Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination.

PubMed

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.

Multiple linear regression analysis

NASA Technical Reports Server (NTRS)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

Testing for gene-environment interaction under exposure misspecification.

PubMed

Sun, Ryan; Carroll, Raymond J; Christiani, David C; Lin, Xihong

2017-11-09

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties. © 2017, The International Biometric Society.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Estimation of octanol/water partition coefficients using LSER parameters

USGS Publications Warehouse

Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.

1998-01-01

The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

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...

Comparison between light scattering and gravimetric samplers for PM10 mass concentration in poultry and pig houses

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.

Testing a single regression coefficient in high dimensional linear models

PubMed Central

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

Testing a single regression coefficient in high dimensional linear models.

PubMed

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.

Synthesis of linear regression coefficients by recovering the within-study covariance matrix from summary statistics.

PubMed

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.

Determination and importance of temperature dependence of retention coefficient (RPHPLC) in QSAR model of nitrazepams' partition coefficient in bile acid micelles.

PubMed

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.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

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.

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

A New Test of Linear Hypotheses in OLS Regression under Heteroscedasticity of Unknown Form

ERIC Educational Resources Information Center

Cai, Li; Hayes, Andrew F.

2008-01-01

When the errors in an ordinary least squares (OLS) regression model are heteroscedastic, hypothesis tests involving the regression coefficients can have Type I error rates that are far from the nominal significance level. Asymptotically, this problem can be rectified with the use of a heteroscedasticity-consistent covariance matrix (HCCM)…

Comparison of various error functions in predicting the optimum isotherm by linear and non-linear regression analysis for the sorption of basic red 9 by activated carbon.

PubMed

Kumar, K Vasanth; Porkodi, K; Rocha, F

2008-01-15

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.

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…

Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

NASA Astrophysics Data System (ADS)

Gusriani, N.; Firdaniza

2018-03-01

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

The Use of Structure Coefficients to Address Multicollinearity in Sport and Exercise Science

ERIC Educational Resources Information Center

Yeatts, Paul E.; Barton, Mitch; Henson, Robin K.; Martin, Scott B.

2017-01-01

A common practice in general linear model (GLM) analyses is to interpret regression coefficients (e.g., standardized ß weights) as indicators of variable importance. However, focusing solely on standardized beta weights may provide limited or erroneous information. For example, ß weights become increasingly unreliable when predictor variables are…

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

Isotherm investigation for the sorption of fluoride onto Bio-F: comparison of linear and non-linear regression method

NASA Astrophysics Data System (ADS)

Yadav, Manish; Singh, Nitin Kumar

2017-12-01

A comparison of the linear and non-linear regression method in selecting the optimum isotherm among three most commonly used adsorption isotherms (Langmuir, Freundlich, and Redlich-Peterson) was made to the experimental data of fluoride (F) sorption onto Bio-F at a solution temperature of 30 ± 1 °C. The coefficient of correlation (r2) was used to select the best theoretical isotherm among the investigated ones. A total of four Langmuir linear equations were discussed and out of which linear form of most popular Langmuir-1 and Langmuir-2 showed the higher coefficient of determination (0.976 and 0.989) as compared to other Langmuir linear equations. Freundlich and Redlich-Peterson isotherms showed a better fit to the experimental data in linear least-square method, while in non-linear method Redlich-Peterson isotherm equations showed the best fit to the tested data set. The present study showed that the non-linear method could be a better way to obtain the isotherm parameters and represent the most suitable isotherm. Redlich-Peterson isotherm was found to be the best representative (r2 = 0.999) for this sorption system. It is also observed that the values of β are not close to unity, which means the isotherms are approaching the Freundlich but not the Langmuir isotherm.

The Bayesian group lasso for confounded spatial data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

2017-01-01

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Measurement of effective air diffusion coefficients for trichloroethene in undisturbed soil cores.

PubMed

Bartelt-Hunt, Shannon L; Smith, James A

2002-06-01

In this study, we measure effective diffusion coefficients for trichloroethene in undisturbed soil samples taken from Picatinny Arsenal, New Jersey. The measured effective diffusion coefficients ranged from 0.0053 to 0.0609 cm2/s over a range of air-filled porosity of 0.23-0.49. The experimental data were compared to several previously published relations that predict diffusion coefficients as a function of air-filled porosity and porosity. A multiple linear regression analysis was developed to determine if a modification of the exponents in Millington's [Science 130 (1959) 100] relation would better fit the experimental data. The literature relations appeared to generally underpredict the effective diffusion coefficient for the soil cores studied in this work. Inclusion of a particle-size distribution parameter, d10, did not significantly improve the fit of the linear regression equation. The effective diffusion coefficient and porosity data were used to recalculate estimates of diffusive flux through the subsurface made in a previous study performed at the field site. It was determined that the method of calculation used in the previous study resulted in an underprediction of diffusive flux from the subsurface. We conclude that although Millington's [Science 130 (1959) 100] relation works well to predict effective diffusion coefficients in homogeneous soils with relatively uniform particle-size distributions, it may be inaccurate for many natural soils with heterogeneous structure and/or non-uniform particle-size distributions.

Linearity and sex-specificity of impact force prediction during a fall onto the outstretched hand using a single-damper-model.

PubMed

Kawalilak, C E; Lanovaz, J L; Johnston, J D; Kontulainen, S A

2014-09-01

To assess the linearity and sex-specificity of damping coefficients used in a single-damper-model (SDM) when predicting impact forces during the worst-case falling scenario from fall heights up to 25 cm. Using 3-dimensional motion tracking and an integrated force plate, impact forces and impact velocities were assessed from 10 young adults (5 males; 5 females), falling from planted knees onto outstretched arms, from a random order of drop heights: 3, 5, 7, 10, 15, 20, and 25 cm. We assessed the linearity and sex-specificity between impact forces and impact velocities across all fall heights using analysis of variance linearity test and linear regression, respectively. Significance was accepted at P<0.05. Association between impact forces and impact velocities up to 25 cm was linear (P=0.02). Damping coefficients appeared sex-specific (males: 627 Ns/m, R(2)=0.70; females: 421 Ns/m; R(2)=0.81; sex combined: 532 Ns/m, R(2)=0.61). A linear damping coefficient used in the SDM proved valid for predicting impact forces from fall heights up to 25 cm. RESULTS suggested the use of sex-specific damping coefficients when estimating impact force using the SDM and calculating the factor-of-risk for wrist fractures.

New method for calculating a mathematical expression for streamflow recession

USGS Publications Warehouse

Rutledge, Albert T.

1991-01-01

An empirical method has been devised to calculate the master recession curve, which is a mathematical expression for streamflow recession during times of negligible direct runoff. The method is based on the assumption that the storage-delay factor, which is the time per log cycle of streamflow recession, varies linearly with the logarithm of streamflow. The resulting master recession curve can be nonlinear. The method can be executed by a computer program that reads a data file of daily mean streamflow, then allows the user to select several near-linear segments of streamflow recession. The storage-delay factor for each segment is one of the coefficients of the equation that results from linear least-squares regression. Using results for each recession segment, a mathematical expression of the storage-delay factor as a function of the log of streamflow is determined by linear least-squares regression. The master recession curve, which is a second-order polynomial expression for time as a function of log of streamflow, is then derived using the coefficients of this function.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Deriving Hounsfield units using grey levels in cone beam computed tomography

PubMed Central

Mah, P; Reeves, T E; McDavid, W D

2010-01-01

Objectives An in vitro study was performed to investigate the relationship between grey levels in dental cone beam CT (CBCT) and Hounsfield units (HU) in CBCT scanners. Methods A phantom containing 8 different materials of known composition and density was imaged with 11 different dental CBCT scanners and 2 medical CT scanners. The phantom was scanned under three conditions: phantom alone and phantom in a small and large water container. The reconstructed data were exported as Digital Imaging and Communications in Medicine (DICOM) and analysed with On Demand 3D® by Cybermed, Seoul, Korea. The relationship between grey levels and linear attenuation coefficients was investigated. Results It was demonstrated that a linear relationship between the grey levels and the attenuation coefficients of each of the materials exists at some “effective” energy. From the linear regression equation of the reference materials, attenuation coefficients were obtained for each of the materials and CT numbers in HU were derived using the standard equation. Conclusions HU can be derived from the grey levels in dental CBCT scanners using linear attenuation coefficients as an intermediate step. PMID:20729181

Advanced Statistical Analyses to Reduce Inconsistency of Bond Strength Data.

PubMed

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.

Meta-analytical synthesis of regression coefficients under different categorization scheme of continuous covariates.

PubMed

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.

Modeling maximum daily temperature using a varying coefficient regression model

Treesearch

Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith

2014-01-01

Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...

42 CFR 433.68 - Permissible health care-related taxes.

Code of Federal Regulations, 2011 CFR

2011-10-01

...-related tax is imposed by a unit of local government, the tax must extend to all items or services or... least squares, the slope (designated as (B) (that is. the value of the x coefficient) of two linear... linear regression, as described in paragraph (e)(2)(i) of this section, for the State's tax program, if...

42 CFR 433.68 - Permissible health care-related taxes.

Code of Federal Regulations, 2013 CFR

2013-10-01

...-related tax is imposed by a unit of local government, the tax must extend to all items or services or... least squares, the slope (designated as (B) (that is. the value of the x coefficient) of two linear... linear regression, as described in paragraph (e)(2)(i) of this section, for the State's tax program, if...

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing

PubMed Central

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

A Weighted Least Squares Approach To Robustify Least Squares Estimates.

ERIC Educational Resources Information Center

Lin, Chowhong; Davenport, Ernest C., Jr.

This study developed a robust linear regression technique based on the idea of weighted least squares. In this technique, a subsample of the full data of interest is drawn, based on a measure of distance, and an initial set of regression coefficients is calculated. The rest of the data points are then taken into the subsample, one after another,…

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

Microbial Transformation of Esters of Chlorinated Carboxylic Acids

PubMed Central

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

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

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.

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing.

PubMed

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © 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.

Calibration Adjustment of the Mid-infrared Analyzer for an Accurate Determination of the Macronutrient Composition of Human Milk.

PubMed

Billard, Hélène; Simon, Laure; Desnots, Emmanuelle; Sochard, Agnès; Boscher, Cécile; Riaublanc, Alain; Alexandre-Gouabau, Marie-Cécile; Boquien, Clair-Yves

2016-08-01

Human milk composition analysis seems essential to adapt human milk fortification for preterm neonates. The Miris human milk analyzer (HMA), based on mid-infrared methodology, is convenient for a unique determination of macronutrients. However, HMA measurements are not totally comparable with reference methods (RMs). The primary aim of this study was to compare HMA results with results from biochemical RMs for a large range of protein, fat, and carbohydrate contents and to establish a calibration adjustment. Human milk was fractionated in protein, fat, and skim milk by covering large ranges of protein (0-3 g/100 mL), fat (0-8 g/100 mL), and carbohydrate (5-8 g/100 mL). For each macronutrient, a calibration curve was plotted by linear regression using measurements obtained using HMA and RMs. For fat, 53 measurements were performed, and the linear regression equation was HMA = 0.79RM + 0.28 (R(2) = 0.92). For true protein (29 measurements), the linear regression equation was HMA = 0.9RM + 0.23 (R(2) = 0.98). For carbohydrate (15 measurements), the linear regression equation was HMA = 0.59RM + 1.86 (R(2) = 0.95). A homogenization step with a disruptor coupled to a sonication step was necessary to obtain better accuracy of the measurements. Good repeatability (coefficient of variation < 7%) and reproducibility (coefficient of variation < 17%) were obtained after calibration adjustment. New calibration curves were developed for the Miris HMA, allowing accurate measurements in large ranges of macronutrient content. This is necessary for reliable use of this device in individualizing nutrition for preterm newborns. © The Author(s) 2015.

The Use of Shrinkage Techniques in the Estimation of Attrition Rates for Large Scale Manpower Models

DTIC Science & Technology

1988-07-27

auto regressive model combined with a linear program that solves for the coefficients using MAD. But this success has diminished with time (Rowe...8217Harrison-Stevens Forcasting and the Multiprocess Dy- namic Linear Model ", The American Statistician, v.40, pp. 12 9 - 1 3 5 . 1986. 8. Box, G. E. P. and...1950. 40. McCullagh, P. and Nelder, J., Generalized Linear Models , Chapman and Hall. 1983. 41. McKenzie, E. General Exponential Smoothing and the

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

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.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

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.

Correlation between lung to thorax transverse area ratio and observed/expected lung area to head circumference ratio in fetuses with left-sided diaphragmatic hernia.

PubMed

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.

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...

Evaluating the Applicability of Phi Coefficient in Indicating Habitat Preferences of Forest Soil Fauna Based on a Single Field Study in Subtropical China.

PubMed

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.

Evaluating the Applicability of Phi Coefficient in Indicating Habitat Preferences of Forest Soil Fauna Based on a Single Field Study in Subtropical China

PubMed Central

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

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water.

PubMed

Lamm, Steven H; Ferdosi, Hamid; Dissen, Elisabeth K; Li, Ji; Ahn, Jaeil

2015-12-07

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1-1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100-150 µg/L arsenic.

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water

PubMed Central

Lamm, Steven H.; Ferdosi, Hamid; Dissen, Elisabeth K.; Li, Ji; Ahn, Jaeil

2015-01-01

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1–1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100–150 µg/L arsenic. PMID:26690190

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

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.

Structured penalties for functional linear models-partially empirical eigenvectors for regression.

PubMed

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.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

TI-59 Programs for Multiple Regression.

DTIC Science & Technology

1980-05-01

general linear hypothesis model of full rank [ Graybill , 19611 can be written as Y = x 8 + C , s-N(O,o 2I) nxl nxk kxl nxl where Y is the vector of n...a "reduced model " solution, and confidence intervals for linear functions of the coefficients can be obtained using (x’x) and a2, based on the t...O107)l UA.LLL. Library ModuIe NASTER -Puter 0NTINA Cards 1 PROGRAM DESCRIPTION (s s 2 ror the general linear hypothesis model Y - XO + C’ calculates

Adjusted variable plots for Cox's proportional hazards regression model.

PubMed

Hall, C B; Zeger, S L; Bandeen-Roche, K J

1996-01-01

Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.

[Stature estimation for Sichuan Han nationality female based on X-ray technology with measurement of lumbar vertebrae].

PubMed

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.

Differential impact of anxiety symptoms and anxiety disorders on treatment outcome for psychotic depression in the STOP-PD study

PubMed Central

Davies, Simon J.C.; Mulsant, Benoit H.; Flint, Alastair J.; Rothschild, Anthony J.; Whyte, Ellen M.; Meyers, Barnett S.

2014-01-01

Background There are conflicting results on the impact of anxiety on depression outcomes. The impact of anxiety has not been studied in major depression with psychotic features (“psychotic depression”). Aims We assessed the impact of specific anxiety symptoms and disorders on the outcomes of psychotic depression. Methods We analyzed data from the Study of Pharmacotherapy for Psychotic Depression that randomized 259 younger and older participants to either olanzapine plus placebo or olanzapine plus sertraline. We assessed the impact of specific anxiety symptoms from the Brief Psychiatric Rating Scale (“tension”, “anxiety” and “somatic concerns” and a composite anxiety score) and diagnoses (panic disorder and GAD) on psychotic depression outcomes using linear or logistic regression. Age, gender, education and benzodiazepine use (at baseline and end) were included as covariates. Results Anxiety symptoms at baseline and anxiety disorder diagnoses differentially impacted outcomes. On adjusted linear regression there was an association between improvement in depressive symptoms and both baseline “tension” (coefficient = 0.784; 95% CI: 0.169–1.400; p = 0.013) and the composite anxiety score (regression coefficient = 0.348; 95% CI: 0.064–0.632; p = 0.017). There was an interaction between “tension” and treatment group, with better responses in those randomized to combination treatment if they had high baseline anxiety scores (coefficient = 1.309; 95% CI: 0.105–2.514; p = 0.033). In contrast, panic disorder was associated with worse clinical outcomes (coefficient = −3.858; 95% CI: –7.281 to −0.434; p = 0.027) regardless of treatment. Conclusions Our results suggest that analysis of the impact of anxiety on depression outcome needs to differentiate psychic and somatic symptoms. PMID:24656524

Comparison of Regression Methods to Compute Atmospheric Pressure and Earth Tidal Coefficients in Water Level Associated with Wenchuan Earthquake of 12 May 2008

NASA Astrophysics Data System (ADS)

He, Anhua; Singh, Ramesh P.; Sun, Zhaohua; Ye, Qing; Zhao, Gang

2016-07-01

The earth tide, atmospheric pressure, precipitation and earthquake fluctuations, especially earthquake greatly impacts water well levels, thus anomalous co-seismic changes in ground water levels have been observed. In this paper, we have used four different models, simple linear regression (SLR), multiple linear regression (MLR), principal component analysis (PCA) and partial least squares (PLS) to compute the atmospheric pressure and earth tidal effects on water level. Furthermore, we have used the Akaike information criterion (AIC) to study the performance of various models. Based on the lowest AIC and sum of squares for error values, the best estimate of the effects of atmospheric pressure and earth tide on water level is found using the MLR model. However, MLR model does not provide multicollinearity between inputs, as a result the atmospheric pressure and earth tidal response coefficients fail to reflect the mechanisms associated with the groundwater level fluctuations. On the premise of solving serious multicollinearity of inputs, PLS model shows the minimum AIC value. The atmospheric pressure and earth tidal response coefficients show close response with the observation using PLS model. The atmospheric pressure and the earth tidal response coefficients are found to be sensitive to the stress-strain state using the observed data for the period 1 April-8 June 2008 of Chuan 03# well. The transient enhancement of porosity of rock mass around Chuan 03# well associated with the Wenchuan earthquake (Mw = 7.9 of 12 May 2008) that has taken its original pre-seismic level after 13 days indicates that the co-seismic sharp rise of water well could be induced by static stress change, rather than development of new fractures.

Comparison of linear and non-linear models for the adsorption of fluoride onto geo-material: limonite.

PubMed

Sahin, Rubina; Tapadia, Kavita

2015-01-01

The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG < 0) and endothermic (ΔH > 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area.

[Correlation between gaseous exchange rate, body temperature, and mitochondrial protein content in the liver of mice].

PubMed

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.

REGRES: A FORTRAN-77 program to calculate nonparametric and ``structural'' parametric solutions to bivariate regression equations

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.

Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes basin. [Saginaw Bay, Michigan and Wisconsin

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).

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.

Approximate Probabilistic Methods for Survivability/Vulnerability Analysis of Strategic Structures.

DTIC Science & Technology

1978-07-15

weapon yield, in kilotons; K = energy coupling factor; C = coefficient determined from linear regression; a, b = exponents determined from linear...hn(l + .582 00 = 0.54 In the case of the applied pressure, according to Perret and Bass (1975), the variabilities in the exponents a and b of Eq. 32...ATTN: WESSF, L. Ingram ATTN: ATC-T ATTN: Library ATTN: F. Brown BMD Systems Command ATTN: J. Strange Deoartment of the Army ATTN: BMDSC-H, N. Hurst

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress.

PubMed

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.

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Echocardiographic Linear Dimensions for Assessment of Right Ventricular Chamber Volume as Demonstrated by Cardiac Magnetic Resonance.

PubMed

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.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

NASA Astrophysics Data System (ADS)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg/day) in Jenderan catchment area.

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions.

PubMed

Fischer, A; Friggens, N C; Berry, D P; Faverdin, P

2018-07-01

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

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)

The Evaluation on the Cadmium Net Concentration for Soil Ecosystems.

PubMed

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.

The Evaluation on the Cadmium Net Concentration for Soil Ecosystems

PubMed Central

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

Determining the response of sea level to atmospheric pressure forcing using TOPEX/POSEIDON data

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Pihos, Greg

1994-01-01

The static response of sea level to the forcing of atmospheric pressure, the so-called inverted barometer (IB) effect, is investigated using TOPEX/POSEIDON data. This response, characterized by the rise and fall of sea level to compensate for the change of atmospheric pressure at a rate of -1 cm/mbar, is not associated with any ocean currents and hence is normally treated as an error to be removed from sea level observation. Linear regression and spectral transfer function analyses are applied to sea level and pressure to examine the validity of the IB effect. In regions outside the tropics, the regression coefficient is found to be consistently close to the theoretical value except for the regions of western boundary currents, where the mesoscale variability interferes with the IB effect. The spectral transfer function shows near IB response at periods of 30 degrees is -0.84 +/- 0.29 cm/mbar (1 standard deviation). The deviation from = 1 cm /mbar is shown to be caused primarily by the effect of wind forcing on sea level, based on multivariate linear regression model involving both pressure and wind forcing. The regression coefficient for pressure resulting from the multivariate analysis is -0.96 +/- 0.32 cm/mbar. In the tropics the multivariate analysis fails because sea level in the tropics is primarily responding to remote wind forcing. However, after removing from the data the wind-forced sea level estimated by a dynamic model of the tropical Pacific, the pressure regression coefficient improves from -1.22 +/- 0.69 cm/mbar to -0.99 +/- 0.46 cm/mbar, clearly revealing an IB response. The result of the study suggests that with a proper removal of the effect of wind forcing the IB effect is valid in most of the open ocean at periods longer than 20 days and spatial scales larger than 500 km.

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

Clinical Relevance of Alternative Endpoints in Colorectal Cancer First-Line Therapy With Bevacizumab: A Retrospective Study.

PubMed

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.

Comparison of Linear and Non-linear Regression Analysis to Determine Pulmonary Pressure in Hyperthyroidism.

PubMed

Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan

2017-01-01

This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second degree where the parabola is its graphical representation.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Global estimation of long-term persistence in annual river runoff

NASA Astrophysics Data System (ADS)

Markonis, Y.; Moustakis, Y.; Nasika, C.; Sychova, P.; Dimitriadis, P.; Hanel, M.; Máca, P.; Papalexiou, S. M.

2018-03-01

Long-term persistence (LTP) of annual river runoff is a topic of ongoing hydrological research, due to its implications to water resources management. Here, we estimate its strength, measured by the Hurst coefficient H, in 696 annual, globally distributed, streamflow records with at least 80 years of data. We use three estimation methods (maximum likelihood estimator, Whittle estimator and least squares variance) resulting in similar mean values of H close to 0.65. Subsequently, we explore potential factors influencing H by two linear (Spearman's rank correlation, multiple linear regression) and two non-linear (self-organizing maps, random forests) techniques. Catchment area is found to be crucial for medium to larger watersheds, while climatic controls, such as aridity index, have higher impact to smaller ones. Our findings indicate that long-term persistence is weaker than found in other studies, suggesting that enhanced LTP is encountered in large-catchment rivers, were the effect of spatial aggregation is more intense. However, we also show that the estimated values of H can be reproduced by a short-term persistence stochastic model such as an auto-regressive AR(1) process. A direct consequence is that some of the most common methods for the estimation of H coefficient, might not be suitable for discriminating short- and long-term persistence even in long observational records.

Prediction of cotton resistance to Helicoverpa armigera based on the percent (+)-gossypol in mature seed

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...

Age and mortality after injury: is the association linear?

PubMed

Friese, R S; Wynne, J; Joseph, B; Hashmi, A; Diven, C; Pandit, V; O'Keeffe, T; Zangbar, B; Kulvatunyou, N; Rhee, P

2014-10-01

Multiple studies have demonstrated a linear association between advancing age and mortality after injury. An inflection point, or an age at which outcomes begin to differ, has not been previously described. We hypothesized that the relationship between age and mortality after injury is non-linear and an inflection point exists. We performed a retrospective cohort analysis at our urban level I center from 2007 through 2009. All patients aged 65 years and older with the admission diagnosis of injury were included. Non-parametric logistic regression was used to identify the functional form between mortality and age. Multivariate logistic regression was utilized to explore the association between age and mortality. Age 65 years was used as the reference. Significance was defined as p < 0.05. A total of 1,107 patients were included in the analysis. One-third required intensive care unit (ICU) admission and 48 % had traumatic brain injury. 229 patients (20.6 %) were 84 years of age or older. The overall mortality was 7.2 %. Our model indicates that mortality is a quadratic function of age. After controlling for confounders, age is associated with mortality with a regression coefficient of 1.08 for the linear term (p = 0.02) and a regression coefficient of -0.006 for the quadratic term (p = 0.03). The model identified 84.4 years of age as the inflection point at which mortality rates begin to decline. The risk of death after injury varies linearly with age until 84 years. After 84 years of age, the mortality rates decline. These findings may reflect the varying severity of comorbidities and differences in baseline functional status in elderly trauma patients. Specifically, a proportion of our injured patient population less than 84 years old may be more frail, contributing to increased mortality after trauma, whereas a larger proportion of our injured patients over 84 years old, by virtue of reaching this advanced age, may, in fact, be less frail, contributing to less risk of death.

An evaluation of bias in propensity score-adjusted non-linear regression models.

PubMed

Wan, Fei; Mitra, Nandita

2018-03-01

Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.

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.

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

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.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Time Series Analysis and Forecasting of Wastewater Inflow into Bandar Tun Razak Sewage Treatment Plant in Selangor, Malaysia

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.

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

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.

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.

[Health expenditures, income inequality, and the marginalization index in Mexico's health system].

PubMed

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.

Factors associated to clinical learning in nursing students in primary health care: an analytical cross-sectional study

PubMed Central

Serrano-Gallardo, Pilar; Martínez-Marcos, Mercedes; Espejo-Matorrales, Flora; Arakawa, Tiemi; Magnabosco, Gabriela Tavares; Pinto, Ione Carvalho

2016-01-01

ABSTRACT Objective: to identify the students' perception about the quality of clinical placements and asses the influence of the different tutoring processes in clinical learning. Methods: analytical cross-sectional study on second and third year nursing students (n=122) about clinical learning in primary health care. The Clinical Placement Evaluation Tool and a synthetic index of attitudes and skills were computed to give scores to the clinical learning (scale 0-10). Univariate, bivariate and multivariate (multiple linear regression) analyses were performed. Results: the response rate was 91.8%. The most commonly identified tutoring process was "preceptor-professor" (45.2%). The clinical placement was assessed as "optimal" by 55.1%, relationship with team-preceptor was considered good by 80.4% of the cases and the average grade for clinical learning was 7.89. The multiple linear regression model with more explanatory capacity included the variables "Academic year" (beta coefficient = 1.042 for third-year students), "Primary Health Care Area (PHC)" (beta coefficient = 0.308 for Area B) and "Clinical placement perception" (beta coefficient = - 0.204 for a suboptimal perception). Conclusions: timeframe within the academic program, location and clinical placement perception were associated with students' clinical learning. Students' perceptions of setting quality were positive and a good team-preceptor relationship is a matter of relevance. PMID:27627124

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

Estimating the total energy demand for supra-maximal exercise using the VO2-power regression from an incremental exercise test.

PubMed

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.

Poor methodological quality and reporting standards of systematic reviews in burn care management.

PubMed

Wasiak, Jason; Tyack, Zephanie; Ware, Robert; Goodwin, Nicholas; Faggion, Clovis M

2017-10-01

The methodological and reporting quality of burn-specific systematic reviews has not been established. The aim of this study was to evaluate the methodological quality of systematic reviews in burn care management. Computerised searches were performed in Ovid MEDLINE, Ovid EMBASE and The Cochrane Library through to February 2016 for systematic reviews relevant to burn care using medical subject and free-text terms such as 'burn', 'systematic review' or 'meta-analysis'. Additional studies were identified by hand-searching five discipline-specific journals. Two authors independently screened papers, extracted and evaluated methodological quality using the 11-item A Measurement Tool to Assess Systematic Reviews (AMSTAR) tool and reporting quality using the 27-item Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Characteristics of systematic reviews associated with methodological and reporting quality were identified. Descriptive statistics and linear regression identified features associated with improved methodological quality. A total of 60 systematic reviews met the inclusion criteria. Six of the 11 AMSTAR items reporting on 'a priori' design, duplicate study selection, grey literature, included/excluded studies, publication bias and conflict of interest were reported in less than 50% of the systematic reviews. Of the 27 items listed for PRISMA, 13 items reporting on introduction, methods, results and the discussion were addressed in less than 50% of systematic reviews. Multivariable analyses showed that systematic reviews associated with higher methodological or reporting quality incorporated a meta-analysis (AMSTAR regression coefficient 2.1; 95% CI: 1.1, 3.1; PRISMA regression coefficient 6·3; 95% CI: 3·8, 8·7) were published in the Cochrane library (AMSTAR regression coefficient 2·9; 95% CI: 1·6, 4·2; PRISMA regression coefficient 6·1; 95% CI: 3·1, 9·2) and included a randomised control trial (AMSTAR regression coefficient 1·4; 95%CI: 0·4, 2·4; PRISMA regression coefficient 3·4; 95% CI: 0·9, 5·8). The methodological and reporting quality of systematic reviews in burn care requires further improvement with stricter adherence by authors to the PRISMA checklist and AMSTAR tool. © 2016 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Objectively measured sedentary time and academic achievement in schoolchildren.

PubMed

Lopes, Luís; Santos, Rute; Mota, Jorge; Pereira, Beatriz; Lopes, Vítor

2017-03-01

This study aimed to evaluate the relationship between objectively measured total sedentary time and academic achievement (AA) in Portuguese children. The sample comprised of 213 children (51.6% girls) aged 9.46 ± 0.43 years, from the north of Portugal. Sedentary time was measured with accelerometry, and AA was assessed using the Portuguese Language and Mathematics National Exams results. Multilevel linear regression models were fitted to assess regression coefficients predicting AA. The results showed that objectively measured total sedentary time was not associated with AA, after adjusting for potential confounders.

Background stratified Poisson regression analysis of cohort data.

PubMed

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Effects of integration time on in-water radiometric profiles.

PubMed

D'Alimonte, Davide; Zibordi, Giuseppe; Kajiyama, Tamito

2018-03-05

This work investigates the effects of integration time on in-water downward irradiance E d , upward irradiance E u and upwelling radiance L u profile data acquired with free-fall hyperspectral systems. Analyzed quantities are the subsurface value and the diffuse attenuation coefficient derived by applying linear and non-linear regression schemes. Case studies include oligotrophic waters (Case-1), as well as waters dominated by Colored Dissolved Organic Matter (CDOM) and Non-Algal Particles (NAP). Assuming a 24-bit digitization, measurements resulting from the accumulation of photons over integration times varying between 8 and 2048ms are evaluated at depths corresponding to: 1) the beginning of each integration interval (Fst); 2) the end of each integration interval (Lst); 3) the averages of Fst and Lst values (Avg); and finally 4) the values weighted accounting for the diffuse attenuation coefficient of water (Wgt). Statistical figures show that the effects of integration time can bias results well above 5% as a function of the depth definition. Results indicate the validity of the Wgt depth definition and the fair applicability of the Avg one. Instead, both the Fst and Lst depths should not be adopted since they may introduce pronounced biases in E u and L u regression products for highly absorbing waters. Finally, the study reconfirms the relevance of combining multiple radiometric casts into a single profile to increase precision of regression products.

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.

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).

A statistical methodology for estimating transport parameters: Theory and applications to one-dimensional advectivec-dispersive systems

USGS Publications Warehouse

Wagner, Brian J.; Gorelick, Steven M.

1986-01-01

A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference contaminant transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2–3 times more reliable than estimates based on temporal data for all parameters except velocity. Comparison of estimated linear and nonlinear confidence intervals based upon Monte Carlo analysis showed that the linear approximation is poor for dispersion coefficient and zero-order production coefficient when data are collected over time. In addition, examples demonstrate transport parameter estimation for two real one-dimensional systems. First, the longitudinal dispersivity and effective porosity of an unsaturated soil are estimated using laboratory column data. We compare the reliability of estimates based upon data from individual laboratory experiments versus estimates based upon pooled data from several experiments. Second, the simulation nonlinear regression procedure is extended to include an additional governing equation that describes delayed storage during contaminant transport. The model is applied to analyze the trends, variability, and interrelationship of parameters in a mourtain stream in northern California.

Distributed Monitoring of the R(sup 2) Statistic for Linear Regression

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Giannella, Chris R.

2011-01-01

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI.

PubMed

Wang, S; Martinez-Lage, M; Sakai, Y; Chawla, S; Kim, S G; Alonso-Basanta, M; Lustig, R A; Brem, S; Mohan, S; Wolf, R L; Desai, A; Poptani, H

2016-01-01

Early assessment of treatment response is critical in patients with glioblastomas. A combination of DTI and DSC perfusion imaging parameters was evaluated to distinguish glioblastomas with true progression from mixed response and pseudoprogression. Forty-one patients with glioblastomas exhibiting enhancing lesions within 6 months after completion of chemoradiation therapy were retrospectively studied. All patients underwent surgery after MR imaging and were histologically classified as having true progression (>75% tumor), mixed response (25%-75% tumor), or pseudoprogression (<25% tumor). Mean diffusivity, fractional anisotropy, linear anisotropy coefficient, planar anisotropy coefficient, spheric anisotropy coefficient, and maximum relative cerebral blood volume values were measured from the enhancing tissue. A multivariate logistic regression analysis was used to determine the best model for classification of true progression from mixed response or pseudoprogression. Significantly elevated maximum relative cerebral blood volume, fractional anisotropy, linear anisotropy coefficient, and planar anisotropy coefficient and decreased spheric anisotropy coefficient were observed in true progression compared with pseudoprogression (P < .05). There were also significant differences in maximum relative cerebral blood volume, fractional anisotropy, planar anisotropy coefficient, and spheric anisotropy coefficient measurements between mixed response and true progression groups. The best model to distinguish true progression from non-true progression (pseudoprogression and mixed) consisted of fractional anisotropy, linear anisotropy coefficient, and maximum relative cerebral blood volume, resulting in an area under the curve of 0.905. This model also differentiated true progression from mixed response with an area under the curve of 0.901. A combination of fractional anisotropy and maximum relative cerebral blood volume differentiated pseudoprogression from nonpseudoprogression (true progression and mixed) with an area under the curve of 0.807. DTI and DSC perfusion imaging can improve accuracy in assessing treatment response and may aid in individualized treatment of patients with glioblastomas. © 2016 by American Journal of Neuroradiology.

Functional Linear Model with Zero-value Coefficient Function at Sub-regions.

PubMed

Zhou, Jianhui; Wang, Nae-Yuh; Wang, Naisyin

2013-01-01

We propose a shrinkage method to estimate the coefficient function in a functional linear regression model when the value of the coefficient function is zero within certain sub-regions. Besides identifying the null region in which the coefficient function is zero, we also aim to perform estimation and inferences for the nonparametrically estimated coefficient function without over-shrinking the values. Our proposal consists of two stages. In stage one, the Dantzig selector is employed to provide initial location of the null region. In stage two, we propose a group SCAD approach to refine the estimated location of the null region and to provide the estimation and inference procedures for the coefficient function. Our considerations have certain advantages in this functional setup. One goal is to reduce the number of parameters employed in the model. With a one-stage procedure, it is needed to use a large number of knots in order to precisely identify the zero-coefficient region; however, the variation and estimation difficulties increase with the number of parameters. Owing to the additional refinement stage, we avoid this necessity and our estimator achieves superior numerical performance in practice. We show that our estimator enjoys the Oracle property; it identifies the null region with probability tending to 1, and it achieves the same asymptotic normality for the estimated coefficient function on the non-null region as the functional linear model estimator when the non-null region is known. Numerically, our refined estimator overcomes the shortcomings of the initial Dantzig estimator which tends to under-estimate the absolute scale of non-zero coefficients. The performance of the proposed method is illustrated in simulation studies. We apply the method in an analysis of data collected by the Johns Hopkins Precursors Study, where the primary interests are in estimating the strength of association between body mass index in midlife and the quality of life in physical functioning at old age, and in identifying the effective age ranges where such associations exist.

Statistical Package User’s Guide.

DTIC Science & Technology

1980-08-01

261 C. STACH Nonparametric Descriptive Statistics ... ......... ... 265 D. CHIRA Coefficient of Concordance...135 I.- -a - - W 7- Test Data: This program was tested using data from John Neter and William Wasserman, Applied Linear Statistical Models: Regression...length of data file e. new fileý name (not same as raw data file) 5. Printout as optioned for only. Comments: Ranked data are used for program CHIRA

[Research on the method of interference correction for nondispersive infrared multi-component gas analysis].

PubMed

Sun, You-Wen; Liu, Wen-Qing; Wang, Shi-Mei; Huang, Shu-Hua; Yu, Xiao-Man

2011-10-01

A method of interference correction for nondispersive infrared multi-component gas analysis was described. According to the successive integral gas absorption models and methods, the influence of temperature and air pressure on the integral line strengths and linetype was considered, and based on Lorentz detuning linetypes, the absorption cross sections and response coefficients of H2O, CO2, CO, and NO on each filter channel were obtained. The four dimension linear regression equations for interference correction were established by response coefficients, the absorption cross interference was corrected by solving the multi-dimensional linear regression equations, and after interference correction, the pure absorbance signal on each filter channel was only controlled by the corresponding target gas concentration. When the sample cell was filled with gas mixture with a certain concentration proportion of CO, NO and CO2, the pure absorbance after interference correction was used for concentration inversion, the inversion concentration error for CO2 is 2.0%, the inversion concentration error for CO is 1.6%, and the inversion concentration error for NO is 1.7%. Both the theory and experiment prove that the interference correction method proposed for NDIR multi-component gas analysis is feasible.

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

PubMed Central

Zhong-xiang, Feng; Shi-sheng, Lu; Wei-hua, Zhang; Nan-nan, Zhang

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. PMID:25610454

Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

PubMed

Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Modeling of adipose/blood partition coefficient for environmental chemicals.

PubMed

Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

2017-12-01

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

Methamphetamine abuse during pregnancy and its health impact on neonates born at Siriraj Hospital, Bangkok, Thailand.

PubMed

Chomchai, Chulathida; Na Manorom, Natawadee; Watanarungsan, Pornchai; Yossuck, Panitan; Chomchai, Summon

2004-03-01

To ascertain the impact of intrauterine methamphetamine exposure on the overall health of newborn infants at Siriraj Hospital, Bangkok, Thailand, birth records of somatic growth parameters and neonatal withdrawal symptoms of 47 infants born to methamphetamine-abusing women during January 2001 to December 2001 were compared to 49 newborns whose mothers did not use methamphetamines during pregnancy. The data on somatic growth was analyzed using linear regression and multiple linear regression. The association between methamphetamine use and withdrawal symptoms was analyzed using the chi-square. Home visitation and maternal interview records were reviewed in order to assess for child-rearing attitude, and psychosocial parameters. Infants of methamphetamine-abusing mothers were found to have a significantly smaller gestational age-adjusted head circumference (regression coefficient = -1.458, p < 0.001) and birth weight (regression coefficient = -217.9, p < or = 0.001) measurements. Methamphetamine exposure was also associated with symptoms of agitation (5/47), vomiting (11/47) and tachypnea (12/47) when compared to the non-exposed group (p < 0r =0.001). Maternal interviews were conducted in 23 cases and showed that: 96% of the cases had inadequate prenatal care (<5 visits), 48% had at least one parent involved in prostitution, 39% of the mothers were unwilling to take their children home, and government or non-government support were provided in only 30% of the cases. In-utero methamphetamine exposure has been shown to adversely effect somatic growth of newborns and cause a variety of withdrawal-like symptoms. These infants are also psychosocially disadvantaged and are at greater risk for abuse and neglect.

A model for predicting sulcus-to-sulcus diameter in posterior chamber phakic intraocular lens candidates: correlation between ocular biometric parameters.

PubMed

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.

Job strain and resting heart rate: a cross-sectional study in a Swedish random working sample.

PubMed

Eriksson, Peter; Schiöler, Linus; Söderberg, Mia; Rosengren, Annika; Torén, Kjell

2016-03-05

Numerous studies have reported an association between stressing work conditions and cardiovascular disease. However, more evidence is needed, and the etiological mechanisms are unknown. Elevated resting heart rate has emerged as a possible risk factor for cardiovascular disease, but little is known about the relation to work-related stress. This study therefore investigated the association between job strain, job control, and job demands and resting heart rate. We conducted a cross-sectional survey of randomly selected men and women in Västra Götalandsregionen, Sweden (West county of Sweden) (n = 1552). Information about job strain, job demands, job control, heart rate and covariates was collected during the period 2001-2004 as part of the INTERGENE/ADONIX research project. Six different linear regression models were used with adjustments for gender, age, BMI, smoking, education, and physical activity in the fully adjusted model. Job strain was operationalized as the log-transformed ratio of job demands over job control in the statistical analyses. No associations were seen between resting heart rate and job demands. Job strain was associated with elevated resting heart rate in the unadjusted model (linear regression coefficient 1.26, 95 % CI 0.14 to 2.38), but not in any of the extended models. Low job control was associated with elevated resting heart rate after adjustments for gender, age, BMI, and smoking (linear regression coefficient -0.18, 95 % CI -0.30 to -0.02). However, there were no significant associations in the fully adjusted model. Low job control and job strain, but not job demands, were associated with elevated resting heart rate. However, the observed associations were modest and may be explained by confounding effects.

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.

Predonation Volume of Future Remnant Cortical Kidney Helps Predict Postdonation Renal Function in Live Kidney Donors.

PubMed

Fananapazir, Ghaneh; Benzl, Robert; Corwin, Michael T; Chen, Ling-Xin; Sageshima, Junichiro; Stewart, Susan L; Troppmann, Christoph

2018-07-01

Purpose To determine whether the predonation computed tomography (CT)-based volume of the future remnant kidney is predictive of postdonation renal function in living kidney donors. Materials and Methods This institutional review board-approved, retrospective, HIPAA-compliant study included 126 live kidney donors who had undergone predonation renal CT between January 2007 and December 2014 as well as 2-year postdonation measurement of estimated glomerular filtration rate (eGFR). The whole kidney volume and cortical volume of the future remnant kidney were measured and standardized for body surface area (BSA). Bivariate linear associations between the ratios of whole kidney volume to BSA and cortical volume to BSA were obtained. A linear regression model for 2-year postdonation eGFR that incorporated donor age, sex, and either whole kidney volume-to-BSA ratio or cortical volume-to-BSA ratio was created, and the coefficient of determination (R 2 ) for the model was calculated. Factors not statistically additive in assessing 2-year eGFR were removed by using backward elimination, and the coefficient of determination for this parsimonious model was calculated. Results Correlation was slightly better for cortical volume-to-BSA ratio than for whole kidney volume-to-BSA ratio (r = 0.48 vs r = 0.44, respectively). The linear regression model incorporating all donor factors had an R 2 of 0.66. The only factors that were significantly additive to the equation were cortical volume-to-BSA ratio and predonation eGFR (P = .01 and P < .01, respectively), and the final parsimonious linear regression model incorporating these two variables explained almost the same amount of variance (R 2 = 0.65) as did the full model. Conclusion The cortical volume of the future remnant kidney helped predict postdonation eGFR at 2 years. The cortical volume-to-BSA ratio should thus be considered for addition as an important variable to living kidney donor evaluation and selection guidelines. © RSNA, 2018.

[Selecting methods of controls concentration for internal quality control and continuity of control chart between different reagent lots for HBsAg qualitative detection].

PubMed

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.

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

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

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

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

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Comparative study of Poincaré plot analysis using short electroencephalogram signals during anaesthesia with spectral edge frequency 95 and bispectral index.

PubMed

Hayashi, K; Yamada, T; Sawa, T

2015-03-01

The return or Poincaré plot is a non-linear analytical approach in a two-dimensional plane, where a timed signal is plotted against itself after a time delay. Its scatter pattern reflects the randomness and variability in the signals. Quantification of a Poincaré plot of the electroencephalogram has potential to determine anaesthesia depth. We quantified the degree of dispersion (i.e. standard deviation, SD) along the diagonal line of the electroencephalogram-Poincaré plot (named as SD1/SD2), and compared SD1/SD2 values with spectral edge frequency 95 (SEF95) and bispectral index values. The regression analysis showed a tight linear regression equation with a coefficient of determination (R(2) ) value of 0.904 (p < 0.0001) between the Poincaré index (SD1/SD2) and SEF95, and a moderate linear regression equation between SD1/SD2 and bispectral index (R(2)  = 0.346, p < 0.0001). Quantification of the Poincaré plot tightly correlates with SEF95, reflecting anaesthesia-dependent changes in electroencephalogram oscillation. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

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.

Large signal-to-noise ratio quantification in MLE for ARARMAX models

NASA Astrophysics Data System (ADS)

Zou, Yiqun; Tang, Xiafei

2014-06-01

It has been shown that closed-loop linear system identification by indirect method can be generally transferred to open-loop ARARMAX (AutoRegressive AutoRegressive Moving Average with eXogenous input) estimation. For such models, the gradient-related optimisation with large enough signal-to-noise ratio (SNR) can avoid the potential local convergence in maximum likelihood estimation. To ease the application of this condition, the threshold SNR needs to be quantified. In this paper, we build the amplitude coefficient which is an equivalence to the SNR and prove the finiteness of the threshold amplitude coefficient within the stability region. The quantification of threshold is achieved by the minimisation of an elaborately designed multi-variable cost function which unifies all the restrictions on the amplitude coefficient. The corresponding algorithm based on two sets of physically realisable system input-output data details the minimisation and also points out how to use the gradient-related method to estimate ARARMAX parameters when local minimum is present as the SNR is small. Then, the algorithm is tested on a theoretical AutoRegressive Moving Average with eXogenous input model for the derivation of the threshold and a gas turbine engine real system for model identification, respectively. Finally, the graphical validation of threshold on a two-dimensional plot is discussed.

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

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.

Structured sparse linear graph embedding.

PubMed

Wang, Haixian

2012-03-01

Subspace learning is a core issue in pattern recognition and machine learning. Linear graph embedding (LGE) is a general framework for subspace learning. In this paper, we propose a structured sparse extension to LGE (SSLGE) by introducing a structured sparsity-inducing norm into LGE. Specifically, SSLGE casts the projection bases learning into a regression-type optimization problem, and then the structured sparsity regularization is applied to the regression coefficients. The regularization selects a subset of features and meanwhile encodes high-order information reflecting a priori structure information of the data. The SSLGE technique provides a unified framework for discovering structured sparse subspace. Computationally, by using a variational equality and the Procrustes transformation, SSLGE is efficiently solved with closed-form updates. Experimental results on face image show the effectiveness of the proposed method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks.

PubMed

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.

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.

Three-parameter modeling of the soil sorption of acetanilide and triazine herbicide derivatives.

PubMed

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.

Regression analysis of sparse asynchronous longitudinal data.

PubMed

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

2015-09-01

We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

Cooperativity between various types of polar solute-solvent interactions in aqueous media.

PubMed

Madeira, Pedro P; Bessa, Ana; Loureiro, Joana A; Álvares-Ribeiro, Luís; Rodrigues, Alírio E; Zaslavsky, Boris Y

2015-08-21

Partition coefficients of seven low molecular weight compounds were measured in multiple aqueous two-phase systems (ATPSs) formed by pairs of different polymers. The ionic composition of each ATPS was varied to include 0.01M sodium phosphate buffer (NaPB), pH 7.4 and 0.1M Na2SO4, 0.15M NaCl, and 0.15M NaClO4 all in 0.01M NaPB, pH 7.4. The differences between the solvent features of the coexisting phases in all the ATPSs were estimated from partitioning of a homologous series of dinitrophenylated-amino acids and by the solvatochromic method. The solute-specific coefficients for the compounds examined were determined by the multiple linear regression analysis using the modified linear solvation energy relationship equation. It is established that the solute specific coefficients characterizing different types of the solute-water interactions (dipole-dipole, dipole-ion, and H-bonding) for a given solute change in the presence of different salt additives in the solute specific manner. It is also found that these characteristics are linearly interrelated. It is suggested that there is a cooperativity between various types of solute-water interactions governed by the solute structure. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Time-resolved perfusion imaging at the angiography suite: preclinical comparison of a new flat-detector application to computed tomography perfusion.

PubMed

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.

QSAR, docking and ADMET studies of artemisinin derivatives for antimalarial activity targeting plasmepsin II, a hemoglobin-degrading enzyme from P. falciparum.

PubMed

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.

Regression equations for disinfection by-products for the Mississippi, Ohio and Missouri rivers

USGS Publications Warehouse

Rathbun, R.E.

1996-01-01

Trihalomethane and nonpurgeable total organic-halide formation potentials were determined for the chlorination of water samples from the Mississippi, Ohio and Missouri Rivers. Samples were collected during the summer and fall of 1991 and the spring of 1992 at twelve locations on the Mississippi from New Orleans to Minneapolis, and on the Ohio and Missouri 1.6 km upstream from their confluences with the Mississippi. Formation potentials were determined as a function of pH, initial free-chlorine concentration, and reaction time. Multiple linear regression analysis of the data indicated that pH, reaction time, and the dissolved organic carbon concentration and/or the ultraviolet absorbance of the water were the most significant variables. The initial free-chlorine concentration had less significance and bromide concentration had little or no significance. Analysis of combinations of the dissolved organic carbon concentration and the ultraviolet absorbance indicated that use of the ultraviolet absorbance alone provided the best prediction of the experimental data. Regression coefficients for the variables were generally comparable to coefficients previously presented in the literature for waters from other parts of the United States.

Relationship between the magnitude of the inbreeding coefficient and milk traits in Holstein and Jersey dairy bull semen used in Brazil.

PubMed

Soares, M P; Gaya, L G; Lorentz, L H; Batistel, F; Rovadoscki, G A; Ticiani, E; Zabot, V; Di Domenico, Q; Madureira, A P; Pértile, S F N

2011-09-06

Artificial insemination has been used to improve production in Brazilian dairy cattle; however, this can lead to problems due to increased inbreeding. To evaluate the effect of the magnitude of inbreeding coefficients on predicted transmitting abilities (PTAs) for milk traits of Holstein and Jersey breeds, data on 392 Holstein and 92 Jersey sires used in Brazil were tabulated. The second-degree polynomial equations and points of maximum or minimal response were estimated to establish the regression equation of the variables as a function of the inbreeding coefficients. The mean inbreeding coefficient of the Holstein bulls was 5.10%; this did not significantly affect the PTA for percent milk fat, protein percentage and protein (P = 0.479, 0.058 and 0.087, respectively). However, the PTAs for milk yield and fat decreased significantly after reaching inbreeding coefficients of 6.43 (P = 0.034) and 5.75 (P = 0.007), respectively. The mean inbreeding coefficient of Jersey bulls was 6.45%; the PTAs for milk yield, fat and protein, in pounds, decreased significantly after reaching inbreeding coefficients of 15.04, 9.83 and 12.82% (P < 0.001, P = 0.002, and P = 0.001, respectively). The linear regression was only significant for fat and protein percentages in the Jersey breed (P = 0.002 and P = 0.005, respectively). The PTAs of Holstein sires were more affected by smaller magnitudes of inbreeding coefficients than those of Jersey sires. It is necessary to monitor the inbreeding coefficients of sires used for artificial insemination in breeding schemes in Brazil, since the low genetic variability of the available sires may lead to reduced production.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Estimation of factors from natural and anthropogenic radioactivity present in the surface soil and comparison with DCF values.

PubMed

Ranade, A K; Pandey, M; Datta, D

2013-01-01

A study was conducted to evaluate the absorbed rate coefficient of (238)U, (232)Th, (40)K and (137)Cs present in soil. A total of 31 soil samples and the corresponding terrestrial dose rates at 1 m from different locations were taken around the Anushaktinagar region, where the litho-logy is dominated by red soil. A linear regression model was developed for the estimation of these factors. The estimated coefficients (nGy h(-1) Bq(-1) kg(-1)) were 0.454, 0.586, 0.035 and 0.392, respectively. The factors calculated were in good agreement with the literature values.

Conference Proceedings on Atmospheric Propagation in the UV, Visible, IR and MM-Wave Region and Related Systems Aspects Held in Copenhagen, Denmark on 9- 13th October 1989

DTIC Science & Technology

1990-03-01

is more likely2 3 . Table 3. Linear Regression Coefficients of Aerosol Concentration and Volumetric Loadings on Wind Speed Size Band Coefficients...vents. S33 UNI L N I m a FE 4 K 26 w " N ~ ~ ~ ~ ~ AN FA CrCS S HI IIq 2-4 C - LA SONDE GRANULOUNTRIQUN. D’autre part, des mesures do granulom ~trie...appel6 NAVY MAR17!- NB’’). Celui-ci rbnulte d’une s~rie de mesures de profils granulom ~tti- ques pris lors de conditions mataorologiques diff6rentes

Drag coefficients for modeling flow through emergent vegetation in the Florida Everglades

USGS Publications Warehouse

Lee, J.K.; Roig, L.C.; Jenter, H.L.; Visser, H.M.

2004-01-01

Hydraulic data collected in a flume fitted with pans of sawgrass were analyzed to determine the vertically averaged drag coefficient as a function of vegetation characteristics. The drag coefficient is required for modeling flow through emergent vegetation at low Reynolds numbers in the Florida Everglades. Parameters of the vegetation, such as the stem population per unit bed area and the average stem/leaf width, were measured for five fixed vegetation layers. The vertically averaged vegetation parameters for each experiment were then computed by weighted average over the submerged portion of the vegetation. Only laminar flow through emergent vegetation was considered, because this is the dominant flow regime of the inland Everglades. A functional form for the vegetation drag coefficient was determined by linear regression of the logarithmic transforms of measured resistance force and Reynolds number. The coefficients of the drag coefficient function were then determined for the Everglades, using extensive flow and vegetation measurements taken in the field. The Everglades data show that the stem spacing and the Reynolds number are important parameters for the determination of vegetation drag coefficient. ?? 2004 Elsevier B.V. All rights reserved.

[Correlation of molecular weight and nanofiltration mass transfer coefficient of phenolic acid composition from Salvia miltiorrhiza].

PubMed

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.

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA.

PubMed

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).

Addressing the identification problem in age-period-cohort analysis: a tutorial on the use of partial least squares and principal components analysis.

PubMed

Tu, Yu-Kang; Krämer, Nicole; Lee, Wen-Chung

2012-07-01

In the analysis of trends in health outcomes, an ongoing issue is how to separate and estimate the effects of age, period, and cohort. As these 3 variables are perfectly collinear by definition, regression coefficients in a general linear model are not unique. In this tutorial, we review why identification is a problem, and how this problem may be tackled using partial least squares and principal components regression analyses. Both methods produce regression coefficients that fulfill the same collinearity constraint as the variables age, period, and cohort. We show that, because the constraint imposed by partial least squares and principal components regression is inherent in the mathematical relation among the 3 variables, this leads to more interpretable results. We use one dataset from a Taiwanese health-screening program to illustrate how to use partial least squares regression to analyze the trends in body heights with 3 continuous variables for age, period, and cohort. We then use another dataset of hepatocellular carcinoma mortality rates for Taiwanese men to illustrate how to use partial least squares regression to analyze tables with aggregated data. We use the second dataset to show the relation between the intrinsic estimator, a recently proposed method for the age-period-cohort analysis, and partial least squares regression. We also show that the inclusion of all indicator variables provides a more consistent approach. R code for our analyses is provided in the eAppendix.

Distinguishing Gasoline Engine Oils of Different Viscosities Using Terahertz Time-Domain Spectroscopy

NASA Astrophysics Data System (ADS)

Adbul-Munaim, Ali Mazin; Reuter, Marco; Koch, Martin; Watson, Dennis G.

2015-07-01

Terahertz-time-domain spectroscopy (THz-TDS) in the range of 0.5-2.0 THz was evaluated for distinguishing among gasoline engine oils of three different grades (SAE 5W-20, 10W-40, and 20W-50) from the same manufacturer. Absorption coefficient showed limited potential and only distinguished ( p < 0.05) the 20W-50 grade from the other two grades in the 1.7-2.0-THz range. Refractive index data demonstrated relatively flat and consistently spaced curves for the three oil grades. ANOVA results confirmed a highly significant difference ( p < 0.0001) in refractive index among each of the three oils across the 0.5-2.0-THz range. Linear regression was applied to refractive index data at 0.25-THz intervals from 0.5 to 2.0 THz to predict kinematic viscosity. All seven linear regression models, intercepts, and refractive index coefficients were highly significant ( p < 0.0001). All models had a similar fit with R 2 ranging from 0.9773 to 0.9827 and RMSE ranging from 6.33 to 7.75. The refractive indices at 1.25 THz produced the best fit. The refractive indices of these oil samples were promising for identification and distinction of oil grades.

Determinants of plasma NT-pro-BNP levels in patients with atrial fibrillation and preserved left ventricular ejection fraction.

PubMed

Letsas, Konstantinos P; Filippatos, Gerasimos S; Pappas, Loukas K; Mihas, Constantinos C; Markou, Virginia; Alexanian, Ioannis P; Efremidis, Michalis; Sideris, Antonios; Maisel, Alan S; Kardaras, Fotios

2009-02-01

The present study aimed to investigate the clinical and echocardiographic determinants of plasma NT-pro-BNP levels in patients with atrial fibrillation (AF) and preserved left ventricular ejection fraction (LVEF). NT-pro-BNP levels were measured in 45 patients with paroxysmal AF, 41 patients with permanent AF and 48 controls. NT-pro-BNP levels were found significantly elevated in patients with paroxysmal (215+/-815 pg/ml) and permanent AF (1,086+/-835 pg/ml) in relation to control population (86.3+/-77.9 pg/ml) (P<0.001). According to the univariate linear regression analysis, age, hypertension, beta-blocker use, left atrial diameter (LAD), LVEF and AF status (paroxysmal or permanent or both) were significantly associated with NT-pro-BNP levels (P<0.05). In multiple linear regression analysis, LVEF (B coefficient: -53.030; CI: -95.738 to -10.322; P: 0.015) and LAD (B coefficient: 285.858; CI: 23.731-547.986; P: 0.033) were significant and independent determinants of NT-pro-BNP levels. Plasma NT-pro-BNP levels were significantly higher in patients with paroxysmal and permanent AF compared to those with sinus rhythm in the setting of preserved left ventricular systolic function. LVEF and LAD were independent predictors of NT-pro-BNP levels.

Review and statistical analysis of the use of ultrasonic velocity for estimating the porosity fraction in polycrystalline materials

NASA Technical Reports Server (NTRS)

Roth, D. J.; Swickard, S. M.; Stang, D. B.; Deguire, M. R.

1991-01-01

A review and statistical analysis of the ultrasonic velocity method for estimating the porosity fraction in polycrystalline materials is presented. Initially, a semiempirical model is developed showing the origin of the linear relationship between ultrasonic velocity and porosity fraction. Then, from a compilation of data produced by many researchers, scatter plots of velocity versus percent porosity data are shown for Al2O3, MgO, porcelain-based ceramics, PZT, SiC, Si3N4, steel, tungsten, UO2,(U0.30Pu0.70)C, and YBa2Cu3O(7-x). Linear regression analysis produces predicted slope, intercept, correlation coefficient, level of significance, and confidence interval statistics for the data. Velocity values predicted from regression analysis of fully-dense materials are in good agreement with those calculated from elastic properties.

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A.; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.

Review and statistical analysis of the ultrasonic velocity method for estimating the porosity fraction in polycrystalline materials

NASA Technical Reports Server (NTRS)

Roth, D. J.; Swickard, S. M.; Stang, D. B.; Deguire, M. R.

1990-01-01

A review and statistical analysis of the ultrasonic velocity method for estimating the porosity fraction in polycrystalline materials is presented. Initially, a semi-empirical model is developed showing the origin of the linear relationship between ultrasonic velocity and porosity fraction. Then, from a compilation of data produced by many researchers, scatter plots of velocity versus percent porosity data are shown for Al2O3, MgO, porcelain-based ceramics, PZT, SiC, Si3N4, steel, tungsten, UO2,(U0.30Pu0.70)C, and YBa2Cu3O(7-x). Linear regression analysis produced predicted slope, intercept, correlation coefficient, level of significance, and confidence interval statistics for the data. Velocity values predicted from regression analysis for fully-dense materials are in good agreement with those calculated from elastic properties.

Detecting trends in raptor counts: power and type I error rates of various statistical tests

USGS Publications Warehouse

Hatfield, J.S.; Gould, W.R.; Hoover, B.A.; Fuller, M.R.; Lindquist, E.L.

1996-01-01

We conducted simulations that estimated power and type I error rates of statistical tests for detecting trends in raptor population count data collected from a single monitoring site. Results of the simulations were used to help analyze count data of bald eagles (Haliaeetus leucocephalus) from 7 national forests in Michigan, Minnesota, and Wisconsin during 1980-1989. Seven statistical tests were evaluated, including simple linear regression on the log scale and linear regression with a permutation test. Using 1,000 replications each, we simulated n = 10 and n = 50 years of count data and trends ranging from -5 to 5% change/year. We evaluated the tests at 3 critical levels (alpha = 0.01, 0.05, and 0.10) for both upper- and lower-tailed tests. Exponential count data were simulated by adding sampling error with a coefficient of variation of 40% from either a log-normal or autocorrelated log-normal distribution. Not surprisingly, tests performed with 50 years of data were much more powerful than tests with 10 years of data. Positive autocorrelation inflated alpha-levels upward from their nominal levels, making the tests less conservative and more likely to reject the null hypothesis of no trend. Of the tests studied, Cox and Stuart's test and Pollard's test clearly had lower power than the others. Surprisingly, the linear regression t-test, Collins' linear regression permutation test, and the nonparametric Lehmann's and Mann's tests all had similar power in our simulations. Analyses of the count data suggested that bald eagles had increasing trends on at least 2 of the 7 national forests during 1980-1989.

Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models.

PubMed

Bhamidipati, Ravi Kanth; Syed, Muzeeb; Mullangi, Ramesh; Srinivas, Nuggehally

2018-02-01

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC inf ) of dalbavancin is a key parameter and AUC inf /MIC ratio is a critical pharmacodynamic marker. 2. Using end of intravenous infusion concentration (i.e. C max ) C max versus AUC inf relationship for dalbavancin was established by regression analyses (i.e. linear, log-log, log-linear and power models) using 21 pairs of subject data. 3. The predictions of the AUC inf were performed using published C max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment. 4. MAE and RMSE values for the various models were comparable. The C max versus AUC inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84-1.14-fold difference) with a RMSE < 10.3%. The external data evaluation showed that the models predicted AUC inf with a RMSE of 3.02-27.46% with fold difference largely contained within 0.64-1.48. 5. Regardless of the regression models, a single time point strategy of using C max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC inf of dalbavancin in patients.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

The relationship between compressive strength and flexural strength of pavement geopolymer grouting material

NASA Astrophysics Data System (ADS)

Zhang, L.; Han, X. X.; Ge, J.; Wang, C. H.

2018-01-01

To determine the relationship between compressive strength and flexural strength of pavement geopolymer grouting material, 20 groups of geopolymer grouting materials were prepared, the compressive strength and flexural strength were determined by mechanical properties test. On the basis of excluding the abnormal values through boxplot, the results show that, the compressive strength test results were normal, but there were two mild outliers in 7days flexural strength test. The compressive strength and flexural strength were linearly fitted by SPSS, six regression models were obtained by linear fitting of compressive strength and flexural strength. The linear relationship between compressive strength and flexural strength can be better expressed by the cubic curve model, and the correlation coefficient was 0.842.

A varying-coefficient method for analyzing longitudinal clinical trials data with nonignorable dropout

PubMed Central

Forster, Jeri E.; MaWhinney, Samantha; Ball, Erika L.; Fairclough, Diane

2011-01-01

Dropout is common in longitudinal clinical trials and when the probability of dropout depends on unobserved outcomes even after conditioning on available data, it is considered missing not at random and therefore nonignorable. To address this problem, mixture models can be used to account for the relationship between a longitudinal outcome and dropout. We propose a Natural Spline Varying-coefficient mixture model (NSV), which is a straightforward extension of the parametric Conditional Linear Model (CLM). We assume that the outcome follows a varying-coefficient model conditional on a continuous dropout distribution. Natural cubic B-splines are used to allow the regression coefficients to semiparametrically depend on dropout and inference is therefore more robust. Additionally, this method is computationally stable and relatively simple to implement. We conduct simulation studies to evaluate performance and compare methodologies in settings where the longitudinal trajectories are linear and dropout time is observed for all individuals. Performance is assessed under conditions where model assumptions are both met and violated. In addition, we compare the NSV to the CLM and a standard random-effects model using an HIV/AIDS clinical trial with probable nonignorable dropout. The simulation studies suggest that the NSV is an improvement over the CLM when dropout has a nonlinear dependence on the outcome. PMID:22101223

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-06-14

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

Relationship between photoreceptor outer segment length and visual acuity in diabetic macular edema.

PubMed

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.

Impact of income and income inequality on infant health outcomes in the United States.

PubMed

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.

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.

Pesticide adsorption in relation to soil properties and soil type distribution in regional scale.

PubMed

Kodešová, Radka; Kočárek, Martin; Kodeš, Vít; Drábek, Ondřej; Kozák, Josef; Hejtmánková, Kateřina

2011-02-15

Study was focused on the evaluation of pesticide adsorption in soils, as one of the parameters, which are necessary to know when assessing possible groundwater contamination caused by pesticides commonly used in agriculture. Batch sorption tests were performed for 11 selected pesticides and 13 representative soils. The Freundlich equations were used to describe adsorption isotherms. Multiple-linear regressions were used to predict the Freundlich adsorption coefficients from measured soil properties. Resulting functions and a soil map of the Czech Republic were used to generate maps of the coefficient distribution. The multiple linear regressions showed that the K(F) coefficient depended on: (a) combination of OM (organic matter content), pH(KCl) and CEC (cation exchange capacity), or OM, SCS (sorption complex saturation) and salinity (terbuthylazine), (b) combination of OM and pH(KCl), or OM, SCS and salinity (prometryne), (c) combination of OM and pH(KCl), or OM and ρ(z) (metribuzin), (d) combination of OM, CEC and clay content, or clay content, CEC and salinity (hexazinone), (e) combination of OM and pH(KCl), or OM and SCS (metolachlor), (f) OM or combination of OM and CaCO(3) (chlorotoluron), (g) OM (azoxystrobin), (h) combination of OM and pH(KCl) (trifluralin), (i) combination of OM and clay content (fipronil), (j) combination of OM and pH(KCl), or OM, pH(KCl) and CaCO(3) (thiacloprid), (k) combination of OM, pH(KCl) and CEC, or sand content, pH(KCl) and salinity (chlormequat chloride). Copyright © 2010 Elsevier B.V. All rights reserved.

Examining geological controls on baseflow index (BFI) using regression analysis: An illustration from the Thames Basin, UK

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.

Mapping diffuse photosynthetically active radiation from satellite data in Thailand

NASA Astrophysics Data System (ADS)

Choosri, P.; Janjai, S.; Nunez, M.; Buntoung, S.; Charuchittipan, D.

2017-12-01

In this paper, calculation of monthly average hourly diffuse photosynthetically active radiation (PAR) using satellite data is proposed. Diffuse PAR was analyzed at four stations in Thailand. A radiative transfer model was used for calculating the diffuse PAR for cloudless sky conditions. Differences between the diffuse PAR under all sky conditions obtained from the ground-based measurements and those from the model are representative of cloud effects. Two models are developed, one describing diffuse PAR only as a function of solar zenith angle, and the second one as a multiple linear regression with solar zenith angle and satellite reflectivity acting linearly and aerosol optical depth acting in logarithmic functions. When tested with an independent data set, the multiple regression model performed best with a higher coefficient of variance R2 (0.78 vs. 0.70), lower root mean square difference (RMSD) (12.92% vs. 13.05%) and the same mean bias difference (MBD) of -2.20%. Results from the multiple regression model are used to map diffuse PAR throughout the country as monthly averages of hourly data.

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

Abnormal dynamics of language in schizophrenia.

PubMed

Stephane, Massoud; Kuskowski, Michael; Gundel, Jeanette

2014-05-30

Language could be conceptualized as a dynamic system that includes multiple interactive levels (sub-lexical, lexical, sentence, and discourse) and components (phonology, semantics, and syntax). In schizophrenia, abnormalities are observed at all language elements (levels and components) but the dynamic between these elements remains unclear. We hypothesize that the dynamics between language elements in schizophrenia is abnormal and explore how this dynamic is altered. We, first, investigated language elements with comparable procedures in patients and healthy controls. Second, using measures of reaction time, we performed multiple linear regression analyses to evaluate the inter-relationships among language elements and the effect of group on these relationships. Patients significantly differed from controls with respect to sub-lexical/lexical, lexical/sentence, and sentence/discourse regression coefficients. The intercepts of the regression slopes increased in the same order above (from lower to higher levels) in patients but not in controls. Regression coefficients between syntax and both sentence level and discourse level semantics did not differentiate patients from controls. This study indicates that the dynamics between language elements is abnormal in schizophrenia. In patients, top-down flow of linguistic information might be reduced, and the relationship between phonology and semantics but not between syntax and semantics appears to be altered. Published by Elsevier Ireland Ltd.

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.

The subtle danger of symmetry restrictions in time series regressions, with application to fertility models.

PubMed

Haynes, S E

1983-10-01

It is widely known that linear restrictions involve bias. What is not known is that some linear restrictions are especially dangerous for hypothesis testing. For some, the expected value of the restricted coefficient does not lie between (among) the true unconstrained coefficients, which implies that the estimate is not a simple average of these coefficients. In this paper, the danger is examined regarding the additive linear restriction almost universally imposed in statistical research--the restriction of symmetry. Symmetry implies that the response of the dependent variable to a unit decrease in an expanatory variable is identical, but of opposite sign, to the response to a unit increase. The 1st section of the paper demonstrates theoretically that a coefficient restricted by symmetry (unlike coefficients embodying other additive restrictions) is not a simple average of the unconstrained coefficients because the relevant interacted variables are inversly correlated by definition. The next section shows that, under the restriction of symmetry, fertility in Finland from 1885-1925 appears to respond in a prolonged manner to infant mortality (significant and positive with a lag of 4-6 years), suggesting a response to expected deaths. However, unscontrained estimates indicate that this finding is spurious. When the restriction is relaxed, the dominant response is rapid (significant and positive with a lag of 1-2 years) and stronger for declines in mortality, supporting an aymmetric response to actual deaths. For 2 reasons, the danger of the symmetry restriction may be especially pervasive. 1st, unlike most other linear constraints, symmetry is passively imposed merely by ignoring the possibility of asymmetry. 2nd, modles in a wide range of fields--including macroeconomics (e.g., demand for money, consumption, and investment models, and the Phillips curve), international economics (e.g., intervention models of central banks), and labor economics (e.g., sticky wage models)--predict asymmetry. The conclusion of the study is that, to avoid spurious hypothesis testing, empirical research should systematically test for asymmetry, especially when predicted by theory.

Matrix diffusion coefficients in volcanic rocks at the Nevada test site: influence of matrix porosity, matrix permeability, and fracture coating minerals.

PubMed

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.

Matrix diffusion coefficients in volcanic rocks at the Nevada test site: Influence of matrix porosity, matrix permeability, and fracture coating minerals

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.

Daily magnesium intake and serum magnesium concentration among Japanese people.

PubMed

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.

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

Correlation of Vitamin D status and orthodontic-induced external apical root resorption.

PubMed

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.

A method for the selection of a functional form for a thermodynamic equation of state using weighted linear least squares stepwise regression

NASA Technical Reports Server (NTRS)

Jacobsen, R. T.; Stewart, R. B.; Crain, R. W., Jr.; Rose, G. L.; Myers, A. F.

1976-01-01

A method was developed for establishing a rational choice of the terms to be included in an equation of state with a large number of adjustable coefficients. The methods presented were developed for use in the determination of an equation of state for oxygen and nitrogen. However, a general application of the methods is possible in studies involving the determination of an optimum polynomial equation for fitting a large number of data points. The data considered in the least squares problem are experimental thermodynamic pressure-density-temperature data. Attention is given to a description of stepwise multiple regression and the use of stepwise regression in the determination of an equation of state for oxygen and nitrogen.

Income inequality, disinvestment in health care and use of dental services.

PubMed

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.

Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

NASA Astrophysics Data System (ADS)

Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.

Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China.

PubMed

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.

Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China

PubMed Central

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

Regression analysis of sparse asynchronous longitudinal data

PubMed Central

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P.

2015-01-01

Summary We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus. PMID:26568699

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.

The problem of natural funnel asymmetries: a simulation analysis of meta-analysis in macroeconomics.

PubMed

Callot, Laurent; Paldam, Martin

2011-06-01

Effect sizes in macroeconomic are estimated by regressions on data published by statistical agencies. Funnel plots are a representation of the distribution of the resulting regression coefficients. They are normally much wider than predicted by the t-ratio of the coefficients and often asymmetric. The standard method of meta-analysts in economics assumes that the asymmetries are because of publication bias causing censoring and adjusts the average accordingly. The paper shows that some funnel asymmetries may be 'natural' so that they occur without censoring. We investigate such asymmetries by simulating funnels by pairs of data generating processes (DGPs) and estimating models (EMs), in which the EM has the problem that it disregards a property of the DGP. The problems are data dependency, structural breaks, non-normal residuals, non-linearity, and omitted variables. We show that some of these problems generate funnel asymmetries. When they do, the standard method often fails. Copyright © 2011 John Wiley & Sons, Ltd. Copyright © 2011 John Wiley & Sons, Ltd.

Factors affecting the use of postincisional analgesics in dogs and cats by Canadian veterinarians in 2001

PubMed Central

Hewson, Caroline J.; Dohoo, Ian R.

2006-01-01

Abstract Factors affecting the postincisional use of analgesics for ovariohysterectomy (OVH) in dogs and cats were assessed by using data collected from 280 Canadian veterinarians, as part of a national, randomized mail survey (response rate 57.8%). Predictors of analgesic usage identified by logistic regression included the presence of at least 1 animal health technician (AHT) per 2 veterinarians (OR = 2.3, P = 0.004), and the veterinarians’ perception of the pain caused by surgery without analgesia (OR = 1.5, P < 0.001). Linear regression identified the following predictors of veterinarians’ perception of pain: the presence of more than 1 AHT per 2 veterinarians (coefficient = 0.42, P = 0.048) and the number of years since graduation (coefficient = −0.073, P < 0.001). Some of these risk factors are similar to those identified in 1994. The results suggest that continuing education may help to increase analgesic usage. Other important contributors may be client education and a valid method of pain assessment. PMID:16734371

Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

PubMed

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.

Verification of spectrophotometric method for nitrate analysis in water samples

NASA Astrophysics Data System (ADS)

Kurniawati, Puji; Gusrianti, Reny; Dwisiwi, Bledug Bernanti; Purbaningtias, Tri Esti; Wiyantoko, Bayu

2017-12-01

The aim of this research was to verify the spectrophotometric method to analyze nitrate in water samples using APHA 2012 Section 4500 NO3-B method. The verification parameters used were: linearity, method detection limit, level of quantitation, level of linearity, accuracy and precision. Linearity was obtained by using 0 to 50 mg/L nitrate standard solution and the correlation coefficient of standard calibration linear regression equation was 0.9981. The method detection limit (MDL) was defined as 0,1294 mg/L and limit of quantitation (LOQ) was 0,4117 mg/L. The result of a level of linearity (LOL) was 50 mg/L and nitrate concentration 10 to 50 mg/L was linear with a level of confidence was 99%. The accuracy was determined through recovery value was 109.1907%. The precision value was observed using % relative standard deviation (%RSD) from repeatability and its result was 1.0886%. The tested performance criteria showed that the methodology was verified under the laboratory conditions.

Society of Thoracic Surgeons Risk Score Predicts Hospital Charges and Resource Utilization After Aortic Valve Replacement

PubMed Central

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

Regression-based adaptive sparse polynomial dimensional decomposition for sensitivity analysis

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro; Abgrall, Remi

2014-11-01

Polynomial dimensional decomposition (PDD) is employed in this work for global sensitivity analysis and uncertainty quantification of stochastic systems subject to a large number of random input variables. Due to the intimate structure between PDD and Analysis-of-Variance, PDD is able to provide simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to polynomial chaos (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of the standard method unaffordable for real engineering applications. In order to address this problem of curse of dimensionality, this work proposes a variance-based adaptive strategy aiming to build a cheap meta-model by sparse-PDD with PDD coefficients computed by regression. During this adaptive procedure, the model representation by PDD only contains few terms, so that the cost to resolve repeatedly the linear system of the least-square regression problem is negligible. The size of the final sparse-PDD representation is much smaller than the full PDD, since only significant terms are eventually retained. Consequently, a much less number of calls to the deterministic model is required to compute the final PDD coefficients.

QSTR of the toxicity of some organophosphorus compounds by using the quantum chemical and topological descriptors.

PubMed

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.

Using air/water/sediment temperature contrasts to identify groundwater seepage locations in small streams

NASA Astrophysics Data System (ADS)

Karan, S.; Sebok, E.; Engesgaard, P. K.

2016-12-01

For identifying groundwater seepage locations in small streams within a headwater catchment, we present a method expanding on the linear regression of air and stream temperatures. Thus, by measuring the temperatures in dual-depth; in the stream column and at the streambed-water interface (SWI), we apply metrics from linear regression analysis of temperatures between air/stream and air/SWI (linear regression slope, intercept and coefficient of determination), and the daily mean temperatures (temperature variance and the average difference between the minimum and maximum daily temperatures). Our study show that using metrics from single-depth stream temperature measurements only are not sufficient to identify substantial groundwater seepage locations within a headwater stream. Conversely, comparing the metrics from dual-depth temperatures show significant differences so that at groundwater seepage locations, temperatures at the SWI, merely explain 43-75 % of the variation opposed to ≥91 % at the corresponding stream column temperatures. The figure showing a box-plot of the variation in daily mean temperature depict that at several locations there is great variation in the range the upper and lower loggers due to groundwater seepage. In general, the linear regression show that at these locations at the SWI, the slopes (<0.25) and intercepts (>6.5oC) are substantially lower and higher, while the mean diel amplitudes (<0.98oC) are decreased compared to remaining locations. The dual-depth approach was applied in a post-glacial fluvial setting, where metrics analyses overall corresponded to field measurements of groundwater fluxes deduced from vertical streambed temperatures and stream flow accretions. Thus, we propose a method reliably identifying groundwater seepage locations along streambed in such settings.

Ethnomathematics: The use of multiple linier regression Y = b 1 X 1 + b 2 X 2 + e in traditional house construction Saka Roras in Songan Village

NASA Astrophysics Data System (ADS)

Darmayasa, J. B.; Wahyudin; Mulyana, T.

2018-01-01

Ethnomathematics may be the connecting bridge between culture and technology and arts. Therefore, the exploration of mathematics values that intersects with cultural anthropology should be significantly conducted. One case containing such issue is the construction of Traditional House of Saka Roras in Bali. Thus, this research aimed to explore the mathematic concept adopted in the construction of such traditional Bale (house) located in Songan Village, Kintamani, Bali. Specifically, this research also aimed to investigate the selection of linear regression coefficient for the saka (pillar) in the Bale. This research applied Embedded Mix-Method Design. Meanwhile, the data collection was conducted by interview, observation and measurement of pillars of 32 Bale Saka Roras. The result of this research revealed that the connection between the width and height of pillars was stated in the formula Y = 26,3 + 18,2X, where X acted as stimulus variable. The coefficient value amounted to 18.2 showed that most preceding architects in Songan Village were more likely to use 19 as the coefficient towards the pillar width than the other coefficients such as 17, 20 and 21 as mentioned in book/palm-leaf manuscript entitled Kosala-Kosali. The last but not least, the researchers also figured out that the pillar width depended on the length of the house-owner candidate’s index finger.

Ridge: a computer program for calculating ridge regression estimates

Treesearch

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.

Linear solvation energy relationship for the adsorption of synthetic organic compounds on single-walled carbon nanotubes in water.

PubMed

Ding, H; Chen, C; Zhang, X

2016-01-01

The linear solvation energy relationship (LSER) was applied to predict the adsorption coefficient (K) of synthetic organic compounds (SOCs) on single-walled carbon nanotubes (SWCNTs). A total of 40 log K values were used to develop and validate the LSER model. The adsorption data for 34 SOCs were collected from 13 published articles and the other six were obtained in our experiment. The optimal model composed of four descriptors was developed by a stepwise multiple linear regression (MLR) method. The adjusted r(2) (r(2)adj) and root mean square error (RMSE) were 0.84 and 0.49, respectively, indicating good fitness. The leave-one-out cross-validation Q(2) ([Formula: see text]) was 0.79, suggesting the robustness of the model was satisfactory. The external Q(2) ([Formula: see text]) and RMSE (RMSEext) were 0.72 and 0.50, respectively, showing the model's strong predictive ability. Hydrogen bond donating interaction (bB) and cavity formation and dispersion interactions (vV) stood out as the two most influential factors controlling the adsorption of SOCs onto SWCNTs. The equilibrium concentration would affect the fitness and predictive ability of the model, while the coefficients varied slightly.

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Data mining-based coefficient of influence factors optimization of test paper reliability

NASA Astrophysics Data System (ADS)

Xu, Peiyao; Jiang, Huiping; Wei, Jieyao

2018-05-01

Test is a significant part of the teaching process. It demonstrates the final outcome of school teaching through teachers' teaching level and students' scores. The analysis of test paper is a complex operation that has the characteristics of non-linear relation in the length of the paper, time duration and the degree of difficulty. It is therefore difficult to optimize the coefficient of influence factors under different conditions in order to get text papers with clearly higher reliability with general methods [1]. With data mining techniques like Support Vector Regression (SVR) and Genetic Algorithm (GA), we can model the test paper analysis and optimize the coefficient of impact factors for higher reliability. It's easy to find that the combination of SVR and GA can get an effective advance in reliability from the test results. The optimal coefficient of influence factors optimization has a practicability in actual application, and the whole optimizing operation can offer model basis for test paper analysis.

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

USGS Publications Warehouse

Granato, Gregory E.

2006-01-01

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and data in subsequent rows. The user may choose the columns that contain the independent (X) and dependent (Y) variable. A third column, if present, may contain metadata such as the sample-collection location and date. The program screens the input files and plots the data. The KTRLine software is a graphical tool that facilitates development of regression models by use of graphs of the regression line with data, the regression residuals (with X or Y), and percentile plots of the cumulative frequency of the X variable, Y variable, and the regression residuals. The user may individually transform the independent and dependent variables to reduce heteroscedasticity and to linearize data. The program plots the data and the regression line. The program also prints model specifications and regression statistics to the screen. The user may save and print the regression results. The program can accept data sets that contain up to about 15,000 XY data points, but because the program must sort the array of all pairwise slopes, the program may be perceptibly slow with data sets that contain more than about 1,000 points.

Wheat flour dough Alveograph characteristics predicted by Mixolab regression models.

PubMed

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.

Simultaneous Quantification of Syringic Acid and Kaempferol in Extracts of Bergenia Species Using Validated High-Performance Thin-Layer Chromatographic-Densitometric Method.

PubMed

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.

Can the prevalence of high blood drug concentrations in a population be estimated by analysing oral fluid? A study of tetrahydrocannabinol and amphetamine.

PubMed

Gjerde, Hallvard; Verstraete, Alain

2010-02-25

To study several methods for estimating the prevalence of high blood concentrations of tetrahydrocannabinol and amphetamine in a population of drug users by analysing oral fluid (saliva). Five methods were compared, including simple calculation procedures dividing the drug concentrations in oral fluid by average or median oral fluid/blood (OF/B) drug concentration ratios or linear regression coefficients, and more complex Monte Carlo simulations. Populations of 311 cannabis users and 197 amphetamine users from the Rosita-2 Project were studied. The results of a feasibility study suggested that the Monte Carlo simulations might give better accuracies than simple calculations if good data on OF/B ratios is available. If using only 20 randomly selected OF/B ratios, a Monte Carlo simulation gave the best accuracy but not the best precision. Dividing by the OF/B regression coefficient gave acceptable accuracy and precision, and was therefore the best method. None of the methods gave acceptable accuracy if the prevalence of high blood drug concentrations was less than 15%. Dividing the drug concentration in oral fluid by the OF/B regression coefficient gave an acceptable estimation of high blood drug concentrations in a population, and may therefore give valuable additional information on possible drug impairment, e.g. in roadside surveys of drugs and driving. If good data on the distribution of OF/B ratios are available, a Monte Carlo simulation may give better accuracy. 2009 Elsevier Ireland Ltd. All rights reserved.

Non-invasive glucose monitoring in patients with Type 1 diabetes: a Multisensor system combining sensors for dielectric and optical characterisation of skin.

PubMed

Caduff, Andreas; Talary, Mark S; Mueller, Martin; Dewarrat, Francois; Klisic, Jelena; Donath, Marc; Heinemann, Lutz; Stahel, Werner A

2009-05-15

In vivo variations of blood glucose (BG) are affecting the biophysical characteristics (e.g. dielectric and optical) of skin and underlying tissue (SAUT) at various frequencies. However, the skin impedance spectra for instance can also be affected by other factors, perturbing the glucose related information, factors such as temperature, skin moisture and sweat, blood perfusion as well as body movements affecting the sensor-skin contact. In order to be able to correct for such perturbing factors, a Multisensor system was developed including sensors to measure the identified factors. To evaluate the quality of glucose monitoring, the Multisensor was applied in 10 patients with Type 1 diabetes. Glucose was administered orally to induce hyperglycaemic excursions at two different study visits. For analysis of the sensor signals, a global multiple linear regression model was derived. The respective coefficients of the variables were determined from the sensor signals of this first study visit (R(2)=0.74, MARD=18.0%--mean absolute relative difference). The identical set of modelling coefficients of the first study visit was re-applied to the test data of the second study visit to evaluate the predictive power of the model (R(2)=0.68, MARD=27.3%). It appears as if the Multisensor together with the global linear regression model applied, allows for tracking glucose changes non-invasively in patients with diabetes without requiring new model coefficients for each visit. Confirmation of these findings in a larger study group and under less experimentally controlled conditions is required for understanding whether a global parameterisation routine is feasible.

Application of the World Health Organization Quality of Life Instrument, Short Form (WHOQOL-BREF) to patients with cataract.

PubMed

Gholami, Ali; Araghi, Mahmood Tavakoli; Shamsabadi, Fatemeh; Bayat, Mahdiye; Dabirkhani, Fatemeh; Moradpour, Farhad; Mansori, Kamyar; Moradi, Yousef; Rajabi, Abdolhalim

2016-01-01

Cataract is a prevalent disease in the elderly, and negatively influences patients' quality of life. This study was conducted to study the application of the World Health Organization Quality of Life Instrument, Short Form (WHOQOL-BREF) to patients with cataract. In this cross-sectional study, 300 patients with cataract were studied in Neyshabur, Iran from July to October 2014. The Iranian version of the WHOQOL-BREF questionnaire was used to measure their quality of life. Cronbach's alpha coefficient, Pearson's correlation coefficient, the paired t-test, the independent t-test, and a linear regression model were used to analyze the data in SPSS version 16.0 (SPSS Inc., Chicago, IL, USA). The mean age of the participants was 68.11±11.98 years, and most were female (53%). The overall observed Cronbach's alpha coefficient for the WHOQOL-BREF was 0.889, ranging from 0.714 to 0.810 in its four domains. The total mean score of the respondents on the WHOQOL-BREF was 13.19. The highest and lowest mean scores were observed in the social relationship domain (14.11) and the physical health domain (12.29), respectively. A backward multiple linear regression model found that duration of disease and marital status were associated with total WHOQOL scores, while age, duration of disease, marital status, and income level were associated with domains one through four, respectively (p<0.05). The reliability analysis conducted in this study indicated that the WHOQOL-BREF scale exhibited an acceptable degree of internal consistency in the measurement of the quality of life of patients with cataract. It was also found that the patients with cataract who were surveyed reported a relatively moderate quality of life.

Genetic parameters for stayability to consecutive calvings in Zebu cattle.

PubMed

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.

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.

Cone-Beam Computed Tomography as a Diagnostic Method for Determination of Gingival Thickness and Distance between Gingival Margin and Bone Crest

PubMed Central

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

A Simple and Specific Stability- Indicating RP-HPLC Method for Routine Assay of Adefovir Dipivoxil in Bulk and Tablet Dosage Form.

PubMed

Darsazan, Bahar; Shafaati, Alireza; Mortazavi, Seyed Alireza; Zarghi, Afshin

2017-01-01

A simple and reliable stability-indicating RP-HPLC method was developed and validated for analysis of adefovir dipivoxil (ADV).The chromatographic separation was performed on a C 18 column using a mixture of acetonitrile-citrate buffer (10 mM at pH 5.2) 36:64 (%v/v) as mobile phase, at a flow rate of 1.5 mL/min. Detection was carried out at 260 nm and a sharp peak was obtained for ADV at a retention time of 5.8 ± 0.01 min. No interferences were observed from its stress degradation products. The method was validated according to the international guidelines. Linear regression analysis of data for the calibration plot showed a linear relationship between peak area and concentration over the range of 0.5-16 μg/mL; the regression coefficient was 0.9999and the linear regression equation was y = 24844x-2941.3. The detection (LOD) and quantification (LOQ) limits were 0.12 and 0.35 μg/mL, respectively. The results proved the method was fast (analysis time less than 7 min), precise, reproducible, and accurate for analysis of ADV over a wide range of concentration. The proposed specific method was used for routine quantification of ADV in pharmaceutical bulk and a tablet dosage form.

Optimal Estimation of Clock Values and Trends from Finite Data

NASA Technical Reports Server (NTRS)

Greenhall, Charles

2005-01-01

We show how to solve two problems of optimal linear estimation from a finite set of phase data. Clock noise is modeled as a stochastic process with stationary dth increments. The covariance properties of such a process are contained in the generalized autocovariance function (GACV). We set up two principles for optimal estimation: with the help of the GACV, these principles lead to a set of linear equations for the regression coefficients and some auxiliary parameters. The mean square errors of the estimators are easily calculated. The method can be used to check the results of other methods and to find good suboptimal estimators based on a small subset of the available data.

On estimation of linear transformation models with nested case–control sampling

PubMed Central

Liu, Mengling

2011-01-01

Nested case–control (NCC) sampling is widely used in large epidemiological cohort studies for its cost effectiveness, but its data analysis primarily relies on the Cox proportional hazards model. In this paper, we consider a family of linear transformation models for analyzing NCC data and propose an inverse selection probability weighted estimating equation method for inference. Consistency and asymptotic normality of our estimators for regression coefficients are established. We show that the asymptotic variance has a closed analytic form and can be easily estimated. Numerical studies are conducted to support the theory and an application to the Wilms’ Tumor Study is also given to illustrate the methodology. PMID:21912975

Marginal regression analysis of recurrent events with coarsened censoring times.

PubMed

Hu, X Joan; Rosychuk, Rhonda J

2016-12-01

Motivated by an ongoing pediatric mental health care (PMHC) study, this article presents weakly structured methods for analyzing doubly censored recurrent event data where only coarsened information on censoring is available. The study extracted administrative records of emergency department visits from provincial health administrative databases. The available information of each individual subject is limited to a subject-specific time window determined up to concealed data. To evaluate time-dependent effect of exposures, we adapt the local linear estimation with right censored survival times under the Cox regression model with time-varying coefficients (cf. Cai and Sun, Scandinavian Journal of Statistics 2003, 30, 93-111). We establish the pointwise consistency and asymptotic normality of the regression parameter estimator, and examine its performance by simulation. The PMHC study illustrates the proposed approach throughout the article. © 2016, The International Biometric Society.

Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Liming, Ye; Yufei, Liu; Chunyan, Li; Han, Chen

2017-05-01

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography.

PubMed

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

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.

Association between serum endogenous secretory receptor for advanced glycation end products and risk of type 2 diabetes mellitus with combined depression in the Chinese population.

PubMed

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.

Daily Magnesium Intake and Serum Magnesium Concentration among Japanese People

PubMed Central

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

Quantification of 11-Nor-9-Carboxy-Δ9-Tetrahydrocannabinol in Human Oral Fluid by Gas Chromatography–Tandem Mass Spectrometry

PubMed Central

Barnes, Allan J.; Scheidweiler, Karl B.; Huestis, Marilyn A.

2015-01-01

A sensitive and specific method for the quantification of 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THCCOOH) in oral fluid collected with the Quantisal and Oral-Eze devices was developed and fully validated. Extracted analytes were derivatized with hexafluoroisopropanol and trifluoroacetic anhydride and quantified by gas chromatography–tandem mass spectrometry with negative chemical ionization. Standard curves, using linear least-squares regression with 1/x2 weighting were linear from 10 to 1000 ng/L with coefficients of determination >0.998 for both collection devices. Bias was 89.2%–112.6%, total imprecision 4.0%–5.1% coefficient of variation, and extraction efficiency >79.8% across the linear range for Quantisal-collected specimens. Bias was 84.6%–109.3%, total imprecision 3.6%–7.3% coefficient of variation, and extraction efficiency >92.6% for specimens collected with the Oral-Eze device at all 3 quality control concentrations (10, 120, and 750 ng/L). This effective high-throughput method reduces analysis time by 9 minutes per sample compared with our current 2-dimensional gas chromatography–mass spectrometry method and extends the capability of quantifying this important oral fluid analyte to gas chromatography–tandem mass spectrometry. This method was applied to the analysis of oral fluid specimens collected from individuals participating in controlled cannabis studies and will be effective for distinguishing passive environmental contamination from active cannabis smoking. PMID:24622724

Quantum Chemically Estimated Abraham Solute Parameters Using Multiple Solvent-Water Partition Coefficients and Molecular Polarizability.

PubMed

Liang, Yuzhen; Xiong, Ruichang; Sandler, Stanley I; Di Toro, Dominic M

2017-09-05

Polyparameter Linear Free Energy Relationships (pp-LFERs), also called Linear Solvation Energy Relationships (LSERs), are used to predict many environmentally significant properties of chemicals. A method is presented for computing the necessary chemical parameters, the Abraham parameters (AP), used by many pp-LFERs. It employs quantum chemical calculations and uses only the chemical's molecular structure. The method computes the Abraham E parameter using density functional theory computed molecular polarizability and the Clausius-Mossotti equation relating the index refraction to the molecular polarizability, estimates the Abraham V as the COSMO calculated molecular volume, and computes the remaining AP S, A, and B jointly with a multiple linear regression using sixty-five solvent-water partition coefficients computed using the quantum mechanical COSMO-SAC solvation model. These solute parameters, referred to as Quantum Chemically estimated Abraham Parameters (QCAP), are further adjusted by fitting to experimentally based APs using QCAP parameters as the independent variables so that they are compatible with existing Abraham pp-LFERs. QCAP and adjusted QCAP for 1827 neutral chemicals are included. For 24 solvent-water systems including octanol-water, predicted log solvent-water partition coefficients using adjusted QCAP have the smallest root-mean-square errors (RMSEs, 0.314-0.602) compared to predictions made using APs estimated using the molecular fragment based method ABSOLV (0.45-0.716). For munition and munition-like compounds, adjusted QCAP has much lower RMSE (0.860) than does ABSOLV (4.45) which essentially fails for these compounds.

Understanding the effect of compositions on electronegativity, atomic radius and thermal stability of Mg-Ni-Y amorphous alloy

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.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly

PubMed Central

2013-01-01

Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042

Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars.

PubMed

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.

Inequality in Maternal Mortality in Iran: An Ecologic Study

PubMed Central

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

Mapping Surface Water DOC in the Northern Gulf of Mexico Using CDOM Absorption Coefficients and Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Kelly, B.; Chelsky, A.; Bulygina, E.; Roberts, B. J.

2017-12-01

Remote sensing techniques have become valuable tools to researchers, providing the capability to measure and visualize important parameters without the need for time or resource intensive sampling trips. Relationships between dissolved organic carbon (DOC), colored dissolved organic matter (CDOM) and spectral data have been used to remotely sense DOC concentrations in riverine systems, however, this approach has not been applied to the northern Gulf of Mexico (GoM) and needs to be tested to determine how accurate these relationships are in riverine-dominated shelf systems. In April, July, and October 2017 we sampled surface water from 80+ sites over an area of 100,000 km2 along the Louisiana-Texas shelf in the northern GoM. DOC concentrations were measured on filtered water samples using a Shimadzu TOC-VCSH analyzer using standard techniques. Additionally, DOC concentrations were estimated from CDOM absorption coefficients of filtered water samples on a UV-Vis spectrophotometer using a modification of the methods of Fichot and Benner (2011). These values were regressed against Landsat visible band spectral data for those same locations to establish a relationship between the spectral data, CDOM absorption coefficients. This allowed us to spatially map CDOM absorption coefficients in the Gulf of Mexico using the Landsat spectral data in GIS. We then used a multiple linear regressions model to derive DOC concentrations from the CDOM absorption coefficients and applied those to our map. This study provides an evaluation of the viability of scaling up CDOM absorption coefficient and remote-sensing derived estimates of DOC concentrations to the scale of the LA-TX shelf ecosystem.

Profile local linear estimation of generalized semiparametric regression model for longitudinal data.

PubMed

Sun, Yanqing; Sun, Liuquan; Zhou, Jie

2013-07-01

This paper studies the generalized semiparametric regression model for longitudinal data where the covariate effects are constant for some and time-varying for others. Different link functions can be used to allow more flexible modelling of longitudinal data. The nonparametric components of the model are estimated using a local linear estimating equation and the parametric components are estimated through a profile estimating function. The method automatically adjusts for heterogeneity of sampling times, allowing the sampling strategy to depend on the past sampling history as well as possibly time-dependent covariates without specifically model such dependence. A [Formula: see text]-fold cross-validation bandwidth selection is proposed as a working tool for locating an appropriate bandwidth. A criteria for selecting the link function is proposed to provide better fit of the data. Large sample properties of the proposed estimators are investigated. Large sample pointwise and simultaneous confidence intervals for the regression coefficients are constructed. Formal hypothesis testing procedures are proposed to check for the covariate effects and whether the effects are time-varying. A simulation study is conducted to examine the finite sample performances of the proposed estimation and hypothesis testing procedures. The methods are illustrated with a data example.

Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform - A case study in Wild Duck Lake Wetland, Beijing, China

NASA Astrophysics Data System (ADS)

Jing, Ran; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Deng, Lei

2017-12-01

Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated.

Estimating Dbh of Trees Employing Multiple Linear Regression of the best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

NASA Astrophysics Data System (ADS)

Ibanez, C. A. G.; Carcellar, B. G., III; Paringit, E. C.; Argamosa, R. J. L.; Faelga, R. A. G.; Posilero, M. A. V.; Zaragosa, G. P.; Dimayacyac, N. A.

2016-06-01

Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett's Test for Spherecity (BTS).

Ozone and sulfur dioxide effects on three tall fescue cultivars

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

Flagler, R.B.; Youngner, V.B.

Although many reports have been published concerning differential susceptibility of various crops and/or cultivars to air pollutants, most have used foliar injury instead of the marketable yield as the factor that determined susceptibility for the crop. In an examination of screening in terms of marketable yield, three cultivars of tall fescue (Festuca arundinacea Schreb.), 'Alta,' 'Fawn,' and 'Kentucky 31,' were exposed to 0-0.40 ppm O/sub 3/ or 0-0.50 ppm SO/sub 2/ 6 h/d, once a week, for 7 and 9 weeks, respectively. Experimental design was a randomized complete block with three replications. Statistical analysis was by standard analysis of variancemore » and regression techniques. Three variables were analyzed: top dry weight (yield), tiller number, and weight per tiller. Ozone had a significant effect on all three variables. Significant linear decreases in yield and weight per tiller occurred with increasing O/sub 3/ concentrations. Linear regressions of these variables on O/sub 3/ concentration produced significantly different regression coefficients. The coefficient for Kentucky 31 was significantly greater than Alta or Fawn, which did not differ from each other. This indicated that Kentucky 31 was more susceptible to O/sub 3/ than either of the other cultivars. Percent reductions in dry weight for the three cultivars at highest O/sub 3/ level were 35, 44, and 53%, respectively, for Fawn, Alta, and Kentucky 31. For weight per tiller, Kentucky 31 had a higher percent reduction than the other cultivars (59 vs. 46 and 44%). Tiller number was generally increased by O/sub 3/, but this variable was not useful for determining differential susceptibility to the pollutant. Sulfur dioxide treatments produced no significant effects on any of the variables analyzed.« less

Correlation of Vitamin D status and orthodontic-induced external apical root resorption

PubMed Central

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

Changes in the timing of snowmelt and streamflow in Colorado: A response to recent warming

USGS Publications Warehouse

Clow, David W.

2010-01-01

Trends in the timing of snowmelt and associated runoff in Colorado were evaluated for the 1978-2007 water years using the regional Kendall test (RKT) on daily snow-water equivalent (SWE) data from snowpack telemetry (SNOTEL) sites and daily streamflow data from headwater streams. The RKT is a robust, nonparametric test that provides an increased power of trend detection by grouping data from multiple sites within a given geographic region. The RKT analyses indicated strong, pervasive trends in snowmelt and streamflow timing, which have shifted toward earlier in the year by a median of 2-3 weeks over the 29-yr study period. In contrast, relatively few statistically significant trends were detected using simple linear regression. RKT analyses also indicated that November-May air temperatures increased by a median of 0.9 degrees C decade-1, while 1 April SWE and maximum SWE declined by a median of 4.1 and 3.6 cm decade-1, respectively. Multiple linear regression models were created, using monthly air temperatures, snowfall, latitude, and elevation as explanatory variables to identify major controlling factors on snowmelt timing. The models accounted for 45% of the variance in snowmelt onset, and 78% of the variance in the snowmelt center of mass (when half the snowpack had melted). Variations in springtime air temperature and SWE explained most of the interannual variability in snowmelt timing. Regression coefficients for air temperature were negative, indicating that warm temperatures promote early melt. Regression coefficients for SWE, latitude, and elevation were positive, indicating that abundant snowfall tends to delay snowmelt, and snowmelt tends to occur later at northern latitudes and high elevations. Results from this study indicate that even the mountains of Colorado, with their high elevations and cold snowpacks, are experiencing substantial shifts in the timing of snowmelt and snowmelt runoff toward earlier in the year.

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis

PubMed Central

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

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis.

PubMed

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.

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.

PubMed

Malloy, Elizabeth J; Morris, Jeffrey S; Adar, Sara D; Suh, Helen; Gold, Diane R; Coull, Brent A

2010-07-01

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.

[Association between distribution of bacillary dysentery and meteorological factors in Beijing, 2004-2015].

PubMed

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.

Mapping the spatial pattern of temperate forest above ground biomass by integrating airborne lidar with Radarsat-2 imagery via geostatistical models

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.

A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.

Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor.

PubMed

Marrero-Ponce, Yovani; Medina-Marrero, Ricardo; Castillo-Garit, Juan A; Romero-Zaldivar, Vicente; Torrens, Francisco; Castro, Eduardo A

2005-04-15

A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.

Cone-beam computed tomography for lung cancer - validation with CT and monitoring tumour response during chemo-radiation therapy.

PubMed

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.

Does lower lifetime fluoridation exposure explain why people outside capital cities have poor clinical oral health?

PubMed

Crocombe, L A; Brennan, D S; Slade, G D

2015-03-26

Australians outside state capital cities have greater caries experience than their counterparts in capital cities. We hypothesized that differing water fluoridation exposures was associated with this disparity. Data were the 2004-06 Australian National Survey of Adult Oral Health. Examiners measured participant decayed, missing and filled teeth and DMFT Index and lifetime fluoridation exposure was quantified. Multivariable linear regression models estimated differences in caries experience between capital city residents and others, with and without adjustment for fluoridation exposure. There was greater mean lifetime fluoridation exposure in state capital cities (59.1%, 95% confidence interval=56.9,61.4) than outside capital cities (42.3, confidence interval=36.9,47.6). People located outside capital city areas had differing socio-demographic characteristics and dental visiting patterns, and a higher mean DMFT (Capital cities=12.9, Non-capital cities=14.3, p=0.02), than people from capital cities. After adjustment for socio-demographic characteristics and dental visits, DMFT of people living in capital cities was less than non-capital city residents (Regression coefficient=0.8, p=0.01). The disparity was no longer statistically significant (Regression coefficient=0.6, p=0.09) after additional adjustment for fluoridation exposure. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Natural bond orbital approach to the transmission of substituent effect through the fulvene and benzene ring systems.

PubMed

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.

Palus Somni - Anomalies in the correlation of Al/Si X-ray fluorescence intensity ratios and broad-spectrum visible albedos. [lunar surface mineralogy

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.

SigrafW: An easy-to-use program for fitting enzyme kinetic data.

PubMed

Leone, Francisco Assis; Baranauskas, José Augusto; Furriel, Rosa Prazeres Melo; Borin, Ivana Aparecida

2005-11-01

SigrafW is Windows-compatible software developed using the Microsoft® Visual Basic Studio program that uses the simplified Hill equation for fitting kinetic data from allosteric and Michaelian enzymes. SigrafW uses a modified Fibonacci search to calculate maximal velocity (V), the Hill coefficient (n), and the enzyme-substrate apparent dissociation constant (K). The estimation of V, K, and the sum of the squares of residuals is performed using a Wilkinson nonlinear regression at any Hill coefficient (n). In contrast to many currently available kinetic analysis programs, SigrafW shows several advantages for the determination of kinetic parameters of both hyperbolic and nonhyperbolic saturation curves. No initial estimates of the kinetic parameters are required, a measure of the goodness-of-the-fit for each calculation performed is provided, the nonlinear regression used for calculations eliminates the statistical bias inherent in linear transformations, and the software can be used for enzyme kinetic simulations either for educational or research purposes. Persons interested in receiving a free copy of the software should contact Dr. F. A. Leone. Copyright © 2005 International Union of Biochemistry and Molecular Biology, Inc.

Visual Search Load Effects on Age-Related Cognitive Decline: Evidence From the Yakumo Longitudinal Study.

PubMed

Hatta, Takeshi; Kato, Kimiko; Hotta, Chie; Higashikawa, Mari; Iwahara, Akihiko; Hatta, Taketoshi; Hatta, Junko; Fujiwara, Kazumi; Nagahara, Naoko; Ito, Emi; Hamajima, Nobuyuki

2017-01-01

The validity of Bucur and Madden's (2010) proposal that an age-related decline is particularly pronounced in executive function measures rather than in elementary perceptual speed measures was examined via the Yakumo Study longitudinal database. Their proposal suggests that cognitive load differentially affects cognitive abilities in older adults. To address their proposal, linear regression coefficients of 104 participants were calculated individually for the digit cancellation task 1 (D-CAT1), where participants search for a given single digit, and the D-CAT3, where they search for 3 digits simultaneously. Therefore, it can be conjectured that the D-CAT1 represents primarily elementary perceptual speed and low-visual search load task. whereas the D-CAT3 represents primarily executive function and high-visual search load task. Regression coefficients from age 65 to 75 for the D-CAT3 showed a significantly steeper decline than that for the D-CAT1, and a large number of participants showed this tendency. These results support the proposal by Brcur and Madden (2010) and suggest that the degree of cognitive load affects age-related cognitive decline.

Factors in Variability of Serial Gabapentin Concentrations in Elderly Patients with Epilepsy.

PubMed

Conway, Jeannine M; Eberly, Lynn E; Collins, Joseph F; Macias, Flavia M; Ramsay, R Eugene; Leppik, Ilo E; Birnbaum, Angela K

2017-10-01

To characterize and quantify the variability of serial gabapentin concentrations in elderly patients with epilepsy. This study included 83 patients (age ≥ 60 yrs) from an 18-center randomized double-blind double-dummy parallel study from the Veterans Affairs Cooperative 428 Study. All patients were taking 1500 mg/day gabapentin. Within-person coefficient of variation (CV) in gabapentin concentrations, measured weekly to bimonthly for up to 52 weeks, then quarterly, was computed. Impact of patient characteristics on gabapentin concentrations (linear mixed model) and CV (linear regression) were estimated. A total of 482 gabapentin concentration measurements were available for analysis. Gabapentin concentrations and intrapatient CVs ranged from 0.5 to 22.6 μg/ml (mean 7.9 μg/ml, standard deviation [SD] 4.1 μg/ml) and 2% to 79% (mean 27.9%, SD 15.3%), respectively, across all visits. Intrapatient CV was higher by 7.3% for those with a body mass index of ≥ 30 kg/m 2 (coefficient = 7.3, p=0.04). CVs were on average 0.5% higher for each 1-unit higher CV in creatinine clearance (coefficient = 0.5, p=0.03) and 1.2% higher for each 1-hour longer mean time after dose (coefficient = 1.2, p=0.04). Substantial intrapatient variability in serial gabapentin concentration was noted in elderly patients with epilepsy. Creatinine clearance, time of sampling relative to dose, and obesity were found to be positively associated with variability. © 2017 Pharmacotherapy Publications, Inc.

A Regression Framework for Effect Size Assessments in Longitudinal Modeling of Group Differences

PubMed Central

Feingold, Alan

2013-01-01

The use of growth modeling analysis (GMA)--particularly multilevel analysis and latent growth modeling--to test the significance of intervention effects has increased exponentially in prevention science, clinical psychology, and psychiatry over the past 15 years. Model-based effect sizes for differences in means between two independent groups in GMA can be expressed in the same metric (Cohen’s d) commonly used in classical analysis and meta-analysis. This article first reviews conceptual issues regarding calculation of d for findings from GMA and then introduces an integrative framework for effect size assessments that subsumes GMA. The new approach uses the structure of the linear regression model, from which effect sizes for findings from diverse cross-sectional and longitudinal analyses can be calculated with familiar statistics, such as the regression coefficient, the standard deviation of the dependent measure, and study duration. PMID:23956615

Fast determination of total ginsenosides content in ginseng powder by near infrared reflectance spectroscopy

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.

THE DISTRIBUTION OF COOK’S D STATISTIC

PubMed Central

Muller, Keith E.; Mok, Mario Chen

2013-01-01

Cook (1977) proposed a diagnostic to quantify the impact of deleting an observation on the estimated regression coefficients of a General Linear Univariate Model (GLUM). Simulations of models with Gaussian response and predictors demonstrate that his suggestion of comparing the diagnostic to the median of the F for overall regression captures an erratically varying proportion of the values. We describe the exact distribution of Cook’s statistic for a GLUM with Gaussian predictors and response. We also present computational forms, simple approximations, and asymptotic results. A simulation supports the accuracy of the results. The methods allow accurate evaluation of a single value or the maximum value from a regression analysis. The approximations work well for a single value, but less well for the maximum. In contrast, the cut-point suggested by Cook provides widely varying tail probabilities. As with all diagnostics, the data analyst must use scientific judgment in deciding how to treat highlighted observations. PMID:24363487

An Investigation of Age-Related Iron Deposition Using Susceptibility Weighted Imaging

PubMed Central

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

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

PubMed Central

Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

2017-01-01

Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

Application of WHOQOL-BREF in Measuring Quality of Life in Health-Care Staff.

PubMed

Gholami, Ali; Jahromi, Leila Moosavi; Zarei, Esmail; Dehghan, Azizallah

2013-07-01

The objective of this study was to evaluate the quality of life of Neyshabur health-care staff and some factors associated with it with use of WHOQOL-BREF scale. This cross-sectional study was conducted on 522 staff of Neyshabur health-care centers from May to July 2011. Cronbach's alpha coefficient was applied to examine the internal consistency of WHOQOL-BREF scale; Pearson's correlation coefficient was used to determine the level of agreement between different domains of WHOQOL-BREF. Paired t-test was used to compare difference between score means of different domains. T-independent test was performed for group analysis and Multiple Linear Regression was used to control confounding effects. In this study, a good internal consistency (α = 0.925) for WHOQOL-BREF and its four domains was observed. The highest and the lowest mean scores of WHOQOL-BREF domains was found for physical health domain (Mean = 15.26) and environmental health domain (Mean = 13.09) respectively. Backward multiple linear regression revealed that existence chronic disease in staff was significantly associated with four domains of WHOQOL-BREF, education years was associated with two domains (Psychological and Environmental) and sex was associated with psychological domain (P < 0.05). The findings from this study confirm that the WHOQOL-BREF questionnaire is a reliable instrument to measure quality of life in health-care staff. From the data, it appears that Neyshabur health-care staff has WHOQOL-BREF scores that might be considered to indicate a relatively moderate quality of life.

The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs.

PubMed

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.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

Association between Personality Traits and Sleep Quality in Young Korean Women

PubMed Central

Kim, Han-Na; Cho, Juhee; Chang, Yoosoo; Ryu, Seungho

2015-01-01

Personality is a trait that affects behavior and lifestyle, and sleep quality is an important component of a healthy life. We analyzed the association between personality traits and sleep quality in a cross-section of 1,406 young women (from 18 to 40 years of age) who were not reporting clinically meaningful depression symptoms. Surveys were carried out from December 2011 to February 2012, using the Revised NEO Personality Inventory and the Pittsburgh Sleep Quality Index (PSQI). All analyses were adjusted for demographic and behavioral variables. We considered beta weights, structure coefficients, unique effects, and common effects when evaluating the importance of sleep quality predictors in multiple linear regression models. Neuroticism was the most important contributor to PSQI global scores in the multiple regression models. By contrast, despite being strongly correlated with sleep quality, conscientiousness had a near-zero beta weight in linear regression models, because most variance was shared with other personality traits. However, conscientiousness was the most noteworthy predictor of poor sleep quality status (PSQI≥6) in logistic regression models and individuals high in conscientiousness were least likely to have poor sleep quality, which is consistent with an OR of 0.813, with conscientiousness being protective against poor sleep quality. Personality may be a factor in poor sleep quality and should be considered in sleep interventions targeting young women. PMID:26030141

Non-stationary hydrologic frequency analysis using B-spline quantile regression

NASA Astrophysics Data System (ADS)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

Wii Balance Board: Reliability and Clinical Use in Assessment of Balance in Healthy Elderly Women.

PubMed

Monteiro-Junior, Renato Sobral; Ferreira, Arthur Sá; Puell, Vivian Neiva; Lattari, Eduardo; Machado, Sérgio; Otero Vaghetti, César Augusto; da Silva, Elirez Bezerra

2015-01-01

Force plate is considered gold standard tool to assess body balance. However the Wii Balance Board (WBB) platform is a trustworthy equipment to assess stabilometric components in young people. Thus, we aim to examine the reliability of measures of center of pressure with WBB in healthy elderly women. Twenty one healthy and physically active women were enrolled in the study (age: 64 ± 7 years; body mass index: 29 ± 5 kg/m2. The WBB was used to assess the center of pressure measures in the individuals. Pressure was linearly applied to different points to test the platform precision. Three assessments were performed, with two of them being held on the same day at a 5- to 10-minute interval, and the third one was performed 48 h later. A linear regression analysis was used to find out linearity, while the intraclass correlation coefficient was used to assess reliability. The platform precision was adequate (R2 = 0.997, P = 0.01). Center of pressure measures showed an excellent reliability (all intraclass correlation coefficient values were > 0.90; p < 0.01). The WBB is a precise and reliable tool of body stability quantitative measure in healthy active elderly women and its use should be encouraged in clinical settings.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

PubMed

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.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

PubMed Central

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

Applicability of Cameriere's and Drusini's age estimation methods to a sample of Turkish adults.

PubMed

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.

Advanced glycation end products and antioxidant status in type 2 diabetic patients with and without peripheral artery disease.

PubMed

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.

Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

NASA Astrophysics Data System (ADS)

Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

2017-09-01

We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

Acute effects of dynamic exercises on the relationship between the motor unit firing rate and the recruitment threshold.

PubMed

Ye, Xin; Beck, Travis W; DeFreitas, Jason M; Wages, Nathan P

2015-04-01

The aim of this study was to compare the acute effects of concentric versus eccentric exercise on motor control strategies. Fifteen men performed six sets of 10 repetitions of maximal concentric exercises or eccentric isokinetic exercises with their dominant elbow flexors on separate experimental visits. Before and after the exercise, maximal strength testing and submaximal trapezoid isometric contractions (40% of the maximal force) were performed. Both exercise conditions caused significant strength loss in the elbow flexors, but the loss was greater following the eccentric exercise (t=2.401, P=.031). The surface electromyographic signals obtained from the submaximal trapezoid isometric contractions were decomposed into individual motor unit action potential trains. For each submaximal trapezoid isometric contraction, the relationship between the average motor unit firing rate and the recruitment threshold was examined using linear regression analysis. In contrast to the concentric exercise, which did not cause significant changes in the mean linear slope coefficient and y-intercept of the linear regression line, the eccentric exercise resulted in a lower mean linear slope and an increased mean y-intercept, thereby indicating that increasing the firing rates of low-threshold motor units may be more important than recruiting high-threshold motor units to compensate for eccentric exercise-induced strength loss. Copyright © 2014 Elsevier B.V. All rights reserved.

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

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

The Digital Shoreline Analysis System (DSAS) Version 4.0 - An ArcGIS extension for calculating shoreline change

USGS Publications Warehouse

Thieler, E. Robert; Himmelstoss, Emily A.; Zichichi, Jessica L.; Ergul, Ayhan

2009-01-01

The Digital Shoreline Analysis System (DSAS) version 4.0 is a software extension to ESRI ArcGIS v.9.2 and above that enables a user to calculate shoreline rate-of-change statistics from multiple historic shoreline positions. A user-friendly interface of simple buttons and menus guides the user through the major steps of shoreline change analysis. Components of the extension and user guide include (1) instruction on the proper way to define a reference baseline for measurements, (2) automated and manual generation of measurement transects and metadata based on user-specified parameters, and (3) output of calculated rates of shoreline change and other statistical information. DSAS computes shoreline rates of change using four different methods: (1) endpoint rate, (2) simple linear regression, (3) weighted linear regression, and (4) least median of squares. The standard error, correlation coefficient, and confidence interval are also computed for the simple and weighted linear-regression methods. The results of all rate calculations are output to a table that can be linked to the transect file by a common attribute field. DSAS is intended to facilitate the shoreline change-calculation process and to provide rate-of-change information and the statistical data necessary to establish the reliability of the calculated results. The software is also suitable for any generic application that calculates positional change over time, such as assessing rates of change of glacier limits in sequential aerial photos, river edge boundaries, land-cover changes, and so on.

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms.

PubMed

Molnos, Sophie; Baumbach, Clemens; Wahl, Simone; Müller-Nurasyid, Martina; Strauch, Konstantin; Wang-Sattler, Rui; Waldenberger, Melanie; Meitinger, Thomas; Adamski, Jerzy; Kastenmüller, Gabi; Suhre, Karsten; Peters, Annette; Grallert, Harald; Theis, Fabian J; Gieger, Christian

2017-09-29

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different "omics" layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .

Isovolumic relaxation period as an index of left ventricular relaxation under different afterload conditions--comparison with the time constant of left ventricular pressure decay in the dog.

PubMed

Ochi, H; Ikuma, I; Toda, H; Shimada, T; Morioka, S; Moriyama, K

1989-12-01

In order to determine whether isovolumic relaxation period (IRP) reflects left ventricular relaxation under different afterload conditions, 17 anesthetized, open chest dogs were studied, and the left ventricular pressure decay time constant (T) was calculated. In 12 dogs, angiotensin II and nitroprusside were administered, with the heart rate constant at 90 beats/min. Multiple linear regression analysis showed that the aortic dicrotic notch pressure (AoDNP) and T were major determinants of IRP, while left ventricular end-diastolic pressure was a minor determinant. Multiple linear regression analysis, correlating T with IRP and AoDNP, did not further improve the correlation coefficient compared with that between T and IRP. We concluded that correction of the IRP by AoDNP is not necessary to predict T from additional multiple linear regression. The effects of ascending aortic constriction or angiotensin II on IRP were examined in five dogs, after pretreatment with propranolol. Aortic constriction caused a significant decrease in IRP and T, while angiotensin II produced a significant increase in IRP and T. IRP was affected by the change of afterload. However, the IRP and T values were always altered in the same direction. These results demonstrate that IRP is substituted for T and it reflects left ventricular relaxation even in different afterload conditions. We conclude that IRP is a simple parameter easily used to evaluate left ventricular relaxation in clinical situations.

Conditional parametric models for storm sewer runoff

NASA Astrophysics Data System (ADS)

Jonsdottir, H.; Nielsen, H. Aa; Madsen, H.; Eliasson, J.; Palsson, O. P.; Nielsen, M. K.

2007-05-01

The method of conditional parametric modeling is introduced for flow prediction in a sewage system. It is a well-known fact that in hydrological modeling the response (runoff) to input (precipitation) varies depending on soil moisture and several other factors. Consequently, nonlinear input-output models are needed. The model formulation described in this paper is similar to the traditional linear models like final impulse response (FIR) and autoregressive exogenous (ARX) except that the parameters vary as a function of some external variables. The parameter variation is modeled by local lines, using kernels for local linear regression. As such, the method might be referred to as a nearest neighbor method. The results achieved in this study were compared to results from the conventional linear methods, FIR and ARX. The increase in the coefficient of determination is substantial. Furthermore, the new approach conserves the mass balance better. Hence this new approach looks promising for various hydrological models and analysis.

Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days.

PubMed

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.

Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

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.

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…

Novel Approach to Estimate Kidney and Cyst Volumes using Mid-Slice Magnetic Resonance Images in Polycystic Kidney Disease

PubMed Central

Bae, Kyongtae T; Tao, Cheng; Wang, Jinhong; Kaya, Diana; Wu, Zhiyuan; Bae, Junu T; Chapman, Arlene B; Torres, Vicente E; Grantham, Jared J; Mrug, Michal; Bennett, William M; Flessner, Michael F; Landsittel, Doug P

2013-01-01

Objective To evaluate whether kidney and cyst volumes can be accurately estimated based on limited area measurements from MR images of patients with autosomal dominant polycystic kidney disease (ADPKD). Materials and Methods MR coronal images of 178 ADPKD participants from the Consortium for Radiologic Imaging Studies of ADPKD (CRISP) were analyzed. For each MR image slice, we measured kidney and renal cyst areas using stereology and region-based thresholding methods, respectively. The kidney and cyst ‘observed’ volumes were calculated by summing up the area measurements of all the slices covering the kidney. To estimate the volume, we selected a coronal mid-slice in each kidney and multiplied its area by the total number of slices (‘PANK2’ for kidney and ‘PANC2’ for cyst). We then compared the kidney and cyst volumes predicted from PANK2 and PANC2, respectively, to the corresponding observed volumes, using a linear regression analysis. Results The kidney volume predicted from PANK2 correlated extremely well with the observed kidney volume: R2=0.994 for right and 0.991 for left kidney. The linear regression coefficient multiplier to PANK2 that best fit the kidney volume was 0.637 (95%CI: 0.629–0.644) for right and 0.624 (95%CI: 0.616–0.633) for left kidney. The correlation between the cyst volume predicted from PANC2 and the observed cyst volume was also very high: R2=0.984 for right and 0.967 for left kidney. The least squares linear regression coefficient for PANC2 was 0.637 (95%CI: 0.624–0.649) for right and 0.608 (95%CI: 0.591–0.625) for left kidney. Conclusion Kidney and cyst volumes can be closely approximated by multiplying the product of the mid-slice area measurement and the total number of slices in the coronal MR images of ADPKD kidneys by 0.61–0.64. This information will help save processing time needed to estimate total kidney and cyst volumes of ADPKD kidneys. PMID:24107679

Society of Thoracic Surgeons Risk Score predicts hospital charges and resource use after aortic valve replacement.

PubMed

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.

Comparison of exercise capacity in COPD and other etiologies of chronic respiratory failure requiring non-invasive mechanical ventilation at home: retrospective analysis of 1-year follow-up.

PubMed

Salturk, Cuneyt; Karakurt, Zuhal; Takir, Huriye Berk; Balci, Merih; Kargin, Feyza; Mocin, Ozlem Yazıcıoglu; Gungor, Gokay; Ozmen, Ipek; Oztas, Selahattin; Yalcinsoy, Murat; Evin, Ruya; Ozturk, Murat; Adiguzel, Nalan

2015-01-01

The objective of this study was to compare the change in 6-minute walking distance (6MWD) in 1 year as an indicator of exercise capacity among patients undergoing home non-invasive mechanical ventilation (NIMV) due to chronic hypercapnic respiratory failure (CHRF) caused by different etiologies. This retrospective cohort study was conducted in a tertiary pulmonary disease hospital in patients who had completed 1-year follow-up under home NIMV because of CHRF with different etiologies (ie, chronic obstructive pulmonary disease [COPD], obesity hypoventilation syndrome [OHS], kyphoscoliosis [KS], and diffuse parenchymal lung disease [DPLD]), between January 2011 and January 2012. The results of arterial blood gas (ABG) analyses and spirometry, and 6MWD measurements with 12-month interval were recorded from the patient files, in addition to demographics, comorbidities, and body mass indices. The groups were compared in terms of 6MWD via analysis of variance (ANOVA) and multiple linear regression (MLR) analysis (independent variables: analysis age, sex, baseline 6MWD, baseline forced expiratory volume in 1 second, and baseline partial carbon dioxide pressure, in reference to COPD group). A total of 105 patients with a mean age (± standard deviation) of 61±12 years of whom 37 had COPD, 34 had OHS, 20 had KS, and 14 had DPLD were included in statistical analysis. There were no significant differences between groups in the baseline and delta values of ABG and spirometry findings. Both univariate ANOVA and MLR showed that the OHS group had the lowest baseline 6MWD and the highest decrease in 1 year (linear regression coefficient -24.48; 95% CI -48.74 to -0.21, P=0.048); while the KS group had the best baseline values and the biggest improvement under home NIMV (linear regression coefficient 26.94; 95% CI -3.79 to 57.66, P=0.085). The 6MWD measurements revealed improvement in exercise capacity test in CHRF patients receiving home NIMV treatment on long-term depends on etiological diagnoses.

Development and evaluation of a reservoir model for the Chain of Lakes in Illinois

USGS Publications Warehouse

Domanski, Marian M.

2017-01-27

Forecasts of flows entering and leaving the Chain of Lakes reservoir on the Fox River in northeastern Illinois are critical information to water-resource managers who determine the optimal operation of the dam at McHenry, Illinois, to help minimize damages to property and loss of life because of flooding on the Fox River. In 2014, the U.S. Geological Survey; the Illinois Department of Natural Resources, Office of Water Resources; and National Weather Service, North Central River Forecast Center began a cooperative study to develop a system to enable engineers and planners to simulate and communicate flows and to prepare proactively for precipitation events in near real time in the upper Fox River watershed. The purpose of this report is to document the development and evaluation of the Chain of Lakes reservoir model developed in this study.The reservoir model for the Chain of Lakes was developed using the Hydrologic Engineering Center–Reservoir System Simulation program. Because of the complex relation between the dam headwater and reservoir pool elevations, the reservoir model uses a linear regression model that relates dam headwater elevation to reservoir pool elevation. The linear regression model was developed using 17 U.S. Geological Survey streamflow measurements, along with the gage height in the reservoir pool and the gage height at the dam headwater. The Nash-Sutcliffe model efficiency coefficients for all three linear regression model variables ranged from 0.90 to 0.98.The reservoir model performance was evaluated by graphically comparing simulated and observed reservoir pool elevation time series during nine periods of high pool elevation. In addition, the peak elevations during these time periods were graphically compared to the closest-in-time observed pool elevation peak. The mean difference in the simulated and observed peak elevations was -0.03 feet, with a standard deviation of 0.19 feet. The Nash-Sutcliffe coefficient for peak prediction was calculated as 0.94. Evaluation of the model based on accuracy of peak prediction and the ability to simulate an elevation time series showed the performance of the model was satisfactory.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

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.

Generating linear regression model to predict motor functions by use of laser range finder during TUG.

PubMed

Adachi, Daiki; Nishiguchi, Shu; Fukutani, Naoto; Hotta, Takayuki; Tashiro, Yuto; Morino, Saori; Shirooka, Hidehiko; Nozaki, Yuma; Hirata, Hinako; Yamaguchi, Moe; Yorozu, Ayanori; Takahashi, Masaki; Aoyama, Tomoki

2017-05-01

The purpose of this study was to investigate which spatial and temporal parameters of the Timed Up and Go (TUG) test are associated with motor function in elderly individuals. This study included 99 community-dwelling women aged 72.9 ± 6.3 years. Step length, step width, single support time, variability of the aforementioned parameters, gait velocity, cadence, reaction time from starting signal to first step, and minimum distance between the foot and a marker placed to 3 in front of the chair were measured using our analysis system. The 10-m walk test, five times sit-to-stand (FTSTS) test, and one-leg standing (OLS) test were used to assess motor function. Stepwise multivariate linear regression analysis was used to determine which TUG test parameters were associated with each motor function test. Finally, we calculated a predictive model for each motor function test using each regression coefficient. In stepwise linear regression analysis, step length and cadence were significantly associated with the 10-m walk test, FTSTS and OLS test. Reaction time was associated with the FTSTS test, and step width was associated with the OLS test. Each predictive model showed a strong correlation with the 10-m walk test and OLS test (P < 0.01), which was not significant higher correlation than TUG test time. We showed which TUG test parameters were associated with each motor function test. Moreover, the TUG test time regarded as the lower extremity function and mobility has strong predictive ability in each motor function test. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Near infrared spectral linearisation in quantifying soluble solids content of intact carambola.

PubMed

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-04-12

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques.

Near Infrared Spectral Linearisation in Quantifying Soluble Solids Content of Intact Carambola

PubMed Central

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-01-01

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques. PMID:23584118

A method for operative quantitative interpretation of multispectral images of biological tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-10-01

A method for operative retrieval of spatial distributions of biophysical parameters of a biological tissue by using a multispectral image of it has been developed. The method is based on multiple regressions between linearly independent components of the diffuse reflection spectrum of the tissue and unknown parameters. Possibilities of the method are illustrated by an example of determining biophysical parameters of the skin (concentrations of melanin, hemoglobin and bilirubin, blood oxygenation, and scattering coefficient of the tissue). Examples of quantitative interpretation of the experimental data are presented.

Variance approach for multi-objective linear programming with fuzzy random of objective function coefficients

NASA Astrophysics Data System (ADS)

Indarsih, Indrati, Ch. Rini

2016-02-01

In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.

Age- and sex-dependent regression models for predicting the live weight of West African Dwarf goat from body measurements.

PubMed

Sowande, O S; Oyewale, B F; Iyasere, O S

2010-06-01

The relationships between live weight and eight body measurements of West African Dwarf (WAD) goats were studied using 211 animals under farm condition. The animals were categorized based on age and sex. Data obtained on height at withers (HW), heart girth (HG), body length (BL), head length (HL), and length of hindquarter (LHQ) were fitted into simple linear, allometric, and multiple-regression models to predict live weight from the body measurements according to age group and sex. Results showed that live weight, HG, BL, LHQ, HL, and HW increased with the age of the animals. In multiple-regression model, HG and HL best fit the model for goat kids; HG, HW, and HL for goat aged 13-24 months; while HG, LHQ, HW, and HL best fit the model for goats aged 25-36 months. Coefficients of determination (R(2)) values for linear and allometric models for predicting the live weight of WAD goat increased with age in all the body measurements, with HG being the most satisfactory single measurement in predicting the live weight of WAD goat. Sex had significant influence on the model with R(2) values consistently higher in females except the models for LHQ and HW.

Cellulose microfibril orientation of Picea abies and its variability at the micron-level determined by Raman imaging.

PubMed

Gierlinger, Notburga; Luss, Saskia; König, Christian; Konnerth, Johannes; Eder, Michaela; Fratzl, Peter

2010-01-01

The functional characteristics of plant cell walls depend on the composition of the cell wall polymers, as well as on their highly ordered architecture at scales from a few nanometres to several microns. Raman spectra of wood acquired with linear polarized laser light include information about polymer composition as well as the alignment of cellulose microfibrils with respect to the fibre axis (microfibril angle). By changing the laser polarization direction in 3 degrees steps, the dependency between cellulose and laser orientation direction was investigated. Orientation-dependent changes of band height ratios and spectra were described by quadratic linear regression and partial least square regressions, respectively. Using the models and regressions with high coefficients of determination (R(2) > 0.99) microfibril orientation was predicted in the S1 and S2 layers distinguished by the Raman imaging approach in cross-sections of spruce normal, opposite, and compression wood. The determined microfibril angle (MFA) in the different S2 layers ranged from 0 degrees to 49.9 degrees and was in coincidence with X-ray diffraction determination. With the prerequisite of geometric sample and laser alignment, exact MFA prediction can complete the picture of the chemical cell wall design gained by the Raman imaging approach at the micron level in all plant tissues.

Leptin but not adiponectin is related to type 2 diabetes mellitus in obese adolescents.

PubMed

Reinehr, Thomas; Woelfle, Joachim; Wiegand, Susanna; Karges, Beate; Meissner, Thomas; Nagl, Katrin; Holl, Reinhard W

2016-06-01

Adipokines have been suggested to be involved in the development of type 2 diabetes mellitus (T2DM). However, studies in humans are controversial and analyzes at the onset of disease are scarce. We compared adiponectin and leptin levels between 74 predominately Caucasian adolescents with T2DM and 74 body mass index (BMI)-, age-, and gender-matched controls without T2DM. Adiponectin and leptin were correlated to age, BMI, hemoglobin A1c (HbA1c), blood pressure, and lipids. Adolescents with T2DM showed significant lower leptin levels as compared with controls (18 ± 12 vs. 37 ± 23 ng/mL, p < 0.001), whereas the adiponectin levels did not differ between the adolescents with and without T2DM (5.0 ± 2.5 vs. 4.9 ± 2.5 µg/mL, p = 0.833). The associations between adiponectin and high-density lipoprotein (HDL) cholesterol (r = 0.42), systolic (r = -0.15), and diastolic blood pressure (r = -0.20) were stronger as the associations of leptin to these parameters (all r < 0.07). In multiple linear regression analysis, leptin was significantly and positively associated with BMI [β-coefficient: 1.3 (95% confidence interval (95% CI): ±0.5), p < 0.001] and female sex [β-coefficient: 9.7 (95% CI: ±6.7), p = 0.005], and negatively with age [β-coefficient: -2.3 (95% CI: ±2.1), p < 0.001] and HbA1c [β-coefficient -3.1 (95% CI: ±2.1), p = 0.011]. Adiponectin was not significantly associated with BMI, HbA1c, age, or gender in multiple linear regression analysis. Because adiponectin levels did not differ between obese adolescents with and without T2DM, hypoadiponectinemia as observed in obesity seems not to be involved in the genesis of T2DM. The relative hypoleptinemia in obese adolescents with T2DM as compared with obese adolescents without T2DM may contribute to the development of T2DM. Future longitudinal studies in humans are necessary to prove this hypothesis. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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…

Multi-sensory landscape assessment: the contribution of acoustic perception to landscape evaluation.

PubMed

Gan, Yonghong; Luo, Tao; Breitung, Werner; Kang, Jian; Zhang, Tianhai

2014-12-01

In this paper, the contribution of visual and acoustic preference to multi-sensory landscape evaluation was quantitatively compared. The real landscapes were treated as dual-sensory ambiance and separated into visual landscape and soundscape. Both were evaluated by 63 respondents in laboratory conditions. The analysis of the relationship between respondent's visual and acoustic preference as well as their respective contribution to landscape preference showed that (1) some common attributes are universally identified in assessing visual, aural and audio-visual preference, such as naturalness or degree of human disturbance; (2) with acoustic and visual preferences as variables, a multi-variate linear regression model can satisfactorily predict landscape preference (R(2 )= 0.740), while the coefficients of determination for a unitary linear regression model were 0.345 and 0.720 for visual and acoustic preference as predicting factors, respectively; (3) acoustic preference played a much more important role in landscape evaluation than visual preference in this study (the former is about 4.5 times of the latter), which strongly suggests a rethinking of the role of soundscape in environment perception research and landscape planning practice.

Acculturation and health-related quality of life: results from the German National Cohort migrant feasibility study.

PubMed

Brand, Tilman; Samkange-Zeeb, Florence; Ellert, Ute; Keil, Thomas; Krist, Lilian; Dragano, Nico; Jöckel, Karl-Heinz; Razum, Oliver; Reiss, Katharina; Greiser, Karin Halina; Zimmermann, Heiko; Becher, Heiko; Zeeb, Hajo

2017-06-01

We assessed the association between acculturation and health-related quality of life (HRQoL) among persons with a Turkish migrant background in Germany. 1226 adults of Turkish origin were recruited in four German cities. Acculturation was assessed using the Frankfurt Acculturation Scale resulting in four groups (integration, assimilation, separation and marginalization). Short Form-8 physical and mental components were used to assess the HRQoL. Associations were analysed with linear regression models. Of the respondents, 20% were classified as integrated, 29% assimilated, 29% separated and 19% as marginalized. Separation was associated with poorer physical and mental health (linear regression coefficient (RC) = -2.3, 95% CI -3.9 to -0.8 and RC = -2.4, 95% CI -4.4 to -0.5, respectively; reference: integration). Marginalization was associated with poorer mental health in descendants of migrants (RC = -6.4, 95% CI -12.0 to -0.8; reference: integration). Separation and marginalization are associated with a poorer HRQoL. Policies should support the integration of migrants, and health promotion interventions should target separated and marginalized migrants to improve their HRQoL.

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.

Estimation of particulate nutrient load using turbidity meter.

PubMed

Yamamoto, K; Suetsugi, T

2006-01-01

The "Nutrient Load Hysteresis Coefficient" was proposed to evaluate the hysteresis of the nutrient loads to flow rate quantitatively. This could classify the runoff patterns of nutrient load into 15 patterns. Linear relationships between the turbidity and the concentrations of particulate nutrients were observed. It was clarified that the linearity was caused by the influence of the particle size on turbidity output and accumulation of nutrients on smaller particles (diameter < 23 microm). The L-Q-Turb method, which is a new method for the estimation of runoff loads of nutrients using a regression curve between the turbidity and the concentrations of particulate nutrients, was developed. This method could raise the precision of the estimation of nutrient loads even if they had strong hysteresis to flow rate. For example, as for the runoff load of total phosphorus load on flood events in a total of eight cases, the averaged error of estimation of total phosphorus load by the L-Q-Turb method was 11%, whereas the averaged estimation error by the regression curve between flow rate and nutrient load was 28%.

Estimating regression coefficients from clustered samples: Sampling errors and optimum sample allocation

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.

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.

QSPR study of polychlorinated diphenyl ethers by molecular electronegativity distance vector (MEDV-4).

PubMed

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.

Developing a stroke severity index based on administrative data was feasible using data mining techniques.

PubMed

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.

Ion radial diffusion in an electrostatic impulse model for stormtime ring current formation

NASA Technical Reports Server (NTRS)

Chen, Margaret W.; Schulz, Michael; Lyons, Larry R.; Gorney, David J.

1992-01-01

Two refinements to the quasi-linear theory of ion radial diffusion are proposed and examined analytically with simulations of particle trajectories. The resonance-broadening correction by Dungey (1965) is applied to the quasi-linear diffusion theory by Faelthammar (1965) for an individual model storm. Quasi-linear theory is then applied to the mean diffusion coefficients resulting from simulations of particle trajectories in 20 model storms. The correction for drift-resonance broadening results in quasi-linear diffusion coefficients with discrepancies from the corresponding simulated values that are reduced by a factor of about 3. Further reductions in the discrepancies are noted following the averaging of the quasi-linear diffusion coefficients, the simulated coefficients, and the resonance-broadened coefficients for the 20 storms. Quasi-linear theory provides good descriptions of particle transport for a single storm but performs even better in conjunction with the present ensemble-averaging.

Estimating the chlorophyll content in the waters of Guanabara Bay from the LANDSAT multispectral scanning digital data

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Bentancurt, J. J. V.; Herz, B. R.; Molion, L. B.

1980-01-01

Detection of water quality in Guanabara Bay using multispectral scanning digital data taken from LANDSAT satellites was examined. To test these processes, an empirical (statistical) approach was choosen to observe the degree of relationship between LANDSAT data and the in situ data taken simultaneously. The linear and nonlinear regression analyses were taken from among those developed by INPE in 1978. Results indicate that the major regression was in the number six MSS band, atmospheric effects, which indicated a correction coefficient of 0.99 and an average error of 6.59 micrograms liter. This error was similar to that obtained in the laboratory. The chlorophyll content was between 0 and 100 micrograms/liter, as taken from the MSS of LANDSAT.

The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan

NASA Astrophysics Data System (ADS)

Reddy, Ramakrushna; Nair, Rajesh R.

2013-10-01

This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source-receiver distance of up to 150 km during the period 1998-2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.

Statistical procedures for evaluating daily and monthly hydrologic model predictions

USGS Publications Warehouse

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.

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

On marker-based parentage verification via non-linear optimization.

PubMed

Boerner, Vinzent

2017-06-15

Parentage verification by molecular markers is mainly based on short tandem repeat markers. Single nucleotide polymorphisms (SNPs) as bi-allelic markers have become the markers of choice for genotyping projects. Thus, the subsequent step is to use SNP genotypes for parentage verification as well. Recent developments of algorithms such as evaluating opposing homozygous SNP genotypes have drawbacks, for example the inability of rejecting all animals of a sample of potential parents. This paper describes an algorithm for parentage verification by constrained regression which overcomes the latter limitation and proves to be very fast and accurate even when the number of SNPs is as low as 50. The algorithm was tested on a sample of 14,816 animals with 50, 100 and 500 SNP genotypes randomly selected from 40k genotypes. The samples of putative parents of these animals contained either five random animals, or four random animals and the true sire. Parentage assignment was performed by ranking of regression coefficients, or by setting a minimum threshold for regression coefficients. The assignment quality was evaluated by the power of assignment (P[Formula: see text]) and the power of exclusion (P[Formula: see text]). If the sample of putative parents contained the true sire and parentage was assigned by coefficient ranking, P[Formula: see text] and P[Formula: see text] were both higher than 0.99 for the 500 and 100 SNP genotypes, and higher than 0.98 for the 50 SNP genotypes. When parentage was assigned by a coefficient threshold, P[Formula: see text] was higher than 0.99 regardless of the number of SNPs, but P[Formula: see text] decreased from 0.99 (500 SNPs) to 0.97 (100 SNPs) and 0.92 (50 SNPs). If the sample of putative parents did not contain the true sire and parentage was rejected using a coefficient threshold, the algorithm achieved a P[Formula: see text] of 1 (500 SNPs), 0.99 (100 SNPs) and 0.97 (50 SNPs). The algorithm described here is easy to implement, fast and accurate, and is able to assign parentage using genomic marker data with a size as low as 50 SNPs.

Are Predictive Equations for Estimating Resting Energy Expenditure Accurate in Asian Indian Male Weightlifters?

PubMed

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.

Association of Cortical β-Amyloid with Erythrocyte Membrane Monounsaturated and Saturated Fatty Acids in Older Adults at Risk of Dementia.

PubMed

Hooper, C; De Souto Barreto, P; Payoux, P; Salabert, A S; Guyonnet, S; Andrieu, S; Sourdet, S; Delrieu, J; Vellas, B

2017-01-01

We examined the relationships between erythrocyte membrane monounsaturated fatty acids (MUFAs) and saturated fatty acids (SFAs) and cortical β-amyloid (Aβ) load in older adults reporting subjective memory complaints. This is a cross-sectional study using data from the Multidomain Alzheimer Preventive Trial (MAPT); a randomised controlled trial. French community dwellers aged 70 or over reporting subjective memory complaints, but free from a diagnosis of clinical dementia. Participants of this study were 61 individuals from the placebo arm of the MAPT trial with data on erythrocyte membrane fatty acid levels and cortical Aβ load. Cortical-to-cerebellar standard uptake value ratios were assessed using [18F] florbetapir positron emission tomography (PET). Fatty acids were measured in erythrocyte cell membranes using gas chromatography. Associations between erythrocyte membrane MUFAs and SFAs and cortical Aβ load were explored using adjusted multiple linear regression models and were considered significant at p ≤ 0.005 (10 comparisons) after correction for multiple testing. We found no significant associations between fatty acids and cortical Aβ load using multiple linear regression adjusted for age, sex, education, cognition, PET-scan to clinical assessment interval, PET-scan to blood collection interval and apolipoprotein E (ApoE) status. The association closest to significance was that between erythrocyte membrane stearic acid and Aβ (B-coefficient 0.03, 95 % CI: 0.00,0.05, p = 0.05). This association, although statistically non-significant, appeared to be stronger amongst ApoE ε4 carriers (B-coefficient 0.04, 95 % CI: -0.01,0.09, p = 0.08) compared to ApoE ε4 non-carriers (B-coefficient 0.02, 95 % CI: -0.01,0.05, p = 0.18) in age and sex stratified analysis. Future research in the form of large longitudinal observational study is needed to validate our findings, particularly regarding the potential association of stearic acid with cortical Aβ.

Colorectal liver metastases: contrast agent diffusion coefficient for quantification of contrast enhancement heterogeneity at MR imaging.

PubMed

Jia, Guang; O'Dell, Craig; Heverhagen, Johannes T; Yang, Xiangyu; Liang, Jiachao; Jacko, Richard V; Sammet, Steffen; Pellas, Theodore; Cole, Patricia; Knopp, Michael V

2008-09-01

To describe and determine the reproducibility of a simplified model to quantitatively measure heterogeneous intralesion contrast agent diffusion in colorectal liver metastases. This HIPAA-compliant retrospective study received institutional review board approval, and written informed consent was obtained from 14 patients (mean age, 61 years +/- 9 [standard deviation]; range, 41-78 years), including 10 men (mean age, 65 years +/- 8; range, 47-78 years) and four women (mean age, 54 years +/- 9; range, 41-59 years), with colorectal liver metastases. Magnetic resonance (MR) imaging was performed twice (first baseline MR image [B(1)] and second baseline MR image [B(2)]) in a single target lesion prior to therapy. Dynamic contrast material-enhanced MR imaging was performed by using a saturation-recovery fast gradient-echo sequence. A simplified contrast agent diffusion model was proposed, and a contrast agent diffusion coefficient (CDC) was calculated. The reproducibility of the CDC measurement was evaluated by using the Bland-Altman plot and a linear regression model. The mean CDC was 0.22 mm(2)/sec (range, 0.01-0.73 mm(2)/sec) on B(1) and 0.24 mm(2)/sec (range, 0.01-0.71 mm(2)/sec) on B(2), with an intraclass correlation coefficient of 0.91 (P < .0001). Bland-Altman plot showed good agreement, with a mean difference in measurement pairs of 0.017 mm(2)/sec +/- 0.096. The slope from the linear regression model was 0.89 (95% confidence interval: 0.63, 1.15) and the intercept was 0.01 (95% confidence interval: -0.08, 0.09). The CDC enables a quantitative description of contrast enhancement heterogeneity in lesions. Given the high reproducibility of the CDC metric, CDC appears promising for further qualification as an imaging biomarker of change measurement in response assessment. http://radiology.rsnajnls.org/cgi/content/full/248/3/901/DC1. RSNA, 2008

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…

Standardization of domestic frying processes by an engineering approach.

PubMed

Franke, K; Strijowski, U

2011-05-01

An approach was developed to enable a better standardization of domestic frying of potato products. For this purpose, 5 domestic fryers differing in heating power and oil capacity were used. A very defined frying process using a highly standardized model product and a broad range of frying conditions was carried out in these fryers and the development of browning representing an important quality parameter was measured. Product-to-oil ratio, oil temperature, and frying time were varied. Quite different color changes were measured in the different fryers although the same frying process parameters were applied. The specific energy consumption for water evaporation (spECWE) during frying related to product amount was determined for all frying processes to define an engineering parameter for characterizing the frying process. A quasi-linear regression approach was applied to calculate this parameter from frying process settings and fryer properties. The high significance of the regression coefficients and a coefficient of determination close to unity confirmed the suitability of this approach. Based on this regression equation, curves for standard frying conditions (SFC curves) were calculated which describe the frying conditions required to obtain the same level of spECWE in the different domestic fryers. Comparison of browning results from the different fryers operated at conditions near the SFC curves confirmed the applicability of the approach. © 2011 Institute of Food Technologists®

Application of face centred central composite design to optimise compression force and tablet diameter for the formulation of mechanically strong and fast disintegrating orodispersible tablets.

PubMed

Pabari, Ritesh M; Ramtoola, Zebunnissa

2012-07-01

A two factor, three level (3(2)) face centred, central composite design (CCD) was applied to investigate the main and interaction effects of tablet diameter and compression force (CF) on hardness, disintegration time (DT) and porosity of mannitol based orodispersible tablets (ODTs). Tablet diameters of 10, 13 and 15 mm, and CF of 10, 15 and 20 kN were studied. Results of multiple linear regression analysis show that both the tablet diameter and CF influence tablet characteristics. A negative value of regression coefficient for tablet diameter showed an inverse relationship with hardness and DT. A positive value of regression coefficient for CF indicated an increase in hardness and DT with increasing CF as a result of the decrease in tablet porosity. Interestingly, at the larger tablet diameter of 15 mm, while hardness increased and porosity decreased with an increase in CF, the DT was resistant to change. The optimised combination was a tablet of 15 mm diameter compressed at 15 kN showing a rapid DT of 37.7s and high hardness of 71.4N. Using these parameters, ODTs containing ibuprofen showed no significant change in DT (ANOVA; p>0.05) irrespective of the hydrophobicity of the ibuprofen. Copyright © 2012 Elsevier B.V. All rights reserved.

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

PubMed

Marill, Keith A

2004-01-01

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

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

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.

Responsive copolymers for enhanced petroleum recovery. Quarterly technical progress report, June 23--September 21, 1994

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

McCormick, C.; Hester, R.

Summaries are given on the technical progress on three tasks of this project. Monomer and polymer synthesis discusses the preparation of 1(7-aminoheptyloxymethyl)naphthalene and poly(maleic anhydride-alt-ethyl vinyl ether). Task 2, Characterization of molecular structure, discusses terpolymer solution preparation, UV analysis, fluorescence analysis, low angle laser light scattering, and viscometry. The paper discusses the effects of hydrophobic groups, the effect of pH, the effect of electrolyte addition, and photophysical studies. Task 3, Solution properties, describes the factorial experimental design for characterizing polymer solutions by light scattering, the light scattering test model, orthogonal factorial test design, linear regression in coded space, confidence levelmore » for coded space test mode coefficients, coefficients of the real space test model, and surface analysis of the model equations.« less

Condensation heat transfer correlation for water-ethanol vapor mixture flowing through a plate heat exchanger

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%.

Tire-road friction coefficient estimation based on the resonance frequency of in-wheel motor drive system

NASA Astrophysics Data System (ADS)

Chen, Long; Bian, Mingyuan; Luo, Yugong; Qin, Zhaobo; Li, Keqiang

2016-01-01

In this paper, a resonance frequency-based tire-road friction coefficient (TRFC) estimation method is proposed by considering the dynamics performance of the in-wheel motor drive system under small slip ratio conditions. A frequency response function (FRF) is deduced for the drive system that is composed of a dynamic tire model and a simplified motor model. A linear relationship between the squared system resonance frequency and the TFRC is described with the FRF. Furthermore, the resonance frequency is identified by the Auto-Regressive eXogenous model using the information of the motor torque and the wheel speed, and the TRFC is estimated thereafter by a recursive least squares filter with the identified resonance frequency. Finally, the effectiveness of the proposed approach is demonstrated through simulations and experimental tests on different road surfaces.

Patient satisfaction with ambulatory care in Germany: effects of patient- and medical practice-related factors.

PubMed

Auras, Silke; Ostermann, Thomas; de Cruppé, Werner; Bitzer, Eva-Maria; Diel, Franziska; Geraedts, Max

2016-12-01

The study aimed to illustrate the effect of the patients' sex, age, self-rated health and medical practice specialization on patient satisfaction. Secondary analysis of patient survey data using multilevel analysis (generalized linear mixed model, medical practice as random effect) using a sequential modelling strategy. We examined the effects of the patients' sex, age, self-rated health and medical practice specialization on four patient satisfaction dimensions: medical practice organization, information, interaction, professional competence. The study was performed in 92 German medical practices providing ambulatory care in general medicine, internal medicine or gynaecology. In total, 9888 adult patients participated in a patient survey using the validated 'questionnaire on satisfaction with ambulatory care-quality from the patient perspective [ZAP]'. We calculated four models for each satisfaction dimension, revealing regression coefficients with 95% confidence intervals (CIs) for all independent variables, and using Wald Chi-Square statistic for each modelling step (model validity) and LR-Tests to compare the models of each step with the previous model. The patients' sex and age had a weak effect (maximum regression coefficient 1.09, CI 0.39; 1.80), and the patients' self-rated health had the strongest positive effect (maximum regression coefficient 7.66, CI 6.69; 8.63) on satisfaction ratings. The effect of medical practice specialization was heterogeneous. All factors studied, specifically the patients' self-rated health, affected patient satisfaction. Adjustment should always be considered because it improves the comparability of patient satisfaction in medical practices with atypically varying patient populations and increases the acceptance of comparisons. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis.

PubMed

Kamruzzaman, Mohammed; Sun, Da-Wen; ElMasry, Gamal; Allen, Paul

2013-01-15

Many studies have been carried out in developing non-destructive technologies for predicting meat adulteration, but there is still no endeavor for non-destructive detection and quantification of adulteration in minced lamb meat. The main goal of this study was to develop and optimize a rapid analytical technique based on near-infrared (NIR) hyperspectral imaging to detect the level of adulteration in minced lamb. Initial investigation was carried out using principal component analysis (PCA) to identify the most potential adulterate in minced lamb. Minced lamb meat samples were then adulterated with minced pork in the range 2-40% (w/w) at approximately 2% increments. Spectral data were used to develop a partial least squares regression (PLSR) model to predict the level of adulteration in minced lamb. Good prediction model was obtained using the whole spectral range (910-1700 nm) with a coefficient of determination (R(2)(cv)) of 0.99 and root-mean-square errors estimated by cross validation (RMSECV) of 1.37%. Four important wavelengths (940, 1067, 1144 and 1217 nm) were selected using weighted regression coefficients (Bw) and a multiple linear regression (MLR) model was then established using these important wavelengths to predict adulteration. The MLR model resulted in a coefficient of determination (R(2)(cv)) of 0.98 and RMSECV of 1.45%. The developed MLR model was then applied to each pixel in the image to obtain prediction maps to visualize the distribution of adulteration of the tested samples. The results demonstrated that the laborious and time-consuming tradition analytical techniques could be replaced by spectral data in order to provide rapid, low cost and non-destructive testing technique for adulterate detection in minced lamb meat. Copyright © 2012 Elsevier B.V. All rights reserved.

Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

PubMed

Shafizadeh-Moghadam, Hossein; Valavi, Roozbeh; Shahabi, Himan; Chapi, Kamran; Shirzadi, Ataollah

2018-07-01

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Peripheral Refraction, Peripheral Eye Length, and Retinal Shape in Myopia.

PubMed

Verkicharla, Pavan K; Suheimat, Marwan; Schmid, Katrina L; Atchison, David A

2016-09-01

To investigate how peripheral refraction and peripheral eye length are related to retinal shape. Relative peripheral refraction (RPR) and relative peripheral eye length (RPEL) were determined in 36 young adults (M +0.75D to -5.25D) along horizontal and vertical visual field meridians out to ±35° and ±30°, respectively. Retinal shape was determined in terms of vertex radius of curvature Rv, asphericity Q, and equivalent radius of curvature REq using a partial coherence interferometry method involving peripheral eye lengths and model eye raytracing. Second-order polynomial fits were applied to RPR and RPEL as functions of visual field position. Linear regressions were determined for the fits' second order coefficients and for retinal shape estimates as functions of central spherical refraction. Linear regressions investigated relationships of RPR and RPEL with retinal shape estimates. Peripheral refraction, peripheral eye lengths, and retinal shapes were significantly affected by meridian and refraction. More positive (hyperopic) relative peripheral refraction, more negative RPELs, and steeper retinas were found along the horizontal than along the vertical meridian and in myopes than in emmetropes. RPR and RPEL, as represented by their second-order fit coefficients, correlated significantly with retinal shape represented by REq. Effects of meridian and refraction on RPR and RPEL patterns are consistent with effects on retinal shape. Patterns derived from one of these predict the others: more positive (hyperopic) RPR predicts more negative RPEL and steeper retinas, more negative RPEL predicts more positive relative peripheral refraction and steeper retinas, and steeper retinas derived from peripheral eye lengths predict more positive RPR.

Normal reference values for bladder wall thickness on CT in a healthy population.

PubMed

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.

The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs

PubMed Central

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

Modeling of tropospheric NO2 column over different climatic zones and land use/land cover types in South Asia

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.

Cerebral and somatic near-infrared spectroscopy measurements during fluid challenge in cardiac surgery patients: a descriptive pilot study.

PubMed

Fellahi, Jean-Luc; Fischer, Marc-Olivier; Rebet, Olivier; Dalbera, Audrey; Massetti, Massimo; Gérard, Jean-Louis; Hanouz, Jean-Luc

2013-04-01

Little is known about changes in near-infrared spectroscopy (NIRS)-derived cerebral (rSO(2)b) and somatic (rSO(2)s) oxygen saturation during a fluid challenge. The authors tested the hypothesis that they could differ from central venous oxygen saturation (ScvO(2)) and from one site to another. A prospective observational study. A teaching university hospital. Fifty consecutive adult patients. Admission to the intensive care unit after cardiac surgery and investigation before and after a fluid challenge. Simultaneous comparative ScvO(2), rSO(2)b, and rSO(2)s data points were collected from a blood-gas analyzer and the EQUANOX monitor (Nonin Medical, Inc, Plymouth, MN). Correlations were determined by linear regression. Multiple stepwise linear regression models were used to assess independent variables associated with changes in ScvO(2), rSO(2)b, and rSO(2)s. A statistically significant relationship was found between absolute values of ScvO(2) and rSO(2)b (r = 0.42, p < 0.001) but not between absolute values of ScvO(2) and rSO(2)s (r = 0.18, p = 0.066). No relationship was found between percent changes in ScvO(2) and rSO(2)b (r = 0.05, p = 0.715) and between percent changes in ScvO(2) and rSO(2)s (r = 0.02, p = 0.886) after the fluid challenge. Cardiac index contributed to the prediction of changes in ScvO(2) (regression coefficient = -4.09, p = 0.006), whereas the mean arterial pressure contributed to the prediction of changes in rSO(2)b (regression coefficient = -0.05, p = 0.027). rSO(2)b and rSO(2)s cannot be used to provide noninvasive estimation of ScvO(2), and trends in rSO(2)b and rSO(2)s cannot be considered as noninvasive surrogates for the trend in ScvO(2) after cardiac surgery. Different independent variables contribute to the prediction of ScvO(2), rSO(2)b, and rSO(2)s. Copyright © 2013 Elsevier Inc. All rights reserved.

Method and Excel VBA Algorithm for Modeling Master Recession Curve Using Trigonometry Approach.

PubMed

Posavec, Kristijan; Giacopetti, Marco; Materazzi, Marco; Birk, Steffen

2017-11-01

A new method was developed and implemented into an Excel Visual Basic for Applications (VBAs) algorithm utilizing trigonometry laws in an innovative way to overlap recession segments of time series and create master recession curves (MRCs). Based on a trigonometry approach, the algorithm horizontally translates succeeding recession segments of time series, placing their vertex, that is, the highest recorded value of each recession segment, directly onto the appropriate connection line defined by measurement points of a preceding recession segment. The new method and algorithm continues the development of methods and algorithms for the generation of MRC, where the first published method was based on a multiple linear/nonlinear regression model approach (Posavec et al. 2006). The newly developed trigonometry-based method was tested on real case study examples and compared with the previously published multiple linear/nonlinear regression model-based method. The results show that in some cases, that is, for some time series, the trigonometry-based method creates narrower overlaps of the recession segments, resulting in higher coefficients of determination R 2 , while in other cases the multiple linear/nonlinear regression model-based method remains superior. The Excel VBA algorithm for modeling MRC using the trigonometry approach is implemented into a spreadsheet tool (MRCTools v3.0 written by and available from Kristijan Posavec, Zagreb, Croatia) containing the previously published VBA algorithms for MRC generation and separation. All algorithms within the MRCTools v3.0 are open access and available free of charge, supporting the idea of running science on available, open, and free of charge software. © 2017, National Ground Water Association.

Impact of divorce on the quality of life in school-age children.

PubMed

Eymann, Alfredo; Busaniche, Julio; Llera, Julián; De Cunto, Carmen; Wahren, Carlos

2009-01-01

To assess psychosocial quality of life in school-age children of divorced parents. A cross-sectional survey was conducted at the pediatric outpatient clinic of a community hospital. Children 5 to 12 years old from married families and divorced families were included. Child quality of life was assessed through maternal reports using a Child Health Questionnaire-Parent Form 50. A multiple linear regression model was constructed including clinically relevant variables significant on univariate analysis (beta coefficient and 95%CI). Three hundred and thirty families were invited to participate and 313 completed the questionnaire. Univariate analysis showed that quality of life was significantly associated with parental separation, child sex, time spent with the father, standard of living, and maternal education. In a multiple linear regression model, quality of life scores decreased in boys -4.5 (-6.8 to -2.3) and increased for time spent with the father 0.09 (0.01 to 0.2). In divorced families, multiple linear regression showed that quality of life scores increased when parents had separated by mutual agreement 6.1 (2.7 to 9.4), when the mother had university level education 5.9 (1.7 to 10.1) and for each year elapsed since separation 0.6 (0.2 to 1.1), whereas scores decreased in boys -5.4 (-9.5 to -1.3) and for each one-year increment of maternal age -0.4 (-0.7 to -0.05). Children's psychosocial quality of life was affected by divorce. The Child Health Questionnaire can be useful to detect a decline in the psychosocial quality of life.

A combined evaluation of the characteristics and acute toxicity of antibiotic wastewater.

PubMed

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.

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

PubMed

Zhang, Hua; Kurgan, Lukasz

2014-12-01

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

Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities

PubMed Central

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

Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities.

PubMed

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.

Penalized spline estimation for functional coefficient regression models.

PubMed

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.

Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer.

PubMed

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.

Influence of socio-economic inequality measured by the Gini coefficient on meningitis incidence caused by Mycobacterium tuberculosis and Haemophilus influenzae in Colombia, 2008-2011.

PubMed

Acevedo-Mendoza, Wilmer F; Buitrago Gómez, Diana Paola; Atehortua-Otero, Miguel Ángel; Páez, Miguel Ángel; Jiménez-Rincón, Manuela; Lagos-Grisales, Guillermo J; Rodríguez-Morales, Alfonso J

2017-03-01

Bacterial meningitis is an important cause of infectious neurological morbidity and mortality. Its incidence has decreased with the introduction of vaccination programmes against preventable agents. However, low-income and middle-income countries with poor access to health care still have a significant burden of the disease. Thus, the relationship between the Gini coefficient and H. influenzae and M. tuberculosis meningitis incidence in Colombia, during 2008-2011, was assessed. In this ecological study, the Gini coefficient was obtained from the Colombian Department of Statistics, incidence rates were calculated (cases/1,000,000 pop) and linear regressions were performed using the Gini coefficient, to assess the relationship between the latter and the incidence of meningitis. It was observed that when inequality increases in the Colombian departments, the incidence of meningitis also increases, with a significant association in the models (p<0.01) for both M. tuberculosis (r²=0.2382; p<0.001) and H. influenzae (r²=0.2509; p<0.001). This research suggests that high Gini coefficient values influence the incidence of Mycobacterium tuberculosis and Haemophilus influenzae meningitis, showing that social inequality is critical to disease occurrence. Early detection, supervised treatment, vaccination coverage, access to health care are efficient control strategies.

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

Tang, Kunkun, E-mail: ktg@illinois.edu; Inria Bordeaux – Sud-Ouest, Team Cardamom, 200 avenue de la Vieille Tour, 33405 Talence; Congedo, Pietro M.

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable formore » real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.« less

Dietary intake in adults at risk for Huntington disease: analysis of PHAROS research participants.

PubMed

Marder, K; Zhao, H; Eberly, S; Tanner, C M; Oakes, D; Shoulson, I

2009-08-04

To examine caloric intake, dietary composition, and body mass index (BMI) in participants in the Prospective Huntington At Risk Observational Study (PHAROS). Caloric intake and macronutrient composition were measured using the National Cancer Institute Food Frequency Questionnaire (FFQ) in 652 participants at risk for Huntington disease (HD) who did not meet clinical criteria for HD. Logistic regression was used to examine the relationship between macronutrients, BMI, caloric intake, and genetic status (CAG <37 vs CAG > or =37), adjusting for age, gender, and education. Linear regression was used to determine the relationship between caloric intake, BMI, and CAG repeat length. A total of 435 participants with CAG <37 and 217 with CAG > or =37 completed the FFQ. Individuals in the CAG > or =37 group had a twofold odds of being represented in the second, third, or fourth quartile of caloric intake compared to the lowest quartile adjusted for age, gender, education, and BMI. This relationship was attenuated in the highest quartile when additionally adjusted for total motor score. In subjects with CAG > or =37, higher caloric intake, but not BMI, was associated with both higher CAG repeat length (adjusted regression coefficient = 0.26, p = 0.032) and 5-year probability of onset of HD (adjusted regression coefficient = 0.024; p = 0.013). Adjusted analyses showed no differences in macronutrient composition between groups. Increased caloric intake may be necessary to maintain body mass index in clinically unaffected individuals with CAG repeat length > or =37. This may be related to increased energy expenditure due to subtle motor impairment or a hypermetabolic state.

Summertime Minimum Streamflow Elasticity to Antecendent Winter Precipitation, Peak Snow Water Equivalent and Summertime Evaporative Demand in the Western US Maritime Mountains

NASA Astrophysics Data System (ADS)

Schaperow, J.; Cooper, M. G.; Cooley, S. W.; Alam, S.; Smith, L. C.; Lettenmaier, D. P.

2017-12-01

As climate regimes shift, streamflows and our ability to predict them will change, as well. Elasticity of summer minimum streamflow is estimated for 138 unimpaired headwater river basins across the maritime western US mountains to better understand how climatologic variables and geologic characteristics interact to determine the response of summer low flows to winter precipitation (PPT), spring snow water equivalent (SWE), and summertime potential evapotranspiration (PET). Elasticities are calculated using log log linear regression, and linear reservoir storage coefficients are used to represent basin geology. Storage coefficients are estimated using baseflow recession analysis. On average, SWE, PET, and PPT explain about 1/3 of the summertime low flow variance. Snow-dominated basins with long timescales of baseflow recession are least sensitive to changes in SWE, PPT, and PET, while rainfall-dominated, faster draining basins are most sensitive. There are also implications for the predictability of summer low flows. The R2 between streamflow and SWE drops from 0.62 to 0.47 from snow-dominated to rain-dominated basins, while there is no corresponding increase in R2 between streamflow and PPT.

Comparison of co-expression measures: mutual information, correlation, and model based indices.

PubMed

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.

Histological Grading of Hepatocellular Carcinomas with Intravoxel Incoherent Motion Diffusion-weighted Imaging: Inconsistent Results Depending on the Fitting Method.

PubMed

Ichikawa, Shintaro; Motosugi, Utaroh; Hernando, Diego; Morisaka, Hiroyuki; Enomoto, Nobuyuki; Matsuda, Masanori; Onishi, Hiroshi

2018-04-10

To compare the abilities of three intravoxel incoherent motion (IVIM) imaging approximation methods to discriminate the histological grade of hepatocellular carcinomas (HCCs). Fifty-eight patients (60 HCCs) underwent IVIM imaging with 11 b-values (0-1000 s/mm 2 ). Slow (D) and fast diffusion coefficients (D * ) and the perfusion fraction (f) were calculated for the HCCs using the mean signal intensities in regions of interest drawn by two radiologists. Three approximation methods were used. First, all three parameters were obtained simultaneously using non-linear fitting (method A). Second, D was obtained using linear fitting (b = 500 and 1000), followed by non-linear fitting for D * and f (method B). Third, D was obtained by linear fitting, f was obtained using the regression line intersection and signals at b = 0, and non-linear fitting was used for D * (method C). A receiver operating characteristic analysis was performed to reveal the abilities of these methods to distinguish poorly-differentiated from well-to-moderately-differentiated HCCs. Inter-reader agreements were assessed using intraclass correlation coefficients (ICCs). The measurements of D, D * , and f in methods B and C (Az-value, 0.658-0.881) had better discrimination abilities than did those in method A (Az-value, 0.527-0.607). The ICCs of D and f were good to excellent (0.639-0.835) with all methods. The ICCs of D * were moderate with methods B (0.580) and C (0.463) and good with method A (0.705). The IVIM parameters may vary depending on the fitting methods, and therefore, further technical refinement may be needed.

Discrimination of serum Raman spectroscopy between normal and colorectal cancer

NASA Astrophysics Data System (ADS)

Li, Xiaozhou; Yang, Tianyue; Yu, Ting; Li, Siqi

2011-07-01

Raman spectroscopy of tissues has been widely studied for the diagnosis of various cancers, but biofluids were seldom used as the analyte because of the low concentration. Herein, serum of 30 normal people, 46 colon cancer, and 44 rectum cancer patients were measured Raman spectra and analyzed. The information of Raman peaks (intensity and width) and that of the fluorescence background (baseline function coefficients) were selected as parameters for statistical analysis. Principal component regression (PCR) and partial least square regression (PLSR) were used on the selected parameters separately to see the performance of the parameters. PCR performed better than PLSR in our spectral data. Then linear discriminant analysis (LDA) was used on the principal components (PCs) of the two regression method on the selected parameters, and a diagnostic accuracy of 88% and 83% were obtained. The conclusion is that the selected features can maintain the information of original spectra well and Raman spectroscopy of serum has the potential for the diagnosis of colorectal cancer.

Quantile Regression Models for Current Status Data

PubMed Central

Ou, Fang-Shu; Zeng, Donglin; Cai, Jianwen

2016-01-01

Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging. PMID:27994307

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy

NASA Astrophysics Data System (ADS)

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-01

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R2 and pseudo R2 were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R2 ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R2 = 0.31), but there was still large variability between patients in R2. The R2 from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy.

PubMed

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-07

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R(2) and pseudo R(2) were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R(2) ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R(2) = 0.31), but there was still large variability between patients in R(2). The R(2) from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

A quantitative property-property relationship for the internal diffusion coefficients of organic compounds in solid materials.

PubMed

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.

Neonates with Bartter syndrome have enormous fluid and sodium requirements.

PubMed

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.

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.

Estimating the effects of wages on obesity.

PubMed

Kim, DaeHwan; Leigh, John Paul

2010-05-01

To estimate the effects of wages on obesity and body mass. Data on household heads, aged 20 to 65 years, with full-time jobs, were drawn from the Panel Study of Income Dynamics for 2003 to 2007. The Panel Study of Income Dynamics is a nationally representative sample. Instrumental variables (IV) for wages were created using knowledge of computer software and state legal minimum wages. Least squares (linear regression) with corrected standard errors were used to estimate the equations. Statistical tests revealed both instruments were strong and tests for over-identifying restrictions were favorable. Wages were found to be predictive (P < 0.05) of obesity and body mass in regressions both before and after applying IVs. Coefficient estimates suggested stronger effects in the IV models. Results are consistent with the hypothesis that low wages increase obesity prevalence and body mass.

[Is the rate of medical publication from Israel similar to other countries? A comparative study of three medical specialties].

PubMed

Zer, Matan; Lindner, Arie; Greenstein, Alexander; Leibovici, Dan

2011-07-01

Academic careers of individual doctors are commonly evaluated by examining the number and quality of authored publications. Similarly, the extent and quality of medical research may be assessed nationwide by measuring the number of publications originating from the country of interest over time. This in turn, may indicate on the quality of medicine practiced. To evaluate the extent and quality of IsraeLi publications we measured the rate and quality of medical publications originating from Israel for two decades in the fields of urology, cardiology and orthopedics, and compared the data to those of other countries. Leading journals in urology, cardiology, and orthopedics were selected. A Medline search (http://www.ncbi.ntm.nih.gov/sites/entrez] was conducted for all the publications originating in Israel between the years 1990-2009 in the selected journals. Data from Israel was compared to those from Italy, France, Germany, Egypt and Turkey. The change in rate of publications was tested using Linear regression. The quality of publications was calculated by multiplying the number of publications by the relevant impact factor. While the urology publications rate in Israel increased by 32.7% in the second study decade as compared with the first, the urology publication rates during the same time period from Italy, France, Germany, Egypt and Turkey were 199%, 115%, 184%, 180% and 227% respectively. The regression coefficient for the urology publication rate was 0.51 for Israel, and 0.78, 0.95, 0.78, 0.87 and 0.97 for the other countries, respectively. The regression coefficient for the change in the quality of publications from Israel was 0.31 and 0.81, 0.75, 0.92, 0.73, and 0.92 for the other countries, respectively. In cardiology, the Israeli publication rate increased by 26% during the second study decade, whereas in the other countries the increments were 46%, 35%, 76%, 80% and 309% respectively. The regression coefficient for Israeli pubLication rate was 0.45, and 0.78, 0.54, 0.62, 0.13 and 0.75 for the other countries, respectively. The regression coefficient of the quality of publications in Israel was 0.3 as opposed to 0.47, 0.36, 0.48, 0.01, and 0.78 respectively. The Israeli publications in orthopedics increased by 9.3% during the second decade compared with the first. At the same time, other countries increased the publication rate in orthopedics by 69%, 121%, 173%, 140% and 296% respectively. The regression coefficient for the publication rate in orthopedics was 0.02 for Israel, and 0.62, 0.64, 0.78, 0.34 and 0.71 for the other countries, respectively. The regression coefficient of the quality of publications in Israel was 0.05 as opposed to 0.67, 0.62, 0.75, 0.31, and 0.66 in the other countries, respectively. Israel lags behind Italy, France, Germany, Egypt and Turkey with regard to the increase of both the number and the quality of medical publications in urology and orthopedics. While the rate and quality of IsraeLi publications in cardiology surpasses those from Egypt, they lag in the number of publications in this medical field behind those of all the rest of the countries examined. In a world of rapid progress and expansion of medical research, Israel has been stagnant in publications in 3 medical specialties, rendering it inferior to other nations.

Terahertz spectral detection of potassium sorbate in milk powder

NASA Astrophysics Data System (ADS)

Li, Pengpeng; Zhang, Yuan; Ge, Hongyi

2017-02-01

The spectral characteristics of potassium sorbate in milk powder in the range of 0.2 2.0 THz have been measured with THz time-domain spectroscopy(THz-TDS). Its absorption and refraction spectra are obtained at room temperature in the nitrogen atmosphere. The results showed that potassium sorbate at 0.98 THz obvious characteristic absorption peak. The simple linear regression(SLR) model was taken to analyze the content of potassium sorbate in milk powder. The results showed that the absorption coefficient increases as the mixture potassium sorbate increases. The research is important to food quality and safety testing.

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

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.

Competing regression models for longitudinal data.

PubMed

Alencar, Airlane P; Singer, Julio M; Rocha, Francisco Marcelo M

2012-03-01

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cellulose microfibril orientation of Picea abies and its variability at the micron-level determined by Raman imaging

PubMed Central

Gierlinger, Notburga; Luss, Saskia; König, Christian; Konnerth, Johannes; Eder, Michaela; Fratzl, Peter

2010-01-01

The functional characteristics of plant cell walls depend on the composition of the cell wall polymers, as well as on their highly ordered architecture at scales from a few nanometres to several microns. Raman spectra of wood acquired with linear polarized laser light include information about polymer composition as well as the alignment of cellulose microfibrils with respect to the fibre axis (microfibril angle). By changing the laser polarization direction in 3° steps, the dependency between cellulose and laser orientation direction was investigated. Orientation-dependent changes of band height ratios and spectra were described by quadratic linear regression and partial least square regressions, respectively. Using the models and regressions with high coefficients of determination (R2 > 0.99) microfibril orientation was predicted in the S1 and S2 layers distinguished by the Raman imaging approach in cross-sections of spruce normal, opposite, and compression wood. The determined microfibril angle (MFA) in the different S2 layers ranged from 0° to 49.9° and was in coincidence with X-ray diffraction determination. With the prerequisite of geometric sample and laser alignment, exact MFA prediction can complete the picture of the chemical cell wall design gained by the Raman imaging approach at the micron level in all plant tissues. PMID:20007198

Robust best linear estimator for Cox regression with instrumental variables in whole cohort and surrogates with additive measurement error in calibration sample

PubMed Central

Wang, Ching-Yun; Song, Xiao

2017-01-01

SUMMARY Biomedical researchers are often interested in estimating the effect of an environmental exposure in relation to a chronic disease endpoint. However, the exposure variable of interest may be measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies an additive measurement error model, but it may not have repeated measurements. The subset in which the surrogate variables are available is called a calibration sample. In addition to the surrogate variables that are available among the subjects in the calibration sample, we consider the situation when there is an instrumental variable available for all study subjects. An instrumental variable is correlated with the unobserved true exposure variable, and hence can be useful in the estimation of the regression coefficients. In this paper, we propose a nonparametric method for Cox regression using the observed data from the whole cohort. The nonparametric estimator is the best linear combination of a nonparametric correction estimator from the calibration sample and the difference of the naive estimators from the calibration sample and the whole cohort. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via intensive simulation studies. The methods are applied to the Nutritional Biomarkers Study of the Women’s Health Initiative. PMID:27546625

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

[Optimal extraction of effective constituents from Aralia elata by central composite design and response surface methodology].

PubMed

Lv, Shao-Wa; Liu, Dong; Hu, Pan-Pan; Ye, Xu-Yan; Xiao, Hong-Bin; Kuang, Hai-Xue

2010-03-01

To optimize the process of extracting effective constituents from Aralia elata by response surface methodology. The independent variables were ethanol concentration, reflux time and solvent fold, the dependent variable was extraction rate of total saponins in Aralia elata. Linear or no-linear mathematic models were used to estimate the relationship between independent and dependent variables. Response surface methodology was used to optimize the process of extraction. The prediction was carried out through comparing the observed and predicted values. Regression coefficient of binomial fitting complex model was as high as 0.9617, the optimum conditions of extraction process were 70% ethanol, 2.5 hours for reflux, 20-fold solvent and 3 times for extraction. The bias between observed and predicted values was -2.41%. It shows the optimum model is highly predictive.

Clinical evaluation of the Technico Stat/Ion system.

PubMed

Slaunwhite, D; Clements, J C; Reynoso, G

1977-02-01

1. We describe our evaluation of the Technicon Stat/Ion, an instrument which performs sodium, chloride and bicarbonate analysis simultaneously. 2. All four of the assays resulted in linear response over the entire clinical range with insignificant carryover between specimens. 3. Precision studies for within-run variation were: sodium 0.3 percent, potassium 0.7 percent, chloride 0.5 percent and bicarbonate 1.6 percent. Day-to-day precision was similar to the within-run precision. 4. Comparison methods for sodium, potassium, chloride and bicarbonate utilizing flame photometry, chloridometry and titration of released carbon dioxide respectively showed the following linear regression and correlation coefficients: sodium y=0.96+5.5 (a=0.988) potassium y=1.01x+0.0 (a=.996) chloride y=0.99x+1.0 (a=.993)bicarbonate y=1.0x+1.2 (alpha=.969).

On the concept of sloped motion for free-floating wave energy converters.

PubMed

Payne, Grégory S; Pascal, Rémy; Vaillant, Guillaume

2015-10-08

A free-floating wave energy converter (WEC) concept whose power take-off (PTO) system reacts against water inertia is investigated herein. The main focus is the impact of inclining the PTO direction on the system performance. The study is based on a numerical model whose formulation is first derived in detail. Hydrodynamics coefficients are obtained using the linear boundary element method package WAMIT. Verification of the model is provided prior to its use for a PTO parametric study and a multi-objective optimization based on a multi-linear regression method. It is found that inclining the direction of the PTO at around 50° to the vertical is highly beneficial for the WEC performance in that it provides a high capture width ratio over a broad region of the wave period range.

On the concept of sloped motion for free-floating wave energy converters

PubMed Central

Payne, Grégory S.; Pascal, Rémy; Vaillant, Guillaume

2015-01-01

A free-floating wave energy converter (WEC) concept whose power take-off (PTO) system reacts against water inertia is investigated herein. The main focus is the impact of inclining the PTO direction on the system performance. The study is based on a numerical model whose formulation is first derived in detail. Hydrodynamics coefficients are obtained using the linear boundary element method package WAMIT. Verification of the model is provided prior to its use for a PTO parametric study and a multi-objective optimization based on a multi-linear regression method. It is found that inclining the direction of the PTO at around 50° to the vertical is highly beneficial for the WEC performance in that it provides a high capture width ratio over a broad region of the wave period range. PMID:26543397

High performance liquid chromatographic assay for the quantitation of total glutathione in plasma

NASA Technical Reports Server (NTRS)

Abukhalaf, Imad K.; Silvestrov, Natalia A.; Menter, Julian M.; von Deutsch, Daniel A.; Bayorh, Mohamed A.; Socci, Robin R.; Ganafa, Agaba A.

2002-01-01

A simple and widely used homocysteine HPLC procedure was applied for the HPLC identification and quantitation of glutathione in plasma. The method, which utilizes SBDF as a derivatizing agent utilizes only 50 microl of sample volume. Linear quantitative response curve was generated for glutathione over a concentration range of 0.3125-62.50 micromol/l. Linear regression analysis of the standard curve exhibited correlation coefficient of 0.999. Limit of detection (LOD) and limit of quantitation (LOQ) values were 5.0 and 15 pmol, respectively. Glutathione recovery using this method was nearly complete (above 96%). Intra-assay and inter-assay precision studies reflected a high level of reliability and reproducibility of the method. The applicability of the method for the quantitation of glutathione was demonstrated successfully using human and rat plasma samples.

Linear ordinary differential equations with constant coefficients. Revisiting the impulsive response method using factorization

NASA Astrophysics Data System (ADS)

Camporesi, Roberto

2011-06-01

We present an approach to the impulsive response method for solving linear constant-coefficient ordinary differential equations based on the factorization of the differential operator. The approach is elementary, we only assume a basic knowledge of calculus and linear algebra. In particular, we avoid the use of distribution theory, as well as of the other more advanced approaches: Laplace transform, linear systems, the general theory of linear equations with variable coefficients and the variation of constants method. The approach presented here can be used in a first course on differential equations for science and engineering majors.

Relationship Between Ktrans and K1 with Simultaneous Versus Separate MR/PET in Rabbits with VX2 Tumors.

PubMed

Lee, Kyung Hee; Kang, Seung Kwan; Goo, Jin Mo; Lee, Jae Sung; Cheon, Gi Jeong; Seo, Seongho; Hwang, Eui Jin

2017-03-01

To compare the relationship between K trans from DCE-MRI and K 1 from dynamic 13 N-NH 3 -PET, with simultaneous and separate MR/PET in the VX-2 rabbit carcinoma model. MR/PET was performed simultaneously and separately, 14 and 15 days after VX-2 tumor implantation at the paravertebral muscle. The K trans and K 1 values were estimated using an in-house software program. The relationships between K trans and K 1 were analyzed using Pearson's correlation coefficients and linear/non-linear regression function. Assuming a linear relationship, K trans and K 1 exhibited a moderate positive correlations with both simultaneous (r=0.54-0.57) and separate (r=0.53-0.69) imaging. However, while the K trans and K 1 from separate imaging were linearly correlated, those from simultaneous imaging exhibited a non-linear relationship. The amount of change in K 1 associated with a unit increase in K trans varied depending on K trans values. The relationship between K trans and K 1 may be mis-interpreted with separate MR and PET acquisition. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Near Infrared Spectrometry of Clinically Significant Fatty Acids Using Multicomponent Regression

NASA Astrophysics Data System (ADS)

Kalinin, A. V.; Krasheninnikov, V. N.; Sviridov, A. P.; Titov, V. N.

2016-11-01

We have developed methods for determining the content of clinically important fatty acids (FAs), primarily saturated palmitic acid, monounsaturated oleic acid, and the sum of polyenoic fatty acids (eicosapentaenoic + docosahexaenoic), in oily media (food products and supplements, fish oils) using different types of near infrared (NIR) spectrometers: Fourier-transform, linear photodiode array, and Raman. Based on a calibration method (regression) by means of projections to latent structures, using standard samples of oil and fat mixtures, we have confirmed the feasibility of reliable and selective quantitative analysis of the above-indicated fatty acids. As a result of comparing the calibration models for Fourier-transform spectrometers in different parts of the NIR range (based on different overtones and combinations of fatty acid absorption), we have provided a basis for selection of the spectral range for a portable linear InGaAs-photodiode array spectrometer. In testing the calibrations of a linear InGaAs-photodiode array spectrometer which is a prototype for a portable instrument, for palmitic and oleic acids and also the sum of the polyenoic fatty acids we have achieved a multiple correlation coefficient of 0.89, 0.85, and 0.96 and a standard error of 0.53%, 1.43%, and 0.39% respectively. We have confirmed the feasibility of using Raman spectra to determine the content of the above-indicated fatty acids in media where water is present.

A long lifetime chemical sensor: study on fluorescence property of fluorescein isothiocyanate and preparation of pH chemical sensor.

PubMed

Ma, Li Ying; Wang, Huai You; Xie, Hui; Xu, Li Xiao

2004-07-01

The fluorescence property of fluorescein isothiocyanate (FITC) in acid-alkaline medium was studied by spectrofluorimetry. The characteristic of FITC response to hydrogen ion has been examined in acid-alkaline solution. A novel pH chemical sensor was prepared based on the relationship between the relative fluorescence intensity of FITC and pH. The measurement of relative fluorescence intensity was carried out at 362 nm with excitation at 250 nm. The excellent linear relationship was obtained between relative fluorescence intensity and pH in the range of pH 1-5. The linear regression equation of the calibration graph is F = 66.871 + 6.605 pH (F is relative fluorescence intensity), with a correlation coefficient of linear regression of 0.9995. Effects of temperature, concentration of FITC on the response to hydrogen ion had been examined. It was important that this chemical sensor was long lifetime, and the property of response to hydrogen ion was stable for at least 70 days. This pH sensor can be used for measuring pH value in water solution. The accuracy is 0.01 pH unit. The results obtained by the pH sensor agreed with those by the pH meter. Obviously, this pH sensor is potential for determining pH change real time in biological system.

A long lifetime chemical sensor: study on fluorescence property of fluorescein isothiocyanate and preparation of pH chemical sensor

NASA Astrophysics Data System (ADS)

Ma, Li Ying; Wang, Huai You; Xie, Hui; Xu, Li Xiao

2004-07-01

The fluorescence property of fluorescein isothiocyanate (FITC) in acid-alkaline medium was studied by spectrofluorimetry. The characteristic of FITC response to hydrogen ion has been examined in acid-alkaline solution. A novel pH chemical sensor was prepared based on the relationship between the relative fluorescence intensity of FITC and pH. The measurement of relative fluorescence intensity was carried out at 362 nm with excitation at 250 nm. The excellent linear relationship was obtained between relative fluorescence intensity and pH in the range of pH 1-5. The linear regression equation of the calibration graph is F=66.871+6.605 pH ( F is relative fluorescence intensity), with a correlation coefficient of linear regression of 0.9995. Effects of temperature, concentration of FITC on the response to hydrogen ion had been examined. It was important that this chemical sensor was long lifetime, and the property of response to hydrogen ion was stable for at least 70 days. This pH sensor can be used for measuring pH value in water solution. The accuracy is 0.01 pH unit. The results obtained by the pH sensor agreed with those by the pH meter. Obviously, this pH sensor is potential for determining pH change real time in biological system.

Smoothing two-dimensional Malaysian mortality data using P-splines indexed by age and year

NASA Astrophysics Data System (ADS)

Kamaruddin, Halim Shukri; Ismail, Noriszura

2014-06-01

Nonparametric regression implements data to derive the best coefficient of a model from a large class of flexible functions. Eilers and Marx (1996) introduced P-splines as a method of smoothing in generalized linear models, GLMs, in which the ordinary B-splines with a difference roughness penalty on coefficients is being used in a single dimensional mortality data. Modeling and forecasting mortality rate is a problem of fundamental importance in insurance company calculation in which accuracy of models and forecasts are the main concern of the industry. The original idea of P-splines is extended to two dimensional mortality data. The data indexed by age of death and year of death, in which the large set of data will be supplied by Department of Statistics Malaysia. The extension of this idea constructs the best fitted surface and provides sensible prediction of the underlying mortality rate in Malaysia mortality case.

Peptide retention prediction using hydrophilic interaction liquid chromatography coupled to mass spectrometry.

PubMed

Badgett, Majors J; Boyes, Barry; Orlando, Ron

2018-02-16

A model that predicts retention for peptides using a HALO ® penta-HILIC column and gradient elution was created. Coefficients for each amino acid were derived using linear regression analysis and these coefficients can be summed to predict the retention of peptides. This model has a high correlation between experimental and predicted retention times (0.946), which is on par with previous RP and HILIC models. External validation of the model was performed using a set of H. pylori samples on the same LC-MS system used to create the model, and the deviation from actual to predicted times was low. Apart from amino acid composition, length and location of amino acid residues on a peptide were examined and two site-specific corrections for hydrophobic residues at the N-terminus as well as hydrophobic residues one spot over from the N-terminus were created. Copyright © 2017 Elsevier B.V. All rights reserved.

Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere.

PubMed

Genc, D Deniz; Yesilyurt, Canan; Tuncel, Gurdal

2010-07-01

Spatial and temporal variations in concentrations of CO, NO, NO(2), SO(2), and PM(10), measured between 1999 and 2000, at traffic-impacted and residential stations in Ankara were investigated. Air quality in residential areas was found to be influenced by traffic activities in the city. Pollutant ratios were proven to be reliable tracers to differentiate between different sources. Air pollution index (API) of the whole city was calculated to evaluate the level of air quality in Ankara. Multiple linear regression model was developed for forecasting API in Ankara. The correlation coefficients were found to be 0.79 and 0.63 for different time periods. The assimilative capacity of Ankara atmosphere was calculated in terms of ventilation coefficient (VC). The relation between API and VC was investigated and found that the air quality in Ankara was determined by meteorology rather than emissions.

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

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.

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle.

PubMed

Boligon, A A; Baldi, F; Mercadante, M E Z; Lobo, R B; Pereira, R J; Albuquerque, L G

2011-06-28

We quantified the potential increase in accuracy of expected breeding value for weights of Nelore cattle, from birth to mature age, using multi-trait and random regression models on Legendre polynomials and B-spline functions. A total of 87,712 weight records from 8144 females were used, recorded every three months from birth to mature age from the Nelore Brazil Program. For random regression analyses, all female weight records from birth to eight years of age (data set I) were considered. From this general data set, a subset was created (data set II), which included only nine weight records: at birth, weaning, 365 and 550 days of age, and 2, 3, 4, 5, and 6 years of age. Data set II was analyzed using random regression and multi-trait models. The model of analysis included the contemporary group as fixed effects and age of dam as a linear and quadratic covariable. In the random regression analyses, average growth trends were modeled using a cubic regression on orthogonal polynomials of age. Residual variances were modeled by a step function with five classes. Legendre polynomials of fourth and sixth order were utilized to model the direct genetic and animal permanent environmental effects, respectively, while third-order Legendre polynomials were considered for maternal genetic and maternal permanent environmental effects. Quadratic polynomials were applied to model all random effects in random regression models on B-spline functions. Direct genetic and animal permanent environmental effects were modeled using three segments or five coefficients, and genetic maternal and maternal permanent environmental effects were modeled with one segment or three coefficients in the random regression models on B-spline functions. For both data sets (I and II), animals ranked differently according to expected breeding value obtained by random regression or multi-trait models. With random regression models, the highest gains in accuracy were obtained at ages with a low number of weight records. The results indicate that random regression models provide more accurate expected breeding values than the traditionally finite multi-trait models. Thus, higher genetic responses are expected for beef cattle growth traits by replacing a multi-trait model with random regression models for genetic evaluation. B-spline functions could be applied as an alternative to Legendre polynomials to model covariance functions for weights from birth to mature age.

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

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.

Progression of Mortality due to Diseases of the Circulatory System and Human Development Index in Rio de Janeiro Municipalities

PubMed Central

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

Progression of Mortality due to Diseases of the Circulatory System and Human Development Index in Rio de Janeiro Municipalities.

PubMed

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.

Accuracy of self-reported physical activity as an indicator of cardiovascular fitness depends on education level.

PubMed

Gerrard, Paul

2012-10-01

To determine whether there is a relationship between the level of education and the accuracy of self-reported physical activity as a proxy measure of aerobic fitness. Data from the National Health and Nutrition Examination from the years 1999 to 2004 were used. Linear regression was performed for measured maximum oxygen consumption (Vo(2)max) versus self-reported physical activity for 5 different levels of education. This was a national survey in the United States. Participants included adults from the general U.S. population (N=3290). None. Coefficients of determination obtained from models for each education level were used to compare how well self-reported physical activity represents cardiovascular fitness. These coefficients were the main outcome measure. Coefficients of determination for Vo(2)max versus reported physical activity increased as the level of education increased. In this preliminary study, self-reported physical activity is a better proxy measure for aerobic fitness in highly educated individuals than in poorly educated individuals. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

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).

Preliminary study of the association between the elimination parameters of phenytoin and phenobarbital.

PubMed

Methaneethorn, Janthima; Panomvana, Duangchit; Vachirayonstien, Thaveechai

2017-09-26

Therapeutic drug monitoring is essential for both phenytoin and phenobarbital therapy given their narrow therapeutic indexes. Nevertheless, the measurement of either phenytoin or phenobarbital concentrations might not be available in some rural hospitals. Information assisting individualized phenytoin and phenobarbital combination therapy is important. This study's objective was to determine the relationship between the maximum rate of metabolism of phenytoin (Vmax) and phenobarbital clearance (CLPB), which can serve as a guide to individualized drug therapy. Data on phenytoin and phenobarbital concentrations of 19 epileptic patients concurrently receiving both drugs were obtained from medical records. Phenytoin and phenobarbital pharmacokinetic parameters were studied at steady-state conditions. The relationship between the elimination parameters of both drugs was determined using simple linear regression. A high correlation coefficient between Vmax and CLPB was found [r=0.744; p<0.001 for Vmax (mg/kg/day) vs. CLPB (L/kg/day)]. Such a relatively strong linear relationship between the elimination parameters of both drugs indicates that Vmax might be predicted from CLPB and vice versa. Regression equations were established for estimating Vmax from CLPB, and vice versa in patients treated with combination of phenytoin and phenobarbital. These proposed equations can be of use in aiding individualized drug therapy.

Use of a tracing task to assess visuomotor performance for evidence of concussion and recuperation.

PubMed

Kelty-Stephen, Damian G; Qureshi Ahmad, Mona; Stirling, Leia

2015-12-01

The likelihood of suffering a concussion while playing a contact sport ranges from 15-45% per year of play. These rates are highly variable as athletes seldom report concussive symptoms, or do not recognize their symptoms. We performed a prospective cohort study (n = 206, aged 10-17) to examine visuomotor tracing to determine the sensitivity for detecting neuromotor components of concussion. Tracing variability measures were investigated for a mean shift with presentation of concussion-related symptoms and a linear return toward baseline over subsequent return visits. Furthermore, previous research relating brain injury to the dissociation of smooth movements into "submovements" led to the expectation that cumulative micropause duration, a measure of motion continuity, might detect likelihood of injury. Separate linear mixed effects regressions of tracing measures indicated that 4 of the 5 tracing measures captured both short-term effects of injury and longer-term effects of recovery with subsequent visits. Cumulative micropause duration has a positive relationship with likelihood of participants having had a concussion. The present results suggest that future research should evaluate how well the coefficients for the tracing parameter in the logistic regression help to detect concussion in novel cases. (c) 2015 APA, all rights reserved).

A pocket-sized metabolic analyzer for assessment of resting energy expenditure.

PubMed

Zhao, Di; Xian, Xiaojun; Terrera, Mirna; Krishnan, Ranganath; Miller, Dylan; Bridgeman, Devon; Tao, Kevin; Zhang, Lihua; Tsow, Francis; Forzani, Erica S; Tao, Nongjian

2014-04-01

The assessment of metabolic parameters related to energy expenditure has a proven value for weight management; however these measurements remain too difficult and costly for monitoring individuals at home. The objective of this study is to evaluate the accuracy of a new pocket-sized metabolic analyzer device for assessing energy expenditure at rest (REE) and during sedentary activities (EE). The new device performs indirect calorimetry by measuring an individual's oxygen consumption (VO2) and carbon dioxide production (VCO2) rates, which allows the determination of resting- and sedentary activity-related energy expenditure. VO2 and VCO2 values of 17 volunteer adult subjects were measured during resting and sedentary activities in order to compare the metabolic analyzer with the Douglas bag method. The Douglas bag method is considered the Gold Standard method for indirect calorimetry. Metabolic parameters of VO2, VCO2, and energy expenditure were compared using linear regression analysis, paired t-tests, and Bland-Altman plots. Linear regression analysis of measured VO2 and VCO2 values, as well as calculated energy expenditure assessed with the new analyzer and Douglas bag method, had the following linear regression parameters (linear regression slope LRS0, and R-squared coefficient, r(2)) with p = 0: LRS0 (SD) = 1.00 (0.01), r(2) = 0.9933 for VO2; LRS0 (SD) = 1.00 (0.01), r(2) = 0.9929 for VCO2; and LRS0 (SD) = 1.00 (0.01), r(2) = 0.9942 for energy expenditure. In addition, results from paired t-tests did not show statistical significant difference between the methods with a significance level of α = 0.05 for VO2, VCO2, REE, and EE. Furthermore, the Bland-Altman plot for REE showed good agreement between methods with 100% of the results within ±2SD, which was equivalent to ≤10% error. The findings demonstrate that the new pocket-sized metabolic analyzer device is accurate for determining VO2, VCO2, and energy expenditure. Copyright © 2013 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Estimation of stature using hand and foot dimensions in Slovak adults.

PubMed

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.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

A fresh look at linear ordinary differential equations with constant coefficients. Revisiting the impulsive response method using factorization

NASA Astrophysics Data System (ADS)

Camporesi, Roberto

2016-01-01

We present an approach to the impulsive response method for solving linear constant-coefficient ordinary differential equations of any order based on the factorization of the differential operator. The approach is elementary, we only assume a basic knowledge of calculus and linear algebra. In particular, we avoid the use of distribution theory, as well as of the other more advanced approaches: Laplace transform, linear systems, the general theory of linear equations with variable coefficients and variation of parameters. The approach presented here can be used in a first course on differential equations for science and engineering majors.

Inland-coastal water interaction: Remote sensing application for shallow-water quality and algal blooms modeling

NASA Astrophysics Data System (ADS)

Melesse, Assefa; Hajigholizadeh, Mohammad; Blakey, Tara

2017-04-01

In this study, Landsat 8 and Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) sensors were used to model the spatiotemporal changes of four water quality parameters: Landsat 8 (turbidity, chlorophyll-a (chl-a), total phosphate, and total nitrogen) and Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) (algal blooms). The study was conducted in Florda bay, south Florida and model outputs were compared with in-situ observed data. The Landsat 8 based study found that, the predictive models to estimate chl-a and turbidity concentrations, developed through the use of stepwise multiple linear regression (MLR), gave high coefficients of determination in dry season (wet season) (R2 = 0.86(0.66) for chl-a and R2 = 0.84(0.63) for turbidity). Total phosphate and TN were estimated using best-fit multiple linear regression models as a function of Landsat TM and OLI,127 and ground data and showed a high coefficient of determination in dry season (wet season) (R2 = 0.74(0.69) for total phosphate and R2 = 0.82(0.82) for TN). Similarly, the ability of SeaWIFS for chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels' benthic class was evaluated. Benthic class was determined through satellite image-based classification methods. It was found that benthic class based chl-a modeling algorithm was better than the existing regionally-tuned approach. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Key words: Landsat, SeaWIFS, water quality, Florida bay, Chl-a, turbidity

Water Intake by Outdoor Temperature Among Children Aged 1-10 Years: Implications for Community Water Fluoridation in the U.S.

PubMed

Beltrán-Aguilar, Eugenio D; Barker, Laurie; Sohn, Woosung; Wei, Liang

2015-01-01

The U.S. water fluoridation recommendations, which have been in place since 1962, were based in part on findings from the 1950s that children's water intake increased with outdoor temperature. We examined whether or not water intake is associated with outdoor temperature. Using linked data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004 and the National Oceanic and Atmospheric Administration, we examined reported 24-hour total and plain water intake in milliliters per kilogram of body weight per day of children aged 1-10 years by maximum outdoor temperature on the day of reported water intake, unadjusted and adjusted for age, sex, race/ethnicity, and poverty status. We applied linear regression methods that were used in previously reported analyses of data from NHANES 1988-1994 and from the 1950s. We found that total water intake was not associated with temperature. Plain water intake was weakly associated with temperature in unadjusted (coefficient 5 0.2, p=0.015) and adjusted (coefficient 5 0.2, p=0.013) linear regression models. However, these models explained little of the individual variation in plain water intake (unadjusted: R(2)=0.005; adjusted: R(2)=0.023). Optimal fluoride concentration in drinking water to prevent caries need not be based on outdoor temperature, given the lack of association between total water intake and outdoor temperature, the weak association between plain water intake and outdoor temperature, and the minimal amount of individual variance in plain water intake explained by outdoor temperature. These findings support the change in the U.S. Public Health Service recommendation for fluoride concentration in drinking water for the prevention of dental caries from temperature-related concentrations to a single concentration that is not related to outdoor temperature.

Relation between histological prostatitis and lower urinary tract symptoms and erectile function.

PubMed

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.

Predictive Regression Equations of Flowmetric and Spirometric Peak Expiratory Flow in Healthy Moroccan Children.

PubMed

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.

The Reliability of Individualized Load-Velocity Profiles.

PubMed

Banyard, Harry G; Nosaka, K; Vernon, Alex D; Haff, G Gregory

2017-11-15

This study examined the reliability of peak velocity (PV), mean propulsive velocity (MPV), and mean velocity (MV) in the development of load-velocity profiles (LVP) in the full depth free-weight back squat performed with maximal concentric effort. Eighteen resistance-trained men performed a baseline one-repetition maximum (1RM) back squat trial and three subsequent 1RM trials used for reliability analyses, with 48-hours interval between trials. 1RM trials comprised lifts from six relative loads including 20, 40, 60, 80, 90, and 100% 1RM. Individualized LVPs for PV, MPV, or MV were derived from loads that were highly reliable based on the following criteria: intra-class correlation coefficient (ICC) >0.70, coefficient of variation (CV) ≤10%, and Cohen's d effect size (ES) <0.60. PV was highly reliable at all six loads. Importantly, MPV and MV were highly reliable at 20, 40, 60, 80 and 90% but not 100% 1RM (MPV: ICC=0.66, CV=18.0%, ES=0.10, standard error of the estimate [SEM]=0.04m·s -1 ; MV: ICC=0.55, CV=19.4%, ES=0.08, SEM=0.04m·s -1 ). When considering the reliable ranges, almost perfect correlations were observed for LVPs derived from PV 20-100% (r=0.91-0.93), MPV 20-90% (r=0.92-0.94) and MV 20-90% (r=0.94-0.95). Furthermore, the LVPs were not significantly different (p>0.05) between trials, movement velocities, or between linear regression versus second order polynomial fits. PV 20-100% , MPV 20-90% , and MV 20-90% are reliable and can be utilized to develop LVPs using linear regression. Conceptually, LVPs can be used to monitor changes in movement velocity and employed as a method for adjusting sessional training loads according to daily readiness.

Continuous blood pressure monitoring via non-invasive radial artery applanation tonometry and invasive arterial catheter demonstrates good agreement in patients undergoing colon carcinoma surgery.

PubMed

Sun, Jing; Chen, Hanjian; Zheng, Jun; Mao, Bin; Zhu, Shengmei; Feng, Jingyi

2017-12-01

Radial artery applanation tonometry (RAAT) has been developed and utilized for continuous arterial pressure monitoring. However, evidence is lacking to clinically verify the RAAT technology and identify appropriate patient groups before routine clinical use. This study aims to evaluate the RAAT technology by comparing systolic blood pressure (SBP), mean blood pressure (MBP) and diastolic blood pressure (DBP) values in patients undergoing colon carcinoma surgery. Blood Pressure (BP) values obtained via RAAT (TL-300, Tensys Medical Inc., San Diego, CA, USA) and conventional arterial catheterization from 30 colon carcinoma surgical patients were collected and compared via Bland-Atman method, linear regression and 4-quadrant plot concordance analysis. For SBPs, MBPs and DBPs, means of the differences (±standard deviation; 95% limits of agreement) were -0.9 (±7.6; -15.7 to 13.9) mmHg, 3.1 (±6.5; -9.6 to 15.8) mmHg and 4.3 (±7.4; -10.3 to 18.8) mmHg, respectively. Linear regression coefficients of determination were 0.8706 for SBPs, 0.8353 for MBPs and 0.6858 for DBPs. Four-quadrant concordance correlation coefficients were 0.8740, 0.8522 and 0.7108 for SBPs, MBPs and DBPs, respectively. A highly selected patient collective undergoing colon carcinoma surgery was studied. BP measurements obtained via the TL-300 had clinically acceptable agreement with that acquired invasively using an arterial catheter. For use in clinical routine, it is necessary to take measures for improvement regarding movement artifacts and dilution of noise. A large sample size of patients under various conditions is also needed to further evaluate the RAAT technology before clinically routine use.

High Right Ventricular Stroke Work Index Is Associated with Worse Kidney Function in Patients with Heart Failure with Preserved Ejection Fraction.

PubMed

Kanjanahattakij, Napatt; Sirinvaravong, Natee; Aguilar, Francisco; Agrawal, Akanksha; Krishnamoorthy, Parasuram; Gupta, Shuchita

2018-01-01

In patients with heart failure with preserved ejection fraction (HFpEF), worse kidney function is associated with worse overall cardiac mechanics. Right ventricular stroke work index (RVSWI) is a parameter of right ventricular function. The aim of our study was to determine the relationship between RVSWI and glomerular filtration rate (GFR) in patients with HFpEF. This was a single-center cross-sectional study. HFpEF is defined as patients with documented heart failure with ejection fraction > 50% and pulmonary wedge pressure > 15 mm Hg from right heart catheterization. RVSWI (normal value 8-12 g/m/beat/m2) was calculated using the formula: RVSWI = 0.0136 × stroke volume index × (mean pulmonary artery pressure - mean right atrial pressure). Univariate and multivariate linear regression analysis was performed to study the correlation between RVSWI and GFR. Ninety-one patients were included in the study. The patients were predominantly female (n = 64, 70%) and African American (n = 61, 67%). Mean age was 66 ± 12 years. Mean GFR was 59 ± 35 mL/min/1.73 m2. Mean RVSWI was 11 ± 6 g/m/beat/m2. Linear regression analysis showed that there was a significant independent inverse relationship between RVSWI and GFR (unstandardized coefficient = -1.3, p = 0.029). In the subgroup with combined post and precapillary pulmonary hypertension (Cpc-PH) the association remained significant (unstandardized coefficient = -1.74, 95% CI -3.37 to -0.11, p = 0.04). High right ventricular workload indicated by high RVSWI is associated with worse renal function in patients with Cpc-PH. Further prospective studies are needed to better understand this association. © 2018 S. Karger AG, Basel.

Modeling Laterality of the Globus Pallidus Internus in Patients With Parkinson's Disease.

PubMed

Sharim, Justin; Yazdi, Daniel; Baohan, Amy; Behnke, Eric; Pouratian, Nader

2017-04-01

Neurosurgical interventions such as deep brain stimulation surgery of the globus pallidus internus (GPi) play an important role in the treatment of medically refractory Parkinson's disease (PD), and require high targeting accuracy. Variability in the laterality of the GPi across patients with PD has not been well characterized. The aim of this report is to identify factors that may contribute to differences in position of the motor region of GPi. The charts and operative reports of 101 PD patients following deep brain stimulation surgery (70 males, aged 11-78 years) representing 201 GPi were retrospectively reviewed. Data extracted for each subject include age, gender, anterior and posterior commissures (AC-PC) distance, and third ventricular width. Multiple linear regression, stepwise regression, and relative importance of regressors analysis were performed to assess the predictive ability of these variables on GPi laterality. Multiple linear regression for target vs. third ventricular width, gender, AC-PC distance, and age were significant for normalized linear regression coefficients of 0.333 (p < 0.0001), 0.206 (p = 0.00219), 0.168 (p = 0.0119), and 0.159 (p = 0.0136), respectively. Third ventricular width, gender, AC-PC distance, and age each account for 44.06% (21.38-65.69%, 95% CI), 20.82% (10.51-35.88%), 21.46% (8.28-37.05%), and 13.66% (2.62-28.64%) of the R 2 value, respectively. Effect size calculation was significant for a change in the GPi laterality of 0.19 mm per mm of ventricular width, 0.11 mm per mm of AC-PC distance, 0.017 mm per year in age, and 0.54 mm increase for male gender. This variability highlights the limitations of indirect targeting alone, and argues for the continued use of MRI as well as intraoperative physiological testing to account for such factors that contribute to patient-specific variability in GPi localization. © 2016 International Neuromodulation Society.

Emission and distribution of phosphine in paddy fields and its relationship with greenhouse gases.

PubMed

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.

Estimating interaction on an additive scale between continuous determinants in a logistic regression model.

PubMed

Knol, Mirjam J; van der Tweel, Ingeborg; Grobbee, Diederick E; Numans, Mattijs E; Geerlings, Mirjam I

2007-10-01

To determine the presence of interaction in epidemiologic research, typically a product term is added to the regression model. In linear regression, the regression coefficient of the product term reflects interaction as departure from additivity. However, in logistic regression it refers to interaction as departure from multiplicativity. Rothman has argued that interaction estimated as departure from additivity better reflects biologic interaction. So far, literature on estimating interaction on an additive scale using logistic regression only focused on dichotomous determinants. The objective of the present study was to provide the methods to estimate interaction between continuous determinants and to illustrate these methods with a clinical example. and results From the existing literature we derived the formulas to quantify interaction as departure from additivity between one continuous and one dichotomous determinant and between two continuous determinants using logistic regression. Bootstrapping was used to calculate the corresponding confidence intervals. To illustrate the theory with an empirical example, data from the Utrecht Health Project were used, with age and body mass index as risk factors for elevated diastolic blood pressure. The methods and formulas presented in this article are intended to assist epidemiologists to calculate interaction on an additive scale between two variables on a certain outcome. The proposed methods are included in a spreadsheet which is freely available at: http://www.juliuscenter.nl/additive-interaction.xls.

Socio-economic factors associated with infant mortality in Italy: an ecological study.

PubMed

Dallolio, Laura; Di Gregori, Valentina; Lenzi, Jacopo; Franchino, Giuseppe; Calugi, Simona; Domenighetti, Gianfranco; Fantini, Maria Pia

2012-08-16

One issue that continues to attract the attention of public health researchers is the possible relationship in high-income countries between income, income inequality and infant mortality (IM). The aim of this study was to assess the associations between IM and major socio-economic determinants in Italy. Associations between infant mortality rates in the 20 Italian regions (2006-2008) and the Gini index of income inequality, mean household income, percentage of women with at least 8 years of education, and percentage of unemployed aged 15-64 years were assessed using Pearson correlation coefficients. Univariate linear regression and multiple stepwise linear regression analyses were performed to determine the magnitude and direction of the effect of the four socio-economic variables on IM. The Gini index and the total unemployment rate showed a positive strong correlation with IM (r = 0.70; p < 0.001 and r = 0.84; p < 0.001 respectively), mean household income showed a strong negative correlation (r = -0.78; p < 0.001), while female educational attainment presented a weak negative correlation (r = -0.45; p < 0.05). Using a multiple stepwise linear regression model, only unemployment rate was independently associated with IM (b = 0.15, p < 0.001). In Italy, a high-income country where health care is universally available, variations in IM were strongly associated with relative and absolute income and unemployment rate. These results suggest that in Italy IM is not only related to income distribution, as demonstrated for other developed countries, but also to economic factors such as absolute income and unemployment. In order to reduce IM and the existing inequalities, the challenge for Italian decision makers is to promote economic growth and enhance employment levels.

Consistency evaluation of values of weight, height, and body mass index in Food Intake and Physical Activity of School Children: the quality control of data entry in the computerized system.

PubMed

Jesus, Gilmar Mercês de; Assis, Maria Alice Altenburg de; Kupek, Emil; Dias, Lizziane Andrade

2017-01-01

The quality control of data entry in computerized questionnaires is an important step in the validation of new instruments. The study assessed the consistency of recorded weight and height on the Food Intake and Physical Activity of School Children (Web-CAAFE) between repeated measures and against directly measured data. Students from the 2nd to the 5th grade (n = 390) had their weight and height directly measured and then filled out the Web-CAAFE. A subsample (n = 92) filled out the Web-CAAFE twice, three hours apart. The analysis included hierarchical linear regression, mixed linear regression model, to evaluate the bias, and intraclass correlation coefficient (ICC), to assess consistency. Univariate linear regression assessed the effect of gender, reading/writing performance, and computer/internet use and possession on residuals of fixed and random effects. The Web-CAAFE showed high values of ICC between repeated measures (body weight = 0.996, height = 0.937, body mass index - BMI = 0.972), and regarding the checked measures (body weight = 0.962, height = 0.882, BMI = 0.828). The difference between means of body weight, height, and BMI directly measured and recorded was 208 g, -2 mm, and 0.238 kg/m², respectively, indicating slight BMI underestimation due to underestimation of weight and overestimation of height. This trend was related to body weight and age. Height and weight data entered in the Web-CAAFE by children were highly correlated with direct measurements and with the repeated entry. The bias found was similar to validation studies of self-reported weight and height in comparison to direct measurements.

An indirect approach to assess the pests on sorghum by remote sensing

NASA Astrophysics Data System (ADS)

Singh, D.; Sao, R.

In today's world of advanced technology various techniques are being used to study ecological parameter and gathering data for agricultural benefits. The major aspects of remote sensing are timely estimates of agriculture crop yield, prediction of pest etc. The damage caused by the pest to crop is well known. Therefore, in this paper, an attempt has to be made to estimate the number of pests on sorghum by remote sensing technique. The studies were made on crop Sorghum (Meethi Sudan) that is a forage variety and the pest observed is a species of grasshopper. The beds of crop sorghum were specially prepared for pests as well as microwave scattering measurements. In first phase of study, dependence of number of pests on sorghum plant parameters (i.e., crop covered moist soil (SM), plant height (PH), leaf area index (LAI), percentage Biomass (BIO), Total chlorophyll (TC)) have been observed by the regression analyses and it was found that pests were more dependent on sorghum chlorophyll than other plant parameters, while climatic conditions were taken as constant. A linear relationship has been obtained between number of pests and TC with quite significant values of coefficient of determination (r^2=0.86). These crop parameters are easily assessable through microwave remote sensing so they can form the basis for prediction of pest remotely. In second phase of study, several observations were carried out for various growth stages of sorghum using bistatic scatterometer for both like polarizations (i.e., HH- and VV-) and different incidence angles at X-band (9.5 GHz). Linear, and multiple regression analysis were carried out to check dependence of scattering coefficient on these crop parameters and it was noticed that scattering coefficient was more dependent on sorghum TC than other plant parameters at X-band. A negative correlation has been obtained between TC and scattering coefficient with quite good values of r^2 (0.82). VV-pol gives better results than HH-pol and incidence angle should be more than 40 degree for both like pols for assessing the sorghum TC at X-band. The TC assessed by the microwave measurements was helpful to estimate the number of pests on sorghum. Combining both phase of study, number of pests was estimated and a quite good agreement (r^2=0.76) was found between observed and estimated pests.

Measurement of true ileal digestibility of phosphorus in some feed ingredients for broiler chickens.

PubMed

Mutucumarana, R K; Ravindran, V; Ravindran, G; Cowieson, A J

2014-12-01

An experiment was conducted to estimate the true ileal digestibility of P in wheat, sorghum, soybean meal, and corn distiller's dried grains with solubles (DDGS) in broiler chickens. Four semipurified diets were formulated from each ingredient (wheat and sorghum: 236.5, 473, 709.5, and 946 g/kg; soybean meal and corn DDGS: 135, 270, 405, and 540 g/kg) to contain graded concentrations of nonphytate P. The experiment was conducted as a randomized complete block design with 4 weight blocks of 16 cages each (5 birds per cage). A total of 320 21-d-old broilers (Ross 308) were assigned to the 16 test diets with 4 replicates per diet. Apparent ileal digestibility coefficients of P were determined by the indicator method and the linear regression method was used to determine the true P digestibility coefficients. The results showed that the apparent ileal P digestibility coefficients of wheat-based diets were not influenced (P>0.05) by increasing dietary P concentrations, whereas those of diets based on sorghum, soybean meal, and corn DDGS differed (P<0.05) at different P concentrations. Apparent ileal P digestibility in broilers fed diets with soybean meal and corn DDGS linearly (P<0.001) increased with increasing P concentrations. True ileal P digestibility coefficients of wheat, sorghum, soybean meal, and corn DDGS were determined to be 0.464, 0.331, 0.798, and 0.727, respectively. Ileal endogenous P losses in birds fed diets with wheat, soybean meal, and corn DDGS were estimated to be 0.080, 0.609, and 0.418 g/kg DMI, respectively. In birds fed sorghum-based diets, endogenous P losses were estimated to be negative (-0.087 g/kg DMI). True digestible P contents of wheat, sorghum, soybean meal, and corn DDGS were determined to be 1.49, 0.78, 5.16, and 5.94 g/kg, respectively. The corresponding nonphytate P contents in wheat, sorghum, soybean meal, and corn DDGS were 1.11, 0.55, 2.15, and 4.36 g/kg, respectively. These differences between digestible P and nonphytate P contents may be suggestive, at least in part, of overestimation of P digestibility under the calcium-deficient conditions used in the regression method.

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification

PubMed Central

Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible. PMID:29666661

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

PubMed

Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

Tribological behaviour and statistical experimental design of sintered iron-copper based composites

NASA Astrophysics Data System (ADS)

Popescu, Ileana Nicoleta; Ghiţă, Constantin; Bratu, Vasile; Palacios Navarro, Guillermo

2013-11-01

The sintered iron-copper based composites for automotive brake pads have a complex composite composition and should have good physical, mechanical and tribological characteristics. In this paper, we obtained frictional composites by Powder Metallurgy (P/M) technique and we have characterized them by microstructural and tribological point of view. The morphology of raw powders was determined by SEM and the surfaces of obtained sintered friction materials were analyzed by ESEM, EDS elemental and compo-images analyses. One lot of samples were tested on a "pin-on-disc" type wear machine under dry sliding conditions, at applied load between 3.5 and 11.5 × 10-1 MPa and 12.5 and 16.9 m/s relative speed in braking point at constant temperature. The other lot of samples were tested on an inertial test stand according to a methodology simulating the real conditions of dry friction, at a contact pressure of 2.5-3 MPa, at 300-1200 rpm. The most important characteristics required for sintered friction materials are high and stable friction coefficient during breaking and also, for high durability in service, must have: low wear, high corrosion resistance, high thermal conductivity, mechanical resistance and thermal stability at elevated temperature. Because of the tribological characteristics importance (wear rate and friction coefficient) of sintered iron-copper based composites, we predicted the tribological behaviour through statistical analysis. For the first lot of samples, the response variables Yi (represented by the wear rate and friction coefficient) have been correlated with x1 and x2 (the code value of applied load and relative speed in braking points, respectively) using a linear factorial design approach. We obtained brake friction materials with improved wear resistance characteristics and high and stable friction coefficients. It has been shown, through experimental data and obtained linear regression equations, that the sintered composites wear rate increases with increasing applied load and relative speed, but in the same conditions, the frictional coefficients slowly decrease.

Measurement of true ileal phosphorus digestibility in meat and bone meal for broiler chickens.

PubMed

Mutucumarana, R K; Ravindran, V; Ravindran, G; Cowieson, A J

2015-07-01

An experiment was conducted to estimate true ileal phosphorus (P:) digestibility of 3 meat and bone meal samples (MBM-1, MBM-2: , and MBM-3:) for broiler chickens. Four semipurified diets were formulated from each sample to contain graded concentrations of P. The experiment was conducted as a completely randomized design with 6 replicates (6 birds per replicate) per dietary treatment. A total of 432 Ross 308 broilers were assigned at 21 d of age to the 12 test diets. The apparent ileal digestibility coefficient of P was determined by the indicator method, and the linear regression method was used to determine the true P digestibility coefficient. The apparent ileal digestibility coefficient of P in birds fed diets containing MBM-1 and MBM-2 was unaffected by increasing dietary concentrations of P (P > 0.05). The apparent ileal digestibility coefficient of P in birds fed the MBM-3 diets decreased with increasing P concentrations (linear, P < 0.001; quadratic, P < 0. 01). In birds fed the MBM-1 and MBM-2 diets, ileal endogenous P losses were estimated to be 0.049 and 0.142 g/kg DM intake (DMI:), respectively. In birds fed the MBM-3 diets, endogenous P loss was estimated to be negative (-0.370 g/kg DMI). True ileal P digestibility of MBM-1, MBM-2, and MBM-3 was determined to be 0.693, 0.608, and 0.420, respectively. True ileal P digestibility coefficients determined for MBM-1 and MBM-2 were similar (P < 0.05), but were higher (P < 0.05) than that for MBM-3. Total P and true digestible P contents of MBM-1, MBM-2, and MBM-3 were determined to be 37.5 and 26.0; 60.2 and 36.6; and 59.8 and 25.1 g/kg, respectively, on an as-fed basis. © 2015 Poultry Science Association Inc.

ACTN3 genotype and physical function and frailty in an elderly Chinese population: the Rugao Longevity and Ageing Study.

PubMed

Ma, Teng; Lu, Deyi; Zhu, Yin-Sheng; Chu, Xue-Feng; Wang, Yong; Shi, Guo-Ping; Wang, Zheng-Dong; Yu, Li; Jiang, Xiao-Yan; Wang, Xiao-Feng

2018-05-01

To examine the associations of the actinin alpha 3 gene (ACTN3) R577X polymorphism with physical performance and frailty in an older Chinese population. Data from 1,463 individuals (57.8% female) aged 70-87 years from the Rugao Longevity and Ageing Study were used. The associations between R577X and timed 5-m walk, grip strength, timed Up and Go test, and frailty index (FI) based on deficits of 23 laboratory tests (FI-Lab) were examined. Analysis of variance and linear regression models were used to evaluate the genetic effects of ACTN3 R577X on physical performance and FI-Lab. The XX and RX genotypes of the ACTN3 R557X polymorphism accounted for 17.1 and 46.9%, respectively. Multivariate regression analysis revealed that in men aged 70-79 years, the ACTN3 577X allele was significantly associated with physical performance (5-m walk time, regression coefficient (β) = 0.258, P = 0.006; grip strength, β = -1.062, P = 0.012; Up and Go test time β = 0.368, P = 0.019). In women aged 70-79 years, a significant association between the ACTN3 577X allele and the FI-Lab score was observed, with a regression coefficient of β = 0.019 (P = 0.003). These findings suggest an age- and gender-specific X-additive model of R577X for 5-m walk time, grip strength, Up and Go Test time, and FI-Lab score. The ACTN3 577X allele is associated with an age- and sex-specific decrease in physical performance and an increase in frailty in an older population.

Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro M.; Abgrall, Rémi

2016-06-01

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.

Functional Capacity and Self-Esteem of People With Cerebral Palsy.

PubMed

Espín-Tello, Sandra Martina; Dickinson, Heather Olivia; Bueno-Lozano, Manuel; Jiménez-Bernadó, María Teresa; Caballero-Navarro, Ana Luisa

We assessed whether functional capacity predicts self-esteem in people with cerebral palsy (CP). We conducted a cross-sectional observational study of 108 people with CP, ages 16-65 yr, who were residents of Spain. Self-esteem was captured using the Rosenberg Self-Esteem Scale (RSES), and functional capacity using the Barthel Index (BI). Sociodemographic characteristics were recorded. The relationship between the RSES score and the BI score was analyzed using linear regression. RSES scores increased significantly as BI scores increased (regression coefficient = 0.047, 95% confidence interval [0.017, 0.078], p = .003). People with a higher level of education, active employment, and independent living arrangements tended to have better functional capacity and higher self-esteem. Greater functional capacity predicted higher self-esteem; this effect is probably partly mediated by education, employment, and living arrangements. Copyright © 2018 by the American Occupational Therapy Association, Inc.

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

Penna, M.L.; Duchiade, M.P.

This study examines the relationship between air pollution, measured as concentration of suspended particulates in the atmosphere, and infant mortality due to pneumonia in the metropolitan area of Rio de Janeiro. Multiple linear regression (progressive or stepwise method) was used to analyze infant mortality due to pneumonia, diarrhea, and all causes in 1980, by geographic area, income level, and degree of contamination. While the variable proportion of families with income equivalent to more than two minimum wages was included in the regressions corresponding to the three types of infant mortality, the average contamination index had a statistically significant coefficient (bmore » = 0.2208; t = 2.670; P = 0.0137) only in the case of mortality due to pneumonia. This would suggest a biological association, but, as in any ecological study, such conclusions should be viewed with caution. The authors believe that air quality indicators are essential to consider in studies of acute respiratory infections in developing countries.« less

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.

Simple method for quick estimation of aquifer hydrogeological parameters

NASA Astrophysics Data System (ADS)

Ma, C.; Li, Y. Y.

2017-08-01

Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.

Performance optimization for rotors in hover and axial flight

NASA Technical Reports Server (NTRS)

Quackenbush, T. R.; Wachspress, D. A.; Kaufman, A. E.; Bliss, D. B.

1989-01-01

Performance optimization for rotors in hover and axial flight is a topic of continuing importance to rotorcraft designers. The aim of this Phase 1 effort has been to demonstrate that a linear optimization algorithm could be coupled to an existing influence coefficient hover performance code. This code, dubbed EHPIC (Evaluation of Hover Performance using Influence Coefficients), uses a quasi-linear wake relaxation to solve for the rotor performance. The coupling was accomplished by expanding of the matrix of linearized influence coefficients in EHPIC to accommodate design variables and deriving new coefficients for linearized equations governing perturbations in power and thrust. These coefficients formed the input to a linear optimization analysis, which used the flow tangency conditions on the blade and in the wake to impose equality constraints on the expanded system of equations; user-specified inequality contraints were also employed to bound the changes in the design. It was found that this locally linearized analysis could be invoked to predict a design change that would produce a reduction in the power required by the rotor at constant thrust. Thus, an efficient search for improved versions of the baseline design can be carried out while retaining the accuracy inherent in a free wake/lifting surface performance analysis.

Pulmonary Catherization Data Correlate Poorly with Renal Function in Heart Failure.

PubMed

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.

Determination of gas-liquid partition coefficients of several organic solutes in trihexyl(tetradecyl)phosphonium bromide using capillary gas chromatography columns.

PubMed

Ronco, Nicolás R; Menestrina, Fiorella; Romero, Lílian M; Castells, Cecilia B

2017-06-09

In this paper, we report gas-liquid partition constants for thirty-five volatile organic solutes in the room temperature ionic liquid trihexyl(tetradecyl)phosphonium bromide measured by gas-liquid chromatography using capillary columns. The relative contribution of gas-liquid partition and interfacial adsorption to retention was evaluated through the use of columns with different the phase ratio. Four capillary columns with exactly known phase ratios were constructed and employed to measure the solute retention factors at four temperatures between 313.15 and 343.15K. The partition coefficients were calculated from the slopes of the linear regression between solute retention factors and the reciprocal of phase ratio at a given temperature according to the gas-liquid chromatographic theory. Gas-liquid interfacial adsorption was detected for a few solutes and it has been considered for the calculations of partition coefficient. Reliable solute's infinite dilution activity coefficients can be obtained when retention data are determined by a unique partitioning mechanism. The partial molar excess enthalpies at infinite dilution have been estimated from the dependence of experimental values of solute activity coefficients with the column temperature. A thorough discussion of the uncertainties of the experimental measurements and the main advantages of the use of capillary columns to acquire the aforementioned relevant thermodynamic information was performed. Copyright © 2017 Elsevier B.V. All rights reserved.

Determination of drying kinetics and convective heat transfer coefficients of ginger slices

NASA Astrophysics Data System (ADS)

Akpinar, Ebru Kavak; Toraman, Seda

2016-10-01

In the present work, the effects of some parametric values on convective heat transfer coefficients and the thin layer drying process of ginger slices were investigated. Drying was done in the laboratory by using cyclone type convective dryer. The drying air temperature was varied as 40, 50, 60 and 70 °C and the air velocity is 0.8, 1.5 and 3 m/s. All drying experiments had only falling rate period. The drying data were fitted to the twelve mathematical models and performance of these models was investigated by comparing the determination of coefficient ( R 2), reduced Chi-square ( χ 2) and root mean square error between the observed and predicted moisture ratios. The effective moisture diffusivity and activation energy were calculated using an infinite series solution of Fick's diffusion equation. The average effective moisture diffusivity values and activation energy values varied from 2.807 × 10-10 to 6.977 × 10-10 m2/s and 19.313-22.722 kJ/mol over the drying air temperature and velocity range, respectively. Experimental data was used to evaluate the values of constants in Nusselt number expression by using linear regression analysis and consequently, convective heat transfer coefficients were determined in forced convection mode. Convective heat transfer coefficient of ginger slices showed changes in ranges 0.33-2.11 W/m2 °C.

[Income inequality, corruption, and life expectancy at birth in Mexico].

PubMed

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.

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.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network.

PubMed

Liu, Ruixin; Zhang, Xiaodong; Zhang, Lu; Gao, Xiaojie; Li, Huiling; Shi, Junhan; Li, Xuelin

2014-06-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network

PubMed Central

LIU, RUIXIN; ZHANG, XIAODONG; ZHANG, LU; GAO, XIAOJIE; LI, HUILING; SHI, JUNHAN; LI, XUELIN

2014-01-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue. PMID:24926369

Aptamer-Based Paper Strip Sensor for Detecting Vibrio fischeri.

PubMed

Shin, Woo-Ri; Sekhon, Simranjeet Singh; Rhee, Sung-Keun; Ko, Jung Ho; Ahn, Ji-Young; Min, Jiho; Kim, Yang-Hoon

2018-05-14

Aptamer-based paper strip sensor for detecting Vibrio fischeri was developed. Our method was based on the aptamer sandwich assay between whole live cells, V. fischeri and DNA aptamer probes. Following 9 rounds of Cell-SELEX and one of the negative-SELEX, V. fischeri Cell Aptamer (VFCA)-02 and -03 were isolated, with the former showing approximately 10-fold greater avidity (in the subnanomolar range) for the target cells when arrayed on a surface. The colorimetric response of a paper sensor based on VFCA-02 was linear in the range of 4 × 10 1 to 4 × 10 5 CFU/mL of target cell by using scanning reader. The linear regression correlation coefficient ( R 2 ) was 0.9809. This system shows promise for use in aptamer-conjugated gold nanoparticle probes in paper strip format for in-field detection of marine bioindicating bacteria.

Computer modeling of multiple-channel input signals and intermodulation losses caused by nonlinear traveling wave tube amplifiers

NASA Technical Reports Server (NTRS)

Stankiewicz, N.

1982-01-01

The multiple channel input signal to a soft limiter amplifier as a traveling wave tube is represented as a finite, linear sum of Gaussian functions in the frequency domain. Linear regression is used to fit the channel shapes to a least squares residual error. Distortions in output signal, namely intermodulation products, are produced by the nonlinear gain characteristic of the amplifier and constitute the principal noise analyzed in this study. The signal to noise ratios are calculated for various input powers from saturation to 10 dB below saturation for two specific distributions of channels. A criterion for the truncation of the series expansion of the nonlinear transfer characteristic is given. It is found that he signal to noise ratios are very sensitive to the coefficients used in this expansion. Improper or incorrect truncation of the series leads to ambiguous results in the signal to noise ratios.

Effects of fatigue on motor unit firing rate versus recruitment threshold relationships.

PubMed

Stock, Matt S; Beck, Travis W; Defreitas, Jason M

2012-01-01

The purpose of this study was to examine the influence of fatigue on the average firing rate versus recruitment threshold relationships for the vastus lateralis (VL) and vastus medialis. Nineteen subjects performed ten maximum voluntary contractions of the dominant leg extensors. Before and after this fatiguing protocol, the subjects performed a trapezoid isometric muscle action of the leg extensors, and bipolar surface electromyographic signals were detected from both muscles. These signals were then decomposed into individual motor unit action potential trains. For each subject and muscle, the relationship between average firing rate and recruitment threshold was examined using linear regression analyses. For the VL, the linear slope coefficients and y-intercepts for these relationships increased and decreased, respectively, after fatigue. For both muscles, many of the motor units decreased their firing rates. With fatigue, recruitment of higher threshold motor units resulted in an increase in slope for the VL. Copyright © 2011 Wiley Periodicals, Inc.

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

USGS Publications Warehouse

Smith, S. Jerrod; Lewis, Jason M.; Graves, Grant M.

2015-09-28

Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

The use of experimental design in the development of an HPLC-ECD method for the analysis of captopril.

PubMed

Khamanga, Sandile M; Walker, Roderick B

2011-01-15

An accurate, sensitive and specific high performance liquid chromatography-electrochemical detection (HPLC-ECD) method that was developed and validated for captopril (CPT) is presented. Separation was achieved using a Phenomenex(®) Luna 5 μm (C(18)) column and a mobile phase comprised of phosphate buffer (adjusted to pH 3.0): acetonitrile in a ratio of 70:30 (v/v). Detection was accomplished using a full scan multi channel ESA Coulometric detector in the "oxidative-screen" mode with the upstream electrode (E(1)) set at +600 mV and the downstream (analytical) electrode (E(2)) set at +950 mV, while the potential of the guard cell was maintained at +1050 mV. The detector gain was set at 300. Experimental design using central composite design (CCD) was used to facilitate method development. Mobile phase pH, molarity and concentration of acetonitrile (ACN) were considered the critical factors to be studied to establish the retention time of CPT and cyclizine (CYC) that was used as the internal standard. Twenty experiments including centre points were undertaken and a quadratic model was derived for the retention time for CPT using the experimental data. The method was validated for linearity, accuracy, precision, limits of quantitation and detection, as per the ICH guidelines. The system was found to produce sharp and well-resolved peaks for CPT and CYC with retention times of 3.08 and 7.56 min, respectively. Linear regression analysis for the calibration curve showed a good linear relationship with a regression coefficient of 0.978 in the concentration range of 2-70 μg/mL. The linear regression equation was y=0.0131x+0.0275. The limits of detection (LOQ) and quantitation (LOD) were found to be 2.27 and 0.6 μg/mL, respectively. The method was used to analyze CPT in tablets. The wide range for linearity, accuracy, sensitivity, short retention time and composition of the mobile phase indicated that this method is better for the quantification of CPT than the pharmacopoeial methods. Copyright © 2010 Elsevier B.V. All rights reserved.

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

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

Tools to support interpreting multiple regression in the face of multicollinearity.

PubMed

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.

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

PubMed

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.

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

Boosting structured additive quantile regression for longitudinal childhood obesity data.

PubMed

Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael

2013-07-25

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis.

PubMed

Yokoo, Takeshi; Serai, Suraj D; Pirasteh, Ali; Bashir, Mustafa R; Hamilton, Gavin; Hernando, Diego; Hu, Houchun H; Hetterich, Holger; Kühn, Jens-Peter; Kukuk, Guido M; Loomba, Rohit; Middleton, Michael S; Obuchowski, Nancy A; Song, Ji Soo; Tang, An; Wu, Xinhuai; Reeder, Scott B; Sirlin, Claude B

2018-02-01

Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R 2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.

Impact of baseline systolic blood pressure on visit-to-visit blood pressure variability: the Kailuan study.

PubMed

Wang, Anxin; Li, Zhifang; Yang, Yuling; Chen, Guojuan; Wang, Chunxue; Wu, Yuntao; Ruan, Chunyu; Liu, Yan; Wang, Yilong; Wu, Shouling

2016-01-01

To investigate the relationship between baseline systolic blood pressure (SBP) and visit-to-visit blood pressure variability in a general population. This is a prospective longitudinal cohort study on cardiovascular risk factors and cardiovascular or cerebrovascular events. Study participants attended a face-to-face interview every 2 years. Blood pressure variability was defined using the standard deviation and coefficient of variation of all SBP values at baseline and follow-up visits. The coefficient of variation is the ratio of the standard deviation to the mean SBP. We used multivariate linear regression models to test the relationships between SBP and standard deviation, and between SBP and coefficient of variation. Approximately 43,360 participants (mean age: 48.2±11.5 years) were selected. In multivariate analysis, after adjustment for potential confounders, baseline SBPs <120 mmHg were inversely related to standard deviation (P<0.001) and coefficient of variation (P<0.001). In contrast, baseline SBPs ≥140 mmHg were significantly positively associated with standard deviation (P<0.001) and coefficient of variation (P<0.001). Baseline SBPs of 120-140 mmHg were associated with the lowest standard deviation and coefficient of variation. The associations between baseline SBP and standard deviation, and between SBP and coefficient of variation during follow-ups showed a U curve. Both lower and higher baseline SBPs were associated with increased blood pressure variability. To control blood pressure variability, a good target SBP range for a general population might be 120-139 mmHg.

Effects of calcium and magnesium on strontium distribution coefficients

USGS Publications Warehouse

Bunde, R.L.; Rosentreter, J.J.; Liszewski, M.J.; Hemming, C.H.; Welhan, J.

1997-01-01

The effects of calcium and magnesium on the distribution of strontium between a surficial sediment and simulated wastewater solutions were measured as part of an investigation to determine strontium transport properties of surficial sediment at the Idaho National Engineering Laboratory (INEL), Idaho. The investigation was conducted by the U.S. Geological Survey and Idaho State University, in cooperation with the U.S. Department of Energy. Batch experimental techniques were used to determine strontium linear sorption isotherms and distribution coefficients (K(d)'s) using simulated wastewater solutions prepared at pH 8.0??0.1 with variable concentrations of calcium and magnesium. Strontium linear sorption isotherm K(d)'s ranged from 12??1 to 85??3 ml/g, increasing as the concentration of calcium and magnesium decreased. The concentration of sorbed strontium and the percentage of strontium retained by the sediment were correlated to aqueous concentrations of strontium, calcium, and magnesium. The effect of these cation concentrations on strontium sorption was quantified using multivariate least-squares regression techniques. Analysis of data from these experiments indicates that increased concentrations of calcium and magnesium in wastewater discharged to waste disposal ponds at the INEL increases the availability of strontium for transport beneath the ponds by decreasing strontium sorption to the surficial sediment.

Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera

PubMed Central

Fu, Longsheng; Sun, Shipeng; Li, Rui; Wang, Shaojin

2016-01-01

This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera. PMID:27376292

High-performance liquid chromatographic determination of certain salicylates and their major metabolites in plasma following topical administration of a liniment to healthy subjects.

PubMed

Dadgar, D; Climax, J; Lambe, R; Darragh, A

1985-08-09

The liniment used is a topical analgesic and anti-inflammatory preparation containing two active constituents, 3-phenylpropylsalicylate and ethyl-5-methoxysalicylate, in solution in isobutyl decanoate. It is known that 3-phenylpropylsalicylate is metabolised to salicylic acid and salicyluric acid and ethyl-5-methoxysalicylate is metabolised to 5-methoxysalicylic acid and gentisic acid. In the present study the separation of the salicylates and their metabolites was carried out on a Waters mu Bondapak C18 column using two different mobile phases, methanol-water (80:20) for the parent drugs and methanol-5% aqueous acetic acid (27:73) for their metabolites. The salicylates and their metabolites were detected by absorption at 310 nm. The limits of detection for parent drugs and metabolites were respectively 0.2 and 0.1 microgram/ml in plasma, using a 1-ml plasma sample and a 20-microliter injection from a reconstituted volume of 250 microliter. Mean percentage coefficients of variation for intra-assay and inter-assay precision were between 3.3 +/- 1.9% to 9.1 +/- 3.7% and 6.8 +/- 2.2% to 15.7 +/- 10.1%, respectively. Linearity, as measured by the correlation coefficient of intra-assay linear regression curves, was better than 0.998 in all cases.

Influence of microarchitecture alterations on ultrasonic backscattering in an experimental simulation of bovine cancellous bone aging.

PubMed

Apostolopoulos, K N; Deligianni, D D

2008-02-01

An experimental model which can simulate physical changes that occur during aging was developed in order to evaluate the effects of change of mineral content and microstructure on ultrasonic properties of bovine cancellous bone. Timed immersion in hydrochloric acid was used to selectively alter the mineral content. Scanning electron microscopy and histological staining of the acid-treated trabeculae demonstrated a heterogeneous structure consisting of a mineralized core and a demineralized layer. The presence of organic matrix contributed very little to normalized broadband ultrasound attenuation (nBUA) and speed of sound. All three ultrasonic parameters, speed of sound, nBUA and backscatter coefficient, were sensitive to changes in apparent density of bovine cancellous bone. A two-component model utilizing a combination of two autocorrelation functions (a densely populated model and a spherical distribution) was used to approximate the backscatter coefficient. The predicted attenuation due to scattering constituted a significant part of the measured total attenuation (due to both scattering and absorption mechanisms) for bovine cancellous bone. Linear regression, performed between trabecular thickness values and estimated from the model correlation lengths, showed significant linear correlation, with R(2)=0.81 before and R(2)=0.80 after demineralization. The accuracy of estimation was found to increase with trabecular thickness.

Prediction of soil organic carbon with different parent materials development using visible-near infrared spectroscopy.

PubMed

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.

Estimating Blade Section Airloads from Blade Leading-Edge Pressure Measurements

NASA Technical Reports Server (NTRS)

vanAken, Johannes M.

2003-01-01

The Tilt-Rotor Aeroacoustic Model (TRAM) test in the Duitse-Nederlandse Wind (DNW) Tunnel acquired blade pressure data for forward flight test conditions of a tiltrotor in helicopter mode. Chordwise pressure data at seven radial locations were integrated to obtain the blade section normal force. The present investigation evaluates the use of linear regression analysis and of neural networks in estimating the blade section normal force coefficient from a limited number of blade leading-edge pressure measurements and representative operating conditions. These network models are subsequently used to estimate the airloads at intermediate radial locations where only blade pressure measurements at the 3.5% chordwise stations are available.

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.

Empirical prediction of peak pressure levels in anthropogenic impulsive noise. Part I: Airgun arrays signals.

PubMed

Galindo-Romero, Marta; Lippert, Tristan; Gavrilov, Alexander

2015-12-01

This paper presents an empirical linear equation to predict peak pressure level of anthropogenic impulsive signals based on its correlation with the sound exposure level. The regression coefficients are shown to be weakly dependent on the environmental characteristics but governed by the source type and parameters. The equation can be applied to values of the sound exposure level predicted with a numerical model, which provides a significant improvement in the prediction of the peak pressure level. Part I presents the analysis for airgun arrays signals, and Part II considers the application of the empirical equation to offshore impact piling noise.

Sensitivity of amplitude-phase characteristics of the surface air temperature annual cycle to variations in annual mean temperature

NASA Astrophysics Data System (ADS)

Eliseev, A. V.; Mokhov, I. I.; Guseva, M. S.

2006-05-01

The ERA40 and NCEP/NCAR data over 1958 1998 were used to estimate the sensitivity of amplitude-phase characteristics (APCs) of the annual cycle (AC) of the surface air temperature (SAT) T s. The results were compared with outputs of the ECHAM4/OPYC3, HadCM3, and INM RAS general circulation models and the IAP RAS climate model of intermediate complexity, which were run with variations in greenhouse gases and sulfate aerosol specified over 1860 2100. The analysis was performed in terms of the linear regression coefficients b of SAT AC APCs on the local annual mean temperature and in terms of the sensitivity characteristic D = br 2, which takes into account not only the linear regression coefficient but also its statistical significance (via the correlation coefficient r). The reanalysis data were used to reveal the features of the tendencies of change in the SAT AC APCs in various regions, including areas near the snow-ice boundary, storm-track ocean regions, large desert areas, and the tropical Pacific. These results agree with earlier observations. The model computations are in fairly good agreement with the reanalysis data in regions of statistically significant variations in SAT AC APCs. The differences between individual models and the reanalysis data can be explained, in particular, in terms of the features of the sea-ice schemes used in the models. Over the land in the middle and high latitudes of the Northern Hemisphere, the absolute values of D for the fall phase time and the interval of exceeding exhibit a positive intermodel correlation with the absolute value of D for the annual-harmonic amplitude. Over the ocean, the models reproducing larger (in modulus) sensitivity parameters of the SAT annual-harmonic amplitude are generally characterized by larger (in modulus) negative sensitivity values of the semiannual-harmonic amplitude T s, 2, especially at latitudes characteristic of the sea-ice boundary. In contrast to the averaged fields of AC APCs and their interannual standard deviations, the sensitivity parameters of the SAT AC APCs on a regional scale vary noticeably for various types of anthropogenic forcing.

Correlation Between Expression of High Temperature Requirement Serine Protease A1 (HtrA1) in Nucleus Pulposus and T2 Value of Magnetic Resonance Imaging.

PubMed

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.

Dual exponential polynomials and linear differential equations

NASA Astrophysics Data System (ADS)

Wen, Zhi-Tao; Gundersen, Gary G.; Heittokangas, Janne

2018-01-01

We study linear differential equations with exponential polynomial coefficients, where exactly one coefficient is of order greater than all the others. The main result shows that a nontrivial exponential polynomial solution of such an equation has a certain dual relationship with the maximum order coefficient. Several examples illustrate our results and exhibit possibilities that can occur.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Multilevel Preconditioners for Reaction-Diffusion Problems with Discontinuous Coefficients

DOE PAGES

Kolev, Tzanio V.; Xu, Jinchao; Zhu, Yunrong

2015-08-23

In this study, we extend some of the multilevel convergence results obtained by Xu and Zhu, to the case of second order linear reaction-diffusion equations. Specifically, we consider the multilevel preconditioners for solving the linear systems arising from the linear finite element approximation of the problem, where both diffusion and reaction coefficients are piecewise-constant functions. We discuss in detail the influence of both the discontinuous reaction and diffusion coefficients to the performance of the classical BPX and multigrid V-cycle preconditioner.

Relationship between parent–infant attachment and parental satisfaction with supportive nursing care

PubMed Central

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

Relationship between parent-infant attachment and parental satisfaction with supportive nursing care.

PubMed

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.

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Threshold regression to accommodate a censored covariate.

PubMed

Qian, Jing; Chiou, Sy Han; Maye, Jacqueline E; Atem, Folefac; Johnson, Keith A; Betensky, Rebecca A

2018-06-22

In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer's amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer's disease. We propose threshold regression approaches for linear regression models with a covariate that is subject to random censoring. Threshold regression methods allow for immediate testing of the significance of the effect of a censored covariate. In addition, they provide for unbiased estimation of the regression coefficient of the censored covariate. We derive the asymptotic properties of the resulting estimators under mild regularity conditions. Simulations demonstrate that the proposed estimators have good finite-sample performance, and often offer improved efficiency over existing methods. We also derive a principled method for selection of the threshold. We illustrate the approach in application to an Alzheimer's disease study that investigated brain amyloid levels in older individuals, as measured through positron emission tomography scans, as a function of maternal age of dementia onset, with adjustment for other covariates. We have developed an R package, censCov, for implementation of our method, available at CRAN. © 2018, The International Biometric Society.

A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

PubMed

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.

Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

PubMed

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Does higher education protect against obesity? Evidence using Mendelian randomization.

PubMed

Böckerman, Petri; Viinikainen, Jutta; Pulkki-Råback, Laura; Hakulinen, Christian; Pitkänen, Niina; Lehtimäki, Terho; Pehkonen, Jaakko; Raitakari, Olli T

2017-08-01

The aim of this explorative study was to examine the effect of education on obesity using Mendelian randomization. Participants (N=2011) were from the on-going nationally representative Young Finns Study (YFS) that began in 1980 when six cohorts (aged 30, 33, 36, 39, 42 and 45 in 2007) were recruited. The average value of BMI (kg/m 2 ) measurements in 2007 and 2011 and genetic information were linked to comprehensive register-based information on the years of education in 2007. We first used a linear regression (Ordinary Least Squares, OLS) to estimate the relationship between education and BMI. To identify a causal relationship, we exploited Mendelian randomization and used a genetic score as an instrument for education. The genetic score was based on 74 genetic variants that genome-wide association studies (GWASs) have found to be associated with the years of education. Because the genotypes are randomly assigned at conception, the instrument causes exogenous variation in the years of education and thus enables identification of causal effects. The years of education in 2007 were associated with lower BMI in 2007/2011 (regression coefficient (b)=-0.22; 95% Confidence Intervals [CI]=-0.29, -0.14) according to the linear regression results. The results based on Mendelian randomization suggests that there may be a negative causal effect of education on BMI (b=-0.84; 95% CI=-1.77, 0.09). The findings indicate that education could be a protective factor against obesity in advanced countries. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust best linear estimator for Cox regression with instrumental variables in whole cohort and surrogates with additive measurement error in calibration sample.

PubMed

Wang, Ching-Yun; Song, Xiao

2016-11-01

Biomedical researchers are often interested in estimating the effect of an environmental exposure in relation to a chronic disease endpoint. However, the exposure variable of interest may be measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies an additive measurement error model, but it may not have repeated measurements. The subset in which the surrogate variables are available is called a calibration sample. In addition to the surrogate variables that are available among the subjects in the calibration sample, we consider the situation when there is an instrumental variable available for all study subjects. An instrumental variable is correlated with the unobserved true exposure variable, and hence can be useful in the estimation of the regression coefficients. In this paper, we propose a nonparametric method for Cox regression using the observed data from the whole cohort. The nonparametric estimator is the best linear combination of a nonparametric correction estimator from the calibration sample and the difference of the naive estimators from the calibration sample and the whole cohort. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via intensive simulation studies. The methods are applied to the Nutritional Biomarkers Study of the Women's Health Initiative. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Effects of carbon components of fine particulate matter (PM2.5) on atherogenic index of plasma].

PubMed

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.

Relationship between Body Composition and Pulmonary Function in Early Adult Life: A Cross-Sectional Analysis Nested in Two Birth Cohort Studies.

PubMed

Oliveira, Paula Duarte de; Wehrmeister, Fernando C; Pérez-Padilla, Rogelio; Gonçalves, Helen; Assunção, Maria Cecília F; Horta, Bernardo Lessa; Gigante, Denise P; Barros, Fernando C; Menezes, Ana Maria Baptista

Overweight/obesity has been reported to worsen pulmonary function (PF). This study aimed to examine the association between PF and several body composition (BC) measures in two population-based cohorts. We performed a cross-sectional analysis of individuals aged 18 and 30 years from two Pelotas Birth Cohorts in southern Brazil. PF was assessed by spirometry. Body measures that were collected included body mass index, waist circumference, skinfold thickness, percentages of total and segmented (trunk, arms and legs) fat mass (FM) and total fat-free mass (FFM). FM and FFM were measured by air-displacement plethysmography (BODPOD) and by dual-energy x-ray absorptiometry (DXA). Associations were verified through linear regressions stratified by sex, and adjusted for weight, height, skin color, and socioeconomic, behavioral, and perinatal variables. A total of 7347 individuals were included in the analyses (3438 and 3909 at 30 and 18 years, respectively). Most BC measures showed a significant positive association between PF and FFM, and a negative association with FM. For each additional percentage point of FM, measured by BOD POD, the forced vital capacity regression coefficient adjusted by height, weight and skin color, at 18 years, was -33 mL (95% CI -38, -29) and -26 mL (95% CI -30, -22), and -30 mL (95% CI -35, -25) and -19 mL (95% CI -23, -14) at 30 years, in men and women, respectively. All the BOD POD regression coefficients for FFM were the same as for the FM coefficients, but in a positive trend (p<0.001 for all associations). All measures that distinguish FM from FFM (skinfold thickness-FM estimation-BOD POD, total and segmental DXA measures-FM and FFM proportions) showed negative trends in the association of FM with PF for both ages and sexes. On the other hand, FFM showed a positive association with PF.

[Culture and quality of life assessment in Chinese populations].

PubMed

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.

Promoting motivation through mode of instruction: The relationship between use of affective teaching techniques and motivation to learn science

NASA Astrophysics Data System (ADS)

Sanchez Rivera, Yamil

The purpose of this study is to add to what we know about the affective domain and to create a valid instrument for future studies. The Motivation to Learn Science (MLS) Inventory is based on Krathwohl's Taxonomy of Affective Behaviors (Krathwohl et al., 1964). The results of the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) demonstrated that the MLS Inventory is a valid and reliable instrument. Therefore, the MLS Inventory is a uni-dimensional instrument composed of 9 items with convergent validity (no divergence). The instrument had a high Chronbach Alpha value of .898 during the EFA analysis and .919 with the CFA analysis. Factor loadings on the 9 items ranged from .617 to .800. Standardized regression weights ranged from .639 to .835 in the CFA analysis. Various indices (RMSEA = .033; NFI = .987; GFI = .985; CFI = 1.000) demonstrated a good fitness of the proposed model. Hierarchical linear modeling was used to statistical analyze data where students' motivation to learn science scores (level-1) were nested within teachers (level-2). The analysis was geared toward identifying if teachers' use of affective behavior (a level-2 classroom variable) was significantly related with students' MLS scores (level-1 criterion variable). Model testing proceeded in three phases: intercept-only model, means-as-outcome model, and a random-regression coefficient model. The intercept-only model revealed an intra-class correlation coefficient of .224 with an estimated reliability of .726. Therefore, data suggested that only 22.4% of the variance in MLS scores is between-classes and the remaining 77.6% is at the student-level. Due to the significant variance in MLS scores, X2(62.756, p<.0001), teachers' TAB scores were added as a level-2 predictor. The regression coefficient was non-significant (p>.05). Therefore, the teachers' self-reported use of affective behaviors was not a significant predictor of students' motivation to learn science.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

SPSS macros to compare any two fitted values from a regression model.

PubMed

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.

Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

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.

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

NASA Astrophysics Data System (ADS)

Hapugoda, J. C.; Sooriyarachchi, M. R.

2017-09-01

Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

Correlation between quantified breast densities from digital mammography and 18F-FDG PET uptake.

PubMed

Lakhani, Paras; Maidment, Andrew D A; Weinstein, Susan P; Kung, Justin W; Alavi, Abass

2009-01-01

To correlate breast density quantified from digital mammograms with mean and maximum standardized uptake values (SUVs) from positron emission tomography (PET). This was a prospective study that included 56 women with a history of suspicion of breast cancer (mean age 49.2 +/- 9.3 years), who underwent 18F-fluoro-2-deoxyglucose (FDG)-PET imaging of their breasts as well as digital mammography. A computer thresholding algorithm was applied to the contralateral nonmalignant breasts to quantitatively estimate the breast density on digital mammograms. The breasts were also classified into one of four Breast Imaging Reporting and Data System categories for density. Comparisons between SUV and breast density were made using linear regression and the Student's t-test. Linear regression of mean SUV versus average breast density showed a positive relationship with a Pearson's correlation coefficient of R(2) = 0.83. The quantified breast densities and mean SUVs were significantly greater for mammographically dense than nondense breasts (p < 0.0001 for both). The average quantified densities and mean SUVs of the breasts were significantly greater for premenopausal than postmenopausal patients (p < 0.05). 8/51 (16%) of the patients had maximum SUVs that equaled 1.6 or greater. There is a positive linear correlation between quantified breast density on digital mammography and FDG uptake on PET. Menopausal status affects the metabolic activity of normal breast tissue, resulting in higher SUVs in pre- versus postmenopausal patients.

Inter-Annual Variability of Burned Area in Brazil Based on a Synergistic use of Information Derived from MODIS and Landsat-TM

NASA Astrophysics Data System (ADS)

Libonati, R.; Dacamara, C. C.; Setzer, A. W.; Morelli, F.

2014-12-01

A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of monthly burned area estimates over Brazil. The method integrates MODIS derived information from two sources; the NASA MCD64A1 Direct Broadcast Monthly Burned Area Product and INPE's Monthly Burned Area MODIS product (AQM-MODIS). The latter product relies on an algorithm that was specifically designed for ecosystems in Brazil, taking advantage of the ability of MIR reflectances to discriminate burned areas. Information from both MODIS products is incorporated by means of a linear regression model where an optimal estimate of the burned area is obtained as a linear combination of burned area estimates from MCD64A1 and AQM-MODIS. The linear regression model is calibrated using as optimal estimates values of burned area derived from Landsat TM during 2005 and 2006 over Jalapão, a region of Cerrado covering an area of 187 x 187 km2. Obtained values of coefficients for MCD64A1 and AQM-MODIS were 0.51 and 0.35, respectively and the root mean square error was 7.6 km2. Robustness of the model was checked by calibrating the model separately for 2005 and 2006 and cross-validating with 2006 and 2005; coefficients for 2005 (2006) were 0.46 (0.54) for MCD64A1 and 0.35 (0.35) for AQM-MODIS and the corresponding root mean square errors for 2006 (2005) were 7.8 (7.4) km2. The linear model was then applied to Brazil as well as to the six Brazilian main biomes, namely Cerrado, Amazônia, Caatinga, Pantanal, Mata Atlântica and Pampa. As to be expected the interannual variability based on the proposed synergistic use of MCD64A1, AQM-MODIS and Landsat Tm data for the period 2005-2010 presents marked differences with the corresponding amounts derived from MCD64A1 alone. For instance during the considered period, values (in 103 km2) from the proposed approach (from MCD64A1) are 399 (142), 232 (62), 559 (259), 274 (73), 219 (31) and 415 (251). Values obtained with the proposed approach may be viewed as an improved alternative to the currently available products over Brazil.

Dietary cyanogen exposure and early child neurodevelopment: An observational study from the Democratic Republic of Congo.

PubMed

Kashala-Abotnes, Espérance; Sombo, Marie-Thérèse; Okitundu, Daniel L; Kunyu, Marcel; Bumoko Makila-Mabe, Guy; Tylleskär, Thorkild; Sikorskii, Alla; Banea, Jean-Pierre; Mumba Ngoyi, Dieudonné; Tshala-Katumbay, Désiré; Boivin, Michael J

2018-01-01

Dietary cyanogen exposure from ingesting bitter (toxic) cassava as a main source of food in sub-Saharan Africa is related to neurological impairments in sub-Saharan Africa. We explored possible association with early child neurodevelopmental outcomes. We undertook a cross-sectional neurodevelopmental assessment of 12-48 month-old children using the Mullen Scale of Early Learning (MSEL) and the Gensini Gavito Scale (GGS). We used the Hopkins Symptoms Checklist-10 (HSCL-10) and Goldberg Depression Anxiety Scale (GDAS) to screen for symptoms of maternal depression-anxiety. We used the cyanogen content in household cassava flour and urinary thiocyanate (SCN) as biomarkers of dietary cyanogen exposure. We employed multivariable generalized linear models (GLM) with Gamma link function to determine predictors of early child neurodevelopmental outcomes. The mean (SD) and median (IQR) of cyanogen content of cassava household flour were above the WHO cut-off points of 10 ppm (52.18 [32·79]) and 50 (30-50) ppm, respectively. Mean (SD) urinary levels of thiocyanate and median (IQR) were respectively 817·81 (474·59) and 688 (344-1032) μmole/l in mothers, and 617·49 (449·48) and 688 (344-688) μmole/l in children reflecting individual high levels as well as a community-wide cyanogenic exposure. The concentration of cyanide in cassava flour was significantly associated with early child neurodevelopment, motor development and cognitive ability as indicated by univariable linear regression (p < 0.05). After adjusting for biological and socioeconomic predictors at multivariable analyses, fine motor proficiency and child neurodevelopment remained the main predictors associated with the concentration of cyanide in cassava flour: coefficients of -0·08 to -.15 (p < 0·01). We also found a significant association between child linear growth, early child neurodevelopment, cognitive ability and motor development at both univariable and multivariable linear regression analyses coefficients of 1.44 to 7.31 (p < 0·01). Dietary cyanogen exposure is associated with early child neurodevelopment, cognitive abilities and motor development, even in the absence of clinically evident paralysis. There is a need for community-wide interventions for better cassava processing practices for detoxification, improved nutrition, and neuro-rehabilitation, all of which are essential for optimal development in exposed children.

Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer12345

PubMed Central

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

2015-01-01

Background: 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. Objectives: 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. Design: 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. Results: 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. Conclusions: 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. PMID:25762808

Age-related apparent diffusion coefficient changes in the normal brain.

PubMed

Watanabe, Memi; Sakai, Osamu; Ozonoff, Al; Kussman, Steven; Jara, Hernán

2013-02-01

To measure the mean diffusional age-related changes of the brain over the full human life span by using diffusion-weighted spin-echo single-shot echo-planar magnetic resonance (MR) imaging and sequential whole-brain apparent diffusion coefficient (ADC) histogram analysis and, secondarily, to build mathematical models of these normal age-related changes throughout human life. After obtaining institutional review board approval, a HIPAA-compliant retrospective search was conducted for brain MR imaging studies performed in 2007 for various clinical indications. Informed consent was waived. The brain data of 414 healthy subjects (189 males and 225 females; mean age, 33.7 years; age range, 2 days to 89.3 years) were obtained with diffusion-weighted spin-echo single-shot echo-planar MR imaging. ADC histograms of the whole brain were generated. ADC peak values, histogram widths, and intracranial volumes were plotted against age, and model parameters were estimated by using nonlinear regression. Four different stages were identified for aging changes in ADC peak values, as characterized by specific mathematical terms: There were age-associated exponential decays for the maturation period and the development period, a constant term for adulthood, and a linear increase for the senescence period. The age dependency of ADC peak value was simulated by using four-term six-coefficient function, including biexponential and linear terms. This model fit the data very closely (R(2) = 0.91). Brain diffusivity as a whole demonstrated age-related changes through four distinct periods of life. These results could contribute to establishing an ADC baseline of the normal brain, covering the full human life span.

Validation of high throughput screening of human sera for detection of anti-PA IgG by Enzyme-Linked Immunosorbent Assay (ELISA) as an emergency response to an anthrax incident

PubMed Central

Semenova, Vera A.; Steward-Clark, Evelene; Maniatis, Panagiotis; Epperson, Monica; Sabnis, Amit; Schiffer, Jarad

2017-01-01

To improve surge testing capability for a response to a release of Bacillus anthracis, the CDC anti-Protective Antigen (PA) IgG Enzyme-Linked Immunosorbent Assay (ELISA) was re-designed into a high throughput screening format. The following assay performance parameters were evaluated: goodness of fit (measured as the mean reference standard r2), accuracy (measured as percent error), precision (measured as coefficient of variance (CV)), lower limit of detection (LLOD), lower limit of quantification (LLOQ), dilutional linearity, diagnostic sensitivity (DSN) and diagnostic specificity (DSP). The paired sets of data for each sample were evaluated by Concordance Correlation Coefficient (CCC) analysis. The goodness of fit was 0.999; percent error between the expected and observed concentration for each sample ranged from −4.6% to 14.4%. The coefficient of variance ranged from 9.0% to 21.2%. The assay LLOQ was 2.6 μg/mL. The regression analysis results for dilutional linearity data were r2 = 0.952, slope = 1.02 and intercept = −0.03. CCC between assays was 0.974 for the median concentration of serum samples. The accuracy and precision components of CCC were 0.997 and 0.977, respectively. This high throughput screening assay is precise, accurate, sensitive and specific. Anti-PA IgG concentrations determined using two different assays proved high levels of agreement. The method will improve surge testing capability 18-fold from 4 to 72 sera per assay plate. PMID:27814939

Validation of high throughput screening of human sera for detection of anti-PA IgG by Enzyme-Linked Immunosorbent Assay (ELISA) as an emergency response to an anthrax incident.

PubMed

Semenova, Vera A; Steward-Clark, Evelene; Maniatis, Panagiotis; Epperson, Monica; Sabnis, Amit; Schiffer, Jarad

2017-01-01

To improve surge testing capability for a response to a release of Bacillus anthracis, the CDC anti-Protective Antigen (PA) IgG Enzyme-Linked Immunosorbent Assay (ELISA) was re-designed into a high throughput screening format. The following assay performance parameters were evaluated: goodness of fit (measured as the mean reference standard r 2 ), accuracy (measured as percent error), precision (measured as coefficient of variance (CV)), lower limit of detection (LLOD), lower limit of quantification (LLOQ), dilutional linearity, diagnostic sensitivity (DSN) and diagnostic specificity (DSP). The paired sets of data for each sample were evaluated by Concordance Correlation Coefficient (CCC) analysis. The goodness of fit was 0.999; percent error between the expected and observed concentration for each sample ranged from -4.6% to 14.4%. The coefficient of variance ranged from 9.0% to 21.2%. The assay LLOQ was 2.6 μg/mL. The regression analysis results for dilutional linearity data were r 2  = 0.952, slope = 1.02 and intercept = -0.03. CCC between assays was 0.974 for the median concentration of serum samples. The accuracy and precision components of CCC were 0.997 and 0.977, respectively. This high throughput screening assay is precise, accurate, sensitive and specific. Anti-PA IgG concentrations determined using two different assays proved high levels of agreement. The method will improve surge testing capability 18-fold from 4 to 72 sera per assay plate. Published by Elsevier Ltd.

A comparison of the experimental subsonic pressure distributions about several bodies of revolution with pressure distributions computed by means of the linearized theory

NASA Technical Reports Server (NTRS)

Matthews, Clarence W

1953-01-01

An analysis is made of the effects of compressibility on the pressure coefficients about several bodies of revolution by comparing experimentally determined pressure coefficients with corresponding pressure coefficients calculated by the use of the linearized equations of compressible flow. The results show that the theoretical methods predict the subsonic pressure-coefficient changes over the central part of the body but do not predict the pressure-coefficient changes near the nose. Extrapolation of the linearized subsonic theory into the mixed subsonic-supersonic flow region fails to predict a rearward movement of the negative pressure-coefficient peak which occurs after the critical stream Mach number has been attained. Two equations developed from a consideration of the subsonic compressible flow about a prolate spheroid are shown to predict, approximately, the change with Mach number of the subsonic pressure coefficients for regular bodies of revolution of fineness ratio 6 or greater.

The impact of professional identity on role stress in nursing students: A cross-sectional study.

PubMed

Sun, Li; Gao, Ying; Yang, Juan; Zang, Xiao-Ying; Wang, Yao-Gang

2016-11-01

As newcomers to the clinical workplace, nursing students will encounter a high degree of role stress, which is an important predictor of burnout and engagement. Professional identity is theorised to be a key factor in providing high-quality care to improve patient outcomes and is thought to mediate the negative effects of a high-stress workplace and improve clinical performance and job retention. To investigate the level of nursing students' professional identity and role stress at the end of the first sub-internship, and to explore the impact of the nursing students' professional identity and other characteristics on role stress. A cross-sectional study. Three nursing schools in China. Nursing students after a 6-month sub-internship in a general hospital (n=474). The Role Stress Scale (score range: 12-60) and the Professional Identity Questionnaire for Nursing students (score range: 17-85) were used to investigate the levels of nursing students' role stress and professional identity. Higher scores indicated higher levels of role stress and professional identity. Basic demographic information about the nursing students was collected. The Pearson correlation, point-biserial correlation and multiple linear regression analysis were used to analyse the data. The mean total scores of the Role Stress Scale and Professional Identity Questionnaire for Nursing Students were 34.04 (SD=6.57) and 57.63 (SD=9.63), respectively. In the bivariate analyses, the following independent variables were found to be significantly associated with the total score of the Role Stress Scale: the total score of the Professional Identity Questionnaire for Nursing Students (r=-0.295, p<0.01), age (r=0.145, p<0.01), whether student was an only child or not (r=-0.114, p<0.05), education level (r=0.295, p<0.01) and whether student had experience in community organisations or not (r=0.151, p<0.01). In the multiple linear regression analysis, the total score of the Professional Identity Questionnaire for Nursing Students (standardised coefficient Beta: -0.260, p<0.001), education level (standardised coefficient Beta: 0.212, p<0.001) and whether or not student had experience in community organisations (standardised coefficient Beta: 0.107, p<0.016) were the factors significantly associated with the total score of the Role Stress Scale. The multiple linear regression model explained 18.2% (adjusted R 2 scores 16.5%) of the Role Stress Scale scores variance. The nursing students' level of role stress at the end of the first sub-internship was high. The students with higher professional identity values had lower role stress levels. Compared with other personal characteristics, professional identity and education level had the strongest impact on the nursing students' level of role stress. This is a new perspective that shows that developing and improving professional identity may prove helpful for nursing students in managing role stress. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effect of the concentration of magnetic grains on the linear-optical-absorption coefficient of ferrofluid-doped lyotropic mesophases: deviation from the Beer-Lambert law.

PubMed

Cuppo, F L S; Gómez, S L; Figueiredo Neto, A M

2004-04-01

In this paper is reported a systematic experimental study of the linear-optical-absorption coefficient of ferrofluid-doped isotropic lyotropic mixtures as a function of the magnetic-grains concentration. The linear optical absorption of ferrolyomesophases increases in a nonlinear manner with the concentration of magnetic grains, deviating from the usual Beer-Lambert law. This behavior is associated to the presence of correlated micelles in the mixture which favors the formation of small-scale aggregates of magnetic grains (dimers), which have a higher absorption coefficient with respect to that of isolated grains. We propose that the indirect heating of the micelles via the ferrofluid grains (hyperthermia) could account for this nonlinear increase of the linear-optical-absorption coefficient as a function of the grains concentration.

Local structure-based image decomposition for feature extraction with applications to face recognition.

PubMed

Qian, Jianjun; Yang, Jian; Xu, Yong

2013-09-01

This paper presents a robust but simple image feature extraction method, called image decomposition based on local structure (IDLS). It is assumed that in the local window of an image, the macro-pixel (patch) of the central pixel, and those of its neighbors, are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images (also called structure images) according to a local structure feature vector. All the structure images, after being down-sampled for dimensionality reduction, are concatenated into one super-vector. Fisher linear discriminant analysis is then used to provide a low-dimensional, compact, and discriminative representation for each super-vector. The proposed method is applied to face recognition and examined using our real-world face image database, NUST-RWFR, and five popular, publicly available, benchmark face image databases (AR, Extended Yale B, PIE, FERET, and LFW). Experimental results show the performance advantages of IDLS over state-of-the-art algorithms.

Mixed models, linear dependency, and identification in age-period-cohort models.

PubMed

O'Brien, Robert M

2017-07-20

This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.

PubMed

Hoffman, Haydn; Lee, Sunghoon I; Garst, Jordan H; Lu, Derek S; Li, Charles H; Nagasawa, Daniel T; Ghalehsari, Nima; Jahanforouz, Nima; Razaghy, Mehrdad; Espinal, Marie; Ghavamrezaii, Amir; Paak, Brian H; Wu, Irene; Sarrafzadeh, Majid; Lu, Daniel C

2015-09-01

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Serial measurement of type-specific human papillomavirus load enables classification of cervical intraepithelial neoplasia lesions according to occurring human papillomavirus-induced pathway.

PubMed

Verhelst, Stefanie; Poppe, Willy A J; Bogers, Johannes J; Depuydt, Christophe E

2017-03-01

This retrospective study examined whether human papillomavirus (HPV) type-specific viral load changes measured in two or three serial cervical smears are predictive for the natural evolution of HPV infections and correlate with histological grades of cervical intraepithelial neoplasia (CIN), allowing triage of HPV-positive women. A cervical histology database was used to select consecutive women with biopsy-proven CIN in 2012 who had at least two liquid-based cytology samples before the diagnosis of CIN. Before performing cytology, 18 different quantitative PCRs allowed HPV type-specific viral load measurement. Changes in HPV-specific load between measurements were assessed by linear regression, with calculation of coefficient of determination (R) and slope. All infections could be classified into one of five categories: (i) clonal progressing process (R≥0.85; positive slope), (ii) simultaneously occurring clonal progressive and transient infection, (iii) clonal regressing process (R≥0.85; negative slope), (iv) serial transient infection with latency [R<0.85; slopes (two points) between 0.0010 and -0.0010 HPV copies/cell/day], and (v) transient productive infection (R<0.85; slope: ±0.0099 HPV copies/cell/day). Three hundred and seven women with CIN were included; 124 had single-type infections and 183 had multiple HPV types. Only with three consecutive measurements could a clonal process be identified in all CIN3 cases. We could clearly demonstrate clonal regressing lesions with a persistent linear decrease in viral load (R≥0.85; -0.003 HPV copies/cell/day) in all CIN categories. Type-specific viral load increase/decrease in three consecutive measurements enabled classification of CIN lesions in clonal HPV-driven transformation (progression/regression) and nonclonal virion-productive (serial transient/transient) processes.

Gene set analysis using variance component tests.

PubMed

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.

Predicting heavy metal concentrations in soils and plants using field spectrophotometry

NASA Astrophysics Data System (ADS)

Muradyan, V.; Tepanosyan, G.; Asmaryan, Sh.; Sahakyan, L.; Saghatelyan, A.; Warner, T. A.

2017-09-01

Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p <0.01) in a 2498 nm spectral range for soils. Pb has a significantly higher correlation at red edge for plants. A regression models and artificial neural network (ANN) for HM prediction were developed. Good results were obtained for the best stress sensitive spectral band ANN (R2 0.9, RPD 2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R2 0.7, RPD 1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R2, rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

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.

Correlation between physicochemical properties of modified clinoptilolite and its performance in the removal of ammonia-nitrogen.

PubMed

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.

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

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.

Understanding multidecadal variability in ENSO amplitude

NASA Astrophysics Data System (ADS)

Russell, A.; Gnanadesikan, A.

2013-12-01

Sea surface temperatures (SSTs) in the tropical Pacific vary as a result of the coupling between the ocean and atmosphere driven largely by the El Niño - Southern Oscillation (ENSO). ENSO has a large impact on the local climate and hydrology of the tropical Pacific, as well as broad-reaching effects on global climate. ENSO amplitude is known to vary on long timescales, which makes it very difficult to quantify its response to climate change and constrain the physical processes that drive it. In order to assess the extent of unforced multidecadal changes in ENSO variability, a linear regression of local SST changes is applied to the GFDL CM2.1 model 4000-yr pre-industrial control run. The resulting regression coefficient strengths, which represent the sensitivity of SST changes to thermocline depth and zonal wind stress, vary by up to a factor of 2 on multi-decadal time scales. This long-term modulation in ocean-atmosphere coupling is highly correlated with ENSO variability, but do not explain the reasons for such variability. Variation in the relationship between SST changes and wind stress points to a role for changing stratification in the central equatorial Pacific in modulating ENSO amplitudes with stronger stratification reducing the response to winds. The main driving mechanism we have identified for higher ENSO variance are changes in the response of zonal winds to SST anomalies. The shifting convection and precipitation patterns associated with the changing state of the atmosphere also contribute to the variability of the regression coefficients. These mechanisms drive much of the variability in ENSO amplitude and hence ocean-atmosphere coupling in the tropical Pacific.

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

NASA Astrophysics Data System (ADS)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2018-01-01

Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

With respect to coefficient of linear thermal expansion, bacterial vegetative cells and spores resemble plastics and metals, respectively.

PubMed

Nakanishi, Koichi; Kogure, Akinori; Fujii, Takenao; Kokawa, Ryohei; Deuchi, Keiji; Kuwana, Ritsuko; Takamatsu, Hiromu

2013-10-09

If a fixed stress is applied to the three-dimensional z-axis of a solid material, followed by heating, the amount of thermal expansion increases according to a fixed coefficient of thermal expansion. When expansion is plotted against temperature, the transition temperature at which the physical properties of the material change is at the apex of the curve. The composition of a microbial cell depends on the species and condition of the cell; consequently, the rate of thermal expansion and the transition temperature also depend on the species and condition of the cell. We have developed a method for measuring the coefficient of thermal expansion and the transition temperature of cells using a nano thermal analysis system in order to study the physical nature of the cells. The tendency was seen that among vegetative cells, the Gram-negative Escherichia coli and Pseudomonas aeruginosa have higher coefficients of linear expansion and lower transition temperatures than the Gram-positive Staphylococcus aureus and Bacillus subtilis. On the other hand, spores, which have low water content, overall showed lower coefficients of linear expansion and higher transition temperatures than vegetative cells. Comparing these trends to non-microbial materials, vegetative cells showed phenomenon similar to plastics and spores showed behaviour similar to metals with regards to the coefficient of liner thermal expansion. We show that vegetative cells occur phenomenon of similar to plastics and spores to metals with regard to the coefficient of liner thermal expansion. Cells may be characterized by the coefficient of linear expansion as a physical index; the coefficient of linear expansion may also characterize cells structurally since it relates to volumetric changes, surface area changes, the degree of expansion of water contained within the cell, and the intensity of the internal stress on the cellular membrane. The coefficient of linear expansion holds promise as a new index for furthering the understanding of the characteristics of cells. It is likely to be a powerful tool for investigating changes in the rate of expansion and also in understanding the physical properties of cells.

With respect to coefficient of linear thermal expansion, bacterial vegetative cells and spores resemble plastics and metals, respectively

PubMed Central

2013-01-01

Background If a fixed stress is applied to the three-dimensional z-axis of a solid material, followed by heating, the amount of thermal expansion increases according to a fixed coefficient of thermal expansion. When expansion is plotted against temperature, the transition temperature at which the physical properties of the material change is at the apex of the curve. The composition of a microbial cell depends on the species and condition of the cell; consequently, the rate of thermal expansion and the transition temperature also depend on the species and condition of the cell. We have developed a method for measuring the coefficient of thermal expansion and the transition temperature of cells using a nano thermal analysis system in order to study the physical nature of the cells. Results The tendency was seen that among vegetative cells, the Gram-negative Escherichia coli and Pseudomonas aeruginosa have higher coefficients of linear expansion and lower transition temperatures than the Gram-positive Staphylococcus aureus and Bacillus subtilis. On the other hand, spores, which have low water content, overall showed lower coefficients of linear expansion and higher transition temperatures than vegetative cells. Comparing these trends to non-microbial materials, vegetative cells showed phenomenon similar to plastics and spores showed behaviour similar to metals with regards to the coefficient of liner thermal expansion. Conclusions We show that vegetative cells occur phenomenon of similar to plastics and spores to metals with regard to the coefficient of liner thermal expansion. Cells may be characterized by the coefficient of linear expansion as a physical index; the coefficient of linear expansion may also characterize cells structurally since it relates to volumetric changes, surface area changes, the degree of expansion of water contained within the cell, and the intensity of the internal stress on the cellular membrane. The coefficient of linear expansion holds promise as a new index for furthering the understanding of the characteristics of cells. It is likely to be a powerful tool for investigating changes in the rate of expansion and also in understanding the physical properties of cells. PMID:24107328

WE-G-18A-02: Calibration-Free Combined KV/MV Short Scan CBCT

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

Wu, M; Loo, B; Bazalova, M

Purpose: To combine orthogonal kilo-voltage (kV) and Mega-voltage (MV) projection data for short scan cone-beam CT to reduce imaging time on current radiation treatment systems, using a calibration-free gain correction method. Methods: Combining two orthogonal projection data sets for kV and MV imaging hardware can reduce the scan angle to as small as 110° (90°+fan) such that the total scan time is ∼18 seconds, or within a breath hold. To obtain an accurate reconstruction, the MV projection data is first linearly corrected using linear regression using the redundant data from the start and end of the sinogram, and then themore » combined data is reconstructed using the FDK method. To correct for the different changes of attenuation coefficients in kV/MV between soft tissue and bone, the forward projection of the segmented bone and soft tissue from the first reconstruction in the redundant region are added to the linear regression model. The MV data is corrected again using the additional information from the segmented image, and combined with kV for a second FDK reconstruction. We simulated polychromatic 120 kVp (conventional a-Si EPID with CsI) and 2.5 MVp (prototype high-DQE MV detector) projection data with Poisson noise using the XCAT phantom. The gain correction and combined kV/MV short scan reconstructions were tested with head and thorax cases, and simple contrast-to-noise ratio measurements were made in a low-contrast pattern in the head. Results: The FDK reconstruction using the proposed gain correction method can effectively reduce artifacts caused by the differences of attenuation coefficients in the kV/MV data. The CNRs of the short scans for kV, MV, and kV/MV are 5.0, 2.6 and 3.4 respectively. The proposed gain correction method also works with truncated projections. Conclusion: A novel gain correction and reconstruction method was developed to generate short scan CBCT from orthogonal kV/MV projections. This work is supported by NIH Grant 5R01CA138426-05.« less

Can DCE-MRI Explain the Heterogeneity in Radiopeptide Uptake Imaged by SPECT in a Pancreatic Neuroendocrine Tumor Model?

PubMed Central

Groen, Harald C.; Niessen, Wiro J.; Bernsen, Monique R.; de Jong, Marion; Veenland, Jifke F.

2013-01-01

Although efficient delivery and distribution of treatment agents over the whole tumor is essential for successful tumor treatment, the distribution of most of these agents cannot be visualized. However, with single-photon emission computed tomography (SPECT), both delivery and uptake of radiolabeled peptides can be visualized in a neuroendocrine tumor model overexpressing somatostatin receptors. A heterogeneous peptide uptake is often observed in these tumors. We hypothesized that peptide distribution in the tumor is spatially related to tumor perfusion, vessel density and permeability, as imaged and quantified by DCE-MRI in a neuroendocrine tumor model. Four subcutaneous CA20948 tumor-bearing Lewis rats were injected with the somatostatin-analog 111In-DTPA-Octreotide (50 MBq). SPECT-CT and MRI scans were acquired and MRI was spatially registered to SPECT-CT. DCE-MRI was analyzed using semi-quantitative and quantitative methods. Correlation between SPECT and DCE-MRI was investigated with 1) Spearman’s rank correlation coefficient; 2) SPECT uptake values grouped into deciles with corresponding median DCE-MRI parametric values and vice versa; and 3) linear regression analysis for median parameter values in combined datasets. In all tumors, areas with low peptide uptake correlated with low perfusion/density/ /permeability for all DCE-MRI-derived parameters. Combining all datasets, highest linear regression was found between peptide uptake and semi-quantitative parameters (R2>0.7). The average correlation coefficient between SPECT and DCE-MRI-derived parameters ranged from 0.52-0.56 (p<0.05) for parameters primarily associated with exchange between blood and extracellular extravascular space. For these parameters a linear relation with peptide uptake was observed. In conclusion, the ‘exchange-related’ DCE-MRI-derived parameters seemed to predict peptide uptake better than the ‘contrast amount- related’ parameters. Consequently, fast and efficient diffusion through the vessel wall into tissue is an important factor for peptide delivery. DCE-MRI helps to elucidate the relation between vascular characteristics, peptide delivery and treatment efficacy, and may form a basis to predict targeting efficiency. PMID:24116203