinical setting. The purpose of this study is to determine if the EQ-5D values can be estimated from commonly available cervical spine disease-specific health-related quality of life measures, much like the SF-6D. The EQ-5D, NDI, neck pain score, and arm pain score were prospectively collected in 3732 patients who presented to the authors' clinic with degenerative cervical spine disorders. Correlation coefficients for paired observations from multiple time points between the NDI, neck pain and arm pain scores, and EQ-5D were determined. Regression models were built to estimate the EQ-5D values from the NDI, neck pain, and arm pain scores. The mean age of the 3732 patients was 53.3 ± 12.2 years, and 43% were male. Correlations between the EQ-5D and the NDI, neck pain score, and arm pain score were statistically significant (p < 0.0001), with correlation coefficients of -0.77, -0.62, and -0.50, respectively. The regression equation 0.98947 + (-0.00705 × NDI) + (-0.00875 × arm pain score) + (-0.00877 × neck pain score) to predict EQ-5D had an R-square of 0.62 and a root mean square error (RMSE) of 0.146. The model using NDI alone had an R-square of 0.59 and a RMSE of 0.150. The model using the individual NDI items had an R-square of 0.46 and an RMSE of 0.172. The correlation coefficient between the observed and estimated EQ-5D scores was 0.79. There was no statistically significant difference between the actual EQ-5D score (0.603 ± 0.235) and the estimated EQ-5D score (0.603 ± 0.185) using the NDI, neck pain score, and arm pain score regression model. However, rounding off the coefficients to fewer than 5 decimal places produced less accurate results. The regression model estimating the EQ-5D from the NDI, neck pain score, and arm pain score accounted for 60% of the variability of the EQ-5D with a relatively large RMSE. This regression model may not be sufficient to accurately or reliably estimate actual EQ-5D values.
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Lowery, Michael G; Calfin, Brenda; Yeh, Shu-Jen; Doan, Tao; Shain, Eric; Hanna, Charles; Hohs, Ronald; Kantor, Stan; Lindberg, John; Khalil, Omar S
2006-01-01
We used the effect of temperature on the localized reflectance of human skin to assess the role of noise sources on the correlation between temperature-induced fractional change in optical density of human skin (DeltaOD(T)) and blood glucose concentration [BG]. Two temperature-controlled optical probes at 30 degrees C contacted the skin, one was then cooled by -10 degrees C; the other was heated by +10 degrees C. DeltaOD(T) upon cooling or heating was correlated with capillary [BG] of diabetic volunteers over a period of three days. Calibration models in the first two days were used to predict [BG] in the third day. We examined the conditions where the correlation coefficient (R2) for predicting [BG] in a third day ranked higher than R2 values resulting from fitting permutations of randomized [BG] to the same DeltaOD(T) values. It was possible to establish a four-term linear regression correlation between DeltaOD(T) upon cooling and [BG] with a correlation coefficient higher than that of an established noise threshold in diabetic patients that were mostly females with less than 20 years of diabetes duration. The ability to predict [BG] values with a correlation coefficient above biological and body-interface noise varied between the cases of cooling and heating.
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Lean body mass is better than body surface area in correcting GFR.
Si, Hongwei; Lei, Zhili; Li, Sijin; Liu, Jianzhong; Geng, Jianhua; Chen, Shengzu
2013-05-01
The Gates-based renography (gGFR) and plasma-based clearance rate (pGFR) are conventionally corrected with body surface area (BSA). Limited studies indicated that the lean body mass (LBM) might be better than BSA in correcting pGFR. Therefore, we suggest that LBM is also better in correcting gGFR and improve the correlation coefficient between gGFR and pGFR. During June 2009 and December 2010, the gGFR and pGFR of 63 patients with hydronephrosis were measured and corrected with BSA and LBM, respectively. The correlation and regression analyses were conducted to illustrate the power of BSA and LBM correction. In a paired t test, there was no significant difference between pGFR and gGFR in patients with stages 1 and 2, although this was significant in stages 3 and 4. However, neither LBM nor BSA correction could eliminate the difference. In all patients (N = 63), the correlation coefficient (r) between pGFR and gGFR was 0.794 (P <; 0.001). After the BSA and LBM correction, r was improved to 0.809 and 0.828, respectively. In all patients, the regression line of pGFR(LBM) - gGFR(LBM) was nearer to the original point and its slope closer to 1 than pGFR(BSA) - gGFR(BSA) and pGFR - gGFR. Similar results were found in the analysis of most stages and subgroups. The commonsense of BSA correction should be seriously reevaluated. Lean body mass can better improve the correlation coefficient between paired GFRs than BSA can and it can be suitable in the correction.
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A non-linear regression method for CT brain perfusion analysis
NASA Astrophysics Data System (ADS)
Bennink, E.; Oosterbroek, J.; Viergever, M. A.; Velthuis, B. K.; de Jong, H. W. A. M.
2015-03-01
CT perfusion (CTP) imaging allows for rapid diagnosis of ischemic stroke. Generation of perfusion maps from CTP data usually involves deconvolution algorithms providing estimates for the impulse response function in the tissue. We propose the use of a fast non-linear regression (NLR) method that we postulate has similar performance to the current academic state-of-art method (bSVD), but that has some important advantages, including the estimation of vascular permeability, improved robustness to tracer-delay, and very few tuning parameters, that are all important in stroke assessment. The aim of this study is to evaluate the fast NLR method against bSVD and a commercial clinical state-of-art method. The three methods were tested against a published digital perfusion phantom earlier used to illustrate the superiority of bSVD. In addition, the NLR and clinical methods were also tested against bSVD on 20 clinical scans. Pearson correlation coefficients were calculated for each of the tested methods. All three methods showed high correlation coefficients (>0.9) with the ground truth in the phantom. With respect to the clinical scans, the NLR perfusion maps showed higher correlation with bSVD than the perfusion maps from the clinical method. Furthermore, the perfusion maps showed that the fast NLR estimates are robust to tracer-delay. In conclusion, the proposed fast NLR method provides a simple and flexible way of estimating perfusion parameters from CT perfusion scans, with high correlation coefficients. This suggests that it could be a better alternative to the current clinical and academic state-of-art methods.
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Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi
2015-01-01
Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.
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Hama, Yohei; Kanazawa, Manabu; Minakuchi, Shunsuke; Uchida, Tatsuro; Sasaki, Yoshiyuki
2014-03-19
In the present study, we developed a novel color scale for visual assessment, conforming to theoretical color changes of a gum, to evaluate masticatoryperformance; moreover, we investigated the reliability and validity of this evaluation method using the color scale. Ten participants (aged 26.30 years) with natural dentition chewed the gum at several chewing strokes. Changes in color were measured using a colorimeter, and then, linearregression expressions that represented changes in gum color were derived. The color scale was developed using these regression expressions. Thirty-two chewed gums were evaluated using colorimeter and were assessed three times using the color scale by six dentists aged 25.27 (mean, 25.8) years, six preclinical dental students aged 21.23 (mean, 22.2) years, and six elderly individuals aged 68.84 (mean, 74.0) years. The intrarater and interrater reliability of evaluations was assessed using intraclass correlation coefficients. Validity of the method compared with a colorimeter was assessed using Spearman's rank correlation coefficient. All intraclass correlation coefficients were > 0.90, and Spearman's rank-correlation coefficients were > 0.95 in all groups. These results indicated that the evaluation method of the color-changeable chewing gum using the newly developed color scale is reliable and valid.
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NASA Astrophysics Data System (ADS)
Deshmukh, A. A.; Kuthe, S. A.; Palikundwar, U. A.
2018-05-01
In the present paper, the consequences of variation in compositions on the electronegativity (ΔX), atomic radius difference (δ) and the thermal stability (ΔTx) of Mg-Ni-Y bulk metallic glasses (BMGs) are evaluated. In order to understand the effect of variation in compositions on ΔX, δ and ΔTx, regression analysis is performed on the experimentally available data. A linear correlation between both δ and ΔX with regression coefficient 0.93 is observed. Further, compositional variation is performed with δ and then it is correlated to the ΔTx by deriving subsequent equations. It is observed that concentration of Mg, Ni and Y are directly proportional to the δ with regression coefficients 0.93, 0.93 and 0.50 respectively. The positive slope of Ni and Y stated that ΔTx will increase if it has more contribution from both Ni and Y. On the other hand negative slope stated that composition of Mg should be selected in such a way that it will have more stability with Ni and Y. The results obtained from mathematical calculations are also tested by regression analysis of ΔTx with the compositions of individual elements in the alloy. These results conclude that there is a strong dependence of ΔTx of the alloy on the compositions of the constituting elements in the alloy.
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Lim, Kok Bin; Ho, Henry; Foo, Keong Tatt; Wong, Michael Yuet Chen; Fook-Chong, Stephanie
2006-12-01
The aims of this study were to define the relationship between intravesical prostatic protrusion (IPP), prostate-specific antigen (PSA) and prostate volume (PV) and to determine which one of them is the best predictor of bladder outlet obstruction (BOO) due to benign prostatic enlargement. A prospective study of 114 male patients older than 50 years examined between November 2001 and 2002 was performed. They were evaluated with digital rectal examination, International Prostate Symptoms Score, PSA, uroflowmetry, postvoid residual urine measurement, IPP and PV using transabdominal ultrasound scan. Statistical analysis included scatter plot with Spearman's correlation coefficients and nominal logistic regression Prostate volume, IPP and PSA showed parallel correlation. Although all three indices had good correlation with BOO index, IPP was the best. The Spearman rho correlation coefficients were 0.314, 0.408 and 0.507 for PV, PSA and IPP, respectively. Using receiver-operator characteristic curves, the areas under the curve for PV, PSA and IPP were 0.637, 0.703 and 0.772, respectively. The positive predictive values of PV, PSA and IPP were 65%, 68% and 72%, respectively. Using a nominal regression model, IPP remained the most significant independent index to determine BOO. All three non-invasive indices correlate with one another. The study showed that IPP is a better predictor for BOO than PSA or PV.
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Roth, Philip L; Le, Huy; Oh, In-Sue; Van Iddekinge, Chad H; Bobko, Philip
2018-06-01
Meta-analysis has become a well-accepted method for synthesizing empirical research about a given phenomenon. Many meta-analyses focus on synthesizing correlations across primary studies, but some primary studies do not report correlations. Peterson and Brown (2005) suggested that researchers could use standardized regression weights (i.e., beta coefficients) to impute missing correlations. Indeed, their beta estimation procedures (BEPs) have been used in meta-analyses in a wide variety of fields. In this study, the authors evaluated the accuracy of BEPs in meta-analysis. We first examined how use of BEPs might affect results from a published meta-analysis. We then developed a series of Monte Carlo simulations that systematically compared the use of existing correlations (that were not missing) to data sets that incorporated BEPs (that impute missing correlations from corresponding beta coefficients). These simulations estimated ρ̄ (mean population correlation) and SDρ (true standard deviation) across a variety of meta-analytic conditions. Results from both the existing meta-analysis and the Monte Carlo simulations revealed that BEPs were associated with potentially large biases when estimating ρ̄ and even larger biases when estimating SDρ. Using only existing correlations often substantially outperformed use of BEPs and virtually never performed worse than BEPs. Overall, the authors urge a return to the standard practice of using only existing correlations in meta-analysis. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Ghoreishi, Mohammad; Abdi-Shahshahani, Mehdi; Peyman, Alireza; Pourazizi, Mohsen
2018-02-21
The aim of this study was to determine the correlation between ocular biometric parameters and sulcus-to-sulcus (STS) diameter. This was a cross-sectional study of preoperative ocular biometry data of patients who were candidates for phakic intraocular lens (IOL) surgery. Subjects underwent ocular biometry analysis, including refraction error evaluation using an autorefractor and Orbscan topography for white-to-white (WTW) corneal diameter and measurement. Pentacam was used to perform WTW corneal diameter and measurements of minimum and maximum keratometry (K). Measurements of STS and angle-to-angle (ATA) were obtained using a 50-MHz B-mode ultrasound device. Anterior optical coherence tomography was performed for anterior chamber depth measurement. Pearson's correlation test and stepwise linear regression analysis were used to find a model to predict STS. Fifty-eight eyes of 58 patients were enrolled. Mean age ± standard deviation of sample was 28.95 ± 6.04 years. The Pearson's correlation coefficient between STS with WTW, ATA, mean K was 0.383, 0.492, and - 0.353, respectively, which was statistically significant (all P < 0.001). Using stepwise linear regression analysis, there is a statistically significant association between STS with WTW (P = 0.011) and mean K (P = 0.025). The standardized coefficient was 0.323 and - 0.284 for WTW and mean K, respectively. The stepwise linear regression analysis equation was: (STS = 9.549 + 0.518 WTW - 0.083 mean K). Based on our result, given the correlation of STS with WTW and mean K and potential of direct and essay measurement of WTW and mean K, it seems that current IOL sizing protocols could be estimating with WTW and mean K.
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Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression
Chen, Yanguang
2016-01-01
In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271
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Pasha, Sharif M; Klok, Frederikus A; van der Bijl, Noortje; de Roos, Albert; Kroft, Lucia J M; Huisman, Menno V
2012-08-01
N-terminal pro-Brain Natriuretic Peptide (NT-pro-BNP) is primarily secreted by left ventricular (LV) stretch and wall tension. Notably, NT-pro-BNP is a prognostic marker in acute pulmonary embolism (PE), which primarily stresses the right ventricle (RV). We sought to evaluate the relative contribution of the RV to NT-pro-BNP levels during PE. A post-hoc analysis of an observational prospective outcome study in 113 consecutive patients with computed tomography (CT)-proven PE and 226 patients in whom PE was clinically suspected but ruled out by CT. In all patients RV and LV function was established by assessing ECG-triggered-CT measured ventricular end-diastolic-volumes and ejection fraction (EF). NT-pro-BNP was assessed in all patients. The correlation between RV and LV end-diastolic-volumes and systolic function was evaluated by multiple linear regression corrected for known confounders. In the PE cohort increased RVEF (β-coefficient (95% confidence interval [CI]) -0.044 (± -0.011); p<0.001) and higher RV end-diastolic-volume (β-coefficient 0.005 (± 0.001); p<0.001) were significantly correlated to NT-pro-BNP, while no correlation was found with LVEF (β-coefficient 0.005 (± 0.010); p=0.587) and LV end-diastolic-volume (β-coefficient -0.003 (± 0.002); p=0.074). In control patients without PE we found a strong correlation between NT-pro-BNP levels and LVEF (β-coefficient -0.027 (± -0.006); p<0.001) although not LV end-diastolic-volume (β-coefficient 0.001 (± 0.001); p=0.418). RVEF (β-coefficient -0.002 (± -0.006); p=0.802) and RV end-diastolic-volume (β-coefficient <0.001 (± 0.001); p=0.730) were not correlated in patients without PE. In PE patients, lower RVEF and higher RV end-diastolic-volume were significantly correlated to NT-pro-BNP levels as compared to control patients without PE. These observations provide pathophysiological ground for the well-known prognostic value of NT-pro-BNP in acute PE.
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Evaluation of estimation methods for organic carbon normalized sorption coefficients
Baker, James R.; Mihelcic, James R.; Luehrs, Dean C.; Hickey, James P.
1997-01-01
A critically evaluated set of 94 soil water partition coefficients normalized to soil organic carbon content (Koc) is presented for 11 classes of organic chemicals. This data set is used to develop and evaluate Koc estimation methods using three different descriptors. The three types of descriptors used in predicting Koc were octanol/water partition coefficient (Kow), molecular connectivity (mXt) and linear solvation energy relationships (LSERs). The best results were obtained estimating Koc from Kow, though a slight improvement in the correlation coefficient was obtained by using a two-parameter regression with Kow and the third order difference term from mXt. Molecular connectivity correlations seemed to be best suited for use with specific chemical classes. The LSER provided a better fit than mXt but not as good as the correlation with Koc. The correlation to predict Koc from Kow was developed for 72 chemicals; log Koc = 0.903* log Kow + 0.094. This correlation accounts for 91% of the variability in the data for chemicals with log Kow ranging from 1.7 to 7.0. The expression to determine the 95% confidence interval on the estimated Koc is provided along with an example for two chemicals of different hydrophobicity showing the confidence interval of the retardation factor determined from the estimated Koc. The data showed that Koc is not likely to be applicable for chemicals with log Kow < 1.7. Finally, the Koc correlation developed using Kow as a descriptor was compared with three nonclass-specific correlations and two 'commonly used' class-specific correlations to determine which method(s) are most suitable.
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Correlation of Vitamin D status and orthodontic-induced external apical root resorption.
Tehranchi, Azita; Sadighnia, Azin; Younessian, Farnaz; Abdi, Amir H; Shirvani, Armin
2017-01-01
Adequate Vitamin D is essential for dental and skeletal health in children and adult. The purpose of this study was to assess the correlation of serum Vitamin D level with external-induced apical root resorption (EARR) following fixed orthodontic treatment. In this cross-sectional study, the prevalence of Vitamin D deficiency (defined by25-hydroxyvitamin-D) was determined in 34 patients (23.5% male; age range 12-23 years; mean age 16.63 ± 2.84) treated with fixed orthodontic treatment. Root resorption of four maxillary incisors was measured using before and after periapical radiographs (136 measured teeth) by means of a design-to-purpose software to optimize data collection. Teeth with a maximum percentage of root resorption (%EARR) were indicated as representative root resorption for each patient. A multiple linear regression model and Pearson correlation coefficient were used to assess the association of Vitamin D status and observed EARR. P < 0.05 was considered statistically significant. The Pearson coefficient between these two variables was determined about 0.15 ( P = 0.38). Regression analysis revealed that Vitamin D status of the patients demonstrated no significant statistical correlation with EARR, after adjustment of confounding variables using linear regression model ( P > 0.05). This study suggests that Vitamin D level is not among the clinical variables that are potential contributors for EARR. The prevalence of Vitamin D deficiency does not differ in patients with higher EARR. These data suggest the possibility that Vitamin D insufficiency may not contribute to the development of more apical root resorption although this remains to be confirmed by further longitudinal cohort studies.
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Standards for Standardized Logistic Regression Coefficients
ERIC Educational Resources Information Center
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
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The Impact of Prior Programming Knowledge on Lecture Attendance and Final Exam
ERIC Educational Resources Information Center
Veerasamy, Ashok Kumar; D'Souza, Daryl; Lindén, Rolf; Laakso, Mikko-Jussi
2018-01-01
In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman's rank correlation coefficient, multiple regression, Kruskal-Wallis, and Bonferroni correction…
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Predicting Student Engagement in Online High Schools
ERIC Educational Resources Information Center
Vieira, Christopher James
2013-01-01
The purpose of this study was to analyze student engagement in online high schools based on demographic information of high school students using a mixed methods research design. Key findings through a multiple regression analysis and Pearson correlation coefficient suggest that although the majority of participants in the study are highly engaged…
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Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages.
Choi, Youn-Kyung; Kim, Jinmi; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Kim, Yong-Il
2016-01-01
This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5-18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P < 0.05). The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.
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Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages
Choi, Youn-Kyung; Kim, Jinmi; Maki, Koutaro; Ko, Ching-Chang
2016-01-01
This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P < 0.05). The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level. PMID:27340668
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Remote sensing of PM2.5 from ground-based optical measurements
NASA Astrophysics Data System (ADS)
Li, S.; Joseph, E.; Min, Q.
2014-12-01
Remote sensing of particulate matter concentration with aerodynamic diameter smaller than 2.5 um(PM2.5) by using ground-based optical measurements of aerosols is investigated based on 6 years of hourly average measurements of aerosol optical properties, PM2.5, ceilometer backscatter coefficients and meteorological factors from Howard University Beltsville Campus facility (HUBC). The accuracy of quantitative retrieval of PM2.5 using aerosol optical depth (AOD) is limited due to changes in aerosol size distribution and vertical distribution. In this study, ceilometer backscatter coefficients are used to provide vertical information of aerosol. It is found that the PM2.5-AOD ratio can vary largely for different aerosol vertical distributions. The ratio is also sensitive to mode parameters of bimodal lognormal aerosol size distribution when the geometric mean radius for the fine mode is small. Using two Angstrom exponents calculated at three wavelengths of 415, 500, 860nm are found better representing aerosol size distributions than only using one Angstrom exponent. A regression model is proposed to assess the impacts of different factors on the retrieval of PM2.5. Compared to a simple linear regression model, the new model combining AOD and ceilometer backscatter can prominently improve the fitting of PM2.5. The contribution of further introducing Angstrom coefficients is apparent. Using combined measurements of AOD, ceilometer backscatter, Angstrom coefficients and meteorological parameters in the regression model can get a correlation coefficient of 0.79 between fitted and expected PM2.5.
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Comparison of Nimbus-7 SMMR and GOES-1 VISSR Atmospheric Liquid Water Content.
NASA Astrophysics Data System (ADS)
Lojou, Jean-Yves; Frouin, Robert; Bernard, René
1991-02-01
Vertically integrated atmospheric liquid water content derived from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperatures and from GOES-1 Visible and Infrared Spin-Scan Radiometer (VISSR) radiances in the visible are compared over the Indian Ocean during MONEX (monsoon experiment). In the retrieval procedure, Wilheit and Chang' algorithm and Stephens' parameterization schemes are applied to the SMMR and VISSR data, respectively. The results indicate that in the 0-100 mg cm2 range of liquid water content considered, the correlation coefficient between the two types of estimates is 0.83 (0.81- 0.85 at the 99 percent confidence level). The Wilheit and Chang algorithm, however, yields values lower than those obtained with Stephens's schemes by 24.5 mg cm2 on the average, and occasionally the SMMR-based values are negative. Alternative algorithms are proposed for use with SMMR data, which eliminate the bias, augment the correlation coefficient, and reduce the rms difference. These algorithms include using the Witheit and Chang formula with modified coefficients (multilinear regression), the Wilheit and Chang formula with the same coefficients but different equivalent atmospheric temperatures for each channel (temperature bias adjustment), and a second-order polynomial in brightness temperatures at 18, 21, and 37 GHz (polynomial development). When applied to a dataset excluded from the regressionn dataset, the multilinear regression algorithm provides the best results, namely a 0.91 correlation coefficient, a 5.2 mg cm2 (residual) difference, and a 2.9 mg cm2 bias. Simply shifting the liquid water content predicted by the Wilheit and Chang algorithm does not yield as good comparison statistics, indicating that the occasional negative values are not due only to a bias. The more accurate SMMR-derived liquid water content allows one to better evaluate cloud transmittance in the solar spectrum, at least in the area and during the period analyzed. Combining this cloud transmittance with a clear sky model would provide ocean surface insulation estimates from SMMR data alone.
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Middleton, Michael S; Haufe, William; Hooker, Jonathan; Borga, Magnus; Dahlqvist Leinhard, Olof; Romu, Thobias; Tunón, Patrik; Hamilton, Gavin; Wolfson, Tanya; Gamst, Anthony; Loomba, Rohit; Sirlin, Claude B
2017-05-01
Purpose To determine the repeatability and accuracy of a commercially available magnetic resonance (MR) imaging-based, semiautomated method to quantify abdominal adipose tissue and thigh muscle volume and hepatic proton density fat fraction (PDFF). Materials and Methods This prospective study was institutional review board- approved and HIPAA compliant. All subjects provided written informed consent. Inclusion criteria were age of 18 years or older and willingness to participate. The exclusion criterion was contraindication to MR imaging. Three-dimensional T1-weighted dual-echo body-coil images were acquired three times. Source images were reconstructed to generate water and calibrated fat images. Abdominal adipose tissue and thigh muscle were segmented, and their volumes were estimated by using a semiautomated method and, as a reference standard, a manual method. Hepatic PDFF was estimated by using a confounder-corrected chemical shift-encoded MR imaging method with hybrid complex-magnitude reconstruction and, as a reference standard, MR spectroscopy. Tissue volume and hepatic PDFF intra- and interexamination repeatability were assessed by using intraclass correlation and coefficient of variation analysis. Tissue volume and hepatic PDFF accuracy were assessed by means of linear regression with the respective reference standards. Results Adipose and thigh muscle tissue volumes of 20 subjects (18 women; age range, 25-76 years; body mass index range, 19.3-43.9 kg/m 2 ) were estimated by using the semiautomated method. Intra- and interexamination intraclass correlation coefficients were 0.996-0.998 and coefficients of variation were 1.5%-3.6%. For hepatic MR imaging PDFF, intra- and interexamination intraclass correlation coefficients were greater than or equal to 0.994 and coefficients of variation were less than or equal to 7.3%. In the regression analyses of manual versus semiautomated volume and spectroscopy versus MR imaging, PDFF slopes and intercepts were close to the identity line, and correlations of determination at multivariate analysis (R 2 ) ranged from 0.744 to 0.994. Conclusion This MR imaging-based, semiautomated method provides high repeatability and accuracy for estimating abdominal adipose tissue and thigh muscle volumes and hepatic PDFF. © RSNA, 2017.
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NASA Astrophysics Data System (ADS)
Zhou, Weiqing; Hu, Shenhua; Ma, Xiangrong; Zhou, Feng
2018-04-01
Condensation heat transfer coefficient (HTC) as a function of outlet vapor quality was investigated using water-ethanol vapor mixture of different ethanol vapor concentrations (0%, 1%, 2%, 5%, 10%, 20%) under three different system pressures (31 kPa, 47 kPa, 83 kPa). A heat transfer coefficient was developed by applying multiple linear regression method to experimental data, taking into account the dimensionless numbers which represents the Marangoni condensation effects, such as Re, Pr, Ja, Ma and Sh. The developed correlation can predict the condensation performance within a deviation range from -22% to 32%. Taking PHE's characteristic into consideration and bringing in Ma number and Sh number, a new correlation was developed, which showed a much more accurate prediction, within a deviation from -3.2% to 7.9%.
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NASA Astrophysics Data System (ADS)
Dyar, M. D.; Carmosino, M. L.; Breves, E. A.; Ozanne, M. V.; Clegg, S. M.; Wiens, R. C.
2012-04-01
A remote laser-induced breakdown spectrometer (LIBS) designed to simulate the ChemCam instrument on the Mars Science Laboratory Rover Curiosity was used to probe 100 geologic samples at a 9-m standoff distance. ChemCam consists of an integrated remote LIBS instrument that will probe samples up to 7 m from the mast of the rover and a remote micro-imager (RMI) that will record context images. The elemental compositions of 100 igneous and highly-metamorphosed rocks are determined with LIBS using three variations of multivariate analysis, with a goal of improving the analytical accuracy. Two forms of partial least squares (PLS) regression are employed with finely-tuned parameters: PLS-1 regresses a single response variable (elemental concentration) against the observation variables (spectra, or intensity at each of 6144 spectrometer channels), while PLS-2 simultaneously regresses multiple response variables (concentrations of the ten major elements in rocks) against the observation predictor variables, taking advantage of natural correlations between elements. Those results are contrasted with those from the multivariate regression technique of the least absolute shrinkage and selection operator (lasso), which is a penalized shrunken regression method that selects the specific channels for each element that explain the most variance in the concentration of that element. To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units. Results demonstrate that the lasso, PLS-1, and PLS-2 all yield comparable results in terms of accuracy for this dataset. However, the interpretability of these methods differs greatly in terms of fundamental understanding of LIBS emissions. PLS techniques generate principal components, linear combinations of intensities at any number of spectrometer channels, which explain as much variance in the response variables as possible while avoiding multicollinearity between principal components. When the selected number of principal components is projected back into the original feature space of the spectra, 6144 correlation coefficients are generated, a small fraction of which are mathematically significant to the regression. In contrast, the lasso models require only a small number (< 24) of non-zero correlation coefficients (β values) to determine the concentration of each of the ten major elements. Causality between the positively-correlated emission lines chosen by the lasso and the elemental concentration was examined. In general, the higher the lasso coefficient (β), the greater the likelihood that the selected line results from an emission of that element. Emission lines with negative β values should arise from elements that are anti-correlated with the element being predicted. For elements except Fe, Al, Ti, and P, the lasso-selected wavelength with the highest β value corresponds to the element being predicted, e.g. 559.8 nm for neutral Ca. However, the specific lines chosen by the lasso with positive β values are not always those from the element being predicted. Other wavelengths and the elements that most strongly correlate with them to predict concentration are obviously related to known geochemical correlations or close overlap of emission lines, while others must result from matrix effects. Use of the lasso technique thus directly informs our understanding of the underlying physical processes that give rise to LIBS emissions by determining which lines can best represent concentration, and which lines from other elements are causing matrix effects.
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Heat transfer and flow friction correlations for perforated plate matrix heat exchangers
NASA Astrophysics Data System (ADS)
Ratna Raju, L.; Kumar, S. Sunil; Chowdhury, K.; Nandi, T. K.
2017-02-01
Perforated plate matrix heat exchangers (MHE) are constructed of high conductivity perforated plates stacked alternately with low conductivity spacers. They are being increasingly used in many cryogenic applications including Claude cycle or Reversed Brayton cycle cryo-refrigerators and liquefiers. Design of high NTU (number of (heat) transfer unit) cryogenic MHEs requires accurate heat transfer coefficient and flow friction factor. Thermo-hydraulic behaviour of perforated plates strongly depends on the geometrical parameters. Existing correlations, however, are mostly expressed as functions of Reynolds number only. This causes, for a given configuration, significant variations in coefficients from one correlation to the other. In this paper we present heat transfer and flow friction correlations as functions of all geometrical and other controlling variables. A FluentTM based numerical model has been developed for heat transfer and pressure drop studies over a stack of alternately arranged perforated plates and spacers. The model is validated with the data from literature. Generalized correlations are obtained through regression analysis over a large number of computed data.
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Sharma, Ashok K; Srivastava, Gopal N; Roy, Ankita; Sharma, Vineet K
2017-01-01
The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better ( R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better ( R 2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.
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Sharma, Ashok K.; Srivastava, Gopal N.; Roy, Ankita; Sharma, Vineet K.
2017-01-01
The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules. PMID:29249969
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The Evaluation on the Cadmium Net Concentration for Soil Ecosystems.
Yao, Yu; Wang, Pei-Fang; Wang, Chao; Hou, Jun; Miao, Ling-Zhan
2017-03-12
Yixing, known as the "City of Ceramics", is facing a new dilemma: a raw material crisis. Cadmium (Cd) exists in extremely high concentrations in soil due to the considerable input of industrial wastewater into the soil ecosystem. The in situ technique of diffusive gradients in thin film (DGT), the ex situ static equilibrium approach (HAc, EDTA and CaCl2), and the dissolved concentration in soil solution, as well as microwave digestion, were applied to predict the Cd bioavailability of soil, aiming to provide a robust and accurate method for Cd bioavailability evaluation in Yixing. Moreover, the typical local cash crops-paddy and zizania aquatica-were selected for Cd accumulation, aiming to select the ideal plants with tolerance to the soil Cd contamination. The results indicated that the biomasses of the two applied plants were sufficiently sensitive to reflect the stark regional differences of different sampling sites. The zizania aquatica could effectively reduce the total Cd concentration, as indicated by the high accumulation coefficients. However, the fact that the zizania aquatica has extremely high transfer coefficients, and its stem, as the edible part, might accumulate large amounts of Cd, led to the conclusion that zizania aquatica was not an ideal cash crop in Yixing. Furthermore, the labile Cd concentrations which were obtained by the DGT technique and dissolved in the soil solution showed a significant correlation with the Cd concentrations of the biota accumulation. However, the ex situ methods and the microwave digestion-obtained Cd concentrations showed a poor correlation with the accumulated Cd concentration in plant tissue. Correspondingly, the multiple linear regression models were built for fundamental analysis of the performance of different methods available for Cd bioavailability evaluation. The correlation coefficients of DGT obtained by the improved multiple linear regression model have not significantly improved compared to the coefficients obtained by the simple linear regression model. The results revealed that DGT was a robust measurement, which could obtain the labile Cd concentrations independent of the physicochemical features' variation in the soil ecosystem. Consequently, these findings provide stronger evidence that DGT is an effective and ideal tool for labile Cd evaluation in Yixing.
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The Evaluation on the Cadmium Net Concentration for Soil Ecosystems
Yao, Yu; Wang, Pei-Fang; Wang, Chao; Hou, Jun; Miao, Ling-Zhan
2017-01-01
Yixing, known as the “City of Ceramics”, is facing a new dilemma: a raw material crisis. Cadmium (Cd) exists in extremely high concentrations in soil due to the considerable input of industrial wastewater into the soil ecosystem. The in situ technique of diffusive gradients in thin film (DGT), the ex situ static equilibrium approach (HAc, EDTA and CaCl2), and the dissolved concentration in soil solution, as well as microwave digestion, were applied to predict the Cd bioavailability of soil, aiming to provide a robust and accurate method for Cd bioavailability evaluation in Yixing. Moreover, the typical local cash crops—paddy and zizania aquatica—were selected for Cd accumulation, aiming to select the ideal plants with tolerance to the soil Cd contamination. The results indicated that the biomasses of the two applied plants were sufficiently sensitive to reflect the stark regional differences of different sampling sites. The zizania aquatica could effectively reduce the total Cd concentration, as indicated by the high accumulation coefficients. However, the fact that the zizania aquatica has extremely high transfer coefficients, and its stem, as the edible part, might accumulate large amounts of Cd, led to the conclusion that zizania aquatica was not an ideal cash crop in Yixing. Furthermore, the labile Cd concentrations which were obtained by the DGT technique and dissolved in the soil solution showed a significant correlation with the Cd concentrations of the biota accumulation. However, the ex situ methods and the microwave digestion-obtained Cd concentrations showed a poor correlation with the accumulated Cd concentration in plant tissue. Correspondingly, the multiple linear regression models were built for fundamental analysis of the performance of different methods available for Cd bioavailability evaluation. The correlation coefficients of DGT obtained by the improved multiple linear regression model have not significantly improved compared to the coefficients obtained by the simple linear regression model. The results revealed that DGT was a robust measurement, which could obtain the labile Cd concentrations independent of the physicochemical features’ variation in the soil ecosystem. Consequently, these findings provide stronger evidence that DGT is an effective and ideal tool for labile Cd evaluation in Yixing. PMID:28287500
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Estimation of stature using hand and foot dimensions in Slovak adults.
Uhrová, Petra; Beňuš, Radoslav; Masnicová, Soňa; Obertová, Zuzana; Kramárová, Daniela; Kyselicová, Klaudia; Dörnhöferová, Michaela; Bodoriková, Silvia; Neščáková, Eva
2015-03-01
Hand and foot dimensions used for stature estimation help to formulate a biological profile in the process of personal identification. Morphological variability of hands and feet shows the importance of generating population-specific equations to estimate stature. The stature, hand length, hand breadth, foot length and foot breadth of 250 young Slovak males and females, aged 18-24 years, were measured according to standard anthropometric procedures. The data were statistically analyzed using independent t-test for sex and bilateral differences. Pearson correlation coefficient was used for assessing relationship between stature and hand/foot parameters, and subsequently linear regression analysis was used to estimate stature. The results revealed significant sex differences in hand and foot dimensions as well as in stature (p<0.05). There was a positive and statistically significant correlation between stature and all measurements in both sexes (p<0.01). The highest correlation coefficient was found for foot length in males (r=0.71) as well as in females (r=0.63). Regression equations were computed separately for each sex. The accuracy of stature prediction ranged from ±4.6 to ±6.1cm. The results of this study indicate that hand and foot dimension can be used to estimate stature for Slovak for the purpose of forensic field. The regression equations can be of use for stature estimation particularly in cases of dismembered bodies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Jürgens, Julian H W; Schulz, Nadine; Wybranski, Christian; Seidensticker, Max; Streit, Sebastian; Brauner, Jan; Wohlgemuth, Walter A; Deuerling-Zheng, Yu; Ricke, Jens; Dudeck, Oliver
2015-02-01
The objective of this study was to compare the parameter maps of a new flat-panel detector application for time-resolved perfusion imaging in the angiography room (FD-CTP) with computed tomography perfusion (CTP) in an experimental tumor model. Twenty-four VX2 tumors were implanted into the hind legs of 12 rabbits. Three weeks later, FD-CTP (Artis zeego; Siemens) and CTP (SOMATOM Definition AS +; Siemens) were performed. The parameter maps for the FD-CTP were calculated using a prototype software, and those for the CTP were calculated with VPCT-body software on a dedicated syngo MultiModality Workplace. The parameters were compared using Pearson product-moment correlation coefficient and linear regression analysis. The Pearson product-moment correlation coefficient showed good correlation values for both the intratumoral blood volume of 0.848 (P < 0.01) and the blood flow of 0.698 (P < 0.01). The linear regression analysis of the perfusion between FD-CTP and CTP showed for the blood volume a regression equation y = 4.44x + 36.72 (P < 0.01) and for the blood flow y = 0.75x + 14.61 (P < 0.01). This preclinical study provides evidence that FD-CTP allows a time-resolved (dynamic) perfusion imaging of tumors similar to CTP, which provides the basis for clinical applications such as the assessment of tumor response to locoregional therapies directly in the angiography suite.
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Lead exposure and the 2010 achievement test scores of children in New York counties
2012-01-01
Background Lead is toxic to cognitive and behavioral functioning in children even at levels well below those producing physical symptoms. Continuing efforts in the U.S. since about the 1970s to reduce lead exposure in children have dramatically reduced the incidence of elevated blood lead levels (with elevated levels defined by the current U.S. Centers for Disease Control threshold of 10 μg/dl). The current study examines how much lead toxicity continues to impair the academic achievement of children of New York State, using 2010 test data. Methods This study relies on three sets of data published for the 57 New York counties outside New York City: school achievement data from the New York State Department of Education, data on incidence of elevated blood lead levels from the New York State Department of Health, and data on income from the U.S. Census Bureau. We studied third grade and eighth grade test scores in English Language Arts and mathematics. Using the county as the unit of analysis, we computed bivariate correlations and regression coefficients, with percent of children achieving at the lowest reported level as the dependent variable and the percent of preschoolers in the county with elevated blood lead levels as the independent variable. Then we repeated those analyses using partial correlations to control for possible confounding effects of family income, and using multiple regressions with income included. Results The bivariate correlations between incidence of elevated lead and number of children in the lowest achievement group ranged between 0.38 and 0.47. The partial correlations ranged from 0.29 to 0.40. The regression coefficients, both bivariate and partial (both estimating the increase in percent of children in the lowest achievement group for every percent increase in the children with elevated blood lead levels), ranged from 0.52 to 1.31. All regression coefficients, when rounded to the nearest integer, were approximately 1. Thus, when the percent of children showing elevated lead increases by one percent, the percent of children in the lowest achievement group, according to the regression equations generated, also increases by about one percent. All associations were significant at the 0.05 level. Conclusion Despite public health advances, and despite the imprecision of measures, an association between the incidence of elevated blood lead and achievement in New York counties is still apparent, not attributable to confounding by income. Efforts to reduce lead exposure should persist with vigor. PMID:22269775
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Li, Dapeng; Yue, Jiawei; Jiang, Lu; Huang, Yonghui; Sun, Jifu; Wu, Yan
2017-04-22
BACKGROUND Degrading enzymes play an important role in the process of disc degeneration. The objective of this study was to investigate the correlation between the expression of high temperature requirement serine protease A1 (HtrA1) in the nucleus pulposus and the T2 value of the nucleus pulposus region in magnetic resonance imaging (MRI). MATERIAL AND METHODS Thirty-six patients who had undergone surgical excision of the nucleus pulposus were examined by MRI before surgery. Pfirrmann grading of the target intervertebral disc was performed according to the sagittal T2-weighted imaging, and the T2 value of the target nucleus pulposus was measured according to the median sagittal T2 mapping. The correlation between the Pfirrmann grade and the T2 value was analyzed. The expression of HtrA1 in the nucleus pulposus was analyzed by RT-PCR and Western blot. The correlation between the expression of HtrA1 and the T2 value was analyzed. RESULTS The T2 value of the nucleus pulposus region was 33.11-167.91 ms, with an average of 86.64±38.73 ms. According to Spearman correlation analysis, there was a rank correlation between T2 value and Pfirrmann grade (P<0.0001), and the correlation coefficient (rs)=-0.93617. There was a linear correlation between the mRNA level of HtrA1 and T2 value in nucleus pulposus tissues (a=3.88, b=-0.019, F=112.63, P<0.0001), normalized regression coefficient=-0.88. There was a linear correlation between the expression level of HtrA1 protein and the T2 value in the nucleus pulposus tissues (a=3.30, b=-0.016, F=93.15, P<0.0001) and normalized regression coefficient=-0.86. CONCLUSIONS The expression of HtrA1 was strongly related to the T2 value, suggesting that HtrA1 plays an important role in the pathological process of intervertebral disc degeneration.
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Equations of prediction for abdominal fat in brown egg-laying hens fed different diets.
Souza, C; Jaimes, J J B; Gewehr, C E
2017-06-01
The objective was to use noninvasive measurements to formulate equations for predicting the abdominal fat weight of laying hens in a noninvasive manner. Hens were fed with different diets; the external body measurements of birds were used as regressors. We used 288 Hy-Line Brown laying hens, distributed in a completely randomized design in a factorial arrangement, submitted for 16 wk to 2 metabolizable energy levels (2,550 and 2,800 kcal/kg) and 3 levels of crude protein in the diet (150, 160, and 170 g/kg), totaling 6 treatments, with 48 hens each. Sixteen hens per treatment of 92 wk age were utilized to evaluate body weight, bird length, tarsus and sternum, greater and lesser diameter of the tarsus, and abdominal fat weight, after slaughter. The equations were obtained by using measures evaluated with regressors through simple and multiple linear regression with the stepwise method of indirect elimination (backward), with P < 0.10 for all variables remaining in the model. The weight of abdominal fat as predicted by the equations and observed values for each bird were subjected to Pearson's correlation analysis. The equations generated by energy levels showed coefficients of determination of 0.50 and 0.74 for 2,800 and 2,550 kcal/kg of metabolizable energy, respectively, with correlation coefficients of 0.71 and 0.84, with a highly significant correlation between the calculated and observed values of abdominal fat. For protein levels of 150, 160, and 170 g/kg in the diet, it was possible to obtain coefficients of determination of 0.75, 0.57, and 0.61, with correlation coefficients of 0.86, 0.75, and 0.78, respectively. Regarding the general equation for predicting abdominal fat weight, the coefficient of determination was 0.62; the correlation coefficient was 0.79. The equations for predicting abdominal fat weight in laying hens, based on external measurements of the birds, showed positive coefficients of determination and correlation coefficients, thus allowing researchers to determine abdominal fat weight in vivo. © 2016 Poultry Science Association Inc.
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A study of Solar-Enso correlation with southern Brazil tree ring index (1955- 1991)
NASA Astrophysics Data System (ADS)
Rigozo, N.; Nordemann, D.; Vieira, L.; Echer, E.
The effects of solar activity and El Niño-Southern Oscillation on tree growth in Southern Brazil were studied by correlation analysis. Trees for this study were native Araucaria (Araucaria Angustifolia)from four locations in Rio Grande do Sul State, in Southern Brazil: Canela (29o18`S, 50o51`W, 790 m asl), Nova Petropolis (29o2`S, 51o10`W, 579 m asl), Sao Francisco de Paula (29o25`S, 50o24`W, 930 m asl) and Sao Martinho da Serra (29o30`S, 53o53`W, 484 m asl). From these four sites, an average tree ring Index for this region was derived, for the period 1955-1991. Linear correlations were made on annual and 10 year running averages of this tree ring Index, of sunspot number Rz and SOI. For annual averages, the correlation coefficients were low, and the multiple regression between tree ring and SOI and Rz indicates that 20% of the variance in tree rings was explained by solar activity and ENSO variability. However, when the 10 year running averages correlations were made, the coefficient correlations were much higher. A clear anticorrelation is observed between SOI and Index (r=-0.81) whereas Rz and Index show a positive correlation (r=0.67). The multiple regression of 10 year running averages indicates that 76% of the variance in tree ring INdex was explained by solar activity and ENSO. These results indicate that the effects of solar activity and ENSO on tree rings are better seen on long timescales.
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Chen, Ying-Jen; Ho, Meng-Yang; Chen, Kwan-Ju; Hsu, Chia-Fen; Ryu, Shan-Jin
2009-08-01
The aims of the present study were to (i) investigate if traditional Chinese word reading ability can be used for estimating premorbid general intelligence; and (ii) to provide multiple regression equations for estimating premorbid performance on Raven's Standard Progressive Matrices (RSPM), using age, years of education and Chinese Graded Word Reading Test (CGWRT) scores as predictor variables. Four hundred and twenty-six healthy volunteers (201 male, 225 female), aged 16-93 years (mean +/- SD, 41.92 +/- 18.19 years) undertook the tests individually under supervised conditions. Seventy percent of subjects were randomly allocated to the derivation group (n = 296), and the rest to the validation group (n = 130). RSPM score was positively correlated with CGWRT score and years of education. RSPM and CGWRT scores and years of education were also inversely correlated with age, but the declining trend for RSPM performance against age was steeper than that for CGWRT performance. Separate multiple regression equations were derived for estimating RSPM scores using different combinations of age, years of education, and CGWRT score for both groups. The multiple regression coefficient of each equation ranged from 0.71 to 0.80 with the standard error of estimate between 7 and 8 RSPM points. When fitting the data of one group to the equations derived from its counterpart group, the cross-validation multiple regression coefficients ranged from 0.71 to 0.79. There were no significant differences in the 'predicted-obtained' RSPM discrepancies between any equations. The regression equations derived in the present study may provide a basis for estimating premorbid RSPM performance.
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Poulin, Patrick; Hop, Cornelis Eca; Salphati, Laurent; Liederer, Bianca M
2013-04-01
Understanding drug distribution and accumulation in tumors would be informative in the assessment of efficacy in targeted therapy; however, existing methods for predicting tissue drug distribution focus on normal tissues and do not incorporate tumors. The main objective of this study was to describe the relationships between tissue-plasma concentration ratios (Kp ) of normal tissues and those of subcutaneous xenograft tumors under nonsteady-state conditions, and establish regression equations that could potentially be used for the prediction of drug levels in several human tumor xenografts in mouse, based solely on a Kp value determined in a normal tissue (e.g., muscle). A dataset of 17 compounds was collected from the literature and from Genentech. Tissue and plasma concentration data in mouse were obtained following oral gavage or intraperitoneal administration. Linear regression analyses were performed between Kp values in several normal tissues (muscle, lung, liver, or brain) and those in human tumor xenografts (CL6, EBC-1, HT-29, PC3, U-87, MCF-7-neo-Her2, or BT474M1.1). The tissue-plasma ratios in normal tissues reasonably correlated with the tumor-plasma ratios in CL6, EBC-1, HT-29, U-87, BT474M1.1, and MCF-7-neo-Her2 xenografts (r(2) in the range 0.62-1) but not with the PC3 xenograft. In general, muscle and lung exhibited the strongest correlation with tumor xenografts, followed by liver. Regression coefficients from brain were low, except between brain and the glioblastoma U-87 xenograft (r(2) in the range 0.62-0.94). Furthermore, reasonably strong correlations were observed between muscle and lung and between muscle and liver (r(2) in the range 0.67-0.96). The slopes of the regressions differed depending on the class of drug (strong vs. weak base) and type of tissue (brain vs. other tissues and tumors). Overall, this study will contribute to our understanding of tissue-plasma partition coefficients for tumors and facilitate the use of physiologically based pharmacokinetics (PBPK) modeling for chemotherapy in oncology studies. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:1355-1369, 2013. Copyright © 2013 Wiley Periodicals, Inc.
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Belief in complementary and alternative medicine is related to age and paranormal beliefs in adults.
Van den Bulck, Jan; Custers, Kathleen
2010-04-01
The use of complementary and alternative medicine (CAM) is widespread, even among people who use conventional medicine. Positive beliefs about CAM are common among physicians and medical students. Little is known about the beliefs regarding CAM among the general public. Among science students, belief in CAM was predicted by belief in the paranormal. In a cross-sectional study, 712 randomly selected adults (>18 years old) responded to the CAM Health Belief Questionnaire (CHBQ) and a paranormal beliefs scale. CAM beliefs were very prevalent in this sample of adult Flemish men and women. Zero-order correlations indicated that belief in CAM was associated with age (r = 0.173 P < 0.001) level of education (r = -0.079 P = 0.039) social desirability (r = -0.119 P = 0.002) and paranormal belief (r = 0.365 P < 0.001). In a multivariate model, two variables predicted CAM beliefs. Support for CAM increased with age (regression coefficient: 0.01; 95% confidence interval (CI): 0.006 to 0.014), but the strongest relationship existed between support for CAM and beliefs in the paranormal. Paranormal beliefs accounted for 14% of the variance of the CAM beliefs (regression coefficient: 0.376; 95%: CI 0.30-0.44). The level of education (regression coefficient: 0.06; 95% CI: -0.014-0.129) and social desirability (regression coefficient: -0.023; 95% CI: -0.048-0.026) did not make a significant contribution to the explained variance (<0.1%, P = 0.867). Support of CAM was very prevalent in this Flemish adult population. CAM beliefs were strongly associated with paranormal beliefs.
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Linear Least Squares for Correlated Data
NASA Technical Reports Server (NTRS)
Dean, Edwin B.
1988-01-01
Throughout the literature authors have consistently discussed the suspicion that regression results were less than satisfactory when the independent variables were correlated. Camm, Gulledge, and Womer, and Womer and Marcotte provide excellent applied examples of these concerns. Many authors have obtained partial solutions for this problem as discussed by Womer and Marcotte and Wonnacott and Wonnacott, which result in generalized least squares algorithms to solve restrictive cases. This paper presents a simple but relatively general multivariate method for obtaining linear least squares coefficients which are free of the statistical distortion created by correlated independent variables.
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Grijalva-Eternod, Carlos S; Wells, Jonathan C K; Girma, Tsinuel; Kæstel, Pernille; Admassu, Bitiya; Friis, Henrik; Andersen, Gregers S
2015-09-01
A midupper arm circumference (MUAC) <115 mm and weight-for-height z score (WHZ) or weight-for-length z score (WLZ) less than -3, all of which are recommended to identify severe wasting in children, often identify different children. The reasons behind this poor agreement are not well understood. We investigated the association between these 2 anthropometric indexes and body composition to help understand why they identify different children as wasted. We analyzed weight, length, MUAC, fat-mass (FM), and fat-free mass (FFM) data from 2470 measurements from 595 healthy Ethiopian infants obtained at birth and at 1.5, 2.5, 3.5, 4.5, and 6 mo of age. We derived WLZs by using 2006 WHO growth standards. We derived length-adjusted FM and FFM values as unexplained residuals after regressing each FM and FFM against length. We used a correlation analysis to assess associations between length, FFM, and FM (adjusted and nonadjusted for length) and the MUAC and WLZ and a multivariable regression analysis to assess the independent variability of length and length-adjusted FM and FFM with either the MUAC or the WLZ as the outcome. At all ages, length showed consistently strong positive correlations with the MUAC but not with the WLZ. Adjustment for length reduced observed correlation coefficients of FM and FFM with the MUAC but increased those for the WLZ. At all ages, both length-adjusted FM and FFM showed an independent association with the WLZ and MUAC with higher regression coefficients for the WLZ. Conversely, length showed greater regression coefficients for the MUAC. At all ages, the MUAC was shown to be more influenced than was the WLZ by the FM variability relative to the FFM variability. The MUAC and WLZ have different associations with body composition, and length influences these associations differently. Our results suggest that the WLZ is a good marker of tissue masses independent of length. The MUAC acts more as a composite index of poor growth indexing jointly tissue masses and length. This trial was registered at www.controlled-trials.com as ISRCTN46718296. © 2015 American Society for Nutrition.
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USDA-ARS?s Scientific Manuscript database
Various Uzbek commercial varieties were grown in the field and these were exposed to cotton bollworm (Helicoverpa armigera) larvae. A significant negative correlation coefficient (r = -0.89) and linear regression (Y = 109.69-5.26X) was observed between the concentration of (+)-gossypol in cotton se...
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Pre-Service Teacher Self-Efficacy for Teaching Students with Disabilities: What Knowledge Matters?
ERIC Educational Resources Information Center
Browarnik, Brooke; Bell, Sherry Mee; McCallum, R. Steve; Smyth, Kelly; Martin, Melissa
2017-01-01
The relation between items assessing knowledge about educating students with disabilities and the Tschannen-Moran and Hoy's Teachers' Sense of Efficacy Scale (TSES; 2001) was explored for 140 preservice, general education teachers using biserial correlation coefficients and a multiple regression equation. From the data collected, 8 correlations…
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Clinical correlates of sex hormones in women: The study of health in Pomerania.
Kische, Hanna; Gross, Stefan; Wallaschofski, Henri; Völzke, Henry; Dörr, Marcus; Nauck, Matthias; Haring, Robin
2016-09-01
Despite associations of sex hormones in women with increased cardiometabolic risk and mortality, the clinical correlates of altered sex hormone concentrations in women are less clearly understood. We investigated a broad range of clinical correlates of sex hormones in women from a large population-based sample. Data from 2560 women from two cohorts of the Study of Health in Pomerania were used. Stepwise multivariable regression models were implemented to investigate a broad range of behavioral, socio-demographic, and cardiometabolic clinical correlates related to total testosterone (TT), free testosterone (fT), androstenedione (ASD), dehydroepiandrosterone-sulfate (DHEAS), estrone (E1), estradiol (E2), and sex hormone-binding globulin (SHBG). Waist circumference and BMI (β-coefficient: -0.03; 95% CI: -0.04; 0.03) were inversely related to SHBG, and BMI was positively related to TT (β-coefficient: 0.005; 95% CI: 0.001; 0.009), fT, E1, and E2. Smoking was positively related to TT (β-coefficient: 0.04; 95% CI: 0.01; 0.06), ASD, and fT. Systolic blood pressure (TT: β-coefficient: 0.002; 95% CI: 0.001; 0.003), hypertension (TT: β-coefficient: 0.05; 95% CI: 0.003; 0.11), low-density lipoprotein (LDL) cholesterol (TT: β-coefficient: 0.02; 95% CI: 0.01; 0.05), and total cholesterol (TT: β-coefficient: -0.03; 95% CI: 0.01; 0.05) were positively related to TT and ASD. Finally, type 2 diabetes mellitus (T2DM), and metabolic syndrome (MetS) were positively related to fT, but inversely related to SHBG. Our population-based study, with sex hormone concentrations measured by liquid chromatography tandem mass spectrometry, revealed associations between clinical correlates including waist circumference, smoking, cohabitation, systolic blood pressure, cholesterol, and MetS with sex hormones. Thus, sex hormones and SHBG may play a role in the cardiovascular risk profile of women. Copyright © 2016 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Afolagboye, Lekan Olatayo; Talabi, Abel Ojo; Oyelami, Charles Adebayo
2017-05-01
This study assessed the possibility of using index tests to determine the mechanical properties of crushed aggregates. The aggregates used in this study were derived from major Precambrian basement rocks in Ado-Ekiti, Nigeria. Regression analyses were performed to determine the empirical relations that mechanical properties of the aggregates may have with the point load strength (IS(50)), Schmidt rebound hammer value (SHR) and unconfined compressive strength (UCS) of the rocks. For all the data, strong correlation coefficients were found between IS(50), SHR, UCS, and mechanical properties of the aggregates. The regression analysis conducted on the different rocks separately showed that correlations coefficients obtained between the IS(50), SHR, UCS and mechanical properties of the aggregates were stronger than those of the grouped rocks. The T-test and F-test showed that the derived models were valid. This study has shown that the mechanical properties of the aggregates can be estimated from IS(50), SHR and USC but the influence of rock type on the relationships should be taken into consideration.
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Determinations of cloud liquid water in the tropics from the SSM/I
NASA Technical Reports Server (NTRS)
Alishouse, John C.; Swift, Calvin; Ruf, Christopher; Snyder, Sheila; Vongsathorn, Jennifer
1989-01-01
Upward-looking microwave radiometric observations were used to validate the SSM/I determinations, and also as a basis for the determination of new coefficients. Due to insufficiency of the initial four channel algorithm for cloud liquid water, the improved algorithm was derived from the CORRAD (the University of Massachusetts autocorrelation radiometer) measurements of cloud liquid water and the matching SSM/I brightness temperatures using the standard linear regression. The correlation coefficients for the possible four channel combinations, and subsequently the best and the worst combinations were determined.
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Ramírez-Vélez, Robinson; Correa-Bautista, Jorge Enrique; González-Ruíz, Katherine; Vivas, Andrés; Triana-Reina, Héctor Reynaldo; Martínez-Torres, Javier; Prieto-Benavides, Daniel Humberto; Carrillo, Hugo Alejandro; Ramos-Sepúlveda, Jeison Alexander; Villa-González, Emilio; García-Hermoso, Antonio
2017-01-17
Recently, a body adiposity index (BAI = (hip circumference)/((height)(1.5)) -18 ) was developed and validated in adult populations. The aim of this study was to evaluate the performance of BAI in estimating percentage body fat (BF%) in a sample of Colombian collegiate young adults. The participants were comprised of 903 volunteers (52% females, mean age = 21.4 years ± 3.3). We used the Lin's concordance correlation coefficient, linear regression, Bland-Altman's agreement analysis, concordance correlation coefficient ( ρc ) and the coefficient of determination ( R ²) between BAI, and BF%; by bioelectrical impedance analysis (BIA)). The correlation between the two methods of estimating BF% was R ² = 0.384, p < 0.001. A paired-sample t -test showed a difference between the methods (BIA BF% = 16.2 ± 3.1, BAI BF% = 30.0 ± 5.4%; p < 0.001). For BIA, bias value was 6.0 ± 6.2 BF% (95% confidence interval (CI) = -6.0 to 18.2), indicating that the BAI method overestimated BF% relative to the reference method. Lin's concordance correlation coefficient was poor ( ρc = 0.014, 95% CI = -0.124 to 0.135; p = 0.414). In Colombian college students, there was poor agreement between BAI- and BIA-based estimates of BF%, and so BAI is not accurate in people with low or high body fat percentage levels.
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On the Asymptotic Relative Efficiency of Planned Missingness Designs.
Rhemtulla, Mijke; Savalei, Victoria; Little, Todd D
2016-03-01
In planned missingness (PM) designs, certain data are set a priori to be missing. PM designs can increase validity and reduce cost; however, little is known about the loss of efficiency that accompanies these designs. The present paper compares PM designs to reduced sample (RN) designs that have the same total number of data points concentrated in fewer participants. In 4 studies, we consider models for both observed and latent variables, designs that do or do not include an "X set" of variables with complete data, and a full range of between- and within-set correlation values. All results are obtained using asymptotic relative efficiency formulas, and thus no data are generated; this novel approach allows us to examine whether PM designs have theoretical advantages over RN designs removing the impact of sampling error. Our primary findings are that (a) in manifest variable regression models, estimates of regression coefficients have much lower relative efficiency in PM designs as compared to RN designs, (b) relative efficiency of factor correlation or latent regression coefficient estimates is maximized when the indicators of each latent variable come from different sets, and (c) the addition of an X set improves efficiency in manifest variable regression models only for the parameters that directly involve the X-set variables, but it substantially improves efficiency of most parameters in latent variable models. We conclude that PM designs can be beneficial when the model of interest is a latent variable model; recommendations are made for how to optimize such a design.
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Refraction data survey: 2nd generation correlation of myopia.
Greene, Peter R; Medina, Antonio
2016-10-01
The objective herein is to provide refraction data, myopia progression rate, prevalence, and 1st and 2nd generation correlations, relevant to whether myopia is random or inherited. First- and second-generation ocular refraction data are assembled from N = 34 families, average of 2.8 children per family. From this group, data are available from N = 165 subjects. Inter-generation regressions are performed on all the data sets, including correlation coefficient r, and myopia prevalence [%]. Prevalence of myopia is [M] = 38.5 %. Prevalence of high myopes with |R| >6 D is [M-] = 20.5 %. Average refraction is
= -1.84 D ± 3.22 (N = 165). For the high myopes, |R| >6 D, prevalence for the parents is [M-] = 25 %, for the 2nd generation [M-] = 16.5 %. Average myopia level for the high myopes, both generations, is = -7.52 D ± 1.31 D (N = 33). Regression parameters are calculated for all the data sets, yielding correlation coefficients in the range r = 0.48-0.72 for some groups of myopes and high myopes, fathers to daughters, and mothers to sons. Also of interest, some categories show essentially no correlation, -0.20 < r < 0.20, indicating that the refractive errors occur randomly. Time series results show myopia diopter rates = -0.50 D/year. -
Correlation of Vitamin D status and orthodontic-induced external apical root resorption
Tehranchi, Azita; Sadighnia, Azin; Younessian, Farnaz; Abdi, Amir H.; Shirvani, Armin
2017-01-01
Background: Adequate Vitamin D is essential for dental and skeletal health in children and adult. The purpose of this study was to assess the correlation of serum Vitamin D level with external-induced apical root resorption (EARR) following fixed orthodontic treatment. Materials and Methods: In this cross-sectional study, the prevalence of Vitamin D deficiency (defined by25-hydroxyvitamin-D) was determined in 34 patients (23.5% male; age range 12–23 years; mean age 16.63 ± 2.84) treated with fixed orthodontic treatment. Root resorption of four maxillary incisors was measured using before and after periapical radiographs (136 measured teeth) by means of a design-to-purpose software to optimize data collection. Teeth with a maximum percentage of root resorption (%EARR) were indicated as representative root resorption for each patient. A multiple linear regression model and Pearson correlation coefficient were used to assess the association of Vitamin D status and observed EARR. P < 0.05 was considered statistically significant. Results: The Pearson coefficient between these two variables was determined about 0.15 (P = 0.38). Regression analysis revealed that Vitamin D status of the patients demonstrated no significant statistical correlation with EARR, after adjustment of confounding variables using linear regression model (P > 0.05). Conclusion: This study suggests that Vitamin D level is not among the clinical variables that are potential contributors for EARR. The prevalence of Vitamin D deficiency does not differ in patients with higher EARR. These data suggest the possibility that Vitamin D insufficiency may not contribute to the development of more apical root resorption although this remains to be confirmed by further longitudinal cohort studies. PMID:29238379
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Carbonell, F; Bellec, P; Shmuel, A
2014-02-01
The effect of regressing out the global average signal (GAS) in resting state fMRI data has become a concern for interpreting functional connectivity analyses. It is not clear whether the reported anti-correlations between the Default Mode and the Dorsal Attention Networks are intrinsic to the brain, or are artificially created by regressing out the GAS. Here we introduce a concept, Impact of the Global Average on Functional Connectivity (IGAFC), for quantifying the sensitivity of seed-based correlation analyses to the regression of the GAS. This voxel-wise IGAFC index is defined as the product of two correlation coefficients: the correlation between the GAS and the fMRI time course of a voxel, times the correlation between the GAS and the seed time course. This definition enables the calculation of a threshold at which the impact of regressing-out the GAS would be large enough to introduce spurious negative correlations. It also yields a post-hoc impact correction procedure via thresholding, which eliminates spurious correlations introduced by regressing out the GAS. In addition, we introduce an Artificial Negative Correlation Index (ANCI), defined as the absolute difference between the IGAFC index and the impact threshold. The ANCI allows a graded confidence scale for ranking voxels according to their likelihood of showing artificial correlations. By applying this method, we observed regions in the Default Mode and Dorsal Attention Networks that were anti-correlated. These findings confirm that the previously reported negative correlations between the Dorsal Attention and Default Mode Networks are intrinsic to the brain and not the result of statistical manipulations. Our proposed quantification of the impact that a confound may have on functional connectivity can be generalized to global effect estimators other than the GAS. It can be readily applied to other confounds, such as systemic physiological or head movement interferences, in order to quantify their impact on functional connectivity in the resting state. © 2013.
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Evaluation of AUC(0-4) predictive methods for cyclosporine in kidney transplant patients.
Aoyama, Takahiko; Matsumoto, Yoshiaki; Shimizu, Makiko; Fukuoka, Masamichi; Kimura, Toshimi; Kokubun, Hideya; Yoshida, Kazunari; Yago, Kazuo
2005-05-01
Cyclosporine (CyA) is the most commonly used immunosuppressive agent in patients who undergo kidney transplantation. Dosage adjustment of CyA is usually based on trough levels. Recently, trough levels have been replacing the area under the concentration-time curve during the first 4 h after CyA administration (AUC(0-4)). The aim of this study was to compare the predictive values obtained using three different methods of AUC(0-4) monitoring. AUC(0-4) was calculated from 0 to 4 h in early and stable renal transplant patients using the trapezoidal rule. The predicted AUC(0-4) was calculated using three different methods: the multiple regression equation reported by Uchida et al.; Bayesian estimation for modified population pharmacokinetic parameters reported by Yoshida et al.; and modified population pharmacokinetic parameters reported by Cremers et al. The predicted AUC(0-4) was assessed on the basis of predictive bias, precision, and correlation coefficient. The predicted AUC(0-4) values obtained using three methods through measurement of three blood samples showed small differences in predictive bias, precision, and correlation coefficient. In the prediction of AUC(0-4) measurement of one blood sample from stable renal transplant patients, the performance of the regression equation reported by Uchida depended on sampling time. On the other hand, the performance of Bayesian estimation with modified pharmacokinetic parameters reported by Yoshida through measurement of one blood sample, which is not dependent on sampling time, showed a small difference in the correlation coefficient. The prediction of AUC(0-4) using a regression equation required accurate sampling time. In this study, the prediction of AUC(0-4) using Bayesian estimation did not require accurate sampling time in the AUC(0-4) monitoring of CyA. Thus Bayesian estimation is assumed to be clinically useful in the dosage adjustment of CyA.
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Dental age assessment of young Iranian adults using third molars: A multivariate regression study.
Bagherpour, Ali; Anbiaee, Najmeh; Partovi, Parnia; Golestani, Shayan; Afzalinasab, Shakiba
2012-10-01
In recent years, a noticeable increase in forensic age estimations of living individuals has been observed. Radiologic assessment of the mineralisation stage of third molars is of particular importance, with regard to the relevant age group. To attain a referral database and regression equations for dental age estimation of unaccompanied minors in an Iranian population was the goal of this study. Moreover, determination was made concerning the probability of an individual being over the age of 18 in case of full third molar(s) development. Using the scoring system of Gleiser and Hunt, modified by Köhler, an investigation of a cross-sectional sample of 1274 orthopantomograms of 885 females and 389 males aged between 15 and 22 years was carried out. Using kappa statistics, intra-observer reliability was tested. With Spearman correlation coefficient, correlation between the scores of all four wisdom teeth, was evaluated. We also carried out the Wilcoxon signed-rank test on asymmetry and calculated the regression formulae. A strong intra-observer agreement was displayed by the kappa value. No significant difference (p-value for upper and lower jaws were 0.07 and 0.59, respectively) was discovered by Wilcoxon signed-rank test for left and right asymmetry. The developmental stage of upper right and upper left third molars yielded the greatest correlation coefficient. The probability of an individual being over the age of 18 is 95.6% for males and 100.0% for females in case four fully developed third molars are present. Taking into consideration gender, location and number of wisdom teeth, regression formulae were arrived at. Use of population-specific standards is recommended as a means of improving the accuracy of forensic age estimates based on third molars mineralisation. To obtain more exact regression formulae, wider age range studies are recommended. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
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Impact of multicollinearity on small sample hydrologic regression models
NASA Astrophysics Data System (ADS)
Kroll, Charles N.; Song, Peter
2013-06-01
Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.
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Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk
Czarnota, Jenna; Gennings, Chris; Wheeler, David C
2015-01-01
In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case–control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome. PMID:26005323
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Assessment of weighted quantile sum regression for modeling chemical mixtures and cancer risk.
Czarnota, Jenna; Gennings, Chris; Wheeler, David C
2015-01-01
In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.
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[Income inequality, corruption, and life expectancy at birth in Mexico].
Idrovo, Alvaro Javier
2005-01-01
To ascertain if the effect of income inequality on life expectancy at birth in Mexico is mediated by corruption, used as a proxy of social capital. An ecological study was carried out with the 32 Mexican federative entities. Global and by sex correlations between life expectancy at birth were estimated by federative entity with the Gini coefficient, the Corruption and Good Government Index, the percentage of Catholics, and the percentage of the population speaking indigenous language. Robust linear regressions, with and without instrumental variables, were used to explore if corruption acts as intermediate variable in the studied relationship. Negative correlations with Spearman's rho near to -0.60 (p < 0.05) and greater than -0.66 (p < 0.05) between life expectancy at birth, the Gini coefficient and the population speaking indigenous language, respectively, were observed. Moreover, the Corruption and Good Government Index correlated with men's life expectancy at birth with Spearman's rho -0.3592 (p < 0.05). Regressions with instruments were more consistent than conventional ones and they show a strong negative effect (p < 0.05) of income inequality on life expectancy at birth. This effect was greater among men. The findings suggest a negative effect of income inequality on life expectancy at birth in Mexico, mediated by corruption levels and other related cultural factors.
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Estimating chlorophyll content of spartina alterniflora at leaf level using hyper-spectral data
NASA Astrophysics Data System (ADS)
Wang, Jiapeng; Shi, Runhe; Liu, Pudong; Zhang, Chao; Chen, Maosi
2017-09-01
Spartina alterniflora, one of most successful invasive species in the world, was firstly introduced to China in 1979 to accelerate sedimentation and land formation via so-called "ecological engineering", and it is now widely distributed in coastal saltmarshes in China. A key question is how to retrieve chlorophyll content to reflect growth status, which has important implication of potential invasiveness. In this work, an estimation model of chlorophyll content of S. alterniflora was developed based on hyper-spectral data in the Dongtan Wetland, Yangtze Estuary, China. The spectral reflectance of S. alterniflora leaves and their corresponding chlorophyll contents were measured, and then the correlation analysis and regression (i.e., linear, logarithmic, quadratic, power and exponential regression) method were established. The spectral reflectance was transformed and the feature parameters (i.e., "san bian", "lv feng" and "hong gu") were extracted to retrieve the chlorophyll content of S. alterniflora . The results showed that these parameters had a large correlation coefficient with chlorophyll content. On the basis of the correlation coefficient, mathematical models were established, and the models of power and exponential based on SDb had the least RMSE and larger R2 , which had a good performance regarding the inversion of chlorophyll content of S. alterniflora.
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NASA Astrophysics Data System (ADS)
Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.
2018-01-01
Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.
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Two SPSS programs for interpreting multiple regression results.
Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo
2010-02-01
When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.
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Impact of income and income inequality on infant health outcomes in the United States.
Olson, Maren E; Diekema, Douglas; Elliott, Barbara A; Renier, Colleen M
2010-12-01
The goal was to investigate the relationships of income and income inequality with neonatal and infant health outcomes in the United States. The 2000-2004 state data were extracted from the Kids Count Data Center. Health indicators included proportion of preterm births (PTBs), proportion of infants with low birth weight (LBW), proportion of infants with very low birth weight (VLBW), and infant mortality rate (IMR). Income was evaluated on the basis of median family income and proportion of federal poverty levels; income inequality was measured by using the Gini coefficient. Pearson correlations evaluated associations between the proportion of children living in poverty and the health indicators. Linear regression evaluated predictive relationships between median household income, proportion of children living in poverty, and income inequality for the 4 health indicators. Median family income was negatively correlated with all birth outcomes (PTB, r = -0.481; LBW, r = -0.295; VLBW, r = -0.133; IMR, r = -0.432), and the Gini coefficient was positively correlated (PTB, r = 0.339; LBW, r = 0.398; VLBW, r = 0.460; IMR, r = 0.114). The Gini coefficient explained a significant proportion of the variance in rate for each outcome in linear regression models with median family income. Among children living in poverty, the role of income decreased as the degree of poverty decreased, whereas the role of income inequality increased. Both income and income inequality affect infant health outcomes in the United States. The health of the poorest infants was affected more by absolute wealth than relative wealth.
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Gong, Gordon; Mattevada, Sravan; O'Bryant, Sid E
2014-04-01
Exposure to arsenic causes many diseases. Most Americans in rural areas use groundwater for drinking, which may contain arsenic above the currently allowable level, 10µg/L. It is cost-effective to estimate groundwater arsenic levels based on data from wells with known arsenic concentrations. We compared the accuracy of several commonly used interpolation methods in estimating arsenic concentrations in >8000 wells in Texas by the leave-one-out-cross-validation technique. Correlation coefficient between measured and estimated arsenic levels was greater with inverse distance weighted (IDW) than kriging Gaussian, kriging spherical or cokriging interpolations when analyzing data from wells in the entire Texas (p<0.0001). Correlation coefficient was significantly lower with cokriging than any other methods (p<0.006) for wells in Texas, east Texas or the Edwards aquifer. Correlation coefficient was significantly greater for wells in southwestern Texas Panhandle than in east Texas, and was higher for wells in Ogallala aquifer than in Edwards aquifer (p<0.0001) regardless of interpolation methods. In regression analysis, the best models are when well depth and/or elevation were entered into the model as covariates regardless of area/aquifer or interpolation methods, and models with IDW are better than kriging in any area/aquifer. In conclusion, the accuracy in estimating groundwater arsenic level depends on both interpolation methods and wells' geographic distributions and characteristics in Texas. Taking well depth and elevation into regression analysis as covariates significantly increases the accuracy in estimating groundwater arsenic level in Texas with IDW in particular. Published by Elsevier Inc.
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Nikol'skii, A A
2017-11-01
Dependence of the sound-signal frequency on the animal body length was studied in 14 ground squirrel species (genus Spermophilus) of Eurasia. Regression analysis of the total sample yielded a low determination coefficient (R 2 = 26%), because the total sample proved to be heterogeneous in terms of signal frequency within the dimension classes of animals. When the total sample was divided into two groups according to signal frequency, two statistically significant models (regression equations) were obtained in which signal frequency depended on the body size at high determination coefficients (R 2 = 73 and 94% versus 26% for the total sample). Thus, the problem of correlation between animal body size and the frequency of their vocal signals does not have a unique solution.
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Correlation of track irregularities and vehicle responses based on measured data
NASA Astrophysics Data System (ADS)
Karis, Tomas; Berg, Mats; Stichel, Sebastian; Li, Martin; Thomas, Dirk; Dirks, Babette
2018-06-01
Track geometry quality and dynamic vehicle response are closely related, but do not always correspond with each other in terms of maximum values and standard deviations. This can often be seen to give poor results in analyses with correlation coefficients or regression analysis. Measured data from both the EU project DynoTRAIN and the Swedish Green Train (Gröna Tåget) research programme is used in this paper to evaluate track-vehicle response for three vehicles. A single degree of freedom model is used as an inspiration to divide track-vehicle interaction into three parts, which are analysed in terms of correlation. One part, the vertical axle box acceleration divided by vehicle speed squared (?) and the second spatial derivative of the vertical track irregularities (?), is shown to be the weak link with lower correlation coefficients than the other parts. Future efforts should therefore be directed towards investigating the relation between axle box accelerations and track irregularity second derivatives.
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Sun, Lili; Zhou, Liping; Yu, Yu; Lan, Yukun; Li, Zhiliang
2007-01-01
Polychlorinated diphenyl ethers (PCDEs) have received more and more concerns as a group of ubiquitous potential persistent organic pollutants (POPs). By using molecular electronegativity distance vector (MEDV-4), multiple linear regression (MLR) models are developed for sub-cooled liquid vapor pressures (P(L)), n-octanol/water partition coefficients (K(OW)) and sub-cooled liquid water solubilities (S(W,L)) of 209 PCDEs and diphenyl ether. The correlation coefficients (R) and the leave-one-out cross-validation (LOO) correlation coefficients (R(CV)) of all the 6-descriptor models for logP(L), logK(OW) and logS(W,L) are more than 0.98. By using stepwise multiple regression (SMR), the descriptors are selected and the resulting models are 5-descriptor model for logP(L), 4-descriptor model for logK(OW), and 6-descriptor model for logS(W,L), respectively. All these models exhibit excellent estimate capabilities for internal sample set and good predictive capabilities for external samples set. The consistency between observed and estimated/predicted values for logP(L) is the best (R=0.996, R(CV)=0.996), followed by logK(OW) (R=0.992, R(CV)=0.992) and logS(W,L) (R=0.983, R(CV)=0.980). By using MEDV-4 descriptors, the QSPR models can be used for prediction and the model predictions can hence extend the current database of experimental values.
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Statistical procedures for evaluating daily and monthly hydrologic model predictions
Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.
2004-01-01
The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.
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ERIC Educational Resources Information Center
Aypay, Ahmet
2010-01-01
The purpose of this study is to examine the ICT usage and academic achievement of Turkish students in PISA 2006 data. The sample of the study included 4942 students from 160 schools. Frequencies, independent samples t-tests, ANOVAs, pearson correlation coefficients, exploratory factor analysis, and regression analysis were used. A high percentage…
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ERIC Educational Resources Information Center
Köhler, Carmen; Pohl, Steffi; Carstensen, Claus H.
2017-01-01
Competence data from low-stakes educational large-scale assessment studies allow for evaluating relationships between competencies and other variables. The impact of item-level nonresponse has not been investigated with regard to statistics that determine the size of these relationships (e.g., correlations, regression coefficients). Classical…
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Ozaki, Toshiro, E-mail: ganronbun@amail.plala.or.jp; Seki, Hiroshi; Shiina, Makoto
2009-09-15
The purpose of the present study was to elucidate a method for predicting the intrahepatic arteriovenous shunt rate from computed tomography (CT) images and biochemical data, instead of from arterial perfusion scintigraphy, because adverse exacerbated systemic effects may be induced in cases where a high shunt rate exists. CT and arterial perfusion scintigraphy were performed in patients with liver metastases from gastric or colorectal cancer. Biochemical data and tumor marker levels of 33 enrolled patients were measured. The results were statistically verified by multiple regression analysis. The total metastatic hepatic tumor volume (V{sub metastasized}), residual hepatic parenchyma volume (V{sub residual};more » calculated from CT images), and biochemical data were treated as independent variables; the intrahepatic arteriovenous (IHAV) shunt rate (calculated from scintigraphy) was treated as a dependent variable. The IHAV shunt rate was 15.1 {+-} 11.9%. Based on the correlation matrixes, the best correlation coefficient of 0.84 was established between the IHAV shunt rate and V{sub metastasized} (p < 0.01). In the multiple regression analysis with the IHAV shunt rate as the dependent variable, the coefficient of determination (R{sup 2}) was 0.75, which was significant at the 0.1% level with two significant independent variables (V{sub metastasized} and V{sub residual}). The standardized regression coefficients ({beta}) of V{sub metastasized} and V{sub residual} were significant at the 0.1 and 5% levels, respectively. Based on this result, we can obtain a predicted value of IHAV shunt rate (p < 0.001) using CT images. When a high shunt rate was predicted, beneficial and consistent clinical monitoring can be initiated in, for example, hepatic arterial infusion chemotherapy.« less
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Vesicular stomatitis forecasting based on Google Trends
Lu, Yi; Zhou, GuangYa; Chen, Qin
2018-01-01
Background Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. Methods American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. Results For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. Conclusion This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast. PMID:29385198
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Utility of correlation techniques in gravity and magnetic interpretation
NASA Technical Reports Server (NTRS)
Chandler, V. W.; Koski, J. S.; Braice, L. W.; Hinze, W. J.
1977-01-01
Internal correspondence uses Poisson's Theorem in a moving-window linear regression analysis between the anomalous first vertical derivative of gravity and total magnetic field reduced to the pole. The regression parameters provide critical information on source characteristics. The correlation coefficient indicates the strength of the relation between magnetics and gravity. Slope value gives delta j/delta sigma estimates of the anomalous source. The intercept furnishes information on anomaly interference. Cluster analysis consists of the classification of subsets of data into groups of similarity based on correlation of selected characteristics of the anomalies. Model studies are used to illustrate implementation and interpretation procedures of these methods, particularly internal correspondence. Analysis of the results of applying these methods to data from the midcontinent and a transcontinental profile shows they can be useful in identifying crustal provinces, providing information on horizontal and vertical variations of physical properties over province size zones, validating long wavelength anomalies, and isolating geomagnetic field removal problems.
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Sinzobahamvya, N; Kopp, T; Photiadis, J; Arenz, C; Schindler, E; Haun, C; Hraska, V; Asfour, B
2010-09-01
Hospital costs are expected to correlate with clinical complexity. Do costs for congenital heart surgery correlate with Aristotle complexity scores? 442 inpatient stays in 2008 were evaluated. Aristotle scores and levels were determined. Costs were estimated according to the German Institute for Hospital Reimbursement system. Pearson and Spearman R correlation coefficients and corresponding goodness-of-fit regression coefficients R2 were calculated. Mean basic and comprehensive Aristotle scores were 7.60 +/- 2.74 and 9.23 +/- 2.94 points, respectively. Mean expenses per hospital stay amounted to 29,369 +/- 30,823 Euros. Aristotle basic and comprehensive scores and levels were positively correlated with hospital costs. With a Spearman R of 1 and related R2 of 0.9436, scores of the 6 Aristotle comprehensive levels correlated best. Mean hospital reimbursement was 26,412 +/- 17,962 Euros. Compensation was higher than expenses for patients in comprehensive levels 1 to 3, but much lower for those in levels 4 to 6. Aristotle comprehensive complexity scores were highly correlated with hospital costs. The Aristotle score could be used as a scale to establish the correct reimbursement after congenital heart surgery. Georg Thieme Verlag KG Stuttgart, New York.
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Serum Liver Fibrosis Markers in the Prognosis of Liver Cirrhosis: A Prospective Observational Study.
Qi, Xingshun; Liu, Xu; Zhang, Yongguo; Hou, Yue; Ren, Linan; Wu, Chunyan; Chen, Jiang; Xia, Chunlian; Zhao, Jiajun; Wang, Di; Zhang, Yanlin; Zhang, Xia; Lin, Hao; Wang, Hezhi; Wang, Jinling; Cui, Zhongmin; Li, Xueyan; Deng, Han; Hou, Feifei; Peng, Ying; Wang, Xueying; Shao, Xiaodong; Li, Hongyu; Guo, Xiaozhong
2016-08-02
BACKGROUND The prognostic role of serum liver fibrosis markers in cirrhotic patients remains unclear. We performed a prospective observational study to evaluate the effect of amino-terminal pro-peptide of type III pro-collagen (PIIINP), collagen IV (CIV), laminin (LN), and hyaluronic acid (HA) on the prognosis of liver cirrhosis. MATERIAL AND METHODS All patients who were diagnosed with liver cirrhosis and admitted to our department were prospectively enrolled. PIIINP, CIV, LN, and HA levels were tested. RESULTS Overall, 108 cirrhotic patients were included. Correlation analysis demonstrated that CIV (coefficient r: 0.658, p<0.001; coefficient r: 0.368, p<0.001), LN (coefficient r: 0.450, p<0.001; coefficient r: 0.343, p<0.001), and HA (coefficient r: 0.325, p=0.001; coefficient r: 0.282, p=0.004) levels, but not PIIINP level (coefficient r: 0.081, p=0.414; coefficient r: 0.090, p=0.363), significantly correlated with Child-Pugh and MELD scores. Logistic regression analysis demonstrated that HA (odds ratio=1.00003, 95% confidence interval [CI]=1.000004-1.000056, p=0.022) was significantly associated with the 6-month mortality. Receiver operating characteristics analysis demonstrated that the area under the curve (AUC) of HA for predicting the 6-month mortality was 0.612 (95%CI=0.508-0.709, p=0.1531). CONCLUSIONS CIV, LN, and HA levels were significantly associated with the severity of liver dysfunction, but might be inappropriate for the prognostic assessment of liver cirrhosis.
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Gíslason, Magnús; Sigurðsson, Sigurður; Guðnason, Vilmundur; Harris, Tamara; Carraro, Ugo; Gargiulo, Paolo
2018-01-01
Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66–96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges’ Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P<0.005). These results highlight the specificities of each muscle quality metric to LEF biometrics, SCHOL, and BMI, and particularly highlight the value of the connective tissue regime in this regard. PMID:29513690
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Edmunds, Kyle; Gíslason, Magnús; Sigurðsson, Sigurður; Guðnason, Vilmundur; Harris, Tamara; Carraro, Ugo; Gargiulo, Paolo
2018-01-01
Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P<0.005). These results highlight the specificities of each muscle quality metric to LEF biometrics, SCHOL, and BMI, and particularly highlight the value of the connective tissue regime in this regard.
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Ballif, Marie; Zürcher, Kathrin; Reid, Stewart E; Boulle, Andrew; Fox, Matthew P; Prozesky, Hans W; Chimbetete, Cleophas; Egger, Matthias; Fenner, Lukas
2018-01-01
Objectives Seasonal variations in tuberculosis diagnoses have been attributed to seasonal climatic changes and indoor crowding during colder winter months. We investigated trends in pulmonary tuberculosis (PTB) diagnosis at antiretroviral therapy (ART) programmes in Southern Africa. Setting Five ART programmes participating in the International Epidemiology Database to Evaluate AIDS in South Africa, Zambia and Zimbabwe. Participants We analysed data of 331 634 HIV-positive adults (>15 years), who initiated ART between January 2004 and December 2014. Primary outcome measure We calculated aggregated averages in monthly counts of PTB diagnoses and ART initiations. To account for time trends, we compared deviations of monthly event counts to yearly averages, and calculated correlation coefficients. We used multivariable regressions to assess associations between deviations of monthly ART initiation and PTB diagnosis counts from yearly averages, adjusted for monthly air temperatures and geographical latitude. As controls, we used Kaposi sarcoma and extrapulmonary tuberculosis (EPTB) diagnoses. Results All programmes showed monthly variations in PTB diagnoses that paralleled fluctuations in ART initiations, with recurrent patterns across 2004–2014. The strongest drops in PTB diagnoses occurred in December, followed by April–May in Zimbabwe and South Africa. This corresponded to holiday seasons, when clinical activities are reduced. We observed little monthly variation in ART initiations and PTB diagnoses in Zambia. Correlation coefficients supported parallel trends in ART initiations and PTB diagnoses (correlation coefficient: 0.28, 95% CI 0.21 to 0.35, P<0.001). Monthly temperatures and latitude did not substantially change regression coefficients between ART initiations and PTB diagnoses. Trends in Kaposi sarcoma and EPTB diagnoses similarly followed changes in ART initiations throughout the year. Conclusions Monthly variations in PTB diagnosis at ART programmes in Southern Africa likely occurred regardless of seasonal variations in temperatures or latitude and reflected fluctuations in clinical activities and changes in health-seeking behaviour throughout the year, rather than climatic factors. PMID:29330173
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Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D
2014-04-15
Cross-sectional cohort. The purpose of this study is to determine whether the EuroQOL-5D (EQ-5D) can be derived from commonly available low back disease-specific health-related quality of life measures. The Oswestry Disability Index (ODI) and numeric rating scales (0-10) for back pain (BP) and leg pain (LP) are widely used disease-specific measures in patients with lumbar degenerative disorders. Increasingly, the EQ-5D is being used as a measure of utility due to ease of administration and scoring. The EQ-5D, ODI, BP, and LP were prospectively collected in 14,544 patients seen in clinic for lumbar degenerative disorders. Pearson correlation coefficients for paired observations from multiple time points between ODI, BP, LP, and EQ-5D were determined. Regression modeling was done to compute the EQ-5D score from the ODI, BP, and LP. The mean age was 53.3 ± 16.4 years and 41% were male. Correlations between the EQ-5D and the ODI, BP, and LP were statistically significant (P < 0.0001) with correlation coefficients of -0.77, -0.50, and -0.57, respectively. The regression equation: [0.97711 + (-0.00687 × ODI) + (-0.01488 × LP) + (-0.01008 × BP)] to predict EQ-5D, had an R2 of 0.61 and a root mean square error of 0.149. The model using ODI alone had an R2 of 0.57 and a root mean square error of 0.156. The model using the individual ODI items had an R2 of 0.64 and a root mean square error of 0.143. The correlation coefficient between the observed and estimated EQ-5D score was 0.78. There was no statistically significant difference between the actual EQ-5D (0.553 ± 0.238) and the estimated EQ-5D score (0.553 ± 0.186) using the ODI, BP, and LP regression model. However, rounding off the coefficients to less than 5 decimal places produced less accurate results. Unlike previous studies showing a robust relationship between low back-specific measures and the Short Form-6D, a similar relationship was not seen between the ODI, BP, LP, and the EQ-5D. Thus, the EQ-5D cannot be accurately estimated from the ODI, BP, and LP. 2.
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Ramírez-Vélez, Robinson; Correa-Bautista, Jorge Enrique; González-Ruíz, Katherine; Vivas, Andrés; Triana-Reina, Héctor Reynaldo; Martínez-Torres, Javier; Prieto-Benavides, Daniel Humberto; Carrillo, Hugo Alejandro; Ramos-Sepúlveda, Jeison Alexander; Villa-González, Emilio; García-Hermoso, Antonio
2017-01-01
Recently, a body adiposity index (BAI = (hip circumference)/((height)(1.5))−18) was developed and validated in adult populations. The aim of this study was to evaluate the performance of BAI in estimating percentage body fat (BF%) in a sample of Colombian collegiate young adults. The participants were comprised of 903 volunteers (52% females, mean age = 21.4 years ± 3.3). We used the Lin’s concordance correlation coefficient, linear regression, Bland–Altman’s agreement analysis, concordance correlation coefficient (ρc) and the coefficient of determination (R2) between BAI, and BF%; by bioelectrical impedance analysis (BIA)). The correlation between the two methods of estimating BF% was R2 = 0.384, p < 0.001. A paired-sample t-test showed a difference between the methods (BIA BF% = 16.2 ± 3.1, BAI BF% = 30.0 ± 5.4%; p < 0.001). For BIA, bias value was 6.0 ± 6.2 BF% (95% confidence interval (CI) = −6.0 to 18.2), indicating that the BAI method overestimated BF% relative to the reference method. Lin’s concordance correlation coefficient was poor (ρc = 0.014, 95% CI = −0.124 to 0.135; p = 0.414). In Colombian college students, there was poor agreement between BAI- and BIA-based estimates of BF%, and so BAI is not accurate in people with low or high body fat percentage levels. PMID:28106719
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Remotely sensing wheat maturation with radar
NASA Technical Reports Server (NTRS)
Bush, T. F.; Ulaby, F. T.
1975-01-01
The scattering properties of wheat were studied in the 8-18 GHz band as a function of frequency, polarization, incidence angle, and crop maturity. Supporting ground truth was collected at the time of measurement. The data indicate that the radar backscattering coefficient is sensitive to both radar system parameters and crop characteristics particularly at incidence angles near nadir. Linear regression analyses of the radar backscattering coefficient on both time and plant moisture content result in rather good correlation. Furthermore, by calculating the average time rate of change of the radar backscattering coefficient it is found that it undergoes rapid variations shortly before and after the wheat is harvested. Both of these analyses suggest methods for estimating wheat maturity and for monitoring the progress of harvest.
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Saloheimo, T; González, S A; Erkkola, M; Milauskas, D M; Meisel, J D; Champagne, C M; Tudor-Locke, C; Sarmiento, O; Katzmarzyk, P T; Fogelholm, M
2015-01-01
Objective: The main aim of this study was to assess the reliability and validity of a food frequency questionnaire with 23 food groups (I-FFQ) among a sample of 9–11-year-old children from three different countries that differ on economical development and income distribution, and to assess differences between country sites. Furthermore, we assessed factors associated with I-FFQ's performance. Methods: This was an ancillary study of the International Study of Childhood Obesity, Lifestyle and the Environment. Reliability (n=321) and validity (n=282) components of this study had the same participants. Participation rates were 95% and 70%, respectively. Participants completed two I-FFQs with a mean interval of 4.9 weeks to assess reliability. A 3-day pre-coded food diary (PFD) was used as the reference method in the validity analyses. Wilcoxon signed-rank tests, intraclass correlation coefficients and cross-classifications were used to assess the reliability of I-FFQ. Spearman correlation coefficients, percentage difference and cross-classifications were used to assess the validity of I-FFQ. A logistic regression model was used to assess the relation of selected variables with the estimate of validity. Analyses based on information in the PFDs were performed to assess how participants interpreted food groups. Results: Reliability correlation coefficients ranged from 0.37 to 0.78 and gross misclassification for all food groups was <5%. Validity correlation coefficients were below 0.5 for 22/23 food groups, and they differed among country sites. For validity, gross misclassification was <5% for 22/23 food groups. Over- or underestimation did not appear for 19/23 food groups. Logistic regression showed that country of participation and parental education were associated (P⩽0.05) with the validity of I-FFQ. Analyses of children's interpretation of food groups suggested that the meaning of most food groups was understood by the children. Conclusion: I-FFQ is a moderately reliable method and its validity ranged from low to moderate, depending on food group and country site. PMID:27152180
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Mussin, Nadiar; Sumo, Marco; Lee, Kwang-Woong; Choi, YoungRok; Choi, Jin Yong; Ahn, Sung-Woo; Yoon, Kyung Chul; Kim, Hyo-Sin; Hong, Suk Kyun; Yi, Nam-Joon; Suh, Kyung-Suk
2017-04-01
Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs . Dr. Liver) in preoperative right liver graft estimation compared with real graft weight. Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots. Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R 2 = 0.719), than for the Rapidia group (R 2 = 0.688). Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation.
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Cubiella, Joaquín; Castells, Antoni; Andreu, Montserrat; Bujanda, Luis; Carballo, Fernando; Jover, Rodrigo; Lanas, Ángel; Morillas, Juan Diego; Salas, Dolores; Quintero, Enrique
2017-03-01
The adenoma detection rate (ADR) is the main quality indicator of colonoscopy. The ADR recommended in fecal immunochemical testing (FIT)-based colorectal cancer screening programs is unknown. Using the COLONPREV (NCT00906997) study dataset, we performed a post-hoc analysis to determine if there was a correlation between the ADR in primary and work-up colonoscopy, and the equivalent figure to the minimal 20% ADR recommended. Colonoscopy was performed in 5722 individuals: 5059 as primary strategy and 663 after a positive FIT result (OC-Sensor™; cut-off level 15 µg/g of feces). We developed a predictive model based on a multivariable lineal regression analysis including confounding variables. The median ADR was 31% (range, 14%-51%) in the colonoscopy group and 55% (range, 21%-83%) in the FIT group. There was a positive correlation in the ADR between primary and work-up colonoscopy (Pearson's coefficient 0.716; p < 0.001). ADR in the FIT group was independently related to ADR in the colonoscopy group: regression coefficient for colonoscopy ADR, 0.71 ( p = 0.009); sex, 0.09 ( p = 0.09); age, 0.3 ( p = 0.5); and region 0.00 ( p = 0.9). The equivalent figure to the 20% ADR was 45% (95% confidence interval, 35%-56%). ADR in primary and work-up colonoscopy of a FIT-positive result are positively and significantly correlated.
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Castells, Antoni; Andreu, Montserrat; Bujanda, Luis; Carballo, Fernando; Jover, Rodrigo; Lanas, Ángel; Morillas, Juan Diego; Salas, Dolores; Quintero, Enrique
2016-01-01
Background The adenoma detection rate (ADR) is the main quality indicator of colonoscopy. The ADR recommended in fecal immunochemical testing (FIT)-based colorectal cancer screening programs is unknown. Methods Using the COLONPREV (NCT00906997) study dataset, we performed a post-hoc analysis to determine if there was a correlation between the ADR in primary and work-up colonoscopy, and the equivalent figure to the minimal 20% ADR recommended. Colonoscopy was performed in 5722 individuals: 5059 as primary strategy and 663 after a positive FIT result (OC-Sensor™; cut-off level 15 µg/g of feces). We developed a predictive model based on a multivariable lineal regression analysis including confounding variables. Results The median ADR was 31% (range, 14%–51%) in the colonoscopy group and 55% (range, 21%–83%) in the FIT group. There was a positive correlation in the ADR between primary and work-up colonoscopy (Pearson’s coefficient 0.716; p < 0.001). ADR in the FIT group was independently related to ADR in the colonoscopy group: regression coefficient for colonoscopy ADR, 0.71 (p = 0.009); sex, 0.09 (p = 0.09); age, 0.3 (p = 0.5); and region 0.00 (p = 0.9). The equivalent figure to the 20% ADR was 45% (95% confidence interval, 35%–56%). Conclusions ADR in primary and work-up colonoscopy of a FIT-positive result are positively and significantly correlated. PMID:28344793
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Time-localized wavelet multiple regression and correlation
NASA Astrophysics Data System (ADS)
Fernández-Macho, Javier
2018-02-01
This paper extends wavelet methodology to handle comovement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multiscale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard pairwise wavelet correlations with rolling windows. Also, the spectral properties of weight functions are investigated and it is argued that some common time windows, such as the usual rectangular rolling window, are not satisfactory on these grounds. The method is illustrated with a multiscale analysis of the comovements of Eurozone stock markets during this century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along time and across timescales featuring an acute divide across timescales at about the quarterly scale. At longer scales, evidence from the long-term correlation structure can be interpreted as stable perfect integration among Euro stock markets. On the other hand, at intramonth and intraweek scales, the short-term correlation structure has been clearly evolving along time, experiencing a sharp increase during financial crises which may be interpreted as evidence of financial 'contagion'.
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Meteorological adjustment of yearly mean values for air pollutant concentration comparison
NASA Technical Reports Server (NTRS)
Sidik, S. M.; Neustadter, H. E.
1976-01-01
Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.
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Ogata, Yasuko; Nagano, Midori; Fukuda, Takashi; Hashimoto, Michio
2011-06-01
The purpose of this study was to examine how the nursing practice environment affects job retention and the turnover rate among hospital nurses. The Practice Environment Scale of the Nursing Work Index (PES-NWI) was applied to investigate the nurse working environment from the viewpoint of hospital nurses in Japan. Methods A postal mail survey was conducted using the PES-NWI questionnaire targeting 2,211 nurses who were working at 91 wards in 5 hospitals situated in the Tokyo metropolitan area from February to March in 2008. In the questionnaire, hospital nurses were asked about characteristics such as sex, age and work experience as a nurse, whether they would work at the same hospital in the next year, the 31 items of the PES-NWI and job satisfaction. Nurse managers were asked to provide staff numbers to calculate the turnover rate of each ward. Logistic regression analyses were carried out, with "intention to retain or leave the workplace next year" as the dependent variable, with composite and 5 sub-scale scores of the PES-NWI and nurse characteristics as independent variables. Correlation coefficients were calculated to investigate the relationship between nurse turnover rates and nursing practice environments. A total of 1,067 full-time nurses (48.3%) from 5 hospitals responded. Almost all of them were men (95.9%), with an average age of 29.2 years old. They had an average of 7.0 years total work experience in hospitals and 5.8 years of experience at their current hospital. Cronbach's alpha coefficients were 0.75 for composite of the PES-NWI, and 0.77-0.85 for the sub-scales. All correlation coefficients between PES-NWI and job satisfaction were significant (P < 0.01). In the logistic regression analysis, a composite of PES-NWI, "Nurse Manager's Ability, Leadership, and Support of Nurses" and "Staffing and Resource Adequacy" among the 5 sub-scales correlated with the intention of nurses to stay on (P < 0.05). The means for turnover rate were 10.4% for nurses and 17.6% for newly hired nurses. These rates were significantly correlated to the composite and some sub-scales of the PES-NWI. The working environment for nurses is important in retaining nurses working at hospitals. We confirmed the reliability and the validity of the PES-NWI scale based on the magnitude of the Cronbach's alpha coefficient and correlation coefficient between the PES-NWI scale and job satisfaction in this study.
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Mechanisms behind the estimation of photosynthesis traits from leaf reflectance observations
NASA Astrophysics Data System (ADS)
Dechant, Benjamin; Cuntz, Matthias; Doktor, Daniel; Vohland, Michael
2016-04-01
Many studies have investigated the reflectance-based estimation of leaf chlorophyll, water and dry matter contents of plants. Only few studies focused on photosynthesis traits, however. The maximum potential uptake of carbon dioxide under given environmental conditions is determined mainly by RuBisCO activity, limiting carboxylation, or the speed of photosynthetic electron transport. These two main limitations are represented by the maximum carboxylation capacity, V cmax,25, and the maximum electron transport rate, Jmax,25. These traits were estimated from leaf reflectance before but the mechanisms underlying the estimation remain rather speculative. The aim of this study was therefore to reveal the mechanisms behind reflectance-based estimation of V cmax,25 and Jmax,25. Leaf reflectance, photosynthetic response curves as well as nitrogen content per area, Narea, and leaf mass per area, LMA, were measured on 37 deciduous tree species. V cmax,25 and Jmax,25 were determined from the response curves. Partial Least Squares (PLS) regression models for the two photosynthesis traits V cmax,25 and Jmax,25 as well as Narea and LMA were studied using a cross-validation approach. Analyses of linear regression models based on Narea and other leaf traits estimated via PROSPECT inversion, PLS regression coefficients and model residuals were conducted in order to reveal the mechanisms behind the reflectance-based estimation. We found that V cmax,25 and Jmax,25 can be estimated from leaf reflectance with good to moderate accuracy for a large number of species and different light conditions. The dominant mechanism behind the estimations was the strong relationship between photosynthesis traits and leaf nitrogen content. This was concluded from very strong relationships between PLS regression coefficients, the model residuals as well as the prediction performance of Narea- based linear regression models compared to PLS regression models. While the PLS regression model for V cmax,25 was fully based on the correlation to Narea, the PLS regression model for Jmax,25 was not entirely based on it. Analyses of the contributions of different parts of the reflectance spectrum revealed that the information contributing to the Jmax,25 PLS regression model in addition to the main source of information, Narea, was mainly located in the visible part of the spectrum (500-900 nm). Estimated chlorophyll content could be excluded as potential source of this extra information. The PLS regression coefficients of the Jmax,25 model indicated possible contributions from chlorophyll fluorescence and cytochrome f content. In summary, we found that the main mechanism behind the estimation of V cmax,25 and Jmax,25 from leaf reflectance observations is the correlation to Narea but that there is additional information related to Jmax,25 mainly in the visible part of the spectrum.
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Comparison of co-expression measures: mutual information, correlation, and model based indices.
Song, Lin; Langfelder, Peter; Horvath, Steve
2012-12-09
Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.
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A combined evaluation of the characteristics and acute toxicity of antibiotic wastewater.
Yu, Xin; Zuo, Jiane; Li, Ruixia; Gan, Lili; Li, Zaixing; Zhang, Fei
2014-08-01
The conventional parameters and acute toxicities of antibiotic wastewater collected from each treatment unit of an antibiotic wastewater treatment plant have been investigated. The investigation of the conventional parameters indicated that the antibiotic wastewater treatment plant performed well under the significant fluctuation in influent water quality. The results of acute toxicity indicated that the toxicity of antibiotic wastewater could be reduced by 94.3 percent on average after treatment. However, treated antibiotic effluents were still toxic to Vibrio fischeri. The toxicity of antibiotic production wastewater could be attributed to the joint effects of toxic compound mixtures in wastewater. Moreover, aerobic biological treatment processes, including sequencing batch reactor (SBR) and aerobic biofilm reactor, played the most important role in reducing toxicity by 92.4 percent. Pearson׳s correlation coefficients revealed that toxicity had a strong and positive linear correlation with organic substances, nitrogenous compounds, S(2-), volatile phenol, cyanide, As, Zn, Cd, Ni and Fe. Ammonia nitrogen (NH4(+)) was the greatest contributor to toxicity according to the stepwise regression method. The multiple regression model was a good fit for [TU50-15 min] as a function of [NH₄(+)] with the determination coefficient of 0.981. Copyright © 2014 Elsevier Inc. All rights reserved.
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Basnet, Bishal Babu; Parajuli, Prakash Kumar; Singh, Raj Kumar; Suwal, Pramita; Shrestha, Pragya; Baral, Dharanidhar
2015-01-01
Establishment of proper occlusal vertical dimension (OVD) is one of the important tasks for successful prosthodontic therapy. An ideal method for determining OVD in terms of cost, time, and instrument requirements has been sought in prosthodontics by various investigators. However, no such single method has been formulated. In the current anthropometric study, the relationship of the length of the thumb to the OVD was tested in two ethnic groups of Nepal, Aryans, and Mongoloids. The result of this study can be useful in determining proper OVD in edentulous patients. The primary aim of the present study was to evaluate the correlation between the length of the thumb and OVD in Aryan and Mongoloid ethnic groups. The secondary aim was to compare the correlation between OVD and other anatomic measurements (eye-ear distance and pupil-to-rima oris distance) in these ethnicities. The OVD, thumb length, eye-ear distance and distance between pupil of eye and rima oris were measured in a total of 500 adult dentulous volunteers. The correlation between OVD and thumb length as well as other anatomic measurements was checked with Pearson's product moment correlation coefficient. Linear regression analysis was performed to determine the relationship of OVD to the length of the thumb. The thumb length was significantly (P≤0.05) correlated with strong and positive values (Pearson's coefficient =0.874 in the whole population, 0.826 in Aryans, and 0.944 in Mongoloids). Regression analysis showed that thumb length was significantly related to OVD in both ethnic groups. Within the limitations of the present study, the result implies that thumb length can be used as an adjunct for establishing OVD in the edentulous patients.
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Huang, L; Fantke, P; Ernstoff, A; Jolliet, O
2017-11-01
Indoor releases of organic chemicals encapsulated in solid materials are major contributors to human exposures and are directly related to the internal diffusion coefficient in solid materials. Existing correlations to estimate the diffusion coefficient are only valid for a limited number of chemical-material combinations. This paper develops and evaluates a quantitative property-property relationship (QPPR) to predict diffusion coefficients for a wide range of organic chemicals and materials. We first compiled a training dataset of 1103 measured diffusion coefficients for 158 chemicals in 32 consolidated material types. Following a detailed analysis of the temperature influence, we developed a multiple linear regression model to predict diffusion coefficients as a function of chemical molecular weight (MW), temperature, and material type (adjusted R 2 of .93). The internal validations showed the model to be robust, stable and not a result of chance correlation. The external validation against two separate prediction datasets demonstrated the model has good predicting ability within its applicability domain (Rext2>.8), namely MW between 30 and 1178 g/mol and temperature between 4 and 180°C. By covering a much wider range of organic chemicals and materials, this QPPR facilitates high-throughput estimates of human exposures for chemicals encapsulated in solid materials. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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Kim, Seong-Gil
2018-01-01
Background The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. Material/Methods This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. Results In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). Conclusions Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement. PMID:29760375
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Kim, Seong-Gil; Kim, Wan-Soo
2018-05-15
BACKGROUND The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. MATERIAL AND METHODS This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. RESULTS In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). CONCLUSIONS Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement.
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Kawada, Tomoyuki; Yamada, Natsuki
2012-01-01
Job satisfaction is an important factor in the occupational lives of workers. In this study, the relationship between one-dimensional scale of job satisfaction and psychological wellbeing was evaluated. A total of 1,742 workers (1,191 men and 551 women) participated. 100-point scale evaluating job satisfaction (0 [extremely dissatisfied] to 100 [extremely satisfied]) and the General Health Questionnaire, 12-item version (GHQ-12) evaluating psychological wellbeing were used. A multiple regression analysis was then used, controlling for gender and age. The change in the GHQ-12 and job satisfaction scores after a two-year interval was also evaluated. The mean age for the subjects was 42.2 years for the men and 36.2 years for the women. The GHQ-12 and job satisfaction scores were significantly correlated in each generation. The partial correlation coefficients between the changes in the two variables, controlling for age, were -0.395 for men and -0.435 for women (p< 0.001). A multiple regression analysis revealed that the 100-point job satisfaction score was associated with the GHQ-12 results (p< 0.001). The adjusted multiple correlation coefficient was 0.275. The 100-point scale, which is a simple and easy tool for evaluating job satisfaction, was significantly associated with psychological wellbeing as judged using the GHQ-12.
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Lu, Hsueh-Kuan; Chen, Yu-Yawn; Yeh, Chinagwen; Chuang, Chih-Lin; Chiang, Li-Ming; Lai, Chung-Liang; Casebolt, Kevin M; Huang, Ai-Chun; Lin, Wen-Long; Hsieh, Kuen-Chang
2017-08-22
The aim of this study was to evaluate leg-to-leg bioelectrical impedance analysis (LBIA) using a four-contact electrode system for measuring abdominal visceral fat area (VFA). The present study recruited 381 (240 male and 141 female) Chinese participants to compare VFA measurements estimated by a standing LBIA system (VFALBIA) with computerized tomography (CT) scanned at the L4-L5 vertebrae (VFA CT ). The total mean body mass index (BMI) was 24.7 ± 4.2 kg/m 2 . Correlation analysis, regression analysis, Bland-Altman plot, and paired sample t-tests were used to analyze the accuracy of the VFA LBIA . For the total subjects, the regression line was VFA LBIA = 0.698 VFA CT + 29.521, (correlation coefficient (r) = 0.789, standard estimate of error (SEE) = 24.470 cm 2 , p < 0.001), Lin's correlation coefficient (CCC) was 0.785; and the limit of agreement (LOA; mean difference ±2 standard deviation) ranged from -43.950 to 67.951 cm 2 , LOA% (given as a percentage of mean value measured by the CT) was 48.2%. VFA LBIA and VFA CT showed significant difference (p < 0.001). Collectively, the current study indicates that LBIA has limited potential to accurately estimate visceral fat in a clinical setting.
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The effect of clouds on the earth's radiation budget
NASA Technical Reports Server (NTRS)
Ziskin, Daniel; Strobel, Darrell F.
1991-01-01
The radiative fluxes from the Earth Radiation Budget Experiment (ERBE) and the cloud properties from the International Satellite Cloud Climatology Project (ISCCP) over Indonesia for the months of June and July of 1985 and 1986 were analyzed to determine the cloud sensitivity coefficients. The method involved a linear least squares regression between co-incident flux and cloud coverage measurements. The calculated slope is identified as the cloud sensitivity. It was found that the correlations between the total cloud fraction and radiation parameters were modest. However, correlations between cloud fraction and IR flux were improved by separating clouds by height. Likewise, correlations between the visible flux and cloud fractions were improved by distinguishing clouds based on optical depth. Calculating correlations between the net fluxes and either height or optical depth segregated cloud fractions were somewhat improved. When clouds were classified in terms of their height and optical depth, correlations among all the radiation components were improved. Mean cloud sensitivities based on the regression of radiative fluxes against height and optical depth separated cloud types are presented. Results are compared to a one-dimensional radiation model with a simple cloud parameterization scheme.
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Prediction of soil organic carbon partition coefficients by soil column liquid chromatography.
Guo, Rongbo; Liang, Xinmiao; Chen, Jiping; Wu, Wenzhong; Zhang, Qing; Martens, Dieter; Kettrup, Antonius
2004-04-30
To avoid the limitation of the widely used prediction methods of soil organic carbon partition coefficients (KOC) from hydrophobic parameters, e.g., the n-octanol/water partition coefficients (KOW) and the reversed phase high performance liquid chromatographic (RP-HPLC) retention factors, the soil column liquid chromatographic (SCLC) method was developed for KOC prediction. The real soils were used as the packing materials of RP-HPLC columns, and the correlations between the retention factors of organic compounds on soil columns (ksoil) and KOC measured by batch equilibrium method were studied. Good correlations were achieved between ksoil and KOC for three types of soils with different properties. All the square of the correlation coefficients (R2) of the linear regression between log ksoil and log KOC were higher than 0.89 with standard deviations of less than 0.21. In addition, the prediction of KOC from KOW and the RP-HPLC retention factors on cyanopropyl (CN) stationary phase (kCN) was comparatively evaluated for the three types of soils. The results show that the prediction of KOC from kCN and KOW is only applicable to some specific types of soils. The results obtained in the present study proved that the SCLC method is appropriate for the KOC prediction for different types of soils, however the applicability of using hydrophobic parameters to predict KOC largely depends on the properties of soil concerned.
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Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.
Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi
2017-09-20
Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
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ERIC Educational Resources Information Center
Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.
2004-01-01
We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…
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NASA Astrophysics Data System (ADS)
Visser, H.; Molenaar, J.
1995-05-01
The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of the model is rather poor, and possible explanations are discussed.
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Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung
2015-01-01
This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.
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1990-05-01
0.759 0.744 0.768 0.753 106 (THUMBBR) THUMB BREADTH -0.652 -0.673 -0.539 -0.663 217 (LIPLGTHH) LIP LENGTH HEADBOARD 0.017 0.019 0.020 51 (FTBRHOR) FOOT...DEPENDENT VARIABLE: (106) THUMB BREADTH (THUBBR) MODEL INDEPENDENT VARIABLE 1 2 3 4 5 INTERCEPT 6.621 5.016 6.267 5.697 4.528 59 (HANDCIRC) HAND...95 (SLLSPEL) SLEEVE LENGTH: SPINE-ELBOW -0.020 -0.019 -C.018 9 (BLFTCIRC) BALL OF FOOT CIRCUMFERENCE -0.032 -0.039 106 (THUMBBR) THUMB BREADTH 0.228
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Sochacki, Kyle R; Jack, Robert A; Bekhradi, Arya; Delgado, Domenica; McCulloch, Patrick C; Harris, Joshua D
2018-06-01
To determine if there are significant differences in preoperative patient-reported outcome (PRO) scores in patients with and without self-reported medication allergies undergoing hip arthroscopy. Consecutive subjects undergoing hip arthroscopy for femoroacetabular impingement (FAI) syndrome by a single surgeon were retrospectively reviewed. PROs were collected within 6 weeks of the date of surgery. PROs included International Hip Outcome Tool (iHOT-12), Hip Outcome Score (HOS), and Short-Form (SF-12) scores. Allergies to medications were self-reported preoperatively within 6 weeks of the date of surgery. Patient demographics were recorded. Bivariate correlations and multivariate regression models were calculated to identify associations with baseline hip outcome scores. Two hundred twelve subjects were analyzed (56% female, mean age 35.1 ± 13.2 years). Seventy-two subjects (34%) self-reported allergies (range 1-10; 41 subjects had 1 allergy; 14 subjects had 2; 8 subjects had 3; 2 subjects had 4; 7 subjects had 5 or more). The most commonly reported allergies included penicillin (18), sulfa (13), and codeine (11). Female gender was significantly correlated with number of allergies (Pearson correlation coefficient, 0.188; P < .001). SF-12 Mental Component Score (MCS) was significantly correlated with HOS-ADL (Pearson correlation coefficient, 0.389; P < .001), HOS-SSS (Pearson correlation coefficient, 0.251; P < .001), and iHOT-12 (Pearson correlation coefficient, 0.385; P < .001). There was no significant correlation between number of allergies and all hip PROs. In all multivariate models, the SF-12 MCS had the strongest association with HOS-ADL, HOS-SSS, and iHOT-12 (P < .001 for all). Allergies were not significantly associated with any hip PROs. In patients undergoing hip arthroscopy for FAI syndrome, self-reported medication allergies are not significantly associated with preoperative patient-reported hip outcome scores. Level III, retrospective comparative case series. Copyright © 2018 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.
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[Study on objectively evaluating skin aging according to areas of skin texture].
Shan, Gaixin; Gan, Ping; He, Ling; Sun, Lu; Li, Qiannan; Jiang, Zheng; He, Xiangqian
2015-02-01
Skin aging principles play important roles in skin disease diagnosis, the evaluation of skin cosmetic effect, forensic identification and age identification in sports competition, etc. This paper proposes a new method to evaluate the skin aging objectively and quantitatively by skin texture area. Firstly, the enlarged skin image was acquired. Then, the skin texture image was segmented by using the iterative threshold method, and the skin ridge image was extracted according to the watershed algorithm. Finally, the skin ridge areas of the skin texture were extracted. The experiment data showed that the average areas of skin ridges, of both men and women, had a good correlation with age (the correlation coefficient r of male was 0.938, and the correlation coefficient r of female was 0.922), and skin texture area and age regression curve showed that the skin texture area increased with age. Therefore, it is effective to evaluate skin aging objectively by the new method presented in this paper.
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Standiford, H C; Bernstein, D; Nipper, H C; Caplan, E; Tatem, B; Hall, J S; Reynolds, J
1981-01-01
Gentamicin levels were determined in 100 serum specimens by a new latex agglutination inhibition card test, a radioimmunoassay (RIA), and a bioassay. Correlation coefficients determined by linear regression analysis demonstrated that the levels obtained by the latex agglutination inhibition card test had a high degree of correlation with the RIA and could be performed much faster and more economically when processing small numbers of specimens. The bioassay had a slightly lower degree of correlation with both the RIA and the latex test and was adversely influenced by concurrently administered antibiotics which could not be eliminated by beta-lactamase. When measuring gentamicin concentrations above 2 micrograms/ml, the coefficient of variation was less than 14% for the latex agglutination assay compared with 15% for the bioassay and 12% for RIA. The latex agglutination inhibition card test is a rapid, accurate, specific, and reproducible method for monitoring gentamicin levels in patients and is particularly applicable for laboratories processing small numbers of specimens. PMID:7247384
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Senior, Samir A; Madbouly, Magdy D; El massry, Abdel-Moneim
2011-09-01
Quantum chemical and topological descriptors of some organophosphorus compounds (OP) were correlated with their toxicity LD(50) as a dermal. The quantum chemical parameters were obtained using B3LYP/LANL2DZdp-ECP optimization. Using linear regression analysis, equations were derived to calculate the theoretical LD(50) of the studied compounds. The inclusion of quantum parameters, having both charge indices and topological indices, affects the toxicity of the studied compounds resulting in high correlation coefficient factors for the obtained equations. Two of the new four firstly supposed descriptors give higher correlation coefficients namely the Heteroatom Corrected Extended Connectivity Randic index ((1)X(HCEC)) and the Density Randic index ((1)X(Den)). The obtained linear equations were applied to predict the toxicity of some related structures. It was found that the sulfur atoms in these compounds must be replaced by oxygen atoms to achieve improved toxicity. Copyright © 2011 Elsevier Ltd. All rights reserved.
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NASA Technical Reports Server (NTRS)
Lee, Chi M.; Schock, Harold J.
1988-01-01
Currently, the heat transfer equation used in the rotary combustion engine (RCE) simulation model is taken from piston engine studies. These relations have been empirically developed by the experimental input coming from piston engines whose geometry differs considerably from that of the RCE. The objective of this work was to derive equations to estimate heat transfer coefficients in the combustion chamber of an RCE. This was accomplished by making detailed temperature and pressure measurements in a direct injection stratified charge (DISC) RCE under a range of conditions. For each specific measurement point, the local gas velocity was assumed equal to the local rotor tip speed. Local physical properties of the fluids were then calculated. Two types of correlation equations were derived and are described in this paper. The first correlation expresses the Nusselt number as a function of the Prandtl number, Reynolds number, and characteristic temperature ratio; the second correlation expresses the forced convection heat transfer coefficient as a function of fluid temperature, pressure and velocity.
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Jović, Ozren
2016-12-15
A novel method for quantitative prediction and variable-selection on spectroscopic data, called Durbin-Watson partial least-squares regression (dwPLS), is proposed in this paper. The idea is to inspect serial correlation in infrared data that is known to consist of highly correlated neighbouring variables. The method selects only those variables whose intervals have a lower Durbin-Watson statistic (dw) than a certain optimal cutoff. For each interval, dw is calculated on a vector of regression coefficients. Adulteration of cold-pressed linseed oil (L), a well-known nutrient beneficial to health, is studied in this work by its being mixed with cheaper oils: rapeseed oil (R), sesame oil (Se) and sunflower oil (Su). The samples for each botanical origin of oil vary with respect to producer, content and geographic origin. The results obtained indicate that MIR-ATR, combined with dwPLS could be implemented to quantitative determination of edible-oil adulteration. Copyright © 2016 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying
2018-01-01
Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.
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Serum Liver Fibrosis Markers in the Prognosis of Liver Cirrhosis: A Prospective Observational Study
Qi, Xingshun; Liu, Xu; Zhang, Yongguo; Hou, Yue; Ren, Linan; Wu, Chunyan; Chen, Jiang; Xia, Chunlian; Zhao, Jiajun; Wang, Di; Zhang, Yanlin; Zhang, Xia; Lin, Hao; Wang, Hezhi; Wang, Jinling; Cui, Zhongmin; Li, Xueyan; Deng, Han; Hou, Feifei; Peng, Ying; Wang, Xueying; Shao, Xiaodong; Li, Hongyu; Guo, Xiaozhong
2016-01-01
Background The prognostic role of serum liver fibrosis markers in cirrhotic patients remains unclear. We performed a prospective observational study to evaluate the effect of amino-terminal pro-peptide of type III pro-collagen (PIIINP), collagen IV (CIV), laminin (LN), and hyaluronic acid (HA) on the prognosis of liver cirrhosis. Material/Methods All patients who were diagnosed with liver cirrhosis and admitted to our department were prospectively enrolled. PIIINP, CIV, LN, and HA levels were tested. Results Overall, 108 cirrhotic patients were included. Correlation analysis demonstrated that CIV (coefficient r: 0.658, p<0.001; coefficient r: 0.368, p<0.001), LN (coefficient r: 0.450, p<0.001; coefficient r: 0.343, p<0.001), and HA (coefficient r: 0.325, p=0.001; coefficient r: 0.282, p=0.004) levels, but not PIIINP level (coefficient r: 0.081, p=0.414; coefficient r: 0.090, p=0.363), significantly correlated with Child-Pugh and MELD scores. Logistic regression analysis demonstrated that HA (odds ratio=1.00003, 95% confidence interval [CI]=1.000004–1.000056, p=0.022) was significantly associated with the 6-month mortality. Receiver operating characteristics analysis demonstrated that the area under the curve (AUC) of HA for predicting the 6-month mortality was 0.612 (95%CI=0.508–0.709, p=0.1531). Conclusions CIV, LN, and HA levels were significantly associated with the severity of liver dysfunction, but might be inappropriate for the prognostic assessment of liver cirrhosis. PMID:27480906
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2014-01-01
Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463
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Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D
2017-11-01
Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.
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Harada, Sei; Hirayama, Akiyoshi; Chan, Queenie; Kurihara, Ayako; Fukai, Kota; Iida, Miho; Kato, Suzuka; Sugiyama, Daisuke; Kuwabara, Kazuyo; Takeuchi, Ayano; Akiyama, Miki; Okamura, Tomonori; Ebbels, Timothy M D; Elliott, Paul; Tomita, Masaru; Sato, Asako; Suzuki, Chizuru; Sugimoto, Masahiro; Soga, Tomoyoshi; Takebayashi, Toru
2018-01-01
Cohort studies with metabolomics data are becoming more widespread, however, large-scale studies involving 10,000s of participants are still limited, especially in Asian populations. Therefore, we started the Tsuruoka Metabolomics Cohort Study enrolling 11,002 community-dwelling adults in Japan, and using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry. The CE-MS method is highly amenable to absolute quantification of polar metabolites, however, its reliability for large-scale measurement is unclear. The aim of this study is to examine reproducibility and validity of large-scale CE-MS measurements. In addition, the study presents absolute concentrations of polar metabolites in human plasma, which can be used in future as reference ranges in a Japanese population. Metabolomic profiling of 8,413 fasting plasma samples were completed using CE-MS, and 94 polar metabolites were structurally identified and quantified. Quality control (QC) samples were injected every ten samples and assessed throughout the analysis. Inter- and intra-batch coefficients of variation of QC and participant samples, and technical intraclass correlation coefficients were estimated. Passing-Bablok regression of plasma concentrations by CE-MS on serum concentrations by standard clinical chemistry assays was conducted for creatinine and uric acid. In QC samples, coefficient of variation was less than 20% for 64 metabolites, and less than 30% for 80 metabolites out of the 94 metabolites. Inter-batch coefficient of variation was less than 20% for 81 metabolites. Estimated technical intraclass correlation coefficient was above 0.75 for 67 metabolites. The slope of Passing-Bablok regression was estimated as 0.97 (95% confidence interval: 0.95, 0.98) for creatinine and 0.95 (0.92, 0.96) for uric acid. Compared to published data from other large cohort measurement platforms, reproducibility of metabolites common to the platforms was similar to or better than in the other studies. These results show that our CE-MS platform is suitable for conducting large-scale epidemiological studies.
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Interpreting Regression Results: beta Weights and Structure Coefficients are Both Important.
ERIC Educational Resources Information Center
Thompson, Bruce
Various realizations have led to less frequent use of the "OVA" methods (analysis of variance--ANOVA--among others) and to more frequent use of general linear model approaches such as regression. However, too few researchers understand all the various coefficients produced in regression. This paper explains these coefficients and their…
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Biases and Standard Errors of Standardized Regression Coefficients
ERIC Educational Resources Information Center
Yuan, Ke-Hai; Chan, Wai
2011-01-01
The paper obtains consistent standard errors (SE) and biases of order O(1/n) for the sample standardized regression coefficients with both random and given predictors. Analytical results indicate that the formulas for SEs given in popular text books are consistent only when the population value of the regression coefficient is zero. The sample…
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NASA Astrophysics Data System (ADS)
Chandramouli, Bharadwaj; Jang, Myoseon; Kamens, Richard M.
The partitioning of a diverse set of semivolatile organic compounds (SOCs) on a variety of organic aerosols was studied using smog chamber experimental data. Existing data on the partitioning of SOCs on aerosols from wood combustion, diesel combustion, and the α-pinene-O 3 reaction was augmented by carrying out smog chamber partitioning experiments on aerosols from meat cooking, and catalyzed and uncatalyzed gasoline engine exhaust. Model compositions for aerosols from meat cooking and gasoline combustion emissions were used to calculate activity coefficients for the SOCs in the organic aerosols and the Pankow absorptive gas/particle partitioning model was used to calculate the partitioning coefficient Kp and quantitate the predictive improvements of using the activity coefficient. The slope of the log K p vs. log p L0 correlation for partitioning on aerosols from meat cooking improved from -0.81 to -0.94 after incorporation of activity coefficients iγ om. A stepwise regression analysis of the partitioning model revealed that for the data set used in this study, partitioning predictions on α-pinene-O 3 secondary aerosol and wood combustion aerosol showed statistically significant improvement after incorporation of iγ om, which can be attributed to their overall polarity. The partitioning model was sensitive to changes in aerosol composition when updated compositions for α-pinene-O 3 aerosol and wood combustion aerosol were used. The octanol-air partitioning coefficient's ( KOA) effectiveness as a partitioning correlator over a variety of aerosol types was evaluated. The slope of the log K p- log K OA correlation was not constant over the aerosol types and SOCs used in the study and the use of KOA for partitioning correlations can potentially lead to significant deviations, especially for polar aerosols.
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Xue, Dan; Yin, Jingyuan
2014-05-01
In this study, we explored the potential applications of the Ozone Monitoring Instrument (OMI) satellite sensor in air pollution research. The OMI planetary boundary layer sulfur dioxide (SO2_PBL) column density and daily average surface SO2 concentration of Shanghai from 2004 to 2012 were analyzed. After several consecutive years of increase, the surface SO2 concentration finally declined in 2007. It was higher in winter than in other seasons. The coefficient between daily average surface SO2 concentration and SO2_PBL was only 0.316. But SO2_PBL was found to be a highly significant predictor of the surface SO2 concentration using the simple regression model. Five meteorological factors were considered in this study, among them, temperature, dew point, relative humidity, and wind speed were negatively correlated with surface SO2 concentration, while pressure was positively correlated. Furthermore, it was found that dew point was a more effective predictor than temperature. When these meteorological factors were used in multiple regression, the determination coefficient reached 0.379. The relationship of the surface SO2 concentration and meteorological factors was seasonally dependent. In summer and autumn, the regression model performed better than in spring and winter. The surface SO2 concentration predicting method proposed in this study can be easily adapted for other regions, especially most useful for those having no operational air pollution forecasting services or having sparse ground monitoring networks.
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Qidwai, Tabish; Yadav, Dharmendra K; Khan, Feroz; Dhawan, Sangeeta; Bhakuni, R S
2012-01-01
This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r(2)) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV(2)) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values.
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Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.
Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo
2016-01-01
In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.
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Heddam, Salim
2014-11-01
The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).
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NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet
2010-05-01
This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.
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Onabotulinum toxin A dosage trends over time for adductor spasmodic dysphonia: A 15-year experience.
Tang, Christopher G; Novakovic, Daniel; Mor, Niv; Blitzer, Andrew
2016-03-01
Although onabotulinum neurotoxin A (BoNTA) has been used for over three decades for the treatment of adductor spasmodic dysphonia, no study has been performed to look at the trend of BoNTA dosages across time. The goal of this study is to evaluate the dosage trends to determine if the dosage necessary for voice improvement in patients increases over time. Charts were reviewed for patients with 15 years or more of experience. Linear regression analysis was performed to determine correlation coefficients and trends. Fifty-five patients receiving BoNTA injections by the senior author (a.b.) for over 15 years were evaluated. Thirty-nine patients (82% female) met inclusion criteria. Patients received injections over an average of 18.6 years ± 1.36 years, with the longest follow-up of 21.5 years. Of 39 patients, 16 (41%) had a negative correlation coefficient (Pearson's r) suggesting a decrease over time, whereas 23 (59%) had a positive correlation coefficient suggesting an increase over time. The mean correlation coefficient was 0.139 ± 0.534 and P < 0.05 in 19 patients and P > 0.05 in 20 patients. R(2) for all patients were less than 0.75. Onabotulinum neurotoxin A injection dosage trends vary depending on the individual over time. Overall, the dose range appears to be stable in the majority of patients, suggesting that tolerance does not play a significant part in dose variation over time. 4. Laryngoscope, 126:678-681, 2016. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.
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Parent-Child Resemblance in Weight Status and Its Correlates in the United States
Liang, Lan; Wang, Youfa
2013-01-01
Background Few studies have examined parent-child resemblance in body weight status using nationally representative data for the US. Design We analyzed Body Mass Index (BMI), weight status, and related correlates for 4,846 boys, 4,725 girls, and their parents based on US nationally representative data from the 2006 and 2007 Medical Expenditure Panel Survey (MEPS). Pearson partial correlation coefficients, percent agreement, weighted kappa coefficients, and binary and multinomial logistic regression were used to examine parent-child resemblance, adjusted for complex sampling design. Results Pearson partial correlation coefficients between parent and child’s BMI measures were 0.15 for father-son pairs, 0.17 for father-daughter pairs, 0.20 for mother-son pairs, and 0.23 for mother-daughter pairs. The weighted kappa coefficients between BMI quintiles of parent and child ranged from −0.02 to 0.25. Odds ratio analyses found children were 2.1 (95% confidence interval (CI): 1.6, 2.8) times more likely to be obese if only their father was obese, 1.9 (95% CI: 1.5, 2.4) times more likely if only their mother was obese, and 3.2 (95% CI: 2.5, 4.2) times more likely if both parents were obese. Conclusions Parent-child resemblance in BMI appears weak and may vary across parent-child dyad types in the US population. However, parental obesity status is associated with children’s obesity status. Use of different measures of parent-child resemblance in body weight status can lead to different conclusions. PMID:23762352
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Arnaoutakis, George J.; George, Timothy J.; Alejo, Diane E.; Merlo, Christian A.; Baumgartner, William A.; Cameron, Duke E.; Shah, Ashish S.
2011-01-01
Context The impact of Society of Thoracic Surgeons (STS) predicted mortality risk score on resource utilization after aortic valve replacement (AVR) has not been previously studied. Objective We hypothesize that increasing STS risk scores in patients having AVR are associated with greater hospital charges. Design, Setting, and Patients Clinical and financial data for patients undergoing AVR at a tertiary care, university hospital over a ten-year period (1/2000–12/2009) were retrospectively reviewed. The current STS formula (v2.61) for in-hospital mortality was used for all patients. After stratification into risk quartiles (Q), index admission hospital charges were compared across risk strata with Rank-Sum tests. Linear regression and Spearman’s coefficient assessed correlation and goodness of fit. Multivariable analysis assessed relative contributions of individual variables on overall charges. Main Outcome Measures Inflation-adjusted index hospitalization total charges Results 553 patients had AVR during the study period. Average predicted mortality was 2.9% (±3.4) and actual mortality was 3.4% for AVR. Median charges were greater in the upper Q of AVR patients [Q1–3,$39,949 (IQR32,708–51,323) vs Q4,$62,301 (IQR45,952–97,103), p=<0.01]. On univariate linear regression, there was a positive correlation between STS risk score and log-transformed charges (coefficient: 0.06, 95%CI 0.05–0.07, p<0.01). Spearman’s correlation R-value was 0.51. This positive correlation persisted in risk-adjusted multivariable linear regression. Each 1% increase in STS risk score was associated with an added $3,000 in hospital charges. Conclusions This study showed increasing STS risk score predicts greater charges after AVR. As competing therapies such as percutaneous valve replacement emerge to treat high risk patients, these results serve as a benchmark to compare resource utilization. PMID:21497834
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Chemical and Physical Predictors of the Nutritive Value of Wheat in Broiler Diets
Ball, M. E. E.; Owens, B.; McCracken, K. J.
2013-01-01
The aim of this study was to establish relationships between chemical and physical parameters of wheat with performance and digestibilities of feed components in broiler chickens fed on wheat-based diets. Ninety-four wheat samples were selected for inclusion in four bird trials. Birds were housed in individual wire metabolism cages from 7 to 28 d and offered water and feed ad libitum. Dry matter intake (DMI), liveweight gain (LWG) and gain:feed were measured weekly. A balance collection was carried out from 14 to 21 d for determination of apparent metabolizable energy (AME), ME:gain, dry matter retention, oil and neutral detergent fibre (NDF) digestibility. At 28 d the birds were humanely killed, the contents of the jejunum removed for determination of in vivo viscosity and the contents of the ileum removed for determination of ileal dry matter, starch and protein digestibility. When wheat parameters were correlated with bird performance data, it was found that specific weight was not significantly (p>0.05) related to bird performance. Bird DMI, LWG and gain:feed were best correlated (p<0.05) with the rate of starch digestion, although the coefficients of correlation (r) were still low (0.246 to 0.523). A negative relationship (p<0.01) between AME and total (r = −0.432) and soluble (r = −0.304) non starch polysaccharide (NSP) was observed in this study. Thousand grain weight (TG) was positively correlated with DMI (r = 0.299), LWG (r = 0.343) and gain:feed (r = 0.371). When establishing multiple regression relationships, correlation coefficients greater than 0.8 were achieved for DMI, LWG, gain:feed and ileal crude protein digestibility. However, the economics involved in determining the parameters involved in the regressions make the process impractical. PMID:25049711
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Aggarwal, P; Kashyap, B
2017-06-01
Rampant use of fluconazole in Candida infections has led to predominance of less susceptible non-albicans Candida over Candida albicans. The aim of the study was to determine if zone diameters around fluconazole disk can be used to estimate the minimum inhibitory concentration (MIC) for clinical isolates of Candida species and vice versa. Categorical agreement between the Clinical & Laboratory Standards Institute (CLSI) recommended disk diffusion and CLSI broth microdilution method was sought for. Antifungal susceptibility testing by disk diffusion and Broth microdilution was done as per CLSI document M44-S3 and CLSI document M27-S4 for Candida isolates respectively. Regression analysis correlating zone diameters to MIC value was done. Pearson's correlation coefficient was calculated to determine correlation between disk zone diameters and MICs. Candida albicans (33.3%) was clearly outnumbered by other non-albicans species predominantly Candida tropicalis (42.5%) and Candida glabrata (18.4%). Ten percent of the strains were resistant to fluconazole by disk diffusion and 13% by broth microdilution. MIC range for Candida albicans and Candida tropicalis ranged from≤0.25-64μg/ml while that of Candida glabrata ranged from≤0.25-128μg/ml. Categorical agreement between disk diffusion and broth microdilution was 86.8%. Pearson's coefficient of correlation was -0.5975 indicating moderate negative correlation between the two variables. Zone sizes can be used to estimate the MIC values, although with limited accuracy. There should be a constant effort to upgrade the guidelines in view of new clinical data, and laboratories should make an active effort to incorporate them. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
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Early functional outcome after lateral UKA is sensitive to postoperative lower limb alignment.
van der List, J P; Chawla, H; Villa, J C; Zuiderbaan, H A; Pearle, A D
2017-03-01
The predictive role of patient-specific characteristics and radiographic parameters on medial unicompartmental knee arthroplasty (UKA) outcomes is well known, but knowledge of these predictors is lacking in lateral UKA. Therefore, purpose of this study was to assess the predictive role of these parameters on short-term functional outcomes of lateral UKA. In this retrospective cohort study, Western Ontario and McMaster Universities Arthritis Index scores were collected at 2-year follow-up (median 2.2 years, range 2.0-4.0 years) in 39 patients who underwent lateral UKA. Patient-specific characteristics included age, BMI and gender, while radiographic parameters included osteoarthritis severity of all three compartments and both preoperative and postoperative hip-knee-ankle alignment. BMI, gender, age and preoperative valgus alignment were not correlated with functional outcomes, while postoperative valgus alignment was correlated with functional outcomes (0.561; p = 0.001). Postoperative valgus of 3°-7° was correlated with better outcomes than more neutral (-2° to 3° valgus) alignment (96.7 vs. 85.6; p = 0.011). Postoperative alignment was a predictor when corrected for patient-specific characteristics (regression coefficient 4.1; p < 0.001) and radiological parameters (regression coefficient 3.8; p = 0.002). Postoperative valgus alignment of 3°-7° was correlated with the best short-term functional outcomes in lateral UKA surgery, while patient-specific parameters and preoperative alignment were not correlated with functional outcomes. Based on these findings, a surgeon should aim for valgus alignment of 3°-7° when performing lateral UKA surgery for optimal functional outcomes. Prognostic study, Level II.
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NASA Technical Reports Server (NTRS)
Banse, Karl; Yong, Marina
1990-01-01
As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.
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Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.
2011-01-01
The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.
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Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki
2014-12-01
This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.
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Soares, Gabriel Porto; Klein, Carlos Henrique; Silva, Nelson Albuquerque de Souza e; de Oliveira, Glaucia Maria Moraes
2016-01-01
Background Diseases of the circulatory system (DCS) are the major cause of death in Brazil and worldwide. Objective To correlate the compensated and adjusted mortality rates due to DCS in the Rio de Janeiro State municipalities between 1979 and 2010 with the Human Development Index (HDI) from 1970 onwards. Methods Population and death data were obtained in DATASUS/MS database. Mortality rates due to ischemic heart diseases (IHD), cerebrovascular diseases (CBVD) and DCS adjusted by using the direct method and compensated for ill-defined causes. The HDI data were obtained at the Brazilian Institute of Applied Research in Economics. The mortality rates and HDI values were correlated by estimating Pearson linear coefficients. The correlation coefficients between the mortality rates of census years 1991, 2000 and 2010 and HDI data of census years 1970, 1980 and 1991 were calculated with discrepancy of two demographic censuses. The linear regression coefficients were estimated with disease as the dependent variable and HDI as the independent variable. Results In recent decades, there was a reduction in mortality due to DCS in all Rio de Janeiro State municipalities, mainly because of the decline in mortality due to CBVD, which was preceded by an elevation in HDI. There was a strong correlation between the socioeconomic indicator and mortality rates. Conclusion The HDI progression showed a strong correlation with the decline in mortality due to DCS, signaling to the relevance of improvements in life conditions. PMID:27849263
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Soares, Gabriel Porto; Klein, Carlos Henrique; Silva, Nelson Albuquerque de Souza E; Oliveira, Glaucia Maria Moraes de
2016-10-01
Diseases of the circulatory system (DCS) are the major cause of death in Brazil and worldwide. To correlate the compensated and adjusted mortality rates due to DCS in the Rio de Janeiro State municipalities between 1979 and 2010 with the Human Development Index (HDI) from 1970 onwards. Population and death data were obtained in DATASUS/MS database. Mortality rates due to ischemic heart diseases (IHD), cerebrovascular diseases (CBVD) and DCS adjusted by using the direct method and compensated for ill-defined causes. The HDI data were obtained at the Brazilian Institute of Applied Research in Economics. The mortality rates and HDI values were correlated by estimating Pearson linear coefficients. The correlation coefficients between the mortality rates of census years 1991, 2000 and 2010 and HDI data of census years 1970, 1980 and 1991 were calculated with discrepancy of two demographic censuses. The linear regression coefficients were estimated with disease as the dependent variable and HDI as the independent variable. In recent decades, there was a reduction in mortality due to DCS in all Rio de Janeiro State municipalities, mainly because of the decline in mortality due to CBVD, which was preceded by an elevation in HDI. There was a strong correlation between the socioeconomic indicator and mortality rates. The HDI progression showed a strong correlation with the decline in mortality due to DCS, signaling to the relevance of improvements in life conditions.
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Mussin, Nadiar; Sumo, Marco; Choi, YoungRok; Choi, Jin Yong; Ahn, Sung-Woo; Yoon, Kyung Chul; Kim, Hyo-Sin; Hong, Suk Kyun; Yi, Nam-Joon; Suh, Kyung-Suk
2017-01-01
Purpose Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs. Dr. Liver) in preoperative right liver graft estimation compared with real graft weight. Methods Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots. Results Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R2 = 0.719), than for the Rapidia group (R2 = 0.688). Conclusion Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation. PMID:28382294
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Villarrasa-Sapiña, Israel; Álvarez-Pitti, Julio; Cabeza-Ruiz, Ruth; Redón, Pau; Lurbe, Empar; García-Massó, Xavier
2018-02-01
Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes-open than eyes-closed condition. Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are open. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Posa, Mihalj; Pilipović, Ana; Lalić, Mladena; Popović, Jovan
2011-02-15
Linear dependence between temperature (t) and retention coefficient (k, reversed phase HPLC) of bile acids is obtained. Parameters (a, intercept and b, slope) of the linear function k=f(t) highly correlate with bile acids' structures. Investigated bile acids form linear congeneric groups on a principal component (calculated from k=f(t)) score plot that are in accordance with conformations of the hydroxyl and oxo groups in a bile acid steroid skeleton. Partition coefficient (K(p)) of nitrazepam in bile acids' micelles is investigated. Nitrazepam molecules incorporated in micelles show modified bioavailability (depo effect, higher permeability, etc.). Using multiple linear regression method QSAR models of nitrazepams' partition coefficient, K(p) are derived on the temperatures of 25°C and 37°C. For deriving linear regression models on both temperatures experimentally obtained lipophilicity parameters are included (PC1 from data k=f(t)) and in silico descriptors of the shape of a molecule while on the higher temperature molecular polarisation is introduced. This indicates the fact that the incorporation mechanism of nitrazepam in BA micelles changes on the higher temperatures. QSAR models are derived using partial least squares method as well. Experimental parameters k=f(t) are shown to be significant predictive variables. Both QSAR models are validated using cross validation and internal validation method. PLS models have slightly higher predictive capability than MLR models. Copyright © 2010 Elsevier B.V. All rights reserved.
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Li, Xiao-yun; Wang, Jia-hua; Huang, Ya-wei; Han, Dong-hai
2011-03-01
Near infrared diffuse reflectance spectroscopy calibrations of fat, protein and DM in raw milk were studied with partial least-squares (PLS) regression using portable short-wave near infrared spectrometer. The results indicated that good calibrations of fat and DM were found, the correlation coefficients were all 0.98, the RMSEC were 0.187 and 0.217, RMSEP were 0.187 and 0.296, the RPDs were 5.02 and 3.20 respectively; the calibration of protein needed to be improved but can be used for practice, the correlation coefficient was 0.95, RMSEC was 0.105, RMSEP was 0.120, and RPD was 2.60. Furthermore, the measuring accuracy was improved by analyzing the correction relation of fat and DM in raw milk This study will probably provide a new on-site method for nondestructive and rapid measurement of milk.
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On the Occurrence of Standardized Regression Coefficients Greater than One.
ERIC Educational Resources Information Center
Deegan, John, Jr.
1978-01-01
It is demonstrated here that standardized regression coefficients greater than one can legitimately occur. Furthermore, the relationship between the occurrence of such coefficients and the extent of multicollinearity present among the set of predictor variables in an equation is examined. Comments on the interpretation of these coefficients are…
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Liu, Jinbao; Han, Jichang; Zhang, Yang; Wang, Huanyuan; Kong, Hui; Shi, Lei
2018-06-05
The storage of soil organic carbon (SOC) should improve soil fertility. Conventional determination of SOC is expensive and tedious. Visible-near infrared reflectance spectroscopy is a practical and cost-effective approach that has been successfully used SOC concentration. Soil spectral inversion model could quickly and efficiently determine SOC content. This paper presents a study dealing with SOC estimation through the combination of soil spectroscopy and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), principal component regression (PCR). Spectral measurements for 106 soil samples were acquired using an ASD FieldSpec 4 standard-res spectroradiometer (350-2500 nm). Six types of transformations and three regression methods were applied to build for the quantification of different parent materials development soil. The results show that (1)the basaltic volcanic clastics development of SOC spectral response bands located in 500 nm, 800 nm; Trachyte spectral response of the soil quality, and the volcanic clastics development at 405 nm, 465 nm, 575 nm, 1105 nm. (2) Basaltic volcanic debris soil development, first deviation of maximum correlation coefficient is 0.8898; thick surface soil of the development of rocky volcanic debris from bottom reflectivity logarithm of first deviation of maximum correlation coefficient is 0.9029. (3) Soil organic matter content of basaltic volcanic clastics development optimal prediction model based on spectral reflectance inverse logarithms of first deviation of SMLR. Independent variable number is 7, Rv 2 = 0.9720, RMSEP = 2.0590, sig = 0.003. Trachyte qualitative volcanic clastics developed soil organic matter content of the optimal prediction model based on spectral reflectance inverse logarithms of first deviation of PLSR. Model number of the independent variables Pc = 5, Rc = 0.9872, Rc 2 = 0.9745, RMSEC = 0.4821, SEC = 0.4906, forecasts determine coefficient Rv 2 = 0.9702, RMSEP = 0.9563, SEP = 0.9711, Bias = 0.0637. Copyright © 2018 Elsevier B.V. All rights reserved.
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Developing a Model for Forecasting Road Traffic Accident (RTA) Fatalities in Yemen
NASA Astrophysics Data System (ADS)
Karim, Fareed M. A.; Abdo Saleh, Ali; Taijoobux, Aref; Ševrović, Marko
2017-12-01
The aim of this paper is to develop a model for forecasting RTA fatalities in Yemen. The yearly fatalities was modeled as the dependent variable, while the number of independent variables included the population, number of vehicles, GNP, GDP and Real GDP per capita. It was determined that all these variables are highly correlated with the correlation coefficient (r ≈ 0.9); in order to avoid multicollinearity in the model, a single variable with the highest r value was selected (real GDP per capita). A simple regression model was developed; the model was very good (R2=0.916); however, the residuals were serially correlated. The Prais-Winsten procedure was used to overcome this violation of the regression assumption. The data for a 20-year period from 1991-2010 were analyzed to build the model; the model was validated by using data for the years 2011-2013; the historical fit for the period 1991 - 2011 was very good. Also, the validation for 2011-2013 proved accurate.
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NASA Technical Reports Server (NTRS)
Kalton, G.
1983-01-01
A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.
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The Outlier Detection for Ordinal Data Using Scalling Technique of Regression Coefficients
NASA Astrophysics Data System (ADS)
Adnan, Arisman; Sugiarto, Sigit
2017-06-01
The aims of this study is to detect the outliers by using coefficients of Ordinal Logistic Regression (OLR) for the case of k category responses where the score from 1 (the best) to 8 (the worst). We detect them by using the sum of moduli of the ordinal regression coefficients calculated by jackknife technique. This technique is improved by scalling the regression coefficients to their means. R language has been used on a set of ordinal data from reference distribution. Furthermore, we compare this approach by using studentised residual plots of jackknife technique for ANOVA (Analysis of Variance) and OLR. This study shows that the jackknifing technique along with the proper scaling may lead us to reveal outliers in ordinal regression reasonably well.
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NASA Astrophysics Data System (ADS)
ul-Haq, Zia; Tariq, Salman; Ali, Muhammad
2017-10-01
An accurate knowledge is needed on the complex relation between atmospheric trace gasses and aerosol variability and their sources to explain trace gases-aerosols-climate interaction and next-generation modeling of climate change and air quality. In this regard, we have used tropospheric Nitrogen Dioxide (NO2), Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) obtained from satellite-based Ozone Monitoring Instrument (OMI)/Aura and Moderate-Resolution Imaging Spectroradiometer (MODIS)/Aqua over South Asia. NO2-AOD correlation with coefficient r = 0.49 is determined over the landmass of South Asia during 2005-2015. Yearly mean NO2-AOD correlation over South Asia shows large variations ranging from r = 0.32 to 0.86 in 2008 and 2009, respectively. The highest correlation ( r = 0.66) is seen over eastern regions of Bangladesh and India, as well as adjoining areas of western Myanmar mostly linked to anthropogenic activities. A significant correlation ( r = 0.59) associated with natural causes is found over some parts of Sistan region, located at the borders of Iran, Pakistan and Afghanistan, and adjoining territory. We find significant positive correlations for monsoon and post-monsoon seasons with r = 0.50 and r = 0.61, respectively. A linear regression on the annual correlation coefficients data suggests that NO2-AOD correlation is strengthening with an increase of 12.9% over South Asia during the study period. The spatial distribution of data slopes reveals positive trends in NO2-AOD correlation over megacities Lahore, Dhaka, Mumbai and Kolkata linked to growing anthropogenic activities. Singrauli city (India) has the highest correlation ( r = 0.62) and 35% increase in correlation coefficient value per year. A negative correlation is observed for megacity Karachi ( r = -0.37) suggesting the non-commonality of NO2 and aerosols emission sources. AE has also been used to discuss its correlation with NO2 over the areas with dominance of fine-mode aerosols.
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NASA Astrophysics Data System (ADS)
Bloomfield, J. P.; Allen, D. J.; Griffiths, K. J.
2009-06-01
SummaryLinear regression methods can be used to quantify geological controls on baseflow index (BFI). This is illustrated using an example from the Thames Basin, UK. Two approaches have been adopted. The areal extents of geological classes based on lithostratigraphic and hydrogeological classification schemes have been correlated with BFI for 44 'natural' catchments from the Thames Basin. When regression models are built using lithostratigraphic classes that include a constant term then the model is shown to have some physical meaning and the relative influence of the different geological classes on BFI can be quantified. For example, the regression constants for two such models, 0.64 and 0.69, are consistent with the mean observed BFI (0.65) for the Thames Basin, and the signs and relative magnitudes of the regression coefficients for each of the lithostratigraphic classes are consistent with the hydrogeology of the Basin. In addition, regression coefficients for the lithostratigraphic classes scale linearly with estimates of log 10 hydraulic conductivity for each lithological class. When a regression is built using a hydrogeological classification scheme with no constant term, the model does not have any physical meaning, but it has a relatively high adjusted R2 value and because of the continuous coverage of the hydrogeological classification scheme, the model can be used for predictive purposes. A model calibrated on the 44 'natural' catchments and using four hydrogeological classes (low-permeability surficial deposits, consolidated aquitards, fractured aquifers and intergranular aquifers) is shown to perform as well as a model based on a hydrology of soil types (BFIHOST) scheme in predicting BFI in the Thames Basin. Validation of this model using 110 other 'variably impacted' catchments in the Basin shows that there is a correlation between modelled and observed BFI. Where the observed BFI is significantly higher than modelled BFI the deviations can be explained by an exogenous factor, catchment urban area. It is inferred that this is may be due influences from sewage discharge, mains leakage, and leakage from septic tanks.
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Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen
2010-09-01
The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.
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Liu, Shu-Shen; Qin, Li-Tang; Liu, Hai-Ling; Yin, Da-Qiang
2008-02-01
Molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was used to describe the structures of 122 nonionic organic compounds (NOCs) and a quantitative relationship between the MEDV descriptors and the bioconcentration factors (BCF) of NOCs in fish was developed using the variable selection and modeling based on prediction (VSMP). It was found that some main structural factors influencing the BCFs of NOCs are the substructures expressed by four atomic types of nos. 2, 3, 5, and 13, i.e., atom groups -CH(2)- or =CH-, -CH< or =C<, -NH(2), and -Cl or -Br where the former two groups exist in the molecular skeleton of NOC and the latter three groups are related closely to the substituting groups on a benzene ring. The best 5-variable model, with the correlation coefficient (r(2)) of 0.9500 and the leave-one-out cross-validation correlation coefficient (q(2)) of 0.9428, was built by multiple linear regressions, which shows a good estimation ability and stability. A predictive power for the external samples was tested by the model from the training set of 80 NOCs and the predictive correlation coefficient (u(2)) for the 42 external samples in the test set was 0.9028.
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[Effects of different excipients on properties of Tongsaimai mixture and pellet molding].
Wang, Jin; Lv, Zhiyang; Wu, Xiaoyan; Di, Liuqing; Dong, Yu; Cai, Baochang
2011-01-01
To study preliminarily on the relationship between properties of the mixture composed of Tongsaimai extract and different excipients and pellet molding. The multivariate regression analysis was used to investigate the correlation of different mixture and pellet molding by measuring the cohesion, liquid-plastic limit of mixture, and the powder properties of pellets. The weighted coefficients of the powder properties were determined by analytic hierarchy process combined with criteria importance through intercriteria correlation. The results showed that liquid-plastic limit seemed to be a major factor, which had positive correlation with pellet molding, while cohesion had negative correlation with pellet molding in the measured range. The physical properties of the mixture has marked influence on pellet molding.
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GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa
Yang, X.; Jin, W.
2010-01-01
Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO3NO2-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO3NO2-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO3NO2-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution. ?? 2010 Elsevier Ltd.
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NASA Astrophysics Data System (ADS)
Ahn, Hyunjun; Jung, Younghun; Om, Ju-Seong; Heo, Jun-Haeng
2014-05-01
It is very important to select the probability distribution in Statistical hydrology. Goodness of fit test is a statistical method that selects an appropriate probability model for a given data. The probability plot correlation coefficient (PPCC) test as one of the goodness of fit tests was originally developed for normal distribution. Since then, this test has been widely applied to other probability models. The PPCC test is known as one of the best goodness of fit test because it shows higher rejection powers among them. In this study, we focus on the PPCC tests for the GEV distribution which is widely used in the world. For the GEV model, several plotting position formulas are suggested. However, the PPCC statistics are derived only for the plotting position formulas (Goel and De, In-na and Nguyen, and Kim et al.) in which the skewness coefficient (or shape parameter) are included. And then the regression equations are derived as a function of the shape parameter and sample size for a given significance level. In addition, the rejection powers of these formulas are compared using Monte-Carlo simulation. Keywords: Goodness-of-fit test, Probability plot correlation coefficient test, Plotting position, Monte-Carlo Simulation ACKNOWLEDGEMENTS This research was supported by a grant 'Establishing Active Disaster Management System of Flood Control Structures by using 3D BIM Technique' [NEMA-12-NH-57] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of Korea.
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Performance Analysis of Occurrences January 1, 2011-December 31, 2011
DOE Office of Scientific and Technical Information (OSTI.GOV)
Ludwig, M
2012-03-16
This report documents the analysis of the occurrences during the period January 1, 2011 through December 31, 2011. The report compares LLNL occurrences by reporting criteria and significance category to see if LLNL is reporting occurrences along similar percentages as other DOE sites. The three-year trends are analyzed. It does not include the analysis of the causes or the lessons learned from the occurrences, as they are analyzed separately. The number and types of occurrences that LLNL reports to DOE varies over time. This variation can be attributed to normally occurring changes in frequency; DOE's or LLNL's heightened interest inmore » a particular subject area; changes in LLNL processes; or emerging problems. Since all of the DOE sites use the same reporting criteria, it is helpful to understand if LLNL is consistent with or diverging from reporting at other sites. This section compares the normalized number of occurrences reported by LLNL and other DOE sites. In order to compare LLNL occurrence reports to occurrence reports from other DOE sites, we normalized (or standardized) the data from the sites. DOE sites vary widely in their budgets, populations, and scope of work and these variations may affect reporting frequency. In addition, reports are required for a wide range of occurrence types, some of which may not be applicable to all DOE sites. For example, one occurrence reporting group is Group 3, Nuclear Safety Basis, and not all sites have nuclear operations. Because limited information is available for all sites, the sites were normalized based on best available information. Site effort hours were extracted from the DOE Computerized Accident Incident Reporting System (CAIRS) and used to normalize (or standardize) the number of occurrences by site. Effort hours are those hours that employees normally work and do not include vacation, holiday hours etc. Sites are responsible for calculating their effort hours and ensuring entry into CAIRS. Out of the 30 DOE sites that reported occurrences into ORPS during January 2011 through December 2011, 28 had effort hours available in CAIRS. Two sites had not submitted effort hours data to CAIRS as of the time data was pulled for this report. In those two cases, third quarter data was used as an estimate of fourth quarter data. The use of estimated data may introduce minor errors in the average, median, and Pearson calculations. Using the effort hours and the frequency of occurrences by site, a rate of occurrence frequency per 100 FTE workers was calculated. This rate is similar to the injury/illness frequency rate: the number of injury/illness cases per 100 FTE workers. To validate that this rate was appropriate to use, we compared the effort hours and the frequency of occurrences by site to determine if a relationship exists between the two, e.g. the more effort hours a site has, the more occurrences they tend to have. This hypothesis was tested using the Pearson Correlation Coefficient Test. The correlation coefficient measures the strength of the linear relationship between effort hours and occurrence frequency. The Pearson Correlation Coefficient Test will determine if the true correlation coefficient is equal to zero (no relationship exists), or if the correlation coefficient is not equal to zero (a relationship exists). Values approaching 1.00 show a more positive correlation. Simple linear regression was also used to display a trend line and to test if a one-way relationship exists between effort hours predicting the number of occurrences a site will have. Using the Pearson Correlation test, for the NNSA sites, effort hours and the number of occurrences are significantly and positively correlated with a correlation coefficient of 0.90, as was also seen in the previous report (correlation coefficient of 0.67). All DOE sites are positively correlated with a coefficient of 0.85. As the effort hours increase, so does the number of occurrences and vice versa. Based on the results of the simple linear regression, effort hours were found to predict the number of occurrences.« less
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A relation between the short time variations of cosmic rays and geomagnetic field change
NASA Technical Reports Server (NTRS)
Saki, T.; Kato, M.
1985-01-01
An event is reported of approx. 37 min periodicity in cosmic ray intensity observed at Akeno(38 deg 47 N, 138 deg 30 E. 900m above s.l., cutoff 10.4 GV) during 1300 approx. 1900 UT on April 25th, 1984, just a day before Forbush decrease of April 26th. This event seemed to be followed by the periodic variations of the geomagnetic field observed at Kakioka (36 deg 23 N, 140 deg 18 E). The regression coefficient between them was obtained approx. 0.07%/10nT. It is shown that in general the power spectral density of cosmic rays in the frequency of 0.0001 approx. 0.001Hz correlates positively with the fluctuations of geomagnetic field (Dst field) around approx. 1.2x0.0001Hz. From the analysis of 47 days data (April 14th to June 13th, 1984) the regression curve was obtained as y=0.275x sup 0.343 with the correlation coefficient of 0.48, where x and y mean Fourier components of Dst field summed over 1.04 approx. 1.39x0.001Hz and cosmic ray power spectral density averaged over 0.0001 approx. 0.001Hz.
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[Effects of carbon components of fine particulate matter (PM2.5) on atherogenic index of plasma].
Fan, Jiao; Qin, Xiaolei; Xue, Xiaodan; Han, Bin; Bai, Zhipeng; Tang, Naijun; Zhang, Liwen
2014-01-01
To evaluate associations between carbon constituents of fine particulate matter (PM2.5) and atherogenic index of plasma (AIP). We collected subjects from two communities by a system sampling, and 112 people aged over 60 years old without cardiovascular disease were recruited. The levels of cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C) of objects, and personal exposure to PM2.5 were measured on December, 2011. Total carbon (TC), organic carbon (OC) and elemental carbon (EC) of PM2.5 were detected and AIP was calculated according to its definition. The value of AIP among the 112 subjects was 0.05 ± 0.26. Personal exposure concentration of PM2.5 and its carbon components (TC,OC and EC) were (164.75 ± 110.67), (53.86 ± 29.65), (44.93 ± 26.37) and (9.49 ± 5.75) µg/m(3), respectively. The Pearson analysis showed the linear relationship between TC,OC,EC and AIP, all significant positive correlations. The correlation coefficients were TC (r = 0.307, P < 0.05),OC (r = 0.287, P < 0.05) and EC (r = 0.252, P < 0.05), respectively. The multiple logistic regression analysis showed that when the AIP risk categories were selected as dependent variable and low risk group as reference group, the regression coefficient of TC,OC and EC was separately 1.03 (95%CI:1.01-1.05), 1.03 (95%CI:1.01-1.05), 1.12 (95%CI:1.02-1.22) in the high risk group; while there was no statistical significance of the regression coefficient and OR in the middle risk group. There was stable associations between the carbon constituents (TC,OC and EC) of fine Particulate Matter (PM2.5) and AIP. The findings suggested that carbon components of PM2.5 should be considered as risk factors of atherogenic.
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Dennison, Jessica L; Stack, Jim; Beatty, Stephen; Nolan, John M
2013-11-01
This study compares in vivo measurements of macular pigment (MP) obtained using customized heterochromatic flicker photometry (cHFP; Macular Metrics Densitometer(™)), dual-wavelength fundus autofluorescence (Heidelberg Spectralis(®) HRA + OCT MultiColor) and single-wavelength fundus reflectance (Zeiss Visucam(®) 200). MP was measured in one eye of 62 subjects on each device. Data from 49 subjects (79%) was suitable for analysis. Agreement between the Densitometer and Spectralis was investigated at various eccentricities using a variety of quantitative and graphical methods, including: Pearson correlation coefficient to measure degree of scatter (precision), accuracy coefficient, concordance correlation coefficient (ccc), paired t-test, scatter and Bland-Altman plots. The relationship between max MP from the Visucam and central MP from the Spectralis and Densitometer was investigated using regression methods. Agreement was strong between the Densitometer and Spectralis at all central eccentricities (e.g. at 0.25° eccentricity: accuracy = 0.97, precision = 0.90, ccc = 0.87). Regression analysis showed a very weak relationship between the Visucam and Densitometer (e.g. Visucam max on Densitometer central MP: R(2) = 0.008, p = 0.843). Regression analysis also demonstrated a weak relationship between MP measured by the Spectralis and Visucam (e.g. Visucam max on Spectralis central MP: R(2) = 0.047, p = 0.348). MP values obtained using the Heidelberg Spectralis are comparable to MP values obtained using the Densitometer. In contrast, MP values obtained using the Zeiss Visucam are not comparable with either the Densitometer or the Spectralis MP measuring devices. Taking cHFP as the current standard to which other MP measuring devices should be compared, the Spectralis is suitable for use in a clinical and research setting, whereas the Visucam is not. Copyright © 2013 Elsevier Ltd. All rights reserved.
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An Investigation of Age-Related Iron Deposition Using Susceptibility Weighted Imaging
Wang, Dan; Li, Wen-Bin; Wei, Xiao-Er; Li, Yue-Hua; Dai, Yong-Ming
2012-01-01
Aim To quantify age-dependent iron deposition changes in healthy subjects using Susceptibility Weighted Imaging (SWI). Materials and Methods In total, 143 healthy volunteers were enrolled. All underwent conventional MR and SWI sequences. Subjects were divided into eight groups according to age. Using phase images to quantify iron deposition in the head of the caudate nucleus and the lenticular nucleus, the angle radian value was calculated and compared between groups. ANOVA/Pearson correlation coefficient linear regression analysis and polynomial fitting were performed to analyze the relationship between iron deposition in the head of the caudate nucleus and lenticular nucleus with age. Results Iron deposition in the lenticular nucleus increased in individuals aged up to 40 years, but did not change in those aged over 40 years once a peak had been reached. In the head of the caudate nucleus, iron deposition peaked at 60 years (p<0.05). The correlation coefficients for iron deposition in the L-head of the caudate nucleus, R-head of the caudate nucleus, L-lenticular nucleus and R-lenticular nucleus with age were 0.67691, 0.48585, 0.5228 and 0.5228 (p<0.001, respectively). Linear regression analyses showed a significant correlation between iron deposition levels in with age groups. Conclusions Iron deposition in the lenticular nucleus was found to increase with age, reaching a plateau at 40 years. Iron deposition in the head of the caudate nucleus also increased with age, reaching a plateau at 60 years. PMID:23226360
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Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy
DOE Office of Scientific and Technical Information (OSTI.GOV)
Shao Yongni; He Yong; Mao Jingyuan
Visible and near-infrared (Vis/NIR) reflectance spectroscopy has been investigated for its ability to nondestructively detect acidity in bayberry juice. What we believe to be a new, better mathematic model is put forward, which we have named principal component analysis-stepwise regression analysis-backpropagation neural network (PCA-SRA-BPNN), to build a correlation between the spectral reflectivity data and the acidity of bayberry juice. In this model, the optimum network parameters,such as the number of input nodes, hidden nodes, learning rate, and momentum, are chosen by the value of root-mean-square (rms) error. The results show that its prediction statistical parameters are correlation coefficient (r) ofmore » 0.9451 and root-mean-square error of prediction(RMSEP) of 0.1168. Partial least-squares (PLS) regression is also established to compare with this model. Before doing this, the influences of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S. Golay first derivative, and wavelet package transform) are compared. The PLS approach with wavelet package transform preprocessing spectra is found to provide the best results, and its prediction statistical parameters are correlation coefficient (r) of 0.9061 and RMSEP of 0.1564. Hence, these two models are both desirable to analyze the data from Vis/NIR spectroscopy and to solve the problem of the acidity prediction of bayberry juice. This supplies basal research to ultimately realize the online measurements of the juice's internal quality through this Vis/NIR spectroscopy technique.« less
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Weather adjustment using seemingly unrelated regression
DOE Office of Scientific and Technical Information (OSTI.GOV)
Noll, T.A.
1995-05-01
Seemingly unrelated regression (SUR) is a system estimation technique that accounts for time-contemporaneous correlation between individual equations within a system of equations. SUR is suited to weather adjustment estimations when the estimation is: (1) composed of a system of equations and (2) the system of equations represents either different weather stations, different sales sectors or a combination of different weather stations and different sales sectors. SUR utilizes the cross-equation error values to develop more accurate estimates of the system coefficients than are obtained using ordinary least-squares (OLS) estimation. SUR estimates can be generated using a variety of statistical software packagesmore » including MicroTSP and SAS.« less
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van Wesenbeeck, Ian; Driver, Jeffrey; Ross, John
2008-04-01
Volatilization of chemicals can be an important form of dissipation in the environment. Rates of evaporative losses from plant and soil surfaces are useful for estimating the potential for food-related dietary residues and operator and bystander exposure, and can be used as source functions for screening models that predict off-site movement of volatile materials. A regression of evaporation on vapor pressure from three datasets containing 82 pesticidal active ingredients and co-formulants, ranging in vapor pressure from 0.0001 to >30,000 Pa was developed for this purpose with a regression correlation coefficient of 0.98.
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NASA Astrophysics Data System (ADS)
Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.
2017-05-01
The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.
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Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace
ERIC Educational Resources Information Center
Culpepper, Steven Andrew; Park, Trevor
2017-01-01
A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…
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Correlation between benzene and testosterone in workers exposed to urban pollution.
Rosati, M V; Sancini, A; Tomei, F; Sacco, C; Traversini, V; De Vita, A; De Cesare, D P; Giammichele, G; De Marco, F; Pagliara, F; Massoni, F; Ricci, L; Tomei, G; Ricci, S
2017-01-01
Many studies have examined the effects of benzene on testosterone. The purpose of this study was to evaluate the possible correlation between the blood levels of benzene and the levels of testosterone. The study involved a group of 148 subjects. For every worker have been made out a blood sample for the evaluation of benzene and testosterone levels and an urine analysis for the evaluation of the levels of trans, trans-muconic acid and S-phenylmercapturic acid. We estimated the Pearson correlation coefficient between the variables in the sample and the urinary metabolites, age, length of service, gender, BMI. For the analysis of the major confounding factors it was performed a multiple linear regression. The Pearson correlation coefficiet showed: 1. a significant inverse correlation between the S-phenyl mercapturic acid and free testosterone; 2. a significant direct correlation between trans-trans muconic acid and BMI. After dividing the sample according to the median of blood benzene (161.0 ng / L), Pearson correlation coefficient showed a significant inverse correlation between the S-phenyl mercapturic acid and free testosterone in the group with values below this median. Our results, to be considered preliminary, suggest that occupational exposure to low levels of benzene, present in urban pollution, affect the blood levels of testosterone. These results need to be confirmed in future studies, with the eventual possibility of including more specific fertility tests.
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NASA Astrophysics Data System (ADS)
Wei, C.; Cheng, K. S.
Using meteorological radar and satellite imagery had become an efficient tool for rainfall forecasting However few studies were aimed to predict quantitative rainfall in small watersheds for flood forecasting by using remote sensing data Due to the terrain shelter and ground clutter effect of Central Mountain Ridges the application of meteorological radar data was limited in mountainous areas of central Taiwan This study devises a new scheme to predict rainfall of a small upstream watershed by combing GOES-9 geostationary weather satellite imagery and ground rainfall records which can be applied for local quantitative rainfall forecasting during periods of typhoon and heavy rainfall Imagery of two typhoon events in 2004 and five correspondent ground raingauges records of Chitou Forest Recreational Area which is located in upstream region of Bei-Shi river were analyzed in this study The watershed accounts for 12 7 square kilometers and altitudes ranging from 1000 m to 1800 m Basin-wide Average Rainfall BAR in study area were estimated by block kriging Cloud Top Temperature CTT from satellite imagery and ground hourly rainfall records were medium correlated The regression coefficient ranges from 0 5 to 0 7 and the value decreases as the altitude of the gauge site increases The regression coefficient of CCT and next 2 to 6 hour accumulated BAR decrease as the time scale increases The rainfall forecasting for BAR were analyzed by Kalman Filtering Technique The correlation coefficient and average hourly deviates between estimated and observed value of BAR for
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Liu, Chang-Fu; He, Xing-Yuan; Chen, Wei; Zhao, Gui-Ling; Xue, Wen-Duo
2008-06-01
Based on the fractal theory of forest growth, stepwise regression was employed to pursue a convenient and efficient method of measuring the three-dimensional green biomass (TGB) of urban forests in small area. A total of thirteen simulation equations of TGB of urban forests in Shenyang City were derived, with the factors affecting the TGB analyzed. The results showed that the coefficients of determination (R2) of the 13 simulation equations ranged from 0.612 to 0.842. No evident pattern was shown in residual analysis, and the precisions were all higher than 87% (alpha = 0.05) and 83% (alpha = 0.01). The most convenient simulation equation was ln Y = 7.468 + 0.926 lnx1, where Y was the simulated TGB and x1 was basal area at breast height per hectare (SDB). The correlations between the standard regression coefficients of the simulation equations and 16 tree characteristics suggested that SDB was the main factor affecting the TGB of urban forests in Shenyang.
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Heat and mass transfer rates during flow of dissociated hydrogen gas over graphite surface
NASA Technical Reports Server (NTRS)
Nema, V. K.; Sharma, O. P.
1986-01-01
To improve upon the performance of chemical rockets, the nuclear reactor has been applied to a rocket propulsion system using hydrogen gas as working fluid and a graphite-composite forming a part of the structure. Under the boundary layer approximation, theoretical predictions of skin friction coefficient, surface heat transfer rate and surface regression rate have been made for laminar/turbulent dissociated hydrogen gas flowing over a flat graphite surface. The external stream is assumed to be frozen. The analysis is restricted to Mach numbers low enough to deal with the situation of only surface-reaction between hydrogen and graphite. Empirical correlations of displacement thickness, local skin friction coefficient, local Nusselt number and local non-dimensional heat transfer rate have been obtained. The magnitude of the surface regression rate is found low enough to ensure the use of graphite as a linear or a component of the system over an extended period without loss of performance.
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Carreon, Leah Y; Glassman, Steven D; McDonough, Christine M; Rampersaud, Raja; Berven, Sigurd; Shainline, Michael
2009-09-01
Cross-sectional cohort. The purpose of this study is to provide a model to allow estimation of utility from the Short Form (SF)-6D using data from the Oswestry Disability Index (ODI), Back Pain Numeric Rating Scale (BPNRS), and the Leg Pain Numeric Rating Scale (LPNRS). Cost-utility analysis provides important information about the relative value of interventions and requires a measure of utility not often available from clinical trial data. The ODI and numeric rating scales for back (BPNRS) and leg pain (LPNRS), are widely used disease-specific measures for health-related quality of life in patients with lumbar degenerative disorders. The purpose of this study is to provide a model to allow estimation of utility from the SF-6D using data from the ODI, BPNRS, and the LPNRS. SF-36, ODI, BPNRS, and LPNRS were prospectively collected before surgery, at 12 and 24 months after surgery in 2640 patients undergoing lumbar fusion for degenerative disorders. Spearman correlation coefficients for paired observations from multiple time points between ODI, BPNRS, and LPNRS, and SF-6D utility scores were determined. Regression modeling was done to compute the SF-6D score from the ODI, BPNRS, and LPNRS. Using a separate, independent dataset of 2174 patients in which actual SF-6D and ODI scores were available, the SF-6D was estimated for each subject and compared to their actual SF-6D. In the development sample, the mean age was 52.5 +/- 15 years and 34% were male. In the validation sample, the mean age was 52.9 +/- 14.2 years and 44% were male. Correlations between the SF-6D and the ODI, BPNRS, and LPNRS were statistically significant (P < 0.0001) with correlation coefficients of 0.82, 0.78, and 0.72, respectively. The regression equation using ODI, BPNRS,and LPNRS to predict SF-6D had an R of 0.69 and a root mean square error of 0.076. The model using ODI alone had an R of 0.67 and a root mean square error of 0.078. The correlation coefficient between the observed and estimated SF-6D score was 0.80. In the validation analysis, there was no statistically significant difference (P = 0.11) between actual mean SF-6D (0.55 +/- 0.12) and the estimated mean SF-6D score (0.55 +/- 0.10) using the ODI regression model. This regression-based algorithm may be used to predict SF-6D scores in studies of lumbar degenerative disease that have collected ODI but not utility scores.
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Carreon, Leah Y.; Glassman, Steven D.; McDonough, Christine M.; Rampersaud, Raja; Berven, Sigurd; Shainline, Michael
2012-01-01
Study Design Cross-sectional cohort Objective The purpose of this study is to provide a model to allow estimation of utility from the SF-6D using data from the ODI, BPNRS, and the LPNRS. Summary of Background Data Cost-utility analysis provides important information about the relative value of interventions and requires a measure of utility not often available from clinical trial data. The Oswestry Disability Index (ODI) and numeric rating scales for back (BPNRS) and leg pain (LPNRS), are widely used disease-specific measures for health-related quality of life in patients with lumbar degenerative disorders. The purpose of this study is to provide a model to allow estimation of utility from the SF-6D using data from the ODI, BPNRS, and the LPNRS. Methods SF-36, ODI, BPNRS and LPNRS were prospectively collected pre-operatively, at 12 and 24 months post-operatively in 2640 patients undergoing lumbar fusion for degenerative disorders. Spearman correlation coefficients for paired observations from multiple time points between ODI, BPNRS and LPNRS and SF-6D utility scores were determined. Regression modeling was done to compute the SF-6D score from the ODI, BPNRS and LPNRS. Using a separate, independent dataset of 2174 patients in which actual SF-6D and ODI scores were available, the SF-6D was estimated for each subject and compared to their actual SF-6D. Results In the development sample, the mean age was 52.5 ± 15 years and 34% were male. In the validation sample the mean age was 52.9 ± 14.2 years and 44% were male. Correlations between the SF-6D and the ODI, BPNRS and LPNRS were statistically significant (p<0.0001) with correlation coefficients of 0.82, 0.78, and 0.72 respectively. The regression equation using ODI, BPNRS and LPNRS to predict SF-6D had an R2 of 0.69 and a root mean square error (RMSE) of 0.076. The model using ODI alone had an R2 of 0.67 and a RMSE of 0.078. The correlation coefficient between the observed and estimated SF-6D score was 0.80. In the validation analysis, there was no statistically significant difference (p=0.11) between actual mean SF-6D (0.55 ± 0.12) and the estimated mean SF-6D score (0.55 ± 0.10) using the ODI regression model. Conclusion This regression-based algorithm may be used to predict SF-6D scores in studies of lumbar degenerative disease that have collected ODI but not utility scores. PMID:19730215
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Byun, Bo-Ram; Kim, Yong-Il; Maki, Koutaro; Son, Woo-Sung
2015-01-01
This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6–18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R 2 had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status. PMID:25878721
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Viability estimation of pepper seeds using time-resolved photothermal signal characterization
NASA Astrophysics Data System (ADS)
Kim, Ghiseok; Kim, Geon-Hee; Lohumi, Santosh; Kang, Jum-Soon; Cho, Byoung-Kwan
2014-11-01
We used infrared thermal signal measurement system and photothermal signal and image reconstruction techniques for viability estimation of pepper seeds. Photothermal signals from healthy and aged seeds were measured for seven periods (24, 48, 72, 96, 120, 144, and 168 h) using an infrared camera and analyzed by a regression method. The photothermal signals were regressed using a two-term exponential decay curve with two amplitudes and two time variables (lifetime) as regression coefficients. The regression coefficients of the fitted curve showed significant differences for each seed groups, depending on the aging times. In addition, the viability of a single seed was estimated by imaging of its regression coefficient, which was reconstructed from the measured photothermal signals. The time-resolved photothermal characteristics, along with the regression coefficient images, can be used to discriminate the aged or dead pepper seeds from the healthy seeds.
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Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru
2017-09-01
Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.
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Psychomotor development index in children younger than 6 years from Argentine provinces.
Lejarraga, Horacio; Kelmansky, Diana M; Masautis, Alicia; Nunes, Fernando
2018-04-01
To obtain a psychomotor development index (PDI) for each Argentine province. Using a national, probabilistic, and stratified sample of 13 323 male and female children younger than 6 years selected for the National Survey on Nutrition and Health (Encuesta Nacional de Nutrición y Salud, ENNyS 2004), we estimated the PDI per province based on compliance with 10 developmental milestones. The median age at attainment (median age) of each milestone was estimated adjusting a logistic regression. The PDI was estimated as 100* (1 + b), where "b" is the regression coefficient of y= a + b x, where "y" is the median age as per the national reference (x) minus the median age at attainment of a milestone. The theoretical value expected for the PDI was 100. The PDI per province ranged between 72.1 and 106.4. Most provinces showed a negative regression coefficient, which indicated a progressive increase of the delay in the age at attainment of milestones. The correlation coefficient between the PDI per province and infant mortality in 2005was extremely high: -0.85, suggesting that both indicators share similar biological and social determinants. The PDI was negative because the higher the mortality, the lower the PDI. We have now a positive health indicator available in Argentina: the psychomotor development index, which is a low-cost, easy to collect, and reliable tool that may be used in national health statistics. Sociedad Argentina de Pediatría.
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Foglia, L.; Hill, Mary C.; Mehl, Steffen W.; Burlando, P.
2009-01-01
We evaluate the utility of three interrelated means of using data to calibrate the fully distributed rainfall‐runoff model TOPKAPI as applied to the Maggia Valley drainage area in Switzerland. The use of error‐based weighting of observation and prior information data, local sensitivity analysis, and single‐objective function nonlinear regression provides quantitative evaluation of sensitivity of the 35 model parameters to the data, identification of data types most important to the calibration, and identification of correlations among parameters that contribute to nonuniqueness. Sensitivity analysis required only 71 model runs, and regression required about 50 model runs. The approach presented appears to be ideal for evaluation of models with long run times or as a preliminary step to more computationally demanding methods. The statistics used include composite scaled sensitivities, parameter correlation coefficients, leverage, Cook's D, and DFBETAS. Tests suggest predictive ability of the calibrated model typical of hydrologic models.
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Validation of the questionnaire on hand function assessment in leprosy.
Ferreira, Telma Leonel; Alvarez, Rosicler Rocha Aiza; Virmond, Marcos da Cunha Lopes
2012-06-01
To validate the psychometric properties of the questionnaire on hand function assessment in leprosy. Study conducted with a convenience sample of 101 consecutive patients in Brasília (Central-Western Brazil), from June 2008 to July 2009. The individuals were adults affected by leprosy, with impairment of the ulnar, median and radial nerves. Interobservers and intraobserver reproducibility was analyzed through successive interviews, and construct validity was analyzed through association between age, clinical form of leprosy, duration of nerve injury, grip and pinch strength measured with a dynamometer, sensibility test performed with Semmes-Weinstein monofilaments and manual ability assessment using the Jebsen test of hand function. Pondered kappa coefficient was calculated and a Bland-Altman plot was constructed to assess the reproducibility of the instrument. For internal consistency, Cronbach's alpha coefficient was utilized. Pearson's correlation coefficient was calculated and a multiple regression model was used. The pondered kappa values for interobservers and intraobserver assessments ranged from 0.86 to 0.97 and from 0.85 to 0.97, respectively. The value of Cronbach's alpha coefficient was 0.967. Pearson's correlation coefficient showed an association (p < 0.001) among duration of nerve injury, grip and pinch strength, cutaneous sensibility and mean score in the Jebsen Test. The mean score of the questionnaire on hand functional assessment in leprosy was associated with operational classification of leprosy, duration of nerve injury, grip strength, cutaneous sensibility and manual ability (p < 0.0001 for the model as a whole). The questionnaire on hand functional assessment in leprosy presents almost perfect interobservers and intraobserver reproducibility, high internal consistency and correlation with operational classification of leprosy, duration of nerve injury, grip strength, cutaneous sensibility in the hands and manual ability.
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Structural features that predict real-value fluctuations of globular proteins.
Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke
2012-05-01
It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.
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Penalized spline estimation for functional coefficient regression models.
Cao, Yanrong; Lin, Haiqun; Wu, Tracy Z; Yu, Yan
2010-04-01
The functional coefficient regression models assume that the regression coefficients vary with some "threshold" variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called "curse of dimensionality" in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter λ, which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter λ: modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty λ accordingly. We demonstrate the proposed approach by both simulation examples and a real data application.
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Combined analysis of magnetic and gravity anomalies using normalized source strength (NSS)
NASA Astrophysics Data System (ADS)
Li, L.; Wu, Y.
2017-12-01
Gravity field and magnetic field belong to potential fields which lead inherent multi-solution. Combined analysis of magnetic and gravity anomalies based on Poisson's relation is used to determinate homology gravity and magnetic anomalies and decrease the ambiguity. The traditional combined analysis uses the linear regression of the reduction to pole (RTP) magnetic anomaly to the first order vertical derivative of the gravity anomaly, and provides the quantitative or semi-quantitative interpretation by calculating the correlation coefficient, slope and intercept. In the calculation process, due to the effect of remanent magnetization, the RTP anomaly still contains the effect of oblique magnetization. In this case the homology gravity and magnetic anomalies display irrelevant results in the linear regression calculation. The normalized source strength (NSS) can be transformed from the magnetic tensor matrix, which is insensitive to the remanence. Here we present a new combined analysis using NSS. Based on the Poisson's relation, the gravity tensor matrix can be transformed into the pseudomagnetic tensor matrix of the direction of geomagnetic field magnetization under the homologous condition. The NSS of pseudomagnetic tensor matrix and original magnetic tensor matrix are calculated and linear regression analysis is carried out. The calculated correlation coefficient, slope and intercept indicate the homology level, Poisson's ratio and the distribution of remanent respectively. We test the approach using synthetic model under complex magnetization, the results show that it can still distinguish the same source under the condition of strong remanence, and establish the Poisson's ratio. Finally, this approach is applied in China. The results demonstrated that our approach is feasible.
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Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D
2017-07-01
Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.
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Pope, L.M.; Arruda, J.A.; Fromm, C.H.
1988-01-01
The formation of carcinogenic trihalomethanes during the treatment of public surface water supplies has become a potentially serious problem. The U. S. Geological Survey, in cooperation with the Kansas Department of Health and Environment , investigated the potential for trihalomethane formation in water from 15 small, public water supply lakes in eastern Kansas from April 1984 through April 1986 in order to define the principal factors that affect or control the potential for trihalomethane formation during the water treatment process. Relations of mean concentrations of trihalomethane-formation potential to selected water quality and lake and watershed physical characteristics were investigated using correlation and regression analysis. Statistically significant, direct relations were developed between trihalomethanes produced in unfiltered and filtered lake water and mean concentrations of total and dissolved organic carbon. Correlation coefficients for these relations ranged from 0.86 to 0.93. Mean values of maximum depth of lake were shown to have statistically significant inverse relations to mean concentrations of trihalomethane-formation potential and total and dissolved organic carbon. Correlation coefficients for these relations ranged from -0.76 to -0.81. (USGS)
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Comparison of Spectral Characteristic between LAPAN-A3 and Sentinel-2A
NASA Astrophysics Data System (ADS)
Zylshal, Z.; Sari, N. M.; Nugroho, J. T.; Kushardono, D.
2017-12-01
Indonesian National Institute of Aeronautics and Space (LAPAN) started building its experimental microsatellite back in 2007 and finally able to launch its first microsatellite dubbed as LAPAN-A1/LAPAN-Tubsat. With the launch of LAPAN-A3/LAPAN-IPB, Indonesian experimental satellite programme hit its third generation. LAPAN-A3 is carrying multiple payloads including multispectral push-broom imager, digital matrix camera, as well as video camera. This paper aims to highlight the spectral differences between LAPAN-A3 and the well-established Sentinel-2A multispectral to investigate the potential of using LAPAN-A3 data to complement the other well-established medium resolution satellite data. Comparisons between corresponding bands and band transformations were performed over a dataset. Three areas of interest were chosen as the test sites. Linear regression and Pearson correlation coefficient were then calculated between the corresponding bands. The preliminary results showed a moderate correlation between the two sensors with Pearson correlation coefficient ranging from 0.39 to 0.65. Some issues were found regarding the radiometric quality over the whole scene of LAPAN-A3.
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A practical formula for determining growth.
Türkoz, Çağrı; Kaygısız, Emine; Ulusoy, Çağrı; Ateş, Can
2017-01-01
We aimed to establish a practical method to evaluate skeletal age using cervical vertebrae. The study consisted of hand-wrist and cephalometric radiographs of 324 subjects (167 girls, 157 boys; age range, 7.3-17.2 years). Skeletal ages of the subjects were calculated from hand-wrist radiographs, and cervical vertebral bodies were measured using cephalometric radiographs. A single formula based on C3 and C4 vertebral body heights with different coefficients for each gender was derived using ridge regression analysis. The correlation coefficients for vertebral and hand-wrist bone age were 0.825 and 0.856 for girls and boys, respectively. The correlations among vertebral bone age and C3 and C4 vertebral body heights were also found to be significant. The intraclass correlation (ICC) score was found to be 0.914, which shows high consistency between the two measurements of the same investigator for each C3 and C4 vertebral body height result. The formula derived for evaluating skeletal age in cephalometric radiographs is reliable and can be applied to both girl and boy subjects for legal requirements or therapeutic needs of age estimation.
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Estimation of old field ecosystem biomass using low altitude imagery
NASA Technical Reports Server (NTRS)
Nor, S. M.; Safir, G.; Burton, T. M.; Hook, J. E.; Schultink, G.
1977-01-01
Color-infrared photography was used to evaluate the biomass of experimental plots in an old-field ecosystem that was treated with different levels of waste water from a sewage treatment facility. Cibachrome prints at a scale of approximately 1:1,600 produced from 35 mm color infrared slides were used to analyze density patterns using prepared tonal density scales and multicell grids registered to ground panels shown on the photograph. Correlation analyses between tonal density and vegetation biomass obtained from ground samples and harvests were carried out. Correlations between mean tonal density and harvest biomass data gave consistently high coefficients ranging from 0.530 to 0.896 at the 0.001 significance level. Corresponding multiple regression analysis resulted in higher correlation coefficients. The results of this study indicate that aerial infrared photography can be used to estimate standing crop biomass on waste water irrigated old field ecosystems. Combined with minimal ground truth data, this technique could enable managers of wastewater irrigation projects to precisely time harvest of such systems for maximal removal of nutrients in harvested biomass.
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NASA Astrophysics Data System (ADS)
Abunama, Taher; Othman, Faridah
2017-06-01
Analysing the fluctuations of wastewater inflow rates in sewage treatment plants (STPs) is essential to guarantee a sufficient treatment of wastewater before discharging it to the environment. The main objectives of this study are to statistically analyze and forecast the wastewater inflow rates into the Bandar Tun Razak STP in Kuala Lumpur, Malaysia. A time series analysis of three years’ weekly influent data (156weeks) has been conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model. Various combinations of ARIMA orders (p, d, q) have been tried to select the most fitted model, which was utilized to forecast the wastewater inflow rates. The linear regression analysis was applied to testify the correlation between the observed and predicted influents. ARIMA (3, 1, 3) model was selected with the highest significance R-square and lowest normalized Bayesian Information Criterion (BIC) value, and accordingly the wastewater inflow rates were forecasted to additional 52weeks. The linear regression analysis between the observed and predicted values of the wastewater inflow rates showed a positive linear correlation with a coefficient of 0.831.
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[Spectral reflectance characteristics and modeling of typical Takyr Solonetzs water content].
Zhang, Jun-hua; Jia, Ke-li
2015-03-01
Based on the analysis of the spectral reflectance of the typical Takyr Solonetzs soil in Ningxia, the relationship of soil water content and spectral reflectance was determined, and a quantitative model for the prediction of soil water content was constructed. The results showed that soil spectral reflectance decreased with the increasing soil water content when it was below the water holding capacity but increased with the increasing soil water content when it was higher than the water holding capacity. Soil water content presented significantly negative correlation with original reflectance (r), smooth reflectance (R), logarithm of reflectance (IgR), and positive correlation with the reciprocal of R and logarithm of reciprocal [lg (1/R)]. The correlation coefficient of soil water content and R in the whole wavelength was 0.0013, 0.0397 higher than r and lgR, respectively. Average correlation coefficient of soil water content with 1/R and [lg (1/R)] at the wavelength of 950-1000 nm was 0.2350 higher than that of 400-950 nm. The relationships of soil water content with the first derivate differential (R') , the first derivate differential of logarithm (lgR)' and the first derivate differential of logarithm of reciprocal [lg(1/R)]' were unstable. Base on the coefficients of r, lg(1/R), R' and (lgR)', different regression models were established to predict soil water content, and the coefficients of determination were 0.7610, 0.8184, 0.8524 and 0.8255, respectively. The determination coefficient for power function model of R'. reached 0.9447, while the fitting degree between the predicted value based on this model and on-site measured value was 0.8279. The model of R' had the highest fitted accuracy, while that of r had the lowest one. The results could provide a scientific basis for soil water content prediction and field irrigation in the Takyr Solonetzs region.
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2009-07-16
0.25 0.26 -0.85 1 SSR SSE R SSTO SSTO = = − 2 2 ˆ( ) : Regression sum of square, ˆwhere : mean value, : value from the fitted line ˆ...Error sum of square : Total sum of square i i i i SSR Y Y Y Y SSE Y Y SSTO SSE SSR = − = − = + ∑ ∑ Statistical analysis: Coefficient of correlation
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Strauss, Ludwig G; Koczan, Dirk; Klippel, Sven; Pan, Leyun; Cheng, Caixia; Willis, Stefan; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia
2008-08-01
18F-FDG kinetics are primarily dependent on the expression of genes associated with glucose transporters and hexokinases but may be modulated by other genes. The dependency of 18F-FDG kinetics on angiogenesis-related gene expression was evaluated in this study. Patients with primary colorectal tumors (n = 25) were examined with PET and 18F-FDG within 2 days before surgery. Tissue specimens were obtained from the tumor and the normal colon during surgery, and gene expression was assessed using gene arrays. Overall, 23 angiogenesis-related genes were identified with a tumor-to-normal ratio exceeding 1.50. Analysis revealed a significant correlation between k1 and vascular endothelial growth factor (VEGF-A, r = 0.51) and between fractal dimension and angiopoietin-2 (r = 0.48). k3 was negatively correlated with VEGF-B (r = -0.46), and a positive correlation was noted for angiopoietin-like 4 gene (r = 0.42). A multiple linear regression analysis was used for the PET parameters to predict the gene expression, and a correlation coefficient of r = 0.75 was obtained for VEGF-A and of r = 0.76 for the angiopoietin-2 expression. Thus, on the basis of these multiple correlation coefficients, angiogenesis-related gene expression contributes to about 50% of the variance of the 18F-FDG kinetic data. The global 18F-FDG uptake, as measured by the standardized uptake value and influx, was not significantly correlated with angiogenesis-associated genes. 18F-FDG kinetics are modulated by angiogenesis-related genes. The transport rate for 18F-FDG (k1) is higher in tumors with a higher expression of VEGF-A and angiopoietin-2. The regression functions for the PET parameters provide the possibility to predict the gene expression of VEGF-A and angiopoietin-2.
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2014-01-01
Background It is important that students have a high academic engagement and satisfaction in order to have good academic achievement. No study measures association of these elements in a short training program. This study aimed to measure the correlation between academic achievement, satisfaction and engagement dimensions in a short training program among premedical students. Methods We carried out a cross sectional study, in August 2013, at Cercle d’Etudiants, Ingénieurs, Médecins et Professeurs de Lycée pour le Triomphe de l’Excellence (CEMPLEX) training center, a center which prepares students for the national common entrance examination into medical schools in Cameroon. We included all students attending this training center during last examination period. They were asked to fill out a questionnaire on paper. Academic engagement was measured using three dimensions: vigor, dedication and absorption. Satisfaction to lessons, for each learning subject was collected. Academic achievement was calculated using mean of the score of all learning subjects affected with their coefficient. Pearson coefficient (r) and multiple regression models were used to measure association. A p value < 0.05 was statistically significant. Results In total, 180 students were analyzed. In univariate linear analysis, we found correlation with academic achievement for vigor (r = 0.338, p = 0.006) and dedication (r = 0.287, p = 0.021) only in male students. In multiple regression linear analysis, academic engagement and satisfaction were correlated to academic achievement only in male students (R2 = 0.159, p = 0.035). No correlation was found in female students and in all students. The independent variables (vigor, dedication, absorption and satisfaction) explained 6.8-24.3% of the variance of academic achievement. Conclusion It is only in male students that academic engagement and satisfaction to lessons are correlated to academic achievement in this short training program for premedical students and this correlation is weak. PMID:24564911
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Bigna, Jean Joel R; Fonkoue, Loic; Tchatcho, Manuela Francette F; Dongmo, Christelle N; Soh, Dorothée M; Um, Joseph Lin Lewis N; Sime, Paule Sandra D; Affana, Landry A; Woum, Albert Ruben N; Noumegni, Steve Raoul N; Tabekou, Alphonce; Wanke, Arlette M; Taffe, Herman Rhais K; Tchoukouan, Miriette Linda N; Anyope, Kevin O; Ella, Stephane Brice E; Mouaha, Berny Vanessa T; Kenne, Edgar Y; Mbessoh, Ulrich Igor K; Tchapmi, Adrienne Y; Tene, Donald F; Voufouo, Steve S; Zogo, Stephanie M; Nouebissi, Linda P; Satcho, Kevine F; Tchoumo, Wati Joel T; Basso, Moise Fabrice; Tcheutchoua, Bertrand Daryl N; Agbor, Ako A
2014-02-24
It is important that students have a high academic engagement and satisfaction in order to have good academic achievement. No study measures association of these elements in a short training program. This study aimed to measure the correlation between academic achievement, satisfaction and engagement dimensions in a short training program among premedical students. We carried out a cross sectional study, in August 2013, at Cercle d'Etudiants, Ingénieurs, Médecins et Professeurs de Lycée pour le Triomphe de l'Excellence (CEMPLEX) training center, a center which prepares students for the national common entrance examination into medical schools in Cameroon. We included all students attending this training center during last examination period. They were asked to fill out a questionnaire on paper. Academic engagement was measured using three dimensions: vigor, dedication and absorption. Satisfaction to lessons, for each learning subject was collected. Academic achievement was calculated using mean of the score of all learning subjects affected with their coefficient. Pearson coefficient (r) and multiple regression models were used to measure association. A p value < 0.05 was statistically significant. In total, 180 students were analyzed. In univariate linear analysis, we found correlation with academic achievement for vigor (r = 0.338, p = 0.006) and dedication (r = 0.287, p = 0.021) only in male students. In multiple regression linear analysis, academic engagement and satisfaction were correlated to academic achievement only in male students (R2 = 0.159, p = 0.035). No correlation was found in female students and in all students. The independent variables (vigor, dedication, absorption and satisfaction) explained 6.8-24.3% of the variance of academic achievement. It is only in male students that academic engagement and satisfaction to lessons are correlated to academic achievement in this short training program for premedical students and this correlation is weak.
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Hidden Connections between Regression Models of Strain-Gage Balance Calibration Data
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert
2013-01-01
Hidden connections between regression models of wind tunnel strain-gage balance calibration data are investigated. These connections become visible whenever balance calibration data is supplied in its design format and both the Iterative and Non-Iterative Method are used to process the data. First, it is shown how the regression coefficients of the fitted balance loads of a force balance can be approximated by using the corresponding regression coefficients of the fitted strain-gage outputs. Then, data from the manual calibration of the Ames MK40 six-component force balance is chosen to illustrate how estimates of the regression coefficients of the fitted balance loads can be obtained from the regression coefficients of the fitted strain-gage outputs. The study illustrates that load predictions obtained by applying the Iterative or the Non-Iterative Method originate from two related regression solutions of the balance calibration data as long as balance loads are given in the design format of the balance, gage outputs behave highly linear, strict statistical quality metrics are used to assess regression models of the data, and regression model term combinations of the fitted loads and gage outputs can be obtained by a simple variable exchange.
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Bonanad, S; Schulz, M; Gordo, A; Spurden, D; Cicchetti, M; Cappelleri, J C; Tolley, C; Staunton, H; Brohan, E
2017-11-01
Adherence to haemophilia A (HA) treatment may be influenced by patients' beliefs about their condition and treatment. Furthermore, difficulties administering treatment may lead to poor adherence. New treatment strategies aim to reduce the burden associated with administration and to improve patient perception of treatment, which, in turn, increase adherence levels. This study aimed to examine patient perception of HA treatment and related factors using patient-reported outcome (PRO) questionnaires and to confirm the psychometric properties of a recently developed questionnaire, the HaemoPREF. A non-interventional, cross-sectional, questionnaire study was conducted with adult HA patients in Spain (n=31), Germany (n=10) and Italy (n=48), who were using ReFacto AF with the FuseNGo administration device. Patients completed the HaemoPREF and other questionnaires measuring related constructs: treatment adherence, satisfaction and well-being, online at two time points. Correlational, regression and psychometric analyses were conducted. PRO scores indicated that patients are satisfied with and adherent to their treatment. Multivariate regression of the HaemoPREF global score identified a number of significant predictors (P≤.05). The HaemoPREF Global Score had a moderate relationship with subscales on the related questionnaires (mean correlation=0.43; range=0.39-0.48). The HaemoPREF demonstrated good test-retest reliability (intraclass correlation coefficient=0.82), internal consistency reliability (Cronbach's alpha range=0.69-0.82) and convergent validity with measures of treatment satisfaction (Spearman correlation coefficient, r=.48) and well-being (r=.41). The findings suggest that patients using ReFacto AF with FuseNGo were satisfied with and adherent to their treatment. The HaemoPREF can identify important concepts relating to patient treatment experience in HA. © 2017 John Wiley & Sons Ltd.
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Relation between histological prostatitis and lower urinary tract symptoms and erectile function.
Mizuno, Taiki; Hiramatsu, Ippei; Aoki, Yusuke; Shimoyama, Hirofumi; Nozaki, Taiji; Shirai, Masato; Lu, Yan; Horie, Shigeo; Tsujimura, Akira
2017-09-01
Chronic prostatitis (CP) significantly worsens a patient's quality of life (QOL), but its etiology is heterogeneous. Although the inflammatory process must be associated with CP symptoms, not all patients with benign prostatic hyperplasia and histological prostatitis complain of CP symptoms. The relation between the severity of histological inflammation and lower urinary tract symptoms (LUTS) and erectile function is not fully understood. This study comprised 26 men with suspected prostate cancer but with no malignant lesion by pathological examination of prostate biopsy specimens. LUTS were assessed by several questionnaires including the International Prostate Symptom Score (IPSS), QOL index, Overactive Bladder Symptom Score (OABSS), and the National Institutes of Health-Chronic Prostatitis Symptom Index (NIH-CPSI), and erectile function was assessed by the Sexual Health Inventory for Men. Prostate volume (PV) measured by transabdominal ultrasound, maximum flow rate by uroflowmetry, and serum concentration of prostate-specific antigen were also evaluated. All data collections were performed before prostate biopsy. Histological prostatitis was assessed by immunohistochemical staining with anti-CD45 antibody as the Quick score. The relation between the Quick score and several factors was assessed by Pearson correlation coefficient and a multivariate linear regression model after adjustment for PV. The Pearson correlation coefficient showed a correlation between the Quick score and several factors including PV, IPSS, QOL index, OABSS, and NIH-CPSI. A multivariate linear regression model after adjustment for PV showed only the NIH-CPSI to be associated with the Quick score. The relation between the Quick score and each domain score of the NIH-CPSI showed only the subscore of urinary symptoms to be an associated factor. We found a correlation only between histological prostatitis and LUTS, but not erectile dysfunction. Especially, the subscore of urinary symptoms (residual feeling and urinary frequency) was associated with histological prostatitis.
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Yadlapati, Ajay; Grogan, Tristan; Elashoff, David; Kelly, Robert B.
2013-01-01
Abstract: Using a novel noninvasive, visible-light optical diffusion oximeter (T-Stat VLS Tissue Oximeter; Spectros Corporation, Portola Valley, CA) to measure the tissue oxygen saturation (StO2) of the buccal mucosa, the correlation between StO2 and central venous oxygen saturation (ScvO2) was examined in children with congenital cyanotic heart disease undergoing a cardiac surgical procedure. Paired StO2 and serum ScvO2 measurements were obtained postoperatively and statistically analyzed for agreement and association. Thirteen children (nine male) participated in the study (age range, 4 days to 18 months). Surgeries included Glenn shunt procedures, Norwood procedures, unifocalization procedures with Blalock-Taussig shunt placement, a Kawashima/Glenn shunt procedure, a Blalock-Taussig shunt placement, and a modified Norwood procedure. A total of 45 paired StO2-ScvO2 measurements was obtained. Linear regression demonstrated a Pearson’s correlation of .58 (95% confidence interval [CI], .35–.75; p < .0001). The regression slope coefficient estimate was .95 (95% CI, .54–1.36) with an interclass correlation coefficient of .48 (95% CI, .22–.68). Below a clinically relevant average ScvO2 value, a receiver operator characteristic analysis yielded an area under the curve of .78. Statistical methods to control for repeatedly measuring the same subjects produced similar results. This study shows a moderate relationship and agreement between StO2 and ScvO2 measurements in pediatric patients with a history of congenital cyanotic heart disease undergoing a cardiac surgical procedure. This real-time monitoring device can act as a valuable adjunct to standard noninvasive monitoring in which serum ScvO2 sampling currently assists in the diagnosis of low cardiac output after pediatric cardiac surgery. PMID:23691783
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Sensitivity Analysis of the Integrated Medical Model for ISS Programs
NASA Technical Reports Server (NTRS)
Goodenow, D. A.; Myers, J. G.; Arellano, J.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Young, M.
2016-01-01
Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Standardized Rank Regression Coefficient (SRRC) are methods of conducting sensitivity analysis on nonlinear simulation models like the Integrated Medical Model (IMM). The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The partial part is so named because adjustments are made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. In SRRC, standardized regression-based coefficients measure the sensitivity of each input, adjusted for all the other inputs, on each output. Because the relative ranking of each of the inputs and outputs is used, as opposed to the values themselves, both methods accommodate the nonlinear relationship of the underlying model. As part of the IMM v4.0 validation study, simulations are available that predict 33 person-missions on ISS and 111 person-missions on STS. These simulated data predictions feed the sensitivity analysis procedures. The inputs to the sensitivity procedures include the number occurrences of each of the one hundred IMM medical conditions generated over the simulations and the associated IMM outputs: total quality time lost (QTL), number of evacuations (EVAC), and number of loss of crew lives (LOCL). The IMM team will report the results of using PRCC and SRRC on IMM v4.0 predictions of the ISS and STS missions created as part of the external validation study. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty could drive changes in overall model output uncertainty. These efforts are an integral part of the overall verification, validation, and credibility review of IMM v4.0.
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Evaluation of keratoconus progression.
Shajari, Mehdi; Steinwender, Gernot; Herrmann, Kim; Kubiak, Kate Barbara; Pavlovic, Ivana; Plawetzki, Elena; Schmack, Ingo; Kohnen, Thomas
2018-06-01
To define variables for the evaluation of keratoconus progression and to determine cut-off values. In this retrospective cohort study (2010-2016), 265 eyes of 165 patients diagnosed with keratoconus underwent two Scheimpflug measurements (Pentacam) that took place 1 year apart ±3 months. Variables used for keratoconus detection were evaluated for progression and a correlation analysis was performed. By logistic regression analysis, a keratoconus progression index (KPI) was defined. Receiver-operating characteristic curve (ROC) analysis was performed and Youden Index calculated to determine cut-off values. Variables used for keratoconus detection showed a weak correlation with each other (eg, correlation r=0.245 between RPImin and Kmax, p<0.001). Therefore, we used parameters that took several variables into consideration (eg, D-index, index of surface variance, index for height asymmetry, KPI). KPI was defined by logistic regression and consisted of a Pachymin coefficient of -0.78 (p=0.001), a maximum elevation of back surface coefficient of 0.27 and coefficient of corneal curvature at the zone 3 mm away from the thinnest point on the posterior corneal surface of -12.44 (both p<0.001). The two variables with the highest Youden Index in the ROC analysis were D-index and KPI: D-index had a cut-off of 0.4175 (70.6% sensitivity) and Youden Index of 0.606. Cut-off for KPI was -0.78196 (84.7% sensitivity) and a Youden Index of 0.747; both 90% specificity. Keratoconus progression should be defined by evaluating parameters that consider several corneal changes; we suggest D-index and KPI to detect progression. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
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Joseph, Mini; Gupta, Riddhi Das; Prema, L; Inbakumari, Mercy; Thomas, Nihal
2017-01-01
The accuracy of existing predictive equations to determine the resting energy expenditure (REE) of professional weightlifters remains scarcely studied. Our study aimed at assessing the REE of male Asian Indian weightlifters with indirect calorimetry and to compare the measured REE (mREE) with published equations. A new equation using potential anthropometric variables to predict REE was also evaluated. REE was measured on 30 male professional weightlifters aged between 17 and 28 years using indirect calorimetry and compared with the eight formulas predicted by Harris-Benedicts, Mifflin-St. Jeor, FAO/WHO/UNU, ICMR, Cunninghams, Owen, Katch-McArdle, and Nelson. Pearson correlation coefficient, intraclass correlation coefficient, and multiple linear regression analysis were carried out to study the agreement between the different methods, association with anthropometric variables, and to formulate a new prediction equation for this population. Pearson correlation coefficients between mREE and the anthropometric variables showed positive significance with suprailiac skinfold thickness, lean body mass (LBM), waist circumference, hip circumference, bone mineral mass, and body mass. All eight predictive equations underestimated the REE of the weightlifters when compared with the mREE. The highest mean difference was 636 kcal/day (Owen, 1986) and the lowest difference was 375 kcal/day (Cunninghams, 1980). Multiple linear regression done stepwise showed that LBM was the only significant determinant of REE in this group of sportspersons. A new equation using LBM as the independent variable for calculating REE was computed. REE for weightlifters = -164.065 + 0.039 (LBM) (confidence interval -1122.984, 794.854]. This new equation reduced the mean difference with mREE by 2.36 + 369.15 kcal/day (standard error = 67.40). The significant finding of this study was that all the prediction equations underestimated the REE. The LBM was the sole determinant of REE in this population. In the absence of indirect calorimetry, the REE equation developed by us using LBM is a better predictor for calculating REE of professional male weightlifters of this region.
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Wylie, James D; Suter, Thomas; Potter, Michael Q; Granger, Erin K; Tashjian, Robert Z
2016-02-17
Patient-reported outcome measures have increasingly accompanied objective examination findings in the evaluation of orthopaedic interventions. Our objective was to determine whether a validated measure of mental health (Short Form-36 Mental Component Summary [SF-36 MCS]) or measures of tear severity on magnetic resonance imaging were more strongly associated with self-assessed shoulder pain and function in patients with symptomatic full-thickness rotator cuff tears. One hundred and sixty-nine patients with full-thickness rotator cuff tears were prospectively enrolled. Patients completed the Short Form-36, visual analog scales for shoulder pain and function, the Simple Shoulder Test (SST), and the American Shoulder and Elbow Surgeons (ASES) instrument at the time of diagnosis. Shoulder magnetic resonance imaging examinations were reviewed to document the number of tendons involved, tear size, tendon retraction, and tear surface area. Age, sex, body mass index, number of medical comorbidities, smoking status, and Workers' Compensation status were recorded. Bivariate correlations and multivariate regression models were calculated to identify associations with baseline shoulder scores. The SF-36 MCS had the strongest correlation with the visual analog scale for shoulder pain (Pearson correlation coefficient, -0.48; p < 0.001), the visual analog scale for shoulder function (Pearson correlation coefficient, -0.33; p < 0.001), the SST (Pearson correlation coefficient, 0.37; p < 0.001), and the ASES score (Pearson correlation coefficient, 0.51; p < 0.001). Tear severity only correlated with the visual analog scale for shoulder function; the Pearson correlation coefficient was 0.19 for tear size (p = 0.018), 0.18 for tendon retraction (p = 0.025), 0.18 for tear area (p = 0.022), and 0.20 for the number of tendons involved (p = 0.011). Tear severity did not correlate with other scores in bivariate correlations (all p > 0.05). In all multivariate models, the SF-36 MCS had the strongest association with the visual analog scale for shoulder pain, the visual analog scale for shoulder function, the SST, and the ASES score (all p < 0.001). Patient mental health may play an influential role in patient-reported pain and function in patients with full-thickness rotator cuff tears. Further studies are needed to determine its effect on the outcome of the treatment of rotator cuff disease. Copyright © 2016 by The Journal of Bone and Joint Surgery, Incorporated.
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Wrong Signs in Regression Coefficients
NASA Technical Reports Server (NTRS)
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
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Kim, Soo-Yeon; Shin, Jaewook; Kim, Dong-Hyun; Kim, Eun-Kyung; Moon, Hee Jung; Yoon, Jung Hyun; You, Jai Kyung; Kim, Min Jung
2018-03-06
To investigate the correlation between conductivity and ADC in invasive ductal carcinoma according to the presence of necrosis on MRI. Eighty-one women with invasive ductal carcinoma ≥1 cm on T2-weighted fast spin echo sequence of preoperative MRI were included. Phase-based MR electric properties tomography was used to reconstruct conductivity. Mean ADC was measured. Necrosis was defined as an area with very high T2 signal intensity. The relationship between conductivity and ADC was examined using Spearman's correlation coefficient (r). Multiple linear regression analysis was performed to identify factors associated with conductivity or ADC. In the total group, conductivity showed negative correlation with ADC (r = -0.357, p = 0.001). This correlation was maintained in the subgroup without necrosis (n = 53, r = -0.455, p = 0.001), but not in the subgroup with necrosis (n = 28, r = -0.080, p = 0.687). The correlation between the two parameters was different according to necrosis (r = -0.455 vs -0.080, p = 0.047). HER2 enriched subtype was independently associated with conductivity (p = 0.029). Necrosis on MRI was independently associated with ADC (p = 0.027). Conductivity shows negative correlation with ADC that is abolished by the presence of necrosis on MRI. • Electric conductivity showed negative correlation with ADC • However, the correlation was abolished by the presence of necrosis on MRI • HER2-enriched subtype was independently associated with conductivity • Necrosis on MRI was independently associated with ADC.
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Li, Jin-ming; Zheng, Huai-jing; Wang, Lu-nan; Deng, Wei
2003-04-01
To establish a model for one choosing controls with a suitable concentration for internal quality control (IQC) with qualitative ELISA detection, and a consecutive plotting method on Levey-Jennings control chart when reagent kit lot is changed. First, a series of control serum with 0.2, 0.5, 1.0, 2.0 and 5.0ng/ml HBsAg respectively were assessed for within-run and between-run precision according to NCCLs EP5 document. Then, a linear regression equation (y=bx + a) with best correlation coefficient (r > 0.99) was established based on S/CO values of the series of control serum. Finally, one could choose controls with S/CO value calculated from the equation (y = bx + a) minus the product of the S/CO value multiplying three-fold between-run CV to be still more than 1.0 for IQC use. For consecutive plotting on Levey-Jennings control chart when ELISA kit lot was changed, the new lot kits were used to detect the same series of HBsAg control serum as above. Then, a new linear regression equation (y2 = b2x2 + a2) with best correlation coefficient was obtained. The old one (y1 =b1x1 + a1) could be obtained based on the mean values from above precision assessment. The S/CO value of a control serum detected by new lot kit could be changed to that detected by old kit lot based on the factor of y2/y1. Therefore, the plotting on primary Levey-Jennings control chart could be continued. The within-run coefficient of variation CV of the ELISA method for control serum with 0.2, 0.5, 1.0, 2.0 and 5.0ng/ml HBsAg were 11.08%, 9.49%, 9.83%, 9.18% and 7.25%, respectively, and between-run CV were 13.25%, 14.03%, 15.11%, 13.29% and 9.92%. The linear regression equation with best correlation coefficient from a test at random was y = 3.509x + 0.180. The suitable concentration of control serum for IQC could be 0.5ng/ml or 1.0ng/ml. The linear regression equation from the old lot and other two new lots of the ELISA kits were y1 = 3.550(x1) + 0.226, y2 = 3.238(x2) +0.388, and y3 =3.428(x3) + 0.148, respectively. Then, the transferring factors of 0.960 (y2/y1) and 0.908 (y3/y1) were obtained. The results shows that the model established for IQC control serum concentration selecting and for consecutive plotting on control chart when the reagent lot is changed is effective and practical.
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[Relationship of insight with depression and suicidal ideation in psychotic disorders].
Patelaros, E; Zournatzis, E; Kontstantakopoulos, G
2015-01-01
The associations of insight into psychosis (i.e., awareness of illness) with clinical variables have been examined by a great number of studies. Most of these studies revealed that the level of insight is negatively correlated with psychotic symptoms but positively correlated with depression and suicidal ideation. The aim of this study was to test these findings in a Greek sample of patients. Forty-three outpatients (30 men and 13 women) with schizophrenia or delusional disorder being followed up at the Mental Health Centre of Kavala took part in the study. Patients with bipolar or schizoaffective disorder were excluded. Patients' mean age was 40.7 years and the mean duration of illness was 18.67 years. All participants were under treatment and clinically stable at the time of the study. We used the Positive and Negative Syndrome Scale (PANSS) for the assessment of positive and negative symptoms, the Schedule for the Assessment of Insight-Expanded (SAI-E) to assess the insight into psychosis, and the Montgomery-Asberg Depression Rating Scale (MADRS) for the evaluation of depression recording separately the score for item 10 as an estimate of suicidal ideation. All the scales used have been adapted to Greek population. We used Spearman rho coefficient to assess the strength of correlations between the scales because the distributions of some scores were not normal. In order to assess the predictive value of insight for depression and suicidal ideation, we used hierarchical linear regression analysis. Correlation coefficients between SAI-E and the clinical scales of psychopathology, depression and suicide ideation was statistically significant at the p<0.01 level. The correlations between the clinical scales and the three subscales of SAI-E were also significant at the aforementioned p level. The regression analysis showed that our model of positive and negative psychopathology and insight explained 47.4% of the variance of depression and 32.2% of the variance of suicidal ideation. The predictive value of insight was critically important, because only after the introduction of the SAI-E score in the analysis our regression models reached statistical significance. Taking into account its limitations regarding the sample size and the chronicity of the illness, our study confirms the positive correlation of insight with depression and suicidal ideation, offering support to the psychological model of insight.
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Emission and distribution of phosphine in paddy fields and its relationship with greenhouse gases.
Chen, Weiyi; Niu, Xiaojun; An, Shaorong; Sheng, Hong; Tang, Zhenghua; Yang, Zhiquan; Gu, Xiaohong
2017-12-01
Phosphine (PH 3 ), as a gaseous phosphide, plays an important role in the phosphorus cycle in ecosystems. In this study, the emission and distribution of phosphine, carbon dioxide (CO 2 ) and methane (CH 4 ) in paddy fields were investigated to speculate the future potential impacts of enhanced greenhouse effect on phosphorus cycle involved in phosphine by the method of Pearson correlation analysis and multiple linear regression analysis. During the whole period of rice growth, there was a significant positive correlation between CO 2 emission flux and PH 3 emission flux (r=0.592, p=0.026, n=14). Similarly, a significant positive correlation of emission flux was also observed between CH 4 and PH 3 (r=0.563, p=0.036, n=14). The linear regression relationship was determined as [PH 3 ] flux =0.007[CO 2 ] flux +0.063[CH 4 ] flux -4.638. No significant differences were observed for all values of matrix-bound phosphine (MBP), soil carbon dioxide (SCO 2 ), and soil methane (SCH 4 ) in paddy soils. However, there was a significant positive correlation between MBP and SCO 2 at heading, flowering and ripening stage. The correlation coefficients were 0.909, 0.890 and 0.827, respectively. In vertical distribution, MBP had the analogical variation trend with SCO 2 and SCH 4 . Through Pearson correlation analysis and multiple stepwise linear regression analysis, pH, redox potential (Eh), total phosphorus (TP) and acid phosphatase (ACP) were identified as the principal factors affecting MBP levels, with correlative rankings of Eh>pH>TP>ACP. The multiple stepwise regression model ([MBP]=0.456∗[ACP]+0.235∗[TP]-1.458∗[Eh]-36.547∗[pH]+352.298) was obtained. The findings in this study hold great reference values to the global biogeochemical cycling of phosphorus in the future. Copyright © 2017 Elsevier B.V. All rights reserved.
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Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach.
Mohammadi, Tayeb; Kheiri, Soleiman; Sedehi, Morteza
2016-01-01
Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.
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Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach
Mohammadi, Tayeb; Sedehi, Morteza
2016-01-01
Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables “number of blood donation” and “number of blood deferral”: as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models. PMID:27703493
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NASA Astrophysics Data System (ADS)
Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa
2011-08-01
In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.
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Finite Element Vibration Modeling and Experimental Validation for an Aircraft Engine Casing
NASA Astrophysics Data System (ADS)
Rabbitt, Christopher
This thesis presents a procedure for the development and validation of a theoretical vibration model, applies this procedure to a pair of aircraft engine casings, and compares select parameters from experimental testing of those casings to those from a theoretical model using the Modal Assurance Criterion (MAC) and linear regression coefficients. A novel method of determining the optimal MAC between axisymmetric results is developed and employed. It is concluded that the dynamic finite element models developed as part of this research are fully capable of modelling the modal parameters within the frequency range of interest. Confidence intervals calculated in this research for correlation coefficients provide important information regarding the reliability of predictions, and it is recommended that these intervals be calculated for all comparable coefficients. The procedure outlined for aligning mode shapes around an axis of symmetry proved useful, and the results are promising for the development of further optimization techniques.
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Active and passive microwave measurements of soil moisture in FIFE
NASA Technical Reports Server (NTRS)
Wang, J. R.; Gogineni, S. P.; Ampe, J.
1992-01-01
During the intensive field campaigns of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) in May-October of 1987, several nearly simultaneous measurements were made with low-altitude flights of the L-band radiometer and C- and X-band scatterometers over two transects in the Konza Prairie Natural Research Area, some 8 km south of Manhattan, Kansas. These measurements showed that although the scatterometers were sensitive to soil moisture variations in most regions under the flight path, the L-band radiometer lost most of its sensitivity in regions unburned for many years. The correlation coefficient derived from the regression between the radar backscattering coefficient and the soil moisture was found to improve with the increase in antenna incidence angle. This is attributed to a steeper falloff of the backscattering coefficient as a function of local incidence at angles near nadir than at angles greater than 30 deg.
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Octanol-air partition coefficients of polybrominated biphenyls.
Hongxia, Zhao; Jingwen, Chen; Xie, Quan; Baocheng, Qu; Xinmiao, Liang
2009-03-01
The octanol-air partition coefficients (K(OA)) for PBB15, PBB26, PBB31, PBB49, PBB103 and PBB153 were determined as a function of temperature using a gas chromatographic retention time technique with 1,1,1-trichloro-2,2-bis (4-chlorophenyl) ethane (p,p'-DDT) as a reference substance. The internal energies of phase change from octanol to air (Delta(OA)U) were calculated for the six compounds and were in the range from 74 to 116 kJ mol(-1). Simple regression equations of log K(OA) versus relative retention times (RRTs) on gas chromatography (GC), and log K(OA) versus molecular connectivity indexes (MCI) were obtained, for which the correlation coefficients (r(2)) were greater than 0.985 at 283.15K and 298.15K. Thus the K(OA) values of the remaining PBBs can be predicted by using their RRTs and MCI according to these relationships.
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Elder abuse and socioeconomic inequalities: a multilevel study in 7 European countries.
Fraga, Sílvia; Lindert, Jutta; Barros, Henrique; Torres-González, Francisco; Ioannidi-Kapolou, Elisabeth; Melchiorre, Maria Gabriella; Stankunas, Mindaugas; Soares, Joaquim F
2014-04-01
To compare the prevalence of elder abuse using a multilevel approach that takes into account the characteristics of participants as well as socioeconomic indicators at city and country level. In 2009, the project on abuse of elderly in Europe (ABUEL) was conducted in seven cities (Stuttgart, Germany; Ancona, Italy; Kaunas, Lithuania, Stockholm, Sweden; Porto, Portugal; Granada, Spain; Athens, Greece) comprising 4467 individuals aged 60-84 years. We used a 3-level hierarchical structure of data: 1) characteristics of participants; 2) mean of tertiary education of each city; and 3) country inequality indicator (Gini coefficient). Multilevel logistic regression was used and proportional changes in Intraclass Correlation Coefficient (ICC) were inspected to assert explained variance between models. The prevalence of elder abuse showed large variations across sites. Adding tertiary education to the regression model reduced the country level variance for psychological abuse (ICC=3.4%), with no significant decrease in the explained variance for the other types of abuse. When the Gini coefficient was considered, the highest drop in ICC was observed for financial abuse (from 9.5% to 4.3%). There is a societal and community level dimension that adds information to individual variability in explaining country differences in elder abuse, highlighting underlying socioeconomic inequalities leading to such behavior. Copyright © 2014 Elsevier Inc. All rights reserved.
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Quantitative prediction of ionization effect on human skin permeability.
Baba, Hiromi; Ueno, Yusuke; Hashida, Mitsuru; Yamashita, Fumiyoshi
2017-04-30
Although skin permeability of an active ingredient can be severely affected by its ionization in a dose solution, most of the existing prediction models cannot predict such impacts. To provide reliable predictors, we curated a novel large dataset of in vitro human skin permeability coefficients for 322 entries comprising chemically diverse permeants whose ionization fractions can be calculated. Subsequently, we generated thousands of computational descriptors, including LogD (octanol-water distribution coefficient at a specific pH), and analyzed the dataset using nonlinear support vector regression (SVR) and Gaussian process regression (GPR) combined with greedy descriptor selection. The SVR model was slightly superior to the GPR model, with externally validated squared correlation coefficient, root mean square error, and mean absolute error values of 0.94, 0.29, and 0.21, respectively. These models indicate that Log D is effective for a comprehensive prediction of ionization effects on skin permeability. In addition, the proposed models satisfied the statistical criteria endorsed in recent model validation studies. These models can evaluate virtually generated compounds at any pH; therefore, they can be used for high-throughput evaluations of numerous active ingredients and optimization of their skin permeability with respect to permeant ionization. Copyright © 2017 Elsevier B.V. All rights reserved.
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Mahdavi, A; Nikmanesh, E; AghaeI, M; Kamran, F; Zahra Tavakoli, Z; Khaki Seddigh, F
2015-01-01
Nurses are the most significant part of human resources in a sanitary and health system. Job satisfaction results in the enhancement of organizational productivity, employee commitment to the organization and ensuring his/ her physical and mental health. The present research was conducted with the aim of predicting the level of job satisfaction based on hardiness and its components among the nurses with tension headache. The research method was correlational. The population consisted of all the nurses with tension headache who referred to the relevant specialists in Tehran. The sample size consisted of 50 individuals who were chosen by using the convenience sampling method and were measured and investigated by using the research tools of "Job Satisfaction Test" of Davis, Lofkvist and Weiss and "Personal Views Survey" of Kobasa. The data analysis was carried out by using the Pearson Correlation Coefficient and the Regression Analysis. The research findings demonstrated that the correlation coefficient obtained for "hardiness", "job satisfaction" was 0.506, and this coefficient was significant at the 0.01 level. Moreover, it was specified that the sense of commitment and challenge were stronger predictors for job satisfaction of nurses with tension headache among the components of hardiness, and, about 16% of the variance of "job satisfaction" could be explained by the two components (sense of commitment and challenge).
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Restoration of acidic mine spoils with sewage sludge: II measurement of solids applied
DOE Office of Scientific and Technical Information (OSTI.GOV)
Stucky, D.J.; Zoeller, A.L.
1980-01-01
Sewage sludge was incorporated in acidic strip mine spoils at rates equivalent to 0, 224, 336, and 448 dry metric tons (dmt)/ha and placed in pots in a greenhouse. Spoil parameters were determined 48 hours after sludge incorporation, Time Planting (P), and five months after orchardgrass (Dactylis glomerata L.) was planted, Time Harvest (H), in the pots. Parameters measured were: pH, organic matter content (OM), cation exchange capacity (CEC), electrical conductivity (EC) and yield. Values for each parameter were significantly different at the two sampling times. Correlation coefficient values were calculated for all parameters versus rates of applied sewage sludgemore » and all parameters versus each other. Multiple regressions were performed, stepwise, for all parameters versus rates of applied sewage sludge. Equations to predict amounts of sewage sludge incorporated in spoils were derived for individual and multiple parameters. Generally, measurements made at Time P achieved the highest correlation coefficient and multiple correlation coefficient values; therefore, the authors concluded data from Time P had the greatest predictability value. The most important value measured to predict rate of applied sewage sludge was pH and some additional accuracy was obtained by including CEC in equation. This experiment indicated that soil properties can be used to estimate amounts of sewage sludge solids required to reclaim acidic mine spoils and to estimate quantities incorporated.« less
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Determination of organic compounds in water using ultraviolet LED
NASA Astrophysics Data System (ADS)
Kim, Chihoon; Ji, Taeksoo; Eom, Joo Beom
2018-04-01
This paper describes a method of detecting organic compounds in water using an ultraviolet LED (280 nm) spectroscopy system and a photodetector. The LED spectroscopy system showed a high correlation between the concentration of the prepared potassium hydrogen phthalate and that calculated by multiple linear regression, indicating an adjusted coefficient of determination ranging from 0.953-0.993. In addition, a comparison between the performance of the spectroscopy system and the total organic carbon analyzer indicated that the difference in concentration was small. Based on the close correlation between the spectroscopy and photodetector absorbance values, organic measurement with a photodetector could be configured for monitoring.
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Population dynamics of pond zooplankton, I. Diaptomus pallidus Herrick
Armitage, K.B.; Saxena, B.; Angino, E.E.
1973-01-01
The simultaneous and lag relationships between 27 environmental variables and seven population components of a perennial calanoid copepod were examined by simple and partial correlations and stepwise regression. The analyses consistently explained more than 70% of the variation of a population component. The multiple correlation coefficient (R) usually was highest in no lag or in 3-week or 4-week lag except for clutch size in which R was highest in 1-week lag. Population control, egg-bearing, and clutch size were affected primarily by environmental components categorized as weather; food apparently was relatively minor in affecting population control or reproduction. ?? 1973 Dr. W. Junk B.V. Publishers.
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Maertens de Noordhout, Charline; Devleesschauwer, Brecht; Salomon, Joshua A; Turner, Heather; Cassini, Alessandro; Colzani, Edoardo; Speybroeck, Niko; Polinder, Suzanne; Kretzschmar, Mirjam E; Havelaar, Arie H; Haagsma, Juanita A
2018-01-01
Abstract Background In 2015, new disability weights (DWs) for infectious diseases were constructed based on data from four European countries. In this paper, we evaluated if country, age, sex, disease experience status, income and educational levels have an impact on these DWs. Methods We analyzed paired comparison responses of the European DW study by participants’ characteristics with separate probit regression models. To evaluate the effect of participants’ characteristics, we performed correlation analyses between countries and within country by respondent characteristics and constructed seven probit regression models, including a null model and six models containing participants’ characteristics. We compared these seven models using Akaike Information Criterion (AIC). Results According to AIC, the probit model including country as covariate was the best model. We found a lower correlation of the probit coefficients between countries and income levels (range rs: 0.97–0.99, P < 0.01) than between age groups (range rs: 0.98–0.99, P < 0.01), educational level (range rs: 0.98–0.99, P < 0.01), sex (rs = 0.99, P < 0.01) and disease status (rs = 0.99, P < 0.01). Within country the lowest correlations of the probit coefficients were between low and high income level (range rs = 0.89–0.94, P < 0.01). Conclusions We observed variations in health valuation across countries and within country between income levels. These observations should be further explored in a systematic way, also in non-European countries. We recommend future researches studying the effect of other characteristics of respondents on health assessment. PMID:29020343
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Normal reference values for bladder wall thickness on CT in a healthy population.
Fananapazir, Ghaneh; Kitich, Aleksandar; Lamba, Ramit; Stewart, Susan L; Corwin, Michael T
2018-02-01
To determine normal bladder wall thickness on CT in patients without bladder disease. Four hundred and nineteen patients presenting for trauma with normal CTs of the abdomen and pelvis were included in our retrospective study. Bladder wall thickness was assessed, and bladder volume was measured using both the ellipsoid formula and an automated technique. Patient age, gender, and body mass index were recorded. Linear regression models were created to account for bladder volume, age, gender, and body mass index, and the multiple correlation coefficient with bladder wall thickness was computed. Bladder volume and bladder wall thickness were log-transformed to achieve approximate normality and homogeneity of variance. Variables that did not contribute substantively to the model were excluded, and a parsimonious model was created and the multiple correlation coefficient was calculated. Expected bladder wall thickness was estimated for different bladder volumes, and 1.96 standard deviation above expected provided the upper limit of normal on the log scale. Age, gender, and bladder volume were associated with bladder wall thickness (p = 0.049, 0.024, and < 0.001, respectively). The linear regression model had an R 2 of 0.52. Age and gender were negligible in contribution to the model, and a parsimonious model using only volume was created for both the ellipsoid and automated volumes (R 2 = 0.52 and 0.51, respectively). Bladder wall thickness correlates with bladder wall volume. The study provides reference bladder wall thicknesses on CT utilizing both the ellipsoid formula and automated bladder volumes.
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Howard, Jeremy T; Jiao, Shihui; Tiezzi, Francesco; Huang, Yijian; Gray, Kent A; Maltecca, Christian
2015-05-30
Feed intake and growth are economically important traits in swine production. Previous genome wide association studies (GWAS) have utilized average daily gain or daily feed intake to identify regions that impact growth and feed intake across time. The use of longitudinal models in GWAS studies, such as random regression, allows for SNPs having a heterogeneous effect across the trajectory to be characterized. The objective of this study is therefore to conduct a single step GWAS (ssGWAS) on the animal polynomial coefficients for feed intake and growth. Corrected daily feed intake (DFI Adj) and average daily weight measurements (DBW Avg) on 8981 (n=525,240 observations) and 5643 (n=283,607 observations) animals were utilized in a random regression model using Legendre polynomials (order=2) and a relationship matrix that included genotyped and un-genotyped animals. A ssGWAS was conducted on the animal polynomials coefficients (intercept, linear and quadratic) for animals with genotypes (DFIAdj: n=855; DBWAvg: n=590). Regions were characterized based on the variance of 10-SNP sliding windows GEBV (WGEBV). A bootstrap analysis (n=1000) was conducted to declare significance. Heritability estimates for the traits trajectory ranged from 0.34-0.52 to 0.07-0.23 for DBWAvg and DFIAdj, respectively. Genetic correlations across age classes were large and positive for both DBWAvg and DFIAdj, albeit age classes at the beginning had a small to moderate genetic correlation with age classes towards the end of the trajectory for both traits. The WGEBV variance explained by significant regions (P<0.001) for each polynomial coefficient ranged from 0.2-0.9 to 0.3-1.01% for DBWAvg and DFIAdj, respectively. The WGEBV variance explained by significant regions for the trajectory was 1.54 and 1.95% for DBWAvg and DFIAdj. Both traits identified candidate genes with functions related to metabolite and energy homeostasis, glucose and insulin signaling and behavior. We have identified regions of the genome that have an impact on the intercept, linear and quadratic terms for DBWAvg and DFIAdj. These results provide preliminary evidence that individual growth and feed intake trajectories are impacted by different regions of the genome at different times.
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Assessment of the vision-specific quality of life using clustered visual field in glaucoma patients.
Sawada, Hideko; Yoshino, Takaiko; Fukuchi, Takeo; Abe, Haruki
2014-02-01
To investigate the significance of vision-specific quality of life (QOL) in glaucoma patients based on the location of visual field defects. We examined 336 eyes of 168 patients. The 25-item National Eye Institute Visual Function Questionnaire was used to evaluate patients' QOL. Visual field testing was performed using the Humphrey Field Analyzer; the visual field was divided into 10 clusters. We defined the eye with better mean deviation as the better eye and the fellow eye as the worse eye. A single linear regression analysis was applied to assess the significance of the relationship between QOL and the clustered visual field. The strongest correlation was observed in the lower paracentral visual field in the better eye. The lower peripheral visual field in the better eye also showed a good correlation. Correlation coefficients in the better eye were generally higher than those in the worse eye. For driving, the upper temporal visual field in the better eye was the most strongly correlated (r=0.509). For role limitation and peripheral vision, the lower peripheral visual field in the better eye had the highest correlation coefficients at 0.459 and 0.425, respectively. Overall, clusters in the lower hemifield in the better eye were more strongly correlated with QOL than those in the worse eye. In particular, the lower paracentral visual field in the better eye was correlated most strongly of all. Driving, however, strongly correlated with the upper hemifield in the better eye.
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Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R
2012-01-01
The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.
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Uncertainty Analysis on Heat Transfer Correlations for RP-1 Fuel in Copper Tubing
NASA Technical Reports Server (NTRS)
Driscoll, E. A.; Landrum, D. B.
2004-01-01
NASA is studying kerosene (RP-1) for application in Next Generation Launch Technology (NGLT). Accurate heat transfer correlations in narrow passages at high temperatures and pressures are needed. Hydrocarbon fuels, such as RP-1, produce carbon deposition (coke) along the inside of tube walls when heated to high temperatures. A series of tests to measure the heat transfer using RP-1 fuel and examine the coking were performed in NASA Glenn Research Center's Heated Tube Facility. The facility models regenerative cooling by flowing room temperature RP-1 through resistively heated copper tubing. A Regression analysis is performed on the data to determine the heat transfer correlation for Nusselt number as a function of Reynolds and Prandtl numbers. Each measurement and calculation is analyzed to identify sources of uncertainty, including RP-1 property variations. Monte Carlo simulation is used to determine how each uncertainty source propagates through the regression and an overall uncertainty in predicted heat transfer coefficient. The implications of these uncertainties on engine design and ways to minimize existing uncertainties are discussed.
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NASA Astrophysics Data System (ADS)
Wang, Daojun; Gong, Jianhua; Ma, Ainai; Li, Wenhang; Wang, Xijun
2005-10-01
There are generally two kinds of approaches to studying geomorphic features in terms of the quantification level and difference of major considerations. One is the earlier qualitative characterization, and the other is the 2-dimension measurement that includes section pattern and projection pattern. With the development of geo-information technology, especially the 3-D geo-visualization and virtual geographic environments (VGE), 3-dimension measurement and dynamic interactive between users and geo-data/geo-graphics can be developed to understand geomorphic features deeply, and to benefit to the effective applications of such features for geographic projects like dam construction. Storage-elevation curve is very useful for site selection of projects and flood dispatching in water conservancy region, but it is just a tool querying one value from the other one. In fact, storage-elevation curve can represent comprehensively the geomorphic features including vertical section, cross section of the stream and the landform nearby. In this paper, we use quadratic regression equation shaped like y = ax2 + bx + c and the DEM data of Hong-Shi-Mao watershed, Zi Chang County, ShaanXi Province, China to find out the relationship between the coefficients of the equation and the geomorphic features based on VGE platform. It's exciting that the coefficient "a" appear to be correlative strongly with the stream scale, and the coefficient "b" may give an index to the valley shape. In the end, we use a sub-basin named Hao-Jia-Gou of the watershed as an application. The result of correlative research about quadratic regression equation and geomorphic features can save computing and improve the efficiency in silt dam systems planning.
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Räth, Christoph; Baum, Thomas; Monetti, Roberto; Sidorenko, Irina; Wolf, Petra; Eckstein, Felix; Matsuura, Maiko; Lochmüller, Eva-Maria; Zysset, Philippe K; Rummeny, Ernst J; Link, Thomas M; Bauer, Jan S
2013-12-01
In this study, we investigated the scaling relations between trabecular bone volume fraction (BV/TV) and parameters of the trabecular microstructure at different skeletal sites. Cylindrical bone samples with a diameter of 8mm were harvested from different skeletal sites of 154 human donors in vitro: 87 from the distal radius, 59/69 from the thoracic/lumbar spine, 51 from the femoral neck, and 83 from the greater trochanter. μCT images were obtained with an isotropic spatial resolution of 26μm. BV/TV and trabecular microstructure parameters (TbN, TbTh, TbSp, scaling indices (< > and σ of α and αz), and Minkowski Functionals (Surface, Curvature, Euler)) were computed for each sample. The regression coefficient β was determined for each skeletal site as the slope of a linear fit in the double-logarithmic representations of the correlations of BV/TV versus the respective microstructure parameter. Statistically significant correlation coefficients ranging from r=0.36 to r=0.97 were observed for BV/TV versus microstructure parameters, except for Curvature and Euler. The regression coefficients β were 0.19 to 0.23 (TbN), 0.21 to 0.30 (TbTh), -0.28 to -0.24 (TbSp), 0.58 to 0.71 (Surface) and 0.12 to 0.16 (<α>), 0.07 to 0.11 (<αz>), -0.44 to -0.30 (σ(α)), and -0.39 to -0.14 (σ(αz)) at the different skeletal sites. The 95% confidence intervals of β overlapped for almost all microstructure parameters at the different skeletal sites. The scaling relations were independent of vertebral fracture status and similar for subjects aged 60-69, 70-79, and >79years. In conclusion, the bone volume fraction-microstructure scaling relations showed a rather universal character. © 2013.
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Benz, Thomas; Angst, Felix; Lehmann, Susanne; Aeschlimann, André
2013-05-04
According to Antonovsky's salutogenic concept, a strong sense of coherence is associated with physical and psychological health. The goal of this study was to analyze the association of Antonovsky's sense of coherence with physical and psychosocial health components in patients with hip and knee osteoarthritis before and after in- and outpatient rehabilitation. Prospective cohort study with 335 patients, 136 (41%) with hip and 199 (59%) with knee osteoarthritis. The outcome was measured by Short Form-36 (SF-36), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Sense of Coherence (SOC-13). Baseline scores of the SF-36 and WOMAC scales and the observed effect sizes after rehabilitation were correlated with the baseline SOC-13. These correlations of the SF-36 scales were compared to the Factor Score Coefficients for the Mental Component Summary of SF-36, which quantify the factor load on the psychosocial dimension. Predictive impact of the baseline SOC-13 for the SF-36 and WOMAC scales (baseline scores and effect sizes) was then determined by multivariate linear regression controlled for possible confounders. At baseline, the SOC-13 correlated with the WOMAC scores between r = 0.18 (stiffness) and r = 0.25 (pain) and with the SF-36 scores between r = 0.10 (physical functioning) and r = 0.53 (mental health). The correlation of these SF-36 correlation coefficients to the Factor Score Coefficient of the SF-36 Mental Component Summary was r = 0.95. The correlations for the effect sizes (baseline → discharge) with the baseline SOC-13 global score were all negative and varied between r = 0.00 (physical functioning) and r = -0.19 (social functioning). In the multivariate linear regression model, the explained variance of the SF-36 scores by the baseline SOC-13 increased continuously from physical to psychosocial health dimensions (from 12.9% to 29.8%). This gradient was consistently observed for both the baseline scores and the effect sizes. The results of the WOMAC were consistent with the physical health scales of SF-36. The sense of coherence was associated with psychosocial health dimensions but hardly with physical health. The higher the load of a scale on the psychosocial dimension the higher was its correlation to the sense of coherence. This is in contrast to the idea of Antonovsky who predicted high associations with both mental and physical health.
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Seasonal ambient air pollution correlates strongly with spontaneous abortion in Mongolia
2014-01-01
Background Air pollution is a major health challenge worldwide and has previously been strongly associated with adverse reproductive health. This study aimed to examine the association between spontaneous abortion and seasonal variation of air pollutants in Ulaanbaatar, Mongolia. Methods Monthly average O3, SO2, NO2, CO, PM10 and PM2.5 levels were measured at Mongolian Government Air Quality Monitoring stations. The medical records of 1219 women admitted to the hospital due to spontaneous abortion between 2009–2011 were examined retrospectively. Fetal deaths per calendar month from January-December, 2011 were counted and correlated with mean monthly levels of various air pollutants by means of regression analysis. Results Regression of ambient pollutants against fetal death as a dose–response toxicity curve revealed very strong dose–response correlations for SO2 r > 0.9 (p < 0.001) while similarly strongly significant correlation coefficients were found for NO2 (r > 0.8), CO (r > 0.9), PM10 (r > 0.9) and PM2.5 (r > 0.8), (p < 0.001), indicating a strong correlation between air pollution and decreased fetal wellbeing. Conclusion The present study identified alarmingly strong statistical correlations between ambient air pollutants and spontaneous abortion. Further studies need to be done to examine possible correlations between personal exposure to air pollutants and pregnancy loss. PMID:24758249
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Arnaoutakis, George J; George, Timothy J; Alejo, Diane E; Merlo, Christian A; Baumgartner, William A; Cameron, Duke E; Shah, Ashish S
2011-09-01
The impact of Society of Thoracic Surgeons predicted mortality risk score on resource use has not been previously studied. We hypothesize that increasing Society of Thoracic Surgeons risk scores in patients undergoing aortic valve replacement are associated with greater hospital charges. Clinical and financial data for patients undergoing aortic valve replacement at The Johns Hopkins Hospital over a 10-year period (January 2000 to December 2009) were reviewed. The current Society of Thoracic Surgeons formula (v2.61) for in-hospital mortality was used for all patients. After stratification into risk quartiles, index admission hospital charges were compared across risk strata with rank-sum and Kruskal-Wallis tests. Linear regression and Spearman's coefficient assessed correlation and goodness of fit. Multivariable analysis assessed relative contributions of individual variables on overall charges. A total of 553 patients underwent aortic valve replacement during the study period. Average predicted mortality was 2.9% (±3.4) and actual mortality was 3.4% for aortic valve replacement. Median charges were greater in the upper quartile of patients undergoing aortic valve replacement (quartiles 1-3, $39,949 [interquartile range, 32,708-51,323] vs quartile 4, $62,301 [interquartile range, 45,952-97,103], P < .01]. On univariate linear regression, there was a positive correlation between Society of Thoracic Surgeons risk score and log-transformed charges (coefficient, 0.06; 95% confidence interval, 0.05-0.07; P < .01). Spearman's correlation R-value was 0.51. This positive correlation persisted in risk-adjusted multivariable linear regression. Each 1% increase in Society of Thoracic Surgeons risk score was associated with an added $3000 in hospital charges. This is the first study to show that increasing Society of Thoracic Surgeons risk score predicts greater charges after aortic valve replacement. As competing therapies, such as percutaneous valve replacement, emerge to treat high-risk patients, these results serve as a benchmark to compare resource use. Copyright © 2011 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.
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Denova-Gutiérrez, Edgar; Ramírez-Silva, Ivonne; Rodríguez-Ramírez, Sonia; Jiménez-Aguilar, Alejandra; Shamah-Levy, Teresa; Rivera-Dommarco, Juan A
2016-01-01
To assess the validity of a 140-item semiquantitative food frequency questionnaire (SFFQ), in Mexican adolescents and adults. Dietary intakes using a SFFQ and two 24-hour dietary recalls (24DRs), in nonconsecutive days during the same week were measured from 178 adolescents and 230 adults participating in the Mexican National Health and Nutrition Survey-2012.Validity was evaluated using correlation coefficients (CC),deattenuated CC, linear regression models, cross-classification analysis, and the Bland-Altman method. In adults, deattenuated correlation coefficients between the SFFQ and the 24DRs ranged from 0.30 for folate to 0.61 for saturated fat. In addition, 63% adults and 62% adolescents were classified in the same and adjacent quartile of nutrient intake when comparing data from SFFQ and 24DRs. The SFFQ had moderate validity for energy, macronutrients and micronutrients. It also had good validity to rank individuals according to their dietary intake of different nutrients.
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Tongue thickness relates to nutritional status in the elderly.
Tamura, Fumiyo; Kikutani, Takeshi; Tohara, Takashi; Yoshida, Mitsuyoshi; Yaegaki, Ken
2012-12-01
Many elderly people under long-term care suffer from malnutrition caused by dysphagia, frequently leading to sarcopenia. Our hypothesis is that sarcopenia may compromise oral function, resulting in dysphagia. The objectives of this study were to evaluate sarcopenia of the lingual muscles by measuring the tongue thickness, and elucidate its relationship with nutritional status. We examined 104 elderly subjects (mean age = 80.3 ± 7.9 years). Anthropometric data, such as triceps skinfold thickness and midarm muscle area (AMA), were obtained. The tongue thickness of the central part was determined using ultrasonography. Measurement was performed twice and the mean value was obtained. The relationship between tongue thickness and nutritional status was analyzed by Pearson's correlation coefficient and Spearman's rank correlation coefficient. AMA and age were identified by multiple-regression analysis as factors influencing tongue thickness. The results of this study suggest that malnutrition may induce sarcopenia not only in the skeletal muscles but also in the tongue.
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Estimation of old field ecosystem biomass using low altitude imagery
NASA Technical Reports Server (NTRS)
Nor, S. M.; Safir, G.; Burton, T. M.; Hook, J. E.; Schultink, G.
1977-01-01
Color-infrared photography was used to evaluate the biomass of experimental plots in an old-field ecosystem that was treated with different levels of waste water from a sewage treatment facility. Cibachrome prints at a scale of approximately 1:1,600 produced from 35 mm color infrared slides were used to analyze density patterns using prepared tonal density scales and multicell grids registered to ground panels shown on the photograph. Correlations between mean tonal density and harvest biomass data gave consistently high coefficients ranging from 0.530 to 0.896 at the 0.001 significance level. Corresponding multiple regression analysis resulted in higher correlation coefficients. The results indicate that aerial infrared photography can be used to estimate standing crop biomass on waste water irrigated old field ecosystems. Combined with minimal ground truth data, this technique could enable managers of waste water irrigation projects to precisely time harvest of such systems for maximal removal of nutrients in harvested biomass.
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Chaurasia, Ashok; Harel, Ofer
2015-02-10
Tests for regression coefficients such as global, local, and partial F-tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F-tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data. Copyright © 2014 John Wiley & Sons, Ltd.
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Wheat flour dough Alveograph characteristics predicted by Mixolab regression models.
Codină, Georgiana Gabriela; Mironeasa, Silvia; Mironeasa, Costel; Popa, Ciprian N; Tamba-Berehoiu, Radiana
2012-02-01
In Romania, the Alveograph is the most used device to evaluate the rheological properties of wheat flour dough, but lately the Mixolab device has begun to play an important role in the breadmaking industry. These two instruments are based on different principles but there are some correlations that can be found between the parameters determined by the Mixolab and the rheological properties of wheat dough measured with the Alveograph. Statistical analysis on 80 wheat flour samples using the backward stepwise multiple regression method showed that Mixolab values using the ‘Chopin S’ protocol (40 samples) and ‘Chopin + ’ protocol (40 samples) can be used to elaborate predictive models for estimating the value of the rheological properties of wheat dough: baking strength (W), dough tenacity (P) and extensibility (L). The correlation analysis confirmed significant findings (P < 0.05 and P < 0.01) between the parameters of wheat dough studied by the Mixolab and its rheological properties measured with the Alveograph. A number of six predictive linear equations were obtained. Linear regression models gave multiple regression coefficients with R²(adjusted) > 0.70 for P, R²(adjusted) > 0.70 for W and R²(adjusted) > 0.38 for L, at a 95% confidence interval. Copyright © 2011 Society of Chemical Industry.
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Kim, Jiwon; Srinivasan, Aparna; Seoane, Tania; Di Franco, Antonino; Peskin, Charles S; McQueen, David M; Paul, Tracy K; Feher, Attila; Geevarghese, Alexi; Rozenstrauch, Meenakshi; Devereux, Richard B; Weinsaft, Jonathan W
2016-09-01
Echocardiography-derived linear dimensions offer straightforward indices of right ventricular (RV) structure but have not been systematically compared with RV volumes on cardiac magnetic resonance (CMR). Echocardiography and CMR were interpreted among patients with coronary artery disease imaged via prospective (90%) and retrospective (10%) registries. For echocardiography, American Society of Echocardiography-recommended RV dimensions were measured in apical four-chamber (basal RV width, mid RV width, and RV length), parasternal long-axis (proximal RV outflow tract [RVOT]), and short-axis (distal RVOT) views. For CMR, RV end-diastolic volume and RV end-systolic volume were quantified using border planimetry. Two hundred seventy-two patients underwent echocardiography and CMR within a narrow interval (0.4 ± 1.0 days); complete acquisition of all American Society of Echocardiography-recommended dimensions was feasible in 98%. All echocardiographic dimensions differed between patients with and those without RV dilation on CMR (P < .05). Basal RV width (r = 0.70), proximal RVOT width (r = 0.68), and RV length (r = 0.61) yielded the highest correlations with RV end-diastolic volume on CMR; end-systolic dimensions yielded similar correlations (r = 0.68, r = 0.66, and r = 0.65, respectively). In multivariate regression, basal RV width (regression coefficient = 1.96 per mm; 95% CI, 1.22-2.70; P < .001), RV length (regression coefficient = 0.97; 95% CI, 0.56-1.37; P < .001), and proximal RVOT width (regression coefficient = 2.62; 95% CI, 1.79-3.44; P < .001) were independently associated with CMR RV end-diastolic volume (r = 0.80). RV end-systolic volume was similarly associated with echocardiographic dimensions (basal RV width: 1.59 per mm [95% CI, 1.06-2.13], P < .001; RV length: 1.00 [95% CI, 0.66-1.34], P < .001; proximal RVOT width: 1.80 [95% CI, 1.22-2.39], P < .001) (r = 0.79). RV linear dimensions provide readily obtainable markers of RV chamber size. Proximal RVOT and basal width are independently associated with CMR volumes, supporting the use of multiple linear dimensions when assessing RV size on echocardiography. Copyright © 2016 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.
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Quality of life, depression, and sexual dysfunction in spouses of female patients with fibromyalgia.
Tutoglu, Ahmet; Boyaci, Ahmet; Koca, Irfan; Celen, Esra; Korkmaz, Nurdan
2014-08-01
The aim of this study was to investigate the effects of the quality of life and psychological condition of female patients with fibromyalgia and their spouses on sexual function. A total of 32 female patients diagnosed with fibromyalgia and their spouses were analyzed. Thirty married couples were included in the study as the control group. The demographic data of the fibromyalgia patients were recorded, a visual analog scale was used to evaluate the level of pain, and the Fibromyalgia Impact Questionnaire was used to evaluate the impact of the symptoms on the quality of life of the patients. The quality of life of both the patients and the control group were evaluated using the Short Form 36 (SF-36), and psychological variables were evaluated using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory. Sexual function was assessed using the Female Sexual Function Index for female participants and the International Index of Erectile Function (IIEF) for male participants. The IIEF erectile dysfunction scores were significantly lower in the spouses of female patients with fibromyalgia than in the control group (p < 0.05), and the BDI scores were significantly higher in the spouses of the female patients with fibromyalgia (p < 0.05). Among the SF-36 scores, the emotional and physical roles were significantly lower in the spouses of the female patients with fibromyalgia (p = 0.003 and p = 0.004, respectively). In all spouses of FMS patients and controls, there was a significantly negative correlation between erectile function, the BDI score, and to be married with FMS patient and positive correlations between erectile function and emotional role, social function, mental health, SF-36 pain score, and general health (p < 0.05 for all). In a linear regression model, BDI, to be married with FMS patient and general health were found to affect erectile function (beta regression coefficient = -0.572, SE = 0.082, p = 0.001; beta regression coefficient = -0.332, SE = 1.619, p = 0.007; beta regression coefficient = 0.445, SE = 0.065, p = 0.005, respectively). Being a spouse of a patient with fibromyalgia might significantly interfere with quality of life and lead to a high rate of sexual dysfunction. Spouses of patients with fibromyalgia might also be investigated for sexual dysfunction and quality of life. Treatment programs for this group should be considered.
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Analysis of quality of life and influencing factors in 197 Chinese patients with port-wine stains
Wang, Juan; Zhu, Yu-you; Wang, Zhong-ying; Yao, Xiu-hua; Zhang, Lan-fang; Lv, Hong; Zhang, Si-ping; Hu, Bai
2017-01-01
Abstract Port-wine stains (PWS) are congenital capillary malformations, usually occurring on the face, neck, and other exposed parts of the skin, that have serious psychological and social impact on the patient. Most researchers focus on the treatment of PWS, but the quality of life (QoL) of PWS patients is seldom researched. The objective of this study is to evaluate the QoL of patients with PWS on exposed parts and explore the factors influencing the QoL of PWS patients. The QoL of 197 cases with PWS on exposed parts were prospectively studied using the Dermatology Life Quality Index questionnaire (DLQI), and the factors influencing the patients’ QoL were analyzed by single-factor analysis and multiple-factor logistic regression analysis. The reliability and validity of the QoL of PWS patients were then assessed by DLQI. A total of 197 valid questionnaires were collected. The DLQI scores in PWS cases ranged from 2 to 16, with 2 to 5 in 52.29% (103/197), 6 to 10 in 42.13% (83/197), and 11 to 20 in 5.58% (11/197). The main score elements of the DLQI focused on symptoms and feelings, daily activities, and social entertainment. Single-factor analysis and multiple-factor logistic regression analysis showed that the main influencing factors were female sex, skin hypertrophy, and lesion area >30 cm2. The inter-item correlation averaged 47.46% and the Cronbach α was 0.740, indicating high internal consistency. Correlation of the 6 dimensions of the DLQI questionnaires with the total scores showed that the Spearman correlation coefficient r ranged from 0.550 to 0.782 (P < .001), with symptoms and feelings having a correlation coefficient of 0.782 and a high correlation with total scores. This study shows that PWS has mild to moderate influence on the QoL of most patients, mainly on daily activities, social entertainment, and feelings. PMID:29390578
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Lapolla, Annunziata; Piarulli, Francesco; Sartore, Giovanni; Ceriello, Antonio; Ragazzi, Eugenio; Reitano, Rachele; Baccarin, Lorenzo; Laverda, Barbara; Fedele, Domenico
2007-03-01
Advanced glycation end products (AGEs), pentosidine and malondialdehyde (MDA), are elevated in type 2 diabetic subjects with coronary and carotid angiopathy. We investigated the relationship of AGEs, MDA, total reactive antioxidant potentials (TRAPs), and vitamin E in type 2 diabetic patients with and without peripheral artery disease (PAD). AGEs, pentosidine, MDA, TRAP, vitamin E, and ankle-brachial index (ABI) were measured in 99 consecutive type 2 diabetic subjects and 20 control subjects. AGEs, pentosidine, and MDA were higher and vitamin E and TRAP were lower in patients with PAD (ABI <0.9) than in patients without PAD (ABI >0.9) (P < 0.001). After multiple regression analysis, a correlation between AGEs and pentosidine, as independent variables, and ABI, as the dependent variable, was found in both patients with and without PAD (r = 0.9198, P < 0.001 and r = 0.5764, P < 0.001, respectively) but not in control subjects. When individual regression coefficients were evaluated, only that due to pentosidine was confirmed as significant. For patients with PAD, considering TRAP, vitamin E, and MDA as independent variables and ABI as the dependent variable produced an overall significant regression (r = 0.6913, P < 0.001). The regression coefficients for TRAP and vitamin E were not significant, indicating that the model is best explained by a single linear regression between MDA and ABI. These findings were also confirmed by principal component analysis. Results show that pentosidine and MDA are strongly associated with PAD in type 2 diabetic patients.
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Liu, Quan; Ma, Li; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing
2018-01-01
Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.
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Hewitt, Angela L.; Popa, Laurentiu S.; Pasalar, Siavash; Hendrix, Claudia M.
2011-01-01
Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of Radj2), followed by position (28 ± 24% of Radj2) and speed (11 ± 19% of Radj2). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower Radj2 values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics. PMID:21795616
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Zhang, Yan; Zou, Hong-Yan; Shi, Pei; Yang, Qin; Tang, Li-Juan; Jiang, Jian-Hui; Wu, Hai-Long; Yu, Ru-Qin
2016-01-01
Determination of benzo[a]pyrene (BaP) in cigarette smoke can be very important for the tobacco quality control and the assessment of its harm to human health. In this study, mid-infrared spectroscopy (MIR) coupled to chemometric algorithm (DPSO-WPT-PLS), which was based on the wavelet packet transform (WPT), discrete particle swarm optimization algorithm (DPSO) and partial least squares regression (PLS), was used to quantify harmful ingredient benzo[a]pyrene in the cigarette mainstream smoke with promising result. Furthermore, the proposed method provided better performance compared to several other chemometric models, i.e., PLS, radial basis function-based PLS (RBF-PLS), PLS with stepwise regression variable selection (Stepwise-PLS) as well as WPT-PLS with informative wavelet coefficients selected by correlation coefficient test (rtest-WPT-PLS). It can be expected that the proposed strategy could become a new effective, rapid quantitative analysis technique in analyzing the harmful ingredient BaP in cigarette mainstream smoke. Copyright © 2015 Elsevier B.V. All rights reserved.
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ERIC Educational Resources Information Center
Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer
2013-01-01
Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…
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Gene set analysis using variance component tests.
Huang, Yen-Tsung; Lin, Xihong
2013-06-28
Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.
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Relationship between sugarcane rust severity and soil properties in louisiana.
Johnson, Richard M; Grisham, Michael P; Richard, Edward P
2007-06-01
ABSTRACT The extent of spatial and temporal variability of sugarcane rust (Puccinia melanocephala) infestation was related to variation in soil properties in five commercial fields of sugarcane (interspecific hybrids of Saccharum spp., cv. LCP 85-384) in southern Louisiana. Sugarcane fields were grid-soil sampled at several intensities and rust ratings were collected at each point over 6 to 7 weeks. Soil properties exhibited significant variability (coefficients of variation = 9 to 70.1%) and were spatially correlated in 39 of 40 cases with a range of spatial correlation varying from 39 to 201 m. Rust ratings were spatially correlated in 32 of 33 cases, with a range varying from 29 to 241 m. Rust ratings were correlated with several soil properties, most notably soil phosphorus (r = 0.40 to 0.81) and soil sulfur (r = 0.36 to 0.68). Multiple linear regression analysis resulted in coefficients of determination that ranged from 0.22 to 0.73, and discriminant analysis further improved the overall predictive ability of rust models. Finally, contour plots of soil properties and rust levels clearly suggested a link between these two parameters. These combined data suggest that sugarcane growers that apply fertilizer in excess of plant requirements will increase the incidence and severity of rust infestations in their fields.
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Rostami, Reza; Sadeghi, Vahid; Zarei, Jamileh; Haddadi, Parvaneh; Mohazzab-Torabi, Saman; Salamati, Payman
2013-04-01
The aim of this study was to compare the Persian version of the wechsler intelligence scale for children - fourth edition (WISC-IV) and cognitive assessment system (CAS) tests, to determine the correlation between their scales and to evaluate the probable concurrent validity of these tests in patients with learning disorders. One-hundered-sixty-two children with learning disorder who were presented at Atieh Comprehensive Psychiatry Center were selected in a consecutive non-randomized order. All of the patients were assessed based on WISC-IV and CAS scores questionnaires. Pearson correlation coefficient was used to analyze the correlation between the data and to assess the concurrent validity of the two tests. Linear regression was used for statistical modeling. The type one error was considered 5% in maximum. There was a strong correlation between total score of WISC-IV test and total score of CAS test in the patients (r=0.75, P<0.001). The correlations among the other scales were mostly high and all of them were statistically significant (P<0.001). A linear regression model was obtained (α = 0.51, β = 0.81 and P<0.001). There is an acceptable correlation between the WISC-IV scales and CAS test in children with learning disorders. A concurrent validity is established between the two tests and their scales.
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Rostami, Reza; Sadeghi, Vahid; Zarei, Jamileh; Haddadi, Parvaneh; Mohazzab-Torabi, Saman; Salamati, Payman
2013-01-01
Objective The aim of this study was to compare the Persian version of the wechsler intelligence scale for children - fourth edition (WISC-IV) and cognitive assessment system (CAS) tests, to determine the correlation between their scales and to evaluate the probable concurrent validity of these tests in patients with learning disorders. Methods One-hundered-sixty-two children with learning disorder who were presented at Atieh Comprehensive Psychiatry Center were selected in a consecutive non-randomized order. All of the patients were assessed based on WISC-IV and CAS scores questionnaires. Pearson correlation coefficient was used to analyze the correlation between the data and to assess the concurrent validity of the two tests. Linear regression was used for statistical modeling. The type one error was considered 5% in maximum. Findings There was a strong correlation between total score of WISC-IV test and total score of CAS test in the patients (r=0.75, P<0.001). The correlations among the other scales were mostly high and all of them were statistically significant (P<0.001). A linear regression model was obtained (α = 0.51, β = 0.81 and P<0.001). Conclusion There is an acceptable correlation between the WISC-IV scales and CAS test in children with learning disorders. A concurrent validity is established between the two tests and their scales. PMID:23724180
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Restoring method for missing data of spatial structural stress monitoring based on correlation
NASA Astrophysics Data System (ADS)
Zhang, Zeyu; Luo, Yaozhi
2017-07-01
Long-term monitoring of spatial structures is of great importance for the full understanding of their performance and safety. The missing part of the monitoring data link will affect the data analysis and safety assessment of the structure. Based on the long-term monitoring data of the steel structure of the Hangzhou Olympic Center Stadium, the correlation between the stress change of the measuring points is studied, and an interpolation method of the missing stress data is proposed. Stress data of correlated measuring points are selected in the 3 months of the season when missing data is required for fitting correlation. Data of daytime and nighttime are fitted separately for interpolation. For a simple linear regression when single point's correlation coefficient is 0.9 or more, the average error of interpolation is about 5%. For multiple linear regression, the interpolation accuracy is not significantly increased after the number of correlated points is more than 6. Stress baseline value of construction step should be calculated before interpolating missing data in the construction stage, and the average error is within 10%. The interpolation error of continuous missing data is slightly larger than that of the discrete missing data. The data missing rate of this method should better not exceed 30%. Finally, a measuring point's missing monitoring data is restored to verify the validity of the method.
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Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A
2016-08-01
The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.
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Blood proteins analysis by Raman spectroscopy method
NASA Astrophysics Data System (ADS)
Artemyev, D. N.; Bratchenko, I. A.; Khristoforova, Yu. A.; Lykina, A. A.; Myakinin, O. O.; Kuzmina, T. P.; Davydkin, I. L.; Zakharov, V. P.
2016-04-01
This work is devoted to study the possibility of plasma proteins (albumin, globulins) concentration measurement using Raman spectroscopy setup. The blood plasma and whole blood were studied in this research. The obtained Raman spectra showed significant variation of intensities of certain spectral bands 940, 1005, 1330, 1450 and 1650 cm-1 for different protein fractions. Partial least squares regression analysis was used for determination of correlation coefficients. We have shown that the proposed method represents the structure and biochemical composition of major blood proteins.
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1990-05-01
MNidsouldcr:. The Q (2 point on top of the right shoulder midway Midpatcllao The between the neck anterior point halfway (right trapezius point) between...of hcad: Thbe K Thwnbip: e tp ofhighest point on the Tbumbtp: Thetip ofhead when the head is the right thumb . in the Frankfort plane. A Trapiori...of the right shoulder. 80 NECK-BUSTPOINT/THELION LENGTH (NKBPLGTH) -- distance between the trapezius landmark at the side of the neck and the
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Considerations for monitoring raptor population trends based on counts of migrants
Titus, K.; Fuller, M.R.; Ruos, J.L.; Meyburg, B-U.; Chancellor, R.D.
1989-01-01
Various problems were identified with standardized hawk count data as annually collected at six sites. Some of the hawk lookouts increased their hours of observation from 1979-1985, thereby confounding the total counts. Data recording and missing data hamper coding of data and their use with modern analytical techniques. Coefficients of variation among years in counts averaged about 40%. The advantages and disadvantages of various analytical techniques are discussed including regression, non-parametric rank correlation trend analysis, and moving averages.
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Digital Correlation Microwave Polarimetry: Analysis and Demonstration
NASA Technical Reports Server (NTRS)
Piepmeier, J. R.; Gasiewski, A. J.; Krebs, Carolyn A. (Technical Monitor)
2000-01-01
The design, analysis, and demonstration of a digital-correlation microwave polarimeter for use in earth remote sensing is presented. We begin with an analysis of three-level digital correlation and develop the correlator transfer function and radiometric sensitivity. A fifth-order polynomial regression is derived for inverting the digital correlation coefficient into the analog statistic. In addition, the effects of quantizer threshold asymmetry and hysteresis are discussed. A two-look unpolarized calibration scheme is developed for identifying correlation offsets. The developed theory and calibration method are verified using a 10.7 GHz and a 37.0 GHz polarimeter. The polarimeters are based upon 1-GS/s three-level digital correlators and measure the first three Stokes parameters. Through experiment, the radiometric sensitivity is shown to approach the theoretical as derived earlier in the paper and the two-look unpolarized calibration method is successfully compared with results using a polarimetric scheme. Finally, sample data from an aircraft experiment demonstrates that the polarimeter is highly-useful for ocean wind-vector measurement.
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Wang, Yan-Cang; Yang, Gui-Jun; Zhu, Jin-Shan; Gu, Xiao-He; Xu, Peng; Liao, Qin-Hong
2014-07-01
For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.
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Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.
Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A
2012-12-01
Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Assessment of oxygen saturation in dental pulp of permanent teeth with periodontal disease.
Giovanella, Larissa Bergesch; Barletta, Fernando Branco; Felippe, Wilson Tadeu; Bruno, Kely Firmino; de Alencar, Ana Helena Gonçalves; Estrela, Carlos
2014-12-01
In individuals with periodontal disease, dental pulp status should be determined before a treatment plan is made. Pulse oximeters are promising diagnostic tools to evaluate pulp vascularization. This study used pulse oximetry to determine the level of oxygen saturation in dental pulp of intact permanent teeth with periodontal attachment loss (PAL) and gingival recession (GR) and to evaluate the correlation between periodontal disease and level of oxygen saturation in the pulp. This study included 67 anterior teeth of 35 patients; all teeth showed intact crowns, PAL, a periodontal pocket (PP), and GR. The teeth underwent periodontal examination, cold and electric pulp testing, and pulse oximetry measurements. The Pearson correlation coefficient and a linear regression coefficient were calculated to evaluate the degree of correlation between periodontal disease markers (PAL, PP, and GR) and the level of oxygen saturation in dental pulp. These tests also evaluated possible associations between oxygen saturation and cold and electric pulp testing. PAL, PP, and GR had negative correlations with oxygen saturation in dental pulp. Conversely, no statistically significant association was found between oxygen saturation in dental pulp and the response to electric sensibility testing. Oxygen saturation was lower in the pulp of permanent teeth with PAL, PP, and GR, indicating that periodontal disease correlates with the level of oxygen saturation in the pulp. Copyright © 2014 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.
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The design of control system of livestock feeding processing
NASA Astrophysics Data System (ADS)
Sihombing, Juna; Napitupulu, Humala L.; Hidayati, Juliza
2018-03-01
PT. XYZ is a company that produces animal feed. One type of animal feed produced is 105 ISA P. In carrying out its production process, PT. XYZ faces the problem of rejected feed amounts during 2014 to June 2015 due to the amount of animal feed that exceeds the standard feed quality of 13% of moisture content and 3% for ash content. Therefore, the researchers analyzed the relationship between factors affecting the quality and extent of damage by using regression and correlation and determine the optimum value of each processing process. Analysis results found that variables affecting product quality are mixing time, steam conditioning temperature and cooling time. The most dominant variable affecting the product moisture content is mixing time with the correlation coefficient of (0.7959) and the most dominant variable affecting the ash content of the product during the processing is mixing time with the correlation coefficient of (0.8541). The design of the proposed product processing control is to run the product processing process with mixing time 235 seconds, steam conditioning temperature 87 0C and cooling time 192 seconds. Product quality 105 ISA P obtained by using this design is with 12.16% moisture content and ash content of 2.59%.
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Bhargava, Dinesh; Karthikeyan, C; Moorthy, N S H N; Trivedi, Piyush
2009-09-01
QSAR study was carried out for a series of piperazinyl phenylalanine derivatives exhibiting VLA-4/VCAM-1 inhibitory activity to find out the structural features responsible for the biological activity. The QSAR study was carried out on V-life Molecular Design Suite software and the derived best QSAR model by partial least square (forward) regression method showed 85.67% variation in biological activity. The statistically significant model with high correlation coefficient (r2=0.85) was selected for further study and the resulted validation parameters of the model, crossed squared correlation coefficient (q2=0.76 and pred_r2=0.42) show the model has good predictive ability. The model showed that the parameters SaaNEindex, SsClcount slogP,and 4PathCount are highly correlated with VLA-4/VCAM-1 inhibitory activity of piperazinyl phenylalanine derivatives. The result of the study suggests that the chlorine atoms in the molecule and fourth order fragmentation patterns in the molecular skeleton favour VLA-4/VCAM-1 inhibition shown by the title compounds whereas lipophilicity and nitrogen bonded to aromatic bond are not conducive for VLA-4/VCAM-1 inhibitory activity.
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Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.
Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H
2016-01-01
Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.
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Yoneoka, Daisuke; Henmi, Masayuki
2017-11-30
Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.
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Linear Regression between CIE-Lab Color Parameters and Organic Matter in Soils of Tea Plantations
NASA Astrophysics Data System (ADS)
Chen, Yonggen; Zhang, Min; Fan, Dongmei; Fan, Kai; Wang, Xiaochang
2018-02-01
To quantify the relationship between the soil organic matter and color parameters using the CIE-Lab system, 62 soil samples (0-10 cm, Ferralic Acrisols) from tea plantations were collected from southern China. After air-drying and sieving, numerical color information and reflectance spectra of soil samples were measured under laboratory conditions using an UltraScan VIS (HunterLab) spectrophotometer equipped with CIE-Lab color models. We found that soil total organic carbon (TOC) and nitrogen (TN) contents were negatively correlated with the L* value (lightness) ( r = -0.84 and -0.80, respectively), a* value (correlation coefficient r = -0.51 and -0.46, respectively) and b* value ( r = -0.76 and -0.70, respectively). There were also linear regressions between TOC and TN contents with the L* value and b* value. Results showed that color parameters from a spectrophotometer equipped with CIE-Lab color models can predict TOC contents well for soils in tea plantations. The linear regression model between color values and soil organic carbon contents showed it can be used as a rapid, cost-effective method to evaluate content of soil organic matter in Chinese tea plantations.
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Herrero, A M; de la Hoz, L; Ordóñez, J A; Herranz, B; Romero de Ávila, M D; Cambero, M I
2008-11-01
The possibilities of using breaking strength (BS) and energy to fracture (EF) for monitoring textural properties of some cooked meat sausages (chopped, mortadella and galantines) were studied. Texture profile analysis (TPA), folding test and physico-chemical measurements were also performed. Principal component analysis enabled these meat products to be grouped into three textural profiles which showed significant (p<0.05) differences mainly for BS, hardness, adhesiveness and cohesiveness. Multivariate analysis indicated that BS, EF and TPA parameters were correlated (p<0.05) for every individual meat product (chopped, mortadella and galantines) and all products together. On the basis of these results, TPA parameters could be used for constructing regression models to predict BS. The resulting regression model for all cooked meat products was BS=-0.160+6.600∗cohesiveness-1.255∗adhesiveness+0.048∗hardness-506.31∗springiness (R(2)=0.745, p<0.00005). Simple linear regression analysis showed significant coefficients of determination between BS (R(2)=0.586, p<0.0001) versus folding test grade (FG) and EF versus FG (R(2)=0.564, p<0.0001).
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Koleilat, Maria; Whaley, Shannon E
2016-06-01
Fruits, vegetables, sweetened foods, and beverages have been found to have positive and negative associations with obesity in early childhood, yet no rapid assessment tools are available to measure intake of these foods among preschoolers. This study examines the test-retest reliability and validity of a 10-item Child Food and Beverage Intake Questionnaire designed to assess fruits, vegetables, and sweetened foods and beverages intake among 2- to 4-year-old children. The Child Food and Beverage Intake Questionnaire was developed for use in periodic phone surveys conducted with low-income families with preschool-aged children. Seventy primary caregivers of 2- to 4-year-old children completed two Child Food and Beverage Intake Questionnaires within a 2-week period for test-retest reliability. Participants also completed three 24-hour recalls to allow assessment of validity. Intraclass correlations were used to examine test-retest reliability. Spearman rank correlation coefficients, Bland-Altman plots, and linear regression analyses were used to examine validity of the Child Food and Beverage Intake Questionnaire compared with three 24-hour recalls. Intraclass correlations between Child Food and Beverage Intake Questionnaire administrations ranged from 0.48 for sweetened drinks to 0.87 for regular sodas. Intraclass correlations for fruits, vegetables, and sweetened food were 0.56, 0.49, and 0.56, respectively. Spearman rank correlation coefficients ranged from 0.15 to 0.59 for beverages, with 0.46 for sugar-sweetened beverages. Spearman rank correlation coefficients for fruits, vegetables, and sweetened food were 0.30, 0.33, and 0.30, respectively. Although observation of the Bland-Altman plots and linear regression analyses showed a slight upward trend in mean differences, with increasing mean intake for five beverage groups, at least 90% of data plots fell within the limits of agreement for all food/beverage groups. The Child Food and Beverage Intake Questionnaire exhibited fair to substantial test-retest reliability and moderate to strong validity in ranking fruits, vegetables, sweetened food, and the majority of beverages consumed by children aged 2 to 4 years old. Although the Child Food and Beverage Intake Questionnaire might not be able to assess the absolute intake of foods and beverages, given the scarcity of an easily administered, valid, and reliable questionnaire to assess nutritional intake among 2- to 4-year-old low-income children, this tool is a useful means for measuring trends in dietary intake among low-income preschoolers. Copyright © 2016 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.
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Brasil, Vinicius Paim; Costa, Juvenal Soares Dias da
2016-01-01
to evaluate trends in rates of hospitalizations owing to ambulatory care sensitive conditions in the municipality of Florianópolis, Santa Catarina, Brazil, from 2001 to 2011, and to assess correlation with the public health expendutures Family Health Strategy (FHS) population coverage. this was an ecological study using Ministry of Health secondary data; data were analyzed using Poisson Regression. the regression coefficient was 0.97, showing a decrease of 3% per year in hospitalizations owing to ambulatory care sensitive conditions, a three-fold increase in FHS coverage and seven times more financial investment per capita in health services, from R$67.65 in 2001 to R$471.03 in 2011; FHS investments per capita in health and population coverage were negatively correlated to the rate of hospitalizations owing to ambulatory care sensitive conditions. financial investment and FHS expansion had led to major reductions in the rate of hospitalizations owing to ambulatory care sensitive conditions.
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Yadav, Dharmendra Kumar; Kalani, Komal; Khan, Feroz; Srivastava, Santosh Kumar
2013-12-01
For the prediction of anticancer activity of glycyrrhetinic acid (GA-1) analogs against the human lung cancer cell line (A-549), a QSAR model was developed by forward stepwise multiple linear regression methodology. The regression coefficient (r(2)) and prediction accuracy (rCV(2)) of the QSAR model were taken 0.94 and 0.82, respectively in terms of correlation. The QSAR study indicates that the dipole moments, size of smallest ring, amine counts, hydroxyl and nitro functional groups are correlated well with cytotoxic activity. The docking studies showed high binding affinity of the predicted active compounds against the lung cancer target EGFR. These active glycyrrhetinic acid derivatives were then semi-synthesized, characterized and in-vitro tested for anticancer activity. The experimental results were in agreement with the predicted values and the ethyl oxalyl derivative of GA-1 (GA-3) showed equal cytotoxic activity to that of standard anticancer drug paclitaxel.
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Analysis and generation of groundwater concentration time series
NASA Astrophysics Data System (ADS)
Crăciun, Maria; Vamoş, Călin; Suciu, Nicolae
2018-01-01
Concentration time series are provided by simulated concentrations of a nonreactive solute transported in groundwater, integrated over the transverse direction of a two-dimensional computational domain and recorded at the plume center of mass. The analysis of a statistical ensemble of time series reveals subtle features that are not captured by the first two moments which characterize the approximate Gaussian distribution of the two-dimensional concentration fields. The concentration time series exhibit a complex preasymptotic behavior driven by a nonstationary trend and correlated fluctuations with time-variable amplitude. Time series with almost the same statistics are generated by successively adding to a time-dependent trend a sum of linear regression terms, accounting for correlations between fluctuations around the trend and their increments in time, and terms of an amplitude modulated autoregressive noise of order one with time-varying parameter. The algorithm generalizes mixing models used in probability density function approaches. The well-known interaction by exchange with the mean mixing model is a special case consisting of a linear regression with constant coefficients.
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Structure-activity relationships between sterols and their thermal stability in oil matrix.
Hu, Yinzhou; Xu, Junli; Huang, Weisu; Zhao, Yajing; Li, Maiquan; Wang, Mengmeng; Zheng, Lufei; Lu, Baiyi
2018-08-30
Structure-activity relationships between 20 sterols and their thermal stabilities were studied in a model oil system. All sterol degradations were found to be consistent with a first-order kinetic model with determination of coefficient (R 2 ) higher than 0.9444. The number of double bonds in the sterol structure was negatively correlated with the thermal stability of sterol, whereas the length of the branch chain was positively correlated with the thermal stability of sterol. A quantitative structure-activity relationship (QSAR) model to predict thermal stability of sterol was developed by using partial least squares regression (PLSR) combined with genetic algorithm (GA). A regression model was built with R 2 of 0.806. Almost all sterol degradation constants can be predicted accurately with R 2 of cross-validation equals to 0.680. Four important variables were selected in optimal QSAR model and the selected variables were observed to be related with information indices, RDF descriptors, and 3D-MoRSE descriptors. Copyright © 2018 Elsevier Ltd. All rights reserved.
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[Developing Perceived Competence Scale (PCS) for Adolescents].
Özer, Arif; Gençtanirim Kurt, Dilek; Kizildağ, Seval; Demırtaş Zorbaz, Selen; Arici Şahın, Fatma; Acar, Tülin; Ergene, Tuncay
2016-01-01
In this study, Perceived Competence Scale was developed to measure high school students' perceived competence. Scale development process was verified on three different samples. Participants of the research are some high school students in 2011-2012 academic terms from Ankara. Participants' numbers are incorporated in exploratory factor analysis, confirmatory factor analysis and test-retest reliability respectively, as follows: 372, 668 and 75. Internal consistency coefficients (Cronbach's and stratified α) are calculated separately for each group. For data analysis Factor 8.02 and LISREL 8.70 package programs were used. According to results of the analyses, internal consistency coefficients (α) are .90 - .93 for academic competence, .82 - .86 for social competence in the samples that exploratory and confirmatory factor analysis performed. For the whole scale internal consistency coefficient (stratified α) is calculated as .91. As a result of test-retest reliability, adjusted correlation coefficients (r) are .94 for social competence and .90 for academic competence. In addition, to fit indexes and regression weights obtained from factor analysis, findings related convergent and discriminant validity, indicating that competence can be addressed in two dimensions which are academic (16 items) and social (14 items).
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NASA Astrophysics Data System (ADS)
Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat
2015-04-01
Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.
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Borges, Germana Jayme; Ruiz, Luis Fernando Naldi; de Alencar, Ana Helena Gonçalves; Porto, Olavo César Lyra; Estrela, Carlos
2015-01-01
The objective of the present study was to assess cone-beam computed tomography (CBCT) as a diagnostic method for determination of gingival thickness (GT) and distance between gingival margin and vestibular (GMBC-V) and interproximal bone crests (GMBC-I). GT and GMBC-V were measured in 348 teeth and GMBC-I was measured in 377 tooth regions of 29 patients with gummy smile. GT was assessed using transgingival probing (TP), ultrasound (US), and CBCT, whereas GMBC-V and GMBC-I were assessed by transsurgical clinical evaluation (TCE) and CBCT. Statistical analyses used independent t-test, Pearson's correlation coefficient, and simple linear regression. Difference was observed for GT: between TP, CBCT, and US considering all teeth; between TP and CBCT and between TP and US in incisors and canines; between TP and US in premolars and first molars. TP presented the highest means for GT. Positive correlation and linear regression were observed between TP and CBCT, TP and US, and CBCT and US. Difference was observed for GMBC-V and GMBC-I using TCE and CBCT, considering all teeth. Correlation and linear regression results were significant for GMBC-V and GMBC-I in incisors, canines, and premolars. CBCT is an effective diagnostic method to visualize and measure GT, GMBC-V, and GMBC-I. PMID:25918737
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Kandala, Sridhar; Nolan, Dan; Laumann, Timothy O.; Power, Jonathan D.; Adeyemo, Babatunde; Harms, Michael P.; Petersen, Steven E.; Barch, Deanna M.
2016-01-01
Abstract Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR. PMID:27571276
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1990-05-01
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Evaluation of Quality of Life and Safety of Seniors in Golestan Province, Iran
Foroushani, Abbas Rahimi; Badakhshan, Abbas; Gholipour, Mahin; Hosseini, Masoumeh
2015-01-01
This study evaluated the criteria for quality of life (QoL) using standardized short-form health survey with only 36 questions (SF-36; Version 2.0) and Consumer Product Safety Commission (CPSC) questionnaires to study the relationship between QoL and living conditions of seniors in Golestan province in Iran. This was an analytical cross-sectional study with descriptive and analytical parts. The population was individuals above 65 years of age in Golestan province in Iran. The sample size was calculated based on the correlation coefficient; a correlation of .2 or greater was considered statistically significant at 80% for the power of the test at the 95% confidence level. The data on QoL of seniors were collected by interview and observation using the CPSC questionnaire for nursing homes and the SF-36 for QoL health indicators. The reliability of the CPSC questionnaire was estimated using Cronbach’s alpha with a coefficient of .838. The SF-36 questionnaire was validated with Cronbach’s alpha with a coefficient of .95. Chi-square and logistic regression were used to interpret the probability of abnormal QoL between levels of independent predictors. The percentage of seniors in overall poor health as a binary outcome was 43.5, and the percentage of unsafe conditions was 49.8. PMID:28138463
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Corticospinal signals recorded with MEAs can predict the volitional forearm forces in rats.
Guo, Yi; Mesut, Sahin; Foulds, Richard A; Adamovich, Sergei V
2013-01-01
We set out to investigate if volitional components in the descending tracts of the spinal cord white matter can be accessed with multi-electrode array (MEA) recording technique. Rats were trained to press a lever connected to a haptic device with force feedback to receive sugar pellets. A flexible-substrate multi-electrode array was chronically implanted into the dorsal column of the cervical spinal cord. Field potentials and multi-unit activities were recorded from the descending axons of the corticospinal tract while the rat performed a lever pressing task. Forelimb forces, recorded with the sensor attached to the lever, were reconstructed using the hand position data and the neural signals through multiple trials over three weeks. The regression coefficients found from the trial set were cross-validated on the other trials recorded on same day. Approximately 30 trials of at least 2 seconds were required for accurate model estimation. The maximum correlation coefficient between the actual and predicted force was 0.7 in the test set. Positional information and its interaction with neural signals improved the correlation coefficient by 0.1 to 0.15. These results suggest that the volitional information contained in the corticospinal tract can be extracted with multi-channel neural recordings made with parenchymal electrodes.
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Vasoconstriction Potency Induced by Aminoamide Local Anesthetics Correlates with Lipid Solubility
Sung, Hui-Jin; Ok, Seong-Ho; Sohn, Jin-Young; Son, Yong Hyeok; Kim, Jun Kyu; Lee, Soo Hee; Han, Jeong Yeol; Lim, Dong Hoon; Shin, Il-Woo; Lee, Heon-Keun; Chung, Young-Kyun; Choi, Mun-Jeoung; Sohn, Ju-Tae
2012-01-01
Aminoamide local anesthetics induce vasoconstriction in vivo and in vitro. The goals of this in vitro study were to investigate the potency of local anesthetic-induced vasoconstriction and to identify the physicochemical property (octanol/buffer partition coefficient, pKa, molecular weight, or potency) of local anesthetics that determines their potency in inducing isolated rat aortic ring contraction. Cumulative concentration-response curves to local anesthetics (levobupivacaine, ropivacaine, lidocaine, and mepivacaine) were obtained from isolated rat aorta. Regression analyses were performed to determine the relationship between the reported physicochemical properties of local anesthetics and the local anesthetic concentration that produced 50% (ED50) of the local anesthetic-induced maximum vasoconstriction. We determined the order of potency (ED50) of vasoconstriction among local anesthetics to be levobupivacaine > ropivacaine > lidocaine > mepivacaine. The relative importance of the independent variables that affect the vasoconstriction potency is octanol/buffer partition coefficient > potency > pKa > molecular weight. The ED50 in endothelium-denuded aorta negatively correlated with the octanol/buffer partition coefficient of local anesthetics (r2 = 0.9563; P < 0.001). The potency of the vasoconstriction in the endothelium-denuded aorta induced by local anesthetics is determined primarily by lipid solubility and, in part, by other physicochemical properties including potency and pKa. PMID:22778542
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Taniguchi, Naoya; Matsuda, Shuichi; Kawaguchi, Takahisa; Tabara, Yasuharu; Ikezoe, Tome; Tsuboyama, Tadao; Ichihashi, Noriaki; Nakayama, Takeo; Matsuda, Fumihiko; Ito, Hiromu
2015-01-01
Cultural and ethnic differences are present both in subjective and objective measures of patient health, but scoring systems do not always reflect these differences, and so validation of outcomes tools in different cultural settings is important. Recently, a revised version of The Knee Society Score® (KSS 2011) was developed, but to our knowledge, the degree that this tool evaluates clinical symptoms, physical activities, and radiographic grades in the general Japanese population is not known. We therefore asked: (1) how KSS 2011 reflects knee conditions and function in the general Japanese population, in particular evaluating changes with increasing patient age; (2) can objective measures of physical function be correlated with KSS 2011; and (3) does radiographic osteoarthritis (OA) grade correlate with KSS 2011? Two hundred twenty-six people in the general Japanese population, aged 35 to 92 years, with and without knee arthritis, voluntarily participated in this cross-sectional study. Residents who had no serious disease or symptoms based on a self-assessment were recruited. This study consisted of a questionnaire including self-administered KSS 2011, physical examination, and weightbearing radiographs of the knee. Leg muscle strength, Timed Up and Go test, and body mass index (BMI) were examined in all the participants. Radiographs were graded according to the Kellgren and Lawrence scale (KL grade). Multivariable linear regression analysis showed that KSS 2011 correlated with age (coefficient: -0.30±0.12, p=0.011), BMI (coefficient: -1.47±0.42, p<0.001), leg muscle strength (coefficient: 0.41±0.13, p=0.002), and Timed Up and Go Test (coefficient: -1.96±0.92, p=0.034), but not sex, as independent variables by a stepwise method. KSS 2011 was also correlated with radiographic OA evaluated by KL grade (coefficient: -12.2±2.9, p<0.001). KSS 2011 reflects symptoms, physical activities, and radiographic OA grades of the knee in an age-dependent manner in the general Japanese population. Level IV, diagnostic study. See Guidelines for Authors for a complete description of levels of evidence.
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ERIC Educational Resources Information Center
Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.
2013-01-01
This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)
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Zhou, Yang; Fu, Xiaping; Ying, Yibin; Fang, Zhenhuan
2015-06-23
A fiber-optic probe system was developed to estimate the optical properties of turbid media based on spatially resolved diffuse reflectance. Because of the limitations in numerical calculation of radiative transfer equation (RTE), diffusion approximation (DA) and Monte Carlo simulations (MC), support vector regression (SVR) was introduced to model the relationship between diffuse reflectance values and optical properties. The SVR models of four collection fibers were trained by phantoms in calibration set with a wide range of optical properties which represented products of different applications, then the optical properties of phantoms in prediction set were predicted after an optimal searching on SVR models. The results indicated that the SVR model was capable of describing the relationship with little deviation in forward validation. The correlation coefficient (R) of reduced scattering coefficient μ'(s) and absorption coefficient μ(a) in the prediction set were 0.9907 and 0.9980, respectively. The root mean square errors of prediction (RMSEP) of μ'(s) and μ(a) in inverse validation were 0.411 cm(-1) and 0.338 cm(-1), respectively. The results indicated that the integrated fiber-optic probe system combined with SVR model were suitable for fast and accurate estimation of optical properties of turbid media based on spatially resolved diffuse reflectance. Copyright © 2015 Elsevier B.V. All rights reserved.
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Development and validation of the neck dissection impairment index: a quality of life measure.
Taylor, Rodney J; Chepeha, Judith C; Teknos, Theodoros N; Bradford, Carol R; Sharma, Pramod K; Terrell, Jeffrey E; Hogikyan, Norman D; Wolf, Gregory T; Chepeha, Douglas B
2002-01-01
To validate a health-related quality-of-life (QOL) instrument for patients following neck dissection and to identify the factors that affect QOL following neck dissection. Cross-sectional validation study. The outpatient clinic of a tertiary care cancer center. Convenience sample of 54 patients previously treated for head and neck cancer who underwent a selective neck dissection or modified radical neck dissection (64 total neck dissections). Patients had a minimum postoperative convalescence of 11 months. Thirty-two underwent accessory nerve-sparing modified radical neck dissection, and 32 underwent selective neck dissection. A 10-item, self-report instrument, the Neck Dissection Impairment Index (NDII), was developed and validated. Reliability was evaluated with test-retest correlation and internal consistency using the Cronbach alpha coefficient. Convergent validity was assessed using the 36-Item Short-Form Health Survey (SF-36) and the Constant Shoulder Scale, a shoulder function test. Multiple variable regression was used to determine variables that most affected QOL following neck dissection The 10-item NDII test-retest correlation was 0.91 (P<.001) with an internal consistency Cronbach alpha coefficient of.95. The NDII correlated with the Constant Shoulder Scale (r = 0.85, P<.001) and with the SF-36 physical functioning (r = 0.50, P<.001) and role-physical functioning (r = 0.60, P<.001) domains. Using multiple variable regression, the variables that contributed most to QOL score were patient's age and weight, radiation treatment, and neck dissection type. The NDII is a valid, reliable instrument for assessing neck dissection impairment. Patient's age, weight, radiation treatment, and neck dissection type were important factors that affect QOL following neck dissection.
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Bouti, Khalid; Benamor, Jouda; Bourkadi, Jamal Eddine
2017-08-01
Peak Expiratory Flow (PEF) has never been characterised among healthy Moroccan school children. To study the relationship between PEF and anthropometric parameters (sex, age, height and weight) in healthy Moroccan school children, to establish predictive equations of PEF; and to compare flowmetric and spirometric PEF with Forced Expiratory Volume in 1 second (FEV1). This cross-sectional study was conducted between April, 2016 and May, 2016. It involved 222 (122 boys and 100 girls) healthy school children living in Ksar el-Kebir, Morocco. We used mobile equipments for realisation of spirometry and peak expiratory flow measurements. SPSS (Version 22.0) was used to calculate Student's t-test, Pearson's correlation coefficient and linear regression. Significant linear correlation was seen between PEF, age and height in boys and girls. The equation for prediction of flowmetric PEF in boys was calculated as 'F-PEF = -187+ 24.4 Age + 1.61 Height' (p-value<0.001, r=0.86), and for girls as 'F-PEF = -151 + 17Age + 1.59Height' (p-value<0.001, r=0.86). The equation for prediction of spirometric PEF in boys was calculated as 'S-PEF = -199+ 9.8Age + 2.67Height' (p-value<0.05, r=0.77), and for girls as 'S-PEF = -181 + 8.5Age + 2.5Height' (p-value<0.001, r=0.83). The boys had higher values than the girls. The performance of the Mini Wright Peak Flow Meter was lower than that of a spirometer. Our study established PEF predictive equations in Moroccan children. Our results appeared to be reliable, as evident by the high correlation coefficient in this sample. PEF can be an alternative of FEV1 in centers without spirometry.
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Du, Z; Zhang, J; Lu, J X; Lu, L P
2018-05-10
Objective: To analyze the distribution characteristics of bacillary dysentery in Beijing during 2004-2015 and evaluate the influence of meteorological factors on the temporal and spatial distribution of bacillary dysentery. Methods: The incidence data of bacterial dysentery and meteorological data in Beijing from 2004 to 2015 were collected. Descriptive epidemiological analysis was conducted to study the distribution characteristics of bacterial dysentery. Linear correlation analysis and multiple linear regression analysis were carried out to investigate the relationship between the incidence of bacillary dysentery and average precipitation, average air temperature, sunshine hours, average wind speed, average air pressure, gale and rain days. Results: A total of 280 704 cases of bacterial dysentery, including 36 deaths, were reported from 2004 to 2015 in Beijing, the average annual incidence was 130.15/100 000. The annual incidence peak was mainly between May and October, the cases occurred during this period accounted for 80.75 % of the total, and the incidence was highest in age group 0 year. The population distribution showed that most cases were children outside child care settings and students, and the sex ratio of the cases was 1.22∶1. The reported incidence of bacillary dysentery was positively associated with average precipitation, average air temperature and rain days with the correlation coefficients of 0.931, 0.878 and 0.888, but it was negatively associated with the average pressure, the correlation coefficient was -0.820. Multiple linear regression equation for fitting analysis of bacillary dysentery and meteorological factors was Y =3.792+0.162 X (1). Conclusion: The reported incidence of bacillary dysentery in Beijing was much higher than national level. The annual incidence peak was during July to August, and the average precipitation was an important meteorological factor influencing the incidence of bacillary dysentery.
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SPSS macros to compare any two fitted values from a regression model.
Weaver, Bruce; Dubois, Sacha
2012-12-01
In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.
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Skeletal Correlates for Body Mass Estimation in Modern and Fossil Flying Birds
Field, Daniel J.; Lynner, Colton; Brown, Christian; Darroch, Simon A. F.
2013-01-01
Scaling relationships between skeletal dimensions and body mass in extant birds are often used to estimate body mass in fossil crown-group birds, as well as in stem-group avialans. However, useful statistical measurements for constraining the precision and accuracy of fossil mass estimates are rarely provided, which prevents the quantification of robust upper and lower bound body mass estimates for fossils. Here, we generate thirteen body mass correlations and associated measures of statistical robustness using a sample of 863 extant flying birds. By providing robust body mass regressions with upper- and lower-bound prediction intervals for individual skeletal elements, we address the longstanding problem of body mass estimation for highly fragmentary fossil birds. We demonstrate that the most precise proxy for estimating body mass in the overall dataset, measured both as coefficient determination of ordinary least squares regression and percent prediction error, is the maximum diameter of the coracoid’s humeral articulation facet (the glenoid). We further demonstrate that this result is consistent among the majority of investigated avian orders (10 out of 18). As a result, we suggest that, in the majority of cases, this proxy may provide the most accurate estimates of body mass for volant fossil birds. Additionally, by presenting statistical measurements of body mass prediction error for thirteen different body mass regressions, this study provides a much-needed quantitative framework for the accurate estimation of body mass and associated ecological correlates in fossil birds. The application of these regressions will enhance the precision and robustness of many mass-based inferences in future paleornithological studies. PMID:24312392
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Kupek, Emil
2006-03-15
Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.
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A new correlation coefficient for bivariate time-series data
NASA Astrophysics Data System (ADS)
Erdem, Orhan; Ceyhan, Elvan; Varli, Yusuf
2014-11-01
The correlation in time series has received considerable attention in the literature. Its use has attained an important role in the social sciences and finance. For example, pair trading in finance is concerned with the correlation between stock prices, returns, etc. In general, Pearson’s correlation coefficient is employed in these areas although it has many underlying assumptions which restrict its use. Here, we introduce a new correlation coefficient which takes into account the lag difference of data points. We investigate the properties of this new correlation coefficient. We demonstrate that it is more appropriate for showing the direction of the covariation of the two variables over time. We also compare the performance of the new correlation coefficient with Pearson’s correlation coefficient and Detrended Cross-Correlation Analysis (DCCA) via simulated examples.
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Coronado-Zarco, Roberto; Diez-García, María del Pilar; Chávez-Arias, Daniel; León-Hernández, Saúl Renán; Cruz-Medina, Eva; Arellano-Hernández, Aurelia
2005-01-01
Bone and skeletal muscle mass loss is related to age. Mechanisms by which they interact have not been well established. To establish a relationship of age with serum levels of IGF-1, skeletal muscle and appendicular muscle mass index, and their influence in isokinetic parameters in osteoporotic female patients. Pearson correlation coefficient and linear regression analyses were used. There were 38 patients with a mean age of 65.16 years (range: 50-84 years), mean appendicular skeletal mass index (ASMI) of 6.3 kg/m2 (range: 4.3-8.3) and mean skeletal mass index (SMI) of 12.4 kg/m2 (range: 9.6-15.7), mean serum IGF-1 levels of 82.97 ng/ml (range: 22-177). Linear regression predicted hip mineral bone density by SMI (p = 0.19) and age (p = 0.017, r = 0.50). Some isokinetic parameters had a positive correlation for work with age. Knee acceleration time had a positive correlation with age. Osteoporosis and sarcopenia may have related pathophysiologic mechanisms. Growth factor study must include the influence of sex hormones. Some isokinetic parameters are determined by the predominant muscle fiber, skeletal mass index and age.
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The association between parental mental health and behavioral disorders in pre-school children
Karimzadeh, Mansoureh; Rostami, Mohammad; Teymouri, Robab; Moazzen, Zahra; Tahmasebi, Siyamak
2017-01-01
Background and Aim Behavioral disorders among children reflect psychological problems of parents, as mental illness of either parent would increase the likelihood of mental disorder in the child. In view of the negative relationship between parents’ and children’s illness, the current study intended to determine the correlation between mental health of parents and behavioral disorders of pre-school children. Methods The present descriptive-correlational research studied 80 children registered at pre-school centers in Pardis Township, Tehran, Iran during 2014–2015 using convenience sampling. The research tools included General Health Questionnaire (GHQ) and Preschool Behavior Questionnaire (PBQ). The resulted data were analyzed using Pearson Product-moment Correlation Coefficient and regression analysis in SPSS 21. Results The research results showed that there was a significant positive correlation between all dimensions of mental health of parents with general behavioral disorders (p<0.001). The results of the regression analysis showed that parents’ depression was the first and the only predictive variable of behavioral disorders in children with 26.8% predictive strength. Conclusion Given the strong relationship between children’s behavioral disorders and parents’ general health, and the significant role of parents’ depression in children’s behavioral disorders, it seems necessary to take measures to decrease the impact of parents’ disorders on children. PMID:28848622
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Park, Jaeyong; Lee, Sang Gil; Bae, Jongjin; Lee, Jung Chul
2015-12-01
[Purpose] This study aimed to provide a predictable evaluation method for the progression of scoliosis in adolescents based on quick and reliable measurements using the naked eye, such as the calcaneal valgus angle of the foot, which can be performed at public facilities such as schools. [Subjects and Methods] Idiopathic scoliosis patients with a Cobb's angle of 10° or more (96 females, 22 males) were included in this study. To identify relationships between factors, Pearson's product-moment correlation coefficient was computed. The degree of scoliosis was set as a dependent variable to predict thoracic and lumbar scoliosis using ankle angle and physique factors. Height, weight, and left and right calcaneal valgus angles were set as independent variables; thereafter, multiple regression analysis was performed. This study extracted variables at a significance level (α) of 0.05 by applying a stepwise method, and calculated a regression equation. [Results] Negative correlation (R=-0.266) was shown between lumbar lordosis and asymmetrical lumbar rotation angles. A correlation (R=0.281) was also demonstrated between left calcaneal valgus angles and asymmetrical thoracic rotation angles. [Conclusion] Prediction of scoliosis progress was revealed to be possible through ocular inspection of the calcaneus and Adams forward bending test and the use of a scoliometer.
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Atanes, Ana C M; Andreoni, Solange; Hirayama, Marcio S; Montero-Marin, Jesús; Barros, Viviam V; Ronzani, Telmo M; Kozasa, Eliza H; Soler, Joaquim; Cebolla, Ausiàs; Garcia-Campayo, Javier; Demarzo, Marcelo M P
2015-09-02
Primary health care professionals (PHPs) usually report high levels of distress and burnout symptoms related to job strain. Mindfulness, defined as non-judgmental-present-moment awareness, seems to be a moderator in the causal association between life stressors and well-being. This study aimed to verify correlations among self-reported mindfulness, perceived stress (PS), and subjective well-being (SW) in Brazilian PHPs. We performed a correlational cross-sectional study in a purposive sample of Brazilian PHPs (physicians, nurses, nursing assistants, and community health workers), working in community-oriented primary care programs (known locally as "Family Health Programs"). We used validated self-reporting instruments: the Mindful Attention Awareness Scale (MAAS), the Perceived Stress Scale (PSS), and the Subjective Well-being Scale (SWS). We performed a multivariate analysis of variance (MANOVA), through regression coefficients (beta) in relation to the professional category (nursing assistant), in addition to the length of time in the same job (under than 6 months) that had indicated the lowest level of PS. Participants (n=450) comprised community health workers (65.8%), nursing assistants (18%), registered nurses (10.0%), and doctors (family physicians) (6.0%); 94% were female and 83.1% had worked in the same position for more than one year. MANOVA regression analysis showed differences across professional categories and length of time in the same job position in relation to mindfulness, PS, and SW. Nurses demonstrated lower levels of mindfulness, higher PS, and SW negative affect, as well as lower SW positive affect. Being at work for 1 year or longer showed a clear association with higher PS and lower SW positive affect, and no significance with mindfulness levels. Pearson's coefficient values indicated strong negative correlations between mindfulness and PS, and medium correlations between mindfulness and SW. In this study, there were clear correlations between mindfulness, PS, and SW across different primary care professional categories and time in the same job position, suggesting specific vulnerabilities that should be addressed through the development of staff awareness, stress prevention, and well-being interventions.
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NASA Technical Reports Server (NTRS)
Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)
1998-01-01
This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.
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NASA Technical Reports Server (NTRS)
Trejo, L. J.; Shensa, M. J.
1999-01-01
This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.
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Properties of a color-changeable chewing gum used to evaluate masticatory performance.
Hama, Yohei; Kanazawa, Manabu; Minakuchi, Shunsuke; Uchida, Tatsuro; Sasaki, Yoshiyuki
2014-04-01
To clarify the basic properties of a color-changeable chewing gum to determine its applicability to evaluations of masticatory performance under different types of dental status. Ten participants with natural dentition aged 26-30 years chewed gum that changes color during several chewing strokes over five repetitions. Changes in color were assessed using a colorimeter, and then L*, a*, and b* values in the CIELAB color system were quantified. Relationships between chewing progression and color changes were assessed using regression analysis and the reliability of color changes was assessed using intraclass correlation coefficients. We then measured 42 dentate participants (age, 22-31 years) and 47 complete denture wearers (age, 44-90 years) to determine the detectability of masticatory performance under two types of dental status. Regression between the number of chewing strokes and the difference between two colors was non-linear. The intraclass correlation coefficients were highest between 60 and 160 chewing strokes. Dentate and edentulous groups significantly differed (Wilcoxon rank sum test) and values were widely distributed within each group. The color of the chewing gum changed over a wide range, which was sufficient to evaluate the masticatory performance of individuals with natural dentition and those with complete dentures. Changes in the color values of the gum reliably reflected masticatory performance. These findings indicate that the color-changeable chewing gum will be useful for evaluating masticatory performance under any dental status. Copyright © 2014 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.
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Aziz, Shamsul Akmar Ab; Nuawi, Mohd Zaki; Nor, Mohd Jailani Mohd
2015-01-01
The objective of this study was to present a new method for determination of hand-arm vibration (HAV) in Malaysian Army (MA) three-tonne truck steering wheels based on changes in vehicle speed using regression model and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique Vibro (I-kaz Vibro). The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals. Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Ƶ(v)(∞)), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ(v)(∞) and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ(v)(∞). The results of the regression model showed that Ƶ(v)(∞) increased when the vehicle speed and HAV exposure increased. For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R(2)) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.
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NASA Astrophysics Data System (ADS)
Zhan, Liwei; Li, Chengwei
2017-02-01
A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.
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Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.
Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J
2008-06-18
Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.
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The response of the temperature of cold-point mesopause to solar activity based on SABER data set
NASA Astrophysics Data System (ADS)
Tang, Chaoli; Liu, Dong; Wei, Heli; Wang, Yingjian; Dai, Congming; Wu, Pengfei; Zhu, Wenyue; Rao, Ruizhong
2016-07-01
The thermal structure and energy balance of upper atmosphere are dominated by solar activity. The response of cold-point mesopause (CPM) to solar activity is an important form. This article presents the response of the temperature of CPM (T-CPM) to solar activity using 14 year Sounding of the Atmosphere using Broadband Emission Radiometry data series over 80°S-80°N regions. These regions are divided into 16 latitude zones with 10° interval, and the spatial areas of 80°S-80°N, 180°W-180°E are divided into 96 lattices with 10°(latitude) × 60°(longitude) grid. The annual-mean values of T-CPM and F10.7 are calculated. The least squares regression method and correlation analysis are applied to these annual-mean series. First, the results show that the global T-CPM is significantly correlated to solar activity at the 0.05 level of significance with correlation coefficient of 0.90. The global solar response of T-CPM is 4.89 ± 0.67 K/100 solar flux unit. Then, for each latitude zone, the solar response of T-CPM and its fluctuation are obtained. The solar response of T-CPM becomes stronger with increasing latitude. The fluctuation ranges of solar response at middle-latitude regions are smaller than those of the equator and high-latitude regions, and the global distribution takes on W shape. The corelationship analysis shows that the T-CPM is significantly correlated to solar activity at the 0.05 level of significance for each latitude zone. The correlation coefficients at middle-latitude regions are higher than those of the equator and high-latitude regions, and the global distribution takes on M shape. At last, for each grid cell, the response of T-CPM to solar activity and their correlation coefficient are presented.
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Wilson, Kathryn M.; Vesper, Hubert W.; Tocco, Paula; Sampson, Laura; Rosén, Johan; Hellenäs, Karl-Erik; Törnqvist, Margareta; Willett, Walter C.
2011-01-01
Objective Acrylamide, a probable human carcinogen, is formed during high-heat cooking of many common foods. The validity of food frequency questionnaire (FFQ) measures of acrylamide intake has not been established. We assessed the validity of acrylamide intake calculated from an FFQ using a biomarker of acrylamide exposure. Methods We calculated acrylamide intake from an FFQ in the Nurses' Health Study II. We measured hemoglobin adducts of acrylamide and its metabolite, glycidamide, in a random sample of 296 women. Correlation and regression analyses were used to assess the relationship between acrylamide intake and adducts. Results The correlation between acrylamide intake and the sum of acrylamide and glycidamide adducts was 0.31 (95% CI: 0.20 – 0.41), adjusted for laboratory batch, energy intake, and age. Further adjustment for BMI, alcohol intake, and correction for random within-person measurement error in adducts gave a correlation of 0.34 (CI: 0.23 – 0.45). The intraclass correlation coefficient for the sum of adducts was 0.77 in blood samples collected 1 to 3 years apart in a subset of 45 women. Intake of several foods significantly predicted adducts in multiple regression. Conclusions Acrylamide intake and hemoglobin adducts of acrylamide and glycidamide were moderately correlated. Within-person consistency in adducts was high over time. PMID:18855107
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The Correlation Between Metacognition Level with Self-Efficacy of Biology Education College Students
NASA Astrophysics Data System (ADS)
Ridlo, S.; Lutfiya, F.
2017-04-01
Self-efficacy is a strong predictor of academic achievement. Self-efficacy refers to the ability of college students to achieve the desired results. The metacognition level can influence college student’s self-efficacy. This study aims to identify college student’s metacognition level and self-efficacy, as well as determine the relationship between self-efficacy and metacognition level for college students of Biology Education 2013, Semarang State University. The ex-post facto quantitative research was conducted on 99 students Academic Year 2015/2016. Saturation sampling technique determined samples. E-D scale collected data for self-efficacy identification. Data for assess the metacognition level collected by Metacognitive Awareness Inventory. Data were analysed quantitatively by Pearson correlation and linear regression. Most college students have the high level of metacognition and average self-efficacy. Pearson correlation coefficient result was 0.367. This result showed that metacognition level and self-efficacy has a weak relationship. Based on linear regression test, self-efficacy influenced by metacognition level up to 13.5%. The results of the study showed that positive and significant relationships exist between metacognition level and self-efficacy. Therefore, if the metacognition level is high, then self-efficacy will also be high (appropriate).
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NASA Technical Reports Server (NTRS)
Acker, James G.; Soebiyanto, Radina; Kiang, Richard; Kempler, Steve
2014-01-01
The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, and air quality research. The use of Giovanni for researching connections between public health issues and Earths environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated (p 0.05) with influenza activity in both temperate and tropical climate. In the two temperate locations studied, specific humidity was negatively correlated with influenza; conversely, in the three tropical locations, specific humidity was positively correlated with influenza. Influenza prediction using the regression models showed good agreement with the observed data (correlation coefficient of 0.50.83).
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Tests of Hypotheses Arising In the Correlated Random Coefficient Model*
Heckman, James J.; Schmierer, Daniel
2010-01-01
This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance of the instrumental variable estimator for that model. We develop tests of the validity of the correlated random coefficient model against the null hypothesis of the uncorrelated random coefficient model. PMID:21170148
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Atsumi, Kazushige; Shioyama, Yoshiyuki; Arimura, Hidetaka; Terashima, Kotaro; Matsuki, Takaomi; Ohga, Saiji; Yoshitake, Tadamasa; Nonoshita, Takeshi; Tsurumaru, Daisuke; Ohnishi, Kayoko; Asai, Kaori; Matsumoto, Keiji; Nakamura, Katsumasa; Honda, Hiroshi
2012-04-01
To determine clinical factors for predicting the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer. The study group consisted of 109 patients with esophageal cancer of T1-4 and Stage I-III who were treated with definitive radiotherapy and achieved a complete response of their primary lesion at Kyushu University Hospital between January 1998 and December 2007. Esophageal stenosis was evaluated using esophagographic images within 3 months after completion of radiotherapy. We investigated the correlation between esophageal stenosis after radiotherapy and each of the clinical factors with regard to tumors and therapy. For validation of the correlative factors for esophageal stenosis, an artificial neural network was used to predict the esophageal stenotic ratio. Esophageal stenosis tended to be more severe and more frequent in T3-4 cases than in T1-2 cases. Esophageal stenosis in cases with full circumference involvement tended to be more severe and more frequent than that in cases without full circumference involvement. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. In the multivariate analysis, T stage, extent of involved circumference, and wall thickness of the tumor region were significantly correlated to esophageal stenosis (p = 0.031, p < 0.0001, and p = 0.0011, respectively). The esophageal stenotic ratio predicted by the artificial neural network, which learned these three factors, was significantly correlated to the actual observed stenotic ratio, with a correlation coefficient of 0.864 (p < 0.001). Our study suggested that T stage, extent of involved circumference, and esophageal wall thickness of the tumor region were useful to predict the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer. Copyright © 2012 Elsevier Inc. All rights reserved.
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Carbonell, Felix; Bellec, Pierre; Shmuel, Amir
2011-01-01
The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.
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Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses
Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.
2017-01-01
Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Pinkham, D; Schueler, E; Diehn, M
Purpose: To demonstrate the efficacy of a novel functional lung imaging method that utilizes single-inhalation, single-energy xenon CT (Xe-CT) lung ventilation scans, and to compare it against the current clinical standard, ventilation single-photon emission CT (V-SPECT). Methods: In an IRB-approved clinical study, 14 patients undergoing thoracic radiotherapy received two successive single inhalation, single energy (80keV) CT images of the entire lung using 100% oxygen and a 70%/30% xenon-oxygen mixture. A subset of ten patients also received concurrent SPECT ventilation scans. Anatomic reproducibility between the two scans was achieved using a custom video biofeedback apparatus. The CT images were registered tomore » each other by deformable registration, and a calculated difference image served as surrogate xenon ventilation map. Both lungs were partitioned into twelve sectors, and a sector-wise correlation was performed between the xenon and V-SPECT scans. A linear regression model was developed with forced expiratory volume (FEV) as a predictor and the coefficient of variation (CoV) as the outcome. Results: The ventilation comparison for five of the patients had either moderate to strong Pearson correlation coefficients (0.47 to 0.69, p<0.05). Of these, four also had moderate to strong Spearman correlation coefficients (0.46 to 0.80, p<0.03). The patients with the strongest correlation had clear regional ventilation deficits. The patient comparisons with the weakest correlations had more homogeneous ventilation distributions, and those patients also had diminished lung function as assessed by spirometry. Analysis of the relationship between CoV and FEV yielded a non-significant trend toward negative correlation (Pearson coefficient −0.60, p<0.15). Conclusion: Significant correlations were found between the Xe-CT and V-SPECT ventilation imagery. The results from this small cohort of patients indicate that single inhalation, single energy Xe-CT has the potential to quantify regional lung ventilation volumetrically with high resolution using widely accessible radiologic equipment. Bill Loo and Peter Maxim are founders of TibaRay, Inc. Bill Loo is also a board member. Bill Loo and Peter Maxim have received research grants from Varian Medical Systems, Inc. and RaySearch Laboratory.« less
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Daily Rainfall Simulation Using Climate Variables and Nonhomogeneous Hidden Markov Model
NASA Astrophysics Data System (ADS)
Jung, J.; Kim, H. S.; Joo, H. J.; Han, D.
2017-12-01
Markov chain is an easy method to handle when we compare it with other ones for the rainfall simulation. However, it also has limitations in reflecting seasonal variability of rainfall or change on rainfall patterns caused by climate change. This study applied a Nonhomogeneous Hidden Markov Model(NHMM) to consider these problems. The NHMM compared with a Hidden Markov Model(HMM) for the evaluation of a goodness of the model. First, we chose Gum river basin in Korea to apply the models and collected daily rainfall data from the stations. Also, the climate variables of geopotential height, temperature, zonal wind, and meridional wind date were collected from NCEP/NCAR reanalysis data to consider external factors affecting the rainfall event. We conducted a correlation analysis between rainfall and climate variables then developed a linear regression equation using the climate variables which have high correlation with rainfall. The monthly rainfall was obtained by the regression equation and it became input data of NHMM. Finally, the daily rainfall by NHMM was simulated and we evaluated the goodness of fit and prediction capability of NHMM by comparing with those of HMM. As a result of simulation by HMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.2076 and 10.8243/131.1304mm each. In case of NHMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.6652 and 10.5112/100.9865mm each. We could verify that the error of daily and monthly rainfall simulated by NHMM was improved by 2.89% and 22.99% compared with HMM. Therefore, it is expected that the results of the study could provide more accurate data for hydrologic analysis. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)
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Characterizing regional soil mineral composition using spectroscopyand geostatistics
Mulder, V.L.; de Bruin, S.; Weyermann, J.; Kokaly, Raymond F.; Schaepman, M.E.
2013-01-01
This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD)was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We testedwhether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was donewith Fixed Rank Kriging (FRK) to represent the mediumand long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 = 0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 = 0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale.
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Sun, Zhi-Jing; Zhu, Lan; Liang, Maolian; Xu, Tao; Lang, Jing-He
2016-08-01
WeChat is a promising tool for capturing electronic data; however, no research has examined its use. This study evaluates the reliability and feasibility of WeChat for administering the Pelvic Floor Impact Questionnaire Short Form 7 questionnaire to women with pelvic floor disorders. Sixty-eight pelvic floor rehabilitation women were recruited between June and December 2015 and crossover randomized to two groups. All participants completed two questionnaire formats. One group completed the paper version followed by the WeChat version; the other group completed the questionnaires in reverse order. Two weeks later, each group completed the two versions in reverse order. The WeChat version's reliability was assessed using intraclass correlation coefficients and test-retest reliability. Forty-two women (61.8%) preferred the WeChat to the paper format, eight (11.8%) preferred the paper format, and 18 (26.5%) had no preference. The younger women preferred WeChat. Completion time was 116.5 (61.3) seconds for the WeChat version and 133.4 (107.0) seconds for the paper version, with no significant difference (P = 0.145). Age and education did not impact completion time (P > 0.05). Consistency between the WeChat and paper versions was excellent. The intraclass correlation coefficients of the Pelvic Floor Impact Questionnaire Short Form 7 and the three subscales ranged from 0.915 to 0.980. The Bland-Altman analysis and linear regression results also showed high consistency. The test-retest study had a Pearson's correlation coefficient of 0.908, demonstrating a strong correlation. WeChat-based questionnaires were well accepted by women with pelvic floor disorders and had good data quality and reliability.
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Comparison of the goals and MISTELS scores for the evaluation of surgeons on training benches.
Wolf, Rémi; Medici, Maud; Fiard, Gaëlle; Long, Jean-Alexandre; Moreau-Gaudry, Alexandre; Cinquin, Philippe; Voros, Sandrine
2018-01-01
Evaluation of surgical technical abilities is a major issue in minimally invasive surgery. Devices such as training benches offer specific scores to evaluate surgeons but cannot transfer in the operating room (OR). A contrario, several scores measure performance in the OR, but have not been evaluated on training benches. Our aim was to demonstrate that the GOALS score, which can effectively grade in the OR the abilities involved in laparoscopy, can be used for evaluation on a laparoscopic testbench (MISTELS). This could lead to training systems that can identify more precisely the skills that have been acquired or must still be worked on. 32 volunteers (surgeons, residents and medical students) performed the 5 tasks of the MISTELS training bench and were simultaneously video-recorded. Their performance was evaluated with the MISTELS score and with the GOALS score based on the review of the recording by two experienced, blinded laparoscopic surgeons. The concurrent validity of the GOALS score was assessed using Pearson and Spearman correlation coefficients with the MISTELS score. The construct validity of the GOALS score was assessed with k-means clustering and accuracy rates. Lastly, abilities explored by each MISTELS task were identified with multiple linear regression. GOALS and MISTELS scores are strongly correlated (Pearson correlation coefficient = 0.85 and Spearman correlation coefficient = 0.82 for the overall score). The GOALS score proves to be valid for construction for the tasks of the training bench, with a better accuracy rate between groups of level after k-means clustering, when compared to the original MISTELS score (accuracy rates, respectively, 0.75 and 0.56). GOALS score is well suited for the evaluation of the performance of surgeons of different levels during the completion of the tasks of the MISTELS training bench.
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He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong
2016-03-01
In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
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Fluctuations in air pollution give risk warning signals of asthma hospitalization
NASA Astrophysics Data System (ADS)
Hsieh, Nan-Hung; Liao, Chung-Min
2013-08-01
Recent studies have implicated that air pollution has been associated with asthma exacerbations. However, the key link between specific air pollutant and the consequent impact on asthma has not been shown. The purpose of this study was to quantify the fluctuations in air pollution time-series dynamics to correlate the relationships between statistical indicators and age-specific asthma hospital admissions. An indicators-based regression model was developed to predict the time-trend of asthma hospital admissions in Taiwan in the period 1998-2010. Five major pollutants such as particulate matters with aerodynamic diameter less than 10 μm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) were included. We used Spearman's rank correlation to detect the relationships between time-series based statistical indicators of standard deviation, coefficient of variation, skewness, and kurtosis and monthly asthma hospitalization. We further used the indicators-guided Poisson regression model to test and predict the impact of target air pollutants on asthma incidence. Here we showed that standard deviation of PM10 data was the most correlated indicators for asthma hospitalization for all age groups, particularly for elderly. The skewness of O3 data gives the highest correlation to adult asthmatics. The proposed regression model shows a better predictability in annual asthma hospitalization trends for pediatrics. Our results suggest that a set of statistical indicators inferred from time-series information of major air pollutants can provide advance risk warning signals in complex air pollution-asthma systems and aid in asthma management that depends heavily on monitoring the dynamics of asthma incidence and environmental stimuli.
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Munhoz, Wagner Cesar; Hsing, Wu Tu
2014-07-01
Studies on the relationships between postural deviations and the temporomandibular system (TS) functional health are controversial and inconclusive. This study stems from the hypothesis that such inconclusiveness is due to authors considering functional pathologies of the TS (FPTS) as a whole, without taking into account subjects' specific FPTS signs and symptoms. Based on the author and collaborators' previous studies, the present study analyzed data on body posture from a sample of 50 subjects with (30) and without (20) FPTS. Correlation analyses were applied, taking as independent variables age, sex, Helkimo anamnestic, occlusal, and dysfunction indices, as well as FPTS specific signs and symptoms. Postural assessments of the head, cervical spine, shoulders, lumbar spine, and hips were the dependent variables. Linear regression equations were built that proved to partially predict the presence and magnitude of body posture deviations by drawing on subjects' characteristics and specific FPTS symptoms. Determination coefficients for these equations ranged from 0.082 to 0.199 in the univariate, and from 0.121 to 0.502 in the multivariate regression analyses. Results show that factors intrinsic to the subjects or the TS may potentially interfere in results of studies that analyze relationships between FPTS and body posture. Furthermore, a trend to specificity was found, e.g. the degree of cervical lordosis was found to correlate to age and FPTS degree of severity, suggesting that some TS pathological features, or malocclusion, age or sex, may be more strongly correlated than others with specific posture patterns.
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Measuring Retention in HIV Care: The Elusive Gold Standard
Mugavero, Michael J.; Westfall, Andrew O.; Zinski, Anne; Davila, Jessica; Drainoni, Mari-Lynn; Gardner, Lytt I.; Keruly, Jeanne C.; Malitz, Faye; Marks, Gary; Metsch, Lisa; Wilson, Tracey E.; Giordano, Thomas P.
2012-01-01
Background Measuring retention in HIV primary care is complex as care includes multiple visits scheduled at varying intervals over time. We evaluated six commonly used retention measures in predicting viral load (VL) suppression and the correlation among measures. Methods Clinic-wide patient-level data from six academic HIV clinics were used for 12-months preceding implementation of the CDC/HRSA Retention in Care intervention. Six retention measures were calculated for each patient based upon scheduled primary HIV provider visits: count and dichotomous missed visits, visit adherence, 6-month gap, 4-month visit constancy, and the HRSA HAB retention measure. Spearman correlation coefficients and separate unadjusted logistic regression models compared retention measures to one another and with 12-month VL suppression, respectively. The discriminatory capacity of each measure was assessed with the c-statistic. Results Among 10,053 patients, 8,235 (82%) had 12-month VL measures, with 6,304 (77%) achieving suppression (VL<400 c/mL). All six retention measures were significantly associated (P<0.0001) with VL suppression (OR;95%CI, c-statistic): missed visit count (0.73;0.71–0.75,0.67), missed visit dichotomous (3.2;2.8–3.6,0.62), visit adherence (3.9;3.5–4.3,0.69), gap (3.0;2.6–3.3,0.61), visit constancy (2.8;2.5–3.0,0.63), HRSA HAB (3.8;3.3–4.4,0.59). Measures incorporating “no show” visits were highly correlated (Spearman coefficient=0.83–0.85), as were measures based solely upon kept visits (Spearman coefficient=0.72–0.77). Correlation coefficients were lower across these two groups of measures (Range=0.16–0.57). Conclusions Six retention measures displayed a wide range of correlation with one another, yet each measure had significant association and modest discrimination for VL suppression. These data suggest there is no clear gold standard, and that selection of a retention measure may be tailored to context. PMID:23011397
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The relationship between the Nasality Severity Index 2.0 and perceptual judgments of hypernasality.
Bettens, Kim; De Bodt, Marc; Maryn, Youri; Luyten, Anke; Wuyts, Floris L; Van Lierde, Kristiane M
2016-01-01
The Nasality Severity Index 2.0 (NSI 2.0) forms a new, multiparametric approach in the identification of hypernasality. The present study aimed to investigate the correlation between the NSI 2.0 scores and the perceptual assessment of hypernasality. Speech samples of 35 patients, representing a range of nasality from normal to severely hypernasal, were rated by four expert speech-language pathologists using visual analogue scaling (VAS) judging the degree of hypernasality, audible nasal airflow (ANA) and speech intelligibility. Inter- and intra-listener reliability was verified using intraclass correlation coefficients. Correlations between NSI 2.0 scores and its parameters (i.e. nasalance score of an oral text and vowel /u/, voice low tone to high tone ratio of the vowel /i/) and the degree of hypernasality were determined using Pearson correlation coefficients. Multiple linear regression analysis was used to investigate the possible influence of ANA and speech intelligibility on the NSI 2.0 scores. Overall good to excellent inter- and intra-listener reliability was found for the perceptual ratings. A moderate, but significant negative correlation between NSI 2.0 scores and perceived hypernasality (r=-0.64) was found, in which a more negative NSI 2.0 score indicates the presence of more severe hypernasality. No significant influence of ANA or intelligibility on the NSI 2.0 was observed based on the regression analysis. Because the NSI 2.0 correlates significantly with perceived hypernasality, it provides an easy-to-interpret severity score of hypernasality which will facilitate the evaluation of therapy outcomes, communication to the patient and other clinicians, and decisions for treatment planning, based on a multiparametric approach. However, research is still necessary to further explore the instrumental correlates of perceived hypernasality. The reader will be able to (1) describe and discuss current issues and influencing variables regarding perceptual ratings of hypernasality; (2) describe and discuss the relationship between the Nasality Severity Index 2.0, a new multiparametric approach to hypernasality, and perceptual judgments of hypernasality based on visual analogue scale ratings; (3) compare these results with the correlations based on a single parameter approach and (4) describe and discuss the possible influence of audible nasal airflow and speech intelligibility on the NSI 2.0 scores. Copyright © 2016 Elsevier Inc. All rights reserved.
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Paseiro-Cerrato, Rafael; Tongchat, Chinawat; Franz, Roland
2016-05-01
This study evaluated the influence of parameters such as temperature and type of low-density polyethylene (LDPE) film on the log Kp/f values of seven model migrants in food simulants. Two different types of LDPE films contaminated by extrusion and immersion were placed in contact with three food simulants including 20% ethanol, 50% ethanol and olive oil under several time-temperature conditions. Results suggest that most log Kp/f values are little affected by these parameters in this study. In addition, the relation between log Kp/f and log Po/w was established for each food simulant and regression lines, as well as correlation coefficients, were calculated. Correlations were compared with data from real foodstuffs. Data presented in this study could be valuable in assigning certain foods to particular food simulants as well as predicting the mass transfer of potential migrants into different types of food or food simulants, avoiding tedious and expensive laboratory analysis. The results could be especially useful for regulatory agencies as well as for the food industry.
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Hydrogel keratophakia: a microkeratome dissection in the monkey model.
Beekhuis, W H; McCarey, B E; Waring, G O; van Rij, G
1986-01-01
High water content intracorneal implants were fabricated from Vistamarc hydrogel (Vistakon, Inc.) at 58%, 68%, and 72% water content and a range of powers from +7.25 to +17.00 dioptres. The Barraquer microkeratome technique was used to implant the lens at 59.0 +/- 9% (+/- SD) depth in the corneas of 14 rhesus monkey eyes. The contralateral eye served as a control. Three eyes were lost to the study because of complications. The remaining 11 animals were followed up for 51 +/- 2 weeks with the refractive yield being 118 +/- 34% and the keratometric yield being 92 +/- 30%. The measured and theoretically expected refractive changes have a linear regression line correlation coefficient of 0.74, whereas the respective keratometric data had a correlation coefficient of 0.04. The measured refraction became stable within 2 to 3 dioptres after 20 postoperative weeks. The hydrogels were well tolerated within the corneal tissue. There was a minimum of interface problems except along the edge of the implant. Implants with abruptly cut edges versus a fine wedge tended to have more light scattering collagen at the implant margin. PMID:3954976
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Lu, Shao Hua; Li, Bao Qiong; Zhai, Hong Lin; Zhang, Xin; Zhang, Zhuo Yong
2018-04-25
Terahertz time-domain spectroscopy has been applied to many fields, however, it still encounters drawbacks in multicomponent mixtures analysis due to serious spectral overlapping. Here, an effective approach to quantitative analysis was proposed, and applied on the determination of the ternary amino acids in foxtail millet substrate. Utilizing three parameters derived from the THz-TDS, the images were constructed and the Tchebichef image moments were used to extract the information of target components. Then the quantitative models were obtained by stepwise regression. The correlation coefficients of leave-one-out cross-validation (R loo-cv 2 ) were more than 0.9595. As for external test set, the predictive correlation coefficients (R p 2 ) were more than 0.8026 and the root mean square error of prediction (RMSE p ) were less than 1.2601. Compared with the traditional methods (PLS and N-PLS methods), our approach is more accurate, robust and reliable, and can be a potential excellent approach to quantify multicomponent with THz-TDS spectroscopy. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Liu, Shu-Shen; Liu, Yan; Yin, Da-Qian; Wang, Xiao-Dong; Wang, Lian-Sheng
2006-02-01
Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.
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Schmitt, Neal; Golubovich, Juliya; Leong, Frederick T L
2011-12-01
The impact of measurement invariance and the provision for partial invariance in confirmatory factor analytic models on factor intercorrelations, latent mean differences, and estimates of relations with external variables is investigated for measures of two sets of widely assessed constructs: Big Five personality and the six Holland interests (RIASEC). In comparing models that include provisions for partial invariance with models that do not, the results indicate quite small differences in parameter estimates involving the relations between factors, one relatively large standardized mean difference in factors between the subgroups compared and relatively small differences in the regression coefficients when the factors are used to predict external variables. The results provide support for the use of partially invariant models, but there does not seem to be a great deal of difference between structural coefficients when the measurement model does or does not include separate estimates of subgroup parameters that differ across subgroups. Future research should include simulations in which the impact of various factors related to invariance is estimated.
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Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy
NASA Astrophysics Data System (ADS)
Guo, Zhiming; Huang, Wenqian; Chen, Liping; Wang, Xiu; Peng, Yankun
This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.
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Copula based prediction models: an application to an aortic regurgitation study
Kumar, Pranesh; Shoukri, Mohamed M
2007-01-01
Background: An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed. Methods: The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the Archimedean copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure. Results: We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction); p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808). From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots) are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0.8907 × (Pre-operative ejection fraction); p = 0.00008 ; 95% confidence interval for slope coefficient (0.4810, 1.3003). For both models differences in the predicted post-operative ejection fractions in the lower range of pre-operative ejection measurements are considerably different and prediction errors due to copula model are smaller. To validate the copula methodology we have re-sampled with replacement fifty independent bootstrap samples and have estimated concordance statistics 0.7722 (p = 0.0224) for the copula model and 0.7237 (p = 0.0604) for the correlation model. The predicted and observed measurements are concordant for both models. The estimates of accuracy components are 0.9233 and 0.8654 for copula and correlation models respectively. Conclusion: Copula-based prediction modeling is demonstrated to be an appropriate alternative to the conventional correlation-based prediction modeling since the correlation-based prediction models are not appropriate to model the dependence in populations with asymmetrical tails. Proposed copula-based prediction model has been validated using the independent bootstrap samples. PMID:17573974
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Impact of soil properties on selected pharmaceuticals adsorption in soils
NASA Astrophysics Data System (ADS)
Kodesova, Radka; Kocarek, Martin; Klement, Ales; Fer, Miroslav; Golovko, Oksana; Grabic, Roman; Jaksik, Ondrej
2014-05-01
The presence of human and veterinary pharmaceuticals in the environment has been recognized as a potential threat. Pharmaceuticals may contaminate soils and consequently surface and groundwater. Study was therefore focused on the evaluation of selected pharmaceuticals adsorption in soils, as one of the parameters, which are necessary to know when assessing contaminant transport in soils. The goals of this study were: (1) to select representative soils of the Czech Republic and to measure soil physical and chemical properties; (2) to measure adsorption isotherms of selected pharmaceuticals; (3) to evaluate impact of soil properties on pharmaceutical adsorptions and to propose pedotransfer rules for estimating adsorption coefficients from the measured soil properties. Batch sorption tests were performed for 6 selected pharmaceuticals (beta blockers Atenolol and Metoprolol, anticonvulsant Carbamazepin, and antibiotics Clarithromycin, Trimetoprim and Sulfamethoxazol) and 13 representative soils (soil samples from surface horizons of 11 different soil types and 2 substrates). The Freundlich equations were used to describe adsorption isotherms. The simple correlations between measured physical and chemical soil properties (soil particle density, soil texture, oxidable organic carbon content, CaCO3 content, pH_H2O, pH_KCl, exchangeable acidity, cation exchange capacity, hydrolytic acidity, basic cation saturation, sorption complex saturation, salinity), and the Freundlich adsorption coefficients were assessed using Pearson correlation coefficient. Then multiple-linear regressions were applied to predict the Freundlich adsorption coefficients from measured soil properties. The largest adsorption was measured for Clarithromycin (average value of 227.1) and decreased as follows: Trimetoprim (22.5), Metoprolol (9.0), Atenolol (6.6), Carbamazepin (2.7), Sulfamethoxazol (1.9). Absorption coefficients for Atenolol and Metoprolol closely correlated (R=0.85), and both were also related to absorption coefficients of Carbamazepin (R=0.67 and 0.68). Positive correlation was found between Trimetoprim absorption coefficients and Atenolol, Metoprolol or Carbamazepin absorption coefficients. The negative relationship was found between absorption coefficients of Sulfomethoxazol and Clarithromycin (R=-0.80). Sulfamethoxazol absorption coefficient was negatively related to pH_H2O, pH_KCL or sorption complex saturation and positively to the hydrolytic acidity or exchangeable acidity. Trimetoprim absorption coefficient was positively related to the oxidable organic carbon content, cation exchange capacity, basic cation saturation or silt content and negatively to particle density or sand content. Clarithromycin absorption coefficient was positively related to pH_H2O, pH_KCL, CaCO3 content, basic cation saturation or sorption complex saturation and negatively to hydrolytic acidity or exchangeable acidity. Atenolol and Metoprolol absorption coefficients were positively related to the oxidable organic carbon content, cation exchange capacity, basic cation saturation, salinity, clay content or silt content, and negatively to the particle density or sand content. Finally Carbamazepin absorption coefficient was positively related to the oxidable organic carbon content, cation exchange capacity or basic cation saturation, and negatively to the particle density or sand content. Evaluated pedotransfer rules for different pharmaceuticals included different sets of soil properties. Absorption coefficients could be predicted from: the hydrolytic acidity (Sulfamethoxazol), the oxidable organic carbon content (Trimetoprim and Carbamazepin), the oxidable organic carbon content, hydrolytic acidity and cation exchange capacity (Clarithromycin), the basic cation saturation (Atenolol and Metoprolol). Acknowledgement: Authors acknowledge the financial support of the Czech Science Foundation (Project No. 13-12477S).
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Detection of Cutting Tool Wear using Statistical Analysis and Regression Model
NASA Astrophysics Data System (ADS)
Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin
2010-10-01
This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.
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Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Kim, Moon S; Chao, Kuanglin; Qin, Jianwei; Fu, Xiaping; Baek, Insuck; Cho, Byoung-Kwan
2016-05-01
Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders. Copyright © 2016 Elsevier B.V. All rights reserved.
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Distance correlation methods for discovering associations in large astrophysical databases
DOE Office of Scientific and Technical Information (OSTI.GOV)
Martínez-Gómez, Elizabeth; Richards, Mercedes T.; Richards, Donald St. P., E-mail: elizabeth.martinez@itam.mx, E-mail: mrichards@astro.psu.edu, E-mail: richards@stat.psu.edu
2014-01-20
High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between different astrophysical variables; hence an ability to detect and verify associations or correlations between variables is important in astrophysical research. In this paper, we apply a recently defined statistical measure called the distance correlation coefficient, which can be used to identify new associations and correlations between astrophysical variables. The distance correlation coefficient applies to variables of any dimension,more » can be used to determine smaller sets of variables that provide equivalent astrophysical information, is zero only when variables are independent, and is capable of detecting nonlinear associations that are undetectable by the classical Pearson correlation coefficient. Hence, the distance correlation coefficient provides more information than the Pearson coefficient. We analyze numerous pairs of variables in the COMBO-17 database with the distance correlation method and with the maximal information coefficient. We show that the Pearson coefficient can be estimated with higher accuracy from the corresponding distance correlation coefficient than from the maximal information coefficient. For given values of the Pearson coefficient, the distance correlation method has a greater ability than the maximal information coefficient to resolve astrophysical data into highly concentrated horseshoe- or V-shapes, which enhances classification and pattern identification. These results are observed over a range of redshifts beyond the local universe and for galaxies from elliptical to spiral.« less
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Clustering Coefficients for Correlation Networks.
Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu
2018-01-01
Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.
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Clustering Coefficients for Correlation Networks
Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu
2018-01-01
Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties. PMID:29599714
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Chen, Yoa; Yu, Yong; He, Cheng-qi
2015-11-01
To establish correlations between joint proprioception, muscle flexion and extension peak torque, and functional ability in patients with knee osteoarthritis (OA). Fifty-six patients with symptomatic knee OA were recruited in this study. Both proprioceptive acuity and muscle strength were measured using the isomed-2000 isokinetic dynamometer. Proprioceptive acuity was evaluated by establishing the joint motion detection threshold (JMDT). Muscle strength was evaluated by Max torque (Nm) and Max torque/weight (Nm/ kg). Functional ability was assessed by the Western Ontario and McMaster Universities Osteoarthritis Index physical function (WOMAC-PF) questionnaire. Correlational analyses were performed between proprioception, muscle strength, and functional ability. A multiple stepwise regression model was established, with WOMAC-PF as dependent variable and patient age, body mass index (BMI), visual analogue scale (VAS)-score, mean grade for Kellgren-Lawrance of both knees, mean strength for quadriceps and hamstring muscles of both knees, and mean JMDT of both knees as independent variables. Poor proprioception (high JMDT) was negatively correlated with muscle strength (P<0.05). There was no significant correlation between knee proprioception (high JMDT) and joint pain (WOMAC pain score), and between knee proprioception (high JMDT) and joint stiffness (WOMAC stiffness score). Poor proprioception (high JMDT) was correlated with limitation in functional ability (WOMAC physical function score r=0.659, P<0.05). WOMAC score was correlated with poor muscle strength (quadriceps muscle strength r = -0.511, P<0.05, hamstring muscle strength r = -0.408, P<0.05). The multiple stepwise regression model showed that high JMDT C standard partial regression coefficient (B) = 0.385, P<0.50 and high VAS-scale score (B=0.347, P<0.05) were significant predictors of WOMAC-PF score. Patients with poor proprioception is associated with poor muscle strength and limitation in functional ability. Patients with symptomatic OA of knees commonly endure with moderate to considerable dysfunction, which is associated with poor proprioception (high JMDT) and high VAS-scale score.
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Estimation of the biserial correlation and its sampling variance for use in meta-analysis.
Jacobs, Perke; Viechtbauer, Wolfgang
2017-06-01
Meta-analyses are often used to synthesize the findings of studies examining the correlational relationship between two continuous variables. When only dichotomous measurements are available for one of the two variables, the biserial correlation coefficient can be used to estimate the product-moment correlation between the two underlying continuous variables. Unlike the point-biserial correlation coefficient, biserial correlation coefficients can therefore be integrated with product-moment correlation coefficients in the same meta-analysis. The present article describes the estimation of the biserial correlation coefficient for meta-analytic purposes and reports simulation results comparing different methods for estimating the coefficient's sampling variance. The findings indicate that commonly employed methods yield inconsistent estimates of the sampling variance across a broad range of research situations. In contrast, consistent estimates can be obtained using two methods that appear to be unknown in the meta-analytic literature. A variance-stabilizing transformation for the biserial correlation coefficient is described that allows for the construction of confidence intervals for individual coefficients with close to nominal coverage probabilities in most of the examined conditions. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
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Regier, M; Derlin, T; Schwarz, D; Laqmani, A; Henes, F O; Groth, M; Buhk, J-H; Kooijman, H; Adam, G
2012-10-01
To investigate the potential correlation of the apparent diffusion coefficient assessed by diffusion-weighted MRI (DWI) and glucose metabolism determined by the standardized uptake value (SUV) at 18F-FDG PET/CT in non-small cell lung cancer (NSCLC). 18F-FDG PET/CT and DWI (TR/TE, 2000/66 ms; b-values, 0 and 500 s/mm(2)) were performed in 41 consecutive patients with histologically verified NSCLC. Analysing the PET-CT data calculation of the mean (SUV(mean)) and maximum (SUV(max)) SUV was performed. By placing a region-of-interest (ROI) encovering the entire tumor mean (ADC(mean)) and minimum ADC (ADC(min)) were determined by two independent radiologists. Results of 18F-FDG PET-CT and DWI were compared on a per-patient basis. For statistical analysis Pearson's correlation coefficient, Bland-Altman and regression analysis were assessed. Data analysis revealed a significant inverse correlation of the ADC(min) and SUV(max) (r=-0.46; p=0.032). Testing the correlation of the ADC(min) and SUV(max) for each histological subtype separately revealed that the inverse correlation was good for both adenocarcinomas (r=-0.47; p=0.03) and squamouscell carcinomas (r=-0.71; p=0.002), respectively. No significant correlation was found for the comparison of ADC(min) and SUV(mean) (r=-0.29; p=0.27), ADC(mean) vs. SUV(mean) (r=-0.28; p=0.31) or ADC(mean) vs. SUV(max) (r=-0.33; p=0.23). The κ-value of 0.88 indicated a good agreement between both observers. This preliminary study is the first to verify the relation between the SUV and the ADC in NSCLC. The significant inverse correlation of these two quantitative imaging approaches points out the association of metabolic activity and tumor cellularity. Therefore, DWI with ADC measurement might represent a new prognostic marker in NSCLC. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
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García-Estévez, Ignacio; Andrés-García, Paula; Alcalde-Eon, Cristina; Giacosa, Simone; Rolle, Luca; Rivas-Gonzalo, Julián C; Quijada-Morín, Natalia; Escribano-Bailón, M Teresa
2015-09-09
The relationship between the agronomic parameters of grapevine and the phenolic composition of skin of Vitis vinifera L. cv. Tempranillo grapes was assessed. The physical and mechanical properties of berries and their skins were also determined and correlated to the chemical composition. Results showed a significant negative correlation between grapevine vigor-related parameters (such as leaf area and bunch weight) and anthocyanin composition, whereas the percentage (w/w) of seeds was negatively correlated with the amount of flavanols of grape skins. Texture properties of grape skins also showed an important relationship with chemical composition. Berry hardness showed a negative correlation with the coumaroyl-anthocyanin derivatives, but it was positively correlated to skin flavanic composition. Moreover, significant regressions with high coefficients of determination were found between phenolic composition and grapevine vigor-related and texture variables, thus pointing out that these parameters might be useful for estimating the phenolic composition of grape skins.
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Correlation-study about the ambient dose rate and the weather conditions
NASA Astrophysics Data System (ADS)
Furuya, Masato; Hatano, Yuko; Aoyama, Tomoo; Igarashi, Yasuhito; Kita, Kazuyuki; Ishizuka, Masahide
2016-04-01
The long-term radiation risks are believed to be heavily affected by the resuspension process. We therefore focus on the surface-atmosphere exchange process of released radioactive materials in this study. Radioactive materials were deposited on the soil and float in the air, and such complicated process are influenced by the weather conditions deeply. We need to reveal the correlation between the weather conditions and the ambient dose rate. In this study, we study the correlation between the weather conditions and the ambient dose rate with the correction of the decrease due to the radioactive decay. We found that there is a negative correlation between the ambient dose rate and the soil water content by the correlation coefficient. Using this result, we reconstruct the ambient dose rate from the weather conditions by the multiple regression analysis and found that the reconstructed data agree with the observation very well. Using Kalman filter, which can be sequentially updates the state estimate, we obtained such a good agreement.
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DFT study on oxidation of HS(CH2) m SH ( m = 1-8) in oxidative desulfurization
NASA Astrophysics Data System (ADS)
Song, Y. Z.; Song, J. J.; Zhao, T. T.; Chen, C. Y.; He, M.; Du, J.
2016-06-01
Density functional theory was employed for calculation of HS(CH2) m SH ( m = 1-8) and its derivatives at B3LYP method at 6-31++g ( d, p) level. Using eigenvalues of LUMO and HOMO for HS(CH2) m SH, the standard electrode potentials were estimated by a stepwise multiple regression techniques (MLR), and obtained as E° = 1.500 + 7.167 × 10-3 HOMO-0.229 LUMO with high correlation coefficients of 0.973 and F values of 43.973.
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Structured penalties for functional linear models-partially empirical eigenvectors for regression.
Randolph, Timothy W; Harezlak, Jaroslaw; Feng, Ziding
2012-01-01
One of the challenges with functional data is incorporating geometric structure, or local correlation, into the analysis. This structure is inherent in the output from an increasing number of biomedical technologies, and a functional linear model is often used to estimate the relationship between the predictor functions and scalar responses. Common approaches to the problem of estimating a coefficient function typically involve two stages: regularization and estimation. Regularization is usually done via dimension reduction, projecting onto a predefined span of basis functions or a reduced set of eigenvectors (principal components). In contrast, we present a unified approach that directly incorporates geometric structure into the estimation process by exploiting the joint eigenproperties of the predictors and a linear penalty operator. In this sense, the components in the regression are 'partially empirical' and the framework is provided by the generalized singular value decomposition (GSVD). The form of the penalized estimation is not new, but the GSVD clarifies the process and informs the choice of penalty by making explicit the joint influence of the penalty and predictors on the bias, variance and performance of the estimated coefficient function. Laboratory spectroscopy data and simulations are used to illustrate the concepts.
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Zhang, Hualing
2014-03-01
To learn characteristics and their mutual relations of self-esteem, self-harmony and interpersonal-harmony of university students, in order to provide the basis for mental health education. With a stratified cluster random sampling method, a questionnaire survey was conducted in 820 university students from 16 classes of four universities, chosen from 30 universities in Anhui Province. Meanwhile, Rosenberg Self-esteem Scale, Self-harmony Scale and Interpersonal-harmony Diagnostic Scale were used for assessment. Self-esteem of university students has an average score of (30.71 +/- 4.77), higher than median thoery 25, and there existed statistical significance in the dimensions of gender (P = 0.004), origin (P = 0.038) and only-child (P = 0.005). University students' self-harmony has an average score of (98.66 +/- 8.69), among which there were 112 students in the group of low score, counting for 13.7%, 442 in that of middle score, counting for 53.95%, 265 in that of high score, counting for 32.33%. And there existed no statistical significance in the total-score of self-harmony and score differences from most of subscales in the dimention of gender and origin, but satistical significance did exist in the dimention of only-child (P = 0.004). It was statistically significant (P = 0.006) on the "stereotype" subscales, on the differences between university students from urban areas and rural areas. Every dimension of self-esteem and self -harmony and interpersonal harmony was correlated and statistically significant. Multiple regression analysis found that when there was a variable in self-esteem, the amount of the variable of self-harmony for explaination of interpersonal conversation dropped from 22.6% to 12%, and standard regression coefficient changing from 0.087 to 0.035. The trouble of interpersonal dating fell from 27.6% to 13.1%, the standard regression coefficient changing from 0.104 to 0.019. The bother of treating people fell from 30.9% to 15%, and the standard regression coefficient changing from 0.079 to 0.020. The problem of heterosexual contact fell from 23.4% to 17.3%, and the standard regression coefficient changing from 0.095 to 0.024. Self-esteem was a mediator variable between self-harmony and interpersonal-harmony. By cultivating university students' level of self-esteem to achieve their self-harmony and interpersonal-harmony, university students' mental health level can be improved.
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Estimation of the simple correlation coefficient.
Shieh, Gwowen
2010-11-01
This article investigates some unfamiliar properties of the Pearson product-moment correlation coefficient for the estimation of simple correlation coefficient. Although Pearson's r is biased, except for limited situations, and the minimum variance unbiased estimator has been proposed in the literature, researchers routinely employ the sample correlation coefficient in their practical applications, because of its simplicity and popularity. In order to support such practice, this study examines the mean squared errors of r and several prominent formulas. The results reveal specific situations in which the sample correlation coefficient performs better than the unbiased and nearly unbiased estimators, facilitating recommendation of r as an effect size index for the strength of linear association between two variables. In addition, related issues of estimating the squared simple correlation coefficient are also considered.
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Jokovic, Aleksandra; Locker, David; Guyatt, Gordan
2006-01-01
Background The Child Perceptions Questionnaire for children aged 11 to 14 years (CPQ11–14) is a 37-item measure of oral-health-related quality of life (OHRQoL) encompassing four domains: oral symptoms, functional limitations, emotional and social well-being. To facilitate its use in clinical settings and population-based health surveys, it was shortened to 16 and 8 items. Item impact and stepwise regression methods were used to produce each version. This paper describes the developmental process, compares the discriminative properties of the resulting four short-forms and evaluates their precision relative to the original CPQ11–14. Methods The item impact method used data from the CPQ11–14 item reduction study to select the questions with the highest impact scores in each domain. The regression method, where the dependent variable was the overall CPQ11–14 score and the independent variables its individual questions, was applied to the data collected in the validity study for the CPQ11–14. The measurement properties (i.e. criterion validity, construct validity, internal consistency reliability and test-retest reliability) of all 4 short-forms were evaluated using the data from the validity and reliability studies for the CPQ11–14. Results All short forms detected substantial variability in children's OHRQoL. The mean scores on the two 16-item questionnaires were almost identical, while on the two 8-item questionnaires they differed by only one score point. The mean scores standardized to 0–100 were higher on the short forms than the original CPQ11–14 (p < 0.001). There were strong significant correlations between all short-form scores and CPQ11–14 scores (0.87–0.98; p < 0.001). Hypotheses concerning construct validity were confirmed: the short-forms' scores were highest in the oro-facial, lower in the orthodontic and lowest in the paediatric dentistry group; all short-form questionnaires were positively correlated with the ratings of oral health and overall well-being, with the correlation coefficient being higher for the latter. The relative validity coefficients were 0.85 to 1.18. Cronbach's alpha and intraclass correlation coefficients ranged 0.71–0.83 and 0.71–0.77, respectively. Conclusion All short forms demonstrated excellent criterion validity and good construct validity. The reliability coefficients exceeded standards for group-level comparisons. However, these are preliminary findings based on the convenience sampling and further testing in replicated studies involving clinical and general samples of children in various settings is necessary to establish measurement sensitivity and discriminative properties of these questionnaires. PMID:16423298
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Separation in Logistic Regression: Causes, Consequences, and Control.
Mansournia, Mohammad Ali; Geroldinger, Angelika; Greenland, Sander; Heinze, Georg
2018-04-01
Separation is encountered in regression models with a discrete outcome (such as logistic regression) where the covariates perfectly predict the outcome. It is most frequent under the same conditions that lead to small-sample and sparse-data bias, such as presence of a rare outcome, rare exposures, highly correlated covariates, or covariates with strong effects. In theory, separation will produce infinite estimates for some coefficients. In practice, however, separation may be unnoticed or mishandled because of software limits in recognizing and handling the problem and in notifying the user. We discuss causes of separation in logistic regression and describe how common software packages deal with it. We then describe methods that remove separation, focusing on the same penalized-likelihood techniques used to address more general sparse-data problems. These methods improve accuracy, avoid software problems, and allow interpretation as Bayesian analyses with weakly informative priors. We discuss likelihood penalties, including some that can be implemented easily with any software package, and their relative advantages and disadvantages. We provide an illustration of ideas and methods using data from a case-control study of contraceptive practices and urinary tract infection.
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Liu, Yan; Salvendy, Gavriel
2009-05-01
This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.
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Seasonal and Regional Variability in North Pacific Upper-Ocean Turbulence
NASA Astrophysics Data System (ADS)
Najjar, R.; Creedon, R.; Cronin, M. F.
2016-02-01
Turbulent diffusion at marine mixed layer base (MLB) plays a fundamental role in the transport of energy between the upper and abyssal ocean. Recent investigations of North Pacific mooring data at Ocean Climate Stations (OCS) Papa (50.1N,144.9W) and KEO (32.3N,144.6E) suggest seasonal and regional variability in thermal diffusivity (κT). In this investigation, it is hypothesized that these observed differences in κT are directly associated with synoptic variability in net surface heat flux (Q0), surface wind stress (τ), mixed layer depth (h), and density stratification at MLB (∂zσ|-h). To test this hypothesis, daily-averaged time series of κT are regressed against those of Q0, τ, h, and ∂zσ|-h at both Papa and KEO over a six year time period (2007-2013). Seasonality of each time series is removed before regression to capture synoptic variability of each variable. Preliminary results of the regression analysis suggest statistically significant correlations between κT and all forcing parameters at both mooring sites. These correlations have well-determined orders of magnitude and signs consistent with the hypothesis. As a result, differences in κT between Papa and KEO may be recast in terms of differences in their correlation coefficients. In order to continue investigation of these parameters and their effects on mean seasonal differences between the two regions, these results will be compared with turbulence predicted by the K-Profile Parameterization ocean turbulence model.
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Lin, Jie; Dai, Yi; Guo, Ya-nan; Xu, Hai-rong; Wang, Xiao-chang
2012-01-01
This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyzed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Pearson’s linear correlation analysis and partial least square (PLS) regression were applied to investigate the relationship between sensory aroma scores and the volatile compounds. Results showed that 60 volatile compounds could be commonly detected in this famous green tea. Terpenes and esters were two major groups characterized, representing 33.89% and 15.53% of the total peak area respectively. Ten compounds were determined to contribute significantly to the perceived aroma quality of Longjing tea, especially linalool (0.701), nonanal (0.738), (Z)-3-hexenyl hexanoate (−0.785), and β-ionone (−0.763). On the basis of these 10 compounds, a model (correlation coefficient of 89.4% and cross-validated correlation coefficient of 80.4%) was constructed to predict the aroma quality of Longjing tea. Summarily, this study has provided a novel option for quality prediction of green tea based on HS-SPME/GC-MS technique. PMID:23225852
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Mohamadirizi, Soheila; Kordi, Masoumeh
2013-09-01
Menstruation signs are among the most common disorders in adolescents and are influenced by various environmental and psychosocial factors. This study aimed to define the association between menstruation signs and anxiety, depression, and stress in school girls in Mashhad in 2011-2012. This was a cross-sectional study on 407 high school girls in Mashhad who were selected through two-step random sampling. The students completed a questionnaire concerning demographic characteristics, menstruation, Depression, Anxiety, and Stress Scale of 21 questions (DASS-21), and menstruation signs in three phases of their menstruation. Data were analyzed by the statistical tests of Pearson correlation coefficient, Student's t-test, one-way analysis of variance (ANOVA), and regression through SPSS version 14. Based on the findings, 74% of the subjects reported pre-menstruation signs, 94% reported signs during bleeding, and 40.8% reported post-menstruation signs. About 44.3% of the subjects had anxiety, 45.5% had depression, and 47.2% had stress. In addition, Pearson correlation coefficient test showed a significant positive correlation between menstruation signs and depression, anxiety, and stress (P < 0.05). With regard to the association between menstruation signs and psycho-cognitive variables, prevention and treatment of these disorders by the authorities of education and training and the Ministry of Health are essential.
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Mass and heat transfer in crushed oil shale
DOE Office of Scientific and Technical Information (OSTI.GOV)
Carley, J.F.; Straub, J.S.; Ott, L.L.
1984-04-01
Heat and mass transfer between gases and oil-shale particles are both important for all proposed retorting processes. Past studies of transfer in packed beds, which have disagreed substantially in their results, have nearly all been done with beds of regular particles of uniform size, whereas oil-shale retorting involves particles of diverse shapes and widely ranging sizes. To resolve these questions, we have made 349 runs in which we measured mass-transfer rates from naphthalene particles of diverse shapes buried in packed beds through which air was passed at room temperature. This technique permits calculation of the mass-transfer coefficient for each activemore » particle in the bed rather than, as in most past studies, for the bed as a whole. The data were analyzed in two ways: (1) by the traditional correlation of Colburn j/sub D/ vs Reynolds number and (2) by multiple regression of the mass-transfer coefficient on air rate, traditional correlation of Colburn j/sub D/ vs Reynolds number and (3) by multiple regression of the mass-transfer coefficient on air rate, sizes of active and inert particles, void fraction, and temperature. Principal findings are: (1) local Reynolds number should be based on active particle size rather than average size for the bed; (2) no appreciable differences were seen between shallow beds and deep ones; (3) mass transfer was 26% faster for spheres and lozenges buried in shale than for all-sphere beds; (4) orientation of lozenges in shale beds has little effect on mass-transfer rate; (5) a useful summarizing equation for either mass or heat transfer in shale beds is log j.epsilon = -.0747 - .6344 log Re + .0592 log/sup 2/Re where j = either j/sub D/ or j/sub H/, the Chilton-Colburn j-factors for mass and heat transfer, Re = the Reynolds number defined for packed beds, and epsilon = the void fraction in the bed. 12 references, 15 figures.« less
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Feature Grouping and Selection Over an Undirected Graph.
Yang, Sen; Yuan, Lei; Lai, Ying-Cheng; Shen, Xiaotong; Wonka, Peter; Ye, Jieping
2012-01-01
High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l ∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l 1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.
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Media violence exposure and physical aggression in fifth-grade children.
Coker, Tumaini R; Elliott, Marc N; Schwebel, David C; Windle, Michael; Toomey, Sara L; Tortolero, Susan R; Hertz, Marci F; Peskin, Melissa F; Schuster, Mark A
2015-01-01
To examine the association of media violence exposure and physical aggression in fifth graders across 3 media types. We analyzed data from a population-based, cross-sectional survey of 5,147 fifth graders and their parents in 3 US metropolitan areas. We used multivariable linear regression and report partial correlation coefficients to examine associations between children's exposure to violence in television/film, video games, and music (reported time spent consuming media and reported frequency of violent content: physical fighting, hurting, shooting, or killing) and the Problem Behavior Frequency Scale. Child-reported media violence exposure was associated with physical aggression after multivariable adjustment for sociodemographics, family and community violence, and child mental health symptoms (partial correlation coefficients: TV, 0.17; video games, 0.15; music, 0.14). This association was significant and independent for television, video games, and music violence exposure in a model including all 3 media types (partial correlation coefficients: TV, 0.11; video games, 0.09; music, 0.09). There was a significant positive interaction between media time and media violence for video games and music but not for television. Effect sizes for the association of media violence exposure and physical aggression were greater in magnitude than for most of the other examined variables. The association between physical aggression and media violence exposure is robust and persistent; the strength of this association of media violence may be at least as important as that of other factors with physical aggression in children, such as neighborhood violence, home violence, child mental health, and male gender. Copyright © 2015 Academic Pediatric Association. All rights reserved.
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NASA Astrophysics Data System (ADS)
Heydarian, Mohammadreza; Kirby, Miranda; Wheatley, Andrew; Fenster, Aaron; Parraga, Grace
2012-03-01
A semi-automated method for generating hyperpolarized helium-3 (3He) measurements of individual slice (2D) or whole lung (3D) gas distribution was developed. 3He MRI functional images were segmented using two-dimensional (2D) and three-dimensional (3D) hierarchical K-means clustering of the 3He MRI signal and in addition a seeded region-growing algorithm was employed for segmentation of the 1H MRI thoracic cavity volume. 3He MRI pulmonary function measurements were generated following two-dimensional landmark-based non-rigid registration of the 3He and 1H pulmonary images. We applied this method to MRI of healthy subjects and subjects with chronic obstructive lung disease (COPD). The results of hierarchical K-means 2D and 3D segmentation were compared to an expert observer's manual segmentation results using linear regression, Pearson correlations and the Dice similarity coefficient. 2D hierarchical K-means segmentation of ventilation volume (VV) and ventilation defect volume (VDV) was strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.97, p<.0001) and mean Dice coefficients were greater than 92% for all subjects. 3D hierarchical K-means segmentation of VV and VDV was also strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.64, p<.0001) and the mean Dice coefficients were greater than 91% for all subjects. Both 2D and 3D semi-automated segmentation of 3He MRI gas distribution provides a way to generate novel pulmonary function measurements.
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Miller, Justin B; Axelrod, Bradley N; Schutte, Christian
2012-01-01
The recent release of the Wechsler Memory Scale Fourth Edition contains many improvements from a theoretical and administration perspective, including demographic corrections using the Advanced Clinical Solutions. Although the administration time has been reduced from previous versions, a shortened version may be desirable in certain situations given practical time limitations in clinical practice. The current study evaluated two- and three-subtest estimations of demographically corrected Immediate and Delayed Memory index scores using both simple arithmetic prorating and regression models. All estimated values were significantly associated with observed index scores. Use of Lin's Concordance Correlation Coefficient as a measure of agreement showed a high degree of precision and virtually zero bias in the models, although the regression models showed a stronger association than prorated models. Regression-based models proved to be more accurate than prorated estimates with less dispersion around observed values, particularly when using three subtest regression models. Overall, the present research shows strong support for estimating demographically corrected index scores on the WMS-IV in clinical practice with an adequate performance using arithmetically prorated models and a stronger performance using regression models to predict index scores.
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The association between hospital outcomes and diagnostic imaging: early findings.
Lee, David W; Foster, David A
2009-11-01
Resource use variation across the United States prompts the important question of whether "more is better" when it comes to health care services. The aim of this study was to examine correlations between the use of 4 common imaging modalities (CT, MR, ultrasound, and radiography) and in-hospital mortality and costs. Using clinical and utilization data for 1.1 million inpatient admissions at 102 US hospitals during 2007, two hospital-specific, risk-adjusted imaging utilization measures for each modality were constructed that controlled for patients' demographic and clinical characteristics and for hospital characteristics were constructed for each modality. First, logistic regression was used to estimate the odds that each type of imaging service would be provided during an admission. Second, the mean number of services per admission was estimated using output from a two-part ordinary least squares model. Hospital-specific, risk-adjusted inpatient mortality and total hospital costs were also computed, and correlations between the imaging utilization measures and the mortality and cost outcome measures were then assessed using Pearson's correlation coefficients (P < .05). The correlation analyses were weighted by hospital admission volume. Hospitals in which patients were more likely to receive imaging services during admissions had lower mortality, even after controlling for potential confounders. Correlation coefficients were -0.2 for all modalities (P = .02-.05). Weaker correlations existed between mean services per admission and mortality, while costs trended insignificantly higher with greater utilization. This study lays the foundation for further exploration of the relationship between resource use and the clinical and economic outcomes associated with imaging utilization.
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The Attenuation of Correlation Coefficients: A Statistical Literacy Issue
ERIC Educational Resources Information Center
Trafimow, David
2016-01-01
Much of the science reported in the media depends on correlation coefficients. But the size of correlation coefficients depends, in part, on the reliability with which the correlated variables are measured. Understanding this is a statistical literacy issue.
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Support vector regression to predict porosity and permeability: Effect of sample size
NASA Astrophysics Data System (ADS)
Al-Anazi, A. F.; Gates, I. D.
2012-02-01
Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function type and loss functions used.
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Guertin, Kristin A; Loftfield, Erikka; Boca, Simina M; Sampson, Joshua N; Moore, Steven C; Xiao, Qian; Huang, Wen-Yi; Xiong, Xiaoqin; Freedman, Neal D; Cross, Amanda J; Sinha, Rashmi
2015-05-01
Coffee intake may be inversely associated with colorectal cancer; however, previous studies have been inconsistent. Serum coffee metabolites are integrated exposure measures that may clarify associations with cancer and elucidate underlying mechanisms. Our aims were 2-fold as follows: 1) to identify serum metabolites associated with coffee intake and 2) to examine these metabolites in relation to colorectal cancer. In a nested case-control study of 251 colorectal cancer cases and 247 matched control subjects from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, we conducted untargeted metabolomics analyses of baseline serum by using ultrahigh-performance liquid-phase chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Usual coffee intake was self-reported in a food-frequency questionnaire. We used partial Pearson correlations and linear regression to identify serum metabolites associated with coffee intake and conditional logistic regression to evaluate associations between coffee metabolites and colorectal cancer. After Bonferroni correction for multiple comparisons (P = 0.05 ÷ 657 metabolites), 29 serum metabolites were positively correlated with coffee intake (partial correlation coefficients: 0.18-0.61; P < 7.61 × 10(-5)); serum metabolites most highly correlated with coffee intake (partial correlation coefficients >0.40) included trigonelline (N'-methylnicotinate), quinate, and 7 unknown metabolites. Of 29 serum metabolites, 8 metabolites were directly related to caffeine metabolism, and 3 of these metabolites, theophylline (OR for 90th compared with 10th percentiles: 0.44; 95% CI: 0.25, 0.79; P-linear trend = 0.006), caffeine (OR for 90th compared with 10th percentiles: 0.56; 95% CI: 0.35, 0.89; P-linear trend = 0.015), and paraxanthine (OR for 90th compared with 10th percentiles: 0.58; 95% CI: 0.36, 0.94; P-linear trend = 0.027), were inversely associated with colorectal cancer. Serum metabolites can distinguish coffee drinkers from nondrinkers; some caffeine-related metabolites were inversely associated with colorectal cancer and should be studied further to clarify the role of coffee in the cause of colorectal cancer. The Prostate, Lung, Colorectal, and Ovarian trial was registered at clinicaltrials.gov as NCT00002540. © 2015 American Society for Nutrition.
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A Millennial-length Reconstruction of the Western Pacific Pattern with Associated Paleoclimate
NASA Astrophysics Data System (ADS)
Wright, W. E.; Guan, B. T.; Wei, K.
2010-12-01
The Western Pacific Pattern (WP) is a lesser known 500 hPa pressure pattern similar to the NAO or PNA. As defined, the poles of the WP index are centered on 60°N over the Kamchatka peninsula and the neighboring Pacific and on 32.5°N over the western north Pacific. However, the area of influence for the southern half of the dipole includes a wide swath from East Asia, across Taiwan, through the Philippine Sea, to the western north Pacific. Tree rings of Taiwanese Chamaecyparis obtusa var. formosana in this extended region show significant correlation with the WP, and with local temperature. The WP is also significantly correlated with atmospheric temperatures over Taiwan, especially at 850hPa and 700 hPa, pressure levels that bracket the tree site. Spectral analysis indicates that variations in the WP occur at relatively high frequency, with most power at less than 5 years. Simple linear regression against high frequency variants of the tree-ring chronology yielded the most significant correlation coefficients. Two reconstructions are presented. The first uses a tree-ring time series produced as the first intrinsic mode function (IMF) from an Ensemble Empirical Mode Decomposition (EEMD), based on the Hilbert-Huang Transform. The significance of the regression using the EEMD-derived time series was much more significant than time series produced using traditional high pass filtering. The second also uses the first IMF of a tree-ring time series, but the dataset was first sorted and partitioned at a specified quantile prior to EEMD decomposition, with the mean of the partitioned data forming the input to the EEMD. The partitioning was done to filter out the less climatically sensitive tree rings, a common problem with shade tolerant trees. Time series statistics indicate that the first reconstruction is reliable to 1241 of the Common Era. Reliability of the second reconstruction is dependent on the development of statistics related to the quantile partitioning, and the consequent reduction in sample depth. However, the correlation coefficients from regressions over the instrumental period greatly exceed those from any other method of chronology generation, and so the technique holds promise. Additional atmospheric parameters having significant correlations against the WPO and tree ring time series with similar spatial patterns are also presented. These include vertical wind shear (850hPa-700hPa) over the northern Philippines and the Philippine Sea, surface Omega and 850hPa v-winds over the East China Sea, Japan and Taiwan. Possible links to changes in the subtropical jet stream will also be discussed.
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Fan, Shou-Zen; Abbod, Maysam F.
2018-01-01
Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients’ anaesthetic level during surgeries. PMID:29844970
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NASA Astrophysics Data System (ADS)
ul-Haq, Zia; Rana, Asim Daud; Tariq, Salman; Mahmood, Khalid; Ali, Muhammad; Bashir, Iqra
2018-03-01
We have applied regression analyses for the modeling of tropospheric NO2 (tropo-NO2) as the function of anthropogenic nitrogen oxides (NOx) emissions, aerosol optical depth (AOD), and some important meteorological parameters such as temperature (Temp), precipitation (Preci), relative humidity (RH), wind speed (WS), cloud fraction (CLF) and outgoing long-wave radiation (OLR) over different climatic zones and land use/land cover types in South Asia during October 2004-December 2015. Simple linear regression shows that, over South Asia, tropo-NO2 variability is significantly linked to AOD, WS, NOx, Preci and CLF. Also zone-5, consisting of tropical monsoon areas of eastern India and Myanmar, is the only study zone over which all the selected parameters show their influence on tropo-NO2 at statistical significance levels. In stepwise multiple linear modeling, tropo-NO2 column over landmass of South Asia, is significantly predicted by the combination of RH (standardized regression coefficient, β = - 49), AOD (β = 0.42) and NOx (β = 0.25). The leading predictors of tropo-NO2 columns over zones 1-5 are OLR, AOD, Temp, OLR, and RH respectively. Overall, as revealed by the higher correlation coefficients (r), the multiple regressions provide reasonable models for tropo-NO2 over South Asia (r = 0.82), zone-4 (r = 0.90) and zone-5 (r = 0.93). The lowest r (of 0.66) has been found for hot semi-arid region in northwestern Indus-Ganges Basin (zone-2). The highest value of β for urban area AOD (of 0.42) is observed for megacity Lahore, located in warm semi-arid zone-2 with large scale crop-residue burning, indicating strong influence of aerosols on the modeled tropo-NO2 column. A statistical significant correlation (r = 0.22) at the 0.05 level is found between tropo-NO2 and AOD over Lahore. Also NOx emissions appear as the highest contributor (β = 0.59) for modeled tropo-NO2 column over megacity Dhaka.
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Theodoratou, Evropi; Zhang, Jian Shayne F.; Kolcic, Ivana; Davis, Andrew M.; Bhopal, Sunil; Nair, Harish; Chan, Kit Yee; Liu, Li; Johnson, Hope; Rudan, Igor; Campbell, Harry
2011-01-01
Background Pneumonia is the leading cause of child deaths globally. The aims of this study were to: a) estimate the number and global distribution of pneumonia deaths for children 1–59 months for 2008 for countries with low (<85%) or no coverage of death certification using single-cause regression models and b) compare these country estimates with recently published ones based on multi-cause regression models. Methods and Findings For 35 low child-mortality countries with <85% coverage of death certification, a regression model based on vital registration data of low child-mortality and >85% coverage of death certification countries was used. For 87 high child-mortality countries pneumonia death estimates were obtained by applying a regression model developed from published and unpublished verbal autopsy data from high child-mortality settings. The total number of 1–59 months pneumonia deaths for the year 2008 for these 122 countries was estimated to be 1.18 M (95% CI 0.77 M–1.80 M), which represented 23.27% (95% CI 17.15%–32.75%) of all 1–59 month child deaths. The country level estimation correlation coefficient between these two methods was 0.40. Interpretation Although the overall number of post-neonatal pneumonia deaths was similar irrespective to the method of estimation used, the country estimate correlation coefficient was low, and therefore country-specific estimates should be interpreted with caution. Pneumonia remains the leading cause of child deaths and is greatest in regions of poverty and high child-mortality. Despite the concerns about gender inequity linked with childhood mortality we could not estimate sex-specific pneumonia mortality rates due to the inadequate data. Life-saving interventions effective in preventing and treating pneumonia mortality exist but few children in high pneumonia disease burden regions are able to access them. To achieve the United Nations Millennium Development Goal 4 target to reduce child deaths by two-thirds in year 2015 will require the scale-up of access to these effective pneumonia interventions. PMID:21966425
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Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan
2014-02-10
Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Meylan, W.M.; Howard, P.H.; Aronson, D.
1999-04-01
A compound`s bioconcentration factor (BDF) is the most commonly used indicator of its tendency to accumulate in aquatic organisms from the surrounding medium. Because it is expensive to measure, the BCF is generally estimated from the octanol/water partition coefficient (K{sub ow}), but currently used regression equations were developed from small data sets that do not adequately represent the wide range of chemical substances now subject to review. To develop and improved method, the authors collected BCF data in a file that contained information on measured BCFs and other key experimental details for 694 chemicals. Log BCF was then regressed againstmore » log K{sub ow} and chemicals with significant deviations from the line of best fit were analyzed by chemical structure. The resulting algorithm classifies a substance as either nonionic or ionic, the latter group including carboxylic acids, sulfonic acids and their salts, and quaternary N compounds. Log BCF for nonionics is estimated from log K{sub ow} and a series of correction factors if applicable; different equations apply for log K{sub ow} 1.0 to 7.0 and >7.0. For ionics, chemicals are categorized by log K{sub ow} and a log BCF in the range 0.5 to 1.75 is assigned. Organometallics, nonionics with long alkyl chains, and aromatic azo compounds receive special treatment. The correlation coefficient and mean error for log BCF indicate that the new method is a significantly better fit to existing data than other methods.« less
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[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].
Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao
2016-03-01
Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.
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Investigation of geomagnetic induced current at high latitude during the storm-time variation
NASA Astrophysics Data System (ADS)
Falayi, E. O.; Ogunmodimu, O.; Bolaji, O. S.; Ayanda, J. D.; Ojoniyi, O. S.
2017-06-01
During the geomagnetic disturbances, the geomagnetically induced current (GIC) are influenced by the geoelectric field flowing in conductive Earth. In this paper, we studied the variability of GICs, the time derivatives of the geomagnetic field (dB/dt), geomagnetic indices: Symmetric disturbance field in H (SYM-H) index, AU (eastward electrojet) and AL (westward electrojet) indices, Interplanetary parameters such as solar wind speed (v), and interplanetary magnetic field (Bz) during the geomagnetic storms on 31 March 2001, 21 October 2001, 6 November 2001, 29 October 2003, 31 October 2003 and 9 November 2004 with high solar wind speed due to a coronal mass ejection. Wavelet spectrum based approach was employed to analyze the GIC time series in a sequence of time scales of one to twenty four hours. It was observed that there are more concentration of power between the 14-24 h on 31 March 2001, 17-24 h on 21 October 2001, 1-7 h on 6 November 2001, two peaks were observed between 5-8 h and 21-24 h on 29 October 2003, 1-3 h on 31 October 2003 and 18-22 h on 9 November 2004. Bootstrap method was used to obtain regression correlations between the time derivative of the geomagnetic field (dB/dt) and the observed values of the geomagnetic induced current on 31 March 2001, 21 October 2001, 6 November 2001, 29 October 2003, 31 October 2003 and 9 November 2004 which shows a distributed cluster of correlation coefficients at around r = -0.567, -0.717, -0.477, -0.419, -0.210 and r = -0.488 respectively. We observed that high energy wavelet coefficient correlated well with bootstrap correlation, while low energy wavelet coefficient gives low bootstrap correlation. It was noticed that the geomagnetic storm has a influence on GIC and geomagnetic field derivatives (dB/dt). This might be ascribed to the coronal mass ejection with solar wind due to particle acceleration processes in the solar atmosphere.
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[Comparison of two methods for rapid determination of C-reactive protein with the Tina-quant].
Oremek, G M; Luksaite, R; Bretschneider, I
2008-03-01
C-reactive protein (CRP) as an acute phase protein is an important diagnostic marker for the presence and course of human processes. Out of the acute phase proteins it is one of those the concentrations increase most rapidly with its sensitivity being superior to other markers of inflammation, such as leukocytosis, erythrocytic sedimentation rate, and fever. This study compared two-point-of-care assays with the standard laboratory method Tina-quant CRP processed on a Hitachi 917: the immunofiltration assay NycoCard CRP Whole Blood and the turbidimetric immunoassay Micros CRP. Both methods are carried in the presence of a patient, by using capillary or venous blood. Seventy-eight blood samples were analyzed first in the standard laboratory routine and then by both rapid test assays. The precision of both assays was determined from the confidence interval. The results were statistically analyzed by arithmetic standard deviation mean method, variation coefficient, Spearman correlation index, Wilcoxon and Bland-Altman tests, and Passing-Bablock regression. NycoCard CRP Whole Blood showed a correlation coefficient of R = 0.9838; the precision had a coefficient of variation of CV = 1.8759% while As compared with Tina-quant CRP had R = 0.9934 and CV = 0.9160%. Both assays indicated the same results as Tina-quant CRP. Both Tina-quant CRP and NycoCard CRP Whole Blood give the best fit for the rapid determination of CRP.
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[Male hormonal profile in workers exposed to toluene in an industrial packaging plant].
Miranda-Figueroa, Genezareth; Paz-Román, María del Pilar; Aguilar-Madrid, Guadalupe; Juárez-Pérez, Cuauhtémoc Arturo; Basurto-Acevedo, Lourdes; Haro-García, Luis
2013-01-01
to identify changes in the male hormonal profile (MHP), consisting of luteinizing hormone (LH), follicle-stimulating hormone (FSH) and testosterone, in workers exposed to toluene. cross-sectional study that included 42 workers, formed in two groups: with high and low exposure to toluene (HET and LET, respectively); the hippuric acid in urine, and the serum concentration in FSH, LH, and testosterone were measured. hippuric acid in subjects with LET was 2.53 ± 1.20 g/g creatinine, and with HET: 6.31 ± 3.83 g/g creatinine (p = 0.02). Seric FSH concentration was 5.12 ± 0.77 and 3.55 ± 0.3 mU/mL (p = 0.02) in LET and HET, respectively; LH: 2.66 ± 0.45 and 2.77 ± 0.21 (p = 0.81), and testosterone 3.91 ± 0.34 and 4.86 ± 0.23 ng/mL (p = 0.04). By regression analysis, the correlation coefficient of FSH with hippuric acid was r = -0.33 (p = 0.031), with coefficient of determination of 11 %; the LH correlation coefficient was r = -0.037 (p = 0.88) and 0.05 %, respectively; and the one for testosterone was r = 0.61 (p = 0.0001) and 34 %. in HET group, FSH and LH serum levels diminished; testosterone shows an opposite response, perhaps explained by a different sensitivity of the male gonads exposed to toluene.
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Cortés-Castell, Ernesto; Juste, Mercedes; Palazón-Bru, Antonio; Monge, Laura; Sánchez-Ferrer, Francisco; Rizo-Baeza, María Mercedes
2017-01-01
Dual-energy X-ray absorptiometry (DXA) provides separate measurements of fat mass, fat-free mass and bone mass, and is a quick, accurate, and safe technique, yet one that is not readily available in routine clinical practice. Consequently, we aimed to develop statistical formulas to predict fat mass (%) and fat mass index (FMI) with simple parameters (age, sex, weight and height). We conducted a retrospective observational cross-sectional study in 416 overweight or obese patients aged 4-18 years that involved assessing adiposity by DXA (fat mass percentage and FMI), body mass index (BMI), sex and age. We randomly divided the sample into two parts (construction and validation). In the construction sample, we developed formulas to predict fat mass and FMI using linear multiple regression models. The formulas were validated in the other sample, calculating the intraclass correlation coefficient via bootstrapping. The fat mass percentage formula had a coefficient of determination of 0.65. This value was 0.86 for FMI. In the validation, the constructed formulas had an intraclass correlation coefficient of 0.77 for fat mass percentage and 0.92 for FMI. Our predictive formulas accurately predicted fat mass and FMI with simple parameters (BMI, sex and age) in children with overweight and obesity. The proposed methodology could be applied in other fields. Further studies are needed to externally validate these formulas.
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White, Sarah A; van den Broek, Nynke R
2004-05-30
Before introducing a new measurement tool it is necessary to evaluate its performance. Several statistical methods have been developed, or used, to evaluate the reliability and validity of a new assessment method in such circumstances. In this paper we review some commonly used methods. Data from a study that was conducted to evaluate the usefulness of a specific measurement tool (the WHO Colour Scale) is then used to illustrate the application of these methods. The WHO Colour Scale was developed under the auspices of the WHO to provide a simple portable and reliable method of detecting anaemia. This Colour Scale is a discrete interval scale, whereas the actual haemoglobin values it is used to estimate are on a continuous interval scale and can be measured accurately using electrical laboratory equipment. The methods we consider are: linear regression, correlation coefficients, paired t-tests plotting differences against mean values and deriving limits of agreement; kappa and weighted kappa statistics, sensitivity and specificity, an intraclass correlation coefficient and the repeatability coefficient. We note that although the definition and properties of each of these methods is well established inappropriate methods continue to be used in medical literature for assessing reliability and validity, as evidenced in the context of the evaluation of the WHO Colour Scale. Copyright 2004 John Wiley & Sons, Ltd.
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Kramer, Gerbrand Maria; Frings, Virginie; Heijtel, Dennis; Smit, E F; Hoekstra, Otto S; Boellaard, Ronald
2017-06-01
The objective of this study was to validate several parametric methods for quantification of 3'-deoxy-3'- 18 F-fluorothymidine ( 18 F-FLT) PET in advanced-stage non-small cell lung carcinoma (NSCLC) patients with an activating epidermal growth factor receptor mutation who were treated with gefitinib or erlotinib. Furthermore, we evaluated the impact of noise on accuracy and precision of the parametric analyses of dynamic 18 F-FLT PET/CT to assess the robustness of these methods. Methods : Ten NSCLC patients underwent dynamic 18 F-FLT PET/CT at baseline and 7 and 28 d after the start of treatment. Parametric images were generated using plasma input Logan graphic analysis and 2 basis functions-based methods: a 2-tissue-compartment basis function model (BFM) and spectral analysis (SA). Whole-tumor-averaged parametric pharmacokinetic parameters were compared with those obtained by nonlinear regression of the tumor time-activity curve using a reversible 2-tissue-compartment model with blood volume fraction. In addition, 2 statistically equivalent datasets were generated by countwise splitting the original list-mode data, each containing 50% of the total counts. Both new datasets were reconstructed, and parametric pharmacokinetic parameters were compared between the 2 replicates and the original data. Results: After the settings of each parametric method were optimized, distribution volumes (V T ) obtained with Logan graphic analysis, BFM, and SA all correlated well with those derived using nonlinear regression at baseline and during therapy ( R 2 ≥ 0.94; intraclass correlation coefficient > 0.97). SA-based V T images were most robust to increased noise on a voxel-level (repeatability coefficient, 16% vs. >26%). Yet BFM generated the most accurate K 1 values ( R 2 = 0.94; intraclass correlation coefficient, 0.96). Parametric K 1 data showed a larger variability in general; however, no differences were found in robustness between methods (repeatability coefficient, 80%-84%). Conclusion: Both BFM and SA can generate quantitatively accurate parametric 18 F-FLT V T images in NSCLC patients before and during therapy. SA was more robust to noise, yet BFM provided more accurate parametric K 1 data. We therefore recommend BFM as the preferred parametric method for analysis of dynamic 18 F-FLT PET/CT studies; however, SA can also be used. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.
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Relationship Between Earthquake b-Values and Crustal Stresses in a Young Orogenic Belt
NASA Astrophysics Data System (ADS)
Wu, Yih-Min; Chen, Sean Kuanhsiang; Huang, Ting-Chung; Huang, Hsin-Hua; Chao, Wei-An; Koulakov, Ivan
2018-02-01
It has been reported that earthquake b-values decrease linearly with the differential stresses in the continental crust and subduction zones. Here we report a regression-derived relation between earthquake b-values and crustal stresses using the Anderson fault parameter (Aϕ) in a young orogenic belt of Taiwan. This regression relation is well established by using a large and complete earthquake catalog for Taiwan. The data set consists of b-values and Aϕ values derived from relocated earthquakes and focal mechanisms, respectively. Our results show that b-values decrease linearly with the Aϕ values at crustal depths with a high correlation coefficient of -0.9. Thus, b-values could be used as stress indicators for orogenic belts. However, the state of stress is relatively well correlated with the surface geological setting with respect to earthquake b-values in Taiwan. Temporal variations in the b-value could constitute one of the main reasons for the spatial heterogeneity of b-values. We therefore suggest that b-values could be highly sensitive to temporal stress variations.
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Raman spectroscopy based screening of IgG positive and negative sera for dengue virus infection
NASA Astrophysics Data System (ADS)
Bilal, M.; Saleem, M.; Bial, Maria; Khan, Saranjam; Ullah, Rahat; Ali, Hina; Ahmed, M.; Ikram, Masroor
2017-11-01
A quantitative analysis for the screening of immunoglobulin-G (IgG) positive human sera samples is presented for the dengue virus infection. The regression model was developed using 79 samples while 20 samples were used to test the performance of the model. The R-square (r 2) value of 0.91 was found through a leave-one-sample-out cross validation method, which shows the validity of this model. This model incorporates the molecular changes associated with IgG. Molecular analysis based on regression coefficients revealed that myristic acid, coenzyme-A, alanine, arabinose, arginine, vitamin C, carotene, fumarate, galactosamine, glutamate, lactic acid, stearic acid, tryptophan and vaccenic acid are positively correlated with IgG; while amide III, collagen, proteins, fatty acids, phospholipids and fucose are negatively correlated. For blindly tested samples, an excellent agreement has been found between the model predicted, and the clinical values of IgG. The parameters, which include sensitivity, specificity, accuracy and the area under the receiver operator characteristic curve, are found to be 100%, 83.3%, 95% and 0.99, respectively, which confirms the high quality of the model.
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NASA Astrophysics Data System (ADS)
Tamimi, Abdallah Ibrahim
Quality management is a fundamental challenge facing businesses. This research attempted to quantify the effect of quality investment on the Cost of Poor Quality (COPQ) in an aerospace company utilizing 3 years of quality data at United Launch Alliance, a Boeing -- Lockheed Martin Joint Venture Company. Statistical analysis tools, like multiple regressions, were used to quantify the relationship between quality investments and COPQ. Strong correlations were evident by the high correlation coefficient R2 and very small p-values in multiple regression analysis. The models in the study helped produce an Excel macro that based on preset constraints, optimized the level of quality spending to minimize COPQ. The study confirmed that as quality investments were increased, the COPQ decreased steadily until a point of diminishing return was reached. The findings may be used to develop an approach to reduce the COPQ and enhance product performance. Achieving superior quality in rocket launching enhances the accuracy, reliability, and mission success of delivering satellites to their precise orbits in pursuit of knowledge, peace, and freedom while assuring safety for the end user.
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Kajbafnezhad, H; Ahadi, H; Heidarie, A; Askari, P; Enayati, M
2012-10-01
The aim of this study was to predict athletic success motivation by mental skills, emotional intelligence and its components. The research sample consisted of 153 male athletes who were selected through random multistage sampling. The subjects completed the Mental Skills Questionnaire, Bar-On Emotional Intelligence questionnaire and the perception of sport success questionnaire. Data were analyzed using Pearson correlation coefficient and multiple regressions. Regression analysis shows that between the two variables of mental skill and emotional intelligence, mental skill is the best predictor for athletic success motivation and has a better ability to predict the success rate of the participants. Regression analysis results showed that among all the components of emotional intelligence, self-respect had a significantly higher ability to predict athletic success motivation. The use of psychological skills and emotional intelligence as an mediating and regulating factor and organizer cause leads to improved performance and can not only can to help athletes in making suitable and effective decisions for reaching a desired goal.
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Age Estimation of Infants Through Metric Analysis of Developing Anterior Deciduous Teeth.
Viciano, Joan; De Luca, Stefano; Irurita, Javier; Alemán, Inmaculada
2018-01-01
This study provides regression equations for estimation of age of infants from the dimensions of their developing deciduous teeth. The sample comprises 97 individuals of known sex and age (62 boys, 35 girls), aged between 2 days and 1,081 days. The age-estimation equations were obtained for the sexes combined, as well as for each sex separately, thus including "sex" as an independent variable. The values of the correlations and determination coefficients obtained for each regression equation indicate good fits for most of the equations obtained. The "sex" factor was statistically significant when included as an independent variable in seven of the regression equations. However, the "sex" factor provided an advantage for age estimation in only three of the equations, compared to those that did not include "sex" as a factor. These data suggest that the ages of infants can be accurately estimated from measurements of their developing deciduous teeth. © 2017 American Academy of Forensic Sciences.
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NASA Astrophysics Data System (ADS)
Piretzidis, D.; Sra, G.; Sideris, M. G.
2016-12-01
This study explores new methods for identifying correlation errors in harmonic coefficients derived from monthly solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission using pattern recognition and neural network algorithms. These correlation errors are evidenced in the differences between monthly solutions and can be suppressed using a de-correlation filter. In all studies so far, the implementation of the de-correlation filter starts from a specific minimum order (i.e., 11 for RL04 and 38 for RL05) until the maximum order of the monthly solution examined. This implementation method has two disadvantages, namely, the omission of filtering correlated coefficients of order less than the minimum order and the filtering of uncorrelated coefficients of order higher than the minimum order. In the first case, the filtered solution is not completely free of correlated errors, whereas the second case results in a monthly solution that suffers from loss of geophysical signal. In the present study, a new method of implementing the de-correlation filter is suggested, by identifying and filtering only the coefficients that show indications of high correlation. Several numerical and geometric properties of the harmonic coefficient series of all orders are examined. Extreme cases of both correlated and uncorrelated coefficients are selected, and their corresponding properties are used to train a two-layer feed-forward neural network. The objective of the neural network is to identify and quantify the correlation by providing the probability of an order of coefficients to be correlated. Results show good performance of the neural network, both in the validation stage of the training procedure and in the subsequent use of the trained network to classify independent coefficients. The neural network is also capable of identifying correlated coefficients even when a small number of training samples and neurons are used (e.g.,100 and 10, respectively).
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Tree-Ring Widths and Snow Cover Depth in High Tauern
NASA Astrophysics Data System (ADS)
Falarz, Malgorzata
2017-12-01
The aim of the study is to examine the correlation of Norway spruce tree-ring widths and the snow cover depth in the High Tauern mountains. The average standardized tree-ring widths indices for Nowary spruce posted by Bednarz and Niedzwiedz (2006) were taken into account. Increment cores were collected from 39 Norway spruces growing in the High Tauern near the upper limit of the forest at altitude of 1700-1800 m, 3 km from the meteorological station at Sonnblick. Moreover, the maximum of snow cover depth in Sonnblick (3105 m a.s.l.) for each winter season in the period from 1938/39 to 1994/95 (57 winter seasons) was taken into account. The main results of the research are as follows: (1) tree-ring widths in a given year does not reveal statistically significant dependency on the maximum snow cover depth observed in the winter season, which ended this year; (2) however, the tested relationship is statistically significant in the case of correlating of the tree-ring widths in a given year with a maximum snow cover depth in a season of previous year. The correlation coefficient for the entire period of the study is not very high (r=0.27) but shows a statistical significance at the 0.05 level; (3) the described relationship is not stable over time. 30-year moving correlations showed no significant dependencies till 1942 and after 1982 (probably due to the so-called divergence phenomenon). However, during the period of 1943-1981 the values of correlation coefficient for moving 30-year periods are statistically significant and range from 0.37 to 0.45; (4) the correlation coefficient between real and calibrated (on the base of the regression equation) values of maximum snow cover depth is statistically significant for calibration period and not significant for verification one; (5) due to a quite short period of statistically significant correlations and not very strict dependencies, the reconstruction of snow cover on Sonnblick for the period before regular measurements seems to be not reasonable.
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Ishak, Nor Azizah; Zahari, Zarina; Justine, Maria
2017-01-01
This study aims (1) to determine the association between kinesiophobia and pain, muscle functions, and functional performances and (2) to determine whether kinesiophobia predicts pain, muscle functions, and functional performance among older persons with low back pain (LBP). This is a correlational study, involving 63 institutionalized older persons (age = 70.98 ± 7.90 years) diagnosed with LBP. Anthropometric characteristics (BMI) and functional performances (lower limb function, balance and mobility, and hand grip strength) were measured. Muscle strength (abdominal and back muscle strength) was assessed using the Baseline® Mechanical Push/Pull Dynamometer, while muscle control (transverse abdominus and multifidus) was measured by using the Pressure Biofeedback Unit. The pain intensity and the level of kinesiophobia were measured using Numerical Rating Scale and Tampa Scale of Kinesiophobia, respectively. Data were analyzed using Pearson's correlation coefficients and multivariate linear regressions. No significant correlations were found between kinesiophobia and pain and muscle functions (all p > 0.05). Kinesiophobia was significantly correlated with mobility and balance ( p = 0.038, r = 0.263). Regressions analysis showed that kinesiophobia was a significant predictor of mobility and balance ( p = 0.038). We can conclude that kinesiophobia predicted mobility and balance in older persons with LBP. Kinesiophobia should be continuously assessed in clinical settings to recognize the obstacles that may affect patient's compliance towards a rehabilitation program in older persons with LBP.
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[Spatial epidemiological study on malaria epidemics in Hainan province].
Wen, Liang; Shi, Run-He; Fang, Li-Qun; Xu, De-Zhong; Li, Cheng-Yi; Wang, Yong; Yuan, Zheng-Quan; Zhang, Hui
2008-06-01
To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics. Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0. The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST). Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.
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2017-01-01
Objectives This study aims (1) to determine the association between kinesiophobia and pain, muscle functions, and functional performances and (2) to determine whether kinesiophobia predicts pain, muscle functions, and functional performance among older persons with low back pain (LBP). Methods This is a correlational study, involving 63 institutionalized older persons (age = 70.98 ± 7.90 years) diagnosed with LBP. Anthropometric characteristics (BMI) and functional performances (lower limb function, balance and mobility, and hand grip strength) were measured. Muscle strength (abdominal and back muscle strength) was assessed using the Baseline® Mechanical Push/Pull Dynamometer, while muscle control (transverse abdominus and multifidus) was measured by using the Pressure Biofeedback Unit. The pain intensity and the level of kinesiophobia were measured using Numerical Rating Scale and Tampa Scale of Kinesiophobia, respectively. Data were analyzed using Pearson's correlation coefficients and multivariate linear regressions. Results No significant correlations were found between kinesiophobia and pain and muscle functions (all p > 0.05). Kinesiophobia was significantly correlated with mobility and balance (p = 0.038, r = 0.263). Regressions analysis showed that kinesiophobia was a significant predictor of mobility and balance (p = 0.038). Conclusion We can conclude that kinesiophobia predicted mobility and balance in older persons with LBP. Kinesiophobia should be continuously assessed in clinical settings to recognize the obstacles that may affect patient's compliance towards a rehabilitation program in older persons with LBP. PMID:28634547
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Barbin, Douglas Fernandes; Valous, Nektarios A; Dias, Adriana Passos; Camisa, Jaqueline; Hirooka, Elisa Yoko; Yamashita, Fabio
2015-11-01
There is an increasing interest in the use of polysaccharides and proteins for the production of biodegradable films. Visible and near-infrared (VIS-NIR) spectroscopy is a reliable analytical tool for objective analyses of biological sample attributes. The objective is to investigate the potential of VIS-NIR spectroscopy as a process analytical technology for compositional characterization of biodegradable materials and correlation to their mechanical properties. Biofilms were produced by single-screw extrusion with different combinations of polybutylene adipate-co-terephthalate, whole oat flour, glycerol, magnesium stearate, and citric acid. Spectral data were recorded in the range of 400-2498nm at 2nm intervals. Partial least square regression was used to investigate the correlation between spectral information and mechanical properties. Results show that spectral information is influenced by the major constituent components, as they are clustered according to polybutylene adipate-co-terephthalate content. Results for regression models using the spectral information as predictor of tensile properties achieved satisfactory results, with coefficients of prediction (R(2)C) of 0.83, 0.88 and 0.92 (calibration models) for elongation, tensile strength, and Young's modulus, respectively. Results corroborate the correlation of NIR spectra with tensile properties, showing that NIR spectroscopy has potential as a rapid analytical technology for non-destructive assessment of the mechanical properties of the films. Copyright © 2015 Elsevier B.V. All rights reserved.