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.

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.

Adherence to preferable behavior for lipid control by high-risk dyslipidemic Japanese patients under pravastatin treatment: the APPROACH-J study.

PubMed

Kitagawa, Yasuhisa; Teramoto, Tamio; Daida, Hiroyuki

2012-01-01

We evaluated the impact of adherence to preferable behavior on serum lipid control assessed by a self-reported questionnaire in high-risk patients taking pravastatin for primary prevention of coronary artery disease. High-risk patients taking pravastatin were followed for 2 years. Questionnaire surveys comprising 21 questions, including 18 questions concerning awareness of health, and current status of diet, exercise, and drug therapy, were conducted at baseline and after 1 year. Potential domains were established by factor analysis from the results of questionnaires, and adherence scores were calculated in each domain. The relationship between adherence scores and lipid values during the 1-year treatment period was analyzed by each domain using multiple regression analysis. A total of 5,792 patients taking pravastatin were included in the analysis. Multiple regression analysis showed a significant correlation in terms of "Intake of high fat/cholesterol/sugar foods" (regression coefficient -0.58, p=0.0105) and "Adherence to instructions for drug therapy" (regression coefficient -6.61, p<0.0001). Low-density lipoprotein cholesterol (LDL-C) values were significantly lower in patients who had an increase in the adherence score in the "Awareness of health" domain compared with those with a decreased score. There was a significant correlation between high-density lipoprotein (HDL-C) values and "Awareness of health" (regression coefficient 0.26; p= 0.0037), "Preferable dietary behaviors" (regression coefficient 0.75; p<0.0001), and "Exercise" (regression coefficient 0.73; p= 0.0002). Similar relations were seen with triglycerides. In patients who have a high awareness of their health, a positive attitude toward lipid-lowering treatment including diet, exercise, and high adherence to drug therapy, is related with favorable overall lipid control even in patients under treatment with pravastatin.

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.

Prediction of anthropometric foot characteristics in children.

PubMed

Morrison, Stewart C; Durward, Brian R; Watt, Gordon F; Donaldson, Malcolm D C

2009-01-01

The establishment of growth reference values is needed in pediatric practice where pathologic conditions can have a detrimental effect on the growth and development of the pediatric foot. This study aims to use multiple regression to evaluate the effects of multiple predictor variables (height, age, body mass, and gender) on anthropometric characteristics of the peripubescent foot. Two hundred children aged 9 to 12 years were recruited, and three anthropometric measurements of the pediatric foot were recorded (foot length, forefoot width, and navicular height). Multiple regression analysis was conducted, and coefficients for gender, height, and body mass all had significant relationships for the prediction of forefoot width and foot length (P < or = .05, r > or = 0.7). The coefficients for gender and body mass were not significant for the prediction of navicular height (P > or = .05), whereas height was (P < or = .05). Normative growth reference values and prognostic regression equations are presented for the peripubescent foot.

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.

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

PubMed

Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

2017-02-01

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

[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…

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

Comparison of Different Shrinkage Formulas in Estimating Population Multiple Correlation Coefficients.

ERIC Educational Resources Information Center

Carter, David S.

1979-01-01

There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

Spectral regression and correlation coefficients of some benzaldimines and salicylaldimines in different solvents

NASA Astrophysics Data System (ADS)

Hammud, Hassan H.; Ghannoum, Amer; Masoud, Mamdouh S.

2006-02-01

Sixteen Schiff bases obtained from the condensation of benzaldehyde or salicylaldehyde with various amines (aniline, 4-carboxyaniline, phenylhydrazine, 2,4-dinitrophenylhydrazine, ethylenediamine, hydrazine, o-phenylenediamine and 2,6-pyridinediamine) are studied with UV-vis spectroscopy to observe the effect of solvents, substituents and other structural factors on the spectra. The bands involving different electronic transitions are interpreted. Computerized analysis and multiple regression techniques were applied to calculate the regression and correlation coefficients based on the equation that relates peak position λmax to the solvent parameters that depend on the H-bonding ability, refractive index and dielectric constant of solvents.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

Simultaneous estimation of transcutaneous bilirubin, hemoglobin, and melanin based on diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Abdul, Wares MD.; Ohtsu, Mizuki; Nakano, Kazuya; Haneishi, Hideaki

2018-02-01

We propose a method to estimate transcutaneous bilirubin, hemoglobin, and melanin based on the diffuse reflectance spectroscopy. In the proposed method, the Monte Carlo simulation-based multiple regression analysis for an absorbance spectrum in the visible wavelength region (460-590 nm) is used to specify the concentrations of bilirubin (Cbil), oxygenated hemoglobin (Coh), deoxygenated hemoglobin (Cdh), and melanin (Cm). Using the absorbance spectrum calculated from the measured diffuse reflectance spectrum as a response variable and the extinction coefficients of bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin, as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin, are then determined from the regression coefficients using conversion vectors that are numerically deduced in advance by the Monte Carlo simulations for light transport in skin. Total hemoglobin concentration (Cth) and tissue oxygen saturation (StO2) are simply calculated from the oxygenated hemoglobin and deoxygenated hemoglobin. In vivo animal experiments with bile duct ligation in rats demonstrated that the estimated Cbil is increased after ligation of bile duct and reaches to around 20 mg/dl at 72 h after the onset of the ligation, which corresponds to the reference value of Cbil measured by a commercially available transcutaneous bilirubin meter. We also performed in vivo experiments with rats while varying the fraction of inspired oxygen (FiO2). Coh and Cdh decreased and increased, respectively, as FiO2 decreased. Consequently, StO2 was dramatically decreased. The results in this study indicate potential of the method for simultaneous evaluation of multiple chromophores in skin tissue.

Use of Thematic Mapper for water quality assessment

NASA Technical Reports Server (NTRS)

Horn, E. M.; Morrissey, L. A.

1984-01-01

The evaluation of simulated TM data obtained on an ER-2 aircraft at twenty-five predesignated sample sites for mapping water quality factors such as conductivity, pH, suspended solids, turbidity, temperature, and depth, is discussed. Using a multiple regression for the seven TM bands, an equation is developed for the suspended solids. TM bands 1, 2, 3, 4, and 6 are used with logarithm conductivity in a multiple regression. The assessment of regression equations for a high coefficient of determination (R-squared) and statistical significance is considered. Confidence intervals about the mean regression point are calculated in order to assess the robustness of the regressions used for mapping conductivity, turbidity, and suspended solids, and by regressing random subsamples of sites and comparing the resultant range of R-squared, cross validation is conducted.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

PubMed

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

Estimation of premorbid general fluid intelligence using traditional Chinese reading performance in Taiwanese samples.

PubMed

Chen, Ying-Jen; Ho, Meng-Yang; Chen, Kwan-Ju; Hsu, Chia-Fen; Ryu, Shan-Jin

2009-08-01

The aims of the present study were to (i) investigate if traditional Chinese word reading ability can be used for estimating premorbid general intelligence; and (ii) to provide multiple regression equations for estimating premorbid performance on Raven's Standard Progressive Matrices (RSPM), using age, years of education and Chinese Graded Word Reading Test (CGWRT) scores as predictor variables. Four hundred and twenty-six healthy volunteers (201 male, 225 female), aged 16-93 years (mean +/- SD, 41.92 +/- 18.19 years) undertook the tests individually under supervised conditions. Seventy percent of subjects were randomly allocated to the derivation group (n = 296), and the rest to the validation group (n = 130). RSPM score was positively correlated with CGWRT score and years of education. RSPM and CGWRT scores and years of education were also inversely correlated with age, but the declining trend for RSPM performance against age was steeper than that for CGWRT performance. Separate multiple regression equations were derived for estimating RSPM scores using different combinations of age, years of education, and CGWRT score for both groups. The multiple regression coefficient of each equation ranged from 0.71 to 0.80 with the standard error of estimate between 7 and 8 RSPM points. When fitting the data of one group to the equations derived from its counterpart group, the cross-validation multiple regression coefficients ranged from 0.71 to 0.79. There were no significant differences in the 'predicted-obtained' RSPM discrepancies between any equations. The regression equations derived in the present study may provide a basis for estimating premorbid RSPM performance.

[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.

Multiple Regression with Varying Levels of Correlation among Predictors: Monte Carlo Sampling from Normal and Non-Normal Populations.

ERIC Educational Resources Information Center

Vasu, Ellen Storey

1978-01-01

The effects of the violation of the assumption of normality in the conditional distributions of the dependent variable, coupled with the condition of multicollinearity upon the outcome of testing the hypothesis that the regression coefficient equals zero, are investigated via a Monte Carlo study. (Author/JKS)

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.

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.

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.

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.

Interquantile Shrinkage in Regression Models

PubMed Central

Jiang, Liewen; Wang, Huixia Judy; Bondell, Howard D.

2012-01-01

Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online. PMID:24363546

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

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.

Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

PubMed

Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

2013-09-01

Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p < 0.01), bizygomatic breadth (p < 0.01) and head height (p < 0.05), and a negative relationship between CI and morphological facial height (p < 0.01) and head circumference (p < 0.01). Moreover, the coefficient and odds ratio of logistic regression analysis showed a greater likelihood for minimum frontal breadth (p < 0.01) and bizygomatic breadth (p < 0.01) to predict round-headedness, and morphological facial height (p < 0.05) and head circumference (p < 0.01) to predict long-headedness. Stepwise regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

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.

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.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Characteristics of low-slope streams that affect O2 transfer rates

USGS Publications Warehouse

Parker, Gene W.; Desimone, Leslie A.

1991-01-01

Multiple-regression techniques were used to derive the reaeration coefficients estimating equation for low sloped streams: K2 = 3.83 MBAS-0.41 SL0.20 H-0.76, where K2 is the reaeration coefficient in base e units per day; MBAS is the methylene blue active substances concentration in milligrams per liter; SL is the water-surface slope in foot per foot; and H is the mean-flow depth in feet. Fourteen hydraulic, physical, and water-quality characteristics were regressed against 29 measured-reaeration coefficients for low-sloped (water surface slopes less than 0.002 foot per foot) streams in Massachusetts and New York. Reaeration coefficients measured from May 1985 to October 1988 ranged from 0.2 to 11.0 base e units per day for 29 low-sloped tracer studies. Concentration of methylene blue active substances is significant because it is thought to be an indicator of concentration of surfactants which could change the surface tension at the air-water interface.

Novel Index (Hepatic Receptor: IHR) to Evaluate Hepatic Functional Reserve Using (99m)Tc-GSA Scintigraphy.

PubMed

Hasegawa, Daisuke; Onishi, Hideo; Matsutomo, Norikazu

2016-02-01

This study aimed to evaluate the novel index of hepatic receptor (IHR) on the regression analysis derived from time activity curve of the liver for hepatic functional reserve. Sixty patients had undergone (99m)Tc-galactosyl serum albumin ((99m)Tc-GSA) scintigraphy in the retrospective clinical study. Time activity curves for liver were obtained by region of interest (ROI) on the whole liver. A novel hepatic functional predictor was calculated with multiple regression analysis of time activity curves. In the multiple regression function, the objective variables were the indocyanine green (ICG) retention rate at 15 min, and the explanatory variables were the liver counts in 3-min intervals until end from beginning. Then, this result was defined by IHR, and we analyzed the correlation between IHR and ICG, uptake ratio of the heart at 15 minutes to that at 3 minutes (HH15), uptake ratio of the liver to the liver plus heart at 15 minutes (LHL15), and index of convexity (IOC). Regression function of IHR was derived as follows: IHR=0.025×L(6)-0.052×L(12)+0.027×L(27). The multiple regression analysis indicated that liver counts at 6 min, 12 min, and 27 min were significantly related to objective variables. The correlation coefficient between IHR and ICG was 0.774, and the correlation coefficient between ICG and conventional indices (HH15, LHL15, and IOC) were 0.837, 0.773, and 0.793, respectively. IHR had good correlation with HH15, LHL15, and IOC. The finding results suggested that IHR would provide clinical benefit for hepatic functional assessment in the (99m)Tc-GSA scintigraphy.

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

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

PubMed

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Overcoming multicollinearity in multiple regression using correlation coefficient

NASA Astrophysics Data System (ADS)

Zainodin, H. J.; Yap, S. J.

2013-09-01

Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.

Estimation of instantaneous heat transfer coefficients for a direct-injection stratified-charge rotary engine

NASA Technical Reports Server (NTRS)

Lee, C. M.; Addy, H. E.; Bond, T. H.; Chun, K. S.; Lu, C. Y.

1987-01-01

The main objective of this report was to derive equations to estimate heat transfer coefficients in both the combustion chamber and coolant pasage of a rotary engine. This was accomplished by making detailed temperature and pressure measurements in a direct-injection stratified-charge rotary engine under a range of conditions. For each sppecific measurement point, the local physical properties of the fluids were calculated. Then an empirical correlation of the coefficients was derived by using a multiple regression program. This correlation expresses the Nusselt number as a function of the Prandtl number and Reynolds number.

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

NASA Astrophysics Data System (ADS)

Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.

2018-03-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

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.

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).

Modified Regression Correlation Coefficient for Poisson Regression Model

NASA Astrophysics Data System (ADS)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

Optimization of selective breeding through analysis of morphological traits in Chinese sea bass (Lateolabrax maculatus).

PubMed

Wang, W; Ma, C Y; Chen, W; Ma, H Y; Zhang, H; Meng, Y Y; Ni, Y; Ma, L B

2016-08-19

Determining correlations between certain traits of economic importance constitutes an essential component of selective activities. In this study, our aim was to provide effective indicators for breeding programs of Lateolabrax maculatus, an important aquaculture species in China. We analyzed correlations between 20 morphometric traits and body weight, using correlation and path analyses. The results indicated that the correlations among all 21 traits were highly significant, with the highest correlation coefficient identified between total length and body weight. The path analysis indicated that total length (X 1 ), body width (X 5 ), distance from first dorsal fin origin to anal fin origin (X 10 ), snout length (X 16 ), eye diameter (X 17 ), eye cross (X 18 ), and slanting distance from snout tip to first dorsal fin origin (X 19 ) significantly affected body weight (Y) directly. The following multiple-regression equation was obtained using stepwise multiple-regression analysis: Y = -472.108 + 1.065X 1 + 7.728X 5 + 1.973X 10 - 7.024X 16 - 4.400X 17 - 3.338X 18 + 2.138X 19 , with an adjusted multiple-correlation coefficient of 0.947. Body width had the largest determinant coefficient, as well as the highest positive direct correlation with body weight. At the same time, high indirect effects with six other morphometric traits on L. maculatus body weight, through body width, were identified. Hence, body width could be a key factor that efficiently indicates significant effects on body weight in L. maculatus.

The Impact of Prior Programming Knowledge on Lecture Attendance and Final Exam

ERIC Educational Resources Information Center

Veerasamy, Ashok Kumar; D'Souza, Daryl; Lindén, Rolf; Laakso, Mikko-Jussi

2018-01-01

In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman's rank correlation coefficient, multiple regression, Kruskal-Wallis, and Bonferroni correction…

Predicting Student Engagement in Online High Schools

ERIC Educational Resources Information Center

Vieira, Christopher James

2013-01-01

The purpose of this study was to analyze student engagement in online high schools based on demographic information of high school students using a mixed methods research design. Key findings through a multiple regression analysis and Pearson correlation coefficient suggest that although the majority of participants in the study are highly engaged…

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.

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.

A Modified Double Multiple Nonlinear Regression Constitutive Equation for Modeling and Prediction of High Temperature Flow Behavior of BFe10-1-2 Alloy

NASA Astrophysics Data System (ADS)

Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying

2018-01-01

Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

PubMed

Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

2017-11-01

Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

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.

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland.

PubMed

Kolasa-Wiecek, Alicja

2015-04-01

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R2=0.90. For N2O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2O emission is the peat and wood fuel consumption. Copyright © 2015. Published by Elsevier B.V.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…

Pre-Service Teacher Self-Efficacy for Teaching Students with Disabilities: What Knowledge Matters?

ERIC Educational Resources Information Center

Browarnik, Brooke; Bell, Sherry Mee; McCallum, R. Steve; Smyth, Kelly; Martin, Melissa

2017-01-01

The relation between items assessing knowledge about educating students with disabilities and the Tschannen-Moran and Hoy's Teachers' Sense of Efficacy Scale (TSES; 2001) was explored for 140 preservice, general education teachers using biserial correlation coefficients and a multiple regression equation. From the data collected, 8 correlations…

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Two SPSS programs for interpreting multiple regression results.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo

2010-02-01

When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.

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.

Practical Guidance for Conducting Mediation Analysis With Multiple Mediators Using Inverse Odds Ratio Weighting

PubMed Central

Nguyen, Quynh C.; Osypuk, Theresa L.; Schmidt, Nicole M.; Glymour, M. Maria; Tchetgen Tchetgen, Eric J.

2015-01-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994–2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. PMID:25693776

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

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.

Examination of the Relation between the Values of Adolescents and Virtual Sensitiveness

ERIC Educational Resources Information Center

Yilmaz, Hasan

2013-01-01

The aim of this study is to examine the relation between the values adolescents have and virtual sensitiveness. The study is carried out on 447 adolescents, 160 of whom are female, 287 males. The Humanistic Values Scale and Virtual Sensitiveness scale were used. Pearson Product Moment Coefficient and multiple regression analysis techniques were…

Simulation of land use change in the three gorges reservoir area based on CART-CA

NASA Astrophysics Data System (ADS)

Yuan, Min

2018-05-01

This study proposes a new method to simulate spatiotemporal complex multiple land uses by using classification and regression tree algorithm (CART) based CA model. In this model, we use classification and regression tree algorithm to calculate land class conversion probability, and combine neighborhood factor, random factor to extract cellular transformation rules. The overall Kappa coefficient is 0.8014 and the overall accuracy is 0.8821 in the land dynamic simulation results of the three gorges reservoir area from 2000 to 2010, and the simulation results are satisfactory.

Relationship of extinction coefficient, air pollution, and meteorological parameters in an urban area during 2007 to 2009.

PubMed

Sabetghadam, Samaneh; Ahmadi-Givi, Farhang

2014-01-01

Light extinction, which is the extent of attenuation of light signal for every distance traveled by light in the absence of special weather conditions (e.g., fog and rain), can be expressed as the sum of scattering and absorption effects of aerosols. In this paper, diurnal and seasonal variations of the extinction coefficient are investigated for the urban areas of Tehran from 2007 to 2009. Cases of visibility impairment that were concurrent with reports of fog, mist, precipitation, or relative humidity above 90% are filtered. The mean value and standard deviation of daily extinction are 0.49 and 0.39 km(-1), respectively. The average is much higher than that in many other large cities in the world, indicating the rather poor air quality over Tehran. The extinction coefficient shows obvious diurnal variations in each season, with a peak in the morning that is more pronounced in the wintertime. Also, there is a very slight increasing trend in the annual variations of atmospheric extinction coefficient, which suggests that air quality has regressed since 2007. The horizontal extinction coefficient decreased from January to July in each year and then increased between July and December, with the maximum value in the winter. Diurnal variation of extinction is often associated with small values for low relative humidity (RH), but increases significantly at higher RH. Annual correlation analysis shows that there is a positive correlation between the extinction coefficient and RH, CO, PM10, SO2, and NO2 concentration, while negative correlation exists between the extinction and T, WS, and O3, implying their unfavorable impact on extinction variation. The extinction budget was derived from multiple regression equations using the regression coefficients. On average, 44% of the extinction is from suspended particles, 3% is from air molecules, about 5% is from NO2 absorption, 0.35% is from RH, and approximately 48% is unaccounted for, which may represent errors in the data as well as contribution of other atmospheric constituents omitted from the analysis. Stronger regression equation is achieved in the summer, meaning that the extinction is more predictable in this season using pollutant concentrations.

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.

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.

Deriving the Intrahepatic Arteriovenous Shunt Rate from CT Images and Biochemical Data Instead of from Arterial Perfusion Scintigraphy in Hepatic Arterial Infusion Chemotherapy

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

Ozaki, Toshiro, E-mail: ganronbun@amail.plala.or.jp; Seki, Hiroshi; Shiina, Makoto

2009-09-15

The purpose of the present study was to elucidate a method for predicting the intrahepatic arteriovenous shunt rate from computed tomography (CT) images and biochemical data, instead of from arterial perfusion scintigraphy, because adverse exacerbated systemic effects may be induced in cases where a high shunt rate exists. CT and arterial perfusion scintigraphy were performed in patients with liver metastases from gastric or colorectal cancer. Biochemical data and tumor marker levels of 33 enrolled patients were measured. The results were statistically verified by multiple regression analysis. The total metastatic hepatic tumor volume (V{sub metastasized}), residual hepatic parenchyma volume (V{sub residual};more » calculated from CT images), and biochemical data were treated as independent variables; the intrahepatic arteriovenous (IHAV) shunt rate (calculated from scintigraphy) was treated as a dependent variable. The IHAV shunt rate was 15.1 {+-} 11.9%. Based on the correlation matrixes, the best correlation coefficient of 0.84 was established between the IHAV shunt rate and V{sub metastasized} (p < 0.01). In the multiple regression analysis with the IHAV shunt rate as the dependent variable, the coefficient of determination (R{sup 2}) was 0.75, which was significant at the 0.1% level with two significant independent variables (V{sub metastasized} and V{sub residual}). The standardized regression coefficients ({beta}) of V{sub metastasized} and V{sub residual} were significant at the 0.1 and 5% levels, respectively. Based on this result, we can obtain a predicted value of IHAV shunt rate (p < 0.001) using CT images. When a high shunt rate was predicted, beneficial and consistent clinical monitoring can be initiated in, for example, hepatic arterial infusion chemotherapy.« less

Quantitative Assessment of Cervical Vertebral Maturation Using Cone Beam Computed Tomography in Korean Girls

PubMed Central

Byun, Bo-Ram; Kim, Yong-Il; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6–18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R 2 had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status. PMID:25878721

A Statistical Method for Synthesizing Mediation Analyses Using the Product of Coefficient Approach Across Multiple Trials

PubMed Central

Huang, Shi; MacKinnon, David P.; Perrino, Tatiana; Gallo, Carlos; Cruden, Gracelyn; Brown, C Hendricks

2016-01-01

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: 1) marginal means for mediation path a, the relation of the independent variable to the mediator; 2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and 3) the between-trial level variance-covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings. PMID:28239330

Forecasting models for sugi (Cryptomeria japonica D. Don) pollen count showing an alternate dispersal rhythm.

PubMed

Ito, Yukiko; Hattori, Reiko; Mase, Hiroki; Watanabe, Masako; Shiotani, Itaru

2008-12-01

Pollen information is indispensable for allergic individuals and clinicians. This study aimed to develop forecasting models for the total annual count of airborne pollen grains based on data monitored over the last 20 years at the Mie Chuo Medical Center, Tsu, Mie, Japan. Airborne pollen grains were collected using a Durham sampler. Total annual pollen count and pollen count from October to December (OD pollen count) of the previous year were transformed to logarithms. Regression analysis of the total pollen count was performed using variables such as the OD pollen count and the maximum temperature for mid-July of the previous year. Time series analysis revealed an alternate rhythm of the series of total pollen count. The alternate rhythm consisted of a cyclic alternation of an "on" year (high pollen count) and an "off" year (low pollen count). This rhythm was used as a dummy variable in regression equations. Of the three models involving the OD pollen count, a multiple regression equation that included the alternate rhythm variable and the interaction of this rhythm with OD pollen count showed a high coefficient of determination (0.844). Of the three models involving the maximum temperature for mid-July, those including the alternate rhythm variable and the interaction of this rhythm with maximum temperature had the highest coefficient of determination (0.925). An alternate pollen dispersal rhythm represented by a dummy variable in the multiple regression analysis plays a key role in improving forecasting models for the total annual sugi pollen count.

Bootstrap evaluation of a young Douglas-fir height growth model for the Pacific Northwest

Treesearch

Nicholas R. Vaughn; Eric C. Turnblom; Martin W. Ritchie

2010-01-01

We evaluated the stability of a complex regression model developed to predict the annual height growth of young Douglas-fir. This model is highly nonlinear and is fit in an iterative manner for annual growth coefficients from data with multiple periodic remeasurement intervals. The traditional methods for such a sensitivity analysis either involve laborious math or...

Estimation of PM2.5 and PM10 using ground-based AOD measurements during KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Koo, J. H.; Kim, J.; Kim, S.; Go, S.; Lee, S.; Lee, H.; Mok, J.; Hong, J.; Lee, J.; Eck, T. F.; Holben, B. N.

2017-12-01

During the KORUS-AQ campaign (2 May - 12 June, 2016), aerosol optical depth (AOD) was obtained at multiple channels using various ground-based instruments at Yonsei University, Seoul: AERONET sunphotometer, SKYNET skyradiometer, Brewer spectrophotometer, and multi-filter rotating shadowband radiometer (MFRSR). At the same location, planetary boundary layer (PBL) height and vertical profile of backscattering coefficients also can be obtained based on the celiometer measurements. Using celiometer products and various AODs, we try to estimate the amount of particular matter (PM2.5 and PM10) and validate with in-situ surface PM2.5 and PM10 measurements from AIRKOREA network. Direct comparison between PM2.5 and AOD reveals that the ultraviolet(UV) channel AOD has better correlations, due to the higher sensitivity of short wavelength to the fine-mode particle. In contrast, PM10 shows the highest correlation with the near-infrared(NIR) AOD. Next, we extract the boundary-layer portion of AOD using either PBL height or vertical profile of backscattering coefficients to compare with PM2.5 and PM10. Both results enhance the correlation, but consideration of weighting factor calculated from backscattering coefficients shows larger contribution to the correlation increase. Finally, we performed the multiple linear regression to estimate PM2.5 and PM10 using AODs. Consideration of meteorology (temperature, wind speed, and relative humidity) can enhance the correlation and also O3 and NO2 consideration highly contributes to the high correlation. This finding implies the importance to consider the ambient condition of secondary aerosol formation related to the PM2.5 variation. Multiple regression model finally finds the correlation 0.7-0.8, and diminishes the wavelength-dependent correlation patterns.

100-point scale evaluating job satisfaction and the results of the 12-item General Health Questionnaire in occupational workers.

PubMed

Kawada, Tomoyuki; Yamada, Natsuki

2012-01-01

Job satisfaction is an important factor in the occupational lives of workers. In this study, the relationship between one-dimensional scale of job satisfaction and psychological wellbeing was evaluated. A total of 1,742 workers (1,191 men and 551 women) participated. 100-point scale evaluating job satisfaction (0 [extremely dissatisfied] to 100 [extremely satisfied]) and the General Health Questionnaire, 12-item version (GHQ-12) evaluating psychological wellbeing were used. A multiple regression analysis was then used, controlling for gender and age. The change in the GHQ-12 and job satisfaction scores after a two-year interval was also evaluated. The mean age for the subjects was 42.2 years for the men and 36.2 years for the women. The GHQ-12 and job satisfaction scores were significantly correlated in each generation. The partial correlation coefficients between the changes in the two variables, controlling for age, were -0.395 for men and -0.435 for women (p< 0.001). A multiple regression analysis revealed that the 100-point job satisfaction score was associated with the GHQ-12 results (p< 0.001). The adjusted multiple correlation coefficient was 0.275. The 100-point scale, which is a simple and easy tool for evaluating job satisfaction, was significantly associated with psychological wellbeing as judged using the GHQ-12.

Prediction of kinase-inhibitor binding affinity using energetic parameters

PubMed Central

Usha, Singaravelu; Selvaraj, Samuel

2016-01-01

The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log (IC50) values of 25 different kinase-inhibitors using multiple regressions which gave a correlation coefficient of 0.93. The regression equation obtained was tested on 93 kinase-inhibitor complexes and an average deviation of 0.92 from the experimental log IC50 values was shown. The same set of descriptors was used to predict binding affinities for a test set of five individual kinase families, with correlation values > 0.9. We show that the protein-ligand interaction energies and partition coefficient values form the major deterministic factors for binding affinity of the ligand for its receptor. PMID:28149052

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.

The importance of regional models in assessing canine cancer incidences in Switzerland

PubMed Central

Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships. PMID:29652921

The importance of regional models in assessing canine cancer incidences in Switzerland.

PubMed

Boo, Gianluca; Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

Surface-water hydrology at three coal-refuse disposal sites in southern Illinois: Staunton 1, New Kathleen, and Superior

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

Mele, L.M.; Prodan, P.F.

1983-04-01

Hydrologic data were collected and analyzed for three coal refuse disposal sites in southern Illinois. The disposal sites were associated with underground mines and consisted of piles of coarse waste (gob) and slurry areas where fine waste rejected from coal washing was deposited. Prereclamation data were available for the Superior washer site in Macoupin County and the New Kathleen site in Perry County. Post-reclamation data were available for the Staunton 1 site in Macoupin County and the New Kathleen site. Data analyzed from each phase (i.e., pre- or post-reclamation) were limited to one year. Storm event runoff coefficients were calculatedmore » for each site. Average runoff coefficients were compared for sites within the same reclamation phase to determine the effects of topographical parameters such as gob pile slope and percentage of drainage basin covered by the gob pile. Average runoff coefficients were then compared for pre- and post-reclamation data. Multiple regression analyses were performed on rainfall-runoff data for each site to determine the significance of independent variables other than rainfall in determining runoff. A generalized regression equation corrected data for topographical differences and included only those independent variables that were significant at all sites. Regression coefficients were compared for pre- and post-reclamation sites. The results of rainfall-runoff analysis indicate that the runoff coefficient increases because of reclamation. It is hypothesized that this effect is due to the placement of a soil cover that is less permeable than gob or slurry and occurs despite reduction in slope and the establishment of vegetation.« less

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.

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.

Anthropometric Survey of US Army Personnel (1988): Correlation Coefficients and Regression Equations. Part 5. Stepwise and Standard Multiple Regression Tables

DTIC Science & Technology

1990-05-01

0.759 0.744 0.768 0.753 106 (THUMBBR) THUMB BREADTH -0.652 -0.673 -0.539 -0.663 217 (LIPLGTHH) LIP LENGTH HEADBOARD 0.017 0.019 0.020 51 (FTBRHOR) FOOT...DEPENDENT VARIABLE: (106) THUMB BREADTH (THUBBR) MODEL INDEPENDENT VARIABLE 1 2 3 4 5 INTERCEPT 6.621 5.016 6.267 5.697 4.528 59 (HANDCIRC) HAND...95 (SLLSPEL) SLEEVE LENGTH: SPINE-ELBOW -0.020 -0.019 -C.018 9 (BLFTCIRC) BALL OF FOOT CIRCUMFERENCE -0.032 -0.039 106 (THUMBBR) THUMB BREADTH 0.228

[Study of blending method for the extracts of herbal plants].

PubMed

Liu, Yongsuo; Cao, Min; Chen, Yuying; Hu, Yuzhu; Wang, Yiming; Luo, Guoan

2006-03-01

The irregularity in herbal plant composition is influenced by multiple factors. As for quality control of traditional Chinese medicine, the most critical challenge is to ensure the dosage content uniformity. This content uniformity can be improved by blending different batches of the extracts of herbal plants. Nonlinear least-squares regression was used to calculate the blending coefficient, which means no great absolute differences allowed for all ingredients. For traditional Chinese medicines, even relatively smaller differences could present to be very important for all the ingredients. The auto-scaling pretreatment was used prior to the calculation of the blending coefficients. The pretreatment buffered the characteristics of individual data for the ingredients in different batches, so an improved auto-scaling pretreatment method was proposed. With the improved auto-scaling pretreatment, the relative. differences decreased after blending different batches of extracts of herbal plants according to the reference samples. And the content uniformity control of the specific ingredients could be achieved by the error control coefficient. In the studies for the extracts of fructus gardeniae, the relative differences of all the ingredients is less than 3% after blending different batches of the extracts. The results showed that nonlinear least-squares regression can be used to calculate the blending coefficient of the herbal plant extracts.

Improving Global Models of Remotely Sensed Ocean Chlorophyll Content Using Partial Least Squares and Geographically Weighted Regression

NASA Astrophysics Data System (ADS)

Gholizadeh, H.; Robeson, S. M.

2015-12-01

Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.

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.

Determination of the spectral values of the real part of the relative refractive index of human blood erythrocytes from the measured directional scattering coefficients

NASA Astrophysics Data System (ADS)

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

2008-07-01

An easily automated method for determining the real part of the refractive index of human blood erythrocytes in the range 0.3 1.2 μm is proposed. The method is operationally and metrologically reliable and is based on the measurement of the coefficients of light scattering from forward and backward hemisphere by two pairs of angles and on the use of multiple regression equations. An engineering solution for constructing a measurement system according to this method is proposed, which makes it possible to maximally reduce the calibration errors and effects of destabilizing factors.

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).

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

Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.

PubMed

Nguyen, Quynh C; Osypuk, Theresa L; Schmidt, Nicole M; Glymour, M Maria; Tchetgen Tchetgen, Eric J

2015-03-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994-2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Independence of heritable influences on the food intake of free-living humans.

PubMed

de Castro, John M

2002-01-01

The time of day of meal ingestion, the number of people present at the meal, the subjective state of hunger, and the estimated before-meal contents in the stomach have been established as influences on the amount eaten in a meal and these influences have been shown to be heritable. Because these factors intercorrelate, the calculated heritabilities for some of these variables might result indirectly from their covariation with one of the other heritable variables. The independence of the heritability of the influence of these four factors was investigated with 110 identical and 102 fraternal same-sex and 53 fraternal mixed-sex adult twin pairs who were paid to maintain 7-d food-intake diaries. From the diary reports, the meal sizes were calculated and subjected to multiple regression analysis using the estimated before-meal stomach contents, the reported number of other people present, the subjective hunger ratings, and the time of day of the meal as predictors. Linear structural modeling was applied to the beta-coefficients from the multiple regression to investigate whether the heritability of the influences of these four variables was independent. Significant genetic effects were found for the beta-coefficients for all four variables, indicating that the heritability of their relationship with intake is to some extent independent and heritable. This suggests that influences of multiple factors on intake are influenced by the genes and become part of the total package of genetically determined physiologic, sociocultural, and psychological processes that regulate energy balance.

Heritability of diurnal changes in food intake in free-living humans.

PubMed

de Castro, J M

2001-09-01

The time of day of meal ingestion, the number of people present at the meal, the subjective state of hunger, and the estimated before-meal contents in the stomach have been established as influences on the amount eaten in a meal, and this influence has been shown to be heritable. Because these factors intercorrelate, the possibility that the calculated heritabilities for some of these variables could result indirectly from their convariation with one of the other heritable variables was assessed. The independence of the heritability of the influence of these four factors was investigated with 110 identical and 102 fraternal same-sex and 53 fraternal mixed-sex adult twin pairs who were paid to maintain 7-d food intake diaries. From the diary reports, the meal sizes were calculated and subjected to multiple regression analysis using the estimated before-meal stomach contents, the reported number of other people present, the subjective hunger ratings, and the time of day of the meal as predictors. Linear structural modeling was applied to the beta coefficients from the multiple regression to investigate whether the heritability of the influences of these four variables was independent. Significant genetic effects were found for the beta coefficients for all four variables, indicating that the heritability of their relationship with intake is to some extent heritable. These results suggest that the influences of multiple factors on intake are influenced by the genes and become part of the total package of genetically determined physiologic, sociocultural, and psychological processes that regulate energy balance.

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

PubMed Central

Yang, Yingbao; Li, Xiaolong; Pan, Xin; Zhang, Yong; Cao, Chen

2017-01-01

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions. PMID:28368301

A Note on the Relationship between the Number of Indicators and Their Reliability in Detecting Regression Coefficients in Latent Regression Analysis

ERIC Educational Resources Information Center

Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.

2004-01-01

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

Integrative Analysis of High-throughput Cancer Studies with Contrasted Penalization

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Shia, BenChang; Ma, Shuangge

2015-01-01

In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms “classic” meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance. PMID:24395534

Preliminary evidence for good psychometric properties of the Norwegian version of the Brief Problems Monitor (BPM).

PubMed

Richter, Jörg

2015-04-01

Methods to assess intervention progress and outcome for frequent use are needed. To provide preliminary information about psychometric properties for the Norwegian version of the Brief Problems Monitor. Cronbach's alpha scores and intra-class correlation coefficients as indicators for internal consistency (reliability) and Pearson correlation coefficients between corresponding subscales of the long and short ASEBA form versions as well as multiple regression coefficients to explore the predictive power of the reduced item-set related to the corresponding scale-scores of the long version were calculated in large, representative data sets of Norwegian children and adolescents. Cronbach's alpha scores of the Norwegian version of the BPM subscales varied between 0.67 (attention BPM-youth) and 0.88 (attention BPM-teacher) and between 0.90 (BPM-youth) and 0.96 (BPM-teacher) for its total problem score. Corresponding subscales from the long versions and the BPM as well as the total problems scores were closely correlated with coefficients of high effect size (all r > 0.80). The variance of the items of the BPM explained about three-quarters or more of the variance in the corresponding subscales of the long version. The Norwegian BPM has good psychometric properties in terms of 1) being acceptable to good internal consistency and in terms of 2) regression coefficients of high effect size from the BPM items to the problem-scale scores of the long versions as validity indicators. Its use in clinical practice and research can be recommended.

Analysis of a Split-Plot Experimental Design Applied to a Low-Speed Wind Tunnel Investigation

NASA Technical Reports Server (NTRS)

Erickson, Gary E.

2013-01-01

A procedure to analyze a split-plot experimental design featuring two input factors, two levels of randomization, and two error structures in a low-speed wind tunnel investigation of a small-scale model of a fighter airplane configuration is described in this report. Standard commercially-available statistical software was used to analyze the test results obtained in a randomization-restricted environment often encountered in wind tunnel testing. The input factors were differential horizontal stabilizer incidence and the angle of attack. The response variables were the aerodynamic coefficients of lift, drag, and pitching moment. Using split-plot terminology, the whole plot, or difficult-to-change, factor was the differential horizontal stabilizer incidence, and the subplot, or easy-to-change, factor was the angle of attack. The whole plot and subplot factors were both tested at three levels. Degrees of freedom for the whole plot error were provided by replication in the form of three blocks, or replicates, which were intended to simulate three consecutive days of wind tunnel facility operation. The analysis was conducted in three stages, which yielded the estimated mean squares, multiple regression function coefficients, and corresponding tests of significance for all individual terms at the whole plot and subplot levels for the three aerodynamic response variables. The estimated regression functions included main effects and two-factor interaction for the lift coefficient, main effects, two-factor interaction, and quadratic effects for the drag coefficient, and only main effects for the pitching moment coefficient.

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.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

2009-01-01

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

[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.

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.

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.

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

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Interpreting Regression Results: beta Weights and Structure Coefficients are Both Important.

ERIC Educational Resources Information Center

Thompson, Bruce

Various realizations have led to less frequent use of the "OVA" methods (analysis of variance--ANOVA--among others) and to more frequent use of general linear model approaches such as regression. However, too few researchers understand all the various coefficients produced in regression. This paper explains these coefficients and their…

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…

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

Effect of coefficient of viscosity and ambient temperature on the flow rate of drug solutions in infusion pumps.

PubMed

Kawabata, Yoshinori

2012-01-01

FOLFOX6 and FOLFIRI regimens are often selected as the first- or second-line treatment for advanced or recurrent colorectal cancer. Patients are now able to undergo at-home treatment by using a portable disposable infusion pump (SUREFUSER(®)A) for continuous intravenous infusion of 5-fluorouracil (5-FU). The duration of continuous 5-FU infusion is normally set at an average of 46 h, but large variations in the duration of infusion are observed. The relationship between the total volume of the drug solution in SUREFUSER(®)A and the duration of infusion was analyzed by regression analysis. In addition, multiple regression analysis of the total volume of the drug solution, dummy variables for temperature, and duration of infusion was carried out. The duration of infusion was affected by the coefficient of viscosity of the drug solution and the ambient temperature. The composition of the drug solutions and the ambient temperature must be considered to ensure correct duration of continuous infusion.

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.

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.

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.

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.

Interpreting Multiple Logistic Regression Coefficients in Prospective Observational Studies

DTIC Science & Technology

1982-11-01

TG HDL -C Males T-C 50-80 MRW pɘ.05 pɘ.10 1HDL-C = high density lipoprotein cholesterol MRW...consider a more complete analy- sis, attempting to uncover the relationship between CHD and TG controlling for covariables such a high density ...for T-C can be re- duced, when among older individuals, elevated T-C may increase the capacity to carry cholesterol in the high density lipoprotein

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.

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…

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.

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.

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.

Restoration of acidic mine spoils with sewage sludge: II measurement of solids applied

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

Stucky, D.J.; Zoeller, A.L.

1980-01-01

Sewage sludge was incorporated in acidic strip mine spoils at rates equivalent to 0, 224, 336, and 448 dry metric tons (dmt)/ha and placed in pots in a greenhouse. Spoil parameters were determined 48 hours after sludge incorporation, Time Planting (P), and five months after orchardgrass (Dactylis glomerata L.) was planted, Time Harvest (H), in the pots. Parameters measured were: pH, organic matter content (OM), cation exchange capacity (CEC), electrical conductivity (EC) and yield. Values for each parameter were significantly different at the two sampling times. Correlation coefficient values were calculated for all parameters versus rates of applied sewage sludgemore » and all parameters versus each other. Multiple regressions were performed, stepwise, for all parameters versus rates of applied sewage sludge. Equations to predict amounts of sewage sludge incorporated in spoils were derived for individual and multiple parameters. Generally, measurements made at Time P achieved the highest correlation coefficient and multiple correlation coefficient values; therefore, the authors concluded data from Time P had the greatest predictability value. The most important value measured to predict rate of applied sewage sludge was pH and some additional accuracy was obtained by including CEC in equation. This experiment indicated that soil properties can be used to estimate amounts of sewage sludge solids required to reclaim acidic mine spoils and to estimate quantities incorporated.« less

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.

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…

Statistical research using the multiple regression analysis in areas of the cast hipereutectoid steel rolls manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Alexa, V.; Serban, S.; Rackov, M.; Čavić, M.

2018-01-01

The cast hipereutectoid steel (usually named Adamite) is a roll manufacturing destined material, having mechanical, chemical properties and Carbon [C] content of which stands between steelandiron, along-withitsalloyelements such as Nickel [Ni], Chrome [Cr], Molybdenum [Mo] and/or other alloy elements. Adamite Rolls are basically alloy steel rolls (a kind of high carbon steel) having hardness ranging from 40 to 55 degrees Shore C, with Carbon [C] percentage ranging from 1.35% until to 2% (usually between 1.2˜2.3%), the extra Carbon [C] and the special alloying element giving an extra wear resistance and strength. First of all the Adamite roll’s prominent feature is the small variation in hardness of the working surface, and has a good abrasion resistance and bite performance. This paper reviews key aspects of roll material properties and presents an analysis of the influences of chemical composition upon the mechanical properties (hardness) of the cast hipereutectoid steel rolls (Adamite). Using the multiple regression analysis (the double and triple regression equations), some mathematical correlations between the cast hipereutectoid steel rolls’ chemical composition and the obtained hardness are presented. In this work several results and evidence obtained by actual experiments are presented. Thus, several variation boundaries for the chemical composition of cast hipereutectoid steel rolls, in view the obtaining the proper values of the hardness, are revealed. For the multiple regression equations, correlation coefficients and graphical representations the software Matlab was used.

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.

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.

On using summary statistics from an external calibration sample to correct for covariate measurement error.

PubMed

Guo, Ying; Little, Roderick J; McConnell, Daniel S

2012-01-01

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded. We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution. The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study. Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.

Cox regression analysis with missing covariates via nonparametric multiple imputation.

PubMed

Hsu, Chiu-Hsieh; Yu, Mandi

2018-01-01

We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to non-parametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression, and the predictive mean matching imputation (PMM) method. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the PMM method is sensitive to misspecification of the covariates included in imputation. The AIPW method is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program.

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.

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.

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.

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).

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.

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.

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.

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

Use of a pretest strategy for physical therapist assistant programs to predict success rate on the national physical therapy exam.

PubMed

Sloas, Stacey B; Keith, Becky; Whitehead, Malcolm T

2013-01-01

This study investigated a pretest strategy that identified physical therapist assistant (PTA) students who were at risk of failure on the National Physical Therapy Examination (NPTE). Program assessment data from five cohorts of PTA students (2005-2009) were used to develop a stepwise multiple regression formula that predicted first-time NPTE licensure scores. Data used included the Nelson-Denny Reading Test, grades from eight core courses, grade point average upon admission to the program, and scores from three mock NPTE exams given during the program. Pearson correlation coefficients were calculated between each of the 15 variables and NPTE scores. Stepwise multiple regression analysis was performed using data collected at the ends of the first, second, and third (final) semesters of the program. Data from the class of 2010 were then used to validate the formula. The end-of-program formula accounted for the greatest variance (57%) in predicted scores. Those students scoring below a predicted scaled score of 620 were identified to be at risk of failure of the licensure exam. These students were counseled, and a remedial plan was developed based on regression predictions prior to them sitting for the licensure exam.

The influence of multiple firing on thermal contraction of ceramic materials used for the fabrication of layered all-ceramic dental restorations.

PubMed

Isgrò, Giuseppe; Kleverlaan, Cornelis J; Wang, Hang; Feilzer, Albert J

2005-06-01

During the production of layered all-ceramic restorations transient and/or residual thermal stresses may be formed which may affect a restoration's longevity. The aim of this study was to evaluate the influence of multiple firings on the thermal behavior of veneering porcelains and a ceramic core. The materials tested were: Empress 2 Core, Empress 2 Veneer and Eris glass-ceramics, Carrara Vincent and an experimental leucite-based veneering porcelain, Vitadur-Alpha aluminous porcelain, and two porcelains designed for titanium (i.e. Duceratin Dentine and Enamel). The thermal contraction coefficient of the materials was measured by means of dilatometery. The thermal contraction coefficient was measured during cooling and calculated over the temperature range of 450-20 degrees C by linear regression. One and two-way analysis of variance together with Tukey post-hoc tests were used as statistical analysis. Repeated firing affects the thermal contraction coefficients of Empress 2 Veneer, Carrara Vincent porcelain and the experimental porcelain. The thermal contraction coefficients of Empress 2 Core were significantly different from Vitadur-Alpha, Carrara Vincent, experimental porcelain, and Duceratin porcelains. The contraction coefficients of Empress 2 Veneer and Eris were closest to that of Empress 2 Core. The Empress 2 Core and Eris glass-ceramics, the aluminous porcelain and Duceratin porcelains showed better thermal stability after repeated firing than leucite porcelains. It can be concluded that due to the thermal stability of glass-ceramic materials, layered all-ceramic restorations of these materials may perform better.

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

Characteristics of youth soccer players aged 13–15 years classified by skill level

PubMed Central

Malina, Robert M; Ribeiro, Basil; Aroso, João; Cumming, Sean P

2007-01-01

Objective To evaluate the growth, maturity status and functional capacity of youth soccer players grouped by level of skill. Subjects The sample included 69 male players aged 13.2–15.1 years from clubs that competed in the highest division for their age group. Methods Height and body mass of players were measured and stage of pubic hair (PH) was assessed at clinical examination. Years of experience in football were obtained at interview. Three tests of functional capacity were administered: dash, vertical jump and endurance shuttle run. Performances on six soccer‐specific tests were converted to a composite score which was used to classify players into quintiles of skill. Multiple analysis of covariance, controlling for age, was used to test differences among skill groups in experience, growth status and functional capacity, whereas multiple linear regression analysis was used to estimate the relative contributions of age, years of training in soccer, stage of PH, height, body mass, the height×weight interaction and functional capacities to the composite skill score. Results The skill groups differed significantly in the intermittent endurance run (p<0.05) but not in the other variables. Only the difference between the highest and lowest skill groups in the endurance shuttle run was significant. Most players in the highest (12 of 14) and high (11 of 14) skill groups were in stages PH 4 and PH 5. Pubertal status and height accounted for 21% of the variance in the skill score; adding aerobic resistance to the regression increased the variance in skill accounted for to 29%. In both regressions, the coefficient for height was negative. Conclusion Adolescent soccer players aged 13–15 years classified by skill do not differ in age, experience, body size, speed and power, but differ in aerobic endurance, specifically at the extremes of skill. Stage of puberty and aerobic resistance (positive coefficients) and height (negative coefficient) are significant predictors of soccer skill (29% of the total explained variance), highlighting the inter‐relationship of growth, maturity and functional characteristics of youth soccer players. PMID:17224444

A non-destructive selection criterion for fibre content in jute : II. Regression approach.

PubMed

Arunachalam, V; Iyer, R D

1974-01-01

An experiment with ten populations of jute, comprising varieties and mutants of the two species Corchorus olitorius and C.capsularis was conducted at two different locations with the object of evolving an effective criterion for selecting superior single plants for fibre yield. At Delhi, variation existed only between varieties as a group and mutants as a group, while at Pusa variation also existed among the mutant populations of C. capsularis.A multiple regression approach was used to find the optimum combination of characters for prediction of fibre yield. A process of successive elimination of characters based on the coefficient of determination provided by individual regression equations was employed to arrive at the optimal set of characters for predicting fibre yield. It was found that plant height, basal and mid-diameters and basal and mid-dry fibre weights would provide such an optimal set.

Cox Regression Models with Functional Covariates for Survival Data.

PubMed

Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M

2015-06-01

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

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.

Quantitative laser-induced breakdown spectroscopy data using peak area step-wise regression analysis: an alternative method for interpretation of Mars science laboratory results

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

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

The ChemCam instrument on the Mars Science Laboratory (MSL) will include a laser-induced breakdown spectrometer (LIBS) to quantify major and minor elemental compositions. The traditional analytical chemistry approach to calibration curves for these data regresses a single diagnostic peak area against concentration for each element. This approach contrasts with a new multivariate method in which elemental concentrations are predicted by step-wise multiple regression analysis based on areas of a specific set of diagnostic peaks for each element. The method is tested on LIBS data from igneous and metamorphosed rocks. Between 4 and 13 partial regression coefficients are needed to describemore » each elemental abundance accurately (i.e., with a regression line of R{sup 2} > 0.9995 for the relationship between predicted and measured elemental concentration) for all major and minor elements studied. Validation plots suggest that the method is limited at present by the small data set, and will work best for prediction of concentration when a wide variety of compositions and rock types has been analyzed.« less

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.

Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

NASA Astrophysics Data System (ADS)

Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat

2015-04-01

Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.

Climatological Modeling of Monthly Air Temperature and Precipitation in Egypt through GIS Techniques

NASA Astrophysics Data System (ADS)

El Kenawy, A.

2009-09-01

This paper describes a method for modeling and mapping four climatic variables (maximum temperature, minimum temperature, mean temperature and total precipitation) in Egypt using a multiple regression approach implemented in a GIS environment. In this model, a set of variables including latitude, longitude, elevation within a distance of 5, 10 and 15 km, slope, aspect, distance to the Mediterranean Sea, distance to the Red Sea, distance to the Nile, ratio between land and water masses within a radius of 5, 10, 15 km, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Normalized Difference Temperature Index (NDTI) and reflectance are included as independent variables. These variables were integrated as raster layers in MiraMon software at a spatial resolution of 1 km. Climatic variables were considered as dependent variables and averaged from quality controlled and homogenized 39 series distributing across the entire country during the period of (1957-2006). For each climatic variable, digital and objective maps were finally obtained using the multiple regression coefficients at monthly, seasonal and annual timescale. The accuracy of these maps were assessed through cross-validation between predicted and observed values using a set of statistics including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean bias Error (MBE) and D Willmott statistic. These maps are valuable in the sense of spatial resolution as well as the number of observatories involved in the current analysis.

Efficacy prediction of cevimeline in patients with Sjögren's syndrome.

PubMed

Yamada, Hiroyuki; Nakagawa, Yoichi; Wakamatsu, Ei; Sumida, Takayuki; Yamachika, Shigeo; Nomura, Yoshiaki; Mishima, Kenji; Saito, Ichiro

2007-08-01

The objective of this study was to examine the clinical and immunological factors influencing the efficacy of cevimeline hydrochloride hydrate (cevimeline) for the treatment of xerostomia in patients with Sjögren's syndrome (SS). Thirty primary SS patients who were medicated with cevimeline were enrolled in this study. Whole stimulated sialometry (WSS) was compared between pre- and posttreatment points (4 weeks after oral cevimeline administration) and the increment rate of WSS was calculated. Multiple regression was employed to examine the relative contributions of the clinical and immunological factors, including age, pretreatment WSS, duration of disease, sialography, minor salivary gland biopsy, anti-Ro/SS-A antibodies, anti-La/SS-B antibodies, and antibodies to muscarinic type 3 receptors to the posttreatment WSS. Patients with normal sialography findings, negative minor salivary gland biopsy, and absence of anti-La/SS-B antibodies had significantly higher increment rates of WSS compared with those with positive findings (p=0.042, 0.002, and 0.018, respectively). Results of the multiple regression analysis showed that sialography (coefficient=-0.867, p=0.004) and minor salivary gland biopsy (coefficient=-0.869, p=0.003) had significant associations with the posttreatment WSS. Our preliminary results demonstrated the relationship between the effect of cevimeline on saliva secretion and the degree of salivary gland destruction evaluated by sialography and histopathological findings in the labial minor salivary glands. These diagnostic approaches could provide useful prognostic information on the efficacy of cevimeline in SS patients.

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.

[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.

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.

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

Nazif, Amina; Mohammed, Nurul Izma; Malakahmad, Amirhossein; Abualqumboz, Motasem S

2018-01-01

The devastating health effects of particulate matter (PM 10 ) exposure by susceptible populace has made it necessary to evaluate PM 10 pollution. Meteorological parameters and seasonal variation increases PM 10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM 10 concentration levels. The analyses were carried out using daily average PM 10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM 10 concentration levels having coefficient of determination (R 2 ) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

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

Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

ERIC Educational Resources Information Center

Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

2013-01-01

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

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.

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.

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.

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.

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.

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.

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.

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.

DFT study on oxidation of HS(CH2) m SH ( m = 1-8) in oxidative desulfurization

NASA Astrophysics Data System (ADS)

Song, Y. Z.; Song, J. J.; Zhao, T. T.; Chen, C. Y.; He, M.; Du, J.

2016-06-01

Density functional theory was employed for calculation of HS(CH2) m SH ( m = 1-8) and its derivatives at B3LYP method at 6-31++g ( d, p) level. Using eigenvalues of LUMO and HOMO for HS(CH2) m SH, the standard electrode potentials were estimated by a stepwise multiple regression techniques (MLR), and obtained as E° = 1.500 + 7.167 × 10-3 HOMO-0.229 LUMO with high correlation coefficients of 0.973 and F values of 43.973.

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

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.

Fish consumption in a sample of people in Bandar Abbas, Iran: application of the theory of planned behavior.

PubMed

Aghamolaei, Teamur; Sadat Tavafian, Sedigheh; Madani, Abdoulhossain

2012-09-01

This study aimed to apply the conceptual framework of the theory of planned behavior (TPB) to explain fish consumption in a sample of people who lived in Bandar Abbass, Iran. We investigated the role of three traditional constructs of TPB that included attitude, social norms, and perceived behavioral control in an effort to characterize the intention to consume fish as well as the behavioral trends that characterize fish consumption. Data were derived from a cross-sectional sample of 321 subjects. Alpha coefficient correlation and linear regression analysis were applied to test the relationships between constructs. The predictors of fish consumption frequency were also evaluated. Multiple regression analysis revealed that attitude, subjective norms, and perceived behavioral control significantly predicted intention to eat fish (R2 = 0.54, F = 128.4, P < 0.001). Multiple regression analysis for the intention to eat fish and perceived behavioral control revealed that both factors significantly predicted fish consumption frequency (R2 = 0.58, F = 223.1, P < 0.001). The results indicated that the models fit well with the data. Attitude, subjective norms, and perceived behavioral control all had significant positive impacts on behavioral intention. Moreover, both intention and perceived behavioral control could be used to predict the frequency of fish consumption.

The Hispanic Americans Baseline Alcohol Survey (HABLAS):Predictive invariance of Demographic Characteristics on Attitudes towards Alcohol across Hispanic National Groups#

PubMed Central

Mills, Britain A.; Caetano, Raul; Bernstein, Ira H.

2011-01-01

This study compares the demographic predictors of items assessing attitudes towards drinking across Hispanic national groups. Data were from the 2006 Hispanic Americans Baseline Alcohol Survey (HABLAS), which used a multistage cluster sample design to interview 5,224 individuals randomly selected from the household population in Miami, New York, Philadelphia, Houston, and Los Angeles. Predictive invariance of demographic predictors of alcohol attitudes over four Hispanic national groups (Puerto Rican, Cuban, Mexican, and South/Central Americans) was examined using multiple-group seemingly unrelated probit regression. The analyses examined whether the influence of various demographic predictors varied across the Hispanic national groups in their regression coefficients, item intercepts, and error correlations. The hypothesis of predictive invariance was supported. Hispanic groups did not differ in how demographic predictors related to individual attitudinal items (regression slopes were invariant). In addition, the groups did not differ in attitudinal endorsement rates once demographic covariates were taken into account (item intercepts were invariant). Although Hispanic groups have different attitudes about alcohol, the influence of multiple demographic characteristics on alcohol attitudes operates similarly across Hispanic groups. Future models of drinking behavior in adult Hispanics need not posit moderating effects of group on the relation between these background characteristics and attitudes. PMID:25379120

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.

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.

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.

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.

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%.

Mass and heat transfer in crushed oil shale

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

Carley, J.F.; Straub, J.S.; Ott, L.L.

1984-04-01

Heat and mass transfer between gases and oil-shale particles are both important for all proposed retorting processes. Past studies of transfer in packed beds, which have disagreed substantially in their results, have nearly all been done with beds of regular particles of uniform size, whereas oil-shale retorting involves particles of diverse shapes and widely ranging sizes. To resolve these questions, we have made 349 runs in which we measured mass-transfer rates from naphthalene particles of diverse shapes buried in packed beds through which air was passed at room temperature. This technique permits calculation of the mass-transfer coefficient for each activemore » particle in the bed rather than, as in most past studies, for the bed as a whole. The data were analyzed in two ways: (1) by the traditional correlation of Colburn j/sub D/ vs Reynolds number and (2) by multiple regression of the mass-transfer coefficient on air rate, traditional correlation of Colburn j/sub D/ vs Reynolds number and (3) by multiple regression of the mass-transfer coefficient on air rate, sizes of active and inert particles, void fraction, and temperature. Principal findings are: (1) local Reynolds number should be based on active particle size rather than average size for the bed; (2) no appreciable differences were seen between shallow beds and deep ones; (3) mass transfer was 26% faster for spheres and lozenges buried in shale than for all-sphere beds; (4) orientation of lozenges in shale beds has little effect on mass-transfer rate; (5) a useful summarizing equation for either mass or heat transfer in shale beds is log j.epsilon = -.0747 - .6344 log Re + .0592 log/sup 2/Re where j = either j/sub D/ or j/sub H/, the Chilton-Colburn j-factors for mass and heat transfer, Re = the Reynolds number defined for packed beds, and epsilon = the void fraction in the bed. 12 references, 15 figures.« less

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.

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.

[High Risk Sex Behaviors and Associated Factors in Young Men in Chengdu].

PubMed

2015-11-01

To determine the prevalence of high risk sex behaviors and associated factors in 18-34 years old men in Chengdu. Methods An anonymous questionnaire survey was conducted in 18-34 years old men selected by multi-stage random sampling in Chengdu. Data of 1536 respondents who reported having sex contacts were analyzed. 23.6% of respondents had multiple sex partners in the past 12 months; 11.8% were involved commercial sex; 9.0% had group sex; 4. 7% had anal sex; 15.6% had never used a condom; 37.7% had sex under the influence of alcohol or drugs. Logistic regression analysis revealed that marital status [married, standardized partial regression coefficient (B) = -0.086, P<0.05] , level of education (bachelor or above, B= -0.063, P<0.05), frequency of exposure to pornography (B=0.058, P<0.05), childhood sexual abuse (B= 0.042, P<0.05), first sexual intercourse at an earlier age (B=0.162, P<0.05), frequency of sex under the influence of alcohol or drugs (B=0.054, P<0.05) were significant predictors of having multiple sexual partners. Sexual orientation, age, smoking, alcohol abuse, drug use, anxiety, depression, childhood physical abuse did not appear to be significant factors associated with having multiple sexual partners. Having multiple sexual partners is the main high risk sex behavior of young men in Chengdu. Childhood sexual abuse and early start of sexual intercourse are the major predictors of having multiple sexual partners.

Quantification of the effects of quality investment on the Cost of Poor Quality: A quasi-experimental study

NASA Astrophysics Data System (ADS)

Tamimi, Abdallah Ibrahim

Quality management is a fundamental challenge facing businesses. This research attempted to quantify the effect of quality investment on the Cost of Poor Quality (COPQ) in an aerospace company utilizing 3 years of quality data at United Launch Alliance, a Boeing -- Lockheed Martin Joint Venture Company. Statistical analysis tools, like multiple regressions, were used to quantify the relationship between quality investments and COPQ. Strong correlations were evident by the high correlation coefficient R2 and very small p-values in multiple regression analysis. The models in the study helped produce an Excel macro that based on preset constraints, optimized the level of quality spending to minimize COPQ. The study confirmed that as quality investments were increased, the COPQ decreased steadily until a point of diminishing return was reached. The findings may be used to develop an approach to reduce the COPQ and enhance product performance. Achieving superior quality in rocket launching enhances the accuracy, reliability, and mission success of delivering satellites to their precise orbits in pursuit of knowledge, peace, and freedom while assuring safety for the end user.

A Semiparametric Change-Point Regression Model for Longitudinal Observations.

PubMed

Xing, Haipeng; Ying, Zhiliang

2012-12-01

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this connection, we propose a semiparametric change-point regression model, in which the error process (stochastic component) is nonparametric and the baseline mean function (functional part) is completely unspecified, the observation times are allowed to be subject-specific, and the number, locations and magnitudes of change-points are unknown and need to be estimated. We further develop an estimation procedure which combines the recent advance in semiparametric analysis based on counting process argument and multiple change-points inference, and discuss its large sample properties, including consistency and asymptotic normality, under suitable regularity conditions. Simulation results show that the proposed methods work well under a variety of scenarios. An application to a real data set is also given.

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.

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.

The relationship of exposure to air pollutants in pregnancy with surrogate markers of endothelial dysfunction in umbilical cord.

PubMed

Poursafa, Parinaz; Baradaran-Mahdavi, Sadegh; Moradi, Bita; Haghjooy Javanmard, Shaghayegh; Tajadini, Mohammadhasan; Mehrabian, Ferdous; Kelishadi, Roya

2016-04-01

This study aims to investigate the association of exposure to ambient air pollution during pregnancy with cord blood concentrations of surrogate markers of endothelial dysfunction. This population-based cohort was conducted from March 2014 to March 2015 among 250 mother-neonate pairs in urban areas of Isfahan, the second large and air-polluted city in Iran. We analyzed the association between the ambient carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particular matter 10 (PM10), and air quality index (AQI) with cord blood levels of endothelin-1, vascular adhesion molecule (VCAM), and intercellular adhesion molecule (ICAM). Multiple regression analysis was conducted after adjustment for potential confounding factors and covariates. The regression coefficient (beta), standard error of the estimate (SE), and 95% confidence intervals for each regression coefficient (95% CI) are reported. Data of 233 mother-neonate pairs were complete, and included in the analysis. Multiple regression analyses showed that AQI, CO and O3 had significant correlation with cord blood ICAM-1 [Beta (SE), 95%CI: 2.93 (0.72), 1.33,5.54; 2.28(1.44), 1.56,5.12; and 2.02(0.01), 1.03,2.04, respectively] as well as with VCAM-1 [2.78(0.91), 1.69,4.57; 2.47(1.47), 1.43,5.37; and 2.01(0.01),1.07,2.04, respectively]. AQI, PM10, and SO2 were significantly associated with Endothelin-1 concentrations [Beta (SE), 95%CI: 10.16(5.08),7.61,14.28; 9.70(3.46), 2.88,16.52; and 1.07(0.02), 1.03,2.11, respectively]. The significant associations of air pollutants with markers of endothelial dysfunction during fetal period may provide another evidence on the adverse health effects of air pollutants on early stages of atherosclerosis from fetal period. Our findings underscore the importance of considering environmental factors in primordial prevention of chronic diseases. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

PubMed

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage and metastasis

PubMed Central

2013-01-01

Background Colorectal cancer is the third leading cause of cancer deaths in the United States. The initial assessment of colorectal cancer involves clinical staging that takes into account the extent of primary tumor invasion, determining the number of lymph nodes with metastatic cancer and the identification of metastatic sites in other organs. Advanced clinical stage indicates metastatic cancer, either in regional lymph nodes or in distant organs. While the genomic and genetic basis of colorectal cancer has been elucidated to some degree, less is known about the identity of specific cancer genes that are associated with advanced clinical stage and metastasis. Methods We compiled multiple genomic data types (mutations, copy number alterations, gene expression and methylation status) as well as clinical meta-data from The Cancer Genome Atlas (TCGA). We used an elastic-net regularized regression method on the combined genomic data to identify genetic aberrations and their associated cancer genes that are indicators of clinical stage. We ranked candidate genes by their regression coefficient and level of support from multiple assay modalities. Results A fit of the elastic-net regularized regression to 197 samples and integrated analysis of four genomic platforms identified the set of top gene predictors of advanced clinical stage, including: WRN, SYK, DDX5 and ADRA2C. These genetic features were identified robustly in bootstrap resampling analysis. Conclusions We conducted an analysis integrating multiple genomic features including mutations, copy number alterations, gene expression and methylation. This integrated approach in which one considers all of these genomic features performs better than any individual genomic assay. We identified multiple genes that robustly delineate advanced clinical stage, suggesting their possible role in colorectal cancer metastatic progression. PMID:24308539

[Research on relations among self-esteem, self-harmony and interpersonal-harmony of university students].

PubMed

Zhang, Hualing

2014-03-01

To learn characteristics and their mutual relations of self-esteem, self-harmony and interpersonal-harmony of university students, in order to provide the basis for mental health education. With a stratified cluster random sampling method, a questionnaire survey was conducted in 820 university students from 16 classes of four universities, chosen from 30 universities in Anhui Province. Meanwhile, Rosenberg Self-esteem Scale, Self-harmony Scale and Interpersonal-harmony Diagnostic Scale were used for assessment. Self-esteem of university students has an average score of (30.71 +/- 4.77), higher than median thoery 25, and there existed statistical significance in the dimensions of gender (P = 0.004), origin (P = 0.038) and only-child (P = 0.005). University students' self-harmony has an average score of (98.66 +/- 8.69), among which there were 112 students in the group of low score, counting for 13.7%, 442 in that of middle score, counting for 53.95%, 265 in that of high score, counting for 32.33%. And there existed no statistical significance in the total-score of self-harmony and score differences from most of subscales in the dimention of gender and origin, but satistical significance did exist in the dimention of only-child (P = 0.004). It was statistically significant (P = 0.006) on the "stereotype" subscales, on the differences between university students from urban areas and rural areas. Every dimension of self-esteem and self -harmony and interpersonal harmony was correlated and statistically significant. Multiple regression analysis found that when there was a variable in self-esteem, the amount of the variable of self-harmony for explaination of interpersonal conversation dropped from 22.6% to 12%, and standard regression coefficient changing from 0.087 to 0.035. The trouble of interpersonal dating fell from 27.6% to 13.1%, the standard regression coefficient changing from 0.104 to 0.019. The bother of treating people fell from 30.9% to 15%, and the standard regression coefficient changing from 0.079 to 0.020. The problem of heterosexual contact fell from 23.4% to 17.3%, and the standard regression coefficient changing from 0.095 to 0.024. Self-esteem was a mediator variable between self-harmony and interpersonal-harmony. By cultivating university students' level of self-esteem to achieve their self-harmony and interpersonal-harmony, university students' mental health level can be improved.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

PubMed

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

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.

Visualizing variations in organizational safety culture across an inter-hospital multifaceted workforce.

PubMed

Kobuse, Hiroe; Morishima, Toshitaka; Tanaka, Masayuki; Murakami, Genki; Hirose, Masahiro; Imanaka, Yuichi

2014-06-01

To develop a reliable and valid questionnaire that can distinguish features of organizational culture for patient safety across subgroups such as hospitals, professions, management/non-management positions and units/wards. We developed a Hospital Organizational Culture Questionnaire based on a conceptual framework incorporating items from a review of existing literature. The questionnaire was administered to hospital staff including doctors, nurses, allied health personnel, and administrative staff at six public hospitals in Japan. Reliability and validity were assessed through exploratory factor analysis, multitrait scaling analysis, Cronbach's alpha coefficient and multiple regression analysis using staff-perceived achievement of safety as the response variable. Discriminative power across subgroups was assessed with radar chart profiling. Of the 3304 hospital staff surveyed, 2924 (88.5%) responded. After exploratory factor analysis and multitrait analysis, the finalized questionnaire was composed of 24 items in the following eight dimensions: improvement orientation, passion for mission, professional growth, resource allocation prioritization, inter-sectional collaboration, responsibility and authority, teamwork, and information sharing. Construct validity and internal consistency of dimensions were confirmed with multitrait analysis and Cronbach's alpha coefficients, respectively. Multiple regression analysis showed that improvement orientation, passion for mission, resource allocation prioritization and information sharing were significantly associated with higher achievement in safety practices. Our questionnaire tool was able to distinguish features of safety culture among different subgroups. Our questionnaire demonstrated excellent validity and reliability, and revealed distinct cultural patterns among different subgroups. Quantitative assessment of organizational safety culture with this tool may further the understanding of associated characteristics of each subgroup and provide insight into organizational readiness for patient safety improvement. © 2014 John Wiley & Sons, Ltd.

A study of Solar-Enso correlation with southern Brazil tree ring index (1955- 1991)

NASA Astrophysics Data System (ADS)

Rigozo, N.; Nordemann, D.; Vieira, L.; Echer, E.

The effects of solar activity and El Niño-Southern Oscillation on tree growth in Southern Brazil were studied by correlation analysis. Trees for this study were native Araucaria (Araucaria Angustifolia)from four locations in Rio Grande do Sul State, in Southern Brazil: Canela (29o18`S, 50o51`W, 790 m asl), Nova Petropolis (29o2`S, 51o10`W, 579 m asl), Sao Francisco de Paula (29o25`S, 50o24`W, 930 m asl) and Sao Martinho da Serra (29o30`S, 53o53`W, 484 m asl). From these four sites, an average tree ring Index for this region was derived, for the period 1955-1991. Linear correlations were made on annual and 10 year running averages of this tree ring Index, of sunspot number Rz and SOI. For annual averages, the correlation coefficients were low, and the multiple regression between tree ring and SOI and Rz indicates that 20% of the variance in tree rings was explained by solar activity and ENSO variability. However, when the 10 year running averages correlations were made, the coefficient correlations were much higher. A clear anticorrelation is observed between SOI and Index (r=-0.81) whereas Rz and Index show a positive correlation (r=0.67). The multiple regression of 10 year running averages indicates that 76% of the variance in tree ring INdex was explained by solar activity and ENSO. These results indicate that the effects of solar activity and ENSO on tree rings are better seen on long timescales.

Importance of Preserving Cross-correlation in developing Statistically Downscaled Climate Forcings and in estimating Land-surface Fluxes and States

NASA Astrophysics Data System (ADS)

Das Bhowmik, R.; Arumugam, S.

2015-12-01

Multivariate downscaling techniques exhibited superiority over univariate regression schemes in terms of preserving cross-correlations between multiple variables- precipitation and temperature - from GCMs. This study focuses on two aspects: (a) develop an analytical solutions on estimating biases in cross-correlations from univariate downscaling approaches and (b) quantify the uncertainty in land-surface states and fluxes due to biases in cross-correlations in downscaled climate forcings. Both these aspects are evaluated using climate forcings available from both historical climate simulations and CMIP5 hindcasts over the entire US. The analytical solution basically relates the univariate regression parameters, co-efficient of determination of regression and the co-variance ratio between GCM and downscaled values. The analytical solutions are compared with the downscaled univariate forcings by choosing the desired p-value (Type-1 error) in preserving the observed cross-correlation. . For quantifying the impacts of biases on cross-correlation on estimating streamflow and groundwater, we corrupt the downscaled climate forcings with different cross-correlation structure.

Predicting athletic success motivation using mental skin and emotional intelligence and its components in male athletes.

PubMed

Kajbafnezhad, H; Ahadi, H; Heidarie, A; Askari, P; Enayati, M

2012-10-01

The aim of this study was to predict athletic success motivation by mental skills, emotional intelligence and its components. The research sample consisted of 153 male athletes who were selected through random multistage sampling. The subjects completed the Mental Skills Questionnaire, Bar-On Emotional Intelligence questionnaire and the perception of sport success questionnaire. Data were analyzed using Pearson correlation coefficient and multiple regressions. Regression analysis shows that between the two variables of mental skill and emotional intelligence, mental skill is the best predictor for athletic success motivation and has a better ability to predict the success rate of the participants. Regression analysis results showed that among all the components of emotional intelligence, self-respect had a significantly higher ability to predict athletic success motivation. The use of psychological skills and emotional intelligence as an mediating and regulating factor and organizer cause leads to improved performance and can not only can to help athletes in making suitable and effective decisions for reaching a desired goal.

Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

PubMed

Lin, Feng-Chang; Zhu, Jun

2012-01-01

We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

PubMed

Brownstein, Naomi C; Cai, Jianwen; Slade, Gary D; Bair, Eric

2015-12-30

In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study. Copyright © 2015 John Wiley & Sons, Ltd.

Serum levels of the immune activation marker neopterin change with age and gender and are modified by race, BMI, and percentage of body fat.

PubMed

Spencer, Monique E; Jain, Alka; Matteini, Amy; Beamer, Brock A; Wang, Nae-Yuh; Leng, Sean X; Punjabi, Naresh M; Walston, Jeremy D; Fedarko, Neal S

2010-08-01

Neopterin, a GTP metabolite expressed by macrophages, is a marker of immune activation. We hypothesize that levels of this serum marker alter with donor age, reflecting increased chronic immune activation in normal aging. In addition to age, we assessed gender, race, body mass index (BMI), and percentage of body fat (%fat) as potential covariates. Serum was obtained from 426 healthy participants whose age ranged from 18 to 87 years. Anthropometric measures included %fat and BMI. Neopterin concentrations were measured by competitive ELISA. The paired associations between neopterin and age, BMI, or %fat were analyzed by Spearman's correlation or by linear regression of log-transformed neopterin, whereas overall associations were modeled by multiple regression of log-transformed neopterin as a function of age, gender, race, BMI, %fat, and interaction terms. Across all participants, neopterin exhibited a positive association with age, BMI, and %fat. Multiple regression modeling of neopterin in women and men as a function of age, BMI, and race revealed that each covariate contributed significantly to neopterin values and that optimal modeling required an interaction term between race and BMI. The covariate %fat was highly correlated with BMI and could be substituted for BMI to yield similar regression coefficients. The association of age and gender with neopterin levels and their modification by race, BMI, or %fat reflect the biology underlying chronic immune activation and perhaps gender differences in disease incidence, morbidity, and mortality.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

NASA Astrophysics Data System (ADS)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

SCI model structure determination program (OSR) user's guide. [optimal subset regression

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.

Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS

USGS Publications Warehouse

Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.

2011-01-01

The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.

Key factors contributing to accident severity rate in construction industry in Iran: a regression modelling approach.

PubMed

Soltanzadeh, Ahmad; Mohammadfam, Iraj; Moghimbeigi, Abbas; Ghiasvand, Reza

2016-03-01

Construction industry involves the highest risk of occupational accidents and bodily injuries, which range from mild to very severe. The aim of this cross-sectional study was to identify the factors associated with accident severity rate (ASR) in the largest Iranian construction companies based on data about 500 occupational accidents recorded from 2009 to 2013. We also gathered data on safety and health risk management and training systems. Data were analysed using Pearson's chi-squared coefficient and multiple regression analysis. Median ASR (and the interquartile range) was 107.50 (57.24- 381.25). Fourteen of the 24 studied factors stood out as most affecting construction accident severity (p<0.05). These findings can be applied in the design and implementation of a comprehensive safety and health risk management system to reduce ASR.

Ecotoxicology of phenylphosphonothioates.

PubMed Central

Francis, B M; Hansen, L G; Fukuto, T R; Lu, P Y; Metcalf, R L

1980-01-01

The phenylphosphonothioate insecticides EPN and leptophos, and several analogs, were evaluated with respect to their delayed neurotoxic effects in hens and their environmental behavior in a terrestrial-aquatic model ecosystem. Acute toxicity to insects was highly correlated with sigma sigma of the substituted phenyl group (regression coefficient r = -0.91) while acute toxicity to mammals was slightly less well correlated (regression coefficient r = -0.71), and neurotoxicity was poorly correlated with sigma sigma (regression coefficient r = -0.35). Both EPN and leptophos were markedly more persistent and bioaccumulative in the model ecosystem than parathion. Desbromoleptophos, a contaminant and metabolite of leptophos, was seen to be a highly stable and persistent terminal residue of leptophos. PMID:6159210

Meteorological influence on predicting surface SO2 concentration from satellite remote sensing in Shanghai, China.

PubMed

Xue, Dan; Yin, Jingyuan

2014-05-01

In this study, we explored the potential applications of the Ozone Monitoring Instrument (OMI) satellite sensor in air pollution research. The OMI planetary boundary layer sulfur dioxide (SO2_PBL) column density and daily average surface SO2 concentration of Shanghai from 2004 to 2012 were analyzed. After several consecutive years of increase, the surface SO2 concentration finally declined in 2007. It was higher in winter than in other seasons. The coefficient between daily average surface SO2 concentration and SO2_PBL was only 0.316. But SO2_PBL was found to be a highly significant predictor of the surface SO2 concentration using the simple regression model. Five meteorological factors were considered in this study, among them, temperature, dew point, relative humidity, and wind speed were negatively correlated with surface SO2 concentration, while pressure was positively correlated. Furthermore, it was found that dew point was a more effective predictor than temperature. When these meteorological factors were used in multiple regression, the determination coefficient reached 0.379. The relationship of the surface SO2 concentration and meteorological factors was seasonally dependent. In summer and autumn, the regression model performed better than in spring and winter. The surface SO2 concentration predicting method proposed in this study can be easily adapted for other regions, especially most useful for those having no operational air pollution forecasting services or having sparse ground monitoring networks.

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.

Psychological quality of life and its association with academic employability skills among newly-registered students from three European faculties

PubMed Central

2011-01-01

Background In accord with new European university reforms initiated by the Bologna Process, our objectives were to assess psychological quality of life (QoL) and to analyse its associations with academic employability skills (AES) among students from the Faculty of Language, Literature, Humanities, Arts and Education, Walferdange Luxembourg (F1, mostly vocational/applied courses); the Faculty of Social and Human Sciences, Liege, Belgium (F2, mainly general courses); and the Faculty of Social Work, Iasi, Romania (F3, mainly vocational/professional courses). Method Students who redoubled or who had studied at other universities were excluded. 355 newly-registered first-year students (145 from F1, 125 from F2, and 85 from F3) were invited to complete an online questionnaire (in French, German, English or Romanian) covering socioeconomic data, the AES scale and the QoL-psychological, QoL-social relationships and QoL-environment subscales as measured with the World Health Organisation Quality of Life short-form (WHOQoL-BREF) questionnaire. Analyses included multiple regressions with interactions. Results QoL-psychological, QoL-social relationships and QoL-environment' scores were highest in F1 (Luxembourg), and the QoL-psychological score in F2 (Belgium) was the lower. AES score was higher in F1 than in F3 (Romania). A positive link was found between QoL-psychological and AES for F1 (correlation coefficient 0.29, p < 0.01) and F3 (correlation coefficient 0.30, p < 0.05), but the association was negative for F2 (correlation coefficient -0.25, p < 0.01). QoL-psychological correlated positively with QoL-social relationships (regression coefficient 0.31, p < 0.001) and QoL-environment (regression coefficient 0.35, p < 0.001). Conclusions Psychological quality of life is associated with acquisition of skills that increase employability from the faculties offering vocational/applied/professional courses in Luxembourg and Romania, but not their academically orientated Belgian counterparts. In the context of developing a European Higher Educational Area, these measurements are major indicators that can be used as a guide to promoting programs geared towards counseling, improvement of the social environment, and services to assist with university work and facilitate achievement of future professional projects. PMID:21501507

Psychological quality of life and its association with academic employability skills among newly-registered students from three European faculties.

PubMed

Baumann, Michèle; Ionescu, Ion; Chau, Nearkasen

2011-04-18

In accord with new European university reforms initiated by the Bologna Process, our objectives were to assess psychological quality of life (QoL) and to analyse its associations with academic employability skills (AES) among students from the Faculty of Language, Literature, Humanities, Arts and Education, Walferdange Luxembourg (F1, mostly vocational/applied courses); the Faculty of Social and Human Sciences, Liege, Belgium (F2, mainly general courses); and the Faculty of Social Work, Iasi, Romania (F3, mainly vocational/professional courses). Students who redoubled or who had studied at other universities were excluded. 355 newly-registered first-year students (145 from F1, 125 from F2, and 85 from F3) were invited to complete an online questionnaire (in French, German, English or Romanian) covering socioeconomic data, the AES scale and the QoL-psychological, QoL-social relationships and QoL-environment subscales as measured with the World Health Organisation Quality of Life short-form (WHOQoL-BREF) questionnaire. Analyses included multiple regressions with interactions. QoL-psychological, QoL-social relationships and QoL-environment' scores were highest in F1 (Luxembourg), and the QoL-psychological score in F2 (Belgium) was the lower. AES score was higher in F1 than in F3 (Romania). A positive link was found between QoL-psychological and AES for F1 (correlation coefficient 0.29, p<0.01) and F3 (correlation coefficient 0.30, p<0.05), but the association was negative for F2 (correlation coefficient -0.25, p<0.01). QoL-psychological correlated positively with QoL-social relationships (regression coefficient 0.31, p<0.001) and QoL-environment (regression coefficient 0.35, p<0.001). Psychological quality of life is associated with acquisition of skills that increase employability from the faculties offering vocational/applied/professional courses in Luxembourg and Romania, but not their academically orientated Belgian counterparts. In the context of developing a European Higher Educational Area, these measurements are major indicators that can be used as a guide to promoting programs geared towards counseling, improvement of the social environment, and services to assist with university work and facilitate achievement of future professional projects.

[Homicide mortality, socioeconomic development, and police violence in the city of São Paulo, Brazil].

PubMed

Peres, Maria Fernanda Tourinho; Cardia, Nancy; de Mesquita Neto, Paulo; Dos Santos, Patrícia Carla; Adorno, Sérgio

2008-04-01

To analyze the association between police violence and homicide mortality rates taking into consideration the effect of contextual variables. This was an environmental, cross-sectional study that included the 96 census districts in the City of São Paulo. The association between the variables was analyzed using Spearman's rank correlation and simple and multiple regression analysis. Univariate analysis revealed a strong and significant association between homicide mortality coefficients and all the indicators of socioeconomic development and police violence. After controlling for potential confounding factors, the association between police violence and homicide mortality coefficients remained strong and significant. This significance was lost only after control for the size of the resident population. The results indicate that police action that violates basic human rights is not the right answer to urban violence. The combination of homicides from interpersonal violence and deaths from police violence results in negative socialization and promotes further violence.

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.

Real-time absorption and scattering characterization of slab-shaped turbid samples obtained by a combination of angular and spatially resolved measurements.

PubMed

Dam, Jan S; Yavari, Nazila; Sørensen, Søren; Andersson-Engels, Stefan

2005-07-10

We present a fast and accurate method for real-time determination of the absorption coefficient, the scattering coefficient, and the anisotropy factor of thin turbid samples by using simple continuous-wave noncoherent light sources. The three optical properties are extracted from recordings of angularly resolved transmittance in addition to spatially resolved diffuse reflectance and transmittance. The applied multivariate calibration and prediction techniques are based on multiple polynomial regression in combination with a Newton--Raphson algorithm. The numerical test results based on Monte Carlo simulations showed mean prediction errors of approximately 0.5% for all three optical properties within ranges typical for biological media. Preliminary experimental results are also presented yielding errors of approximately 5%. Thus the presented methods show a substantial potential for simultaneous absorption and scattering characterization of turbid media.

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.

Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression.

PubMed

Parisi Kern, Andrea; Ferreira Dias, Michele; Piva Kulakowski, Marlova; Paulo Gomes, Luciana

2015-05-01

Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.

PubMed

Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L

2016-02-10

Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure. Copyright © 2015 John Wiley & Sons, Ltd.

[Habitat suitability index of larval Japanese Halfbeak (Hyporhamphus sajori) in Bohai Sea based on geographically weighted regression.

PubMed

Zhao, Yang; Zhang, Xue Qing; Bian, Xiao Dong

2018-01-01

To investigate the early supplementary processes of fishre sources in the Bohai Sea, the geographically weighted regression (GWR) was introduced to the habitat suitability index (HSI) model. The Bohai Sea larval Japanese Halfbeak HSI GWR model was established with four environmental variables, including sea surface temperature (SST), sea surface salinity (SSS), water depth (DEP), and chlorophyll a concentration (Chl a). Results of the simulation showed that the four variables had different performances in August 2015. SST and Chl a were global variables, and had little impacts on HSI, with the regression coefficients of -0.027 and 0.006, respectively. SSS and DEP were local variables, and had larger impacts on HSI, while the average values of absolute values of their regression coefficients were 0.075 and 0.129, respectively. In the central Bohai Sea, SSS showed a negative correlation with HSI, and the most negative correlation coefficient was -0.3. In contrast, SSS was correlated positively but weakly with HSI in the three bays of Bohai Sea, and the largest correlation coefficient was 0.1. In particular, DEP and HSI were negatively correlated in the entire Bohai Sea, while they were more negatively correlated in the three bays of Bohai than in the central Bohai Sea, and the most negative correlation coefficient was -0.16 in the three bays. The Poisson regression coefficient of the HSI GWR model was 0.705, consistent with field measurements. Therefore, it could provide a new method for the research on fish habitats in the future.

Total energy expenditure in adults with cerebral palsy as assessed by doubly labeled water.

PubMed

Johnson, R K; Hildreth, H G; Contompasis, S H; Goran, M I

1997-09-01

To characterize total energy expenditure (TEE) in free-living adults with cerebral palsy (CP) using the doubly labeled water technique, and to determine those physiologic variables and characteristics of CP that were markers of TEE in adults with CP. TEE was measured using the doubly labeled water technique in 30 free-living adults with CP (12 women, 18 men). To determine the best markers of TEE, the following factors were examined: CP status, resting metabolic rate (RMR), anthropometric characteristics and body composition by means of dual-energy x-ray absorptiometry (DXA) and skinfold thickness measurements, energy cost of leisure-time activities, and oral-motor impairment. Means +/- standard deviations, t tests, Pearson product-moment correlation coefficients, Spearman rank correlation coefficients, chi 2, stepwise multiple-correlation regression analysis, and analysis of covariance were used to examine the relationships among variables of interest. TEE was highly variable in the sample (mean = 2,455 +/- 622 kcal/day for men and 1,986 +/- 363 kcal/day for women). Stepwise regression analysis showed that TEE was best predicted in the sample by RMR, percentage body fat determined by DXA, ambulation status, and sex (multiple R = .68, P = .003). When practical, easily measured variables were used, TEE was best predicted by height, ambulation status, percentage body fat by skinfold thickness measurements, and sex (multiple R = .61, P. = 018). The contribution of energy expended in physical activity to TEE was significantly higher in the ambulatory subjects than the nonambulatory subjects (25% vs 16%, respectively; P = .009). The high degree of variability in TEE, largely attributable to high interindividual variation in energy expended in physical activity, makes it difficult to provide general guidelines for energy requirements for adults with CP. Because ambulation status was an important predictor of TEE, it must be accounted for in estimating energy requirements in this population.

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.

Advantage of multiple spot urine collections for estimating daily sodium excretion: comparison with two 24-h urine collections as reference.

PubMed

Uechi, Ken; Asakura, Keiko; Ri, Yui; Masayasu, Shizuko; Sasaki, Satoshi

2016-02-01

Several estimation methods for 24-h sodium excretion using spot urine sample have been reported, but accurate estimation at the individual level remains difficult. We aimed to clarify the most accurate method of estimating 24-h sodium excretion with different numbers of available spot urine samples. A total of 370 participants from throughout Japan collected multiple 24-h urine and spot urine samples independently. Participants were allocated randomly into a development and a validation dataset. Two estimation methods were established in the development dataset using the two 24-h sodium excretion samples as reference: the 'simple mean method' estimated by multiplying the sodium-creatinine ratio by predicted 24-h creatinine excretion, whereas the 'regression method' employed linear regression analysis. The accuracy of the two methods was examined by comparing the estimated means and concordance correlation coefficients (CCC) in the validation dataset. Mean sodium excretion by the simple mean method with three spot urine samples was closest to that by 24-h collection (difference: -1.62  mmol/day). CCC with the simple mean method increased with an increased number of spot urine samples at 0.20, 0.31, and 0.42 using one, two, and three samples, respectively. This method with three spot urine samples yielded higher CCC than the regression method (0.40). When only one spot urine sample was available for each study participant, CCC was higher with the regression method (0.36). The simple mean method with three spot urine samples yielded the most accurate estimates of sodium excretion. When only one spot urine sample was available, the regression method was preferable.

The Associations of Serum Lipids with Vitamin D Status.

PubMed

Wang, Ying; Si, Shaoyan; Liu, Junli; Wang, Zongye; Jia, Haiying; Feng, Kai; Sun, Lili; Song, Shu Jun

2016-01-01

Vitamin D deficiency has been associated with some disorders including cardiovascular diseases. Dyslipidemia is a major risk factor for cardiovascular diseases. However, data about the relationships between vitamin D and lipids are inconsistent. The relationship of vitamin D and Atherogenic Index of Plasma (AIP), as an excellent predictor of level of small and dense LDL, has not been reported. The objective of this study was to investigate the effects of vitamin D status on serum lipids in Chinese adults. The study was carried out using 1475 participants from the Center for Physical Examination, 306 Hospital of PLA in Beijing, China. Fasting blood samples were collected and serum concentrations of 25(OH)D, total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) were measured. AIP was calculated based on the formula: log [TG/HDL-C]. Multiple linear regression analysis was used to estimate the associations between serum 25(OH)D and lipids. The association between the occurrences of dyslipidemias and vitamin D levels was assessed by multiple logistic regression analysis. Confounding factors, age and BMI, were used for the adjustment. The median of serum 25(OH)D concentration was 47 (27-92.25) nmol/L in all subjects. The overall percentage of 25(OH)D ≦ 50 nmol/L was 58.5% (males 54.4%, females 63.7%). The serum 25(OH)D levels were inversely associated with TG (β coefficient = -0.24, p < 0.001) and LDL-C (β coefficient = -0.34, p < 0.001) and positively associated with TC (β coefficient = 0.35, p < 0.002) in men. The associations between serum 25(OH)D and LDL-C (β coefficient = -0.25, p = 0.01) and TC (β coefficient = 0.39, p = 0.001) also existed in women. The serum 25(OH)D concentrations were negatively associated with AIP in men (r = -0.111, p < 0.01) but not in women. In addition, vitamin D deficient men had higher AIP values than vitamin D sufficient men. Furthermore, the occurrences of dyslipidemias (reduced HDL-C, elevated TG and elevated AIP) correlated with lower 25(OH)D levels in men, whereas the higher TC and LDL-C associated with higher 25(OH)D levels in women. It seems that the serum 25(OH)D levels are closely associated with the serum lipids and AIP. Vitamin D deficiency may be associated with the increased risk of dyslipidemias, especially in men. The association between vitamin D status and serum lipids may differ by genders.

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.

Influence of Health Behaviors and Occupational Stress on Prediabetic State among Male Office Workers.

PubMed

Ryu, Hosihn; Moon, Jihyeon; Jung, Jiyeon

2018-06-14

This study examined the influence of health behaviors and occupational stress on the prediabetic state of male office workers, and identified related risks and influencing factors. The study used a cross-sectional design and performed an integrative analysis on data from regular health checkups, health questionnaires, and a health behavior-related survey of employees of a company, using Spearman’s correlation coefficients and multiple logistic regression analysis. The results showed significant relationships of prediabetic state with health behaviors and occupational stress. Among health behaviors, a diet without vegetables and fruits (Odds Ratio (OR) = 3.74, 95% Confidence Interval (CI) = 1.93⁻7.66) was associated with a high risk of prediabetic state. In the subscales on occupational stress, organizational system in the 4th quartile (OR = 4.83, 95% CI = 2.40⁻9.70) was significantly associated with an increased likelihood of prediabetic state. To identify influencing factors of prediabetic state, the multiple logistic regression was performed using regression models. The results showed that dietary habits (β = 1.20, p = 0.002), total occupational stress score (β = 1.33, p = 0.024), and organizational system (β = 1.13, p = 0.009) were significant influencing factors. The present findings indicate that active interventions are needed at workplace for the systematic and comprehensive management of health behaviors and occupational stress that influence prediabetic state of office workers.

Multiple regression based imputation for individualizing template human model from a small number of measured dimensions.

PubMed

Nohara, Ryuki; Endo, Yui; Murai, Akihiko; Takemura, Hiroshi; Kouchi, Makiko; Tada, Mitsunori

2016-08-01

Individual human models are usually created by direct 3D scanning or deforming a template model according to the measured dimensions. In this paper, we propose a method to estimate all the necessary dimensions (full set) for the human model individualization from a small number of measured dimensions (subset) and human dimension database. For this purpose, we solved multiple regression equation from the dimension database given full set dimensions as the objective variable and subset dimensions as the explanatory variables. Thus, the full set dimensions are obtained by simply multiplying the subset dimensions to the coefficient matrix of the regression equation. We verified the accuracy of our method by imputing hand, foot, and whole body dimensions from their dimension database. The leave-one-out cross validation is employed in this evaluation. The mean absolute errors (MAE) between the measured and the estimated dimensions computed from 4 dimensions (hand length, breadth, middle finger breadth at proximal, and middle finger depth at proximal) in the hand, 2 dimensions (foot length, breadth, and lateral malleolus height) in the foot, and 1 dimension (height) and weight in the whole body are computed. The average MAE of non-measured dimensions were 4.58% in the hand, 4.42% in the foot, and 3.54% in the whole body, while that of measured dimensions were 0.00%.

Estimating Time to Event From Longitudinal Categorical Data: An Analysis of Multiple Sclerosis Progression.

PubMed

Mandel, Micha; Gauthier, Susan A; Guttmann, Charles R G; Weiner, Howard L; Betensky, Rebecca A

2007-12-01

The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing of one point of EDSS (relative progression). Survival methods for time to progression are not adequate for such data since they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. This approach enables derivation of covariate-specific survival curves, obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix. Large sample theory and resampling methods are employed to derive pointwise confidence intervals, which perform well in simulation. Methods for generating survival curves for time to EDSS of a certain level, time to increase of EDSS of at least one point, and time to two consecutive visits with EDSS greater than three are described explicitly. The regression models described are easily implemented using standard software packages. Survival curves are obtained from the regression results using packages that support simple matrix calculation. We present and demonstrate our method on data collected at the Partners MS center in Boston, MA. We apply our approach to progression defined by time to two consecutive visits with EDSS greater than three, and calculate crude (without covariates) and covariate-specific curves.

The association between financial literacy and Problematic Internet Shopping in a multinational sample.

PubMed

Lam, Lawrence T; Lam, Mary K

2017-12-01

To examine the association between financial literacy and Problematic Internet Shopping in adults. This cross-sectional online survey recruited participants, aged between 18 and 60 years, through an online research facility. The sample consisted of multinational participants from mainly three continents including Europe, North America, and Asia. Problematic Internet Shopping was assessed using the Bergen Shopping Addiction Scale (BSAS). Financial Literacy was measured by the Financial Literacy subscale of the Financial Wellbeing Questionnaire. Multiple linear regression analyses were conducted to elucidate the relationship between the study and outcome variables with adjustment for other potential risk factors. Of the total of 997 respondents with an average age of 30.9 (s.d. = 8.8), 135 (13.8%) could be classified as having a high risk of being Problematic Internet Shoppers. Results from the multiple regression analyses suggested a significant and negative relationship between financial literacy and Problematic Internet Shopping with a regression coefficient of - 0.13, after controlling for the effects of potential risk factors such as age, region of birth, employment, income, shopping frequency, self-regulation and anxiety (t = - 6.42, p < 0.001). The clinical management of PIS should include a financial counselling as a component of the treatment regime. Enhancement of financial literacy in the general population, particularly among young people, will likely have a positive effect on the occurrence of PIS.

Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors.

PubMed

Baek, Hyun Jae; Kim, Ko Keun; Kim, Jung Soo; Lee, Boreom; Park, Kwang Suk

2010-02-01

A new method of blood pressure (BP) estimation using multiple regression with pulse arrival time (PAT) and two confounding factors was evaluated in clinical and unconstrained monitoring situations. For the first analysis with clinical data, electrocardiogram (ECG), photoplethysmogram (PPG) and invasive BP signals were obtained by a conventional patient monitoring device during surgery. In the second analysis, ECG, PPG and non-invasive BP were measured using systems developed to obtain data under conditions in which the subject was not constrained. To enhance the performance of BP estimation methods, heart rate (HR) and arterial stiffness were considered as confounding factors in regression analysis. The PAT and HR were easily extracted from ECG and PPG signals. For arterial stiffness, the duration from the maximum derivative point to the maximum of the dicrotic notch in the PPG signal, a parameter called TDB, was employed. In two experiments that normally cause BP variation, the correlation between measured BP and the estimated BP was investigated. Multiple-regression analysis with the two confounding factors improved correlation coefficients for diastolic blood pressure and systolic blood pressure to acceptable confidence levels, compared to existing methods that consider PAT only. In addition, reproducibility for the proposed method was determined using constructed test sets. Our results demonstrate that non-invasive, non-intrusive BP estimation can be obtained using methods that can be applied in both clinical and daily healthcare situations.

In vivo imaging of scattering and absorption properties of exposed brain using a digital red-green-blue camera

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Yoshida, Keiichiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2014-03-01

We investigate a method to estimate the spectral images of reduced scattering coefficients and the absorption coefficients of in vivo exposed brain tissues in the range from visible to near-infrared wavelength (500-760 nm) based on diffuse reflectance spectroscopy using a digital RGB camera. In the proposed method, the multi-spectral reflectance images of in vivo exposed brain are reconstructed from the digital red, green blue images using the Wiener estimation algorithm. The Monte Carlo simulation-based multiple regression analysis for the absorbance spectra is then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin are estimated as the absorption parameters whereas the scattering amplitude a and the scattering power b in the expression of μs'=aλ-b as the scattering parameters, respectively. The spectra of absorption and reduced scattering coefficients are reconstructed from the absorption and scattering parameters, and finally, the spectral images of absorption and reduced scattering coefficients are estimated. The estimated images of absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of reduced scattering coefficients showed a broad scattering spectrum, exhibiting larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. In vivo experiments with exposed brain of rats during CSD confirmed the possibility of the method to evaluate both hemodynamics and changes in tissue morphology due to electrical depolarization.

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.

A novel mathematical model considering change of diffusion coefficient for predicting dissolution behavior of acetaminophen from wax matrix dosage form.

PubMed

Nitanai, Yuta; Agata, Yasuyoshi; Iwao, Yasunori; Itai, Shigeru

2012-05-30

From wax matrix dosage forms, drug and water-soluble polymer are released into the external solvent over time. As a consequence, the pore volume inside the wax matrix particles is increased and the diffusion coefficient of the drug is altered. In the present study, we attempted to derive a novel empirical mathematical model, namely, a time-dependent diffusivity (TDD) model, that assumes the change in the drug's diffusion coefficient can be used to predict the drug release from spherical wax matrix particles. Wax matrix particles were prepared by using acetaminophen (APAP), a model drug; glyceryl monostearate (GM), a wax base; and aminoalkyl methacrylate copolymer E (AMCE), a functional polymer that dissolves below pH 5.0 and swells over pH 5.0. A three-factor, three-level (3(3)) Box-Behnken design was used to evaluate the effects of several of the variables in the model formulation, and the release of APAP from wax matrix particles was evaluated by the paddle method at pH 4.0 and pH 6.5. When comparing the goodness of fit to the experimental data between the proposed TDD model and the conventional pure diffusion model, a better correspondence was observed for the TDD model in all cases. Multiple regression analysis revealed that an increase in AMCE loading enhanced the diffusion coefficient with time, and that this increase also had a significant effect on drug release behavior. Furthermore, from the results of the multiple regression analysis, a formulation with desired drug release behavior was found to satisfy the criteria of the bitter taste masking of APAP without lowering the bioavailability. That is to say, the amount of APAP released remains below 15% for 10 min at pH 6.5 and exceeds 90% within 30 min at pH 4.0. The predicted formulation was 15% APAP loading, 8.25% AMCE loading, and 400 μm mean particle diameter. When wax matrix dosage forms were prepared accordingly, the predicted drug release behavior agreed well with experimental values at each pH level. Therefore, the proposed model is feasible as a useful tool for predicting drug release behavior, as well as for designing the formulation of wax matrix dosage forms. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

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.

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.

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.

Development and evaluation of an electromagnetic hypersensitivity questionnaire for Japanese people

PubMed Central

Tokiya, Mikiko; Mizuki, Masami; Miyata, Mikio; Kanatani, Kumiko T.; Takagi, Airi; Tsurikisawa, Naomi; Kame, Setsuko; Katoh, Takahiko; Tsujiuchi, Takuya; Kumano, Hiroaki

2016-01-01

The purpose of the present study was to evaluate the validity and reliability of a Japanese version of an electromagnetic hypersensitivity (EHS) questionnaire, originally developed by Eltiti et al. in the United Kingdom. Using this Japanese EHS questionnaire, surveys were conducted on 1306 controls and 127 self‐selected EHS subjects in Japan. Principal component analysis of controls revealed eight principal symptom groups, namely, nervous, skin‐related, head‐related, auditory and vestibular, musculoskeletal, allergy‐related, sensory, and heart/chest‐related. The reliability of the Japanese EHS questionnaire was confirmed by high to moderate intraclass correlation coefficients in a test–retest analysis, and high Cronbach's α coefficients (0.853–0.953) from each subscale. A comparison of scores of each subscale between self‐selected EHS subjects and age‐ and sex‐matched controls using bivariate logistic regression analysis, Mann–Whitney U‐ and χ 2 tests, verified the validity of the questionnaire. This study demonstrated that the Japanese EHS questionnaire is reliable and valid, and can be used for surveillance of EHS individuals in Japan. Furthermore, based on multiple logistic regression and receiver operating characteristic analyses, we propose specific preliminary criteria for screening EHS individuals in Japan. Bioelectromagnetics. 37:353–372, 2016. © 2016 The Authors. Bioelectromagnetics Published by Wiley Periodicals, Inc. PMID:27324106

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

Thermal requirements of Dermanyssus gallinae (De Geer, 1778) (Acari: Dermanyssidae).

PubMed

Tucci, Edna Clara; do Prado, Angelo P; de Araújo, Raquel Pires

2008-01-01

The thermal requirements for development of Dermanyssus gallinae were studied under laboratory conditions at 15, 20, 25, 30 and 35 degrees C, a 12h photoperiod and 60-85% RH. The thermal requirements for D. gallinae were as follows. Preoviposition: base temperature 3.4 degrees C, thermal constant (k) 562.85 degree-hours, determination coefficient (R(2)) 0.59, regression equation: Y= -0.006035 + 0.001777x. Egg: base temperature 10.60 degrees C, thermal constant (k) 689.65 degree-hours, determination coefficient (R(2)) 0.94, regression equation: Y= -0.015367 + 0.001450x. Larva: base temperature 9.82 degrees C, thermal constant (k) 464.91 degree-hours, determination coefficient (R(2)) 0.87, regression equation: Y= -0.021123 + 0.002151x. Protonymph: base temperature 10.17 degrees C, thermal constant (k) 504.49 degree-hours, determination coefficient (R(2)) 0.90, regression equation: Y= -0.020152 + 0.001982x. Deutonymph: base temperature 11.80 degrees C, thermal constant (k) 501.11 degree-hours, determination coefficient (R(2)) 0.99, regression equation: Y= -0.023555 + 0.001996x. The results obtained showed that 15 to 42 generations of Dermanyssus gallinae may occur during the year in the State of São Paulo, as estimated based on isotherm charts. Dermanyssus gallinae may develop continually in the State of São Paulo, with a population decrease in the winter. There were differences between the developmental stages of D. gallinae in relation to thermal requirements.

Evaluation of drainage-area ratio method used to estimate streamflow for the Red River of the North Basin, North Dakota and Minnesota

USGS Publications Warehouse

Emerson, Douglas G.; Vecchia, Aldo V.; Dahl, Ann L.

2005-01-01

The drainage-area ratio method commonly is used to estimate streamflow for sites where no streamflow data were collected. To evaluate the validity of the drainage-area ratio method and to determine if an improved method could be developed to estimate streamflow, a multiple-regression technique was used to determine if drainage area, main channel slope, and precipitation were significant variables for estimating streamflow in the Red River of the North Basin. A separate regression analysis was performed for streamflow for each of three seasons-- winter, spring, and summer. Drainage area and summer precipitation were the most significant variables. However, the regression equations generally overestimated streamflows for North Dakota stations and underestimated streamflows for Minnesota stations. To correct the bias in the residuals for the two groups of stations, indicator variables were included to allow both the intercept and the coefficient for the logarithm of drainage area to depend on the group. Drainage area was the only significant variable in the revised regression equations. The exponents for the drainage-area ratio were 0.85 for the winter season, 0.91 for the spring season, and 1.02 for the summer season.

[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.

Evaluation of AUC(0-4) predictive methods for cyclosporine in kidney transplant patients.

PubMed

Aoyama, Takahiko; Matsumoto, Yoshiaki; Shimizu, Makiko; Fukuoka, Masamichi; Kimura, Toshimi; Kokubun, Hideya; Yoshida, Kazunari; Yago, Kazuo

2005-05-01

Cyclosporine (CyA) is the most commonly used immunosuppressive agent in patients who undergo kidney transplantation. Dosage adjustment of CyA is usually based on trough levels. Recently, trough levels have been replacing the area under the concentration-time curve during the first 4 h after CyA administration (AUC(0-4)). The aim of this study was to compare the predictive values obtained using three different methods of AUC(0-4) monitoring. AUC(0-4) was calculated from 0 to 4 h in early and stable renal transplant patients using the trapezoidal rule. The predicted AUC(0-4) was calculated using three different methods: the multiple regression equation reported by Uchida et al.; Bayesian estimation for modified population pharmacokinetic parameters reported by Yoshida et al.; and modified population pharmacokinetic parameters reported by Cremers et al. The predicted AUC(0-4) was assessed on the basis of predictive bias, precision, and correlation coefficient. The predicted AUC(0-4) values obtained using three methods through measurement of three blood samples showed small differences in predictive bias, precision, and correlation coefficient. In the prediction of AUC(0-4) measurement of one blood sample from stable renal transplant patients, the performance of the regression equation reported by Uchida depended on sampling time. On the other hand, the performance of Bayesian estimation with modified pharmacokinetic parameters reported by Yoshida through measurement of one blood sample, which is not dependent on sampling time, showed a small difference in the correlation coefficient. The prediction of AUC(0-4) using a regression equation required accurate sampling time. In this study, the prediction of AUC(0-4) using Bayesian estimation did not require accurate sampling time in the AUC(0-4) monitoring of CyA. Thus Bayesian estimation is assumed to be clinically useful in the dosage adjustment of CyA.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175

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.

Relationship between body composition and postural control in prepubertal overweight/obese children: A cross-sectional study.

PubMed

Villarrasa-Sapiña, Israel; Álvarez-Pitti, Julio; Cabeza-Ruiz, Ruth; Redón, Pau; Lurbe, Empar; García-Massó, Xavier

2018-02-01

Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes-open than eyes-closed condition. Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are open. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Change in bone mineral density and its determinants in pre- and perimenopausal Chinese women: the Hong Kong Perimenopausal Women Osteoporosis Study.

PubMed

Ho, S C; Chan, S G; Yip, Y B; Chan, C S Y; Woo, J L F; Sham, A

2008-12-01

This 30-month study investigating bone change and its determinants in 438 perimenopausal Chinese women revealed that the fastest bone loss occurred in women undergoing menopausal transition but maintenance of body weight and physical fitness were beneficial for bone health. Soy protein intake also seemed to exert a protective effect. This 30-month follow-up study aims to investigate change in bone mineral density and its determinants in Hong Kong Chinese perimenopausal women. Four hundred and thirty-eight women aged 45 to 55 years were recruited through random telephone dialing and primary care clinic. Bone mass, body composition, lifestyle measurements were obtained at baseline and at 9-, 18- and 30-month follow-ups. Univariate and stepwise multiple regression analyses were performed with the regression coefficients of BMD/C (derived from baseline and follow-up measurements) as the outcome variables. Menopausal status was classified as pre- or postmenopausal or transitional. Menopausal status was the strongest determinant of bone changes. An annual bone loss of about 0.5% was observed among premenopausal, 2% to 2.5% among transitional, and about 1.5% in postmenopausal women. Multiple regression analyses, revealed that a positive regression slope of body weight was protective for follow-up bone loss at all sites. Number of pregnancy, soy protein intake and walking were protective for total body BMC. Higher baseline LM was also protective for neck of femur BMD. Maintenance of body weight and physical fitness were observed to have a protective effect on for bone loss in Chinese perimenopausal women.

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.

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

Relationship between self-esteem and living conditions among stroke survivors at home.

PubMed

Shida, Junko; Sugawara, Kyoko; Goto, Junko; Sekito, Yoshiko

2014-10-01

To clarify the relationship between self-esteem of stroke survivors at home and their living conditions. Study participants were stroke survivors who lived at home and commuted to one of two medical facilities in the Tohoku region of Japan. Stroke survivors were recruited for the present study when they came to the hospital for a routine visit. The researcher or research assistant explained the study objective and methods to the stroke survivor, and the questionnaire survey was conducted. Survey contents included the Japanese version of the Rosenberg Self-Esteem Scale (RSE) and questions designed to assess living conditions. A total of 65 participants with complete RSE data were included in the analysis. The mean (standard deviation) age of participants was 70.9 years (± 11.1), with a mean RSE score of 32.12 (± 8.32). Only a minor decrease in participant self-esteem was observed, even after having experienced a stroke. Factors associated with self-esteem, including "independent bathing" (standardized partial regression coefficient, β = 0.405, P < 0.001), "being needed by family members" (β = 0.389, P < 0.001), "independent grooming" (β = 0.292, P = 0.009), and "sleep satisfaction" (β = 0.237, P = 0.017), were analyzed by stepwise multiple regression analysis. The multiple correlation coefficient adjusted for the degrees of freedom was 0.738 (P < 0.001). Our analysis revealed that the maintenance of activities of daily living, and the presence of a suitable environment that enhances physical function recovery and promotes activity and participation, are necessary to improve self-esteem in stroke survivors living at home. © 2013 The Authors. Japan Journal of Nursing Science © 2013 Japan Academy of Nursing Science.

Development and validation of the neck dissection impairment index: a quality of life measure.

PubMed

Taylor, Rodney J; Chepeha, Judith C; Teknos, Theodoros N; Bradford, Carol R; Sharma, Pramod K; Terrell, Jeffrey E; Hogikyan, Norman D; Wolf, Gregory T; Chepeha, Douglas B

2002-01-01

To validate a health-related quality-of-life (QOL) instrument for patients following neck dissection and to identify the factors that affect QOL following neck dissection. Cross-sectional validation study. The outpatient clinic of a tertiary care cancer center. Convenience sample of 54 patients previously treated for head and neck cancer who underwent a selective neck dissection or modified radical neck dissection (64 total neck dissections). Patients had a minimum postoperative convalescence of 11 months. Thirty-two underwent accessory nerve-sparing modified radical neck dissection, and 32 underwent selective neck dissection. A 10-item, self-report instrument, the Neck Dissection Impairment Index (NDII), was developed and validated. Reliability was evaluated with test-retest correlation and internal consistency using the Cronbach alpha coefficient. Convergent validity was assessed using the 36-Item Short-Form Health Survey (SF-36) and the Constant Shoulder Scale, a shoulder function test. Multiple variable regression was used to determine variables that most affected QOL following neck dissection The 10-item NDII test-retest correlation was 0.91 (P<.001) with an internal consistency Cronbach alpha coefficient of.95. The NDII correlated with the Constant Shoulder Scale (r = 0.85, P<.001) and with the SF-36 physical functioning (r = 0.50, P<.001) and role-physical functioning (r = 0.60, P<.001) domains. Using multiple variable regression, the variables that contributed most to QOL score were patient's age and weight, radiation treatment, and neck dissection type. The NDII is a valid, reliable instrument for assessing neck dissection impairment. Patient's age, weight, radiation treatment, and neck dissection type were important factors that affect QOL following neck dissection.

[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.

Uterine Artery Embolization in 101 Cases of Uterine Fibroids: Do Size, Location, and Number of Fibroids Affect Therapeutic Success and Complications?

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

Firouznia, Kavous, E-mail: k_firouznia@yahoo.com; Ghanaati, Hossein; Sanaati, Mina

The purpose of this study was to evaluate whether the size, location, or number of fibroids affects therapeutic efficacy or complications of uterine artery embolization (UAE). Patients with symptomatic uterine fibroids (n = 101) were treated by selective bilateral UAE using 500- to 710-{mu}m polyvinyl alcohol (PVA) particles. Baseline measures of clinical symptoms, sonography, and MRI taken before the procedure were compared to those taken 1, 3, 6, and 12 months later. Complications and outcomes were analyzed for associations with fibroid size, location, and number. Reductions in mean fibroid volume were similar in patients with single (66.6 {+-} 21.5%) andmore » multiple (67.4 {+-} 25.0%) fibroids (p-value = 0.83). Menstrual improvement occurred in patients with single (93.3%) and multiple (72.2%) fibroids (p = 0.18). Changes in submucosal and other fibroids were not significantly different between the two groups (p's > 0.56). Linear regression analysis between primary fibroid volume as independent variable and percentage reduction of fibroid volume after 1 year yielded an R{sup 2} of 0.083 and the model coefficient was not statistically significant (p = 0.072). Multivariate regression models revealed no statistically or clinically significant coefficients or odds ratios for three independent variables (primary fibroid size, total number, and fibroid location) and all outcome variables (percent reduction of uterus and fibroid volumes in 1 year, improvement of clinical symptoms [menstrual, bulk related, and urinary] in 1 year, and complications after UAE). In conclusion, neither the success rate nor the probability of complications was affected by the primary fibroid size, location, or total number of fibroids.« less

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

New 1,6-heptadienes with pyrimidine bases attached: Syntheses and spectroscopic analyses

NASA Astrophysics Data System (ADS)

Hammud, Hassan H.; Ghannoum, Amer M.; Fares, Fares A.; Abramian, Lara K.; Bouhadir, Kamal H.

2008-06-01

A simple, high yielding synthesis leading to the functionalization of some pyrimidine bases with a 1,6-heptadienyl moiety spaced from the N - 1 position by a methylene group is described. A key step in this synthesis involves a Mitsunobu reaction by coupling 3N-benzoyluracil and 3N-benzoylthymine to 2-allyl-pent-4-en-1-ol followed by alkaline hydrolysis of the 3N-benzoyl protecting groups. This protocol should eventually lend itself to the synthesis of a host of N-alkylated nucleoside analogs. The absorption and emission properties of these pyrimidine derivatives ( 3- 6) were studied in solvents of different physical properties. Computerized analysis and multiple regression techniques were applied to calculate the regression and correlation coefficients based on the equation that relates peak position λmax to the solvent parameters that depend on the H-bonding ability, refractive index, and dielectric constant of solvents.

Quantitative analysis of aircraft multispectral-scanner data and mapping of water-quality parameters in the James River in Virginia

NASA Technical Reports Server (NTRS)

Johnson, R. W.; Bahn, G. S.

1977-01-01

Statistical analysis techniques were applied to develop quantitative relationships between in situ river measurements and the remotely sensed data that were obtained over the James River in Virginia on 28 May 1974. The remotely sensed data were collected with a multispectral scanner and with photographs taken from an aircraft platform. Concentration differences among water quality parameters such as suspended sediment, chlorophyll a, and nutrients indicated significant spectral variations. Calibrated equations from the multiple regression analysis were used to develop maps that indicated the quantitative distributions of water quality parameters and the dispersion characteristics of a pollutant plume entering the turbid river system. Results from further analyses that use only three preselected multispectral scanner bands of data indicated that regression coefficients and standard errors of estimate were not appreciably degraded compared with results from the 10-band analysis.

Measuring the impact of multiple sclerosis on psychosocial functioning: the development of a new self-efficacy scale.

PubMed

Airlie, J; Baker, G A; Smith, S J; Young, C A

2001-06-01

To develop a scale to measure self-efficacy in neurologically impaired patients with multiple sclerosis and to assess the scale's psychometric properties. Cross-sectional questionnaire study in a clinical setting, the retest questionnaire returned by mail after completion at home. Regional multiple sclerosis (MS) outpatient clinic or the Clinical Trials Unit (CTU) at a large neuroscience centre in the UK. One hundred persons with MS attending the Walton Centre for Neurology and Neurosurgery and Clatterbridge Hospital, Wirral, as outpatients. Cognitively impaired patients were excluded at an initial clinic assessment. Patients were asked to provide demographic data and complete the self-efficacy scale along with the following validated scales: Hospital Anxiety and Depression Scale, Rosenberg Self-Esteem Scale, Impact, Stigma and Mastery and Rankin Scales. The Rankin Scale and Barthel Index were also assessed by the physician. A new 11-item self-efficacy scale was constructed consisting of two domains of control and personal agency. The validity of the scale was confirmed using Cronbach's alpha analysis of internal consistency (alpha = 0.81). The test-retest reliability of the scale over two weeks was acceptable with an intraclass correlation coefficient of 0.79. Construct validity was investigated using Pearson's product moment correlation coefficient resulting in significant correlations with depression (r= -0.52) anxiety (r =-0.50) and mastery (r= 0.73). Multiple regression analysis demonstrated that these factors accounted for 70% of the variance of scores on the self-efficacy scale, with scores on mastery, anxiety and perceived disability being independently significant. Assessment of the psychometric properties of this new self-efficacy scale suggest that it possesses good validity and reliability in patients with multiple sclerosis.

Time-localized wavelet multiple regression and correlation

NASA Astrophysics Data System (ADS)

Fernández-Macho, Javier

2018-02-01

This paper extends wavelet methodology to handle comovement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multiscale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard pairwise wavelet correlations with rolling windows. Also, the spectral properties of weight functions are investigated and it is argued that some common time windows, such as the usual rectangular rolling window, are not satisfactory on these grounds. The method is illustrated with a multiscale analysis of the comovements of Eurozone stock markets during this century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along time and across timescales featuring an acute divide across timescales at about the quarterly scale. At longer scales, evidence from the long-term correlation structure can be interpreted as stable perfect integration among Euro stock markets. On the other hand, at intramonth and intraweek scales, the short-term correlation structure has been clearly evolving along time, experiencing a sharp increase during financial crises which may be interpreted as evidence of financial 'contagion'.

Neutropenia is independently associated with sub-therapeutic serum concentration of vancomycin.

PubMed

Choi, Min Hyuk; Choe, Yeon Hwa; Lee, Sang-Guk; Jeong, Seok Hoon; Kim, Jeong-Ho

2017-02-01

We aimed to identify the impact of the presence of neutropenia on serum vancomycin concentration (SVC). A retrospective study was conducted from January 2005 to December 2015. The study population was comprised of adult patients who were performed serum concentration of vancomycin. Patients with renal failure or using non-conventional dosages of vancomycin were excluded. A total of 1307 adult patients were included in this study, of whom 163 (12.4%) were neutropenic. Patients with neutropenia presented significantly lower SVCs than non-neutropenic patients (P<0.0001). Multiple linear regressions showed significant association between neutropenia and trough SVC (beta coefficients, -2.351; P=0.004). Multiple logistic regression analysis also revealed a significant association between sub-therapeutic vancomycin concentrations (trough SVC values<10mg/l) and neutropenia (odds ratio, 1.75, P=0.029) CONCLUSIONS: The presence of neutropenia is significantly associated with low SVC, even after adjusting for other variables. Therefore, neutropenic patients had a higher risk of sub-therapeutic SVC compared with non-neutropenic patients. We recommended that vancomycin therapy should be monitored with TDM-guided optimization of dosage and intervals, especially in neutropenic patients. Copyright © 2016 Elsevier B.V. All rights reserved.

Cross-sectional associations of cortical β-amyloid with erythrocyte membrane long-chain polyunsaturated fatty acids in older adults with subjective memory complaints.

PubMed

Hooper, Claudie; De Souto Barreto, Philipe; Payoux, Pierre; Salabert, Anne Sophie; Guyonnet, Sophie; Andrieu, Sandrine; Vellas, Bruno

2017-08-01

Omega-3 (n-3) and 6 (n-6) polyunsaturated fatty acids (PUFAs) have been associated with reduced cognitive decline in observational studies. Hence, we examined the cross-sectional associations between cortical β-amyloid (Aβ) and erythrocyte membrane PUFAs in 61 non-demented elderly individuals reporting subjective memory complaints from the Multidomain Alzheimer Preventive Trial placebo arm. Cortical-to-cerebellar standard uptake value ratios were obtained using [ 18 F] florbetapir positron emission tomography. Fatty acids were measured in erythrocyte membranes by gas chromatography. Associations were explored using adjusted multiple linear regression models and were considered significant at p ≤ 0.005 after correction for multiple testing (10 comparisons). We found no significant associations between cortical Aβ and erythrocyte membrane PUFAs. The associations closest to significance after adjustment were those between Aβ and erythrocyte membrane arachidonic acid (without apolipoprotein E status adjustment: B-coefficient, 0.03; CI, 0.01, 0.05; p = 0.02. Including Apolipoprotein E adjustment: B-coefficient, 0.03; CI, 0.00, 0.06; p = 0.04) and Aβ and erythrocyte membrane linoleic acid (without apolipoprotein E status adjustment: B-coefficient, -0.02; CI, -0.04, 0.00; p = 0.02. Including Apolipoprotein E adjustment: B-coefficient, -0.02; CI, -0.04, 0.00; p = 0.09). Furthermore, the association between Aβ and erythrocyte membrane arachidonic acid seemed to be specific to Apolipoprotein E ε4 non-carriers (B-coefficient 0.03, CI: 0.00, 0.06, p = 0.03, n = 36). In contrast, no association was found between Aβ and erythrocyte membrane linoleic acid in Apolipoprotein E ε4 stratified analysis. Investigating the relationships between Aβ and PUFAs longitudinally would provide further evidence as to whether fatty acids, particularly arachidonic acid and linoleic acid, might modulate cognition through Aβ-dependent mechanisms. © 2017 International Society for Neurochemistry.

Factor Scores, Structure Coefficients, and Communality Coefficients

ERIC Educational Resources Information Center

Goodwyn, Fara

2012-01-01

This paper presents heuristic explanations of factor scores, structure coefficients, and communality coefficients. Common misconceptions regarding these topics are clarified. In addition, (a) the regression (b) Bartlett, (c) Anderson-Rubin, and (d) Thompson methods for calculating factor scores are reviewed. Syntax necessary to execute all four…

Improved model of the retardance in citric acid coated ferrofluids using stepwise regression

NASA Astrophysics Data System (ADS)

Lin, J. F.; Qiu, X. R.

2017-06-01

Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.

Estimating EQ-5D values from the Neck Disability Index and numeric rating scales for neck and arm pain.

PubMed

Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D

2014-09-01

The Neck Disability Index (NDI) and numeric rating scales (0 to 10) for neck pain and arm pain are widely used cervical spine disease-specific measures. Recent studies have shown that there is a strong relationship between the SF-6D and the NDI such that using a simple linear regression allows for the estimation of an SF-6D value from the NDI alone. Due to ease of administration and scoring, the EQ-5D is increasingly being used as a measure of utility in the clinical setting. The purpose of this study is to determine if the EQ-5D values can be estimated from commonly available cervical spine disease-specific health-related quality of life measures, much like the SF-6D. The EQ-5D, NDI, neck pain score, and arm pain score were prospectively collected in 3732 patients who presented to the authors' clinic with degenerative cervical spine disorders. Correlation coefficients for paired observations from multiple time points between the NDI, neck pain and arm pain scores, and EQ-5D were determined. Regression models were built to estimate the EQ-5D values from the NDI, neck pain, and arm pain scores. The mean age of the 3732 patients was 53.3 ± 12.2 years, and 43% were male. Correlations between the EQ-5D and the NDI, neck pain score, and arm pain score were statistically significant (p < 0.0001), with correlation coefficients of -0.77, -0.62, and -0.50, respectively. The regression equation 0.98947 + (-0.00705 × NDI) + (-0.00875 × arm pain score) + (-0.00877 × neck pain score) to predict EQ-5D had an R-square of 0.62 and a root mean square error (RMSE) of 0.146. The model using NDI alone had an R-square of 0.59 and a RMSE of 0.150. The model using the individual NDI items had an R-square of 0.46 and an RMSE of 0.172. The correlation coefficient between the observed and estimated EQ-5D scores was 0.79. There was no statistically significant difference between the actual EQ-5D score (0.603 ± 0.235) and the estimated EQ-5D score (0.603 ± 0.185) using the NDI, neck pain score, and arm pain score regression model. However, rounding off the coefficients to fewer than 5 decimal places produced less accurate results. The regression model estimating the EQ-5D from the NDI, neck pain score, and arm pain score accounted for 60% of the variability of the EQ-5D with a relatively large RMSE. This regression model may not be sufficient to accurately or reliably estimate actual EQ-5D values.

Comparing the index-flood and multiple-regression methods using L-moments

NASA Astrophysics Data System (ADS)

Malekinezhad, H.; Nachtnebel, H. P.; Klik, A.

In arid and semi-arid regions, the length of records is usually too short to ensure reliable quantile estimates. Comparing index-flood and multiple-regression analyses based on L-moments was the main objective of this study. Factor analysis was applied to determine main influencing variables on flood magnitude. Ward’s cluster and L-moments approaches were applied to several sites in the Namak-Lake basin in central Iran to delineate homogeneous regions based on site characteristics. Homogeneity test was done using L-moments-based measures. Several distributions were fitted to the regional flood data and index-flood and multiple-regression methods as two regional flood frequency methods were compared. The results of factor analysis showed that length of main waterway, compactness coefficient, mean annual precipitation, and mean annual temperature were the main variables affecting flood magnitude. The study area was divided into three regions based on the Ward’s method of clustering approach. The homogeneity test based on L-moments showed that all three regions were acceptably homogeneous. Five distributions were fitted to the annual peak flood data of three homogeneous regions. Using the L-moment ratios and the Z-statistic criteria, GEV distribution was identified as the most robust distribution among five candidate distributions for all the proposed sub-regions of the study area, and in general, it was concluded that the generalised extreme value distribution was the best-fit distribution for every three regions. The relative root mean square error (RRMSE) measure was applied for evaluating the performance of the index-flood and multiple-regression methods in comparison with the curve fitting (plotting position) method. In general, index-flood method gives more reliable estimations for various flood magnitudes of different recurrence intervals. Therefore, this method should be adopted as regional flood frequency method for the study area and the Namak-Lake basin in central Iran. To estimate floods of various return periods for gauged catchments in the study area, the mean annual peak flood of the catchments may be multiplied by corresponding values of the growth factors, and computed using the GEV distribution.

Quantification and regionalization of groundwater recharge in South-Central Kansas: Integrating field characterization, statistical analysis, and GIS

USGS Publications Warehouse

Sophocleous, M.

2000-01-01

A practical methodology for recharge characterization was developed based on several years of field-oriented research at 10 sites in the Great Bend Prairie of south-central Kansas. This methodology combines the soil-water budget on a storm-by-storm year-round basis with the resulting watertable rises. The estimated 1985-1992 average annual recharge was less than 50mm/year with a range from 15 mm/year (during the 1998 drought) to 178 mm/year (during the 1993 flood year). Most of this recharge occurs during the spring months. To regionalize these site-specific estimates, an additional methodology based on multiple (forward) regression analysis combined with classification and GIS overlay analyses was developed and implemented. The multiple regression analysis showed that the most influential variables were, in order of decreasing importance, total annual precipitation, average maximum springtime soil-profile water storage, average shallowest springtime depth to watertable, and average springtime precipitation rate. Therefore, four GIS (ARC/INFO) data "layers" or coverages were constructed for the study region based on these four variables, and each such coverage was classified into the same number of data classes to avoid biasing the results. The normalized regression coefficients were employed to weigh the class rankings of each recharge-affecting variable. This approach resulted in recharge zonations that agreed well with the site recharge estimates. During the "Great Flood of 1993," when rainfall totals exceeded normal levels by -200% in the northern portion of the study region, the developed regionalization methodology was tested against such extreme conditions, and proved to be both practical, based on readily available or easily measurable data, and robust. It was concluded that the combination of multiple regression and GIS overlay analyses is a powerful and practical approach to regionalizing small samples of recharge estimates.

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.

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.

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

Novel risk score of contrast-induced nephropathy after percutaneous coronary intervention.

PubMed

Ji, Ling; Su, XiaoFeng; Qin, Wei; Mi, XuHua; Liu, Fei; Tang, XiaoHong; Li, Zi; Yang, LiChuan

2015-08-01

Contrast-induced nephropathy (CIN) post-percutaneous coronary intervention (PCI) is a major cause of acute kidney injury. In this study, we established a comprehensive risk score model to assess risk of CIN after PCI procedure, which could be easily used in a clinical environment. A total of 805 PCI patients, divided into analysis cohort (70%) and validation cohort (30%), were enrolled retrospectively in this study. Risk factors for CIN were identified using univariate analysis and multivariate logistic regression in the analysis cohort. Risk score model was developed based on multiple regression coefficients. Sensitivity and specificity of the new risk score system was validated in the validation cohort. Comparisons between the new risk score model and previous reported models were applied. The incidence of post-PCI CIN in the analysis cohort (n = 565) was 12%. Considerably high CIN incidence (50%) was observed in patients with chronic kidney disease (CKD). Age >75, body mass index (BMI) >25, myoglobin level, cardiac function level, hypoalbuminaemia, history of chronic kidney disease (CKD), Intra-aortic balloon pump (IABP) and peripheral vascular disease (PVD) were identified as independent risk factors of post-PCI CIN. A novel risk score model was established using multivariate regression coefficients, which showed highest sensitivity and specificity (0.917, 95%CI 0.877-0.957) compared with previous models. A new post-PCI CIN risk score model was developed based on a retrospective study of 805 patients. Application of this model might be helpful to predict CIN in patients undergoing PCI procedure. © 2015 Asian Pacific Society of Nephrology.

Inverse roles of emotional labour on health and job satisfaction among long-term care workers in Japan.

PubMed

Tsukamoto, Erika; Abe, Takeru; Ono, Michikazu

2015-01-01

Emotional labour increases among long-term care workers because providing care and services to impaired elders causes conflicting interpersonal emotions. Thus, we investigated the associations between emotional labour, general health and job satisfaction among long-term care workers. We conducted a cross-sectional study among 132 established, private day care centres in Tokyo using a mail survey. The outcome variables included two health-related variables and four job satisfaction variables: physical and psychological health, satisfaction with wages, interpersonal relationships, work environment and job satisfaction. We performed multiple regression analyses to identify significant factors. Directors from 36 facilities agreed to participate. A total of 123 responses from long-term care workers were analysed. Greater emotional dissonance was associated with better physical and psychological health and worse work environment satisfaction (partial regression coefficient: -2.93, p = .0389; -3.32, p = .0299; -1.92, p = .0314, respectively). Fewer negative emotions were associated with more job satisfaction (partial regression coefficient: -1.87, p = .0163). We found that emotional labour was significantly inversely associated with health and job satisfaction. Our findings indicated that the emotional labour of long-term care workers has a negative and positive influence on health and workplace satisfaction, and suggests that care quality and stable employment among long-term care workers might affect their emotional labour. Therefore, we think a programme to support emotional labour among long-term care workers in an organized manner and a self-care programme to educate workers regarding emotional labour would be beneficial.

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.

Evaluation of Relationship between Trunk Muscle Endurance and Static Balance in Male Students

PubMed Central

Barati, Amirhossein; SafarCherati, Afsaneh; Aghayari, Azar; Azizi, Faeze; Abbasi, Hamed

2013-01-01

Purpose Fatigue of trunk muscle contributes to spinal instability over strenuous and prolonged physical tasks and therefore may lead to injury, however from a performance perspective, relation between endurance efficient core muscles and optimal balance control has not been well-known. The purpose of this study was to examine the relationship of trunk muscle endurance and static balance. Methods Fifty male students inhabitant of Tehran university dormitory (age 23.9±2.4, height 173.0±4.5 weight 70.7±6.3) took part in the study. Trunk muscle endurance was assessed using Sørensen test of trunk extensor endurance, trunk flexor endurance test, side bridge endurance test and static balance was measured using single-limb stance test. A multiple linear regression analysis was applied to test if the trunk muscle endurance measures significantly predicted the static balance. Results There were positive correlations between static balance level and trunk flexor, extensor and lateral endurance measures (Pearson correlation test, r=0.80 and P<0.001; r=0.71 and P<0.001; r=0.84 and P<0.001, respectively). According to multiple regression analysis for variables predicting static balance, the linear combination of trunk muscle endurance measures was significantly related to the static balance (F (3,46) = 66.60, P<0.001). Endurance of trunk flexor, extensor and lateral muscles were significantly associated with the static balance level. The regression model which included these factors had the sample multiple correlation coefficient of 0.902, indicating that approximately 81% of the variance of the static balance is explained by the model. Conclusion There is a significant relationship between trunk muscle endurance and static balance. PMID:24800004

Relationships among personality traits, metabolic syndrome, and metabolic syndrome scores: The Kakegawa cohort study.

PubMed

Ohseto, Hisashi; Ishikuro, Mami; Kikuya, Masahiro; Obara, Taku; Igarashi, Yuko; Takahashi, Satomi; Kikuchi, Daisuke; Shigihara, Michiko; Yamanaka, Chizuru; Miyashita, Masako; Mizuno, Satoshi; Nagai, Masato; Matsubara, Hiroko; Sato, Yuki; Metoki, Hirohito; Tachibana, Hirofumi; Maeda-Yamamoto, Mari; Kuriyama, Shinichi

2018-04-01

Metabolic syndrome and the presence of metabolic syndrome components are risk factors for cardiovascular disease (CVD). However, the association between personality traits and metabolic syndrome remains controversial, and few studies have been conducted in East Asian populations. We measured personality traits using the Japanese version of the Eysenck Personality Questionnaire (Revised Short Form) and five metabolic syndrome components-elevated waist circumference, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure, and elevated fasting glucose-in 1322 participants aged 51.1±12.7years old from Kakegawa city, Japan. Metabolic syndrome score (MS score) was defined as the number of metabolic syndrome components present, and metabolic syndrome as having the MS score of 3 or higher. We performed multiple logistic regression analyses to examine the relationship between personality traits and metabolic syndrome components and multiple regression analyses to examine the relationship between personality traits and MS scores adjusted for age, sex, education, income, smoking status, alcohol use, and family history of CVD and diabetes mellitus. We also examine the relationship between personality traits and metabolic syndrome presence by multiple logistic regression analyses. "Extraversion" scores were higher in those with metabolic syndrome components (elevated waist circumference: P=0.001; elevated triglycerides: P=0.01; elevated blood pressure: P=0.004; elevated fasting glucose: P=0.002). "Extraversion" was associated with the MS score (coefficient=0.12, P=0.0003). No personality trait was significantly associated with the presence of metabolic syndrome. Higher "extraversion" scores were related to higher MS scores, but no personality trait was significantly associated with the presence of metabolic syndrome. Copyright © 2018 Elsevier Inc. All rights reserved.

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows.

PubMed

Visentin, G; McDermott, A; McParland, S; Berry, D P; Kenny, O A; Brodkorb, A; Fenelon, M A; De Marchi, M

2015-09-01

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Hepatic Fibrosis, Inflammation, and Steatosis: Influence on the MR Viscoelastic and Diffusion Parameters in Patients with Chronic Liver Disease.

PubMed

Leitão, Helena S; Doblas, Sabrina; Garteiser, Philippe; d'Assignies, Gaspard; Paradis, Valérie; Mouri, Feryel; Geraldes, Carlos F G C; Ronot, Maxime; Van Beers, Bernard E

2017-04-01

Purpose To determine the relationship of liver fibrosis, inflammation, and steatosis with the magnetic resonance (MR) viscoelastic and diffusion parameters in patients with chronic liver disease and to compare the diagnostic accuracy of the imaging parameters in staging liver fibrosis. Materials and Methods Consecutive patients with chronic liver disease scheduled for liver biopsy were prospectively recruited from November 2010 to October 2012 for this institutional review board-approved study after they provided written informed consent. Sixty-eight patients underwent three-dimensional MR elastography and intravoxel incoherent motion diffusion-weighted MR imaging with a 1.5-T MR system. Fibrosis, inflammation, and steatosis were assessed with the METAVIR and steatosis, activity, and fibrosis (or SAF) scoring systems. Spearman correlation and multiple regression analyses were performed to determine the relationship between liver fibrosis, inflammation, steatosis, and alanine aminotransferase (ALT) levels and viscoelastic and diffusion parameters. The accuracy of three-dimensional MR elastography and diffusion-weighted MR imaging in the determination of fibrosis stage was assessed with Obuchowski measures. Results At multiple regression analysis, fibrosis was the only variable associated with viscoelastic parameters (β = 0.6, P < .001, R 2 = 0.33 for shear modulus; β = 0.6, P < .001, R 2 = 0.32 for elasticity). Fibrosis had a weaker independent association with the apparent diffusion coefficient (β = -0.3, P = .02, R 2 = 0.33) than did steatosis (β = -0.5, P < .001, R 2 = 0.33). Steatosis was the only factor independently associated with the pure diffusion coefficient (β = -0.4, P = .002, R 2 = 0.22). Inflammation and ALT level were not associated with the viscoelastic or diffusion parameters. The diagnostic accuracy of fibrosis staging was significantly higher when measuring the shear modulus rather than the apparent diffusion coefficient (Obuchowski measures, 0.82 ± 0.04 vs 0.30 ± 0.06; P < .001). Conclusion Fibrosis is independently associated with the MR viscoelastic parameters and is less associated with the diffusion parameters than is steatosis. These results and those of diagnostic accuracy suggest that MR elastography should be preferred over diffusion-weighted MR imaging in the staging of liver fibrosis. © RSNA, 2016.

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.

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.

Population dynamics of pond zooplankton, I. Diaptomus pallidus Herrick

USGS Publications Warehouse

Armitage, K.B.; Saxena, B.; Angino, E.E.

1973-01-01

The simultaneous and lag relationships between 27 environmental variables and seven population components of a perennial calanoid copepod were examined by simple and partial correlations and stepwise regression. The analyses consistently explained more than 70% of the variation of a population component. The multiple correlation coefficient (R) usually was highest in no lag or in 3-week or 4-week lag except for clutch size in which R was highest in 1-week lag. Population control, egg-bearing, and clutch size were affected primarily by environmental components categorized as weather; food apparently was relatively minor in affecting population control or reproduction. ?? 1973 Dr. W. Junk B.V. Publishers.

On Using the Average Intercorrelation Among Predictor Variables and Eigenvector Orientation to Choose a Regression Solution.

ERIC Educational Resources Information Center

Mugrage, Beverly; And Others

Three ridge regression solutions are compared with ordinary least squares regression and with principal components regression using all components. Ridge regression, particularly the Lawless-Wang solution, out-performed ordinary least squares regression and the principal components solution on the criteria of stability of coefficient and closeness…

Shrinkage regression-based methods for microarray missing value imputation.

PubMed

Wang, Hsiuying; Chiu, Chia-Chun; Wu, Yi-Ching; Wu, Wei-Sheng

2013-01-01

Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets. To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do. Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Multispectral imaging of absorption and scattering properties of in vivo exposed rat brain using a digital red-green-blue camera.

PubMed

Yoshida, Keiichiro; Nishidate, Izumi; Ishizuka, Tomohiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2015-05-01

In order to estimate multispectral images of the absorption and scattering properties in the cerebral cortex of in vivo rat brain, we investigated spectral reflectance images estimated by the Wiener estimation method using a digital RGB camera. A Monte Carlo simulation-based multiple regression analysis for the corresponding spectral absorbance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) was then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentrations of oxygenated hemoglobin and that of deoxygenated hemoglobin were estimated as the absorption parameters, whereas the coefficient a and the exponent b of the reduced scattering coefficient spectrum approximated by a power law function were estimated as the scattering parameters. The spectra of absorption and reduced scattering coefficients were reconstructed from the absorption and scattering parameters, and the spectral images of absorption and reduced scattering coefficients were then estimated. In order to confirm the feasibility of this method, we performed in vivo experiments on exposed rat brain. The estimated images of the absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of the reduced scattering coefficients had a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. The changes in the estimated absorption and scattering parameters during normoxia, hyperoxia, and anoxia indicate the potential applicability of the method by which to evaluate the pathophysiological conditions of in vivo brain due to the loss of tissue viability.

Glyphosate sorption to soils of Argentina. Estimation of affinity coeficient by pedotransfer function

NASA Astrophysics Data System (ADS)

De Geronimo, Eduardo; Aparicio, Virginia; Costa, José Luis

2017-04-01

Argentine agricultural production is fundamentally based on a technological package that combines direct seeding and glyphosate with transgenic crops (soybean, maize and cotton). Therefore, glyphosate is the most employed herbicide in the country, where 180 to 200 million liters are applied every year. Glyphosate is strongly sorbed to soil by binding to clay minerals, layer silicates, metal oxides, non-crystalline materials or organic matter. Sorption of glyphosate is a reversible process that regulates the half-life and mobility of the herbicide and it is therefore related to the risk of contaminating courses of surface and groundwater. However, this behavior may vary depending on the characteristics of the soil on which it is applied. In addition, pH is a determining factor since it modifies the net charge in the molecule and, with it, the force of the electrostatic interaction between the glyphosate and the components of the soil. For a reliable risk assessment of groundwater contamination from pesticides precise predictions of sorption coefficients are needed. The aim of this work is to study the affinity of glyphosate to different soils of Argentina and create a model to estimate the glyphosate Freundlich sorption coefficient (Kf) from easily measurable soil properties. Adsorption of glyphosate was investigated on 12 different agricultural soils of Argentina using batch equilibration technique and fit to Freundlich sorption model. The correlation coefficients and the effects of soil characteristic factors on glyphosate adsorption parameter were analyzed through principal component and multiple lineal regression analysis. Results indicate that pH and clay contents were found to be the most significant soil factors which affect the glyphosate adsorption process. The Freundlich (Kf) pedotransfer function obtained by stepwise regression analysis was Kf = 735.2*Clay - 104.2*pH + 0.7*Polsen - 3.8*Alin. A 97.9% of the variation of glyphosate sorption coefficient could be attributed to the variation of the soil clay contents, pH, Polsen and Alin.

Maternal overprotection score of the Parental Bonding Instrument predicts the outcome of cognitive behavior therapy by trainees for depression.

PubMed

Asano, Motoshi; Esaki, Kosei; Wakamatsu, Aya; Kitajima, Tomoko; Narita, Tomohiro; Naitoh, Hiroshi; Ozaki, Norio; Iwata, Nakao

2013-07-01

The purpose of this study was to predict the outcome of cognitive behavior therapy (CBT) by trainees for major depressive disorder (MDD) based on the Parental Bonding Instrument (PBI). The hypothesis was that the higher level of care and/or lower level of overprotection score would predict a favorable outcome of CBT by trainees. The subjects were all outpatients with MDD treated with CBT as a training case. All the subjects were asked to fill out the Japanese version of the PBI before commencing the course of psychotherapy. The difference between the first and the last Beck Depression Inventory (BDI) score was used to represent the improvement of the intensity of depression by CBT. In order to predict improvement (the difference of the BDI scores) as the objective variable, multiple regression analysis was performed using maternal overprotection score and baseline BDI score as the explanatory variables. The multiple regression model was significant (P = 0.0026) and partial regression coefficient for the maternal overprotection score and the baseline BDI was -0.73 (P = 0.0046) and 0.88 (P = 0.0092), respectively. Therefore, when a patient's maternal overprotection score of the PBI was lower, a better outcome of CBT was expected. The hypothesis was partially supported. This result would be useful in determining indications for CBT by trainees for patients with MDD. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

Innovating patient care delivery: DSRIP's interrupted time series analysis paradigm.

PubMed

Shenoy, Amrita G; Begley, Charles E; Revere, Lee; Linder, Stephen H; Daiger, Stephen P

2017-12-08

Adoption of Medicaid Section 1115 waiver is one of the many ways of innovating healthcare delivery system. The Delivery System Reform Incentive Payment (DSRIP) pool, one of the two funding pools of the waiver has four categories viz. infrastructure development, program innovation and redesign, quality improvement reporting and lastly, bringing about population health improvement. A metric of the fourth category, preventable hospitalization (PH) rate was analyzed in the context of eight conditions for two time periods, pre-reporting years (2010-2012) and post-reporting years (2013-2015) for two hospital cohorts, DSRIP participating and non-participating hospitals. The study explains how DSRIP impacted Preventable Hospitalization (PH) rates of eight conditions for both hospital cohorts within two time periods. Eight PH rates were regressed as the dependent variable with time, intervention and post-DSRIP Intervention as independent variables. PH rates of eight conditions were then consolidated into one rate for regressing with the above independent variables to evaluate overall impact of DSRIP. An interrupted time series regression was performed after accounting for auto-correlation, stationarity and seasonality in the dataset. In the individual regression model, PH rates showed statistically significant coefficients for seven out of eight conditions in DSRIP participating hospitals. In the combined regression model, the coefficient of the PH rate showed a statistically significant decrease with negative p-values for regression coefficients in DSRIP participating hospitals compared to positive/increased p-values for regression coefficients in DSRIP non-participating hospitals. Several macro- and micro-level factors may have likely contributed DSRIP hospitals outperforming DSRIP non-participating hospitals. Healthcare organization/provider collaboration, support from healthcare professionals, DSRIP's design, state reimbursement and coordination in care delivery methods may have led to likely success of DSRIP. IV, a retrospective cohort study based on longitudinal data. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

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.

Tongue thickness relates to nutritional status in the elderly.

PubMed

Tamura, Fumiyo; Kikutani, Takeshi; Tohara, Takashi; Yoshida, Mitsuyoshi; Yaegaki, Ken

2012-12-01

Many elderly people under long-term care suffer from malnutrition caused by dysphagia, frequently leading to sarcopenia. Our hypothesis is that sarcopenia may compromise oral function, resulting in dysphagia. The objectives of this study were to evaluate sarcopenia of the lingual muscles by measuring the tongue thickness, and elucidate its relationship with nutritional status. We examined 104 elderly subjects (mean age = 80.3 ± 7.9 years). Anthropometric data, such as triceps skinfold thickness and midarm muscle area (AMA), were obtained. The tongue thickness of the central part was determined using ultrasonography. Measurement was performed twice and the mean value was obtained. The relationship between tongue thickness and nutritional status was analyzed by Pearson's correlation coefficient and Spearman's rank correlation coefficient. AMA and age were identified by multiple-regression analysis as factors influencing tongue thickness. The results of this study suggest that malnutrition may induce sarcopenia not only in the skeletal muscles but also in the tongue.

Estimation of old field ecosystem biomass using low altitude imagery

NASA Technical Reports Server (NTRS)

Nor, S. M.; Safir, G.; Burton, T. M.; Hook, J. E.; Schultink, G.

1977-01-01

Color-infrared photography was used to evaluate the biomass of experimental plots in an old-field ecosystem that was treated with different levels of waste water from a sewage treatment facility. Cibachrome prints at a scale of approximately 1:1,600 produced from 35 mm color infrared slides were used to analyze density patterns using prepared tonal density scales and multicell grids registered to ground panels shown on the photograph. Correlations between mean tonal density and harvest biomass data gave consistently high coefficients ranging from 0.530 to 0.896 at the 0.001 significance level. Corresponding multiple regression analysis resulted in higher correlation coefficients. The results indicate that aerial infrared photography can be used to estimate standing crop biomass on waste water irrigated old field ecosystems. Combined with minimal ground truth data, this technique could enable managers of waste water irrigation projects to precisely time harvest of such systems for maximal removal of nutrients in harvested biomass.

Mathematical modeling of tetrahydroimidazole benzodiazepine-1-one derivatives as an anti HIV agent

NASA Astrophysics Data System (ADS)

Ojha, Lokendra Kumar

2017-07-01

The goal of the present work is the study of drug receptor interaction via QSAR (Quantitative Structure-Activity Relationship) analysis for 89 set of TIBO (Tetrahydroimidazole Benzodiazepine-1-one) derivatives. MLR (Multiple Linear Regression) method is utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and biological activity (IC50). The best QSAR model was selected having a correlation coefficient (r) of 0.9299 and Standard Error of Estimation (SEE) of 0.5022, Fisher Ratio (F) of 159.822 and Quality factor (Q) of 1.852. This model is statistically significant and strongly favours the substitution of sulphur atom, IS i.e. indicator parameter for -Z position of the TIBO derivatives. Two other parameter logP (octanol-water partition coefficient) and SAG (Surface Area Grid) also played a vital role in the generation of best QSAR model. All three descriptor shows very good stability towards data variation in leave-one-out (LOO).

QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls.

PubMed

Yu, S; Gao, S; Gan, Y; Zhang, Y; Ruan, X; Wang, Y; Yang, L; Shi, J

2016-04-01

Quantitative structure-property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol-water (KOW) and organic carbon-water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure-activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.

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.

Solid harmonic wavelet scattering for predictions of molecule properties

NASA Astrophysics Data System (ADS)

Eickenberg, Michael; Exarchakis, Georgios; Hirn, Matthew; Mallat, Stéphane; Thiry, Louis

2018-06-01

We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory (DFT). Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute invariant "solid harmonic scattering coefficients" that account for different types of interactions at different scales. Multilinear regressions of various physical properties of molecules are computed from these invariant coefficients. Numerical experiments show that these regressions have near state-of-the-art performance, even with relatively few training examples. Predictions over small sets of scattering coefficients can reach a DFT precision while being interpretable.

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.

Linear viral load increase of a single HPV-type in women with multiple HPV infections predicts progression to cervical cancer.

PubMed

Depuydt, Christophe E; Thys, Sofie; Beert, Johan; Jonckheere, Jef; Salembier, Geert; Bogers, Johannes J

2016-11-01

Persistent high-risk human papillomavirus (HPV) infection is strongly associated with development of high-grade cervical intraepithelial neoplasia or cancer (CIN3+). In single type infections, serial type-specific viral-load measurements predict the natural history of the infection. In infections with multiple HPV-types, the individual type-specific viral-load profile could distinguish progressing HPV-infections from regressing infections. A case-cohort natural history study was established using samples from untreated women with multiple HPV-infections who developed CIN3+ (n = 57) or cleared infections (n = 88). Enriched cell pellet from liquid based cytology samples were subjected to a clinically validated real-time qPCR-assay (18 HPV-types). Using serial type-specific viral-load measurements (≥3) we calculated HPV-specific slopes and coefficient of determination (R(2) ) by linear regression. For each woman slopes and R(2) were used to calculate which HPV-induced processes were ongoing (progression, regression, serial transient, transient). In transient infections with multiple HPV-types, each single HPV-type generated similar increasing (0.27copies/cell/day) and decreasing (-0.27copies/cell/day) viral-load slopes. In CIN3+, at least one of the HPV-types had a clonal progressive course (R(2)  ≥ 0.85; 0.0025copies/cell/day). In selected CIN3+ cases (n = 6), immunostaining detecting type-specific HPV 16, 31, 33, 58 and 67 RNA showed an even staining in clonal populations (CIN3+), whereas in transient virion-producing infections the RNA-staining was less in the basal layer compared to the upper layer where cells were ready to desquamate and release newly-formed virions. RNA-hybridization patterns matched the calculated ongoing processes measured by R(2) and slope in serial type-specific viral-load measurements preceding the biopsy. In women with multiple HPV-types, serial type-specific viral-load measurements predict the natural history of the different HPV-types and elucidates HPV-genotype attribution. © 2016 UICC.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

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.

Lead exposure and the 2010 achievement test scores of children in New York counties

PubMed Central

2012-01-01

Background Lead is toxic to cognitive and behavioral functioning in children even at levels well below those producing physical symptoms. Continuing efforts in the U.S. since about the 1970s to reduce lead exposure in children have dramatically reduced the incidence of elevated blood lead levels (with elevated levels defined by the current U.S. Centers for Disease Control threshold of 10 μg/dl). The current study examines how much lead toxicity continues to impair the academic achievement of children of New York State, using 2010 test data. Methods This study relies on three sets of data published for the 57 New York counties outside New York City: school achievement data from the New York State Department of Education, data on incidence of elevated blood lead levels from the New York State Department of Health, and data on income from the U.S. Census Bureau. We studied third grade and eighth grade test scores in English Language Arts and mathematics. Using the county as the unit of analysis, we computed bivariate correlations and regression coefficients, with percent of children achieving at the lowest reported level as the dependent variable and the percent of preschoolers in the county with elevated blood lead levels as the independent variable. Then we repeated those analyses using partial correlations to control for possible confounding effects of family income, and using multiple regressions with income included. Results The bivariate correlations between incidence of elevated lead and number of children in the lowest achievement group ranged between 0.38 and 0.47. The partial correlations ranged from 0.29 to 0.40. The regression coefficients, both bivariate and partial (both estimating the increase in percent of children in the lowest achievement group for every percent increase in the children with elevated blood lead levels), ranged from 0.52 to 1.31. All regression coefficients, when rounded to the nearest integer, were approximately 1. Thus, when the percent of children showing elevated lead increases by one percent, the percent of children in the lowest achievement group, according to the regression equations generated, also increases by about one percent. All associations were significant at the 0.05 level. Conclusion Despite public health advances, and despite the imprecision of measures, an association between the incidence of elevated blood lead and achievement in New York counties is still apparent, not attributable to confounding by income. Efforts to reduce lead exposure should persist with vigor. PMID:22269775

"L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North at Fargo and Grand Forks, North Dakota, 2003-12

USGS Publications Warehouse

Galloway, Joel M.

2014-01-01

The Red River of the North (hereafter referred to as “Red River”) Basin is an important hydrologic region where water is a valuable resource for the region’s economy. Continuous water-quality monitors have been operated by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, Minnesota Pollution Control Agency, City of Fargo, City of Moorhead, City of Grand Forks, and City of East Grand Forks at the Red River at Fargo, North Dakota, from 2003 through 2012 and at Grand Forks, N.Dak., from 2007 through 2012. The purpose of the monitoring was to provide a better understanding of the water-quality dynamics of the Red River and provide a way to track changes in water quality. Regression equations were developed that can be used to estimate concentrations and loads for dissolved solids, sulfate, chloride, nitrate plus nitrite, total phosphorus, and suspended sediment using explanatory variables such as streamflow, specific conductance, and turbidity. Specific conductance was determined to be a significant explanatory variable for estimating dissolved solids concentrations at the Red River at Fargo and Grand Forks. The regression equations provided good relations between dissolved solid concentrations and specific conductance for the Red River at Fargo and at Grand Forks, with adjusted coefficients of determination of 0.99 and 0.98, respectively. Specific conductance, log-transformed streamflow, and a seasonal component were statistically significant explanatory variables for estimating sulfate in the Red River at Fargo and Grand Forks. Regression equations provided good relations between sulfate concentrations and the explanatory variables, with adjusted coefficients of determination of 0.94 and 0.89, respectively. For the Red River at Fargo and Grand Forks, specific conductance, streamflow, and a seasonal component were statistically significant explanatory variables for estimating chloride. For the Red River at Grand Forks, a time component also was a statistically significant explanatory variable for estimating chloride. The regression equations for chloride at the Red River at Fargo provided a fair relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.66 and the equation for the Red River at Grand Forks provided a relatively good relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.77. Turbidity and streamflow were statistically significant explanatory variables for estimating nitrate plus nitrite concentrations at the Red River at Fargo and turbidity was the only statistically significant explanatory variable for estimating nitrate plus nitrite concentrations at Grand Forks. The regression equation for the Red River at Fargo provided a relatively poor relation between nitrate plus nitrite concentrations, turbidity, and streamflow, with an adjusted coefficient of determination of 0.46. The regression equation for the Red River at Grand Forks provided a fair relation between nitrate plus nitrite concentrations and turbidity, with an adjusted coefficient of determination of 0.73. Some of the variability that was not explained by the equations might be attributed to different sources contributing nitrates to the stream at different times. Turbidity, streamflow, and a seasonal component were statistically significant explanatory variables for estimating total phosphorus at the Red River at Fargo and Grand Forks. The regression equation for the Red River at Fargo provided a relatively fair relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.74. The regression equation for the Red River at Grand Forks provided a good relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.87. For the Red River at Fargo, turbidity and streamflow were statistically significant explanatory variables for estimating suspended-sediment concentrations. For the Red River at Grand Forks, turbidity was the only statistically significant explanatory variable for estimating suspended-sediment concentration. The regression equation at the Red River at Fargo provided a good relation between suspended-sediment concentration, turbidity, and streamflow, with an adjusted coefficient of determination of 0.95. The regression equation for the Red River at Grand Forks provided a good relation between suspended-sediment concentration and turbidity, with an adjusted coefficient of determination of 0.96.

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.

Correlation and simple linear regression.

PubMed

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

2003-06-01

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

Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

NASA Astrophysics Data System (ADS)

Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

2017-03-01

Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

[Burnout syndrome and suicide risk among primary care nurses].

PubMed

Tomás-Sábado, Joaquín; Maynegre-Santaulària, Montserrat; Pérez-Bartolomé, Meritxell; Alsina-Rodríguez, Marta; Quinta-Barbero, Roser; Granell-Navas, Sergi

2010-01-01

To observe the prevalence of the burnout syndrome and the relationship with suicide risk, self-esteem, anxiety and depression, in a sample of primary care nurses. Observational, cross-sectional and correlational study. The sample consisted of 146 nursing professionals, 131 women and 15 men, with an average age of 44.02 years (SD=10.89). Participants responded to a questionnaire which included the Spanish forms of the Maslach burnout inventory (MBI), the Plutchik Suicide Risk Scale (SR), the Kuwait University Anxiety Scale (KUAS), the Self-Rating Depression Scale (SDS) and the Rosenberg Self-esteem Scale (RSES). In the inferential statistical analysis, Pearson's r coefficients and multiple linear regression were calculated. Significant correlations between suicidal risk and anxiety, depression, self-esteem, emotional exhaustion and personal performance, were obtained. In the multiple regression analysis, depression was the main predictor of suicidal risk, followed by anxiety and emotional exhaustion. The scores obtained in burnout and suicidal risk were, in general, lower than those observed in other studies, emphasising the high level observed in personal performance, which reflects reasonable professional satisfaction. The results show the important role of working atmosphere and early recognition of mental disorders in burnout and suicidal risk prevention. Copyright (c) 2009 Elsevier España, S.L. All rights reserved.

The effect of working in an infection isolation room on hospital nurses' job satisfaction.

PubMed

Kagan, Ilya; Fridman, Shoshana; Shalom, Esther; Melnikov, Semyon

2018-03-01

To examine how the nature of working in a carbapenemase-producing Klebsiella pneumoniae infection isolation room affects nurses' job performance and job satisfaction. Job satisfaction is under intensive research as a factor in the retention of nursing staff. In a cross-sectional design study, a convenience sample of 87 registered nurses who had worked in carbapenemase-producing Klebsiella pneumoniae isolation rooms in a tertiary medical centre in Israel answered a self-administered questionnaire. Data were analysed by descriptive statistics, Pearson correlation coefficients, t tests, one-way ANOVA and multiple regression analysis. Job satisfaction was significantly correlated with perceived knowledge of carbapenemase-producing Klebsiella pneumoniae, with personal experience of working in an isolation room and the perceived level of professional functioning. Multiple regression analysis found that the quality of the nurses' personal experience of isolation room work and their perceived level of professional functioning there explained 33% of the variance in job satisfaction. Managers need to take into account that prolonged work in isolation can negatively impinge upon both performance and job satisfaction. Managers can consider refraining from lengthy nurse assignment to the isolation room. This would also apply to other areas of nursing practice where work is performed in isolation. © 2017 John Wiley & Sons Ltd.

Sagittal and Vertical Craniofacial Growth Pattern and Timing of Circumpubertal Skeletal Maturation: A Multiple Regression Study

PubMed Central

Rosso, Luigi; Riatti, Riccardo

2016-01-01

The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6–16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, −0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases. PMID:27995136

[The importance of handprint morphometry for determining the human body length].

PubMed

Grigor'eva, M A

2018-01-01

Handprint morphometry for the purpose of personality identification still remains a relatively novel approach. The methods employed for the measurements are not infrequently difficult to reproduce and therefore cause controversy. The objective of the present study was to introduce the system of methods for the measurement of handprints suitable for the reliable determination of the human body length. The study included the measurement of the size of 40 handprints left by124 adult subjects (52 men and 72 women). Two methods of the regression analysis, stepwise and forced inclusion, were applied to the combined group of handprints to select the equations with the high (R>0.800) coefficients of multiple correlation with the body length. 13 equations of multiple regression were obtained and analyzed. The standard error of estimating (SEE) varied from 4.30 to 5.19 cm. The best results were obtained with the equations constructed from the sizes I, II, and III of the rays without their distal phalanges. It was shown that the body length can be successfully reconstructed within the height range from 168 to 183 cm for men and from 157 to 176 cm for women. The examples of the use of the equations for the purpose of expertise of illegible and incomplete handprints are presented.

Five-Hole Flow Angle Probe Calibration for the NASA Glenn Icing Research Tunnel

NASA Technical Reports Server (NTRS)

Gonsalez, Jose C.; Arrington, E. Allen

1999-01-01

A spring 1997 test section calibration program is scheduled for the NASA Glenn Research Center Icing Research Tunnel following the installation of new water injecting spray bars. A set of new five-hole flow angle pressure probes was fabricated to properly calibrate the test section for total pressure, static pressure, and flow angle. The probes have nine pressure ports: five total pressure ports on a hemispherical head and four static pressure ports located 14.7 diameters downstream of the head. The probes were calibrated in the NASA Glenn 3.5-in.-diameter free-jet calibration facility. After completing calibration data acquisition for two probes, two data prediction models were evaluated. Prediction errors from a linear discrete model proved to be no worse than those from a full third-order multiple regression model. The linear discrete model only required calibration data acquisition according to an abridged test matrix, thus saving considerable time and financial resources over the multiple regression model that required calibration data acquisition according to a more extensive test matrix. Uncertainties in calibration coefficients and predicted values of flow angle, total pressure, static pressure. Mach number. and velocity were examined. These uncertainties consider the instrumentation that will be available in the Icing Research Tunnel for future test section calibration testing.

Effects of homogenization process parameters on physicochemical properties of astaxanthin nanodispersions prepared using a solvent-diffusion technique

PubMed Central

Anarjan, Navideh; Jafarizadeh-Malmiri, Hoda; Nehdi, Imededdine Arbi; Sbihi, Hassen Mohamed; Al-Resayes, Saud Ibrahim; Tan, Chin Ping

2015-01-01

Nanodispersion systems allow incorporation of lipophilic bioactives, such as astaxanthin (a fat soluble carotenoid) into aqueous systems, which can improve their solubility, bioavailability, and stability, and widen their uses in water-based pharmaceutical and food products. In this study, response surface methodology was used to investigate the influences of homogenization time (0.5–20 minutes) and speed (1,000–9,000 rpm) in the formation of astaxanthin nanodispersions via the solvent-diffusion process. The product was characterized for particle size and astaxanthin concentration using laser diffraction particle size analysis and high performance liquid chromatography, respectively. Relatively high determination coefficients (ranging from 0.896 to 0.969) were obtained for all suggested polynomial regression models. The overall optimal homogenization conditions were determined by multiple response optimization analysis to be 6,000 rpm for 7 minutes. In vitro cellular uptake of astaxanthin from the suggested individual and multiple optimized astaxanthin nanodispersions was also evaluated. The cellular uptake of astaxanthin was found to be considerably increased (by more than five times) as it became incorporated into optimum nanodispersion systems. The lack of a significant difference between predicted and experimental values confirms the suitability of the regression equations connecting the response variables studied to the independent parameters. PMID:25709435

Sagittal and Vertical Craniofacial Growth Pattern and Timing of Circumpubertal Skeletal Maturation: A Multiple Regression Study.

PubMed

Perinetti, Giuseppe; Rosso, Luigi; Riatti, Riccardo; Contardo, Luca

2016-01-01

The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6-16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, -0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases.

A New Metric for Land-Atmosphere Coupling Strength: Applications on Observations and Modeling

NASA Astrophysics Data System (ADS)

Tang, Q.; Xie, S.; Zhang, Y.; Phillips, T. J.; Santanello, J. A., Jr.; Cook, D. R.; Riihimaki, L.; Gaustad, K.

2017-12-01

A new metric is proposed to quantify the land-atmosphere (LA) coupling strength and is elaborated by correlating the surface evaporative fraction and impacting land and atmosphere variables (e.g., soil moisture, vegetation, and radiation). Based upon multiple linear regression, this approach simultaneously considers multiple factors and thus represents complex LA coupling mechanisms better than existing single variable metrics. The standardized regression coefficients quantify the relative contributions from individual drivers in a consistent manner, avoiding the potential inconsistency in relative influence of conventional metrics. Moreover, the unique expendable feature of the new method allows us to verify and explore potentially important coupling mechanisms. Our observation-based application of the new metric shows moderate coupling with large spatial variations at the U.S. Southern Great Plains. The relative importance of soil moisture vs. vegetation varies by location. We also show that LA coupling strength is generally underestimated by single variable methods due to their incompleteness. We also apply this new metric to evaluate the representation of LA coupling in the Accelerated Climate Modeling for Energy (ACME) V1 Contiguous United States (CONUS) regionally refined model (RRM). This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-734201

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

NASA Astrophysics Data System (ADS)

Wheeler, David C.; Waller, Lance A.

2009-03-01

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.

Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks

PubMed Central

Hewitt, Angela L.; Popa, Laurentiu S.; Pasalar, Siavash; Hendrix, Claudia M.

2011-01-01

Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of Radj2), followed by position (28 ± 24% of Radj2) and speed (11 ± 19% of Radj2). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower Radj2 values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics. PMID:21795616

Increased urinary leukotriene E4 excretion in obstructive sleep apnea: effects of obesity and hypoxia.

PubMed

Stanke-Labesque, Françoise; Bäck, Magnus; Lefebvre, Blandine; Tamisier, Renaud; Baguet, Jean-Philippe; Arnol, Nathalie; Lévy, Patrick; Pépin, Jean-Louis

2009-08-01

Low-grade inflammation may potentially explain the relationship between obstructive sleep apnea syndrome (OSA) and cardiovascular events. However, the respective contribution of intermittent hypoxia and confounders, such as obesity, is still debated. To monitor urinary leukotriene E(4) (U-LTE(4)), a validated marker of proinflammatory cysteinyl leukotriene production, in OSA; to determine the influence of obesity and other confounders on U-LTE(4) concentrations; to examine the mechanisms involved through transcriptional profiling of the leukotriene pathway in peripheral blood mononuclear cells (PBMCs); and to investigate the effect of continuous positive air pressure (CPAP) on U-LTE(4) concentrations. We measured U-LTE(4) by liquid chromatography-tandem mass spectrometry. The U-LTE(4) concentrations were increased (P = .019) in 40 nonobese patients with OSA carefully matched for age, sex, and body mass index (BMI) to 25 control subjects, and correlated (r = 0.0312; P = .017) to the percentage of time spent with mean oxygen saturation (SaO(2)) less than 90%. In a larger cohort of patients with OSA (n = 72), U-LTE(4) increased as a function of BMI (r = 0.445; P = .0002). In those patients, the expression levels of 5-lipoxygenase activating protein mRNA in mononuclear cells exhibited a similar pattern. A stepwise multiple linear regression analysis performed in this cohort identified BMI (P = .001; regression coefficient, 3.33) and percentage of time spent with SaO(2) <90% (P = .001; regression coefficient, 1.01) as independent predictors of U-LTE(4) concentrations. Compared with baseline, CPAP reduced by 22% (P = .006) U-LTE(4) concentrations only in patients with OSA with normal BMI. Obesity, and to a lesser extent hypoxia severity, are determinant of U-LTE(4) production in patients with OSA.

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

[Development of an automatic pneumatic tourniquet system that determines pressures in synchrony with systolic blood pressure].

PubMed

Liu, Hongyun; Li, Kaiyuan; Zhang, Zhengbo; Guo, Junyan; Wang, Weidong

2012-11-01

The correlation coefficients between arterial occlusion pressure and systolic blood pressure, diastolic blood pressure, limb circumference, body mass etc were obtained through healthy volunteer experiments, in which tourniquet were applied on upper/lower extremities. The prediction equations were derived from the data of experiments by multiple regression analysis. Based on the microprocessor C8051F340, a new pneumatic tourniquet system that can determine tourniquet pressure in synchrony with systolic blood pressure was developed and verified the function and stability of designed system. Results showed that the pneumatic tourniquet which automatically adjusts occlusion pressure in accordance with systolic blood pressure could stop the flow of blood to get a bloodless field.

The scavenging of silver by manganese and iron oxides in stream sediments collected from two drainage areas of Colorado

USGS Publications Warehouse

Chao, T.T.; Anderson, B.J.

1974-01-01

Stream sediments of two well-weathered and aerated drainage areas of Colorado containing anomalous amounts of silver were allowed to react by shaking with nitric acid of different concentrations (1-10M). Silver, manganese, and iron simultaneously dissolved were determined by atomic absorption. The relationship between silver dissolution and the dissolution of manganese and/or iron was evaluated by linear and multiple regression analyses. The highly significant correlation coefficient (r = 0.913) between silver and manganese dissolution suggests that manganese oxides are the major control on the scavenging of silver in these stream sediments, whereas iron oxides only play a secondary role in this regard. ?? 1974.

Near-infrared and Mid-infrared Spectroscopic Techniques for a Fast and Nondestructive Quality Control of Thymi herba.

PubMed

Pezzei, Cornelia K; Schönbichler, Stefan A; Hussain, Shah; Kirchler, Christian G; Huck-Pezzei, Verena A; Popp, Michael; Krolitzek, Justine; Bonn, Günther K; Huck, Christian W

2018-04-01

In this study, novel near-infrared and attenuated total reflectance mid-infrared spectroscopic methods coupled with multivariate data analysis were established enabling the determination of thymol, rosmarinic acid, and the antioxidant capacity of Thymi herba. A new high-performance liquid chromatography method and UV-Vis spectroscopy were applied as reference methods. Partial least squares regressions were carried out as cross and test set validations. To reduce systematic errors, different data pretreatments, such as multiplicative scatter correction, 1st derivative, or 2nd derivative, were applied on the spectra. The performances of the two infrared spectroscopic techniques were evaluated and compared. In general, attenuated total reflectance mid-infrared spectroscopy demonstrated a slightly better predictive power (thymol: coefficient of determination = 0.93, factors = 3, ratio of performance to deviation = 3.94; rosmarinic acid: coefficient of determination = 0.91, factors = 3, ratio of performance to deviation = 3.35, antioxidant capacity: coefficient of determination = 0.87, factors = 2, ratio of performance to deviation = 2.80; test set validation) than near-infrared spectroscopy (thymol: coefficient of determination = 0.90, factors = 6, ratio of performance to deviation = 3.10; rosmarinic acid: coefficient of determination = 0.92, factors = 6, ratio of performance to deviation = 3.61, antioxidant capacity: coefficient of determination = 0.91, factors = 6, ratio of performance to deviation = 3.42; test set validation). The capability of infrared vibrational spectroscopy as a quick and simple analytical tool to replace conventional time and chemical consuming analyses for the quality control of T. herba could be demonstrated. Georg Thieme Verlag KG Stuttgart · New York.

Femur-bending properties as influenced by gravity. V - Strength vs. calcium and gravity in rats exposed for 2 weeks

NASA Technical Reports Server (NTRS)

Wunder, Charles C.; Cook, Kenneth M.; Watkins, Stanley R.; Moressi, William J.

1987-01-01

The dependence of gravitationally related changes in femur bone strength on the comparable changes in calcium content was investigated in rats exposed to chronic simulations of altered gravity from the 28th to 42nd day of age. Zero G was simulated by harness suspension and 3 G by centrifugation. Bone strength (S) was determined by bending (using modified quasi-static cantilever bending methods and equipment described by Wunder et al., 1977 and 1979) and Ca content (C, by mass pct) determined by atomic absorption spectrometry; results were compared with data obtained on both normal and harnessed control animals at 1 G. Multiple regression showed significant dependence of S upon earth's gravity, independent from C, for which there was no significant coefficient of partial regression. It is suggested that the lack of S/C correlation might have been due to the fact that considerable fraction of the calcium in these young, developing bones has not yet crystallized into the hydroxyapatite which provides strength.

Effects of metal- and fiber-reinforced composite root canal posts on flexural properties.

PubMed

Kim, Su-Hyeon; Oh, Tack-Oon; Kim, Ju-Young; Park, Chun-Woong; Baek, Seung-Ho; Park, Eun-Seok

2016-01-01

The aim of this study was to observe the effects of different test conditions on the flexural properties of root canal post. Metal- and fiber-reinforced composite root canal posts of various diameters were measured to determine flexural properties using a threepoint bending test at different conditions. In this study, the span length/post diameter ratio of root canal posts varied from 3.0 to 10.0. Multiple regression models for maximum load as a dependent variable were statistically significant. The models for flexural properties as dependent variables were statistically significant, but linear regression models could not be fitted to data sets. At a low span length/post diameter ratio, the flexural properties were distorted by occurrence of shear stress in short samples. It was impossible to obtain high span length/post diameter ratio with root canal posts. The addition of parameters or coefficients is necessary to appropriately represent the flexural properties of root canal posts.

QSAR and docking based semi-synthesis and in vitro evaluation of 18 β-glycyrrhetinic acid derivatives against human lung cancer cell line A-549.

PubMed

Yadav, Dharmendra Kumar; Kalani, Komal; Khan, Feroz; Srivastava, Santosh Kumar

2013-12-01

For the prediction of anticancer activity of glycyrrhetinic acid (GA-1) analogs against the human lung cancer cell line (A-549), a QSAR model was developed by forward stepwise multiple linear regression methodology. The regression coefficient (r(2)) and prediction accuracy (rCV(2)) of the QSAR model were taken 0.94 and 0.82, respectively in terms of correlation. The QSAR study indicates that the dipole moments, size of smallest ring, amine counts, hydroxyl and nitro functional groups are correlated well with cytotoxic activity. The docking studies showed high binding affinity of the predicted active compounds against the lung cancer target EGFR. These active glycyrrhetinic acid derivatives were then semi-synthesized, characterized and in-vitro tested for anticancer activity. The experimental results were in agreement with the predicted values and the ethyl oxalyl derivative of GA-1 (GA-3) showed equal cytotoxic activity to that of standard anticancer drug paclitaxel.

Evaluation of aroma enhancement for "Ecolly" dry white wines by mixed inoculation of selected Rhodotorula mucilaginosa and Saccharomyces cerevisiae.

PubMed

Wang, Xing-Chen; Li, Ai-Hua; Dizy, Marta; Ullah, Niamat; Sun, Wei-Xuan; Tao, Yong-Sheng

2017-08-01

To improve the aroma profile of Ecolly dry white wine, the simultaneous and sequential inoculations of selected Rhodotorula mucilaginosa and Saccharomyces cerevisiae were performed in wine making of this work. The two yeasts were mixed in various ratios for making the mixed inoculum. The amount of volatiles and aroma characteristics were determined the following year. Mixed fermentation improved both the varietal and fermentative aroma compound composition, especially that of (Z)-3-hexene-1-ol, nerol oxide, certain acetates and ethyls group compounds. Citrus, sweet fruit, acid fruit, berry, and floral aroma traits were enhanced by mixed fermentation; however, an animal note was introduced upon using higher amounts of R. mucilaginosa. Aroma traits were regressed with volatiles as observed by the partial least-square regression method. Analysis of correlation coefficients revealed that the aroma traits were the multiple interactions of volatile compounds, with the fermentative volatiles having more impact on aroma than varietal compounds. Copyright © 2017 Elsevier Ltd. All rights reserved.

Thermal sensation and comfort during exposure to local airflow to face or legs.

PubMed

Yamashita, Kazuaki; Matsuo, Juntaro; Tochihara, Yutaka; Kondo, Youichiro; Takayama, Shizuka; Nagayama, Hiroki

2005-01-01

The present study examined the contribution of local airflow temperature to thermal sensation and comfort in humans. Eight healthy male students were exposed to local airflow to their faces (summer condition) or legs (winter condition) for 30 minutes. Local airflow temperature (Tf) was maintained at 18 degrees C to 36 degrees C, and ambient temperature (Ta) was maintained at 17.4 degrees C to 31.4 degrees C. Each subject was exposed to 16 conditions chosen from the combination of Tf and Ta. Based on the results of multiple regression analysis, the standardized partial regression coefficient of Tf and Ta were determined to be 0.93 and 0.13 in the summer condition, and 0.71 and 0.36 in the winter condition at the end of the exposure. Also, thermal comfort was observed to depend closely on the interrelation between Tf and Ta. The present data suggested that local airflow temperature is an important thermal factor regarding thermal sensation and comfort.

Atmospheric concentrations, sources and gas-particle partitioning of PAHs in Beijing after the 29th Olympic Games.

PubMed

Ma, Wan-Li; Sun, De-Zhi; Shen, Wei-Guo; Yang, Meng; Qi, Hong; Liu, Li-Yan; Shen, Ji-Min; Li, Yi-Fan

2011-07-01

A comprehensive sampling campaign was carried out to study atmospheric concentration of polycyclic aromatic hydrocarbons (PAHs) in Beijing and to evaluate the effectiveness of source control strategies in reducing PAHs pollution after the 29th Olympic Games. The sub-cooled liquid vapor pressure (logP(L)(o))-based model and octanol-air partition coefficient (K(oa))-based model were applied based on each seasonal dateset. Regression analysis among log K(P), logP(L)(o) and log K(oa) exhibited high significant correlations for four seasons. Source factors were identified by principle component analysis and contributions were further estimated by multiple linear regression. Pyrogenic sources and coke oven emission were identified as major sources for both the non-heating and heating seasons. As compared with literatures, the mean PAH concentrations before and after the 29th Olympic Games were reduced by more than 60%, indicating that the source control measures were effective for reducing PAHs pollution in Beijing. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimization by response surface methodology of lutein recovery from paprika leaves using accelerated solvent extraction.

PubMed

Kang, Jae-Hyun; Kim, Suna; Moon, BoKyung

2016-08-15

In this study, we used response surface methodology (RSM) to optimize the extraction conditions for recovering lutein from paprika leaves using accelerated solvent extraction (ASE). The lutein content was quantitatively analyzed using a UPLC equipped with a BEH C18 column. A central composite design (CCD) was employed for experimental design to obtain the optimized combination of extraction temperature (°C), static time (min), and solvent (EtOH, %). The experimental data obtained from a twenty sample set were fitted to a second-order polynomial equation using multiple regression analysis. The adjusted coefficient of determination (R(2)) for the lutein extraction model was 0.9518, and the probability value (p=0.0000) demonstrated a high significance for the regression model. The optimum extraction conditions for lutein were temperature: 93.26°C, static time: 5 min, and solvent: 79.63% EtOH. Under these conditions, the predicted extraction yield of lutein was 232.60 μg/g. Copyright © 2016 Elsevier Ltd. All rights reserved.

SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients.

PubMed

Weaver, Bruce; Wuensch, Karl L

2013-09-01

Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 - α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.

Quantitative Structure-Activity Relationship of Insecticidal Activity of Benzyl Ether Diamidine Derivatives

NASA Astrophysics Data System (ADS)

Zhai, Mengting; Chen, Yan; Li, Jing; Zhou, Jun

2017-12-01

The molecular electrongativity distance vector (MEDV-13) was used to describe the molecular structure of benzyl ether diamidine derivatives in this paper, Based on MEDV-13, The three-parameter (M 3, M 15, M 47) QSAR model of insecticidal activity (pIC 50) for 60 benzyl ether diamidine derivatives was constructed by leaps-and-bounds regression (LBR) . The traditional correlation coefficient (R) and the cross-validation correlation coefficient (R CV ) were 0.975 and 0.971, respectively. The robustness of the regression model was validated by Jackknife method, the correlation coefficient R were between 0.971 and 0.983. Meanwhile, the independent variables in the model were tested to be no autocorrelation. The regression results indicate that the model has good robust and predictive capabilities. The research would provide theoretical guidance for the development of new generation of anti African trypanosomiasis drugs with efficiency and low toxicity.

Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selection

PubMed Central

Zheng, Qi; Peng, Limin

2016-01-01

Quantile regression provides a flexible platform for evaluating covariate effects on different segments of the conditional distribution of response. As the effects of covariates may change with quantile level, contemporaneously examining a spectrum of quantiles is expected to have a better capacity to identify variables with either partial or full effects on the response distribution, as compared to focusing on a single quantile. Under this motivation, we study a general adaptively weighted LASSO penalization strategy in the quantile regression setting, where a continuum of quantile index is considered and coefficients are allowed to vary with quantile index. We establish the oracle properties of the resulting estimator of coefficient function. Furthermore, we formally investigate a BIC-type uniform tuning parameter selector and show that it can ensure consistent model selection. Our numerical studies confirm the theoretical findings and illustrate an application of the new variable selection procedure. PMID:28008212

Prognostic factors in multiple myeloma: selection using Cox's proportional hazard model.

PubMed

Pasqualetti, P; Collacciani, A; Maccarone, C; Casale, R

1996-01-01

The pretreatment characteristics of 210 patients with multiple myeloma, observed between 1980 and 1994, were evaluated as potential prognostic factors for survival. Multivariate analysis according to Cox's proportional hazard model identified in the 160 dead patients with myeloma, among 26 different single prognostic variables, the following factors in order of importance: beta 2-microglobulin; bone marrow plasma cell percentage, hemoglobinemia, degree of lytic bone lesions, serum creatinine, and serum albumin. By analysis of these variables a prognostic index (PI), that considers the regression coefficients derived by Cox's model of all significant factors, was obtained. Using this it was possible to separate the whole patient group into three stages: stage I (PI < 1.485, 67 patients), stage II (PI: 1.485-2.090, 76 patients), and stage III (PI > 2.090, 67 patients), with a median survivals of 68, 36 and 13 months (P < 0.0001), respectively. Also the responses to therapy (P < 0.0001) and the survival curves (P < 0.00001) presented significant differences among the three subgroups. Knowledge of these factors could be of value in predicting prognosis and in planning therapy in patients with multiple myeloma.

Robust, Adaptive Functional Regression in Functional Mixed Model Framework.

PubMed

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

2011-09-01

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

Robust, Adaptive Functional Regression in Functional Mixed Model Framework

PubMed Central

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

2012-01-01

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

Application of multivariate chemometric techniques for simultaneous determination of five parameters of cottonseed oil by single bounce attenuated total reflectance Fourier transform infrared spectroscopy.

PubMed

Talpur, M Younis; Kara, Huseyin; Sherazi, S T H; Ayyildiz, H Filiz; Topkafa, Mustafa; Arslan, Fatma Nur; Naz, Saba; Durmaz, Fatih; Sirajuddin

2014-11-01

Single bounce attenuated total reflectance (SB-ATR) Fourier transform infrared (FTIR) spectroscopy in conjunction with chemometrics was used for accurate determination of free fatty acid (FFA), peroxide value (PV), iodine value (IV), conjugated diene (CD) and conjugated triene (CT) of cottonseed oil (CSO) during potato chips frying. Partial least square (PLS), stepwise multiple linear regression (SMLR), principal component regression (PCR) and simple Beer׳s law (SBL) were applied to develop the calibrations for simultaneous evaluation of five stated parameters of cottonseed oil (CSO) during frying of French frozen potato chips at 170°C. Good regression coefficients (R(2)) were achieved for FFA, PV, IV, CD and CT with value of >0.992 by PLS, SMLR, PCR, and SBL. Root mean square error of prediction (RMSEP) was found to be less than 1.95% for all determinations. Result of the study indicated that SB-ATR FTIR in combination with multivariate chemometrics could be used for accurate and simultaneous determination of different parameters during the frying process without using any toxic organic solvent. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Enhancement of Seebeck coefficient in graphene superlattices by electron filtering technique

NASA Astrophysics Data System (ADS)

Mishra, Shakti Kumar; Kumar, Amar; Kaushik, Chetan Prakash; Dikshit, Biswaranjan

2018-01-01

We show theoretically that the Seebeck coefficient and the thermoelectric figure of merit can be increased by using electron filtering technique in graphene superlattice based thermoelectric devices. The average Seebeck coefficient for graphene-based thermoelectric devices is proportional to the integral of the distribution of Seebeck coefficient versus energy of electrons. The low energy electrons in the distribution curve are found to reduce the average Seebeck coefficient as their contribution is negative. We show that, with electron energy filtering technique using multiple graphene superlattice heterostructures, the low energy electrons can be filtered out and the Seebeck coefficient can be increased. The multiple graphene superlattice heterostructures can be formed by graphene superlattices with different periodic electric potentials applied above the superlattice. The overall electronic band gap of the multiple heterostructures is dependent upon the individual band gap of the graphene superlattices and can be tuned by varying the periodic electric potentials. The overall electronic band gap of the multiple heterostructures has to be properly chosen such that, the low energy electrons which cause negative Seebeck distribution in single graphene superlattice thermoelectric devices fall within the overall band gap formed by the multiple heterostructures. Although the electrical conductance is decreased in this technique reducing the thermoelectric figure of merit, the overall figure of merit is increased due to huge increase in Seebeck coefficient and its square dependency upon the Seebeck coefficient. This is an easy technique to make graphene superlattice based thermoelectric devices more efficient and has the potential to significantly improve the technology of energy harvesting and sensors.

Visualization of hemodynamics and light scattering in exposed brain of rat using multispectral image reconstruction based on Wiener estimation method

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Ishizuka, Tomohiro; Yoshida, Keiichiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2015-07-01

We investigate a method to estimate the spectral images of reduced scattering coefficients and the absorption coefficients of in vivo exposed brain tissues in the range from visible to near-infrared wavelength (500-760 nm) based on diffuse reflectance spectroscopy using a digital RGB camera. In the proposed method, the multi-spectral reflectance images of in vivo exposed brain are reconstructed from the digital red, green, blue images using the Wiener estimation algorithm. The Monte Carlo simulation-based multiple regression analysis for the absorbance spectra is then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin are estimated as the absorption parameters whereas the scattering amplitude a and the scattering power b in the expression of μs'=aλ-b as the scattering parameters, respectively. The spectra of absorption and reduced scattering coefficients are reconstructed from the absorption and scattering parameters, and finally, the spectral images of absorption and reduced scattering coefficients are estimated. We performed simultaneous recordings of spectral diffuse reflectance images and of the electrophysiological signals for in vivo exposed rat brain during the cortical spreading depression evoked by the topical application of KCl. Changes in the total hemoglobin concentration and the tissue oxygen saturation imply the temporary change in cerebral blood flow during CSD. Change in the reduced scattering coefficient was observed before the profound increase in the total hemoglobin concentration, and its occurrence was synchronized with the negative dc shift of the local field potential.

Depression is a predictor for balance in people with multiple sclerosis.

PubMed

Alghwiri, Alia A; Khalil, Hanan; Al-Sharman, Alham; El-Salem, Khalid

2018-05-26

Balance impairments are common and multifactorial among people with multiple sclerosis (MS). Depression is the most common psychological disorder in MS population and is strongly correlated with MS disease. Depression might be one of the factors that contribute to balance deficits in this population. However, the relationship between depression and balance impairments has not been explored in people with MS. To investigate the association between depression and balance impairments in people with MS. Cross sectional design was used in patients with MS. The Activities-specific Balance Confidence scale (ABC) and Berg Balance Scale (BBS) was used to assess balance. Beck Depression Inventory (BDI-II) was used to quantify depression and Kurtizki Expanded Disability Status Scale (EDSS) was utilized for the evaluation of MS disability severity. Pearson correlation coefficient was used to examine the association between depression and balance measurements. Multiple linear stepwise regressions were also conducted to find out if depression is a potential predictor for balance deficits. Seventy-five individuals with MS (Female = 69%) with a mean age (SD) of 38.8 (10) and a mean (SD) EDSS score of 3.0 (1.4) were recruited in this study. Depression was present in 53% of the patients. Depression was significantly correlated with balance measurements and EDSS. However, multiple linear stepwise regressions found that only depression and age significantly predict balance. Depression and balance were found frequent and associated in people with MS. Importantly depression was a significant predictor for balance impairments in individuals with MS. Balance rehabilitation may be hindered by depression. Therefore, depression should be evaluated and treated properly in individuals with MS. Copyright © 2018 Elsevier B.V. All rights reserved.

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

Heavy metal bioaccumulation by Miscanthus sacchariflorus and its potential for removing metals from the Dongting Lake wetlands, China.

PubMed

Yao, Xin; Niu, Yandong; Li, Youzhi; Zou, Dongsheng; Ding, Xiaohui; Bian, Hualin

2018-05-09

Bioaccumulation of five heavy metals (Cd, Cu, Mn, Pb, and Zn) in six plant organs (panicle, leaf, stem, root, rhizome, and bud) of the emergent and perennial plant species, Miscanthus sacchariflorus, were investigated to estimate the plant's potential for accumulating heavy metals in the wetlands of Dongting Lake. We found the highest Cd concentrations in the panicles and leaves; while the highest Cu and Mn were observed in the roots, the highest Pb in the panicles, and the highest Zn in the panicles and buds. In contrast, the lowest Cd concentrations were detected in the stem, roots, and buds; the lowest Cu concentrations in the leaves and stems; the lowest Mn concentrations in the panicles, rhizomes, and buds; the lowest Pb concentrations in the stems; and the lowest Zn concentrations in the leaves, stems, and rhizomes. Mean Cu concentration in the plant showed a positive regression coefficient with plot elevation, soil organic matter content, and soil Cu concentration, whereas it showed a negative regression coefficient with soil moisture and electrolyte leakage. Mean Mn concentration showed positive and negative regression coefficients with soil organic matter and soil moisture, respectively. Mean Pb concentration exhibited positive regression coefficient with plot elevation and soil total P concentration, and Zn concentration showed a positive regression coefficient with soil available P and total P concentrations. However, there was no significant regression coefficient between mean Cd concentration in the plant and the investigated environmental parameters. Stems and roots were the main organs involved in heavy metal accumulation from the environment. The mean quantities of heavy metals accumulated in the plant tissues were 2.2 mg Cd, 86.7 mg Cu, 290.3 mg Mn, 15.9 mg Pb, and 307 mg Zn per square meter. In the Dongting Lake wetlands, 0.7 × 10 3  kg Cd, 22.9 × 10 3  kg Cu, 77.5 × 10 3  kg Mn, 3.1 × 10 3  kg Pb, and 95.9 × 10 3  kg Zn per year were accumulated by aboveground organs and removed from the lake through harvesting for paper manufacture.

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.

Association Between Second Metatarsal Length and Forefoot Loading Under the Second Metatarsophalangeal Joint.

PubMed

Fleischer, Adam E; Hshieh, Shenche; Crews, Ryan T; Waverly, Brett J; Jones, Jacob M; Klein, Erin E; Weil, Lowell; Weil, Lowell Scott

2018-05-01

Metatarsal length is believed to play a role in plantar plate dysfunction, although the mechanism through which progressive injury occurs is still uncertain. We aimed to clarify whether length of the second metatarsal was associated with increased plantar pressure measurements in the forefoot while walking. Weightbearing radiographs and corresponding pedobarographic data from 100 patients in our practice walking without a limp were retrospectively reviewed. Radiographs were assessed for several anatomic relationships, including metatarsal length, by a single rater. Pearson correlation analyses and multiple linear regression models were used to determine whether metatarsal length was associated with forefoot loading parameters. The relative length of the second to first metatarsal was positively associated with the ratio of peak pressure beneath the respective metatarsophalangeal joints ( r = 0.243, P = .015). The relative length of the second to third metatarsal was positively associated with the ratios of peak pressure ( r = 0.292, P = .003), pressure-time integral ( r = 0.249, P = .013), and force-time integral ( r = 0.221, P = .028) beneath the respective metatarsophalangeal joints. Although the variability in loading predicted by the various regression analyses was not large (4%-14%), the relative length of the second metatarsal (to the first and to the third) was maintained in each of the multiple regression models and remained the strongest predictor (highest standardized β-coefficient) in each of the models. Patients with longer second metatarsals exhibited relatively higher loads beneath the second metatarsophalangeal joint during barefoot walking. These findings provide a mechanism through which elongated second metatarsals may contribute to plantar plate injuries. Level III, comparative study.

Sensitivity of ALOS/PALSAR imagery to forest degradation by fire in northern Amazon

NASA Astrophysics Data System (ADS)

Martins, Flora da Silva Ramos Vieira; dos Santos, João Roberto; Galvão, Lênio Soares; Xaud, Haron Abrahim Magalhães

2016-07-01

We evaluated the sensitivity of the full polarimetric Phased Array type L-band Synthetic Aperture Radar (PALSAR), onboard the Advanced Land Observing Satellite (ALOS), to forest degradation caused by fires in northern Amazon, Brazil. We searched for changes in PALSAR signal and tri-dimensional polarimetric responses for different classes of fire disturbance defined by fire frequency and severity. Since the aboveground biomass (AGB) is affected by fire, multiple regression models to estimate AGB were obtained for the whole set of coherent and incoherent attributes (general model) and for each set separately (specific models). The results showed that the polarimetric L-band PALSAR attributes were sensitive to variations in canopy structure and AGB caused by forest fire. However, except for the unburned and thrice burned classes, no single PALSAR attribute was able to discriminate between the intermediate classes of forest degradation by fire. Both the coherent and incoherent polarimetric attributes were important to explain AGB variations in tropical forests affected by fire. The HV backscattering coefficient, anisotropy, double-bounce component, orientation angle, volume index and HH-VV phase difference were PALSAR attributes selected from multiple regression analysis to estimate AGB. The general regression model, combining phase and power radar metrics, presented better results than specific models using coherent or incoherent attributes. The polarimetric responses indicated the dominance of VV-oriented backscattering in primary forest and lightly burned forests. The HH-oriented backscattering predominated in heavily and frequently burned forests. The results suggested a greater contribution of horizontally arranged constituents such as fallen trunks or branches in areas severely affected by fire.

Retinal microvascular network geometry and cognitive abilities in community-dwelling older people: The Lothian Birth Cohort 1936 study

PubMed Central

McGrory, Sarah; Taylor, Adele M; Kirin, Mirna; Corley, Janie; Pattie, Alison; Cox, Simon R; Dhillon, Baljean; Wardlaw, Joanna M; Doubal, Fergus N; Starr, John M; Trucco, Emanuele; MacGillivray, Thomas J; Deary, Ian J

2017-01-01

Aim To examine the relationship between retinal vascular morphology and cognitive abilities in a narrow-age cohort of community-dwelling older people. Methods Digital retinal images taken at age ∼73 years from 683 participants of the Lothian Birth Cohort 1936 (LBC1936) were analysed with Singapore I Vessel Assessment (SIVA) software. Multiple regression models were applied to determine cross-sectional associations between retinal vascular parameters and general cognitive ability (g), memory, processing speed, visuospatial ability, crystallised cognitive ability and change in IQ from childhood to older age. Results After adjustment for cognitive ability at age 11 years and cardiovascular risk factors, venular length-to-diameter ratio was nominally significantly associated with processing speed (β=−0.116, p=0.01) and g (β=−0.079, p=0.04). Arteriolar length-to-diameter ratio was associated with visuospatial ability (β=0.092, p=0.04). Decreased arteriolar junctional exponent deviation and increased arteriolar branching coefficient values were associated with less relative decline in IQ between childhood and older age (arteriolar junctional exponent deviation: β=−0.101, p=0.02; arteriolar branching coefficient: β=0.089, p=0.04). Data are presented as standardised β coefficients (β) reflecting change in cognitive domain score associated with an increase of 1 SD unit in retinal parameter. None of these nominally significant associations remained significant after correction for multiple statistical testing. Conclusions Retinal parameters contributed <1% of the variance in the majority of associations observed. Whereas retinal analysis may have potential for early detection of some types of age-related cognitive decline and dementia, our results present little evidence that retinal vascular features are associated with non-pathological cognitive ageing. PMID:28400371

A Quantitative Structure-Property Relationship (QSPR) Study of Aliphatic Alcohols by the Method of Dividing the Molecular Structure into Substructure

PubMed Central

Liu, Fengping; Cao, Chenzhong; Cheng, Bin

2011-01-01

A quantitative structure–property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol–water partition coefficient (lg POW), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure–property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX1CH) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable. PMID:21731451

The Use of Alternative Regression Methods in Social Sciences and the Comparison of Least Squares and M Estimation Methods in Terms of the Determination of Coefficient

ERIC Educational Resources Information Center

Coskuntuncel, Orkun

2013-01-01

The purpose of this study is two-fold; the first aim being to show the effect of outliers on the widely used least squares regression estimator in social sciences. The second aim is to compare the classical method of least squares with the robust M-estimator using the "determination of coefficient" (R[superscript 2]). For this purpose,…

Molecular electronegativity distance vector model for the prediction of bioconcentration factors in fish.

PubMed

Liu, Shu-Shen; Qin, Li-Tang; Liu, Hai-Ling; Yin, Da-Qiang

2008-02-01

Molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was used to describe the structures of 122 nonionic organic compounds (NOCs) and a quantitative relationship between the MEDV descriptors and the bioconcentration factors (BCF) of NOCs in fish was developed using the variable selection and modeling based on prediction (VSMP). It was found that some main structural factors influencing the BCFs of NOCs are the substructures expressed by four atomic types of nos. 2, 3, 5, and 13, i.e., atom groups -CH(2)- or =CH-, -CH< or =C<, -NH(2), and -Cl or -Br where the former two groups exist in the molecular skeleton of NOC and the latter three groups are related closely to the substituting groups on a benzene ring. The best 5-variable model, with the correlation coefficient (r(2)) of 0.9500 and the leave-one-out cross-validation correlation coefficient (q(2)) of 0.9428, was built by multiple linear regressions, which shows a good estimation ability and stability. A predictive power for the external samples was tested by the model from the training set of 80 NOCs and the predictive correlation coefficient (u(2)) for the 42 external samples in the test set was 0.9028.

Corticospinal signals recorded with MEAs can predict the volitional forearm forces in rats.

PubMed

Guo, Yi; Mesut, Sahin; Foulds, Richard A; Adamovich, Sergei V

2013-01-01

We set out to investigate if volitional components in the descending tracts of the spinal cord white matter can be accessed with multi-electrode array (MEA) recording technique. Rats were trained to press a lever connected to a haptic device with force feedback to receive sugar pellets. A flexible-substrate multi-electrode array was chronically implanted into the dorsal column of the cervical spinal cord. Field potentials and multi-unit activities were recorded from the descending axons of the corticospinal tract while the rat performed a lever pressing task. Forelimb forces, recorded with the sensor attached to the lever, were reconstructed using the hand position data and the neural signals through multiple trials over three weeks. The regression coefficients found from the trial set were cross-validated on the other trials recorded on same day. Approximately 30 trials of at least 2 seconds were required for accurate model estimation. The maximum correlation coefficient between the actual and predicted force was 0.7 in the test set. Positional information and its interaction with neural signals improved the correlation coefficient by 0.1 to 0.15. These results suggest that the volitional information contained in the corticospinal tract can be extracted with multi-channel neural recordings made with parenchymal electrodes.

Multicollinearity and Regression Analysis

NASA Astrophysics Data System (ADS)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

QSAR modeling of flotation collectors using principal components extracted from topological indices.

PubMed

Natarajan, R; Nirdosh, Inderjit; Basak, Subhash C; Mills, Denise R

2002-01-01

Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.

Impact of angiogenesis-related gene expression on the tracer kinetics of 18F-FDG in colorectal tumors.

PubMed

Strauss, Ludwig G; Koczan, Dirk; Klippel, Sven; Pan, Leyun; Cheng, Caixia; Willis, Stefan; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2008-08-01

18F-FDG kinetics are primarily dependent on the expression of genes associated with glucose transporters and hexokinases but may be modulated by other genes. The dependency of 18F-FDG kinetics on angiogenesis-related gene expression was evaluated in this study. Patients with primary colorectal tumors (n = 25) were examined with PET and 18F-FDG within 2 days before surgery. Tissue specimens were obtained from the tumor and the normal colon during surgery, and gene expression was assessed using gene arrays. Overall, 23 angiogenesis-related genes were identified with a tumor-to-normal ratio exceeding 1.50. Analysis revealed a significant correlation between k1 and vascular endothelial growth factor (VEGF-A, r = 0.51) and between fractal dimension and angiopoietin-2 (r = 0.48). k3 was negatively correlated with VEGF-B (r = -0.46), and a positive correlation was noted for angiopoietin-like 4 gene (r = 0.42). A multiple linear regression analysis was used for the PET parameters to predict the gene expression, and a correlation coefficient of r = 0.75 was obtained for VEGF-A and of r = 0.76 for the angiopoietin-2 expression. Thus, on the basis of these multiple correlation coefficients, angiogenesis-related gene expression contributes to about 50% of the variance of the 18F-FDG kinetic data. The global 18F-FDG uptake, as measured by the standardized uptake value and influx, was not significantly correlated with angiogenesis-associated genes. 18F-FDG kinetics are modulated by angiogenesis-related genes. The transport rate for 18F-FDG (k1) is higher in tumors with a higher expression of VEGF-A and angiopoietin-2. The regression functions for the PET parameters provide the possibility to predict the gene expression of VEGF-A and angiopoietin-2.

Analysis of quality of life and influencing factors in 197 Chinese patients with port-wine stains

PubMed Central

Wang, Juan; Zhu, Yu-you; Wang, Zhong-ying; Yao, Xiu-hua; Zhang, Lan-fang; Lv, Hong; Zhang, Si-ping; Hu, Bai

2017-01-01

Abstract Port-wine stains (PWS) are congenital capillary malformations, usually occurring on the face, neck, and other exposed parts of the skin, that have serious psychological and social impact on the patient. Most researchers focus on the treatment of PWS, but the quality of life (QoL) of PWS patients is seldom researched. The objective of this study is to evaluate the QoL of patients with PWS on exposed parts and explore the factors influencing the QoL of PWS patients. The QoL of 197 cases with PWS on exposed parts were prospectively studied using the Dermatology Life Quality Index questionnaire (DLQI), and the factors influencing the patients’ QoL were analyzed by single-factor analysis and multiple-factor logistic regression analysis. The reliability and validity of the QoL of PWS patients were then assessed by DLQI. A total of 197 valid questionnaires were collected. The DLQI scores in PWS cases ranged from 2 to 16, with 2 to 5 in 52.29% (103/197), 6 to 10 in 42.13% (83/197), and 11 to 20 in 5.58% (11/197). The main score elements of the DLQI focused on symptoms and feelings, daily activities, and social entertainment. Single-factor analysis and multiple-factor logistic regression analysis showed that the main influencing factors were female sex, skin hypertrophy, and lesion area >30 cm2. The inter-item correlation averaged 47.46% and the Cronbach α was 0.740, indicating high internal consistency. Correlation of the 6 dimensions of the DLQI questionnaires with the total scores showed that the Spearman correlation coefficient r ranged from 0.550 to 0.782 (P < .001), with symptoms and feelings having a correlation coefficient of 0.782 and a high correlation with total scores. This study shows that PWS has mild to moderate influence on the QoL of most patients, mainly on daily activities, social entertainment, and feelings. PMID:29390578

Antecedents of organizational citizenship behavior among Iranian nurses: a multicenter study.

PubMed

Taghinezhad, Fakhredin; Safavi, Mahboobe; Raiesifar, Afsaneh; Yahyavi, Sayed Hossein

2015-10-08

Organizational citizenship behavior (OCB) improves efficiency and employees' participation and generally provides a good ambiance. This study was conducted to determine the role of job satisfaction (JS), organizational commitment (OC) and procedural justice (PJ) in explaining OCB among nurses working in fifteen educational-treatment centers in Tehran-Iran, to provide guidelines for health care managers' further understanding of how to encourage citizenship behavior among nurses. In this multi-center descriptive-correlational study 373 nurses were evaluated through a Multi-stage cluster sampling method after obtaining approval from the Ethics Committee of Islamic Azad University, Tehran Medical Branch and Tehran University of Medical Sciences Research Deputy. Nurses who signed the informed consent and holding a bachelor or master degree, having a minimum one year of job experience and not having organizational management position during the questionnaire distribution were included in the study. In order to collect data, Demographic questionnaire, Podsakoff et al. (Leadersh Q 1(2):107-142, 1990) OCB questionnaire, OC questionnaire, Aelterman et al. (Educ Stud 33(3):285-297, 2007) JS questionnaire and PJ questionnaire were used. These questionnaires were translated into Persian and content validity was confirmed by an expert group; their reliability was calculated by the internal consistency Cronbach alpha coefficient and it was satisfied. Data were analyzed by descriptive statistics, Comparative mean tests, correlation coefficient and multiple-regression in the SPSS software version 11. The general mean and all five aspects of OCB that ranked higher than 3 were evaluated in a "quite desired" state. The mean for perceived procedural justice, the general mean for JS and the mean of general grade for OC from the nurses' was in "quite desired" state. Finding from multiple regression indicated that OC and PJ exhibit about 19 % of OCB variance totally which is statistically significant (P < 0.01). JS had no significant impact on explaining OCB. OC was the strongest predictor of nurses' OCB followed by perceived procedural justice. So, improving these factors can initiate better citizenship behavior among nurses.

Association of academic performance of premedical students to satisfaction and engagement in a short training program: a cross sectional study presenting gender differences

PubMed Central

2014-01-01

Background It is important that students have a high academic engagement and satisfaction in order to have good academic achievement. No study measures association of these elements in a short training program. This study aimed to measure the correlation between academic achievement, satisfaction and engagement dimensions in a short training program among premedical students. Methods We carried out a cross sectional study, in August 2013, at Cercle d’Etudiants, Ingénieurs, Médecins et Professeurs de Lycée pour le Triomphe de l’Excellence (CEMPLEX) training center, a center which prepares students for the national common entrance examination into medical schools in Cameroon. We included all students attending this training center during last examination period. They were asked to fill out a questionnaire on paper. Academic engagement was measured using three dimensions: vigor, dedication and absorption. Satisfaction to lessons, for each learning subject was collected. Academic achievement was calculated using mean of the score of all learning subjects affected with their coefficient. Pearson coefficient (r) and multiple regression models were used to measure association. A p value < 0.05 was statistically significant. Results In total, 180 students were analyzed. In univariate linear analysis, we found correlation with academic achievement for vigor (r = 0.338, p = 0.006) and dedication (r = 0.287, p = 0.021) only in male students. In multiple regression linear analysis, academic engagement and satisfaction were correlated to academic achievement only in male students (R2 = 0.159, p = 0.035). No correlation was found in female students and in all students. The independent variables (vigor, dedication, absorption and satisfaction) explained 6.8-24.3% of the variance of academic achievement. Conclusion It is only in male students that academic engagement and satisfaction to lessons are correlated to academic achievement in this short training program for premedical students and this correlation is weak. PMID:24564911

Comparison of the color of natural teeth measured by a colorimeter and Shade Vision System.

PubMed

Cho, Byeong-Hoon; Lim, Yong-Kyu; Lee, Yong-Keun

2007-10-01

The objectives were to measure the difference in the color and color parameters of natural teeth measured by a tristimulus colorimeter (CM, used as a reference) and Shade Vision System (SV), and to determine the influence of color parameters on the color difference between the values measured by two instruments. Color of 12 maxillary and mandibular anterior teeth was measured by CM and SV for 47 volunteers (number of teeth=564). Color parameters such as CIE L*, a* and b* values, chroma and hue angle measured by two instruments were compared. Chroma was calculated as C*ab=(a*2 = b*2)1/2, and hue angle was calculated as h degrees =arctan(b*/a*). The influence of color parameters measured by CM on the color difference (DeltaE*(ab)) between the values measured by two instruments was analyzed with multiple regression analysis (alpha=0.01). Mean DeltaE*(ab) value between the values measured by two instruments was 21.7 (+/-3.7), and the mean difference in lightness (CIE L*) and chroma was 16.2 (+/-3.9) and 13.2 (+/-3.0), respectively. Difference in hue angle was high as 132.7 (+/-53.3) degrees . Except for the hue angle, all the color parameters showed significant correlations and the coefficient of determination (r(2)) was in the range of 0.089-0.478. Based on multiple regression analysis, the standardized partial correlation coefficient (beta) of the included predictors for the color difference was -0.710 for CIE L* and -0.300 for C*(ab) (p<0.01). All the color parameters showed significant but weak correlations except for hue angle. When lightness and chroma of teeth were high, color difference between the values measured by two instruments was small. Clinical accuracy of two instruments should be investigated further.

Association of academic performance of premedical students to satisfaction and engagement in a short training program: a cross sectional study presenting gender differences.

PubMed

Bigna, Jean Joel R; Fonkoue, Loic; Tchatcho, Manuela Francette F; Dongmo, Christelle N; Soh, Dorothée M; Um, Joseph Lin Lewis N; Sime, Paule Sandra D; Affana, Landry A; Woum, Albert Ruben N; Noumegni, Steve Raoul N; Tabekou, Alphonce; Wanke, Arlette M; Taffe, Herman Rhais K; Tchoukouan, Miriette Linda N; Anyope, Kevin O; Ella, Stephane Brice E; Mouaha, Berny Vanessa T; Kenne, Edgar Y; Mbessoh, Ulrich Igor K; Tchapmi, Adrienne Y; Tene, Donald F; Voufouo, Steve S; Zogo, Stephanie M; Nouebissi, Linda P; Satcho, Kevine F; Tchoumo, Wati Joel T; Basso, Moise Fabrice; Tcheutchoua, Bertrand Daryl N; Agbor, Ako A

2014-02-24

It is important that students have a high academic engagement and satisfaction in order to have good academic achievement. No study measures association of these elements in a short training program. This study aimed to measure the correlation between academic achievement, satisfaction and engagement dimensions in a short training program among premedical students. We carried out a cross sectional study, in August 2013, at Cercle d'Etudiants, Ingénieurs, Médecins et Professeurs de Lycée pour le Triomphe de l'Excellence (CEMPLEX) training center, a center which prepares students for the national common entrance examination into medical schools in Cameroon. We included all students attending this training center during last examination period. They were asked to fill out a questionnaire on paper. Academic engagement was measured using three dimensions: vigor, dedication and absorption. Satisfaction to lessons, for each learning subject was collected. Academic achievement was calculated using mean of the score of all learning subjects affected with their coefficient. Pearson coefficient (r) and multiple regression models were used to measure association. A p value < 0.05 was statistically significant. In total, 180 students were analyzed. In univariate linear analysis, we found correlation with academic achievement for vigor (r = 0.338, p = 0.006) and dedication (r = 0.287, p = 0.021) only in male students. In multiple regression linear analysis, academic engagement and satisfaction were correlated to academic achievement only in male students (R2 = 0.159, p = 0.035). No correlation was found in female students and in all students. The independent variables (vigor, dedication, absorption and satisfaction) explained 6.8-24.3% of the variance of academic achievement. It is only in male students that academic engagement and satisfaction to lessons are correlated to academic achievement in this short training program for premedical students and this correlation is weak.

Quality of life among people living with hypertension in a rural Vietnam community.

PubMed

Ha, Ninh Thi; Duy, Hoa Thi; Le, Ninh Hoang; Khanal, Vishnu; Moorin, Rachael

2014-08-11

To respond to growing prevalence of hypertension in Vietnam, it is critical to have an in-depth understanding about quality of life (QOL) among people living with hypertension and related factors. This study aimed to measure QOL among hypertensive people in a rural community in Vietnam, and its association with socio-demographic characteristics and factors related to treatment. This study was conducted in a rural community located 60 km from Ho Chi Minh City. Face-to-face interviews were conducted among 275 hypertensive people aged 50 years and above using WHOQOL-BREF questionnaire. Descriptive statistics were used to examine mean scores of quality of life. Cronbach's alpha coefficient and Pearson's correlation coefficient were applied to estimate the internal consistency, and the level of agreement between different domains of WHOQOL-BREF, respectively. Independent T-test and ANOVA test followed by multiple linear regression analyses were used to measure the association between QOL domains and independent variables. Both overall WHOQOL-BREF and each domain had a good internal consistency, ranging from 0.65 to 0.88. The QOL among hypertensive patients was found moderate in all domains, except for psychological domain that was fairly low (mean = 49.4). Backward multiple linear regressions revealed that being men, married, attainment of higher education, having physical activities at moderate level, and adherence to treatment were positively associated with QOL. However, older age and presence of co-morbidity were negatively associated with QOL. WHOQOL-BREF is a reliable instrument to measure QOL among hypertensive patients. The results revealed low QOL in psychological domain and inequality in QOL across socio-demographic characteristics. Given the results, encouraging physical activities and strengthening treatment adherence should be considered to improve QOL of hypertensive people, especially for psychological aspect. Actions to improve QOL among hypertensive patients targeted towards women, lower educated and unmarried patients are needed in the setting.

Influencing factors of alexithymia in Chinese medical students: a cross-sectional study.

PubMed

Zhu, Yaxin; Luo, Ting; Liu, Jie; Qu, Bo

2017-04-04

A much higher prevalence of alexithymia has been reported in medical students compared with the general population, and alexithymia is a risk factor that increases vulnerability to mental disorders. Our aim was to evaluate the level of alexithymia in Chinese medical students and to explore its influencing factors. A cross-sectional study of 1,950 medical students at Shenyang Medical College was conducted in May 2014 to evaluate alexithymia in medical students using the Chinese version of the 20-item Toronto Alexithymia Scale (TAS-20). The reliability of the questionnaire was assessed by Cronbach's α coefficient and mean inter-item correlations. Confirmatory factor analysis (CFA) was used to evaluate construct validity. The relationships between alexithymia and influencing factors were examined using Student's t-test, analysis of variance, and multiple linear regression analysis. Statistical analysis was performed using SPSS 21.0. Of the 1,950 medical students, 1,886 (96.7%) completed questionnaires. Overall, Cronbach's α coefficient of the TAS-20 questionnaire was 0.868. The results of CFA showed that the original three-factor structure produced an acceptable fit to the data. By univariate analysis, gender, grade (academic year of study), smoking behavior, alcohol use, physical activity, history of living with parents during childhood, and childhood trauma were influencing factors of TAS-20 scores (p < 0.05). Multiple linear regression analysis showed that gender, physical activity, grade, living with parents, and childhood trauma also had statistically significant association with total TAS-20 score (p < 0.05). Gender, physical activity, grade, history of living with parents during childhood, and childhood trauma were all factors determining the level of alexithymia. To prevent alexithymia, it will be advisable to promote adequate physical activity and pay greater attention to male medical students and those who are in the final year of training.

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.

Parametric regression model for survival data: Weibull regression model as an example

PubMed Central

2016-01-01

Weibull regression model is one of the most popular forms of parametric regression model that it provides estimate of baseline hazard function, as well as coefficients for covariates. Because of technical difficulties, Weibull regression model is seldom used in medical literature as compared to the semi-parametric proportional hazard model. To make clinical investigators familiar with Weibull regression model, this article introduces some basic knowledge on Weibull regression model and then illustrates how to fit the model with R software. The SurvRegCensCov package is useful in converting estimated coefficients to clinical relevant statistics such as hazard ratio (HR) and event time ratio (ETR). Model adequacy can be assessed by inspecting Kaplan-Meier curves stratified by categorical variable. The eha package provides an alternative method to model Weibull regression model. The check.dist() function helps to assess goodness-of-fit of the model. Variable selection is based on the importance of a covariate, which can be tested using anova() function. Alternatively, backward elimination starting from a full model is an efficient way for model development. Visualization of Weibull regression model after model development is interesting that it provides another way to report your findings. PMID:28149846

A regression-kriging model for estimation of rainfall in the Laohahe basin

NASA Astrophysics Data System (ADS)

Wang, Hong; Ren, Li L.; Liu, Gao H.

2009-10-01

This paper presents a multivariate geostatistical algorithm called regression-kriging (RK) for predicting the spatial distribution of rainfall by incorporating five topographic/geographic factors of latitude, longitude, altitude, slope and aspect. The technique is illustrated using rainfall data collected at 52 rain gauges from the Laohahe basis in northeast China during 1986-2005 . Rainfall data from 44 stations were selected for modeling and the remaining 8 stations were used for model validation. To eliminate multicollinearity, the five explanatory factors were first transformed using factor analysis with three Principal Components (PCs) extracted. The rainfall data were then fitted using step-wise regression and residuals interpolated using SK. The regression coefficients were estimated by generalized least squares (GLS), which takes the spatial heteroskedasticity between rainfall and PCs into account. Finally, the rainfall prediction based on RK was compared with that predicted from ordinary kriging (OK) and ordinary least squares (OLS) multiple regression (MR). For correlated topographic factors are taken into account, RK improves the efficiency of predictions. RK achieved a lower relative root mean square error (RMSE) (44.67%) than MR (49.23%) and OK (73.60%) and a lower bias than MR and OK (23.82 versus 30.89 and 32.15 mm) for annual rainfall. It is much more effective for the wet season than for the dry season. RK is suitable for estimation of rainfall in areas where there are no stations nearby and where topography has a major influence on rainfall.

The solar wind effect on cosmic rays and solar activity

NASA Technical Reports Server (NTRS)

Fujimoto, K.; Kojima, H.; Murakami, K.

1985-01-01

The relation of cosmic ray intensity to solar wind velocity is investigated, using neutron monitor data from Kiel and Deep River. The analysis shows that the regression coefficient of the average intensity for a time interval to the corresponding average velocity is negative and that the absolute effect increases monotonously with the interval of averaging, tau, that is, from -0.5% per 100km/s for tau = 1 day to -1.1% per 100km/s for tau = 27 days. For tau 27 days the coefficient becomes almost constant independently of the value of tau. The analysis also shows that this tau-dependence of the regression coefficiently is varying with the solar activity.

Interpretation of the Coefficients in the Fit y = at + bx + c

ERIC Educational Resources Information Center

Farnsworth, David L.

2006-01-01

The goals of this note are to derive formulas for the coefficients a and b in the least-squares regression plane y = at + bx + c for observations (t[subscript]i,x[subscript]i,y[subscript]i), i = 1, 2, ..., n, and to present meanings for the coefficients a and b. In this note, formulas for the coefficients a and b in the least-squares fit are…

Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

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

Shao Yongni; He Yong; Mao Jingyuan

Visible and near-infrared (Vis/NIR) reflectance spectroscopy has been investigated for its ability to nondestructively detect acidity in bayberry juice. What we believe to be a new, better mathematic model is put forward, which we have named principal component analysis-stepwise regression analysis-backpropagation neural network (PCA-SRA-BPNN), to build a correlation between the spectral reflectivity data and the acidity of bayberry juice. In this model, the optimum network parameters,such as the number of input nodes, hidden nodes, learning rate, and momentum, are chosen by the value of root-mean-square (rms) error. The results show that its prediction statistical parameters are correlation coefficient (r) ofmore » 0.9451 and root-mean-square error of prediction(RMSEP) of 0.1168. Partial least-squares (PLS) regression is also established to compare with this model. Before doing this, the influences of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S. Golay first derivative, and wavelet package transform) are compared. The PLS approach with wavelet package transform preprocessing spectra is found to provide the best results, and its prediction statistical parameters are correlation coefficient (r) of 0.9061 and RMSEP of 0.1564. Hence, these two models are both desirable to analyze the data from Vis/NIR spectroscopy and to solve the problem of the acidity prediction of bayberry juice. This supplies basal research to ultimately realize the online measurements of the juice's internal quality through this Vis/NIR spectroscopy technique.« less

QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase IIα

PubMed Central

Alam, Sarfaraz; Khan, Feroz

2014-01-01

Due to the high mortality rate in India, the identification of novel molecules is important in the development of novel and potent anticancer drugs. Xanthones are natural constituents of plants in the families Bonnetiaceae and Clusiaceae, and comprise oxygenated heterocycles with a variety of biological activities along with an anticancer effect. To explore the anticancer compounds from xanthone derivatives, a quantitative structure activity relationship (QSAR) model was developed by the multiple linear regression method. The structure–activity relationship represented by the QSAR model yielded a high activity–descriptors relationship accuracy (84%) referred by regression coefficient (r2=0.84) and a high activity prediction accuracy (82%). Five molecular descriptors – dielectric energy, group count (hydroxyl), LogP (the logarithm of the partition coefficient between n-octanol and water), shape index basic (order 3), and the solvent-accessible surface area – were significantly correlated with anticancer activity. Using this QSAR model, a set of virtually designed xanthone derivatives was screened out. A molecular docking study was also carried out to predict the molecular interaction between proposed compounds and deoxyribonucleic acid (DNA) topoisomerase IIα. The pharmacokinetics parameters, such as absorption, distribution, metabolism, excretion, and toxicity, were also calculated, and later an appraisal of synthetic accessibility of organic compounds was carried out. The strategy used in this study may provide understanding in designing novel DNA topoisomerase IIα inhibitors, as well as for other cancer targets. PMID:24516330

Development of a model for predicting reaction rate constants of organic chemicals with ozone at different temperatures.

PubMed

Li, Xuehua; Zhao, Wenxing; Li, Jing; Jiang, Jingqiu; Chen, Jianji; Chen, Jingwen

2013-08-01

To assess the persistence and fate of volatile organic compounds in the troposphere, the rate constants for the reaction with ozone (kO3) are needed. As kO3 values are only available for hundreds of compounds, and experimental determination of kO3 is costly and time-consuming, it is of importance to develop predictive models on kO3. In this study, a total of 379 logkO3 values at different temperatures were used to develop and validate a model for the prediction of kO3, based on quantum chemical descriptors, Dragon descriptors and structural fragments. Molecular descriptors were screened by stepwise multiple linear regression, and the model was constructed by partial least-squares regression. The cross validation coefficient QCUM(2) of the model is 0.836, and the external validation coefficient Qext(2) is 0.811, indicating that the model has high robustness and good predictive performance. The most significant descriptor explaining logkO3 is the BELm2 descriptor with connectivity information weighted atomic masses. kO3 increases with increasing BELm2, and decreases with increasing ionization potential. The applicability domain of the proposed model was visualized by the Williams plot. The developed model can be used to predict kO3 at different temperatures for a wide range of organic chemicals, including alkenes, cycloalkenes, haloalkenes, alkynes, oxygen-containing compounds, nitrogen-containing compounds (except primary amines) and aromatic compounds. Copyright © 2013 Elsevier Ltd. All rights reserved.

Low-level lead exposure and the IQ of children. A meta-analysis of modern studies

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

Needleman, H.L.; Gatsonis, C.A.

1990-02-02

We identified 24 modern studies of childhood exposures to lead in relation to IQ. From this population, 12 that employed multiple regression analysis with IQ as the dependent variable and lead as the main effect and that controlled for nonlead covariates were selected for a quantitative, integrated review or meta-analysis. The studies were grouped according to type of tissue analyzed for lead. There were 7 blood and 5 tooth lead studies. Within each group, we obtained joint P values by two different methods and average effect sizes as measured by the partial correlation coefficients. We also investigated the sensitivity ofmore » the results to any single study. The sample sizes ranged from 75 to 724. The sign of the regression coefficient for lead was negative in 11 of 12 studies. The negative partial r's for lead ranged from -.27 to -.003. The power to find an effect was limited, below 0.6 in 7 of 12 studies. The joint P values for the blood lead studies were less than .0001 for both methods of analysis (95% confidence interval for group partial r, -.15 {plus minus} .05), while for the tooth lead studies they were .0005 and .004, respectively (95% confidence interval for group partial r, -.08 {plus minus} .05). The hypothesis that lead impairs children's IQ at low dose is strongly supported by this quantitative review. The effect is robust to the impact of any single study.« less

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. Aim The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Methods Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Results Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. Conclusion To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research. PMID:26080057

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.

Science Teaching Efficacy of Preservice Elementary Teachers: Examination of the Multiple Factors Reported as Influential

NASA Astrophysics Data System (ADS)

Taştan Kırık, Özgecan

2013-12-01

This study explores the science teaching efficacy beliefs of pr-service elementary teachers and the relationship between efficacy beliefs and multiple factors such as antecedent factors (participation in extracurricular activities and number of science and science teaching methods courses taken), conceptual understanding, classroom management beliefs and science teaching attitudes. Science education majors ( n = 71) and elementary education majors ( n = 262) were compared with respect to these variables. Finally, the predictors of two constructs of science teaching efficacy beliefs, personal science teaching efficacy (PSTE) and science teaching outcome expectancy (STOE), were examined by multiple linear regression analysis. According to the results, participation in extracurricular activities has a significant but low correlation with science concept knowledge, science teaching attitudes, PSTE and STOE. In addition, there is a small but significant correlation between science concept knowledge and outcome expectancy, which leads the idea that preservice elementary teachers' conceptual understanding in science contributes to their science teaching self-efficacy. This study reveals a moderate correlation between science teaching attitudes and STOE and a high correlation between science teaching attitudes and PSTE. Additionally, although the correlation coefficient is low, the number of methodology courses was found to be one of the correlates of science teaching attitudes. Furthermore, students of both majors generally had positive self-efficacy beliefs on both the STOE and PSTE. Specifically, science education majors had higher science teaching self-efficacy than elementary education majors. Regression results showed that science teaching attitude is the major factor in predicting both PSTE and STOE for both groups.

Universal Algorithms for Plant Phenotyping: Are we there yet?

NASA Astrophysics Data System (ADS)

Kakani, V. G.; Kambham, R. R.; Zhao, D.; Foster, A. J.; Gowda, P. H.

2017-12-01

Hyperspectral remote sensing offers ability to capture spectral signatures of plant morpho-physio-biochemical traits at multiple scales (leaf to canopy to aerial). Experimental results on plant phenotype from pot, growth chamber and field studies at multiple location were used in this study. Pigment, leaf/plant water status, plant nutrient status, plant height, leaf area, fresh and dry weights of biomass and its components are correlated with hyperspectral reflectance signatures. Leaf reflectance was collected with spectroradiometer having a light source. Canopy hyperspectral reflectance was collected from 1.5 m above the canopy using a spectroradiometer, while multispectral images were acquired from aerial platforms ( 400m). Several statistical methods including simple ratios, principal component analysis, and partial least squares regression were used to identify hyperspectral reflectance bands that were tightly associated with plant phenotypic traits. Leaf level spectra best described the morpho-physio-biochemical traits (R2 = 0.6-0.9), while canopy reflectance best described plant height (R2 = 0.65), leaf area index (R2 = 0.67-0.74) and biomass (R2 = 0.69-0.78), while aerial spectra improved canopy level regression coefficients for plant height (R2 = 0.93) and leaf area index (R2 = 0.89). The comparison of multi-level spectra and resolution, clearly showed the advantage of hyperspectral reflectance data over the multispectral reflectance data, particularly for understanding the basis for spectral reflectance differences among species and traits. In conclusion, high resolution (1-2 cm) spectral imagery can help to bridge the gap across multiple levels of phenotype measurement.

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

Neither fixed nor random: weighted least squares meta-regression.

PubMed

Stanley, T D; Doucouliagos, Hristos

2017-03-01

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Interpreting Bivariate Regression Coefficients: Going beyond the Average

ERIC Educational Resources Information Center

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

An integrated, ethically driven environmental model of clinical decision making in emergency settings.

PubMed

Wolf, Lisa

2013-02-01

To explore the relationship between multiple variables within a model of critical thinking and moral reasoning. A quantitative descriptive correlational design using a purposive sample of 200 emergency nurses. Measured variables were accuracy in clinical decision-making, moral reasoning, perceived care environment, and demographics. Analysis was by bivariate correlation using Pearson's product-moment correlation coefficients, chi square and multiple linear regression analysis. The elements as identified in the integrated ethically-driven environmental model of clinical decision-making (IEDEM-CD) corrected depict moral reasoning and environment of care as factors significantly affecting accuracy in decision-making. The integrated, ethically driven environmental model of clinical decision making is a framework useful for predicting clinical decision making accuracy for emergency nurses in practice, with further implications in education, research and policy. A diagnostic and therapeutic framework for identifying and remediating individual and environmental challenges to accurate clinical decision making. © 2012, The Author. International Journal of Nursing Knowledge © 2012, NANDA International.

Classification of arrhythmia using hybrid networks.

PubMed

Haseena, Hassan H; Joseph, Paul K; Mathew, Abraham T

2011-12-01

Reliable detection of arrhythmias based on digital processing of Electrocardiogram (ECG) signals is vital in providing suitable and timely treatment to a cardiac patient. Due to corruption of ECG signals with multiple frequency noise and presence of multiple arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a challenging task. This paper focuses a Fuzzy C- Mean (FCM) clustered Probabilistic Neural Network (PNN) and Multi Layered Feed Forward Network (MLFFN) for the discrimination of eight types of ECG beats. Parameters such as fourth order Auto Regressive (AR) coefficients along with Spectral Entropy (SE) are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis of Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) arrhythmia database shows that FCM clustered PNNs is superior in cardiac arrhythmia classification than FCM clustered MLFFN with an overall accuracy of 99.05%, 97.14%, respectively.

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

NASA Technical Reports Server (NTRS)

Perrott, M. H.; Cohen, R. J.

1996-01-01

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Comprehensive model for predicting perceptual image quality of smart mobile devices.

PubMed

Gong, Rui; Xu, Haisong; Luo, M R; Li, Haifeng

2015-01-01

An image quality model for smart mobile devices was proposed based on visual assessments of several image quality attributes. A series of psychophysical experiments were carried out on two kinds of smart mobile devices, i.e., smart phones and tablet computers, in which naturalness, colorfulness, brightness, contrast, sharpness, clearness, and overall image quality were visually evaluated under three lighting environments via categorical judgment method for various application types of test images. On the basis of Pearson correlation coefficients and factor analysis, the overall image quality could first be predicted by its two constituent attributes with multiple linear regression functions for different types of images, respectively, and then the mathematical expressions were built to link the constituent image quality attributes with the physical parameters of smart mobile devices and image appearance factors. The procedure and algorithms were applicable to various smart mobile devices, different lighting conditions, and multiple types of images, and performance was verified by the visual data.

Liquid detection with InGaAsP semiconductor lasers having multiple short external cavities.

PubMed

Zhu, X; Cassidy, D T

1996-08-20

A liquid detection system consisting of a diode laser with multiple short external cavities (MSXC's) is reported. The MSXC diode laser operates single mode on one of 18 distinct modes that span a range of 72 nm. We selected the modes by setting the length of one of the external cavities using a piezoelectric positioner. One can measure the transmission through cells by modulating the injection current at audio frequencies and using phase-sensitive detection to reject the ambient light and reduce 1/f noise. A method to determine regions of single-mode operation by the rms of the output of the laser is described. The transmission data were processed by multivariate calibration techniques, i.e., partial least squares and principal component regression. Water concentration in acetone was used to demonstrate the performance of the system. A correlation coefficient of R(2) = 0.997 and 0.29% root-mean-square error of prediction are found for water concentration over the range of 2-19%.

Factors of the Development of Water Erosion in the Zone of Recreation Activity in the Ol'khon Region

NASA Astrophysics Data System (ADS)

Znamenskaya, T. I.; Vanteeva, J. V.; Solodyankina, S. V.

2018-02-01

Specific features of water erosion of thin soils under conditions of nonpercolative water regime and intense recreational loads were studied in the Ol'khon region (Irkutsk oblast). An experiment on the transfer of terrigenous particles under the impact of rainfall simulation was performed. A thorough description of landscape characteristics affecting water erosion development was made. As a result, a multiple regression equation linking the transported matter with the slope steepness, projective cover of vegetation, the degree of vegetation degradation, and the fine sand content in the upper soil horizon was developed; the multiple correlation coefficient R reached 0.86. On this basis, the map of water erosion assessment for the study area was compiled with the use of landscape and topographic maps. The maximum intensity of water erosion is typical of the anthropogenically transformed landscapes on steep slopes with the low vegetative cover on the mountainous noncalcareous steppe soils and on thin loamy sandy surface-gravelly chestnut-like soils.

Relationship among several measurements of slipperiness obtained in a laboratory environment.

PubMed

Chang, Wen-Ruey; Chang, Chien-Chi

2018-04-01

Multiple sensing mechanisms could be used in forming responses to avoid slips, but previous studies, correlating only two parameters, revealed a limited picture of this complex system. In this study, the participants walked as fast as possible without a slip under 15 conditions of different degrees of slipperiness. The relationships among various response parameters, including perceived slipperiness rating, utilized coefficient of friction (UCOF), slipmeter measurement and kinematic parameters, were evaluated. The results showed that the UCOF, perceived rating and heel angle had higher adjusted R 2 values as dependent variables in the multiple linear regressions with the remaining variables in the final pool as independent variables. Although each variable in the final data pool could reflect some measurement of slipperiness, these three variables are more inclusive than others in representing the other variables and were bigger predictors of other variables, so they could be better candidates for measurements of slipperiness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sociodemographic patterns of household water-use costs in Puerto Rico.

PubMed

Yu, Xue; Ghasemizadeh, Reza; Padilla, Ingrid; Meeker, John D; Cordero, Jose F; Alshawabkeh, Akram

2015-08-15

Variability of household water-use costs across different sociodemographic groups in Puerto Rico is evaluated using census microdata from the Integrated Public Use Microdata Series (IPUMS). Multivariate analyses such as multiple linear regression (MLR) and factor analysis (FA) are used to classify, extract and interpret the household water-use costs. The FA results suggest two principal varifactors in explaining the variability of household water-use costs (64% in 2000 and 50% in 2010), which are grouped into a soft coefficient (social, economic and demographic characteristics of household residents, i.e., age, size, income, education) and a hard coefficient (dwelling conditions, i.e., number of rooms, units in the building, building age). The demographic profile of a high water-use household in Puerto Rico tends to be that of renters, people who live in larger or older buildings, people living in metro areas, or those with higher education level and higher income. The findings and discussions from this study will help decision makers to plan holistic and integrated water management to achieve water sustainability. Copyright © 2015 Elsevier B.V. All rights reserved.

Sociodemographic patterns of household water-use costs in Puerto Rico

PubMed Central

Yu, Xue; Ghasemizadeh, Reza; Padilla, Ingrid; Meeker, John D.; Cordero, Jose F.; Alshawabkeh, Akram

2015-01-01

Variability of household water-use costs across different sociodemographic groups in Puerto Rico is evaluated using census microdata from the Integrated Public Use Microdata Series (IPUMS). Multivariate analyses such as Multiple Linear Regression (MLR) and Factor Analysis (FA) are used to classify, extract and interpret the household water-use costs. The FA results suggest two principal varifactors in explaining the variability of household water-use costs (64% in 2000 and 50% in 2010), which are grouped into a soft coefficient (social, economic and demographic characteristics of household residents, i.e. age, size, income, education) and a hard coefficient (dwelling conditions, i.e. number of rooms, units in the building, building age). The demographic profile of a high water-use household in Puerto Rico tends to be that of renters, people who live in larger or older buildings, people living in metro areas, or those with higher education level and higher income. The findings and discussions from this study will help decision makers to plan holistic and integrated water management to achieve water sustainability. PMID:25897735

Energy performance and savings potentials with skylights

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

Arasteh, D.; Johnson, R.; Selkowitz, S.

1984-12-01

This study systematically explores the energy effects of skylight systems in a prototypical office building module and examines the savings from daylighting. For specific climates, roof/skylight characteristics are identified that minimize total energy or peak electrical demand. Simplified techniques for energy performance calculation are also presented based on a multiple regression analysis of our data base so that one may easily evaluate daylighting's effects on total and component energy loads and electrical peaks. This provides additional insights into the influence of skylight parameters on energy consumption and electrical peaks. We use the DOE-2.1B energy analysis program with newly incorporated daylightingmore » algorithms to determine hourly, monthly, and annual impacts of daylighting strategies on electrical lighting consumption, cooling, heating, fan power, peak electrical demands, and total energy use. A data base of more than 2000 parametric simulations for 14 US climates has been generated. Parameters varied include skylight-to-roof ratio, shading coefficient, visible transmittance, skylight well light loss, electric lighting power density, roof heat transfer coefficient, and electric lighting control type. 14 references, 13 figures, 4 tables.« less

Comparison of Spectral Characteristic between LAPAN-A3 and Sentinel-2A

NASA Astrophysics Data System (ADS)

Zylshal, Z.; Sari, N. M.; Nugroho, J. T.; Kushardono, D.

2017-12-01

Indonesian National Institute of Aeronautics and Space (LAPAN) started building its experimental microsatellite back in 2007 and finally able to launch its first microsatellite dubbed as LAPAN-A1/LAPAN-Tubsat. With the launch of LAPAN-A3/LAPAN-IPB, Indonesian experimental satellite programme hit its third generation. LAPAN-A3 is carrying multiple payloads including multispectral push-broom imager, digital matrix camera, as well as video camera. This paper aims to highlight the spectral differences between LAPAN-A3 and the well-established Sentinel-2A multispectral to investigate the potential of using LAPAN-A3 data to complement the other well-established medium resolution satellite data. Comparisons between corresponding bands and band transformations were performed over a dataset. Three areas of interest were chosen as the test sites. Linear regression and Pearson correlation coefficient were then calculated between the corresponding bands. The preliminary results showed a moderate correlation between the two sensors with Pearson correlation coefficient ranging from 0.39 to 0.65. Some issues were found regarding the radiometric quality over the whole scene of LAPAN-A3.

Estimation of old field ecosystem biomass using low altitude imagery

NASA Technical Reports Server (NTRS)

Nor, S. M.; Safir, G.; Burton, T. M.; Hook, J. E.; Schultink, G.

1977-01-01

Color-infrared photography was used to evaluate the biomass of experimental plots in an old-field ecosystem that was treated with different levels of waste water from a sewage treatment facility. Cibachrome prints at a scale of approximately 1:1,600 produced from 35 mm color infrared slides were used to analyze density patterns using prepared tonal density scales and multicell grids registered to ground panels shown on the photograph. Correlation analyses between tonal density and vegetation biomass obtained from ground samples and harvests were carried out. Correlations between mean tonal density and harvest biomass data gave consistently high coefficients ranging from 0.530 to 0.896 at the 0.001 significance level. Corresponding multiple regression analysis resulted in higher correlation coefficients. The results of this study indicate that aerial infrared photography can be used to estimate standing crop biomass on waste water irrigated old field ecosystems. Combined with minimal ground truth data, this technique could enable managers of wastewater irrigation projects to precisely time harvest of such systems for maximal removal of nutrients in harvested biomass.

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls.

PubMed

Yuan, Jintao; Yu, Shuling; Zhang, Ting; Yuan, Xuejie; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-06-01

Octanol/water (K(OW)) and octanol/air (K(OA)) partition coefficients are two important physicochemical properties of organic substances. In current practice, K(OW) and K(OA) values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure-property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of K(OW) and K(OA). In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure-activity relationship, were used to predict generator-column-derived log K(OW) and log K(OA) values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the K(OW) and K(OA) of PCBs. Copyright © 2016 Elsevier Inc. All rights reserved.

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...

Estimating EQ-5D values from the Oswestry Disability Index and numeric rating scales for back and leg pain.

PubMed

Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D

2014-04-15

Cross-sectional cohort. The purpose of this study is to determine whether the EuroQOL-5D (EQ-5D) can be derived from commonly available low back disease-specific health-related quality of life measures. The Oswestry Disability Index (ODI) and numeric rating scales (0-10) for back pain (BP) and leg pain (LP) are widely used disease-specific measures in patients with lumbar degenerative disorders. Increasingly, the EQ-5D is being used as a measure of utility due to ease of administration and scoring. The EQ-5D, ODI, BP, and LP were prospectively collected in 14,544 patients seen in clinic for lumbar degenerative disorders. Pearson correlation coefficients for paired observations from multiple time points between ODI, BP, LP, and EQ-5D were determined. Regression modeling was done to compute the EQ-5D score from the ODI, BP, and LP. The mean age was 53.3 ± 16.4 years and 41% were male. Correlations between the EQ-5D and the ODI, BP, and LP were statistically significant (P < 0.0001) with correlation coefficients of -0.77, -0.50, and -0.57, respectively. The regression equation: [0.97711 + (-0.00687 × ODI) + (-0.01488 × LP) + (-0.01008 × BP)] to predict EQ-5D, had an R2 of 0.61 and a root mean square error of 0.149. The model using ODI alone had an R2 of 0.57 and a root mean square error of 0.156. The model using the individual ODI items had an R2 of 0.64 and a root mean square error of 0.143. The correlation coefficient between the observed and estimated EQ-5D score was 0.78. There was no statistically significant difference between the actual EQ-5D (0.553 ± 0.238) and the estimated EQ-5D score (0.553 ± 0.186) using the ODI, BP, and LP regression model. However, rounding off the coefficients to less than 5 decimal places produced less accurate results. Unlike previous studies showing a robust relationship between low back-specific measures and the Short Form-6D, a similar relationship was not seen between the ODI, BP, LP, and the EQ-5D. Thus, the EQ-5D cannot be accurately estimated from the ODI, BP, and LP. 2.

QSAR study of curcumine derivatives as HIV-1 integrase inhibitors.

PubMed

Gupta, Pawan; Sharma, Anju; Garg, Prabha; Roy, Nilanjan

2013-03-01

A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r(2)) 0.891 and cross validated r(2) (r(2) cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity were important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r(2) pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules.

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.

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

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.

Correlation and prediction of dynamic human isolated joint strength from lean body mass

NASA Technical Reports Server (NTRS)

Pandya, Abhilash K.; Hasson, Scott M.; Aldridge, Ann M.; Maida, James C.; Woolford, Barbara J.

1992-01-01

A relationship between a person's lean body mass and the amount of maximum torque that can be produced with each isolated joint of the upper extremity was investigated. The maximum dynamic isolated joint torque (upper extremity) on 14 subjects was collected using a dynamometer multi-joint testing unit. These data were reduced to a table of coefficients of second degree polynomials, computed using a least squares regression method. All the coefficients were then organized into look-up tables, a compact and convenient storage/retrieval mechanism for the data set. Data from each joint, direction and velocity, were normalized with respect to that joint's average and merged into files (one for each curve for a particular joint). Regression was performed on each one of these files to derive a table of normalized population curve coefficients for each joint axis, direction, and velocity. In addition, a regression table which included all upper extremity joints was built which related average torque to lean body mass for an individual. These two tables are the basis of the regression model which allows the prediction of dynamic isolated joint torques from an individual's lean body mass.

Do the methods used to analyse missing data really matter? An examination of data from an observational study of Intermediate Care patients.

PubMed

Kaambwa, Billingsley; Bryan, Stirling; Billingham, Lucinda

2012-06-27

Missing data is a common statistical problem in healthcare datasets from populations of older people. Some argue that arbitrarily assuming the mechanism responsible for the missingness and therefore the method for dealing with this missingness is not the best option-but is this always true? This paper explores what happens when extra information that suggests that a particular mechanism is responsible for missing data is disregarded and methods for dealing with the missing data are chosen arbitrarily. Regression models based on 2,533 intermediate care (IC) patients from the largest evaluation of IC done and published in the UK to date were used to explain variation in costs, EQ-5D and Barthel index. Three methods for dealing with missingness were utilised, each assuming a different mechanism as being responsible for the missing data: complete case analysis (assuming missing completely at random-MCAR), multiple imputation (assuming missing at random-MAR) and Heckman selection model (assuming missing not at random-MNAR). Differences in results were gauged by examining the signs of coefficients as well as the sizes of both coefficients and associated standard errors. Extra information strongly suggested that missing cost data were MCAR. The results show that MCAR and MAR-based methods yielded similar results with sizes of most coefficients and standard errors differing by less than 3.4% while those based on MNAR-methods were statistically different (up to 730% bigger). Significant variables in all regression models also had the same direction of influence on costs. All three mechanisms of missingness were shown to be potential causes of the missing EQ-5D and Barthel data. The method chosen to deal with missing data did not seem to have any significant effect on the results for these data as they led to broadly similar conclusions with sizes of coefficients and standard errors differing by less than 54% and 322%, respectively. Arbitrary selection of methods to deal with missing data should be avoided. Using extra information gathered during the data collection exercise about the cause of missingness to guide this selection would be more appropriate.

Cerebrospinal fluid norepinephrine and cognition in subjects across the adult age span

PubMed Central

Wang, Lucy Y.; Murphy, Richard R.; Hanscom, Brett; Li, Ge; Millard, Steven P.; Petrie, Eric C.; Galasko, Douglas R.; Sikkema, Carl; Raskind, Murray A.; Wilkinson, Charles W.; Peskind, Elaine R.

2013-01-01

Adequate central nervous system noradrenergic activity enhances cognition, but excessive noradrenergic activity may have adverse effects on cognition. Previous studies have also demonstrated that noradrenergic activity is higher in older than younger adults. We aimed to determine relationships between cerebrospinal fluid (CSF) norepinephrine (NE) concentration and cognitive performance by using data from a CSF bank that includes samples from 258 cognitively normal participants aged 21–100 years. After adjusting for age, gender, education, and ethnicity, higher CSF NE levels (units of 100 pg/mL) are associated with poorer performance on tests of attention, processing speed, and executive function (Trail Making A: regression coefficient 1.5, standard error [SE] 0.77, p = 0.046; Trail Making B: regression coefficient 5.0, SE 2.2, p = 0.024; Stroop Word-Color Interference task: regression coefficient 6.1, SE 2.0, p = 0.003). Findings are consistent with the earlier literature relating excess noradrenergic activity with cognitive impairment. PMID:23639207

Cerebrospinal fluid norepinephrine and cognition in subjects across the adult age span.

PubMed

Wang, Lucy Y; Murphy, Richard R; Hanscom, Brett; Li, Ge; Millard, Steven P; Petrie, Eric C; Galasko, Douglas R; Sikkema, Carl; Raskind, Murray A; Wilkinson, Charles W; Peskind, Elaine R

2013-10-01

Adequate central nervous system noradrenergic activity enhances cognition, but excessive noradrenergic activity may have adverse effects on cognition. Previous studies have also demonstrated that noradrenergic activity is higher in older than younger adults. We aimed to determine relationships between cerebrospinal fluid (CSF) norepinephrine (NE) concentration and cognitive performance by using data from a CSF bank that includes samples from 258 cognitively normal participants aged 21-100 years. After adjusting for age, gender, education, and ethnicity, higher CSF NE levels (units of 100 pg/mL) are associated with poorer performance on tests of attention, processing speed, and executive function (Trail Making A: regression coefficient 1.5, standard error [SE] 0.77, p = 0.046; Trail Making B: regression coefficient 5.0, SE 2.2, p = 0.024; Stroop Word-Color Interference task: regression coefficient 6.1, SE 2.0, p = 0.003). Findings are consistent with the earlier literature relating excess noradrenergic activity with cognitive impairment. Published by Elsevier Inc.

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Air Pollutants, Climate, and the Prevalence of Pediatric Asthma in Urban Areas of China

PubMed Central

Zhang, Juanjuan; Yan, Li; Fu, Wenlong; Yi, Jing; Chen, Yuzhi; Liu, Chuanhe; Xu, Dongqun; Wang, Qiang

2016-01-01

Background. Prevalence of childhood asthma varies significantly among regions, while its reasons are not clear yet with only a few studies reporting relevant causes for this variation. Objective. To investigate the potential role of city-average levels of air pollutants and climatic factors in order to distinguish differences in asthma prevalence in China and explain their reasons. Methods. Data pertaining to 10,777 asthmatic patients were obtained from the third nationwide survey of childhood asthma in China's urban areas. Annual mean concentrations of air pollutants and other climatic factors were obtained for the same period from several government departments. Data analysis was implemented with descriptive statistics, Pearson correlation coefficient, and multiple regression analysis. Results. Pearson correlation analysis showed that the situation of childhood asthma was strongly linked with SO2, relative humidity, and hours of sunshine (p < 0.05). Multiple regression analysis indicated that, among the predictor variables in the final step, SO2 was found to be the most powerful predictor variable amongst all (β = −19.572, p < 0.05). Furthermore, results had shown that hours of sunshine (β = −0.014, p < 0.05) was a significant component summary predictor variable. Conclusion. The findings of this study do not suggest that air pollutants or climate, at least in terms of children, plays a major role in explaining regional differences in asthma prevalence in China. PMID:27556031

Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple-trait random regression models.

PubMed

Kheirabadi, Khabat; Rashidi, Amir; Alijani, Sadegh; Imumorin, Ikhide

2014-11-01

We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer-Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple-trait random regression models (MT-RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test-day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and -2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data. © 2014 Japanese Society of Animal Science.

Biomechanical, anthropometric, and psychological determinants of barbell back squat strength.

PubMed

Vigotsky, Andrew D; Bryanton, Megan A; Nuckols, Greg; Beardsley, Chris; Contreras, Bret; Evans, Jessica; Schoenfeld, Brad J

2018-02-27

Previous investigations of strength have only focused on biomechanical or psychological determinants, while ignoring the potential interplay and relative contributions of these variables. The purpose of this study was to investigate the relative contributions of biomechanical, anthropometric, and psychological variables to the prediction of maximum parallel barbell back squat strength. Twenty-one college-aged participants (male = 14; female = 7; age = 23 ± 3 years) reported to the laboratory for two visits. The first visit consisted of anthropometric, psychometric, and parallel barbell back squat one-repetition maximum (1RM) testing. On the second visit, participants performed isometric dynamometry testing for the knee, hip, and spinal extensors in a sticking point position-specific manner. Multiple linear regression and correlations were used to investigate the combined and individual relationships between biomechanical, anthropometric, and psychological variables and squat 1RM. Multiple regression revealed only one statistically predictive determinant: fat free mass normalized to height (standardized estimate ± SE = 0.6 ± 0.3; t(16) = 2.28; p = 0.037). Correlation coefficients for individual variables and squat 1RM ranged from r = -0.79-0.83, with biomechanical, anthropometric, experiential, and sex predictors showing the strongest relationships, and psychological variables displaying the weakest relationships. These data suggest that back squat strength in a heterogeneous population is multifactorial and more related to physical rather than psychological variables.

Role of Alexithymia, Anxiety, and Depression in Predicting Self-Efficacy in Academic Students

PubMed Central

2017-01-01

Objective. Little research is available on the predictive factors of self-efficacy in college students. The aim of the present study is to examine the role of alexithymia, anxiety, and depression in predicting self-efficacy in academic students. Design. In a cross-sectional study, a total of 133 students at Babol University of Medical Sciences (Medicine, Dentistry, and Paramedicine) participated in the study between 2014 and 2015. All participants completed the Toronto Alexithymia Scale (TAS-20), College Academic Self-Efficacy Scale (CASES), and 14 items on anxiety and depression derived from the 28 items of the General Health Questionnaire (28-GHQ). Results. Pearson correlation coefficients revealed negative significant relationships between alexithymia and the three subscales with student self-efficacy. There was no significant correlation between anxiety/depression symptoms and student self-efficacy. A backward multiple regression analysis revealed that alexithymia was a negative significant predictor of self-efficacy in academic students (B = −0.512, P < 0.001). The prevalence of alexithymia was 21.8% in students. Multiple backward logistic analysis regression revealed that number of passed semesters, gender, mother's education, father's education, and doctoral level did not accurately predict alexithymia in college students. Conclusion. As alexithymia is prevalent in college students and affects self-efficacy and academic functioning, we suggest it should be routinely evaluated by mental physicians at universities. PMID:28154839

Noninvasive Assessment of Advanced Fibrosis Based on Hepatic Volume in Patients with Nonalcoholic Fatty Liver Disease.

PubMed

Hayashi, Tatsuya; Saitoh, Satoshi; Fukuzawa, Kei; Tsuji, Yoshinori; Takahashi, Junji; Kawamura, Yusuke; Akuta, Norio; Kobayashi, Masahiro; Ikeda, Kenji; Fujii, Takeshi; Miyati, Tosiaki; Kumada, Hiromitsu

2017-09-15

Noninvasive liver fibrosis evaluation was performed in patients with nonalcoholic fatty liver disease (NAFLD). We used a quantitative method based on the hepatic volume acquired from gadoxetate disodium-enhanced (Gd-EOB-DTPA-enhanced) magnetic resonance imaging (MRI) for diagnosing advanced fibrosis in patients with NAFLD. A total of 130 patients who were diagnosed with NAFLD and underwent Gd-EOB-DTPA-enhanced MRI were retrospectively included. Histological data were available for 118 patients. Hepatic volumetric parameters, including the left hepatic lobe to right hepatic lobe volume ratio (L/R ratio), were measured. The usefulness of the L/R ratio for diagnosing fibrosis ≥F3-4 and F4 was assessed using the area under the receiver operating characteristic (AUROC) curve. Multiple regression analysis was performed to identify variables (age, body mass index, serum fibrosis markers, and histological features) that were associated with the L/R ratio. The L/R ratio demonstrated good performance in differentiating advanced fibrosis (AUROC, 0.80; 95% confidence interval, 0.72 to 0.88) from cirrhosis (AUROC, 0.87; 95% confidence interval, 0.75 to 0.99). Multiple regression analysis showed that only fibrosis was significantly associated with the L/R ratio (coefficient, 0.121; p<0.0001). The L/R ratio, which is not influenced by pathological parameters other than fibrosis, is useful for diagnosing cirrhosis in patients with NAFLD.

The role of family expressed emotion and perceived social support in predicting addiction relapse.

PubMed

Atadokht, Akbar; Hajloo, Nader; Karimi, Masoud; Narimani, Mohammad

2015-03-01

Emotional conditions governing the family and patients' perceived social support play important roles in the treatment or relapse process of the chronic disease. The current study aimed to investigate the role of family expressed emotion and perceived social support in prediction of addiction relapse. The descriptive-correlation method was used in the current study. The study population consisted of the individuals referred to the addiction treatment centers in Ardabil from October 2013 to January 2014. The subjects (n = 80) were randomly selected using cluster sampling method. To collect data, expressed emotion test by Cole and Kazaryan, and Multidimensional Scale of Perceived Social Support (MSPSS) were used, and the obtained data was analyzed using the Pearson's correlation coefficient and multiple regression analyses. Results showed a positive relationship between family expressed emotions and the frequency of relapse (r = 0.26, P = 0.011) and a significant negative relationship between perceived social support and the frequency of relapse (r = -0.34, P = 0.001). Multiple regression analysis also showed that perceived social support from family and the family expressed emotions significantly explained 12% of the total variance of relapse frequency. These results have implications for addicted people, their families and professionals working in addiction centers to use the emotional potential of families especially their expressed emotions and the perceived social support of addicts to increase the success rate of addiction treatment.

Role of Alexithymia, Anxiety, and Depression in Predicting Self-Efficacy in Academic Students.

PubMed

Faramarzi, Mahbobeh; Khafri, Soraya

2017-01-01

Objective . Little research is available on the predictive factors of self-efficacy in college students. The aim of the present study is to examine the role of alexithymia, anxiety, and depression in predicting self-efficacy in academic students. Design . In a cross-sectional study, a total of 133 students at Babol University of Medical Sciences (Medicine, Dentistry, and Paramedicine) participated in the study between 2014 and 2015. All participants completed the Toronto Alexithymia Scale (TAS-20), College Academic Self-Efficacy Scale (CASES), and 14 items on anxiety and depression derived from the 28 items of the General Health Questionnaire (28-GHQ). Results . Pearson correlation coefficients revealed negative significant relationships between alexithymia and the three subscales with student self-efficacy. There was no significant correlation between anxiety/depression symptoms and student self-efficacy. A backward multiple regression analysis revealed that alexithymia was a negative significant predictor of self-efficacy in academic students ( B = -0.512, P < 0.001). The prevalence of alexithymia was 21.8% in students. Multiple backward logistic analysis regression revealed that number of passed semesters, gender, mother's education, father's education, and doctoral level did not accurately predict alexithymia in college students. Conclusion . As alexithymia is prevalent in college students and affects self-efficacy and academic functioning, we suggest it should be routinely evaluated by mental physicians at universities.

Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala

PubMed Central

Bianconi, André; Zuben, Cláudio J. Von; Serapião, Adriane B. de S.; Govone, José S.

2010-01-01

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies. PMID:20569135

The Detection and Interpretation of Interaction Effects between Continuous Variables in Multiple Regression.

ERIC Educational Resources Information Center

Jaccard, James; And Others

1990-01-01

Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)

Validation of the questionnaire on hand function assessment in leprosy.

PubMed

Ferreira, Telma Leonel; Alvarez, Rosicler Rocha Aiza; Virmond, Marcos da Cunha Lopes

2012-06-01

To validate the psychometric properties of the questionnaire on hand function assessment in leprosy. Study conducted with a convenience sample of 101 consecutive patients in Brasília (Central-Western Brazil), from June 2008 to July 2009. The individuals were adults affected by leprosy, with impairment of the ulnar, median and radial nerves. Interobservers and intraobserver reproducibility was analyzed through successive interviews, and construct validity was analyzed through association between age, clinical form of leprosy, duration of nerve injury, grip and pinch strength measured with a dynamometer, sensibility test performed with Semmes-Weinstein monofilaments and manual ability assessment using the Jebsen test of hand function. Pondered kappa coefficient was calculated and a Bland-Altman plot was constructed to assess the reproducibility of the instrument. For internal consistency, Cronbach's alpha coefficient was utilized. Pearson's correlation coefficient was calculated and a multiple regression model was used. The pondered kappa values for interobservers and intraobserver assessments ranged from 0.86 to 0.97 and from 0.85 to 0.97, respectively. The value of Cronbach's alpha coefficient was 0.967. Pearson's correlation coefficient showed an association (p < 0.001) among duration of nerve injury, grip and pinch strength, cutaneous sensibility and mean score in the Jebsen Test. The mean score of the questionnaire on hand functional assessment in leprosy was associated with operational classification of leprosy, duration of nerve injury, grip strength, cutaneous sensibility and manual ability (p < 0.0001 for the model as a whole). The questionnaire on hand functional assessment in leprosy presents almost perfect interobservers and intraobserver reproducibility, high internal consistency and correlation with operational classification of leprosy, duration of nerve injury, grip strength, cutaneous sensibility in the hands and manual ability.

Inbreeding coefficients and degree of consanguineous marriages in Spain: a review.

PubMed

Fuster, Vicente; Colantonio, Sonia Edith

2003-01-01

The contribution of consanguineous marriages corresponding to uncle-niece or aunt-nephew (C12), first cousin (C22), first cousin once removed (C23), and second cousin (C33) to the inbreeding coefficient (alpha) was analyzed from a sample of Spanish areas and periods. Multiple regressions were performed taking as independent variables the different degrees of consanguinity previously selected (C12, C22, C23, and C33) and as dependent variable the inbreeding coefficient (alpha). According to the results obtained for any degree and period, rural frequencies always surpass urban. However, the pattern is similar in both areas. In the period where consanguinity was more elevated (1890-1929) the C22/C33 ratio increased. Its variation is not due to C22 and C33 changes in the same way. In rural areas, this ratio surpasses the expected value by a factor of 2-3, but in urban areas it was 7-10 times larger, in some cases due to migration. While in rural Spain the C33 frequency was approximately 1.5 times C22, in cities C22 was 1.5 times C33. The best fit among the various types of consanguineous matings and alpha involves a lineal relationship. Regardless of the number of variables contributing significantly to alpha, C22 matings are always present. Moreover, their standardized (beta) coefficients are the highest. The above indicates that this consanguineous relationship conditions the inbreeding coefficient the most. In the period of greater consanguinity, close relationships, uncle-niece C12, and first cousin once removed (C23) make a significant contribution to alpha. In rural Spain second cousins (C33) always significantly determined alpha; however, in cities the inbreeding variation was mainly due to C12 and C23. Copyright 2003 Wiley-Liss, Inc.

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Model for predicting the injury severity score.

PubMed

Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

2015-07-01

To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  < 0.05. To select objective variables, the stepwise method was used. A total of 122 patients were included in this study. The formula for predicting the injury severity score (ISS) was as follows: ISS = 13.252-0.078(mean blood pressure) + 0.12(fibrin degradation products). The P -value of this formula from analysis of variance was <0.001, and the multiple correlation coefficient (R) was 0.739 (R 2  = 0.546). The multiple correlation coefficient adjusted for the degrees of freedom was 0.538. The Durbin-Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

Facial convective heat exchange coefficients in cold and windy environments estimated from human experiments

NASA Astrophysics Data System (ADS)

Ben Shabat, Yael; Shitzer, Avraham

2012-07-01

Facial heat exchange convection coefficients were estimated from experimental data in cold and windy ambient conditions applicable to wind chill calculations. Measured facial temperature datasets, that were made available to this study, originated from 3 separate studies involving 18 male and 6 female subjects. Most of these data were for a -10°C ambient environment and wind speeds in the range of 0.2 to 6 m s-1. Additional single experiments were for -5°C, 0°C and 10°C environments and wind speeds in the same range. Convection coefficients were estimated for all these conditions by means of a numerical facial heat exchange model, applying properties of biological tissues and a typical facial diameter of 0.18 m. Estimation was performed by adjusting the guessed convection coefficients in the computed facial temperatures, while comparing them to measured data, to obtain a satisfactory fit ( r 2 > 0.98, in most cases). In one of the studies, heat flux meters were additionally used. Convection coefficients derived from these meters closely approached the estimated values for only the male subjects. They differed significantly, by about 50%, when compared to the estimated female subjects' data. Regression analysis was performed for just the -10°C ambient temperature, and the range of experimental wind speeds, due to the limited availability of data for other ambient temperatures. The regressed equation was assumed in the form of the equation underlying the "new" wind chill chart. Regressed convection coefficients, which closely duplicated the measured data, were consistently higher than those calculated by this equation, except for one single case. The estimated and currently used convection coefficients are shown to diverge exponentially from each other, as wind speed increases. This finding casts considerable doubts on the validity of the convection coefficients that are used in the computation of the "new" wind chill chart and their applicability to humans in cold and windy environments.

Facial convective heat exchange coefficients in cold and windy environments estimated from human experiments.

PubMed

Ben Shabat, Yael; Shitzer, Avraham

2012-07-01

Facial heat exchange convection coefficients were estimated from experimental data in cold and windy ambient conditions applicable to wind chill calculations. Measured facial temperature datasets, that were made available to this study, originated from 3 separate studies involving 18 male and 6 female subjects. Most of these data were for a -10°C ambient environment and wind speeds in the range of 0.2 to 6 m s(-1). Additional single experiments were for -5°C, 0°C and 10°C environments and wind speeds in the same range. Convection coefficients were estimated for all these conditions by means of a numerical facial heat exchange model, applying properties of biological tissues and a typical facial diameter of 0.18 m. Estimation was performed by adjusting the guessed convection coefficients in the computed facial temperatures, while comparing them to measured data, to obtain a satisfactory fit (r(2) > 0.98, in most cases). In one of the studies, heat flux meters were additionally used. Convection coefficients derived from these meters closely approached the estimated values for only the male subjects. They differed significantly, by about 50%, when compared to the estimated female subjects' data. Regression analysis was performed for just the -10°C ambient temperature, and the range of experimental wind speeds, due to the limited availability of data for other ambient temperatures. The regressed equation was assumed in the form of the equation underlying the "new" wind chill chart. Regressed convection coefficients, which closely duplicated the measured data, were consistently higher than those calculated by this equation, except for one single case. The estimated and currently used convection coefficients are shown to diverge exponentially from each other, as wind speed increases. This finding casts considerable doubts on the validity of the convection coefficients that are used in the computation of the "new" wind chill chart and their applicability to humans in cold and windy environments.

Magnitude, frequency, and trends of floods at gaged and ungaged sites in Washington, based on data through water year 2014

USGS Publications Warehouse

Mastin, Mark C.; Konrad, Christopher P.; Veilleux, Andrea G.; Tecca, Alison E.

2016-09-20

An investigation into the magnitude and frequency of floods in Washington State computed the annual exceedance probability (AEP) statistics for 648 U.S. Geological Survey unregulated streamgages in and near the borders of Washington using the recorded annual peak flows through water year 2014. This is an updated report from a previous report published in 1998 that used annual peak flows through the water year 1996. New in this report, a regional skew coefficient was developed for the Pacific Northwest region that includes areas in Oregon, Washington, Idaho and western Montana within the Columbia River drainage basin south of the United States-Canada border, the coastal areas of Oregon and western Washington, and watersheds draining into Puget Sound, Washington. The skew coefficient is an important term in the Log Pearson Type III equation used to define the distribution of the log-transformed annual peaks. The Expected Moments Algorithm was used to fit historical and censored peak-flow data to the log Pearson Type III distribution. A Multiple Grubb-Beck test was employed to censor low outliers of annual peak flows to improve on the frequency distribution. This investigation also includes a section on observed trends in annual peak flows that showed significant trends (p-value < 0.05) in 21 of 83 long-term sites, but with small magnitude Kendall tau values suggesting a limited monotonic trend in the time series of annual peaks. Most of the sites with a significant trend in western Washington were positive and all the sites with significant trends (three sites) in eastern Washington were negative.Multivariate regression analysis with measured basin characteristics and the AEP statistics at long-term, unregulated, and un-urbanized (defined as drainage basins with less than 5 percent impervious land cover for this investigation) streamgages within Washington and some in Idaho and Oregon that are near the Washington border was used to develop equations to estimate AEP statistics at ungaged basins. Washington was divided into four regions to improve the accuracy of the regression equations; a set of equations for eight selected AEPs and for each region were constructed. Selected AEP statistics included the annual peak flows that equaled or exceeded 50, 20, 10, 4, 2, 1, 0.5 and 0.2 percent of the time equivalent to peak flows for peaks with a 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively. Annual precipitation and drainage area were the significant basin characteristics in the regression equations for all four regression regions in Washington and forest cover was significant for the two regression regions in eastern Washington. Average standard error of prediction for the regional regression equations ranged from 70.19 to 125.72 percent for Regression Regions 1 and 2 on the eastern side of the Cascade Mountains and from 43.22 to 58.04 percent for Regression Regions 3 and 4 on the western side of the Cascade Mountains. The pseudo coefficient of determination (where a value of 100 signifies a perfect regression model) ranged from 68.39 to 90.68 for Regression Regions 1 and 2, and 92.35 to 95.44 for Regions 3 and 4.The calculated AEP statistics for the streamgages and the regional regression equations are expected to be incorporated into StreamStats after the publication of this report. StreamStats is the interactive Web-based map tool created by the U.S. Geological Survey to allow the user to choose a streamgage and obtain published statistics or choose ungaged locations where the program automatically applies the regional regression equations and computes the estimates of the AEP statistics.

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks.

PubMed

Campbell, J Elliott; Moen, Jeremie C; Ney, Richard A; Schnoor, Jerald L

2008-03-01

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively.

Radon-222 concentrations in ground water and soil gas on Indian reservations in Wisconsin

USGS Publications Warehouse

DeWild, John F.; Krohelski, James T.

1995-01-01

For sites with wells finished in the sand and gravel aquifer, the coefficient of determination (R2) of the regression of concentration of radon-222 in ground water as a function of well depth is 0.003 and the significance level is 0.32, which indicates that there is not a statistically significant relation between radon-222 concentrations in ground water and well depth. The coefficient of determination of the regression of radon-222 in ground water and soil gas is 0.19 and the root mean square error of the regression line is 271 picocuries per liter. Even though the significance level (0.036) indicates a statistical relation, the root mean square error of the regression is so large that the regression equation would not give reliable predictions. Because of an inadequate number of samples, similar statistical analyses could not be performed for sites with wells finished in the crystalline and sedimentary bedrock aquifers.

First-principles multiple-barrier diffusion theory. The case study of interstitial diffusion in CdTe

DOE PAGES

Yang, Ji -Hui; Park, Ji -Sang; Kang, Joongoo; ...

2015-02-17

The diffusion of particles in solid-state materials generally involves several sequential thermal-activation processes. However, presently, diffusion coefficient theory only deals with a single barrier, i.e., it lacks an accurate description to deal with multiple-barrier diffusion. Here, we develop a general diffusion coefficient theory for multiple-barrier diffusion. Using our diffusion theory and first-principles calculated hopping rates for each barrier, we calculate the diffusion coefficients of Cd, Cu, Te, and Cl interstitials in CdTe for their full multiple-barrier diffusion pathways. As a result, we found that the calculated diffusivity agrees well with the experimental measurement, thus justifying our theory, which is generalmore » for many other systems.« less

Modeling Group Differences in OLS and Orthogonal Regression: Implications for Differential Validity Studies

ERIC Educational Resources Information Center

Kane, Michael T.; Mroch, Andrew A.

2010-01-01

In evaluating the relationship between two measures across different groups (i.e., in evaluating "differential validity") it is necessary to examine differences in correlation coefficients and in regression lines. Ordinary least squares (OLS) regression is the standard method for fitting lines to data, but its criterion for optimal fit…

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.

Association between overuse of mobile phones on quality of sleep and general health among occupational health and safety students.

PubMed

Eyvazlou, Meysam; Zarei, Esmaeil; Rahimi, Azin; Abazari, Malek

2016-01-01

Concerns about health problems due to the increasing use of mobile phones are growing. Excessive use of mobile phones can affect the quality of sleep as one of the important issues in the health literature and general health of people. Therefore, this study investigated the relationship between the excessive use of mobile phones and general health and quality of sleep on 450 Occupational Health and Safety (OH&S) students in five universities of medical sciences in the North East of Iran in 2014. To achieve this objective, special questionnaires that included Cell Phone Overuse Scale, Pittsburgh's Sleep Quality Index (PSQI) and General Health Questionnaire (GHQ) were used, respectively. In addition to descriptive statistical methods, independent t-test, Pearson correlation, analysis of variance (ANOVA) and multiple regression tests were performed. The results revealed that half of the students had a poor level of sleep quality and most of them were considered unhealthy. The Pearson correlation co-efficient indicated a significant association between the excessive use of mobile phones and the total score of general health and the quality of sleep. In addition, the results of the multiple regression showed that the excessive use of mobile phones has a significant relationship between each of the four subscales of general health and the quality of sleep. Furthermore, the results of the multivariate regression indicated that the quality of sleep has a simultaneous effect on each of the four scales of the general health. Overall, a simultaneous study of the effects of the mobile phones on the quality of sleep and the general health could be considered as a trigger to employ some intervention programs to improve their general health status, quality of sleep and consequently educational performance.

Depression, anxiety and general psychopathology in breast cancer patients: a cross-sectional control study.

PubMed

Fafouti, M; Paparrigopoulos, T; Zervas, Y; Rabavilas, A; Malamos, N; Liappas, I; Tzavara, C

2010-01-01

A significant proportion of breast cancer patients experience psychiatric morbidity. The present study compared the psychopathological profile (depression, anxiety and general psychopathology) of Greek women with breast cancer with a group of healthy controls. Patients (n=109) were recruited from a specialized oncology breast cancer department and healthy controls (n=71) from a breast outpatient clinic. General psychopathology was assessed by the SCL-90-R. The Montgomery-Asberg Depression Rating Scale (MADRS) and the Spielberger State-Trait Anxiety Inventory (STAI) were used for assessing depression and anxiety. Demographics and clinical characteristics were also recorded. Data were modeled using multiple regression analysis. The mean age was 54.7±18.1 years for the control group and 51.2±9.5 years for the patient group (p=0.288). Mean scores on SCL-90-R, MADRS and STAI were significantly higher in the cancer group compared to controls (p<0.05). Multiple regression analysis revealed that breast cancer was independently and positively associated with all psychological measures (p<0.05). Regression coefficients ranged from 0.19 (SCL-90-R, psychotism) to 0.33 (MADRS). Lower anger/aggressiveness and anxiety were found in highly educated women; divorced/widowed women scored higher on obsessionality and MADRS compared to married women. Psychiatric treatment was associated with higher scores on somatization, depression, phobic anxiety and general psychopathology. Anxiety, depression, and overall psychopathology are more frequent in breast cancer patients compared to controls. Disease makes a larger independent contribution to all psychopathological measures than any other investigated variable. Therefore, breast cancer patients should be closely followed up in order to identify and timely treat any mental health problems that may arise.

Reference values of brachial-ankle pulse wave velocity according to age and blood pressure in a central Asia population.

PubMed

Yiming, Gulinuer; Zhou, Xianhui; Lv, Wenkui; Peng, Yi; Zhang, Wenhui; Cheng, Xinchun; Li, Yaodong; Xing, Qiang; Zhang, Jianghua; Zhou, Qina; Zhang, Ling; Lu, Yanmei; Wang, Hongli; Tang, Baopeng

2017-01-01

Brachial-ankle pulse wave velocity (baPWV), a direct measure of aortic stiffness, has increasingly become an important assessment for cardiovascular risk. The present study established the reference and normal values of baPWV in a Central Asia population in Xinjiang, China. We recruited participants from a central Asia population in Xinjiang, China. We performed multiple regression analysis to investigate the determinants of baPWV. The median and 10th-90th percentiles were calculated to establish the reference and normal values based on these categories. In total, 5,757 Han participants aged 15-88 years were included in the present study. Spearman correlation analysis showed that age (r = 0.587, p < 0.001) and mean blood pressure (MBP, r = 0.599, p <0.001) were the major factors influencing the values of baPWV in the reference population. Furthermore, in the multiple linear regression analysis, the standardized regression coefficients of age (0.445) and MBP (0.460) were much higher than those of body mass index, triglyceride, and glycemia (-0.054, 0.035, and 0.033, respectively). In the covariance analysis, after adjustment for age and MBP, only diabetes was the significant independent determinant of baPWV (p = 0.009). Thus, participants with diabetes were excluded from the reference value population. The reference values ranged from 14.3 to 25.2 m/s, and the normal values ranged from 13.9 to 21.2 m/s. This is the first study that has established the reference and normal values for baPWV according to age and blood pressure in a Central Asia population.

Salivary gland ultrasonography as a primary imaging tool for predicting efficacy of xerostomia treatment in patients with Sjögren's syndrome.

PubMed

Takagi, Yukinori; Sumi, Misa; Nakamura, Hideki; Sato, Shuntaro; Kawakami, Atsushi; Nakamura, Takashi

2016-02-01

To evaluate ultrasonography (US) grading of salivary gland disease as a predictor of treatment efficacy for impaired salivary function in xerostomia patients with or without Sjögren's syndrome (SS). We retrospectively analysed the prognostic importance of salivary US grading in 317 patients (168 with SS and 149 without SS). US images of the parotid and submandibular glands in each patient were individually categorized into grades 0-4 based on the extent of damage to the gland; and the sum total grade of the two gland types on either side was assigned a US score of 0-8 for each patient. The relative importance of US score and demographic and clinical variables was assessed using stepwise multiple regression analysis after various durations of xerostomia treatment. Multiple regression analysis indicated that the baseline US score before treatment was the most important factor [standardized regression coefficient (β) = -0.523, t-statistic (t) = -7.967, P < 0.001] in predicting negative outcomes in SS patients. Treatment duration (β = 0.277, t = 4.225, P < 0.001) was also a significant but less important positive variable. On the other hand, US grading did not effectively predict treatment outcomes in non-SS patients, with treatment duration (β = 0.199, t = 2.486, P = 0.014) and baseline salivary flow rate before treatment (β = -0.172, t = -2.159, P = 0.032) being significant but weak predictors of positive and negative outcome, respectively. Salivary gland US grading may help to predict outcomes of treatment for impaired salivary function in patients with SS. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

Does the utilization of dental services associate with masticatory performance in a Japanese urban population?: the Suita study

PubMed Central

Kikui, Miki; Kida, Momoyo; Kosaka, Takayuki; Yamamoto, Masaaki; Yoshimuta, Yoko; Yasui, Sakae; Nokubi, Takashi; Maeda, Yoshinobu; Kokubo, Yoshihiro; Watanabe, Makoto; Miyamoto, Yoshihiro

2015-01-01

Abstract There are numerous reports on the relationship between regular utilization of dental care services and oral health, but most are based on questionnaires and subjective evaluation. Few have objectively evaluated masticatory performance and its relationship to utilization of dental care services. The purpose of this study was to identify the effect of regular utilization of dental services on masticatory performance. The subjects consisted of 1804 general residents of Suita City, Osaka Prefecture (760 men and 1044 women, mean age 66.5 ± 7.9 years). Regular utilization of dental services and oral hygiene habits (frequency of toothbrushing and use of interdental aids) was surveyed, and periodontal status, occlusal support, and masticatory performance were measured. Masticatory performance was evaluated by a chewing test using gummy jelly. The correlation between age, sex, regular dental utilization, oral hygiene habits, periodontal status or occlusal support, and masticatory performance was analyzed using Spearman's correlation test and t‐test. In addition, multiple linear regression analysis was carried out to investigate the relationship of regular dental utilization with masticatory performance after controlling for other factors. Masticatory performance was significantly correlated to age when using Spearman's correlation test, and to regular dental utilization, periodontal status, or occlusal support with t‐test. Multiple linear regression analysis showed that regular utilization of dental services was significantly related to masticatory performance even after adjusting for age, sex, oral hygiene habits, periodontal status, and occlusal support (standardized partial regression coefficient β = 0.055). These findings suggested that the regular utilization of dental care services is an important factor influencing masticatory performance in a Japanese urban population. PMID:29744141

Does the utilization of dental services associate with masticatory performance in a Japanese urban population?: the Suita study.

PubMed

Kikui, Miki; Ono, Takahiro; Kida, Momoyo; Kosaka, Takayuki; Yamamoto, Masaaki; Yoshimuta, Yoko; Yasui, Sakae; Nokubi, Takashi; Maeda, Yoshinobu; Kokubo, Yoshihiro; Watanabe, Makoto; Miyamoto, Yoshihiro

2015-12-01

There are numerous reports on the relationship between regular utilization of dental care services and oral health, but most are based on questionnaires and subjective evaluation. Few have objectively evaluated masticatory performance and its relationship to utilization of dental care services. The purpose of this study was to identify the effect of regular utilization of dental services on masticatory performance. The subjects consisted of 1804 general residents of Suita City, Osaka Prefecture (760 men and 1044 women, mean age 66.5 ± 7.9 years). Regular utilization of dental services and oral hygiene habits (frequency of toothbrushing and use of interdental aids) was surveyed, and periodontal status, occlusal support, and masticatory performance were measured. Masticatory performance was evaluated by a chewing test using gummy jelly. The correlation between age, sex, regular dental utilization, oral hygiene habits, periodontal status or occlusal support, and masticatory performance was analyzed using Spearman's correlation test and t -test. In addition, multiple linear regression analysis was carried out to investigate the relationship of regular dental utilization with masticatory performance after controlling for other factors. Masticatory performance was significantly correlated to age when using Spearman's correlation test, and to regular dental utilization, periodontal status, or occlusal support with t -test. Multiple linear regression analysis showed that regular utilization of dental services was significantly related to masticatory performance even after adjusting for age, sex, oral hygiene habits, periodontal status, and occlusal support (standardized partial regression coefficient β  = 0.055). These findings suggested that the regular utilization of dental care services is an important factor influencing masticatory performance in a Japanese urban population.

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

Causal relationship between the AHSG gene and BMD through fetuin-A and BMI: multiple mediation analysis.

PubMed

Sritara, C; Thakkinstian, A; Ongphiphadhanakul, B; Chailurkit, L; Chanprasertyothin, S; Ratanachaiwong, W; Vathesatogkit, P; Sritara, P

2014-05-01

Using mediation analysis, a causal relationship between the AHSG gene and bone mineral density (BMD) through fetuin-A and body mass index (BMI) mediators was suggested. Fetuin-A, a multifunctional protein of hepatic origin, is associated with bone mineral density. It is unclear if this association is causal. This study aimed at clarification of this issue. A cross-sectional study was conducted among 1,741 healthy workers from the Electricity Generating Authority of Thailand (EGAT) cohort. The alpha-2-Heremans-Schmid glycoprotein (AHSG) rs2248690 gene was genotyped. Three mediation models were constructed using seemingly unrelated regression analysis. First, the ln[fetuin-A] group was regressed on the AHSG gene. Second, the BMI group was regressed on the AHSG gene and the ln[fetuin-A] group. Finally, the BMD model was constructed by fitting BMD on two mediators (ln[fetuin-A] and BMI) and the independent AHSG variable. All three analyses were adjusted for confounders. The prevalence of the minor T allele for the AHSG locus was 15.2%. The AHSG locus was highly related to serum fetuin-A levels (P < 0.001). Multiple mediation analyses showed that AHSG was significantly associated with BMD through the ln[fetuin-A] and BMI pathway, with beta coefficients of 0.0060 (95% CI 0.0038, 0.0083) and 0.0030 (95% CI 0.0020, 0.0045) at the total hip and lumbar spine, respectively. About 27.3 and 26.0% of total genetic effects on hip and spine BMD, respectively, were explained by the mediation effects of fetuin-A and BMI. Our study suggested evidence of a causal relationship between the AHSG gene and BMD through fetuin-A and BMI mediators.

Sensitivity Analysis of Mechanical Parameters of Different Rock Layers to the Stability of Coal Roadway in Soft Rock Strata

PubMed Central

Zhao, Zeng-hui; Wang, Wei-ming; Gao, Xin; Yan, Ji-xing

2013-01-01

According to the geological characteristics of Xinjiang Ili mine in western area of China, a physical model of interstratified strata composed of soft rock and hard coal seam was established. Selecting the tunnel position, deformation modulus, and strength parameters of each layer as influencing factors, the sensitivity coefficient of roadway deformation to each parameter was firstly analyzed based on a Mohr-Columb strain softening model and nonlinear elastic-plastic finite element analysis. Then the effect laws of influencing factors which showed high sensitivity were further discussed. Finally, a regression model for the relationship between roadway displacements and multifactors was obtained by equivalent linear regression under multiple factors. The results show that the roadway deformation is highly sensitive to the depth of coal seam under the floor which should be considered in the layout of coal roadway; deformation modulus and strength of coal seam and floor have a great influence on the global stability of tunnel; on the contrary, roadway deformation is not sensitive to the mechanical parameters of soft roof; roadway deformation under random combinations of multi-factors can be deduced by the regression model. These conclusions provide theoretical significance to the arrangement and stability maintenance of coal roadway. PMID:24459447

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models.

PubMed

Bilgili, Mehmet; Sahin, Besir; Sangun, Levent

2013-01-01

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the "Enter" method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685-3.65% and 0.9988-0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.

An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models

USGS Publications Warehouse

Li, Ji; Gray, B.R.; Bates, D.M.

2008-01-01

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.

Energy Analysis of a Complementary Heating System Combining Solar Energy and Coal for a Rural Residential Building in Northwest China.

PubMed

Zhen, Xiaofei; Li, Jinping; Abdalla Osman, Yassir Idris; Feng, Rong; Zhang, Xuemin; Kang, Jian

2018-01-01

In order to utilize solar energy to meet the heating demands of a rural residential building during the winter in the northwestern region of China, a hybrid heating system combining solar energy and coal was built. Multiple experiments to monitor its performance were conducted during the winter in 2014 and 2015. In this paper, we analyze the efficiency of the energy utilization of the system and describe a prototype model to determine the thermal efficiency of the coal stove in use. Multiple linear regression was adopted to present the dual function of multiple factors on the daily heat-collecting capacity of the solar water heater; the heat-loss coefficient of the storage tank was detected as well. The prototype model shows that the average thermal efficiency of the stove is 38%, which means that the energy input for the building is divided between the coal and solar energy, 39.5% and 60.5% energy, respectively. Additionally, the allocation of the radiation of solar energy projecting into the collecting area of the solar water heater was obtained which showed 49% loss with optics and 23% with the dissipation of heat, with only 28% being utilized effectively.

Long term high resolution rainfall runoff observations for improved water balance uncertainty and database QA-QC in the Walnut Gulch Experimental Watershed.

NASA Astrophysics Data System (ADS)

Bitew, M. M.; Goodrich, D. C.; Demaria, E.; Heilman, P.; Kautz, M. A.

2017-12-01

Walnut Gulch is a semi-arid environment experimental watershed and Long Term Agro-ecosystem Research (LTAR) site managed by USDA-ARS Southwest Watershed Research Center for which high-resolution long-term hydro-climatic data are available across its 150 km2 drainage area. In this study, we present the analysis of 50 years of continuous hourly rainfall data to evaluate runoff control and generation processes for improving the QA-QC plans of Walnut Gulch to create high-quality data set that is critical for reducing water balance uncertainties. Multiple linear regression models were developed to relate rainfall properties, runoff characteristics and watershed properties. The rainfall properties were summarized to event based total depth, maximum intensity, duration, the location of the storm center with respect to the outlet, and storm size normalized to watershed area. We evaluated the interaction between the runoff and rainfall and runoff as antecedent moisture condition (AMC), antecedent runoff condition (ARC) and, runoff depth and duration for each rainfall events. We summarized each of the watershed properties such as contributing area, slope, shape, channel length, stream density, channel flow area, and percent of the area of retention stock ponds for each of the nested catchments in Walnut Gulch. The evaluation of the model using basic and categorical statistics showed good predictive skill throughout the watersheds. The model produced correlation coefficients ranging from 0.4-0.94, Nash efficiency coefficients up to 0.77, and Kling-Gupta coefficients ranging from 0.4 to 0.98. The model predicted 92% of all runoff generations and 98% of no-runoff across all sub-watersheds in Walnut Gulch. The regression model also indicated good potential to complement the QA-QC procedures in place for Walnut Gulch dataset publications developed over the years since the 1960s through identification of inconsistencies in rainfall and runoff relations.

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.

Life-space mobility and social support in elderly adults with orthopaedic disorders.

PubMed

Suzuki, Tomoko; Kitaike, Tadashi; Ikezaki, Sumie

2014-03-01

The purpose of this cross-sectional survey was to explore relationships between life-space mobility and the related factors in elderly Japanese people who attend orthopaedic clinics. The study measures included surveys of life-space mobility (Life-space Assessment (LSA) score), social support (social network diversity and social ties), physical ability (instrumental self-maintenance, intellectual activity, social role), orthopaedic factors (diseases and symptoms) and demographic information. The questionnaire was distributed to 156 subjects; 152 persons responded, yielding 140 valid responses. Mean age of the sample was 76.0 ± 6.4 (range, 65-96 years), with 57.9% women (n = 81). In a multiple regression analysis, the six factors were significantly associated with LSA. Standardized partial regression coefficients (β) were gender (0.342), instrumental self-maintenance (0.297), social network diversity (0.217), age (-0.170), difficulty of motion (-0.156) and intellectual activity (0.150), with an adjusted R(2) = 0.488. These results suggest that outpatient health-care providers need to intervene in not only addressing orthopaedic factors but also promoting social support among elderly Japanese. © 2014 Wiley Publishing Asia Pty Ltd.

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.

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.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Bootstrap Methods: A Very Leisurely Look.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Winstead, Wayland H.

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…

A simple equation to estimate body fat percentage in children with overweightness or obesity: a retrospective study.

PubMed

Cortés-Castell, Ernesto; Juste, Mercedes; Palazón-Bru, Antonio; Monge, Laura; Sánchez-Ferrer, Francisco; Rizo-Baeza, María Mercedes

2017-01-01

Dual-energy X-ray absorptiometry (DXA) provides separate measurements of fat mass, fat-free mass and bone mass, and is a quick, accurate, and safe technique, yet one that is not readily available in routine clinical practice. Consequently, we aimed to develop statistical formulas to predict fat mass (%) and fat mass index (FMI) with simple parameters (age, sex, weight and height). We conducted a retrospective observational cross-sectional study in 416 overweight or obese patients aged 4-18 years that involved assessing adiposity by DXA (fat mass percentage and FMI), body mass index (BMI), sex and age. We randomly divided the sample into two parts (construction and validation). In the construction sample, we developed formulas to predict fat mass and FMI using linear multiple regression models. The formulas were validated in the other sample, calculating the intraclass correlation coefficient via bootstrapping. The fat mass percentage formula had a coefficient of determination of 0.65. This value was 0.86 for FMI. In the validation, the constructed formulas had an intraclass correlation coefficient of 0.77 for fat mass percentage and 0.92 for FMI. Our predictive formulas accurately predicted fat mass and FMI with simple parameters (BMI, sex and age) in children with overweight and obesity. The proposed methodology could be applied in other fields. Further studies are needed to externally validate these formulas.

Control of interior surface materials for speech privacy in high-speed train cabins.

PubMed

Jang, H S; Lim, H; Jeon, J Y

2017-05-01

The effect of interior materials with various absorption coefficients on speech privacy was investigated in a 1:10 scale model of one high-speed train cabin geometry. The speech transmission index (STI) and privacy distance (r P ) were measured in the train cabin to quantify speech privacy. Measurement cases were selected for the ceiling, sidewall, and front and back walls and were classified as high-, medium- and low-absorption coefficient cases. Interior materials with high absorption coefficients yielded a low r P , and the ceiling had the largest impact on both the STI and r P among the interior elements. Combinations of the three cases were measured, and the maximum reduction in r P by the absorptive surfaces was 2.4 m, which exceeds the space between two rows of chairs in the high-speed train. Additionally, the contribution of the interior elements to speech privacy was analyzed using recorded impulse responses and a multiple regression model for r P using the equivalent absorption area. The analysis confirmed that the ceiling was the most important interior element for improving speech privacy. These results can be used to find the relative decrease in r P in the acoustic design of interior materials to improve speech privacy in train cabins. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

NT-pro-BNP levels in patients with acute pulmonary embolism are correlated to right but not left ventricular volume and function.

PubMed

Pasha, Sharif M; Klok, Frederikus A; van der Bijl, Noortje; de Roos, Albert; Kroft, Lucia J M; Huisman, Menno V

2012-08-01

N-terminal pro-Brain Natriuretic Peptide (NT-pro-BNP) is primarily secreted by left ventricular (LV) stretch and wall tension. Notably, NT-pro-BNP is a prognostic marker in acute pulmonary embolism (PE), which primarily stresses the right ventricle (RV). We sought to evaluate the relative contribution of the RV to NT-pro-BNP levels during PE. A post-hoc analysis of an observational prospective outcome study in 113 consecutive patients with computed tomography (CT)-proven PE and 226 patients in whom PE was clinically suspected but ruled out by CT. In all patients RV and LV function was established by assessing ECG-triggered-CT measured ventricular end-diastolic-volumes and ejection fraction (EF). NT-pro-BNP was assessed in all patients. The correlation between RV and LV end-diastolic-volumes and systolic function was evaluated by multiple linear regression corrected for known confounders. In the PE cohort increased RVEF (β-coefficient (95% confidence interval [CI]) -0.044 (± -0.011); p<0.001) and higher RV end-diastolic-volume (β-coefficient 0.005 (± 0.001); p<0.001) were significantly correlated to NT-pro-BNP, while no correlation was found with LVEF (β-coefficient 0.005 (± 0.010); p=0.587) and LV end-diastolic-volume (β-coefficient -0.003 (± 0.002); p=0.074). In control patients without PE we found a strong correlation between NT-pro-BNP levels and LVEF (β-coefficient -0.027 (± -0.006); p<0.001) although not LV end-diastolic-volume (β-coefficient 0.001 (± 0.001); p=0.418). RVEF (β-coefficient -0.002 (± -0.006); p=0.802) and RV end-diastolic-volume (β-coefficient <0.001 (± 0.001); p=0.730) were not correlated in patients without PE. In PE patients, lower RVEF and higher RV end-diastolic-volume were significantly correlated to NT-pro-BNP levels as compared to control patients without PE. These observations provide pathophysiological ground for the well-known prognostic value of NT-pro-BNP in acute PE.

Equations of prediction for abdominal fat in brown egg-laying hens fed different diets.

PubMed

Souza, C; Jaimes, J J B; Gewehr, C E

2017-06-01

The objective was to use noninvasive measurements to formulate equations for predicting the abdominal fat weight of laying hens in a noninvasive manner. Hens were fed with different diets; the external body measurements of birds were used as regressors. We used 288 Hy-Line Brown laying hens, distributed in a completely randomized design in a factorial arrangement, submitted for 16 wk to 2 metabolizable energy levels (2,550 and 2,800 kcal/kg) and 3 levels of crude protein in the diet (150, 160, and 170 g/kg), totaling 6 treatments, with 48 hens each. Sixteen hens per treatment of 92 wk age were utilized to evaluate body weight, bird length, tarsus and sternum, greater and lesser diameter of the tarsus, and abdominal fat weight, after slaughter. The equations were obtained by using measures evaluated with regressors through simple and multiple linear regression with the stepwise method of indirect elimination (backward), with P < 0.10 for all variables remaining in the model. The weight of abdominal fat as predicted by the equations and observed values for each bird were subjected to Pearson's correlation analysis. The equations generated by energy levels showed coefficients of determination of 0.50 and 0.74 for 2,800 and 2,550 kcal/kg of metabolizable energy, respectively, with correlation coefficients of 0.71 and 0.84, with a highly significant correlation between the calculated and observed values of abdominal fat. For protein levels of 150, 160, and 170 g/kg in the diet, it was possible to obtain coefficients of determination of 0.75, 0.57, and 0.61, with correlation coefficients of 0.86, 0.75, and 0.78, respectively. Regarding the general equation for predicting abdominal fat weight, the coefficient of determination was 0.62; the correlation coefficient was 0.79. The equations for predicting abdominal fat weight in laying hens, based on external measurements of the birds, showed positive coefficients of determination and correlation coefficients, thus allowing researchers to determine abdominal fat weight in vivo. © 2016 Poultry Science Association Inc.

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

Dierauf, Timothy; Kurtz, Sarah; Riley, Evan

This paper provides a recommended method for evaluating the AC capacity of a photovoltaic (PV) generating station. It also presents companion guidance on setting the facilitys capacity guarantee value. This is a principles-based approach that incorporates plant fundamental design parameters such as loss factors, module coefficients, and inverter constraints. This method has been used to prove contract guarantees for over 700 MW of installed projects. The method is transparent, and the results are deterministic. In contrast, current industry practices incorporate statistical regression where the empirical coefficients may only characterize the collected data. Though these methods may work well when extrapolationmore » is not required, there are other situations where the empirical coefficients may not adequately model actual performance.This proposed Fundamentals Approach method provides consistent results even where regression methods start to lose fidelity.« less

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)…

[Long-term outcome analysis of subjective and objective parameters after breast reduction in 159 cases: Patients judge differently from plastic surgeons].

PubMed

Osinga, Rik; Babst, Doris; Bodmer, Elvira S; Link, Bjoern C; Fritsche, Elmar; Hug, Urs

2017-12-01

This work assessed both subjective and objective postoperative parameters after breast reduction surgery and compared between patients and plastic surgeons. After an average postoperative observation period of 6.7 ± 2.7 (2 - 13) years, 159 out of 259 patients (61 %) were examined. The mean age at the time of surgery was 37 ± 14 (15 - 74) years. The postoperative anatomy of the breast and other anthropometric parameters were measured in cm with the patient in an upright position. The visual analogue scale (VAS) values for symmetry, size, shape, type of scar and overall satisfaction both from the patient's and from four plastic surgeons' perspectives were assessed and compared. Patients rated the postoperative result significantly better than surgeons. Good subjective ratings by patients for shape, symmetry and sensitivity correlated with high scores for overall assessment. Shape had the strongest influence on overall satisfaction (regression coefficient 0.357; p < 0.001), followed by symmetry (regression coefficient 0.239; p < 0.001) and sensitivity (regression coefficient 0.109; p = 0.040) of the breast. The better the subjective rating for symmetry by the patient, the smaller the measured difference of the jugulum-mamillary distance between left and right (regression coefficient -0.773; p = 0.002) and the smaller the difference in height of the lowest part of the breast between left and right (regression coefficient -0.465; p = 0.035). There was no significant correlation between age, weight, height, BMI, resected weight of the breast, postoperative breast size or type of scar with overall satisfaction. After breast reduction surgery, long-term outcome is rated significantly better by patients than by plastic surgeons. Good subjective ratings by patients for shape, symmetry and sensitivity correlated with high scores for overall assessment. Shape had the strongest influence on overall satisfaction, followed by symmetry and sensitivity of the breast. Postoperative size of the breast, resection weight, type of scar, age or BMI was not of significant influence. Symmetry was the only assessed subjective parameter of this study that could be objectified by postoperative measurements. Georg Thieme Verlag KG Stuttgart · New York.

False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

Use of Multiple Regression and Use-Availability Analyses in Determining Habitat Selection by Gray Squirrels (Sciurus Carolinensis)

Treesearch

John W. Edwards; Susan C. Loeb; David C. Guynn

1994-01-01

Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection. We compared these techniques using behavioral observations (n = 5534) and telemetry locations (n = 2089) of...

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.

Building Regression Models: The Importance of Graphics.

ERIC Educational Resources Information Center

Dunn, Richard

1989-01-01

Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

Belief in complementary and alternative medicine is related to age and paranormal beliefs in adults.

PubMed

Van den Bulck, Jan; Custers, Kathleen

2010-04-01

The use of complementary and alternative medicine (CAM) is widespread, even among people who use conventional medicine. Positive beliefs about CAM are common among physicians and medical students. Little is known about the beliefs regarding CAM among the general public. Among science students, belief in CAM was predicted by belief in the paranormal. In a cross-sectional study, 712 randomly selected adults (>18 years old) responded to the CAM Health Belief Questionnaire (CHBQ) and a paranormal beliefs scale. CAM beliefs were very prevalent in this sample of adult Flemish men and women. Zero-order correlations indicated that belief in CAM was associated with age (r = 0.173 P < 0.001) level of education (r = -0.079 P = 0.039) social desirability (r = -0.119 P = 0.002) and paranormal belief (r = 0.365 P < 0.001). In a multivariate model, two variables predicted CAM beliefs. Support for CAM increased with age (regression coefficient: 0.01; 95% confidence interval (CI): 0.006 to 0.014), but the strongest relationship existed between support for CAM and beliefs in the paranormal. Paranormal beliefs accounted for 14% of the variance of the CAM beliefs (regression coefficient: 0.376; 95%: CI 0.30-0.44). The level of education (regression coefficient: 0.06; 95% CI: -0.014-0.129) and social desirability (regression coefficient: -0.023; 95% CI: -0.048-0.026) did not make a significant contribution to the explained variance (<0.1%, P = 0.867). Support of CAM was very prevalent in this Flemish adult population. CAM beliefs were strongly associated with paranormal beliefs.

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

ERIC Educational Resources Information Center

Smith, Kent W.; Sasaki, M. S.

1979-01-01

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

Skeletal height estimation from regression analysis of sternal lengths in a Northwest Indian population of Chandigarh region: a postmortem study.

PubMed

Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H

2011-03-20

Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P < 0.001). The multiple regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the subjects of known sex, as they underestimate the male and overestimate female stature. However, intercostal lengths were found to be the poor estimators of stature (P < 0.05). And also sternal lengths exhibit weaker correlation coefficients and higher standard errors of estimate. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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.

Predicting out-of-office blood pressure level using repeated measurements in the clinic: an observational cohort study

PubMed Central

Sheppard, James P.; Holder, Roger; Nichols, Linda; Bray, Emma; Hobbs, F.D. Richard; Mant, Jonathan; Little, Paul; Williams, Bryan; Greenfield, Sheila; McManus, Richard J.

2014-01-01

Objectives: Identification of people with lower (white-coat effect) or higher (masked effect) blood pressure at home compared to the clinic usually requires ambulatory or home monitoring. This study assessed whether changes in SBP with repeated measurement at a single clinic predict subsequent differences between clinic and home measurements. Methods: This study used an observational cohort design and included 220 individuals aged 35–84 years, receiving treatment for hypertension, but whose SBP was not controlled. The characteristics of change in SBP over six clinic readings were defined as the SBP drop, the slope and the quadratic coefficient using polynomial regression modelling. The predictive abilities of these characteristics for lower or higher home SBP readings were investigated with logistic regression and repeated operating characteristic analysis. Results: The single clinic SBP drop was predictive of the white-coat effect with a sensitivity of 90%, specificity of 50%, positive predictive value of 56% and negative predictive value of 88%. Predictive values for the masked effect and those of the slope and quadratic coefficient were slightly lower, but when the slope and quadratic variables were combined, the sensitivity, specificity, positive and negative predictive values for the masked effect were improved to 91, 48, 24 and 97%, respectively. Conclusion: Characteristics obtainable from multiple SBP measurements in a single clinic in patients with treated hypertension appear to reasonably predict those unlikely to have a large white-coat or masked effect, potentially allowing better targeting of out-of-office monitoring in routine clinical practice. PMID:25144295

Breast feeding and resilience against psychosocial stress.

PubMed

Montgomery, S M; Ehlin, A; Sacker, A

2006-12-01

Some early life exposures may result in a well controlled stress response, which can reduce stress related anxiety. Breast feeding may be a marker of some relevant exposures. To assess whether breast feeding is associated with modification of the relation between parental divorce and anxiety. Observational study using longitudinal birth cohort data. Linear regression was used to assess whether breast feeding modifies the association of parental divorce/separation with anxiety using stratification and interaction testing. Data were obtained from the 1970 British Cohort Study, which is following the lives of those born in one week in 1970 and living in Great Britain. This study uses information collected at birth and at ages 5 and 10 years for 8958 subjects. Class teachers answered a question on anxiety among 10 year olds using an analogue scale (range 0-50) that was log transformed to minimise skewness. Among 5672 non-breast fed subjects, parental divorce/separation was associated with a statistically significantly raised risk of anxiety, with a regression coefficient (95% CI) of 9.4 (6.1 to 12.8). Among the breast fed group this association was much lower: 2.2 (-2.6 to 7.0). Interaction testing confirmed statistically significant effect modification by breast feeding, independent of simultaneous adjustment for multiple potential confounding factors, producing an interaction coefficient of -7.0 (-12.8 to -1.2), indicating a 7% reduction in anxiety after adjustment. Breast feeding is associated with resilience against the psychosocial stress linked with parental divorce/separation. This could be because breast feeding is a marker of exposures related to maternal characteristics and parent-child interaction.

Predictive value of age of walking for later motor performance in children with mental retardation.

PubMed

Kokubun, M; Haishi, K; Okuzumi, H; Hosobuchi, T; Koike, T

1996-12-01

The purpose of the present study was to clarify the predictive value of age of walking for later motor performance in children with mental retardation. While paying due attention to other factors, our investigation focused on the relationship between a subject's age of walking, and his or her subsequent beam-walking performance. The subjects were 85 children with mental retardation with an average age of 13 years and 3 months. Beam-walking performance was measured by a procedure developed by the authors. Five low beams (5 cm) which varied in width (12.5, 10, 7.5, 5 and 2.5 cm) were employed. The performance of subjects was scored from zero to five points according to the width of the beam that they were able to walk without falling off. From the results of multiple regression analysis, three independent variables were found to be significantly related to beam-walking performance. The age of walking was the most basic variable: partial correlation coefficient (PCC) = -45; standardized partial regression coefficient (SPRC) = -0.41. The next variable in importance was walking duration (PCC = 0.38; SPRC = 0.31). The autism variable also contributed significantly (PCC = 0.28; SPRC = 0.22). Therefore, within the age range used in the present study, the age of walking in children with mental retardation was thought to have sufficient predictive value, even when the variables which might have possibly affected their subsequent performance were taken into consideration; the earlier the age of walking, the better the beam-walking performance.

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

PubMed Central

Lee, Seunggeun; Teslovich, Tanya M.; Boehnke, Michael; Lin, Xihong

2013-01-01

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels. PMID:23768515

Cotton dust and endotoxin exposure-response relationships in cotton textile workers

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

Kennedy, S.M.; Christiani, D.C.; Eisen, E.A.

Endotoxin exposure has been implicated in the etiology of lung disease in cotton workers. We investigated this potential relationship in 443 cotton workers from 2 factories in Shanghai and 439 control subjects from a nearby silk mill. A respiratory questionnaire was administered and pre- and postshift forced expiratory volume (FVC) and flow in one second (FEV1) were determined for each worker. Multiple area air samples were analyzed for total elutriated dust concentration (range: 0.15 to 2.5 mg/m3) and endotoxin (range: 0.002 to 0.55 microgram U.S. Reference Endotoxin/m3). The cotton worker population was stratified by current and cumulative dust or endotoxinmore » exposure. Groups were compared for FEV1, FVC, FEV1/FVC%, % change in FEV1 over the shift (delta FEV1%), and prevalences of chronic bronchitis and byssinosis, and linear and logistic regression models were constructed. No dose-response relationships were demonstrated comparing dust concentration to any pulmonary function or symptom variable. A dose-response trend was seen with the current endotoxin level and FEV1, delta FEV1%, and the prevalence of byssinosis and chronic bronchitis, except for the highest exposure level group in which a reversal of the trend was seen. The regression coefficients for current endotoxin exposure were significant (p less than 0.05) in the models for FEV1 and chronic bronchitis but not in the models for delta FEV1% (i.e., acute change in FEV1) or byssinosis prevalence. The coefficient for dust level was never significant in the models.« less

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.

Implications of combined Ovariectomy/Multi-Deficiency Diet on rat bone with age-related variation in Bone Parameters and Bone Loss at Multiple Skeletal Sites by DEXA

PubMed Central

Govindarajan, Parameswari; Schlewitz, Gudrun; Schliefke, Nathalie; Weisweiler, David; Alt, Volker; Thormann, Ulrich; Lips, Katrin Susanne; Wenisch, Sabine; Langheinrich, Alexander C.; Zahner, Daniel; Hemdan, Nasr Y.; Böcker, Wolfgang; Schnettler, Reinhard; Heiss, Christian

2013-01-01

Background Osteoporosis is a multi-factorial, chronic, skeletal disease highly prevalent in post-menopausal women and is influenced by hormonal and dietary factors. Because animal models are imperative for disease diagnostics, the present study establishes and evaluates enhanced osteoporosis obtained through combined ovariectomy and deficient diet by DEXA (dual-energy X-ray absorptiometry) for a prolonged time period. Material/Methods Sprague-Dawley rats were randomly divided into sham (laparotomized) and OVX-diet (ovariectomized and fed with deficient diet) groups. Different skeletal sites were scanned by DEXA at the following time points: M0 (baseline), M12 (12 months post-surgery), and M14 (14 months post-surgery). Parameters analyzed included BMD (bone mineral density), BMC (bone mineral content), bone area, and fat (%). Regression analysis was performed to determine the interrelationships between BMC, BMD, and bone area from M0 to M14. Results BMD and BMC were significantly lower in OVX-diet rats at M12 and M14 compared to sham rats. The Z-scores were below −5 in OVX-diet rats at M12, but still decreased at M14 in OVX-diet rats. Bone area and percent fat were significantly lower in OVX-diet rats at M14 compared to sham rats. The regression coefficients for BMD vs. bone area, BMC vs. bone area, and BMC vs. BMD of OVX-diet rats increased with time. This is explained by differential percent change in BMD, BMC, and bone area with respect to time and disease progression. Conclusions Combined ovariectomy and deficient diet in rats caused significant reduction of BMD, BMC, and bone area, with nearly 40% bone loss after 14 months, indicating the development of severe osteoporosis. An increasing regression coefficient of BMD vs. bone area with disease progression emphasizes bone area as an important parameter, along with BMD and BMC, for prediction of fracture risk. PMID:23446183

Implications of combined ovariectomy/multi-deficiency diet on rat bone with age-related variation in bone parameters and bone loss at multiple skeletal sites by DEXA.

PubMed

Govindarajan, Parameswari; Schlewitz, Gudrun; Schliefke, Nathalie; Weisweiler, David; Alt, Volker; Thormann, Ulrich; Lips, Katrin Susanne; Wenisch, Sabine; Langheinrich, Alexander C; Zahner, Daniel; Hemdan, Nasr Y; Böcker, Wolfgang; Schnettler, Reinhard; Heiss, Christian

2013-02-28

Osteoporosis is a multi-factorial, chronic, skeletal disease highly prevalent in post-menopausal women and is influenced by hormonal and dietary factors. Because animal models are imperative for disease diagnostics, the present study establishes and evaluates enhanced osteoporosis obtained through combined ovariectomy and deficient diet by DEXA (dual-energy X-ray absorptiometry) for a prolonged time period. Sprague-Dawley rats were randomly divided into sham (laparotomized) and OVX-diet (ovariectomized and fed with deficient diet) groups. Different skeletal sites were scanned by DEXA at the following time points: M0 (baseline), M12 (12 months post-surgery), and M14 (14 months post-surgery). Parameters analyzed included BMD (bone mineral density), BMC (bone mineral content), bone area, and fat (%). Regression analysis was performed to determine the interrelationships between BMC, BMD, and bone area from M0 to M14. BMD and BMC were significantly lower in OVX-diet rats at M12 and M14 compared to sham rats. The Z-scores were below -5 in OVX-diet rats at M12, but still decreased at M14 in OVX-diet rats. Bone area and percent fat were significantly lower in OVX-diet rats at M14 compared to sham rats. The regression coefficients for BMD vs. bone area, BMC vs. bone area, and BMC vs. BMD of OVX-diet rats increased with time. This is explained by differential percent change in BMD, BMC, and bone area with respect to time and disease progression. Combined ovariectomy and deficient diet in rats caused significant reduction of BMD, BMC, and bone area, with nearly 40% bone loss after 14 months, indicating the development of severe osteoporosis. An increasing regression coefficient of BMD vs. bone area with disease progression emphasizes bone area as an important parameter, along with BMD and BMC, for prediction of fracture risk.

The effect of social desirability trait on self-reported dietary measures among multi-ethnic female health center employees.

PubMed

Hébert, J R; Peterson, K E; Hurley, T G; Stoddard, A M; Cohen, N; Field, A E; Sorensen, G

2001-08-01

To evaluate the effect of social desirability trait, the tendency to respond in a manner consistent with societal expectations, on self-reported fruit, vegetable, and macronutrient intake. A 61-item food frequency questionnaire (FFQ), 7-item fruit and vegetable screener, and a single question on combined fruit and vegetable intake were completed by 132 female employees at five health centers in eastern Massachusetts. Intake of fruit and vegetables derived from all three methods and macronutrients from the FFQ were fit as dependent variables in multiple linear regression models (overall and by race/ethnicity and education); independent variables included 3-day mean intakes derived from 24-hour recalls (24HR) and score on the 33-point Marlowe-Crowne Social Desirability scale (the regression coefficient for which reflects its effect on estimates of dietary intake based on the comparison method relative to 24HR). Results are based on the 93 women with complete data and FFQ-derived caloric intake between 450 and 4500 kcal/day. In women with college education, FFQ-derived estimates of total caloric were associated with under-reporting by social desirability trait (e.g., the regression coefficient for total caloric intake was -23.6 kcal/day/point in that group versus 36.1 kcal/day/point in women with education less than college) (difference = 59.7 kcal/day/point, 95% confidence interval (CI) = 13.2, 106.2). Except for the single question on which women with college education tended to under-report (difference =.103 servings/day/point, 95% CI = 0.003, 0.203), there was no association of social desirability trait with self-reported fruit and vegetable intake. The effect of social desirability trait on FFQ reports of macronutrient intake appeared to differ by education, but not by ethnicity or race. The results of this study may have important implications for epidemiologic studies of diet and health in women.