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Sample records for linear regression lr

  1. Linear and support vector regressions based on geometrical correlation of data

    Directory of Open Access Journals (Sweden)

    Kaijun Wang

    2007-10-01

    Full Text Available Linear regression (LR and support vector regression (SVR are widely used in data analysis. Geometrical correlation learning (GcLearn was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation. This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR and SVR will have better prediction performance than traditional LR and SVR for prediction tasks when good inner correlations are obtained and predictions by traditional LR and SVR are far away from their neighbor training data under inner correlation. This gives the applicable condition of GcLearn method.

  2. Inverse estimation of multiple muscle activations based on linear logistic regression.

    Science.gov (United States)

    Sekiya, Masashi; Tsuji, Toshiaki

    2017-07-01

    This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.

  3. Linear regression and sensitivity analysis in nuclear reactor design

    International Nuclear Information System (INIS)

    Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.

    2015-01-01

    Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data

  4. Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

    Science.gov (United States)

    Choi, Jae-Seok; Kim, Munchurl

    2017-03-01

    Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower

  5. Regularized Label Relaxation Linear Regression.

    Science.gov (United States)

    Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu

    2018-04-01

    Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.

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

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

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

  7. A prospective validation of the IOTA logistic regression models (LR1 and LR2) in comparison to subjective pattern recognition for the diagnosis of ovarian cancer.

    Science.gov (United States)

    Nunes, Natalie; Ambler, Gareth; Hoo, Wee-Liak; Naftalin, Joel; Foo, Xulin; Widschwendter, Martin; Jurkovic, Davor

    2013-11-01

    This study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer. This was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared. The AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92-0.97] and 0.93 (95% CI, 0.90-0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84-24.6) and a LR-ve of 0.049 (95% CI, 0.022-0.107). The corresponding LR+ve and LR-ve for LR1 were 3.33 (95% CI, 2.85-3.55) and 0.03 (95% CI, 0.01-0.10), and for LR2 were 3.58 (95% CI, 2.77-4.63) and 0.052 (95% CI, 0.022-0.123). The accuracy of PR was 0.942 (95% CI, 0.908-0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781-0.870) for LR1 and 0.836 (95% CI, 0.788-0.872) for LR2 (P IOTA LR1 and LR2 were similar in nonexpert's hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.

  8. A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant

    International Nuclear Information System (INIS)

    Bunyamin, Muhammad Afif; Yap, Keem Siah; Aziz, Nur Liyana Afiqah Abdul; Tiong, Sheih Kiong; Wong, Shen Yuong; Kamal, Md Fauzan

    2013-01-01

    This paper presents a new approach of gas emission estimation in power generation plant using a hybrid Genetic Algorithm (GA) and Linear Regression (LR) (denoted as GA-LR). The LR is one of the approaches that model the relationship between an output dependant variable, y, with one or more explanatory variables or inputs which denoted as x. It is able to estimate unknown model parameters from inputs data. On the other hand, GA is used to search for the optimal solution until specific criteria is met causing termination. These results include providing good solutions as compared to one optimal solution for complex problems. Thus, GA is widely used as feature selection. By combining the LR and GA (GA-LR), this new technique is able to select the most important input features as well as giving more accurate prediction by minimizing the prediction errors. This new technique is able to produce more consistent of gas emission estimation, which may help in reducing population to the environment. In this paper, the study's interest is focused on nitrous oxides (NOx) prediction. The results of the experiment are encouraging.

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

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

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

  10. Applied linear regression

    CERN Document Server

    Weisberg, Sanford

    2013-01-01

    Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

  11. Approximate Solution of LR Fuzzy Sylvester Matrix Equations

    Directory of Open Access Journals (Sweden)

    Xiaobin Guo

    2013-01-01

    Full Text Available The fuzzy Sylvester matrix equation AX~+X~B=C~ in which A,B are m×m and n×n crisp matrices, respectively, and C~ is an m×n LR fuzzy numbers matrix is investigated. Based on the Kronecker product of matrices, we convert the fuzzy Sylvester matrix equation into an LR fuzzy linear system. Then we extend the fuzzy linear system into two systems of linear equations according to the arithmetic operations of LR fuzzy numbers. The fuzzy approximate solution of the original fuzzy matrix equation is obtained by solving the crisp linear systems. The existence condition of the LR fuzzy solution is also discussed. Some examples are given to illustrate the proposed method.

  12. Linear regression

    CERN Document Server

    Olive, David J

    2017-01-01

    This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

  13. Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

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    Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

    2015-11-03

    Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

  14. Modeling ionospheric foF 2 response during geomagnetic storms using neural network and linear regression techniques

    Science.gov (United States)

    Tshisaphungo, Mpho; Habarulema, John Bosco; McKinnell, Lee-Anne

    2018-06-01

    In this paper, the modeling of the ionospheric foF 2 changes during geomagnetic storms by means of neural network (NN) and linear regression (LR) techniques is presented. The results will lead to a valuable tool to model the complex ionospheric changes during disturbed days in an operational space weather monitoring and forecasting environment. The storm-time foF 2 data during 1996-2014 from Grahamstown (33.3°S, 26.5°E), South Africa ionosonde station was used in modeling. In this paper, six storms were reserved to validate the models and hence not used in the modeling process. We found that the performance of both NN and LR models is comparable during selected storms which fell within the data period (1996-2014) used in modeling. However, when validated on storm periods beyond 1996-2014, the NN model gives a better performance (R = 0.62) compared to LR model (R = 0.56) for a storm that reached a minimum Dst index of -155 nT during 19-23 December 2015. We also found that both NN and LR models are capable of capturing the ionospheric foF 2 responses during two great geomagnetic storms (28 October-1 November 2003 and 6-12 November 2004) which have been demonstrated to be difficult storms to model in previous studies.

  15. Recursive Algorithm For Linear Regression

    Science.gov (United States)

    Varanasi, S. V.

    1988-01-01

    Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.

  16. Discriminative Elastic-Net Regularized Linear Regression.

    Science.gov (United States)

    Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

    2017-03-01

    In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

  17. Prospective evaluation of IOTA logistic regression models LR1 and LR2 in comparison with subjective pattern recognition for diagnosis of ovarian cancer in an outpatient setting.

    Science.gov (United States)

    Nunes, N; Ambler, G; Foo, X; Widschwendter, M; Jurkovic, D

    2018-06-01

    To determine whether International Ovarian Tumor Analysis (IOTA) logistic regression models LR1 and LR2 developed for the preoperative diagnosis of ovarian cancer could also be used to differentiate between benign and malignant adnexal tumors in the population of women attending gynecology outpatient clinics. This was a single-center prospective observational study of consecutive women attending our gynecological diagnostic outpatient unit, recruited between May 2009 and January 2012. All the women were first examined by a Level-II ultrasound operator. In those diagnosed with adnexal tumors, the IOTA-LR1/2 protocol was used to evaluate the masses. The LR1 and LR2 models were then used to assess the risk of malignancy. Subsequently, the women were also examined by a Level-III examiner, who used pattern recognition to differentiate between benign and malignant tumors. Women with an ultrasound diagnosis of malignancy were offered surgery, while asymptomatic women with presumed benign lesions were offered conservative management with a minimum follow-up of 12 months. The initial diagnosis was compared with two reference standards: histological findings and/or a comparative assessment of tumor morphology on follow-up ultrasound scans. All women for whom the tumor classification on follow-up changed from benign to malignant were offered surgery. In the final analysis, 489 women who had either or both of the reference standards were included. Their mean age was 50 years (range, 16-91 years) and 45% were postmenopausal. Of the included women, 342/489 (69.9%) had surgery and 147/489 (30.1%) were managed conservatively. The malignancy rate was 137/489 (28.0%). Overall, sensitivities of LR1 and LR2 for the diagnosis of malignancy were 97.1% (95% CI, 92.7-99.2%) and 94.9% (95% CI, 89.8-97.9%) and specificities were 77.3% (95% CI, 72.5-81.5%) and 76.7% (95% CI, 71.9-81.0%), respectively (P > 0.05). In comparison with pattern recognition (sensitivity 94.2% (95% CI, 88

  18. Ultrasound-based logistic regression model LR2 versus magnetic resonance imaging for discriminating between benign and malignant adnexal masses: a prospective study.

    Science.gov (United States)

    Shimada, Kanane; Matsumoto, Koji; Mimura, Takashi; Ishikawa, Tetsuya; Munechika, Jiro; Ohgiya, Yoshimitsu; Kushima, Miki; Hirose, Yusuke; Asami, Yuka; Iitsuka, Chiaki; Miyamoto, Shingo; Onuki, Mamiko; Tsunoda, Hajime; Matsuoka, Ryu; Ichizuka, Kiyotake; Sekizawa, Akihiko

    2018-06-01

    The diagnostic performances of the International Ovarian Tumor Analysis (IOTA) ultrasound-based logistic regression model (LR2) and magnetic resonance imaging (MRI) in discriminating between benign and malignant adnexal masses have not been directly compared in a single study. Using the IOTA LR2 model and subjective interpretation of MRI findings by experienced radiologists, 265 consecutive patients with adnexal masses were preoperatively evaluated in two hospitals between February 2014 and December 2015. Definitive histological diagnosis of excised tissues was used as a gold standard. From the 265 study subjects, 54 (20.4%) tumors were histologically diagnosed as malignant (including 11 borderline and 3 metastatic tumors). Preoperative diagnoses of malignant tumors showed 91.7% total agreement between IOTA LR2 and MRI, with a kappa value of 0.77 [95% confidence interval (CI), 0.68-0.86]. Sensitivity of IOTA LR2 (0.94, 95% CI, 0.85-0.98) for predicting malignant tumors was similar to that of MRI (0.96, 95% CI, 0.87-0.99; P = 0.99), whereas specificity of IOTA LR2 (0.98, 95% CI, 0.95-0.99) was significantly higher than that of MRI (0.91, 95% CI, 0.87-0.95; P = 0.002). Combined IOTA LR2 and MRI results gave the greatest sensitivity (1.00, 95% CI, 0.93-1.00) and had similar specificity (0.91, 95% CI, 0.86-0.94) to MRI. The IOTA LR2 model had a similar sensitivity to MRI for discriminating between benign and malignant tumors and a higher specificity compared with MRI. Our findings suggest that the IOTA LR2 model, either alone or in conjunction with MRI, should be included in preoperative evaluation of adnexal masses.

  19. Linear regression in astronomy. II

    Science.gov (United States)

    Feigelson, Eric D.; Babu, Gutti J.

    1992-01-01

    A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

  20. Correlation and simple linear regression.

    Science.gov (United States)

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

    2003-06-01

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

  1. Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

    Science.gov (United States)

    Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

    2016-09-01

    The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

  2. LR: Compact connectivity representation for triangle meshes

    Energy Technology Data Exchange (ETDEWEB)

    Gurung, T; Luffel, M; Lindstrom, P; Rossignac, J

    2011-01-28

    We propose LR (Laced Ring) - a simple data structure for representing the connectivity of manifold triangle meshes. LR provides the option to store on average either 1.08 references per triangle or 26.2 bits per triangle. Its construction, from an input mesh that supports constant-time adjacency queries, has linear space and time complexity, and involves ordering most vertices along a nearly-Hamiltonian cycle. LR is best suited for applications that process meshes with fixed connectivity, as any changes to the connectivity require the data structure to be rebuilt. We provide an implementation of the set of standard random-access, constant-time operators for traversing a mesh, and show that LR often saves both space and traversal time over competing representations.

  3. Assessment of co-seismic landslide susceptibility using LR and ...

    Indian Academy of Sciences (India)

    Suchita Shrestha

    2018-03-28

    Mar 28, 2018 ... statistical methods, namely Logistic Regression. (LR) and Analysis ... The LR method has been applied by various researchers ... many bivariate models need that the independent variables be .... through a point (per unit contour length), and tan β is the ..... by multivariate statistical techniques; Nat. Hazards ...

  4. Piecewise linear regression splines with hyperbolic covariates

    International Nuclear Information System (INIS)

    Cologne, John B.; Sposto, Richard

    1992-09-01

    Consider the problem of fitting a curve to data that exhibit a multiphase linear response with smooth transitions between phases. We propose substituting hyperbolas as covariates in piecewise linear regression splines to obtain curves that are smoothly joined. The method provides an intuitive and easy way to extend the two-phase linear hyperbolic response model of Griffiths and Miller and Watts and Bacon to accommodate more than two linear segments. The resulting regression spline with hyperbolic covariates may be fit by nonlinear regression methods to estimate the degree of curvature between adjoining linear segments. The added complexity of fitting nonlinear, as opposed to linear, regression models is not great. The extra effort is particularly worthwhile when investigators are unwilling to assume that the slope of the response changes abruptly at the join points. We can also estimate the join points (the values of the abscissas where the linear segments would intersect if extrapolated) if their number and approximate locations may be presumed known. An example using data on changing age at menarche in a cohort of Japanese women illustrates the use of the method for exploratory data analysis. (author)

  5. Comparison of linear and zero-inflated negative binomial regression models for appraisal of risk factors associated with dental caries.

    Science.gov (United States)

    Batra, Manu; Shah, Aasim Farooq; Rajput, Prashant; Shah, Ishrat Aasim

    2016-01-01

    Dental caries among children has been described as a pandemic disease with a multifactorial nature. Various sociodemographic factors and oral hygiene practices are commonly tested for their influence on dental caries. In recent years, a recent statistical model that allows for covariate adjustment has been developed and is commonly referred zero-inflated negative binomial (ZINB) models. To compare the fit of the two models, the conventional linear regression (LR) model and ZINB model to assess the risk factors associated with dental caries. A cross-sectional survey was conducted on 1138 12-year-old school children in Moradabad Town, Uttar Pradesh during months of February-August 2014. Selected participants were interviewed using a questionnaire. Dental caries was assessed by recording decayed, missing, or filled teeth (DMFT) index. To assess the risk factor associated with dental caries in children, two approaches have been applied - LR model and ZINB model. The prevalence of caries-free subjects was 24.1%, and mean DMFT was 3.4 ± 1.8. In LR model, all the variables were statistically significant. Whereas in ZINB model, negative binomial part showed place of residence, father's education level, tooth brushing frequency, and dental visit statistically significant implying that the degree of being caries-free (DMFT = 0) increases for group of children who are living in urban, whose father is university pass out, who brushes twice a day and if have ever visited a dentist. The current study report that the LR model is a poorly fitted model and may lead to spurious conclusions whereas ZINB model has shown better goodness of fit (Akaike information criterion values - LR: 3.94; ZINB: 2.39) and can be preferred if high variance and number of an excess of zeroes are present.

  6. [From clinical judgment to linear regression model.

    Science.gov (United States)

    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.

  7. Post-processing through linear regression

    Science.gov (United States)

    van Schaeybroeck, B.; Vannitsem, S.

    2011-03-01

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

  8. Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

    Science.gov (United States)

    Fan, Xitao; Wang, Lin

    The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

  9. Post-processing through linear regression

    Directory of Open Access Journals (Sweden)

    B. Van Schaeybroeck

    2011-03-01

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

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

  10. Learning a Nonnegative Sparse Graph for Linear Regression.

    Science.gov (United States)

    Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

    2015-09-01

    Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.

  11. Removing Malmquist bias from linear regressions

    Science.gov (United States)

    Verter, Frances

    1993-01-01

    Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.

  12. Linear regression in astronomy. I

    Science.gov (United States)

    Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh

    1990-01-01

    Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.

  13. Adaptive local surface refinement based on LR NURBS and its application to contact

    Science.gov (United States)

    Zimmermann, Christopher; Sauer, Roger A.

    2017-12-01

    A novel adaptive local surface refinement technique based on Locally Refined Non-Uniform Rational B-Splines (LR NURBS) is presented. LR NURBS can model complex geometries exactly and are the rational extension of LR B-splines. The local representation of the parameter space overcomes the drawback of non-existent local refinement in standard NURBS-based isogeometric analysis. For a convenient embedding into general finite element codes, the Bézier extraction operator for LR NURBS is formulated. An automatic remeshing technique is presented that allows adaptive local refinement and coarsening of LR NURBS. In this work, LR NURBS are applied to contact computations of 3D solids and membranes. For solids, LR NURBS-enriched finite elements are used to discretize the contact surfaces with LR NURBS finite elements, while the rest of the body is discretized by linear Lagrange finite elements. For membranes, the entire surface is discretized by LR NURBS. Various numerical examples are shown, and they demonstrate the benefit of using LR NURBS: Compared to uniform refinement, LR NURBS can achieve high accuracy at lower computational cost.

  14. Use of probabilistic weights to enhance linear regression myoelectric control.

    Science.gov (United States)

    Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J

    2015-12-01

    Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts' law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p linear regression control. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

  15. Linear regression crash prediction models : issues and proposed solutions.

    Science.gov (United States)

    2010-05-01

    The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

  16. Biostatistics Series Module 6: Correlation and Linear Regression.

    Science.gov (United States)

    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 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.

  17. Linear regression and the normality assumption.

    Science.gov (United States)

    Schmidt, Amand F; Finan, Chris

    2017-12-16

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

  18. Controlling attribute effect in linear regression

    KAUST Repository

    Calders, Toon; Karim, Asim A.; Kamiran, Faisal; Ali, Wasif Mohammad; Zhang, Xiangliang

    2013-01-01

    In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.

  19. Controlling attribute effect in linear regression

    KAUST Repository

    Calders, Toon

    2013-12-01

    In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.

  20. Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression

    NARCIS (Netherlands)

    D.E. Allen (David); A.K. Singh (Abhay); R.J. Powell (Robert); M.J. McAleer (Michael); J. Taylor (James); L. Thomas (Lyn)

    2013-01-01

    textabstractThe purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the

  1. Determination of regression laws: Linear and nonlinear

    International Nuclear Information System (INIS)

    Onishchenko, A.M.

    1994-01-01

    A detailed mathematical determination of regression laws is presented in the article. Particular emphasis is place on determining the laws of X j on X l to account for source nuclei decay and detector errors in nuclear physics instrumentation. Both linear and nonlinear relations are presented. Linearization of 19 functions is tabulated, including graph, relation, variable substitution, obtained linear function, and remarks. 6 refs., 1 tab

  2. Some investigations and use of LR-115 track detectors for radon measurements

    CERN Document Server

    Amrani, D

    2001-01-01

    Closed passive integrating radon dosimeters based on the use of cellulose nitrate (LR-115 type II) have been developed for assessment of long term radon exposure. This paper presents and comments the results of investigations, of registration efficiency, calibration factors, linearity tests and lower limit of detection for LR-115 detectors from different batches.

  3. Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.

    Science.gov (United States)

    Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J

    2016-04-01

    The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

  4. Augmenting Data with Published Results in Bayesian Linear Regression

    Science.gov (United States)

    de Leeuw, Christiaan; Klugkist, Irene

    2012-01-01

    In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…

  5. Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

    CERN Document Server

    Faraway, Julian J

    2005-01-01

    Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

  6. Use of probabilistic weights to enhance linear regression myoelectric control

    Science.gov (United States)

    Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

    2015-12-01

    Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

  7. Steganalysis using logistic regression

    Science.gov (United States)

    Lubenko, Ivans; Ker, Andrew D.

    2011-02-01

    We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

  8. Finite Algorithms for Robust Linear Regression

    DEFF Research Database (Denmark)

    Madsen, Kaj; Nielsen, Hans Bruun

    1990-01-01

    The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...

  9. Direction of Effects in Multiple Linear Regression Models.

    Science.gov (United States)

    Wiedermann, Wolfgang; von Eye, Alexander

    2015-01-01

    Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

  10. Simple and multiple linear regression: sample size considerations.

    Science.gov (United States)

    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.

  11. Who Will Win?: Predicting the Presidential Election Using Linear Regression

    Science.gov (United States)

    Lamb, John H.

    2007-01-01

    This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

  12. Quantum algorithm for linear regression

    Science.gov (United States)

    Wang, Guoming

    2017-07-01

    We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.

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

    Science.gov (United States)

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

    2017-01-01

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

  14. A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

    Science.gov (United States)

    Meaney, Christopher; Moineddin, Rahim

    2014-01-24

    In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

  15. A test for the parameters of multiple linear regression models ...

    African Journals Online (AJOL)

    A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

  16. Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

    Science.gov (United States)

    Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

    2017-07-01

    This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

  17. Testing hypotheses for differences between linear regression lines

    Science.gov (United States)

    Stanley J. Zarnoch

    2009-01-01

    Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

  18. Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

    Science.gov (United States)

    Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

    2011-01-01

    Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

  19. Multiple Linear Regression: A Realistic Reflector.

    Science.gov (United States)

    Nutt, A. T.; Batsell, R. R.

    Examples of the use of Multiple Linear Regression (MLR) techniques are presented. This is done to show how MLR aids data processing and decision-making by providing the decision-maker with freedom in phrasing questions and by accurately reflecting the data on hand. A brief overview of the rationale underlying MLR is given, some basic definitions…

  20. Identification of Influential Points in a Linear Regression Model

    Directory of Open Access Journals (Sweden)

    Jan Grosz

    2011-03-01

    Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

  1. SPLINE LINEAR REGRESSION USED FOR EVALUATING FINANCIAL ASSETS 1

    Directory of Open Access Journals (Sweden)

    Liviu GEAMBAŞU

    2010-12-01

    Full Text Available One of the most important preoccupations of financial markets participants was and still is the problem of determining more precise the trend of financial assets prices. For solving this problem there were written many scientific papers and were developed many mathematical and statistical models in order to better determine the financial assets price trend. If until recently the simple linear models were largely used due to their facile utilization, the financial crises that affected the world economy starting with 2008 highlight the necessity of adapting the mathematical models to variation of economy. A simple to use model but adapted to economic life realities is the spline linear regression. This type of regression keeps the continuity of regression function, but split the studied data in intervals with homogenous characteristics. The characteristics of each interval are highlighted and also the evolution of market over all the intervals, resulting reduced standard errors. The first objective of the article is the theoretical presentation of the spline linear regression, also referring to scientific national and international papers related to this subject. The second objective is applying the theoretical model to data from the Bucharest Stock Exchange

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

    Science.gov (United States)

    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...

  3. Determination of a Differential Item Functioning Procedure Using the Hierarchical Generalized Linear Model

    Directory of Open Access Journals (Sweden)

    Tülin Acar

    2012-01-01

    Full Text Available The aim of this research is to compare the result of the differential item functioning (DIF determining with hierarchical generalized linear model (HGLM technique and the results of the DIF determining with logistic regression (LR and item response theory–likelihood ratio (IRT-LR techniques on the test items. For this reason, first in this research, it is determined whether the students encounter DIF with HGLM, LR, and IRT-LR techniques according to socioeconomic status (SES, in the Turkish, Social Sciences, and Science subtest items of the Secondary School Institutions Examination. When inspecting the correlations among the techniques in terms of determining the items having DIF, it was discovered that there was significant correlation between the results of IRT-LR and LR techniques in all subtests; merely in Science subtest, the results of the correlation between HGLM and IRT-LR techniques were found significant. DIF applications can be made on test items with other DIF analysis techniques that were not taken to the scope of this research. The analysis results, which were determined by using the DIF techniques in different sample sizes, can be compared.

  4. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    International Nuclear Information System (INIS)

    Althuwaynee, Omar F; Pradhan, Biswajeet; Ahmad, Noordin

    2014-01-01

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies

  5. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    Science.gov (United States)

    Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

    2014-06-01

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

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

    Science.gov (United States)

    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.

  7. Stochastic development regression on non-linear manifolds

    DEFF Research Database (Denmark)

    Kühnel, Line; Sommer, Stefan Horst

    2017-01-01

    We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is based on stochastic development of Euclidean diffusion...... processes to the manifold. Defining the data distribution as the transition distribution of the mapped stochastic process, parameters of the model, the non-linear analogue of design matrix and intercept, are found via maximum likelihood. The model is intrinsically related to the geometry encoded...

  8. Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

    Science.gov (United States)

    Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

    2013-01-01

    In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

  9. Linear regression methods a ccording to objective functions

    OpenAIRE

    Yasemin Sisman; Sebahattin Bektas

    2012-01-01

    The aim of the study is to explain the parameter estimation methods and the regression analysis. The simple linear regressionmethods grouped according to the objective function are introduced. The numerical solution is achieved for the simple linear regressionmethods according to objective function of Least Squares and theLeast Absolute Value adjustment methods. The success of the appliedmethods is analyzed using their objective function values.

  10. Estimating monotonic rates from biological data using local linear regression.

    Science.gov (United States)

    Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

    2017-03-01

    Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

  11. Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study.

    Science.gov (United States)

    Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y

    2008-02-18

    The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

  12. Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression

    Directory of Open Access Journals (Sweden)

    Land Walker H

    2011-01-01

    Full Text Available Abstract Background When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison. Results The SL approach is capable of generating odds ratios for main effects and risk factor interactions that better capture nonlinear relationships between exposure variables and outcome in comparison with LR. Conclusions The integration of SL methods in epidemiology may improve both the understanding and interpretation of complex exposure/disease relationships.

  13. Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

    Science.gov (United States)

    Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi

    2013-01-01

    Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. PMID:23984382

  14. Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

    Directory of Open Access Journals (Sweden)

    Xuanping Zhang

    2013-01-01

    Full Text Available Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR, which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds.

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

    Science.gov (United States)

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

    2012-12-01

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

  16. Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

    Directory of Open Access Journals (Sweden)

    Gascón Adrià

    2017-10-01

    Full Text Available We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013, and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

  17. A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

    Science.gov (United States)

    Smith, Paul F; Ganesh, Siva; Liu, Ping

    2013-10-30

    Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2006-07-01

    Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

  20. Analysis of γ spectra in airborne radioactivity measurements using multiple linear regressions

    International Nuclear Information System (INIS)

    Bao Min; Shi Quanlin; Zhang Jiamei

    2004-01-01

    This paper describes the net peak counts calculating of nuclide 137 Cs at 662 keV of γ spectra in airborne radioactivity measurements using multiple linear regressions. Mathematic model is founded by analyzing every factor that has contribution to Cs peak counts in spectra, and multiple linear regression function is established. Calculating process adopts stepwise regression, and the indistinctive factors are eliminated by F check. The regression results and its uncertainty are calculated using Least Square Estimation, then the Cs peak net counts and its uncertainty can be gotten. The analysis results for experimental spectrum are displayed. The influence of energy shift and energy resolution on the analyzing result is discussed. In comparison with the stripping spectra method, multiple linear regression method needn't stripping radios, and the calculating result has relation with the counts in Cs peak only, and the calculating uncertainty is reduced. (authors)

  1. A 3D graphene-based biosensor as an early microcystin-LR screening tool in sources of drinking water supply

    International Nuclear Information System (INIS)

    Zhang, Wei; Han, Changseok; Jia, Baoping; Saint, Christopher; Nadagouda, Mallikarjuna; Falaras, Polycarpos; Sygellou, Labrini; Vogiazi, Vasileia; Dionysiou, Dionysios D.

    2017-01-01

    Highlights: • 3D graphene-based biosensors can detect MC-LR with remarkable sensitivity. • Good linear correlation between electron-transfer resistance and MC-LR concentration. • A detection limit of 0.04 μg/L MC-LR was accomplished. - Abstract: Recent advances in graphene synthesis and understanding of properties have led to enormous applications in a variety of areas. Graphene and its unique electrical properties can favor electrochemical biosensor applications for aqueous toxin monitoring. Graphene-based biosensors can be used as an alternative to time-consuming, expensive and non-portable conventional methods of analysis involved in water quality monitoring and assessment. In this work, we showcased a three-dimensional (3D) graphene-based biosensor for microcystin-LR (MC-LR) detection and quantification. We report the efficient functionalization and immobilization of microcystin-LR and its antibodies on the facile synthesized CVD 3D graphene. The modified graphene electrodes were characterized by X-ray photoelectron spectroscopy and micro-Raman spectroscopy. Cyclic voltammetry and electrochemical impedance spectroscopy were used to electrochemically characterize the biochemical events on the electrodes. Specifically, as-prepared 3D graphene-based biosensors can detect MC-LR with remarkable sensitivity due to its macro-porous structure and large surface area, and high conductivity. A very good linear correlation of the electron-transfer resistance (R"2 = 0.93) was achieved over 0.05 and 20 μg/L MC-LR concentration range. Also, a detection limit of 0.05 μg/L was accomplished, which is much lower than the World Health Organization (WHO) provisional guideline limit of MC-LR concentration (i.e. 1 μg/L) in drinking water.

  2. Microcystin-LR Induced Immunotoxicity in Mammals

    Directory of Open Access Journals (Sweden)

    Yaqoob Lone

    2016-01-01

    Full Text Available Microcystins are toxic molecules produced by cyanobacterial blooms due to water eutrophication. Exposure to microcystins is a global health problem because of its association with various other pathological effects and people all over the world are exposed to microcystins on a regular basis. Evidence shows that microcystin-LR (MC-LR may adversely affect the immune system, but its specific effects on immune functions are lacking. In the present review, immunotoxicological effects associated with MC-LR in animals, humans, and in vitro models have been reported. Overall, the data shows that chronic exposure to MC-LR has the potential to impair vital immune responses which could lead to increased risk of various diseases including cancers. Studies in animal and in vitro models have provided some pivotal understanding into the potential mechanisms of MC-LR related immunotoxicity suggesting that further investigation, particularly in humans, is required to better understand the relationship between development of disease and the MC-LR exposure.

  3. Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

    Science.gov (United States)

    Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

    2017-12-01

    The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

  4. OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

    Science.gov (United States)

    Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

    2012-01-01

    The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

  5. Generalised Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions

    DEFF Research Database (Denmark)

    Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf

    We consider the semiparametric generalised linear regression model which has mainstream empirical models such as the (partially) linear mean regression, logistic and multinomial regression as special cases. As an extension to related literature we allow a misclassified covariate to be interacted...

  6. Neutrosophic Correlation and Simple Linear Regression

    Directory of Open Access Journals (Sweden)

    A. A. Salama

    2014-09-01

    Full Text Available Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache. Recently, Salama et al., introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we introduce and study the neutrosophic simple linear regression model. Possible applications to data processing are touched upon.

  7. Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

    OpenAIRE

    KELEŞ, Taliha; ALTUN, Murat

    2016-01-01

    Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...

  8. Single image super-resolution using locally adaptive multiple linear regression.

    Science.gov (United States)

    Yu, Soohwan; Kang, Wonseok; Ko, Seungyong; Paik, Joonki

    2015-12-01

    This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.

  9. Flexible regression models for estimating postmortem interval (PMI) in forensic medicine.

    Science.gov (United States)

    Muñoz Barús, José Ignacio; Febrero-Bande, Manuel; Cadarso-Suárez, Carmen

    2008-10-30

    Correct determination of time of death is an important goal in forensic medicine. Numerous methods have been described for estimating postmortem interval (PMI), but most are imprecise, poorly reproducible and/or have not been validated with real data. In recent years, however, some progress in PMI estimation has been made, notably through the use of new biochemical methods for quantifying relevant indicator compounds in the vitreous humour. The best, but unverified, results have been obtained with [K+] and hypoxanthine [Hx], using simple linear regression (LR) models. The main aim of this paper is to offer more flexible alternatives to LR, such as generalized additive models (GAMs) and support vector machines (SVMs) in order to obtain improved PMI estimates. The present study, based on detailed analysis of [K+] and [Hx] in more than 200 vitreous humour samples from subjects with known PMI, compared classical LR methodology with GAM and SVM methodologies. Both proved better than LR for estimation of PMI. SVM showed somewhat greater precision than GAM, but GAM offers a readily interpretable graphical output, facilitating understanding of findings by legal professionals; there are thus arguments for using both types of models. R code for these methods is available from the authors, permitting accurate prediction of PMI from vitreous humour [K+], [Hx] and [U], with confidence intervals and graphical output provided. Copyright 2008 John Wiley & Sons, Ltd.

  10. Teaching the Concept of Breakdown Point in Simple Linear Regression.

    Science.gov (United States)

    Chan, Wai-Sum

    2001-01-01

    Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…

  11. High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

    Science.gov (United States)

    Laurens, L M L; Wolfrum, E J

    2013-12-18

    One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

  12. Establishment of regression dependences. Linear and nonlinear dependences

    International Nuclear Information System (INIS)

    Onishchenko, A.M.

    1994-01-01

    The main problems of determination of linear and 19 types of nonlinear regression dependences are completely discussed. It is taken into consideration that total dispersions are the sum of measurement dispersions and parameter variation dispersions themselves. Approaches to all dispersions determination are described. It is shown that the least square fit gives inconsistent estimation for industrial objects and processes. The correction methods by taking into account comparable measurement errors for both variable give an opportunity to obtain consistent estimation for the regression equation parameters. The condition of the correction technique application expediency is given. The technique for determination of nonlinear regression dependences taking into account the dependence form and comparable errors of both variables is described. 6 refs., 1 tab

  13. Applied logistic regression

    CERN Document Server

    Hosmer, David W; Sturdivant, Rodney X

    2013-01-01

     A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

  14. Prediction of long-residue properties of potential blends from mathematically mixed infrared spectra of pure crude oils by partial least-squares regression models

    NARCIS (Netherlands)

    de Peinder, P.; Visser, T.; Petrauskas, D.D.; Salvatori, F.; Soulimani, F.; Weckhuysen, B.M.

    2009-01-01

    Research has been carried out to determine the feasibility of partial least-squares (PLS) regression models to predict the long-residue (LR) properties of potential blends from infrared (IR) spectra that have been created by linearly co-adding the IR spectra of crude oils. The study is the follow-up

  15. A simple highly sensitive and selective aptamer-based colorimetric sensor for environmental toxins microcystin-LR in water samples.

    Science.gov (United States)

    Li, Xiuyan; Cheng, Ruojie; Shi, Huijie; Tang, Bo; Xiao, Hanshuang; Zhao, Guohua

    2016-03-05

    A simple and highly sensitive aptamer-based colorimetric sensor was developed for selective detection of Microcystin-LR (MC-LR). The aptamer (ABA) was employed as recognition element which could bind MC-LR with high-affinity, while gold nanoparticles (AuNPs) worked as sensing materials whose plasma resonance absorption peaks red shifted upon binding of the targets at a high concentration of sodium chloride. With the addition of MC-LR, the random coil aptamer adsorbed on Au NPs altered into regulated structure to form MC-LR-aptamer complexes and broke away from the surface of Au NPs, leading to the aggregation of AuNPs, and the color converted from red to blue due to the interparticle plasmon coupling. Results showed that our aptamer-based colorimetric sensor exhibited rapid and sensitive detection performance for MC-LR with linear range from 0.5 nM to 7.5 μM and the detection limit reached 0.37 nM. Meanwhile, the pollutants usually coexisting with MC-LR in pollutant water samples had not demonstrated disturbance for detecting of MC-LR. The mechanism was also proposed suggesting that high affinity interaction between aptamer and MC-LR significantly enhanced the sensitivity and selectivity for MC-LR detection. Besides, the established method was utilized in analyzing real water samples and splendid sensitivity and selectivity were obtained as well. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. The number of subjects per variable required in linear regression analyses.

    Science.gov (United States)

    Austin, Peter C; Steyerberg, Ewout W

    2015-06-01

    To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  17. Homogeneous time-resolved fluoroimmunoassay of microcystin-LR using layered WS2 nanosheets as a transducer

    Science.gov (United States)

    Qin, Xiaodan; Wang, Yuanxiu; Song, Bo; Wang, Xin; Ma, Hua; Yuan, Jingli

    2017-06-01

    A homogeneous time-resolved fluoroimmunoassay method for rapid and sensitive detection of microcystin-LR (MC-LR) in water samples was developed based on the interaction between water-soluble WS2 nanosheets and the conjugate of MC-LR with a luminescent Eu3+ complex BHHBCB-Eu3+ (BHHBCB: 1,2-bis[4‧-(1″,1″,1″,2″,2″,3″,3″-heptafluoro-4″,6″-hexanedion-6″-yl)- benzyl]-4-chlorosulfobenzene). The large lateral dimensions and high surface areas of two-dimensional layered WS2 nanosheets enable easy adsorption of the MC-LR-BHHBCB-Eu3+ conjugate, that lead to efficient quenching of the luminescence of Eu3+ complex via energy transfer or electron transfer process. However, the addition of monoclonal anti-MC-LR antibody can induce the formation of MC-LR-BHHBCB-Eu3+/antibody immune complex, which prevents the interaction between WS2 nanosheets and MC-LR-BHHBCB-Eu3+ to result in the restoration of Eu3+ luminescence. This signal transduction mechanism made it possible for analysis of the target MC-LR in a homogeneous system. The present method has advantages of rapidity and simplicity since the B/F (bound reagent/free reagent) separation steps, the solid-phase carrier and antibody labeling or modification process are not necessary. The proposed immunosensing system displayed a wide linear range, good precision and accuracy, and comparable sensitivity with a detection limit of 0.3 μg l-1, which satisfied the World Health Organization (WHO) provisional guideline limit of 1.0 μg l-1 for MC-LR in drinking water.

  18. How Robust Is Linear Regression with Dummy Variables?

    Science.gov (United States)

    Blankmeyer, Eric

    2006-01-01

    Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…

  19. Solving LR Conflicts Through Context Aware Scanning

    Science.gov (United States)

    Leon, C. Rodriguez; Forte, L. Garcia

    2011-09-01

    This paper presents a new algorithm to compute the exact list of tokens expected by any LR syntax analyzer at any point of the scanning process. The lexer can, at any time, compute the exact list of valid tokens to return only tokens in this set. In the case than more than one matching token is in the valid set, the lexer can resort to a nested LR parser to disambiguate. Allowing nested LR parsing requires some slight modifications when building the LR parsing tables. We also show how LR parsers can parse conflictive and inherently ambiguous languages using a combination of nested parsing and context aware scanning. These expanded lexical analyzers can be generated from high level specifications.

  20. Common pitfalls in statistical analysis: Linear regression analysis

    Directory of Open Access Journals (Sweden)

    Rakesh Aggarwal

    2017-01-01

    Full Text Available In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

  1. Tightness of M-estimators for multiple linear regression in time series

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...

  2. Unifying LL and LR parsing

    NARCIS (Netherlands)

    W.H.L.M. Pijls (Wim)

    1993-01-01

    textabstractIn parsing theory, LL parsing and LR parsing are regarded to be two distinct methods. In this paper the relation between these methods is clarified.As shown in literature on parsing theory, for every context-free grammar, a so-called non-deterministic LR(0) automaton can be constructed.

  3. BRGLM, Interactive Linear Regression Analysis by Least Square Fit

    International Nuclear Information System (INIS)

    Ringland, J.T.; Bohrer, R.E.; Sherman, M.E.

    1985-01-01

    1 - Description of program or function: BRGLM is an interactive program written to fit general linear regression models by least squares and to provide a variety of statistical diagnostic information about the fit. Stepwise and all-subsets regression can be carried out also. There are facilities for interactive data management (e.g. setting missing value flags, data transformations) and tools for constructing design matrices for the more commonly-used models such as factorials, cubic Splines, and auto-regressions. 2 - Method of solution: The least squares computations are based on the orthogonal (QR) decomposition of the design matrix obtained using the modified Gram-Schmidt algorithm. 3 - Restrictions on the complexity of the problem: The current release of BRGLM allows maxima of 1000 observations, 99 variables, and 3000 words of main memory workspace. For a problem with N observations and P variables, the number of words of main memory storage required is MAX(N*(P+6), N*P+P*P+3*N, and 3*P*P+6*N). Any linear model may be fit although the in-memory workspace will have to be increased for larger problems

  4. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

    Science.gov (United States)

    Kawashima, Issaku; Kumano, Hiroaki

    2017-01-01

    Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  5. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Issaku Kawashima

    2017-07-01

    Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  6. Stochastic development regression on non-linear manifolds

    DEFF Research Database (Denmark)

    Kühnel, Line; Sommer, Stefan Horst

    2017-01-01

    We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is based on stochastic development of Euclidean diffusion...... processes to the manifold. Defining the data distribution as the transition distribution of the mapped stochastic process, parameters of the model, the non-linear analogue of design matrix and intercept, are found via maximum likelihood. The model is intrinsically related to the geometry encoded...... in the connection of the manifold. We propose an estimation procedure which applies the Laplace approximation of the likelihood function. A simulation study of the performance of the model is performed and the model is applied to a real dataset of Corpus Callosum shapes....

  7. EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression.

    Science.gov (United States)

    Lian, Yao; Ge, Meng; Pan, Xian-Ming

    2014-12-19

    B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task. In this work, based on the antigen's primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728. We have presented a reliable method for the identification of linear B cell epitope using antigen's primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/ .

  8. The number of subjects per variable required in linear regression analyses

    NARCIS (Netherlands)

    P.C. Austin (Peter); E.W. Steyerberg (Ewout)

    2015-01-01

    textabstractObjectives To determine the number of independent variables that can be included in a linear regression model. Study Design and Setting We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression

  9. Distributed Monitoring of the R2 Statistic for Linear Regression

    Data.gov (United States)

    National Aeronautics and Space Administration — The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and...

  10. Treating experimental data of inverse kinetic method by unitary linear regression analysis

    International Nuclear Information System (INIS)

    Zhao Yusen; Chen Xiaoliang

    2009-01-01

    The theory of treating experimental data of inverse kinetic method by unitary linear regression analysis was described. Not only the reactivity, but also the effective neutron source intensity could be calculated by this method. Computer code was compiled base on the inverse kinetic method and unitary linear regression analysis. The data of zero power facility BFS-1 in Russia were processed and the results were compared. The results show that the reactivity and the effective neutron source intensity can be obtained correctly by treating experimental data of inverse kinetic method using unitary linear regression analysis and the precision of reactivity measurement is improved. The central element efficiency can be calculated by using the reactivity. The result also shows that the effect to reactivity measurement caused by external neutron source should be considered when the reactor power is low and the intensity of external neutron source is strong. (authors)

  11. An improved multiple linear regression and data analysis computer program package

    Science.gov (United States)

    Sidik, S. M.

    1972-01-01

    NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

  12. On macroeconomic values investigation using fuzzy linear regression analysis

    Directory of Open Access Journals (Sweden)

    Richard Pospíšil

    2017-06-01

    Full Text Available The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts.

  13. Fuzzy Linear Regression for the Time Series Data which is Fuzzified with SMRGT Method

    Directory of Open Access Journals (Sweden)

    Seçil YALAZ

    2016-10-01

    Full Text Available Our work on regression and classification provides a new contribution to the analysis of time series used in many areas for years. Owing to the fact that convergence could not obtained with the methods used in autocorrelation fixing process faced with time series regression application, success is not met or fall into obligation of changing the models’ degree. Changing the models’ degree may not be desirable in every situation. In our study, recommended for these situations, time series data was fuzzified by using the simple membership function and fuzzy rule generation technique (SMRGT and to estimate future an equation has created by applying fuzzy least square regression (FLSR method which is a simple linear regression method to this data. Although SMRGT has success in determining the flow discharge in open channels and can be used confidently for flow discharge modeling in open canals, as well as in pipe flow with some modifications, there is no clue about that this technique is successful in fuzzy linear regression modeling. Therefore, in order to address the luck of such a modeling, a new hybrid model has been described within this study. In conclusion, to demonstrate our methods’ efficiency, classical linear regression for time series data and linear regression for fuzzy time series data were applied to two different data sets, and these two approaches performances were compared by using different measures.

  14. Process optimization for microcystin-LR degradation by Response Surface Methodology and mechanism analysis in gas-liquid hybrid discharge system.

    Science.gov (United States)

    Zhang, Yi; Wei, Hanyu; Xin, Qing; Wang, Mingang; Wang, Qi; Wang, Qiang; Cong, Yanqing

    2016-12-01

    A gas-liquid hybrid discharge system was applied to microcystin-LR (MC-LR) degradation. MC-LR degradation was completed after 1 min under a pulsed high voltage of 16 kV, gas-liquid interface gap of 10 mm and oxygen flow rate of 160 L/h. The Box-Behnken Design was proposed in Response Surface Methodology to evaluate the influence of pulsed high voltage, electrode distance and oxygen flow rate on MC-LR removal efficiency. Multiple regression analysis, focused on multivariable factors, was employed and a reduced cubic model was developed. The ANOVA analysis shows that the model is significant and the model prediction on MC-LR removal was also validated with experimental data. The optimum conditions for the process are obtained at pulsed voltage of 16 kV, gas-liquid interface gap of 10 mm and oxygen flow rate of 120 L/h with ta removal efficiency of MC-LR of 96.6%. The addition of catalysts (TiO 2 or Fe 2+ ) in the gas-liquid hybrid discharge system was found to enhance the removal of MC-LR. The intermediates of MC-LR degradation were analyzed by liquid chromatography/mass spectrometry. The degradation pathway proposed envisaged the oxidation of hydroxyl radicals and ozone, and attack of high-energy electrons on the unsaturated double bonds of Adda and Mdha, with MC-LR finally decomposing into small molecular products. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

    Science.gov (United States)

    Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

    2017-01-01

    This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  16. Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

    Science.gov (United States)

    Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

    2014-07-01

    The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

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

    Science.gov (United States)

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

    2016-01-01

    Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19

  18. Analysis of dental caries using generalized linear and count regression models

    Directory of Open Access Journals (Sweden)

    Javali M. Phil

    2013-11-01

    Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.

  19. Regression of non-linear coupling of noise in LIGO detectors

    Science.gov (United States)

    Da Silva Costa, C. F.; Billman, C.; Effler, A.; Klimenko, S.; Cheng, H.-P.

    2018-03-01

    In 2015, after their upgrade, the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors started acquiring data. The effort to improve their sensitivity has never stopped since then. The goal to achieve design sensitivity is challenging. Environmental and instrumental noise couple to the detector output with different, linear and non-linear, coupling mechanisms. The noise regression method we use is based on the Wiener–Kolmogorov filter, which uses witness channels to make noise predictions. We present here how this method helped to determine complex non-linear noise couplings in the output mode cleaner and in the mirror suspension system of the LIGO detector.

  20. Fitting program for linear regressions according to Mahon (1996)

    Energy Technology Data Exchange (ETDEWEB)

    2018-01-09

    This program takes the users' Input data and fits a linear regression to it using the prescription presented by Mahon (1996). Compared to the commonly used York fit, this method has the correct prescription for measurement error propagation. This software should facilitate the proper fitting of measurements with a simple Interface.

  1. Antilisterial activity of a broad-spectrum bacteriocin, enterocin LR/6 from Enterococcus faecium LR/6.

    Science.gov (United States)

    Kumar, Manoj; Srivastava, Sheela

    2010-10-01

    Enterocin LR/6, a purified bacteriocin, exhibited broad inhibitory spectrum both against related as well as some food-borne pathogens such as Listeria monocytogenes, Yersinia enterocolitica, Aeromonas sp., Shigella sp., and Bacillus licheniformis. In this investigation, we have focused on L. monocytogenes as the target organism, as it is not only an important pathogen but can also survive over a wide range of environmental conditions such as refrigeration temperature, low pH, and high-salt concentration. This allows the pathogen to overcome many food preservation and safety barriers and poses a potential risk to human health. The enterocin LR/6 showed a bactericidal action against L. monocytogenes and completely inhibited the growth on agar plates, supplemented with 200 AU/ml of enterocin LR/6. The effectiveness of enterocin LR/6 in completely killing a population of acid-adapted (pH 5.2, 2 h) L. monocytogenes exposed to different temperatures (4-37 degrees C), pH (2.5-8.0), and osmotic (up to 30% NaCl) stress is reported here. This paper focuses on the key issue of killing of the acid-adapted L. monocytogenes cells under adverse environmental conditions.

  2. Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

    Science.gov (United States)

    Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

    2015-08-01

    Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

  3. A SOCIOLOGICAL ANALYSIS OF THE CHILDBEARING COEFFICIENT IN THE ALTAI REGION BASED ON METHOD OF FUZZY LINEAR REGRESSION

    Directory of Open Access Journals (Sweden)

    Sergei Vladimirovich Varaksin

    2017-06-01

    Full Text Available Purpose. Construction of a mathematical model of the dynamics of childbearing change in the Altai region in 2000–2016, analysis of the dynamics of changes in birth rates for multiple age categories of women of childbearing age. Methodology. A auxiliary analysis element is the construction of linear mathematical models of the dynamics of childbearing by using fuzzy linear regression method based on fuzzy numbers. Fuzzy linear regression is considered as an alternative to standard statistical linear regression for short time series and unknown distribution law. The parameters of fuzzy linear and standard statistical regressions for childbearing time series were defined with using the built in language MatLab algorithm. Method of fuzzy linear regression is not used in sociological researches yet. Results. There are made the conclusions about the socio-demographic changes in society, the high efficiency of the demographic policy of the leadership of the region and the country, and the applicability of the method of fuzzy linear regression for sociological analysis.

  4. A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

    Science.gov (United States)

    Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

    2016-04-01

    Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Ludbrook, John

    2012-04-01

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

  6. Short communication: Emergence of a new race of leaf rust with combined virulence to Lr14a and Lr72 genes on durum wheat

    Energy Technology Data Exchange (ETDEWEB)

    Soleiman, N.H; Solis, I.; Soliman, M.H.; Sillero, J.C.; Villegas, D.; Alvaro, F.; Royo, C.; Serra, J.; Ammar, K.; Martínez-Moreno, F.

    2016-11-01

    Leaf rust is a foliar disease caused by the fungus Puccinia triticina that may severely reduce durum wheat yield. Resistance to this pathogen is common in modern durum germplasm but is frequently based on Lr72 and Lr14a. After accounts of races with virulence to Lr14a gene in France in 2000, the present study reports the detection in 2013 for the first time of a new race with virulence to Lr14a and Lr72. The aim of this work was to characterize the virulence pattern of four Spanish isolates with virulence to Lr14a, and to discuss the consequences of this presence. Rusted leaves from cultivars ‘Don Jaime’ (Lr14a) and ‘Gallareta’ (Lr72) were collected in 2013 in the field at two Spanish sites, one in the south (near Cadiz) and another in the north (near Girona). Spores from single pustule for each cultivar and site were multiplied on susceptible cultivar ‘Don Rafael’. Then, the four isolates were inoculated on a set of 19 isogenic lines Thatcher to characterize their virulence spectrum. All isolates presented the same virulence pattern. They were virulent on both Lr14a and Lr72 and the race was named DBB/BS. This race was very similar to those reported in 2009-11, but with added virulence to Lr14a. The resistance based on Lr14a has therefore been overcome in Spain, by a new race that has likely emerged via stepwise mutation from the local predominating races. This information is important to guide breeders in their breeding programmes and gene deployment strategies. (Author)

  7. LRSYS, PASCAL LR(1) Parser Generator System

    International Nuclear Information System (INIS)

    O'Hair, K.

    1991-01-01

    Description of program or function: LRSYS is a complete LR(1) parser generator system written entirely in a portable subset of Pascal. The system, LRSYS, includes a grammar analyzer program (LR) which reads a context-free (BNF) grammar as input and produces LR(1) parsing tables as output, a lexical analyzer generator (LEX) which reads regular expressions created by the REG process as input and produces lexical tables as output, and various parser skeletons that get merged with the tables to produce complete parsers (SMAKE). Current parser skeletons include Pascal, FORTRAN 77, and C. In addition, the CRAY1, DEC VAX11 version contains LRLTRAN and CFT- FORTRAN 77 skeletons. Other language skeletons can easily be added to the system. LRSYS is based on the LR program (NESC Abstract 822)

  8. Linear regression analysis: part 14 of a series on evaluation of scientific publications.

    Science.gov (United States)

    Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

    2010-11-01

    Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

  9. Effects of microcystin-LR, cylindrospermopsin and a microcystin-LR/cylindrospermopsin mixture on growth, oxidative stress and mineral content in lettuce plants (Lactuca sativa L.).

    Science.gov (United States)

    Freitas, Marisa; Azevedo, Joana; Pinto, Edgar; Neves, Joana; Campos, Alexandre; Vasconcelos, Vitor

    2015-06-01

    Toxic cyanobacterial blooms are documented worldwide as an emerging environmental concern. Recent studies support the hypothesis that microcystin-LR (MC-LR) and cylindrospermopsin (CYN) produce toxic effects in crop plants. Lettuce (Lactuca sativa L.) is an important commercial leafy vegetable that supplies essential elements for human nutrition; thus, the study of its sensitivity to MC-LR, CYN and a MC-LR/CYN mixture is of major relevance. This study aimed to assess the effects of environmentally relevant concentrations (1, 10 and 100 µg/L) of MC-LR, CYN and a MC-LR/CYN mixture on growth, antioxidant defense system and mineral content in lettuce plants. In almost all treatments, an increase in root fresh weight was obtained; however, the fresh weight of leaves was significantly decreased in plants exposed to 100 µg/L concentrations of each toxin and the toxin mixture. Overall, GST activity was significantly increased in roots, contrary to GPx activity, which decreased in roots and leaves. The mineral content in lettuce leaves changed due to its exposure to cyanotoxins; in general, the mineral content decreased with MC-LR and increased with CYN, and apparently these effects are time and concentration-dependent. The effects of the MC-LR/CYN mixture were almost always similar to the single cyanotoxins, although MC-LR seems to be more toxic than CYN. Our results suggest that lettuce plants in non-early stages of development are able to cope with lower concentrations of MC-LR, CYN and the MC-LR/CYN mixture; however, higher concentrations (100 µg/L) can affect both lettuce yield and nutritional quality. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Linear Regression on Sparse Features for Single-Channel Speech Separation

    DEFF Research Database (Denmark)

    Schmidt, Mikkel N.; Olsson, Rasmus Kongsgaard

    2007-01-01

    In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech...... mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly improved when regression analysis is performed on the sparse, non-negative features, both...

  11. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

    Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  12. The Relationship between Economic Growth and Money Laundering – a Linear Regression Model

    Directory of Open Access Journals (Sweden)

    Daniel Rece

    2009-09-01

    Full Text Available This study provides an overview of the relationship between economic growth and money laundering modeled by a least squares function. The report analyzes statistically data collected from USA, Russia, Romania and other eleven European countries, rendering a linear regression model. The study illustrates that 23.7% of the total variance in the regressand (level of money laundering is “explained” by the linear regression model. In our opinion, this model will provide critical auxiliary judgment and decision support for anti-money laundering service systems.

  13. Data Transformations for Inference with Linear Regression: Clarifications and Recommendations

    Science.gov (United States)

    Pek, Jolynn; Wong, Octavia; Wong, C. M.

    2017-01-01

    Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to…

  14. Alpins and thibos vectorial astigmatism analyses: proposal of a linear regression model between methods

    Directory of Open Access Journals (Sweden)

    Giuliano de Oliveira Freitas

    2013-10-01

    Full Text Available PURPOSE: To determine linear regression models between Alpins descriptive indices and Thibos astigmatic power vectors (APV, assessing the validity and strength of such correlations. METHODS: This case series prospectively assessed 62 eyes of 31 consecutive cataract patients with preoperative corneal astigmatism between 0.75 and 2.50 diopters in both eyes. Patients were randomly assorted among two phacoemulsification groups: one assigned to receive AcrySof®Toric intraocular lens (IOL in both eyes and another assigned to have AcrySof Natural IOL associated with limbal relaxing incisions, also in both eyes. All patients were reevaluated postoperatively at 6 months, when refractive astigmatism analysis was performed using both Alpins and Thibos methods. The ratio between Thibos postoperative APV and preoperative APV (APVratio and its linear regression to Alpins percentage of success of astigmatic surgery, percentage of astigmatism corrected and percentage of astigmatism reduction at the intended axis were assessed. RESULTS: Significant negative correlation between the ratio of post- and preoperative Thibos APVratio and Alpins percentage of success (%Success was found (Spearman's ρ=-0.93; linear regression is given by the following equation: %Success = (-APVratio + 1.00x100. CONCLUSION: The linear regression we found between APVratio and %Success permits a validated mathematical inference concerning the overall success of astigmatic surgery.

  15. On the null distribution of Bayes factors in linear regression

    Science.gov (United States)

    We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...

  16. Implementing fuzzy polynomial interpolation (FPI and fuzzy linear regression (LFR

    Directory of Open Access Journals (Sweden)

    Maria Cristina Floreno

    1996-05-01

    Full Text Available This paper presents some preliminary results arising within a general framework concerning the development of software tools for fuzzy arithmetic. The program is in a preliminary stage. What has been already implemented consists of a set of routines for elementary operations, optimized functions evaluation, interpolation and regression. Some of these have been applied to real problems.This paper describes a prototype of a library in C++ for polynomial interpolation of fuzzifying functions, a set of routines in FORTRAN for fuzzy linear regression and a program with graphical user interface allowing the use of such routines.

  17. Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

    Directory of Open Access Journals (Sweden)

    Soyoung Park

    2017-07-01

    Full Text Available This study mapped and analyzed groundwater potential using two different models, logistic regression (LR and multivariate adaptive regression splines (MARS, and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70% were used for model training, whereas the other 365 locations (30% were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

  18. Effect of calcium and magnesium on the antimicrobial action of enterocin LR/6 produced by Enterococcus faecium LR/6.

    Science.gov (United States)

    Kumar, Manoj; Srivastava, Sheela

    2011-06-01

    Enterococci are well-known producers of antimicrobial peptides (enterocins) that possess potential as biopreservatives in food. In this study, divalent cations and release of intracellular potassium were used to assess the mechanism of interaction and killing of enterocin LR/6 produced by Enterococcus faecium LR/6 on three target Gram-positive and Gram-negative bacteria, namely Micrococcus luteus, Enterococcus sp. strain LR/3 and Escherichia coli K-12. Whilst treatment with enterocin LR/6 in all cases led to a significant loss of viability, suggesting a bactericidal mode of action, E. coli K-12 showed better tolerance than the other two strains. Bacteriocins have generally been reported to create pores in the membrane of sensitive cells and this function is diminished by divalent cations. In this study it was shown that Ca(2+) and Mg(2+) markedly improved the viability of enterocin LR/6-treated cells in a concentration-dependent manner. K(+) release as a sign of membrane leakiness was higher in M. luteus compared with the other two test strains. In agreement with the viability response, pre-exposure to Ca(2+) and Mg(2+) substantially reduced the amount of K(+) leakage by M. luteus and Enterococcus sp.; in the case of E. coli K-12, no leakage of K(+) was recorded. These results suggest that enterocin LR/6, which possesses good antibacterial potential, may not be very effective as a preservative in foods containing high concentrations of calcium and magnesium. Copyright © 2011 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

  19. Application of range-test in multiple linear regression analysis in ...

    African Journals Online (AJOL)

    Application of range-test in multiple linear regression analysis in the presence of outliers is studied in this paper. First, the plot of the explanatory variables (i.e. Administration, Social/Commercial, Economic services and Transfer) on the dependent variable (i.e. GDP) was done to identify the statistical trend over the years.

  20. Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

    Directory of Open Access Journals (Sweden)

    C. Wu

    2018-03-01

    Full Text Available Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS, Deming regression (DR, orthogonal distance regression (ODR, weighted ODR (WODR, and York regression (YR. We first introduce a new data generation scheme that employs the Mersenne twister (MT pseudorandom number generator. The numerical simulations are also improved by (a refining the parameterization of nonlinear measurement uncertainties, (b inclusion of a linear measurement uncertainty, and (c inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot was developed to facilitate the implementation of error-in-variables regressions.

  1. Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

    Science.gov (United States)

    Wu, Cheng; Zhen Yu, Jian

    2018-03-01

    Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

  2. Research on the multiple linear regression in non-invasive blood glucose measurement.

    Science.gov (United States)

    Zhu, Jianming; Chen, Zhencheng

    2015-01-01

    A non-invasive blood glucose measurement sensor and the data process algorithm based on the metabolic energy conservation (MEC) method are presented in this paper. The physiological parameters of human fingertip can be measured by various sensing modalities, and blood glucose value can be evaluated with the physiological parameters by the multiple linear regression analysis. Five methods such as enter, remove, forward, backward and stepwise in multiple linear regression were compared, and the backward method had the best performance. The best correlation coefficient was 0.876 with the standard error of the estimate 0.534, and the significance was 0.012 (sig. regression equation was valid. The Clarke error grid analysis was performed to compare the MEC method with the hexokinase method, using 200 data points. The correlation coefficient R was 0.867 and all of the points were located in Zone A and Zone B, which shows the MEC method provides a feasible and valid way for non-invasive blood glucose measurement.

  3. Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

    Directory of Open Access Journals (Sweden)

    Qiutong Jin

    2016-06-01

    Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

  4. Investigation of linear regression of EPR dosimetric signal of the man tooth enamel

    International Nuclear Information System (INIS)

    Pivovarov, S.P.; Rukhin, A.B.; Zhakparov, R.K.; Vasilevskaya, L.A.

    2001-01-01

    The experimental relations of the EPR radiation signal in samples of man tooth enamel of three donors of different age up to doses 1350 Gy are examined. To all of them the linear regression is applicable. The considerable errors leading to apparent non-linearity are eliminated most. (author)

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

    Science.gov (United States)

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

    2013-10-01

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

  6. Optimal choice of basis functions in the linear regression analysis

    International Nuclear Information System (INIS)

    Khotinskij, A.M.

    1988-01-01

    Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs

  7. Electrochemical Aptatoxisensor Responses on Nanocomposites Containing Electro-Deposited Silver Nanoparticles on Poly(Propyleneimine Dendrimer for the Detection of Microcystin-LR in Freshwater

    Directory of Open Access Journals (Sweden)

    Mawethu P. Bilibana

    2016-11-01

    Full Text Available A sensitive and reagentless electrochemical aptatoxisensor was developed on cobalt (II salicylaldiimine metallodendrimer (SDD–Co(II doped with electro-synthesized silver nanoparticles (AgNPs for microcystin-LR (L, l-leucine; R, l-arginine, or MC-LR, detection in the nanomolar range. The GCE|SDD–Co(II|AgNPs aptatoxisensor was fabricated with 5’ thiolated aptamer through self-assembly on the modified surface of the glassy carbon electrode (GCE and the electronic response was measured using cyclic voltammetry (CV. Specific binding of MC-LR with the aptamer on GCE|SDD–Co(II|AgNPs aptatoxisensor caused the formation of a complex that resulted in steric hindrance and electrostatic repulsion culminating in variation of the corresponding peak current of the electrochemical probe. The aptatoxisensor showed a linear response for MC-LR between 0.1 and 1.1 µg·L−1 and the calculated limit of detection (LOD was 0.04 µg·L−1. In the detection of MC-LR in water samples, the aptatoxisensor proved to be highly sensitive and stable, performed well in the presence of interfering analog and was comparable to the conventional analytical techniques. The results demonstrate that the constructed MC-LR aptatoxisensor is a suitable device for routine quantification of MC-LR in freshwater and environmental samples.

  8. LINEAR REGRESSION MODEL ESTİMATİON FOR RIGHT CENSORED DATA

    Directory of Open Access Journals (Sweden)

    Ersin Yılmaz

    2016-05-01

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

  9. Accident dynamics of LR-0 reactor

    International Nuclear Information System (INIS)

    Vorisek, M.; Tinka, I.

    1981-01-01

    The results are given of calculating the accident dynamics of the LR-0 light water experimental zero power reactor. Calculations of the time dependence of power, the total released energy, the temperature of fuel and its cladding were made using program FATRAP for different values of the total inserted reactivity. Using the results, an analysis is made of hypothetic accident states of the LR-0 reactor. The results are shown graphically. (J.B.)

  10. A pM leveled photoelectrochemical sensor for microcystin-LR based on surface molecularly imprinted TiO2@CNTs nanostructure.

    Science.gov (United States)

    Liu, Meichuan; Ding, Xue; Yang, Qiwei; Wang, Yu; Zhao, Guohua; Yang, Nianjun

    2017-06-05

    A simple and highly sensitive photoelectrochemical (PEC) sensor towards Microcystin-LR (MC-LR), a kind of typical cyanobacterial toxin in water samples, was developed on a surface molecular imprinted TiO 2 coated multiwalled carbon nanotubes (MI-TiO 2 @CNTs) hybrid nanostructure. It was synthesized using a feasible two-step sol-gel method combining with in situ surface molecular imprinting technique (MIT). With a controllable core-shell tube casing structure, the resultant MI-TiO 2 @CNTs are enhanced greatly in visible-light driven response capacity. In comparison with the traditional TiO 2 (P25) and non-imprinted (NI-)TiO 2 @CNTs, the MI-TiO 2 @CNTs based PEC sensor showed a much higher photoelectric oxidation capacity towards MC-LR. Using this sensor, the determination of MC-LR was doable in a wide linear range from 1.0pM to 3.0nM with a high photocurrent response sensitivity. An outstanding selectivity towards MC-LR was further achieved with this sensor, proven by simultaneously monitoring 100-fold potential co-existing interferences. The superiority of the obtained MC-LR sensor in sensitivity and selectivity is mainly attributed to the high specific surface area and excellent photoelectric activity of TiO 2 @CNTs heterojunction structure, as well as the abundant active recognition sites on its functionalized molecular imprinting surface. A promising PEC analysis platform with high sensitivity and selectivity for MC-LR has thus been provided. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

    Directory of Open Access Journals (Sweden)

    Drzewiecki Wojciech

    2016-12-01

    Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.

  12. Weighted functional linear regression models for gene-based association analysis.

    Science.gov (United States)

    Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

    2018-01-01

    Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

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

    Science.gov (United States)

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

    2011-01-01

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

  14. The hydration enthalpies of Md3+ and Lr3+

    International Nuclear Information System (INIS)

    Bruechle, W.; Schaedel, M.; Scherer, U.W.; Kratz, J.V.

    1987-10-01

    Lawrencium (3-min 260 Lr) and lighter actinides were produced in the bombardement of a 249 Bk target with 18 O ions and loaded onto a cation exchange column in 0.05 M α-hydroxy-isobutyrate solution at pH = 4.85 together with the radioactive lanthanide tracers 166 Ho, 171 Er, and 171 Tm. In elutions with 0.12 M α-hydroxy-isobutyrate solution (pH = 4.85) trivalent Lr was eluted exactly together with the Er tracer and Md close to Ho. Lr elutes much later than expected based on the known elution positions of the lighter actinides and the expected analogy to the elution positions of the homologous lanthanides. From the measured elution positions, ionic radii were calculated for Lr 3+ and Md 3+ . Semiempirical models allow the calculation of the heat of hydration from the ionic radii, resulting in ΔH hyd ≅ - 3654 kJ/mol for Md 3+ and ΔH hyd ≅ - 3689 kJ/mol for Lr 3+ . (orig.)

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

    NARCIS (Netherlands)

    Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.

    2014-01-01

    Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate

  16. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI.

    Science.gov (United States)

    Dikaios, Nikolaos; Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki; Abd-Alazeez, Mohamed; Kirkham, Alex; Allen, Clare; Ahmed, Hashim; Emberton, Mark; Freeman, Alex; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit

    2015-02-01

    We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.

  17. L-R asymmetry in gut's

    International Nuclear Information System (INIS)

    Karadayi, H.R.

    1982-01-01

    An idea of L-R asymmetry is proposed for the grand unification schemes. The idea provides an intrinsic mechanism to obtain standard model charges of fermions in the case of more than one weak gauge boson. It is elaborated within a scheme based on the partial symmetry SU(4)sub(C)xSU(2)sub(L)xSU(2)sub(R) where the coupling constants gsub(L) and gsub(R) corresponding to the chiral SU(2) factors are assumed to be different from each other. Then, the embedding of this structure within the simple symmetry SO(10) is shown. In both cases, a consistent description of vector particle masses is given. These two schemes are considered as primary models to realize the L-R asymmetry idea due to the lack of family unification. However, in a subsequent work, we will show that the SO(14) unification of the three families can be obtained within the framework of L-R asymmetry. All formulations are carried out with the aid of a mathematical method that we recently proposed for the Lie algebra representations of classical groups. (author)

  18. Roles of miRNAs in microcystin-LR-induced Sertoli cell toxicity

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Yuan [Immunology and Reproduction Biology Laboratory & State Key Laboratory of Analytical Chemistry for Life Science, Medical School, Nanjing University, Nanjing, Jiangsu 210093 (China); Jiangsu Key Laboratory of Molecular Medicine, Nanjing University, Nanjing, Jiangsu 210093 (China); Wang, Hui [The Centre for Individualized Medication, Linköping University Hospital, Linköping University, Linköping SE-58185 (Sweden); Wang, Cong [Immunology and Reproduction Biology Laboratory & State Key Laboratory of Analytical Chemistry for Life Science, Medical School, Nanjing University, Nanjing, Jiangsu 210093 (China); Jiangsu Key Laboratory of Molecular Medicine, Nanjing University, Nanjing, Jiangsu 210093 (China); Qiu, Xuefeng [Department of Urology, Affiliated Drum Tower Hospital, School of Medicine, Nanjing University, Nanjing 210008 (China); Benson, Mikael [The Centre for Individualized Medication, Linköping University Hospital, Linköping University, Linköping SE-58185 (Sweden); Yin, Xiaoqin [Immunology and Reproduction Biology Laboratory & State Key Laboratory of Analytical Chemistry for Life Science, Medical School, Nanjing University, Nanjing, Jiangsu 210093 (China); Jiangsu Key Laboratory of Molecular Medicine, Nanjing University, Nanjing, Jiangsu 210093 (China); Xiang, Zou [Department of Microbiology and Immunology, Mucosal Immunobiology and Vaccine Research Center, Institute of Biomedicine, University of Gothenburg, Gothenburg (Sweden); Li, Dongmei, E-mail: lidm@nju.edu.cn [Immunology and Reproduction Biology Laboratory & State Key Laboratory of Analytical Chemistry for Life Science, Medical School, Nanjing University, Nanjing, Jiangsu 210093 (China); Jiangsu Key Laboratory of Molecular Medicine, Nanjing University, Nanjing, Jiangsu 210093 (China); and others

    2015-08-15

    Microcystin (MC)-LR, a cyclic heptapeptide, is a potent reproductive system toxin. To understand the molecular mechanisms of MC-induced reproductive system cytotoxicity, we evaluated global changes of miRNA and mRNA expression in mouse Sertoli cells following MC-LR treatment. Our results revealed that the exposure to MC-LR resulted in an altered miRNA expression profile that might be responsible for the modulation of mRNA expression. Bio-functional analysis indicated that the altered genes were involved in specific cellular processes, including cell death and proliferation. Target gene analysis suggested that junction injury in Sertoli cells exposed to MC-LR might be mediated by miRNAs through the regulation of the Sertoli cell-Sertoli cell pathway. Collectively, these findings may enhance our understanding on the modes of action of MC-LR on mouse Sertoli cells as well as the molecular mechanisms underlying the toxicity of MC-LR on the male reproductive system. - Highlights: • miRNAs were altered in Sertoli cells exposed to MC-LR. • Alerted genes were involved in different cell functions including the cell morphology. • MC-LR adversely affected Sertoli cell junction formation through the regulating miRNAs.

  19. A method for fitting regression splines with varying polynomial order in the linear mixed model.

    Science.gov (United States)

    Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

    2006-02-15

    The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

  20. Computer software for linear and nonlinear regression in organic NMR

    International Nuclear Information System (INIS)

    Canto, Eduardo Leite do; Rittner, Roberto

    1991-01-01

    Calculation involving two variable linear regressions, require specific procedures generally not familiar to chemist. For attending the necessity of fast and efficient handling of NMR data, a self explained and Pc portable software has been developed, which allows user to produce and use diskette recorded tables, containing chemical shift or any other substituent physical-chemical measurements and constants (σ T , σ o R , E s , ...)

  1. Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

    Science.gov (United States)

    Enders, Felicity

    2013-12-01

    Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

  2. Power properties of invariant tests for spatial autocorrelation in linear regression

    NARCIS (Netherlands)

    Martellosio, F.

    2006-01-01

    Many popular tests for residual spatial autocorrelation in the context of the linear regression model belong to the class of invariant tests. This paper derives a number of exact properties of the power function of such tests. In particular, we extend the work of Krämer (2005, Journal of Statistical

  3. Long-term outcomes of ductal carcinoma in situ of the breast: a systematic review, meta-analysis and meta-regression analysis

    International Nuclear Information System (INIS)

    Stuart, Kirsty E.; Houssami, Nehmat; Taylor, Richard; Hayen, Andrew; Boyages, John

    2015-01-01

    To summarize data on long-term ipsilateral local recurrence (LR) and breast cancer death rate (BCDR) for patients with ductal carcinoma in situ (DCIS) who received different treatments. Systematic review and study-level meta-analysis of prospective (n = 5) and retrospective (n = 21) studies of patients with pure DCIS and with median or mean follow-up time of ≥10 years. Meta-regression was performed to assess and adjust for effects of potential confounders – the average age of women, period of initial treatment, and of bias – follow-up duration on recurrence- and death-rates in each treatment group. LR and BCDR rates by local treatment used were reported. Outside of randomized trials, remaining studies were likely to have tailored patient treatment according to the clinical situation. Nine thousand four hundred and four DCIS cases in 9391 patients with 10-year follow-up were included. The adjusted meta-regression LR rate for mastectomy was 2.6 % (95 % CI, 0.8–4.5); breast-conserving surgery with radiotherapy (RT), 13.6 % (95 % CI, 9.8–17.4); breast-conserving surgery without RT, 25.5 % (95 % CI, 18.1–32.9); and biopsy-only (residual predominately low-grade DCIS following inadequate excision), 27.8 % (95 % CI, 8.4–47.1). RT + tamoxifen (TAM) in conservation surgery (CS) patients resulted in lower LR compared to one or no adjuvant treatments: LR rate for CS + RT + TAM, 9.7 %; CS + RT(no TAM), 14.1 %; CS + TAM(no RT), 24.7 %; CS(alone), 25.1 % (linear trend for treatment P < 0.0001). Compared to CS + RT + TAM, a significantly higher invasive LR was observed for CS(alone), odds ratio (OR) 2.61 (P < 0.0001); CS + TAM(no RT), OR 2.52 (P = 0.001); CS + RT(no TAM), OR 1.59 (P = 0.022). BCDR was similar for mastectomy, breast-conserving surgery with or without RT (1.3–2.0 %) and non-significantly higher for biopsy-only (2.7 %). Additionally, the 15-year follow-up was reported where all like-studies had ≥ 15-year data sets; the biopsy-only patients had a

  4. Predicting recovery of cognitive function soon after stroke: differential modeling of logarithmic and linear regression.

    Science.gov (United States)

    Suzuki, Makoto; Sugimura, Yuko; Yamada, Sumio; Omori, Yoshitsugu; Miyamoto, Masaaki; Yamamoto, Jun-ichi

    2013-01-01

    Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, PLogarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.

  5. Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

    Science.gov (United States)

    Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

    2017-04-01

    To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

  6. truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

    Directory of Open Access Journals (Sweden)

    Maria Karlsson

    2014-05-01

    Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

  7. COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

    Directory of Open Access Journals (Sweden)

    K. Seetharaman

    2015-08-01

    Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

  8. Synthesis of visible light sensitized S, N and C co-doped polymorphic TiO2 for Microcystin-LR MC-LR removal

    Science.gov (United States)

    Microcystin-LR (MC-LR) is considered as one of the most widespread and toxic cyanotoxins, which had been discovered to be hepatotoxic, cytotoxic and neurotoxic. It is the only cyanotoxin that has been proposed by Word Health Organization (WHO) for a provisional guideline (1 ppb) ...

  9. Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods

    Directory of Open Access Journals (Sweden)

    Ying-Hsin Chang

    2013-01-01

    Full Text Available Human estrogen receptor (ER isoforms, ERα and ERβ, have long been an important focus in the field of biology. To better understand the structural features associated with the binding of ERα ligands to ERα and modulate their function, several QSAR models, including CoMFA, CoMSIA, SVR, and LR methods, have been employed to predict the inhibitory activity of 68 raloxifene derivatives. In the SVR and LR modeling, 11 descriptors were selected through feature ranking and sequential feature addition/deletion to generate equations to predict the inhibitory activity toward ERα. Among four descriptors that constantly appear in various generated equations, two agree with CoMFA and CoMSIA steric fields and another two can be correlated to a calculated electrostatic potential of ERα.

  10. Analysis of interactive fixed effects dynamic linear panel regression with measurement error

    OpenAIRE

    Nayoung Lee; Hyungsik Roger Moon; Martin Weidner

    2011-01-01

    This paper studies a simple dynamic panel linear regression model with interactive fixed effects in which the variable of interest is measured with error. To estimate the dynamic coefficient, we consider the least-squares minimum distance (LS-MD) estimation method.

  11. Multicollinearity in applied economics research and the Bayesian linear regression

    OpenAIRE

    EISENSTAT, Eric

    2016-01-01

    This article revises the popular issue of collinearity amongst explanatory variables in the context of a multiple linear regression analysis, particularly in empirical studies within social science related fields. Some important interpretations and explanations are highlighted from the econometrics literature with respect to the effects of multicollinearity on statistical inference, as well as the general shortcomings of the once fervent search for methods intended to detect and mitigate thes...

  12. Robust best linear estimation for regression analysis using surrogate and instrumental variables.

    Science.gov (United States)

    Wang, C Y

    2012-04-01

    We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

  13. Relative Importance for Linear Regression in R: The Package relaimpo

    Directory of Open Access Journals (Sweden)

    Ulrike Gromping

    2006-09-01

    Full Text Available Relative importance is a topic that has seen a lot of interest in recent years, particularly in applied work. The R package relaimpo implements six different metrics for assessing relative importance of regressors in the linear model, two of which are recommended - averaging over orderings of regressors and a newly proposed metric (Feldman 2005 called pmvd. Apart from delivering the metrics themselves, relaimpo also provides (exploratory bootstrap confidence intervals. This paper offers a brief tutorial introduction to the package. The methods and relaimpo’s functionality are illustrated using the data set swiss that is generally available in R. The paper targets readers who have a basic understanding of multiple linear regression. For the background of more advanced aspects, references are provided.

  14. Recursive and non-linear logistic regression: moving on from the original EuroSCORE and EuroSCORE II methodologies.

    Science.gov (United States)

    Poullis, Michael

    2014-11-01

    EuroSCORE II, despite improving on the original EuroSCORE system, has not solved all the calibration and predictability issues. Recursive, non-linear and mixed recursive and non-linear regression analysis were assessed with regard to sensitivity, specificity and predictability of the original EuroSCORE and EuroSCORE II systems. The original logistic EuroSCORE, EuroSCORE II and recursive, non-linear and mixed recursive and non-linear regression analyses of these risk models were assessed via receiver operator characteristic curves (ROC) and Hosmer-Lemeshow statistic analysis with regard to the accuracy of predicting in-hospital mortality. Analysis was performed for isolated coronary artery bypass grafts (CABGs) (n = 2913), aortic valve replacement (AVR) (n = 814), mitral valve surgery (n = 340), combined AVR and CABG (n = 517), aortic (n = 350), miscellaneous cases (n = 642), and combinations of the above cases (n = 5576). The original EuroSCORE had an ROC below 0.7 for isolated AVR and combined AVR and CABG. None of the methods described increased the ROC above 0.7. The EuroSCORE II risk model had an ROC below 0.7 for isolated AVR only. Recursive regression, non-linear regression, and mixed recursive and non-linear regression all increased the ROC above 0.7 for isolated AVR. The original EuroSCORE had a Hosmer-Lemeshow statistic that was above 0.05 for all patients and the subgroups analysed. All of the techniques markedly increased the Hosmer-Lemeshow statistic. The EuroSCORE II risk model had a Hosmer-Lemeshow statistic that was significant for all patients (P linear regression failed to improve on the original Hosmer-Lemeshow statistic. The mixed recursive and non-linear regression using the EuroSCORE II risk model was the only model that produced an ROC of 0.7 or above for all patients and procedures and had a Hosmer-Lemeshow statistic that was highly non-significant. The original EuroSCORE and the EuroSCORE II risk models do not have adequate ROC and Hosmer

  15. [Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

    Science.gov (United States)

    Ling, Ru; Liu, Jiawang

    2011-12-01

    To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

  16. Antioxidant Effect of Curcumin Against Microcystin- LR-Induced ...

    African Journals Online (AJOL)

    Purpose: To investigate the effect of curcumin on microcystin-LR (MC-LR)- induced renal oxidative damage in Balb/c mice. Methods: 40 male Balb/c mice were assigned randomly to 4 groups each having 10 mice. One group served as normal (saline treated) while another group was used as curcumin control. The third ...

  17. Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

    Science.gov (United States)

    Madarang, Krish J; Kang, Joo-Hyon

    2014-06-01

    Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

  18. Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

    Science.gov (United States)

    Drzewiecki, Wojciech

    2016-12-01

    In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

  19. Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

    Science.gov (United States)

    Almedeij, Jaber

    2012-01-01

    Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984

  20. Monitoring of microcystin-LR in Luvuvhu River catchment ...

    African Journals Online (AJOL)

    The main aim of this study is to assess the levels of microcystin-LR in Luvuvhu River catchment and to assess the physicochemical parameters that may promote the growth of cyanobacteria. The level of microcystin-LR in some of the sampling sites was <0.18 ìg/l except for one site (Luvuvhu River just before the confluence ...

  1. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

    Science.gov (United States)

    Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

    2018-01-01

    Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

  2. Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis.

    Science.gov (United States)

    Khalil, Mohamed H; Shebl, Mostafa K; Kosba, Mohamed A; El-Sabrout, Karim; Zaki, Nesma

    2016-08-01

    This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens' eggs. Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.

  3. User's Guide to the Weighted-Multiple-Linear Regression Program (WREG version 1.0)

    Science.gov (United States)

    Eng, Ken; Chen, Yin-Yu; Kiang, Julie.E.

    2009-01-01

    Streamflow is not measured at every location in a stream network. Yet hydrologists, State and local agencies, and the general public still seek to know streamflow characteristics, such as mean annual flow or flood flows with different exceedance probabilities, at ungaged basins. The goals of this guide are to introduce and familiarize the user with the weighted multiple-linear regression (WREG) program, and to also provide the theoretical background for program features. The program is intended to be used to develop a regional estimation equation for streamflow characteristics that can be applied at an ungaged basin, or to improve the corresponding estimate at continuous-record streamflow gages with short records. The regional estimation equation results from a multiple-linear regression that relates the observable basin characteristics, such as drainage area, to streamflow characteristics.

  4. Linear Regression Analysis

    CERN Document Server

    Seber, George A F

    2012-01-01

    Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

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

    Science.gov (United States)

    Lunt, Mark

    2015-07-01

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

  6. Laboratory studies of dissolved radiolabelled microcystin-LR in lake water

    DEFF Research Database (Denmark)

    Hyenstrand, Per; Rohrlack, Thomas; Beattie, Kenneth A

    2003-01-01

    The fate of dissolved microcystin-LR was studied in laboratory experiments using surface water taken from a eutrophic lake. Based on initial range finding, a concentration of 50 microg l(-1) dissolved 14C-microcystin-LR was selected for subsequent time-course experiments. The first was performed ...... fractions. The study demonstrated that biodegradation of dissolved microcystin-LR occurred in water collected at a lake surface with carbon dioxide as a major end-product....

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

    Science.gov (United States)

    Frasier, Timothy R

    2016-03-01

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

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

    Science.gov (United States)

    Wan, Fei; Mitra, Nandita

    2018-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Eric R. Edelman

    2017-06-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  11. Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

    KAUST Repository

    Abdul Jameel, Abdul Gani; Naser, Nimal; Emwas, Abdul-Hamid M.; Dooley, Stephen; Sarathy, Mani

    2016-01-01

    An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN

  12. Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression.

    Science.gov (United States)

    Song, Chao; Kwan, Mei-Po; Zhu, Jiping

    2017-04-08

    An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.

  13. Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

    Science.gov (United States)

    Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

    2016-03-01

    In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

    Science.gov (United States)

    Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

    2006-01-01

    Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

  15. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

    Directory of Open Access Journals (Sweden)

    Xu Yu

    2018-01-01

    Full Text Available Cross-domain collaborative filtering (CDCF solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR. We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

  16. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI

    Energy Technology Data Exchange (ETDEWEB)

    Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim; Emberton, Mark [University College London, Research Department of Urology, London (United Kingdom); Kirkham, Alex; Allen, Clare [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)

    2014-09-17

    We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. (orig.)

  17. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI

    International Nuclear Information System (INIS)

    Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit; Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki; Abd-Alazeez, Mohamed; Ahmed, Hashim; Emberton, Mark; Kirkham, Alex; Allen, Clare; Freeman, Alex

    2015-01-01

    We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. (orig.)

  18. SCREENING OF Lr GENES PROVIDING RESISTANCE TO LEAF RUST IN WHEATH USING MULTIPLEX PCR METHOD

    Directory of Open Access Journals (Sweden)

    Mehmet AYBEKE

    2015-12-01

    Full Text Available Leaf rust is a fungal disease in wheat that causes significant decrease in yield around the world. In Turkey, several genes, including leaf rust-resistant (Lr Lr9, Lr19, Lr24 and Lr28, have been found to induce disease resistance. To obtain resistant cultivars during the breeding process, screening of these genes in various specimens is crucial. Thus, we aimed in the present study primarily to improve the multiplex polymerase chain reaction (PCR methodology by which four Lr genes could be simultaneously screened in plant samples carrying these genes. Serial PCR experiments were carried out for determination of optimal PCR conditions for each Lr gene and in all studies nursery lines were used. PCR conditions were determined as follows: 35 cycles of 95°C for denaturation (30 s, 58°C for annealing (30 s and 72°C for elongation (60 s, with an initial 94°C denaturation (3 min and a 72°C extension (30 min. The primers used in the PCR runs were as follows: Lr9F: TCCTTTTATTCCGCACGCCGG, Lr9R: CCACACTACCCCAAAGAGACG; Lr19F: CATCCTTGGGGACCTC, Lr19R: CCAGCTCGCATACATCCA; Lr24F: TCTAGTCTGTACATGGGGGC, Lr24R: TGGCACATGAACTCCATACG; Lr28F: CCCGGCATAAGTCTATGGTT, Lr28R: CAATGAATGAGATACGTGAA. We found that the optimum annealing temperature for all four genes was 61°C and extension temperatures were 62°C or 64°C. Finally, using this new PCR method, we successfully screened these genes in specimens carrying only one single Lr gene. Optimal multiplex PCR conditions were; denaturation at 94°C for 1 min, 35 extension cycles [94°C for 30 s, 57–61ºC (ideal 61°C for 30 s, and 64–68°C for 2 min] and final extension at 72°C for 30 min. In addition, we achieved positive results when running the optimised multiplex PCR tests on Lr19, Lr24 and Lr28. Future studies are planned to expand new wide multiplex PCR method to include all other Lr genes.

  19. Carbon 13 nuclear magnetic resonance chemical shifts empiric calculations of polymers by multi linear regression and molecular modeling

    International Nuclear Information System (INIS)

    Da Silva Pinto, P.S.; Eustache, R.P.; Audenaert, M.; Bernassau, J.M.

    1996-01-01

    This work deals with carbon 13 nuclear magnetic resonance chemical shifts empiric calculations by multi linear regression and molecular modeling. The multi linear regression is indeed one way to obtain an equation able to describe the behaviour of the chemical shift for some molecules which are in the data base (rigid molecules with carbons). The methodology consists of structures describer parameters definition which can be bound to carbon 13 chemical shift known for these molecules. Then, the linear regression is used to determine the equation significant parameters. This one can be extrapolated to molecules which presents some resemblances with those of the data base. (O.L.). 20 refs., 4 figs., 1 tab

  20. SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

    Science.gov (United States)

    As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

  1. Multiple regression technique for Pth degree polynominals with and without linear cross products

    Science.gov (United States)

    Davis, J. W.

    1973-01-01

    A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

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

    Science.gov (United States)

    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.

  3. [Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

    Science.gov (United States)

    Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

    2017-05-10

    We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P valuelinear regression P value). The statistical power of CAT test decreased, while the result of linear regression analysis remained the same when population size was reduced by 100 times and AMI incidence rate remained unchanged. The two statistical methods have their advantages and disadvantages. It is necessary to choose statistical method according the fitting degree of data, or comprehensively analyze the results of two methods.

  4. Microcystin-LR equivalent concentrations in fish tissue during a ...

    African Journals Online (AJOL)

    The effects of a decomposing cyanobacteria bloom on water quality and the accumulation of microcystin-LR equivalent toxin in fish at Loskop Dam were studied in May 2012. Enzyme-linked immunosorbent assay [ELISA] was used to confirm the presence of microcystin-LR equivalent in the water and to determine the ...

  5. USE OF THE SIMPLE LINEAR REGRESSION MODEL IN MACRO-ECONOMICAL ANALYSES

    Directory of Open Access Journals (Sweden)

    Constantin ANGHELACHE

    2011-10-01

    Full Text Available The article presents the fundamental aspects of the linear regression, as a toolbox which can be used in macroeconomic analyses. The article describes the estimation of the parameters, the statistical tests used, the homoscesasticity and heteroskedasticity. The use of econometrics instrument in macroeconomics is an important factor that guarantees the quality of the models, analyses, results and possible interpretation that can be drawn at this level.

  6. Comprehensive insights into microcystin-LR effects on hepatic lipid metabolism using cross-omics technologies

    International Nuclear Information System (INIS)

    Zhang, Zongyao; Zhang, Xu-Xiang; Wu, Bing; Yin, Jinbao; Yu, Yunjiang; Yang, Liuyan

    2016-01-01

    Highlights: • Use of cross-omics technologies to evaluate toxic effects of microcystin-LR. • Disturbance of hepatic lipid metabolism by oral exposure to microcystin-LR. • Crucial roles of gut microbial community shift in the metabolic disturbance induced by microcystin-LR. - Abstract: Microcystin-LR (MC-LR) can induce hepatic tissue damages and molecular toxicities, but its effects on lipid metabolism remain unknown. This study investigated the effects of MC-LR exposure on mice lipid metabolism and uncovered the underlying mechanism through metabonomic, transcriptomic and metagenomic analyses after administration of mice with MC-LR by gavage for 28 d. Increased liver weight and abdominal fat weight, and evident hepatic lipid vacuoles accumulation were observed in the mice fed with 0.2 mg/kg/d MC-LR. Serum nuclear magnetic resonance analysis showed that MC-LR treatment altered the levels of serum metabolites including triglyceride, unsaturated fatty acid (UFA) and very low density lipoprotein. Digital Gene Expression technology was used to reveal differential expression of hepatic transcriptomes, demonstrating that MC-LR treatment disturbed hepatic UFA biosynthesis and activated peroxisome proliferator-activated receptor (PPAR) signaling pathways via Pparγ, Fabp1 and Fabp2 over-expression. Metagenomic analyses of gut microbiota revealed that MC-LR exposure also increased abundant ratio of Firmicutes vs. Bacteroidetes in gut and altered biosynthetic pathways of various microbial metabolic and pro-inflammatory molecules. In conclusion, oral MC-LR exposure can induce hepatic lipid metabolism disorder mediated by UFA biosynthesis and PPAR activation, and gut microbial community shift may play an important role in the metabolic disturbance.

  7. Comprehensive insights into microcystin-LR effects on hepatic lipid metabolism using cross-omics technologies

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Zongyao [State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023 (China); Center for Environmental Health Research, South China Institute of Environmental Sciences, The Ministry of Environmental Protection of PRC, Guangzhou 510655 (China); Zhang, Xu-Xiang, E-mail: zhangxx@nju.edu.cn [State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023 (China); Wu, Bing; Yin, Jinbao [State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023 (China); Yu, Yunjiang [Center for Environmental Health Research, South China Institute of Environmental Sciences, The Ministry of Environmental Protection of PRC, Guangzhou 510655 (China); Yang, Liuyan, E-mail: yangly@nju.edu.cn [State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023 (China)

    2016-09-05

    Highlights: • Use of cross-omics technologies to evaluate toxic effects of microcystin-LR. • Disturbance of hepatic lipid metabolism by oral exposure to microcystin-LR. • Crucial roles of gut microbial community shift in the metabolic disturbance induced by microcystin-LR. - Abstract: Microcystin-LR (MC-LR) can induce hepatic tissue damages and molecular toxicities, but its effects on lipid metabolism remain unknown. This study investigated the effects of MC-LR exposure on mice lipid metabolism and uncovered the underlying mechanism through metabonomic, transcriptomic and metagenomic analyses after administration of mice with MC-LR by gavage for 28 d. Increased liver weight and abdominal fat weight, and evident hepatic lipid vacuoles accumulation were observed in the mice fed with 0.2 mg/kg/d MC-LR. Serum nuclear magnetic resonance analysis showed that MC-LR treatment altered the levels of serum metabolites including triglyceride, unsaturated fatty acid (UFA) and very low density lipoprotein. Digital Gene Expression technology was used to reveal differential expression of hepatic transcriptomes, demonstrating that MC-LR treatment disturbed hepatic UFA biosynthesis and activated peroxisome proliferator-activated receptor (PPAR) signaling pathways via Pparγ, Fabp1 and Fabp2 over-expression. Metagenomic analyses of gut microbiota revealed that MC-LR exposure also increased abundant ratio of Firmicutes vs. Bacteroidetes in gut and altered biosynthetic pathways of various microbial metabolic and pro-inflammatory molecules. In conclusion, oral MC-LR exposure can induce hepatic lipid metabolism disorder mediated by UFA biosynthesis and PPAR activation, and gut microbial community shift may play an important role in the metabolic disturbance.

  8. Detecting nonsense for Chinese comments based on logistic regression

    Science.gov (United States)

    Zhuolin, Ren; Guang, Chen; Shu, Chen

    2016-07-01

    To understand cyber citizens' opinion accurately from Chinese news comments, the clear definition on nonsense is present, and a detection model based on logistic regression (LR) is proposed. The detection of nonsense can be treated as a binary-classification problem. Besides of traditional lexical features, we propose three kinds of features in terms of emotion, structure and relevance. By these features, we train an LR model and demonstrate its effect in understanding Chinese news comments. We find that each of proposed features can significantly promote the result. In our experiments, we achieve a prediction accuracy of 84.3% which improves the baseline 77.3% by 7%.

  9. Single camera multi-view anthropometric measurement of human height and mid-upper arm circumference using linear regression.

    Science.gov (United States)

    Liu, Yingying; Sowmya, Arcot; Khamis, Heba

    2018-01-01

    Manually measured anthropometric quantities are used in many applications including human malnutrition assessment. Training is required to collect anthropometric measurements manually, which is not ideal in resource-constrained environments. Photogrammetric methods have been gaining attention in recent years, due to the availability and affordability of digital cameras. The primary goal is to demonstrate that height and mid-upper arm circumference (MUAC)-indicators of malnutrition-can be accurately estimated by applying linear regression to distance measurements from photographs of participants taken from five views, and determine the optimal view combinations. A secondary goal is to observe the effect on estimate error of two approaches which reduce complexity of the setup, computational requirements and the expertise required of the observer. Thirty-one participants (11 female, 20 male; 18-37 years) were photographed from five views. Distances were computed using both camera calibration and reference object techniques from manually annotated photos. To estimate height, linear regression was applied to the distances between the top of the participants head and the floor, as well as the height of a bounding box enclosing the participant's silhouette which eliminates the need to identify the floor. To estimate MUAC, linear regression was applied to the mid-upper arm width. Estimates were computed for all view combinations and performance was compared to other photogrammetric methods from the literature-linear distance method for height, and shape models for MUAC. The mean absolute difference (MAD) between the linear regression estimates and manual measurements were smaller compared to other methods. For the optimal view combinations (smallest MAD), the technical error of measurement and coefficient of reliability also indicate the linear regression methods are more reliable. The optimal view combination was the front and side views. When estimating height by linear

  10. Robust linear registration of CT images using random regression forests

    Science.gov (United States)

    Konukoglu, Ender; Criminisi, Antonio; Pathak, Sayan; Robertson, Duncan; White, Steve; Haynor, David; Siddiqui, Khan

    2011-03-01

    Global linear registration is a necessary first step for many different tasks in medical image analysis. Comparing longitudinal studies1, cross-modality fusion2, and many other applications depend heavily on the success of the automatic registration. The robustness and efficiency of this step is crucial as it affects all subsequent operations. Most common techniques cast the linear registration problem as the minimization of a global energy function based on the image intensities. Although these algorithms have proved useful, their robustness in fully automated scenarios is still an open question. In fact, the optimization step often gets caught in local minima yielding unsatisfactory results. Recent algorithms constrain the space of registration parameters by exploiting implicit or explicit organ segmentations, thus increasing robustness4,5. In this work we propose a novel robust algorithm for automatic global linear image registration. Our method uses random regression forests to estimate posterior probability distributions for the locations of anatomical structures - represented as axis aligned bounding boxes6. These posterior distributions are later integrated in a global linear registration algorithm. The biggest advantage of our algorithm is that it does not require pre-defined segmentations or regions. Yet it yields robust registration results. We compare the robustness of our algorithm with that of the state of the art Elastix toolbox7. Validation is performed via 1464 pair-wise registrations in a database of very diverse 3D CT images. We show that our method decreases the "failure" rate of the global linear registration from 12.5% (Elastix) to only 1.9%.

  11. Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings

    International Nuclear Information System (INIS)

    Chung, William

    2012-01-01

    Highlights: ► Fuzzy linear regression method is used for developing benchmarking systems. ► The systems can be used to benchmark energy efficiency of commercial buildings. ► The resulting benchmarking model can be used by public users. ► The resulting benchmarking model can capture the fuzzy nature of input–output data. -- Abstract: Benchmarking systems from a sample of reference buildings need to be developed to conduct benchmarking processes for the energy efficiency of commercial buildings. However, not all benchmarking systems can be adopted by public users (i.e., other non-reference building owners) because of the different methods in developing such systems. An approach for benchmarking the energy efficiency of commercial buildings using statistical regression analysis to normalize other factors, such as management performance, was developed in a previous work. However, the field data given by experts can be regarded as a distribution of possibility. Thus, the previous work may not be adequate to handle such fuzzy input–output data. Consequently, a number of fuzzy structures cannot be fully captured by statistical regression analysis. This present paper proposes the use of fuzzy linear regression analysis to develop a benchmarking process, the resulting model of which can be used by public users. An illustrative example is given as well.

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

    Science.gov (United States)

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

    2018-03-26

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

  13. Piecewise linear regression techniques to analyze the timing of head coach dismissals in Dutch soccer clubs

    NARCIS (Netherlands)

    Schryver, T. de; Eisinga, R.

    2010-01-01

    The key question in research on dismissals of head coaches in sports clubs is not whether they should happen but when they will happen. This paper applies piecewise linear regression to advance our understanding of the timing of head coach dismissals. Essentially, the regression sacrifices degrees

  14. An Introduction to Graphical and Mathematical Methods for Detecting Heteroscedasticity in Linear Regression.

    Science.gov (United States)

    Thompson, Russel L.

    Homoscedasticity is an important assumption of linear regression. This paper explains what it is and why it is important to the researcher. Graphical and mathematical methods for testing the homoscedasticity assumption are demonstrated. Sources of homoscedasticity and types of homoscedasticity are discussed, and methods for correction are…

  15. Error analysis of dimensionless scaling experiments with multiple points using linear regression

    International Nuclear Information System (INIS)

    Guercan, Oe.D.; Vermare, L.; Hennequin, P.; Bourdelle, C.

    2010-01-01

    A general method of error estimation in the case of multiple point dimensionless scaling experiments, using linear regression and standard error propagation, is proposed. The method reduces to the previous result of Cordey (2009 Nucl. Fusion 49 052001) in the case of a two-point scan. On the other hand, if the points follow a linear trend, it explains how the estimated error decreases as more points are added to the scan. Based on the analytical expression that is derived, it is argued that for a low number of points, adding points to the ends of the scanned range, rather than the middle, results in a smaller error estimate. (letter)

  16. Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

    Science.gov (United States)

    Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

    2013-09-01

    Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore

  17. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system

    International Nuclear Information System (INIS)

    Fang, Tingting; Lahdelma, Risto

    2016-01-01

    Highlights: • Social factor is considered for the linear regression models besides weather file. • Simultaneously optimize all the coefficients for linear regression models. • SARIMA combined with linear regression is used to forecast the heat demand. • The accuracy for both linear regression and time series models are evaluated. - Abstract: Forecasting heat demand is necessary for production and operation planning of district heating (DH) systems. In this study we first propose a simple regression model where the hourly outdoor temperature and wind speed forecast the heat demand. Weekly rhythm of heat consumption as a social component is added to the model to significantly improve the accuracy. The other type of model is the seasonal autoregressive integrated moving average (SARIMA) model with exogenous variables as a combination to take weather factors, and the historical heat consumption data as depending variables. One outstanding advantage of the model is that it peruses the high accuracy for both long-term and short-term forecast by considering both exogenous factors and time series. The forecasting performance of both linear regression models and time series model are evaluated based on real-life heat demand data for the city of Espoo in Finland by out-of-sample tests for the last 20 full weeks of the year. The results indicate that the proposed linear regression model (T168h) using 168-h demand pattern with midweek holidays classified as Saturdays or Sundays gives the highest accuracy and strong robustness among all the tested models based on the tested forecasting horizon and corresponding data. Considering the parsimony of the input, the ease of use and the high accuracy, the proposed T168h model is the best in practice. The heat demand forecasting model can also be developed for individual buildings if automated meter reading customer measurements are available. This would allow forecasting the heat demand based on more accurate heat consumption

  18. Convergence diagnostics for Eigenvalue problems with linear regression model

    International Nuclear Information System (INIS)

    Shi, Bo; Petrovic, Bojan

    2011-01-01

    Although the Monte Carlo method has been extensively used for criticality/Eigenvalue problems, a reliable, robust, and efficient convergence diagnostics method is still desired. Most methods are based on integral parameters (multiplication factor, entropy) and either condense the local distribution information into a single value (e.g., entropy) or even disregard it. We propose to employ the detailed cycle-by-cycle local flux evolution obtained by using mesh tally mechanism to assess the source and flux convergence. By applying a linear regression model to each individual mesh in a mesh tally for convergence diagnostics, a global convergence criterion can be obtained. We exemplify this method on two problems and obtain promising diagnostics results. (author)

  19. Regressão linear geograficamente ponderada em ambiente SIG

    Directory of Open Access Journals (Sweden)

    Luís Eduardo Ximenes Carvalho

    2009-10-01

    Full Text Available

    Este artigo aborda considerações teóricas e resultados da implementação em ambiente SIG de um modelo confirmatório de estatística espacial — regressão linear geograficamente ponderada (RGP — não disponível em ambiente livre. Os aspectos teóricos deste modelo local de regressão espacial foram amplamente discutidos em virtude da escassa bibliografia existente. O modelo RGP foi implementado na linguagem de programação GISDK do SIG-T TransCAD, utilizando compreensivamente as ferramentas de manipulação, tratamento georreferenciado dos dados e rotinas de análise espacial disponibilizadas em plataformas SIG. Ao final, espera-se ter desenvolvido, ainda que de maneira parcial, uma importante ferramenta que contribuirá para a compreensão e refinamento da modelagem de fenômenos geográficos tão amplamente analisados em estudos de Planejamento de Transportes.

  20. Molecular and analysis of a phenylalanine ammonia-lyase gene (LrPAL2) from Lycoris radiata.

    Science.gov (United States)

    Jiang, Yumei; Xia, Bing; Liang, Lijian; Li, Xiaodan; Xu, Sheng; Peng, Feng; Wang, Ren

    2013-03-01

    Phenylalanine ammonia-lyase (PAL), the first enzyme of phenylpropanoid biosynthesis, participates in the biosynthesis of flavonoids, lignins, stilbenes and many other compounds. In this study, we cloned a 2,326 bp full-length PAL2 gene from Lycoris radiata by using degenerate oligonucleotide primer PCR (DOP-PCR) and the rapid amplification of cDNA ends method. The cDNA contains a 2,124 bp coding region encoding 707 amino acids. The LrPAL2 shares about 77.0 % nucleic acid identity and 83 % amino acid identity with LrPAL1. Furthermore, genome sequence analysis demonstrated that LrPAL2 gene contains one intron and two exons. The 5' flanking sequence of LrPAL2 was also cloned by self-formed adaptor PCR (SEFA-PCR), and a group of putative cis-acting elements such as TATA box, CAAT box, G box, TC-rich repeats, CGTCA motif and TCA-element were identified. The LrPAL2 was detected in all tissues examined, with high abundance in bulbs at leaf sprouting stage and in petals at blooming stage. Besides, LrPAL2 drastically responded to MJ, SNP and UV, moderately responded to GA and SA, and a little increased under wounding. Comparison of LrPAL2 expression and LrPAL1 expression demonstrated that LrPAL2 can be more significantly induced than LrPAL1 under the above treatments, and LrPAL2 transcripts accumulated prominently at blooming stage, especially in petals, while LrPAL1 transcripts did not accumulated significantly at blooming stage. All these results suggested that LrPAL2 might play distinct roles in different branches of the phenylpropanoid pathway.

  1. Enhancement of Visual Field Predictions with Pointwise Exponential Regression (PER) and Pointwise Linear Regression (PLR).

    Science.gov (United States)

    Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph

    2016-03-01

    The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.

  2. Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

    Science.gov (United States)

    Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun

    2013-10-01

    Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.

  3. [Multiple linear regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis].

    Science.gov (United States)

    Ma, Yu-Feng; Wang, Qing-Fu; Chen, Zhao-Jun; Du, Chun-Lin; Li, Jun-Hai; Huang, Hu; Shi, Zong-Ting; Yin, Yue-Shan; Zhang, Lei; A-Di, Li-Jiang; Dong, Shi-Yu; Wu, Ji

    2012-05-01

    To perform Multiple Linear Regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis, and to analyze their relationship with clinical and biomechanical concepts. From March 2011 to July 2011, 140 patients (250 knees) were reviewed, including 132 knees in the left and 118 knees in the right; ranging in age from 40 to 71 years, with an average of 54.68 years. The MB-RULER measurement software was applied to measure femoral angle, tibial angle, femorotibial angle, joint gap angle from antero-posterir and lateral position of X-rays. The WOMAC scores were also collected. Then multiple regression equations was applied for the linear regression analysis of correlation between the X-ray measurement and WOMAC scores. There was statistical significance in the regression equation of AP X-rays value and WOMAC scores (Pregression equation of lateral X-ray value and WOMAC scores (P>0.05). 1) X-ray measurement of knee joint can reflect the WOMAC scores to a certain extent. 2) It is necessary to measure the X-ray mechanical axis of knee, which is important for diagnosis and treatment of osteoarthritis. 3) The correlation between tibial angle,joint gap angle on antero-posterior X-ray and WOMAC scores is significant, which can be used to assess the functional recovery of patients before and after treatment.

  4. Removal of microcystin-LR from drinking water using a bamboo-based charcoal adsorbent modified with chitosan.

    Science.gov (United States)

    Zhang, Hangjun; Zhu, Guoying; Jia, Xiuying; Ding, Ying; Zhang, Mi; Gao, Qing; Hu, Ciming; Xu, Shuying

    2011-01-01

    A new kind of low-cost syntactic adsorbent from bamboo charcoal and chitosan was developed for the removal of microcystin-LR from drinking water. Removal efficiency was higher for the syntactic adsorbent when the amount of bamboo charcoal was increased. The optimum dose ratio of bamboo charcoal to chitosan was 6:4, and the optimum amount was 15 mg/L; equilibrium time was 6 hr. The adsorption isotherm was non-linear and could be simulated by the Freundlich model (R2 = 0.9337). Adsorption efficiency was strongly affected by pH and natural organic matter (NOM). Removal efficiency was 16% higher at pH 3 than at pH 9. Efficiency rate was reduced by 15% with 25 mg/L NOM (UV254 = 0.089 cm(-1)) in drinking water. This study demonstrated that the bamboo charcoal modified with chitosan can effectively remove microcystin-LR from drinking water.

  5. Using the classical linear regression model in analysis of the dependences of conveyor belt life

    Directory of Open Access Journals (Sweden)

    Miriam Andrejiová

    2013-12-01

    Full Text Available The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of the article is about regression model design, point and interval estimation of parameters, verification of statistical significance of the model, and about the parameters of the proposed regression model. The second part of the article deals with identification of influential and extreme values that can have an impact on estimation of regression model parameters. The third part focuses on assumptions of the classical regression model, i.e. on verification of independence assumptions, normality and homoscedasticity of residuals.

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

    Science.gov (United States)

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

    2008-01-01

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

  7. Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

    Science.gov (United States)

    Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

    2014-01-01

    The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

  8. Kinetic and mechanistic study of microcystin-LR degradation by nitrous acid under ultraviolet irradiation

    International Nuclear Information System (INIS)

    Ma, Qingwei; Ren, Jing; Huang, Honghui; Wang, Shoubing; Wang, Xiangrong; Fan, Zhengqiu

    2012-01-01

    Highlights: ► For the first time, degradation of MC-LR by nitrous acid under UV 365 nm was discovered. ► The effects of factors on MC-LR degradation were analyzed based on kinetic study. ► Mass spectrometry was applied for identification of intermediates and products. ► Special intermediates involved in this study were identified. ► Degradation mechanisms were proposed according to the results of LC–MS analysis. - Abstract: Degradation of microcystin-LR (MC-LR) in the presence of nitrous acid (HNO 2 ) under irradiation of 365 nm ultraviolet (UV) was studied for the first time. The influence of initial conditions including pH value, NaNO 2 concentration, MC-LR concentration and UV intensity were studied. MC-LR was degraded in the presence of HNO 2 ; enhanced degradation of MC-LR was observed with 365 nm UV irradiation, caused by the generation of hydroxyl radicals through the photolysis of HNO 2 . The degradation processes of MC-LR could well fit the pseudo-first-order kinetics. Mass spectrometry was applied for identification of the byproducts and the analysis of degradation mechanisms. Major degradation pathways were proposed according to the results of LC–MS analysis. The degradation of MC-LR was initiated via three major pathways: attack of hydroxyl radicals on the conjugated carbon double bonds of Adda, attack of hydroxyl radicals on the benzene ring of Adda, and attack of nitrosonium ion on the benzene ring of Adda.

  9. Kinetic and mechanistic study of microcystin-LR degradation by nitrous acid under ultraviolet irradiation.

    Science.gov (United States)

    Ma, Qingwei; Ren, Jing; Huang, Honghui; Wang, Shoubing; Wang, Xiangrong; Fan, Zhengqiu

    2012-05-15

    Degradation of microcystin-LR (MC-LR) in the presence of nitrous acid (HNO(2)) under irradiation of 365nm ultraviolet (UV) was studied for the first time. The influence of initial conditions including pH value, NaNO(2) concentration, MC-LR concentration and UV intensity were studied. MC-LR was degraded in the presence of HNO(2); enhanced degradation of MC-LR was observed with 365nm UV irradiation, caused by the generation of hydroxyl radicals through the photolysis of HNO(2). The degradation processes of MC-LR could well fit the pseudo-first-order kinetics. Mass spectrometry was applied for identification of the byproducts and the analysis of degradation mechanisms. Major degradation pathways were proposed according to the results of LC-MS analysis. The degradation of MC-LR was initiated via three major pathways: attack of hydroxyl radicals on the conjugated carbon double bonds of Adda, attack of hydroxyl radicals on the benzene ring of Adda, and attack of nitrosonium ion on the benzene ring of Adda. Copyright © 2012 Elsevier B.V. All rights reserved.

  10. A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.

    Science.gov (United States)

    Chen, Yi; Shao, Ying; Yan, Jie; Yuan, Ti-Fei; Qu, Yanwen; Lee, Elizabeth; Wang, Shuihua

    2017-01-01

    Alzheimer's disease patients are increasing rapidly every year. Scholars tend to use computer vision methods to develop automatic diagnosis system. (Background) In 2015, Gorji et al. proposed a novel method using pseudo Zernike moment. They tested four classifiers: learning vector quantization neural network, pattern recognition neural network trained by Levenberg-Marquardt, by resilient backpropagation, and by scaled conjugate gradient. This study presents an improved method by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Therefore, it can be used to detect Alzheimer's disease. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  11. MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY

    OpenAIRE

    Chayalakshmi C.L

    2018-01-01

    MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY ABSTRACT Calculation of boiler efficiency is essential if its parameters need to be controlled for either maintaining or enhancing its efficiency. But determination of boiler efficiency using conventional method is time consuming and very expensive. Hence, it is not recommended to find boiler efficiency frequently. The work presented in this paper deals with establishing the statistical mo...

  12. The wheat resistance gene Lr34 results in the constitutive induction of multiple defense pathways in transgenic barley.

    Science.gov (United States)

    Chauhan, Harsh; Boni, Rainer; Bucher, Rahel; Kuhn, Benjamin; Buchmann, Gabriele; Sucher, Justine; Selter, Liselotte L; Hensel, Goetz; Kumlehn, Jochen; Bigler, Laurent; Glauser, Gaëtan; Wicker, Thomas; Krattinger, Simon G; Keller, Beat

    2015-10-01

    The wheat gene Lr34 encodes an ABCG-type transporter which provides durable resistance against multiple pathogens. Lr34 is functional as a transgene in barley, but its mode of action has remained largely unknown both in wheat and barley. Here we studied gene expression in uninfected barley lines transgenic for Lr34. Genes from multiple defense pathways contributing to basal and inducible disease resistance were constitutively active in seedlings and mature leaves. In addition, the hormones jasmonic acid and salicylic acid were induced to high levels, and increased levels of lignin as well as hordatines were observed. These results demonstrate a strong, constitutive re-programming of metabolism by Lr34. The resistant Lr34 allele (Lr34res) encodes a protein that differs by two amino acid polymorphisms from the susceptible Lr34sus allele. The deletion of a single phenylalanine residue in Lr34sus was sufficient to induce the characteristic Lr34-based responses. Combination of Lr34res and Lr34sus in the same plant resulted in a reduction of Lr34res expression by 8- to 20-fold when the low-expressing Lr34res line BG8 was used as a parent. Crosses with the high-expressing Lr34res line BG9 resulted in an increase of Lr34sus expression by 13- to 16-fold in progenies that inherited both alleles. These results indicate an interaction of the two Lr34 alleles on the transcriptional level. Reduction of Lr34res expression in BG8 crosses reduced the negative pleiotropic effects of Lr34res on barley growth and vigor without compromising disease resistance, suggesting that transgenic combination of Lr34res and Lr34sus can result in agronomically useful resistance. © 2015 The Authors The Plant Journal © 2015 John Wiley & Sons Ltd.

  13. Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

    Science.gov (United States)

    Caimmi, R.

    2011-08-01

    Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both

  14. Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold.

    Science.gov (United States)

    Glass, Edmund R; Dozmorov, Mikhail G

    2016-10-06

    The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis. We evaluated the effect of sample sizes, cell type-specific proportion variability, and mean squared error on sensitivity of cell type-specific differential expression detection using linear regression. Each parameter affected variability of cell type-specific expression estimates and, subsequently, the sensitivity of differential expression detection. We provide the R package, LRCDE, which performs linear regression-based cell type-specific differential expression (deconvolution) detection on a gene-by-gene basis. Accounting for variability around cell type-specific gene expression estimates, it computes per-gene t-statistics of differential detection, p-values, t-statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic metrics. The sensitivity of linear regression-based cell type-specific differential expression detection differed for each gene as a function of mean squared error, per group sample sizes, and variability of the proportions

  15. On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression.

    Science.gov (United States)

    Pek, Jolynn; Chalmers, R Philip; Monette, Georges

    2016-01-01

    When statistical models are employed to provide a parsimonious description of empirical relationships, the extent to which strong conclusions can be drawn rests on quantifying the uncertainty in parameter estimates. In multiple linear regression (MLR), regression weights carry two kinds of uncertainty represented by confidence sets (CSs) and exchangeable weights (EWs). Confidence sets quantify uncertainty in estimation whereas the set of EWs quantify uncertainty in the substantive interpretation of regression weights. As CSs and EWs share certain commonalities, we clarify the relationship between these two kinds of uncertainty about regression weights. We introduce a general framework describing how CSs and the set of EWs for regression weights are estimated from the likelihood-based and Wald-type approach, and establish the analytical relationship between CSs and sets of EWs. With empirical examples on posttraumatic growth of caregivers (Cadell et al., 2014; Schneider, Steele, Cadell & Hemsworth, 2011) and on graduate grade point average (Kuncel, Hezlett & Ones, 2001), we illustrate the usefulness of CSs and EWs for drawing strong scientific conclusions. We discuss the importance of considering both CSs and EWs as part of the scientific process, and provide an Online Appendix with R code for estimating Wald-type CSs and EWs for k regression weights.

  16. Significance tests to determine the direction of effects in linear regression models.

    Science.gov (United States)

    Wiedermann, Wolfgang; Hagmann, Michael; von Eye, Alexander

    2015-02-01

    Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. A Monte Carlo simulation study was performed to examine the behaviour of the procedures under various degrees of associations, sample sizes, and distributional properties of the underlying population. An empirical example is given which illustrates the application of the tests in practice. © 2014 The British Psychological Society.

  17. Multiple linear regression analysis

    Science.gov (United States)

    Edwards, T. R.

    1980-01-01

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

  18. Kinetic and mechanistic study of microcystin-LR degradation by nitrous acid under ultraviolet irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Qingwei; Ren, Jing [Department of Environmental Science and Engineering, Fudan University, Shanghai 200433 (China); Huang, Honghui [Key Laboratory of Fisheries Ecology Environment, Ministry of Agriculture, Guangzhou 510300 (China); Wang, Shoubing [Department of Environmental Science and Engineering, Fudan University, Shanghai 200433 (China); Wang, Xiangrong, E-mail: xrxrwang@vip.sina.com [Department of Environmental Science and Engineering, Fudan University, Shanghai 200433 (China); Fan, Zhengqiu, E-mail: zhqfan@fudan.edu.cn [Department of Environmental Science and Engineering, Fudan University, Shanghai 200433 (China)

    2012-05-15

    Highlights: Black-Right-Pointing-Pointer For the first time, degradation of MC-LR by nitrous acid under UV 365 nm was discovered. Black-Right-Pointing-Pointer The effects of factors on MC-LR degradation were analyzed based on kinetic study. Black-Right-Pointing-Pointer Mass spectrometry was applied for identification of intermediates and products. Black-Right-Pointing-Pointer Special intermediates involved in this study were identified. Black-Right-Pointing-Pointer Degradation mechanisms were proposed according to the results of LC-MS analysis. - Abstract: Degradation of microcystin-LR (MC-LR) in the presence of nitrous acid (HNO{sub 2}) under irradiation of 365 nm ultraviolet (UV) was studied for the first time. The influence of initial conditions including pH value, NaNO{sub 2} concentration, MC-LR concentration and UV intensity were studied. MC-LR was degraded in the presence of HNO{sub 2}; enhanced degradation of MC-LR was observed with 365 nm UV irradiation, caused by the generation of hydroxyl radicals through the photolysis of HNO{sub 2}. The degradation processes of MC-LR could well fit the pseudo-first-order kinetics. Mass spectrometry was applied for identification of the byproducts and the analysis of degradation mechanisms. Major degradation pathways were proposed according to the results of LC-MS analysis. The degradation of MC-LR was initiated via three major pathways: attack of hydroxyl radicals on the conjugated carbon double bonds of Adda, attack of hydroxyl radicals on the benzene ring of Adda, and attack of nitrosonium ion on the benzene ring of Adda.

  19. Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Maokuan Zheng

    2017-01-01

    Full Text Available The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2. The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.

  20. A Linear Regression Model for Global Solar Radiation on Horizontal Surfaces at Warri, Nigeria

    Directory of Open Access Journals (Sweden)

    Michael S. Okundamiya

    2013-10-01

    Full Text Available The growing anxiety on the negative effects of fossil fuels on the environment and the global emission reduction targets call for a more extensive use of renewable energy alternatives. Efficient solar energy utilization is an essential solution to the high atmospheric pollution caused by fossil fuel combustion. Global solar radiation (GSR data, which are useful for the design and evaluation of solar energy conversion system, are not measured at the forty-five meteorological stations in Nigeria. The dearth of the measured solar radiation data calls for accurate estimation. This study proposed a temperature-based linear regression, for predicting the monthly average daily GSR on horizontal surfaces, at Warri (latitude 5.020N and longitude 7.880E an oil city located in the south-south geopolitical zone, in Nigeria. The proposed model is analyzed based on five statistical indicators (coefficient of correlation, coefficient of determination, mean bias error, root mean square error, and t-statistic, and compared with the existing sunshine-based model for the same study. The results indicate that the proposed temperature-based linear regression model could replace the existing sunshine-based model for generating global solar radiation data. Keywords: air temperature; empirical model; global solar radiation; regression analysis; renewable energy; Warri

  1. Partitioning of late gestation energy expenditure in ewes using indirect calorimetry and a linear regression approach

    DEFF Research Database (Denmark)

    Kiani, Alishir; Chwalibog, André; Nielsen, Mette O

    2007-01-01

    Late gestation energy expenditure (EE(gest)) originates from energy expenditure (EE) of development of conceptus (EE(conceptus)) and EE of homeorhetic adaptation of metabolism (EE(homeorhetic)). Even though EE(gest) is relatively easy to quantify, its partitioning is problematic. In the present...... study metabolizable energy (ME) intake ranges for twin-bearing ewes were 220-440, 350- 700, 350-900 kJ per metabolic body weight (W0.75) at week seven, five, two pre-partum respectively. Indirect calorimetry and a linear regression approach were used to quantify EE(gest) and then partition to EE......(conceptus) and EE(homeorhetic). Energy expenditure of basal metabolism of the non-gravid tissues (EE(bmng)), derived from the intercept of the linear regression equation of retained energy [kJ/W0.75] and ME intake [kJ/W(0.75)], was 298 [kJ/ W0.75]. Values of the intercepts of the regression equations at week seven...

  2. Suppression Situations in Multiple Linear Regression

    Science.gov (United States)

    Shieh, Gwowen

    2006-01-01

    This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

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

    Science.gov (United States)

    Ng, Kar Yong; Awang, Norhashidah

    2018-01-06

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

  4. Microcystin-LR Induces Apoptosis via NF-κB /iNOS Pathway in INS-1 Cells

    Directory of Open Access Journals (Sweden)

    Kai Shen

    2011-07-01

    Full Text Available Cyanobacterial toxins, especially the microcystins, are found in eutrophied waters throughout the world, and their potential to impact on human and animal health is a cause for concern. Microcystin-LR (MC-LR is one of the common toxic microcystin congeners and occurs frequently in diverse water systems. Recent work suggested that apoptosis plays a major role in the toxic effects induced by MC-LR in hepatocytes. However, the roles of MC-LR in pancreatic beta cells have not been fully established. The aim of the present study was to assess possible in vitro effects of MC-LR on cell apoptosis in the rat insulinoma cell line, INS-1. Our results demonstrated that MC-LR promoted selectively activation of NF-κB (increasing nuclear p50/p65 translocation and increased the mRNA and protein levels of induced nitric oxide synthase (iNOS. The chronic treatment with MC-LR stimulated nitric oxide (NO production derived from iNOS and induced apoptosis in a dose dependent manner in INS-1 cells. Meanwhile, this effect was inhibited by the NF-κB inhibitor PDTC, which reversed the apoptosis induced by MC-LR. Our observations indicate that MC-LR induced cell apoptosis via an iNOS-dependent pathway. A well-known nuclear transcription factor, NF-κB, is activated and mediates intracellular nitric oxide synthesis. We suggest that the apoptosis induced by chronic MC-LR in vivo presents a possible cause of β-cell dysfunction, as a key environmental factor in the development of diabetes mellitus.

  5. Bayesian linear regression : different conjugate models and their (in)sensitivity to prior-data conflict

    NARCIS (Netherlands)

    Walter, G.M.; Augustin, Th.; Kneib, Thomas; Tutz, Gerhard

    2010-01-01

    The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior). Two approaches for Bayesian linear regression modeling based on

  6. A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

    Science.gov (United States)

    Cooper, Paul D.

    2010-01-01

    A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

  7. Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

    Science.gov (United States)

    MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

    2005-01-01

    Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

  8. Oxidação de microcistinas-LR em águas pelo íon ferrato(VI Aqueous oxidation of microcystin-LR by ferrate(VI

    Directory of Open Access Journals (Sweden)

    Sérgio João de Luca

    2010-03-01

    Full Text Available Toxinas de cianobactérias têm se tornado um grave problema na produção segura de água para consumo humano e animal. Técnicas convencionais de tratamento falham em atingir padrões de potabilidade. O ferrato(VI de potássio, um composto oxidante e coagulante, mostra potencialidade no tratamento de águas contaminadas. Neste trabalho, são apresentados resultados da oxidação pelo ferrato(VI de uma toxina gerada por cianobactérias, a microcistina-LR. Ensaios de cinética de oxidação e de teste de jarros mostram um valor médio de 0,012 min-1 para a constante de taxa de reação de pseudoprimeira ordem, para concentrações de MC-LR de 100 a 200 µg.L-1 na água bruta. Dosagens de 1,6 a 5,0 mg.L-1 de ferrato(VI sugerem o atendimento ao padrão de potabilidade para microcistinas, mostrando que o oxidante poderá ser empregado como coadjuvante no tratamento de água.Algae toxins are becoming a severe problem in the water treatment industry, especially for human and animal consumption. Traditional treatment processes have failed in complying with water supply standards. Potassium ferrate(VI is a powerful oxidant, disinfectant and, also, a coagulant. In this paper, the results of microcystin-LR oxidation by ferrate(VI ion are presented. Kinetic and jar tests showed a average value of 0,012 min-1 for the pseudo first order reaction rate constant, for 100 and 200 µg.L-1 concentration of MC-LR. Ferrate(VI dosages between 1.6 and 5.0 mg.L-1 suggest that water supply standards for MC-LR can be reached, which means that the oxidant may be employed as coadjuvant in water treatment.

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

    Science.gov (United States)

    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…

  10. Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

    Science.gov (United States)

    Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

    2013-01-01

    This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

  11. Healthcare Expenditures Associated with Depression Among Individuals with Osteoarthritis: Post-Regression Linear Decomposition Approach.

    Science.gov (United States)

    Agarwal, Parul; Sambamoorthi, Usha

    2015-12-01

    Depression is common among individuals with osteoarthritis and leads to increased healthcare burden. The objective of this study was to examine excess total healthcare expenditures associated with depression among individuals with osteoarthritis in the US. Adults with self-reported osteoarthritis (n = 1881) were identified using data from the 2010 Medical Expenditure Panel Survey (MEPS). Among those with osteoarthritis, chi-square tests and ordinary least square regressions (OLS) were used to examine differences in healthcare expenditures between those with and without depression. Post-regression linear decomposition technique was used to estimate the relative contribution of different constructs of the Anderson's behavioral model, i.e., predisposing, enabling, need, personal healthcare practices, and external environment factors, to the excess expenditures associated with depression among individuals with osteoarthritis. All analysis accounted for the complex survey design of MEPS. Depression coexisted among 20.6 % of adults with osteoarthritis. The average total healthcare expenditures were $13,684 among adults with depression compared to $9284 among those without depression. Multivariable OLS regression revealed that adults with depression had 38.8 % higher healthcare expenditures (p regression linear decomposition analysis indicated that 50 % of differences in expenditures among adults with and without depression can be explained by differences in need factors. Among individuals with coexisting osteoarthritis and depression, excess healthcare expenditures associated with depression were mainly due to comorbid anxiety, chronic conditions and poor health status. These expenditures may potentially be reduced by providing timely intervention for need factors or by providing care under a collaborative care model.

  12. INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

    Science.gov (United States)

    Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

  13. The wheat Lr34 multipathogen resistance gene confers resistance to anthracnose and rust in sorghum.

    Science.gov (United States)

    Schnippenkoetter, Wendelin; Lo, Clive; Liu, Guoquan; Dibley, Katherine; Chan, Wai Lung; White, Jodie; Milne, Ricky; Zwart, Alexander; Kwong, Eunjung; Keller, Beat; Godwin, Ian; Krattinger, Simon G; Lagudah, Evans

    2017-11-01

    The ability of the wheat Lr34 multipathogen resistance gene (Lr34res) to function across a wide taxonomic boundary was investigated in transgenic Sorghum bicolor. Increased resistance to sorghum rust and anthracnose disease symptoms following infection with the biotrophic pathogen Puccinia purpurea and the hemibiotroph Colletotrichum sublineolum, respectively, occurred in transgenic plants expressing the Lr34res ABC transporter. Transgenic sorghum lines that highly expressed the wheat Lr34res gene exhibited immunity to sorghum rust compared to the low-expressing single copy Lr34res genotype that conferred partial resistance. Pathogen-induced pigmentation mediated by flavonoid phytoalexins was evident on transgenic sorghum leaves following P. purpurea infection within 24-72 h, which paralleled Lr34res gene expression. Elevated expression of flavone synthase II, flavanone 4-reductase and dihydroflavonol reductase genes which control the biosynthesis of flavonoid phytoalexins characterized the highly expressing Lr34res transgenic lines 24-h post-inoculation with P. purpurea. Metabolite analysis of mesocotyls infected with C. sublineolum showed increased levels of 3-deoxyanthocyanidin metabolites were associated with Lr34res expression, concomitant with reduced symptoms of anthracnose. © 2017 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

  14. Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

    Science.gov (United States)

    Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

    2013-01-01

    Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

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

    Science.gov (United States)

    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

  16. Computer software for linear and nonlinear regression in organic NMR; Programa de computador para regressao linear e nao linear em R.M.N. organica

    Energy Technology Data Exchange (ETDEWEB)

    Canto, Eduardo Leite do; Rittner, Roberto [Universidade Estadual de Campinas, SP (Brazil). Inst. de Quimica

    1992-12-31

    Calculation involving two variable linear regressions, require specific procedures generally not familiar to chemist. For attending the necessity of fast and efficient handling of NMR data, a self explained and Pc portable software has been developed, which allows user to produce and use diskette recorded tables, containing chemical shift or any other substituent physical-chemical measurements and constants ({sigma}{sub T}, {sigma}{sup o}{sub R}, E{sub s}, ...) 9 refs., 1 fig.

  17. Microcystin-LR in surface water of Ponjavica river

    Directory of Open Access Journals (Sweden)

    Natić Dejan

    2012-01-01

    Full Text Available Background/Aim. Cyanobacterial toxins befall a group of various compounds according to chemical structure and health effects on people and animals. The most significant in this large group of compounds are microcystins. Their presence in water used for human consumption causes serious health problems, liver beeing the target organ. Microcystins are spread all over the world. Waterblooms of cyanobacterias and their cyanotoxins are also common in the majority of surface waters in Serbia. The aim of this study was to propose HPLC method for determination of mikrocystin-LR, to validate the method and to use it for determination of microcystin-LR in the surface water of the river Ponjavica. The Ponjavica is very eutrophic water and has ideal conditions for the cyanobacterial growth. Methods. Sample of water form the Ponjavica river were collected during the summer 2008. Coupled columns (HLB, Sep-Pak, were used for sample preparation and HPLC/PDA method was used for quantification of microcystin- LR. Results. Parameters of validation show that the proposed method is simple, fast, sensitive (0.1 mg/L and selective with the yield of 89%-92%. The measuring uncertainty of

  18. Photocatalytic Cellulosic Electrospun Fibers for the Degradation of Potent Cyanobacteria Toxin Microcystin-LR

    Science.gov (United States)

    2012-01-01

    visible light activated or UV light activated), the surface area of the fiber mat, and loading solution pH all have an effect on the distribution of...photocatalysis with nanoparticles (such as titania, TiO2 ) show tremendous promise as a simple and energy efficient tech- nology for water purification and...LR (MC-LR). MC- LR is one of the most commonly found cyanobacteria toxins generated by the more frequently occurring cyanobacteria algae blooms in

  19. A novel photocatalytic material for removing microcystin-LR under visible light irradiation: degradation characteristics and mechanisms.

    Directory of Open Access Journals (Sweden)

    Xin Sui

    Full Text Available Microcystin-LR (MC-LR, a common toxic species in contaminated aquatic systems, persists for long periods because of its cyclic structure. Ag3PO4 is an environment-friendly photocatalyst with relatively good degradation capacity for hazardous organic pollutants. This study aimed to investigate the degradation capacity of Ag3PO4 for MC-LR under visible light.An Ag3PO4 photocatalyst was synthesized by the ion-exchange method and characterized by X-ray diffraction, field-emission scanning electron microscope, and UV-Vis spectrophotometer. MC-LR was quantified in each sample through high-performance liquid chromatograph. The degradation efficiency of MC-LR was affected by initial pH, initial Ag3PO4 concentration, initial MC-LR concentration, and recycle experiments. The degradation intermediates of MC-LR were examined by liquid chromatography-mass spectrometry (LC/MS.The degradation process can be well fitted with the pseudo-first-order kinetic model. The maximum MC-LR degradation rate of 99.98% can be obtained within 5 h under the following optimum conditions: pH of 5.01, Ag3PO4 concentration of 26.67 g/L, and MC-LR concentration of 9.06 mg/L. Nine intermediates were detected and analyzed by LC/MS. Three main degradation pathways were proposed based on the molecular weight of the intermediates and the reaction mechanism: (1 hydroxylation on the aromatic ring of Adda, (2 hydroxylation on the diene bonds of Adda, and (3 internal interactions on the cyclic structure of MC-LR.Ag3PO4 is a highly efficient catalyst for MC-LR degradation in aqueous solutions.

  20. Straight line fitting and predictions: On a marginal likelihood approach to linear regression and errors-in-variables models

    Science.gov (United States)

    Christiansen, Bo

    2015-04-01

    Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.

  1. The detection of influential subsets in linear regression using an influence matrix

    OpenAIRE

    Peña, Daniel; Yohai, Víctor J.

    1991-01-01

    This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalue...

  2. Multivariate linear regression of high-dimensional fMRI data with multiple target variables.

    Science.gov (United States)

    Valente, Giancarlo; Castellanos, Agustin Lage; Vanacore, Gianluca; Formisano, Elia

    2014-05-01

    Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets. Copyright © 2013 Wiley Periodicals, Inc.

  3. An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

    Science.gov (United States)

    Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

    2011-01-01

    This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

  4. Uncertainty of pesticide residue concentration determined from ordinary and weighted linear regression curve.

    Science.gov (United States)

    Yolci Omeroglu, Perihan; Ambrus, Árpad; Boyacioglu, Dilek

    2018-03-28

    Determination of pesticide residues is based on calibration curves constructed for each batch of analysis. Calibration standard solutions are prepared from a known amount of reference material at different concentration levels covering the concentration range of the analyte in the analysed samples. In the scope of this study, the applicability of both ordinary linear and weighted linear regression (OLR and WLR) for pesticide residue analysis was investigated. We used 782 multipoint calibration curves obtained for 72 different analytical batches with high-pressure liquid chromatography equipped with an ultraviolet detector, and gas chromatography with electron capture, nitrogen phosphorus or mass spectrophotometer detectors. Quality criteria of the linear curves including regression coefficient, standard deviation of relative residuals and deviation of back calculated concentrations were calculated both for WLR and OLR methods. Moreover, the relative uncertainty of the predicted analyte concentration was estimated for both methods. It was concluded that calibration curve based on WLR complies with all the quality criteria set by international guidelines compared to those calculated with OLR. It means that all the data fit well with WLR for pesticide residue analysis. It was estimated that, regardless of the actual concentration range of the calibration, relative uncertainty at the lowest calibrated level ranged between 0.3% and 113.7% for OLR and between 0.2% and 22.1% for WLR. At or above 1/3 of the calibrated range, uncertainty of calibration curve ranged between 0.1% and 16.3% for OLR and 0% and 12.2% for WLR, and therefore, the two methods gave comparable results.

  5. Pitavastatin attenuates the PDGF-induced LR11/uPA receptor-mediated migration of smooth muscle cells

    International Nuclear Information System (INIS)

    Jiang, Meizi; Bujo, Hideaki; Zhu, Yanjuan; Yamazaki, Hiroyuki; Hirayama, Satoshi; Kanaki, Tatsuro; Shibasaki, Manabu; Takahashi, Kazuo; Schneider, Wolfgang J.; Saito, Yasushi

    2006-01-01

    Statins, inhibitors of HMG-CoA reductase, elicit various actions on vascular cells including the modulation of proliferation and migration of smooth muscle cells (SMCs). Here, we have elucidated the mechanism by which statins, in particular pitavastatin, attenuate the migration activity of SMCs. The expression of LR11, a member of the LDL receptor family and an enhancer of cell surface localization of urokinase-type plasminogen activator receptor (uPAR), is increased in cultured SMCs by treatment with PDGF-BB. Pitavastatin attenuates the PDGF-BB -induced surface expression of LR11 and uPAR. The increased migration of SMCs observed both upon overexpression of LR11 and via stimulation of secretion of soluble LR11 is not reversed by pitavastatin. In vivo studies showed that the SMCs expressing LR11 in plaques are almost congruent with intimal cells expressing nonmuscle myosin heavy chain (SMemb). Pitavastatin reduced the expression of LR11 and SMemb, and the levels of LR11, uPAR, and SMemb in cultured intimal SMCs were reduced to those seen in medial SMCs. We propose that this statin reduces PDGF-induced migration through the attenuation of the LR11/uPAR system in SMCs. Modulation of the LR11/uPAR system with statins suggests a novel treatment strategy for atherogenesis based on suppression of intimal SMC migration

  6. The regression-calibration method for fitting generalized linear models with additive measurement error

    OpenAIRE

    James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll

    2003-01-01

    This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...

  7. LR-90 prevents methylglyoxal-induced oxidative stress and apoptosis in human endothelial cells

    Science.gov (United States)

    Figarola, James L.; Singhal, Jyotsana; Rahbar, Samuel; Awasthi, Sanjay

    2014-01-01

    Methylglyoxal (MGO) is a highly reactive dicarbonyl compound known to induce cellular injury and cytoxicity, including apoptosis in vascular cells. Vascular endothelial cell apoptosis has been implicated in the pathophysiology and progression of atherosclerosis. We investigated whether the advanced glycation end-product inhibitor LR-90 could prevent MGO-induced apoptosis in human umbilical vascular endothelial cells (HUVECs). HUVECs were pre-treated with LR-90 and then stimulated with MGO. Cell morphology, cytotoxicity and apoptosis were evaluated by light microscopy, MTT assay, and Annexin V-FITC and propidium iodide double staining, respectively. Levels of Bax, Bcl-2, cytochrome c, mitogen-activated protein kinases (MAPKs) and caspase activities were assessed by Western blotting. Reactive oxygen species (ROS) generation and mitochondrial membrane potential (MMP) were measured with fluorescent probes. LR-90 dose-dependently prevented MGO-associated HUVEC cytotoxicity and apoptotic biochemical changes such as loss of MMP, increased Bax/Bcl-2 protein ratio, mitochondrial cytochrome c release and activation of caspase-3 and 9. Additionally, LR-90 blocked intracellular ROS formation and MAPK (p44/p42, p38, JNK) activation, though the latter seem to be not directly involved in MGO-induced HUVEC apoptosis. LR-90 prevents MGO-induced HUVEC apoptosis by inhibiting ROS and associated mitochondrial-dependent apoptotic signaling cascades, suggesting that LR-90 possess cytoprotective ability which could be beneficial in prevention of diabetic related-atherosclerosis. PMID:24615331

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

    Directory of Open Access Journals (Sweden)

    Yubo Wang

    2017-06-01

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

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

    Science.gov (United States)

    Wang, Yubo; Veluvolu, Kalyana C

    2017-06-14

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

  10. Adsorption of microcystin-LR on mesoporous carbons and its potential use in drinking water source.

    Science.gov (United States)

    Park, Jeong-Ann; Jung, Sung-Mok; Yi, In-Geol; Choi, Jae-Woo; Kim, Song-Bae; Lee, Sang-Hyup

    2017-06-01

    Microcystin-LR (MC-LR) is a common toxin derived from cyanobacterial blooms an effective, rapid and non-toxic method needs to be developed for its removal from drinking water treatment plants (DWTP). For an adsorption-based method, mesoporous carbon can be a promising supplemental adsorbent. The effect of mesoporous carbon (MC1, MC2, and MC3) properties and water quality parameters on the adsorption of MC-LR were investigated and the results were analyzed by kinetic, isotherm, thermodynamic, Derjaguin-Landau-Verwey-Overbeek (DLVO), and intraparticle diffusion models. MC1 was the most appropriate type for the removal of MC-LR with a maximum adsorption capacity of 35,670.49 μg/g. Adsorption of MC-LR is a spontaneous reaction dominated by van der Waals interactions. Pore sizes of 8.5-14 nm enhance the pore diffusion of MC-LR from the surface to the mesopores of MC1. The adsorption capacity was not sensitive to changes in the pH (3.2-8.0) and the existence of organic matter (2-5 mg/L). Furthermore, the final concentration of MC-LR was below the WHO guideline level after a 10-min reaction with 20 mg/L of MC1 in the Nak-Dong River, a drinking water source. The MC-LR adsorption mainly competed with humic substances (500-1000 g/mole); however, they did not have a great effect on adsorption. Copyright © 2017. Published by Elsevier Ltd.

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

    Science.gov (United States)

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

    2017-01-01

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

  12. Two Paradoxes in Linear Regression Analysis

    Science.gov (United States)

    FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

    2016-01-01

    Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

  13. How to deal with continuous and dichotomic outcomes in epidemiological research: linear and logistic regression analyses

    NARCIS (Netherlands)

    Tripepi, Giovanni; Jager, Kitty J.; Stel, Vianda S.; Dekker, Friedo W.; Zoccali, Carmine

    2011-01-01

    Because of some limitations of stratification methods, epidemiologists frequently use multiple linear and logistic regression analyses to address specific epidemiological questions. If the dependent variable is a continuous one (for example, systolic pressure and serum creatinine), the researcher

  14. BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE.

    Science.gov (United States)

    Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G

    2015-12-01

    The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.

  15. Toward Customer-Centric Organizational Science: A Common Language Effect Size Indicator for Multiple Linear Regressions and Regressions With Higher-Order Terms.

    Science.gov (United States)

    Krasikova, Dina V; Le, Huy; Bachura, Eric

    2018-01-22

    To address a long-standing concern regarding a gap between organizational science and practice, scholars called for more intuitive and meaningful ways of communicating research results to users of academic research. In this article, we develop a common language effect size index (CLβ) that can help translate research results to practice. We demonstrate how CLβ can be computed and used to interpret the effects of continuous and categorical predictors in multiple linear regression models. We also elaborate on how the proposed CLβ index is computed and used to interpret interactions and nonlinear effects in regression models. In addition, we test the robustness of the proposed index to violations of normality and provide means for computing standard errors and constructing confidence intervals around its estimates. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  16. A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

    Science.gov (United States)

    Paek, Insu

    2012-01-01

    Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…

  17. Linear and nonlinear analysis of airflow recordings to help in sleep apnoea–hypopnoea syndrome diagnosis

    International Nuclear Information System (INIS)

    Gutiérrez-Tobal, G C; Hornero, R; Álvarez, D; Marcos, J V; Del Campo, F

    2012-01-01

    This paper focuses on the analysis of single-channel airflow (AF) signal to help in sleep apnoea–hypopnoea syndrome (SAHS) diagnosis. The respiratory rate variability (RRV) series is derived from AF by measuring time between consecutive breathings. A set of statistical, spectral and nonlinear features are extracted from both signals. Then, the forward stepwise logistic regression (FSLR) procedure is used in order to perform feature selection and classification. Three logistic regression (LR) models are obtained by applying FSLR to features from AF, RRV and both signals simultaneously. The diagnostic performance of single features and LR models is assessed and compared in terms of sensitivity, specificity, accuracy and area under the receiver-operating characteristics curve (AROC). The highest accuracy (82.43%) and AROC (0.903) are reached by the LR model derived from the combination of AF and RRV features. This result suggests that AF and RRV provide useful information to detect SAHS. (paper)

  18. A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

    Science.gov (United States)

    Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

    2011-09-01

    Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

  19. Introduction to regression graphics

    CERN Document Server

    Cook, R Dennis

    2009-01-01

    Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

  20. Crystallization and preliminary crystallographic analysis of human LR11 Vps10p domain

    International Nuclear Information System (INIS)

    Nakata, Zenzaburo; Nagae, Masamichi; Yasui, Norihisa; Bujo, Hideaki; Nogi, Terukazu; Takagi, Junichi

    2010-01-01

    LR11/sorLA contains in its extracellular region a large (∼700-residue) Vps10p domain that is implicated in its intracellular protein-trafficking function. Here, the expression, purification, crystallization and preliminary crystallographic characterization of this domain are described. Low-density lipoprotein receptor (LDLR) relative with 11 binding repeats (LR11; also known as sorLA) is genetically associated with late-onset Alzheimer’s disease and is thought to be involved in neurodegenerative processes. LR11 contains a vacuolar protein-sorting 10 protein (Vps10p) domain. As this domain has been implicated in protein–protein interaction in other receptors, its structure and function are of great biological interest. Human LR11 Vps10p domain was expressed in mammalian cells and the purified protein was crystallized using the hanging-drop vapour-diffusion method. Enzymatic deglycosylation of the sample was critical to obtaining diffraction-quality crystals. Deglycosylated LR11 Vps10p-domain crystals belonged to the hexagonal space group P6 1 22. A diffraction data set was collected to 2.4 Å resolution and a clear molecular-replacement solution was obtained

  1. Life-cycle exposure to microcystin-LR interferes with the reproductive endocrine system of male zebrafish.

    Science.gov (United States)

    Su, Yujing; Li, Li; Hou, Jie; Wu, Ning; Lin, Wang; Li, Guangyu

    2016-06-01

    Recently, MC-LR reproductive toxicity drew great attention. Limited information was available on endocrine-disrupting effects of MC-LR on the reproduction system in fish. In the present study, zebrafish hatchlings (5 d post-fertilization) were exposed to 0, 0.3, 3 and 30μg/L MC-LR for 90 d until they reached sexual maturity. Male zebrafish were selected, and changes in growth and developmental parameters, testicular histological structure as well as the levels of gonadal steroid hormones were studied along with the related-gene transcriptional responses in the hypothalamic-pituitary-gonadal axis (HPG-axis). The results, for the first time, show a life cycle exposure to MC-LR causes growth inhibition, testicular damage and delayed sperm maturation. A significant decrease in T/E2 ratio indicated that MC-LR disrupted sex steroid hormones balance. The changes in transcriptional responses of HPG-axis related genes revealed that MC-LR promoted the conversion of T to E2 in circulating blood. It was also noted that vtg1 mRNA expression in the liver was up-regulated, which implied that MC-LR could induce estrogenic-like effects at environmentally relevant concentrations and long-term exposure. Our findings indicated that a life cycle exposure to MC-LR causes endocrine disruption with organic and functional damage of the testis, which might compromise the quality of life for the survivors and pose a potent threat on fish reproduction and thus population dynamics in MCs-contaminated aquatic environments. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Detoxification of microcystin-LR in water by Portulaca oleracea cv.

    Science.gov (United States)

    Isobe, Takatoshi; Okuhata, Hiroshi; Miyasaka, Hitoshi; Jeon, Bong-Seok; Park, Ho-Dong

    2014-03-01

    Microcystin-LR (0.02 μg/ml) in the hydroculture medium of Portulaca oleracea cv., became below the detection level (<0.0001 μg/ml) by HPLC analysis after 7 days. The toxicity of microcystin estimated with protein phosphatase inhibition assay, however, remained at 37% of the initial level, indicating that microcystin-LR was transformed by P. oleracea cv. into unknown compound(s) of lower toxicity. Copyright © 2013 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

  3. Hippocampal atrophy and developmental regression as first sign of linear scleroderma "en coup de sabre".

    Science.gov (United States)

    Verhelst, Helene E; Beele, Hilde; Joos, Rik; Vanneuville, Benedicte; Van Coster, Rudy N

    2008-11-01

    An 8-year-old girl with linear scleroderma "en coup de sabre" is reported who, at preschool age, presented with intractable simple partial seizures more than 1 year before skin lesions were first noticed. MRI revealed hippocampal atrophy, controlaterally to the seizures and ipsilaterally to the skin lesions. In the following months, a mental and motor regression was noticed. Cerebral CT scan showed multiple foci of calcifications in the affected hemisphere. In previously reported patients the skin lesions preceded the neurological signs. To the best of our knowledge, hippocampal atrophy was not earlier reported as presenting symptom of linear scleroderma. Linear scleroderma should be included in the differential diagnosis in patients with unilateral hippocampal atrophy even when the typical skin lesions are not present.

  4. Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

    Science.gov (United States)

    Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

    2017-01-01

    In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

  5. Main: 1LR5 [RPSD[Archive

    Lifescience Database Archive (English)

    Full Text Available Aux311; Zea Mays Molecule: Auxin Binding Protein 1; Chain: A, B, C, D; Engineered: Yes; Mutation: Yes Protei...-.|EMBL; L08425; AAA33430.1; -.|PIR; S16262; S16262.|PDB; 1LR5; X-ray; A/B/C/D=39-201.|PDB; 1LRH; X-ray; A/B/C/D=39-201.|Mai

  6. Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

    Directory of Open Access Journals (Sweden)

    Avval Zhila Mohajeri

    2015-01-01

    Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

  7. A computer tool for a minimax criterion in binary response and heteroscedastic simple linear regression models.

    Science.gov (United States)

    Casero-Alonso, V; López-Fidalgo, J; Torsney, B

    2017-01-01

    Binary response models are used in many real applications. For these models the Fisher information matrix (FIM) is proportional to the FIM of a weighted simple linear regression model. The same is also true when the weight function has a finite integral. Thus, optimal designs for one binary model are also optimal for the corresponding weighted linear regression model. The main objective of this paper is to provide a tool for the construction of MV-optimal designs, minimizing the maximum of the variances of the estimates, for a general design space. MV-optimality is a potentially difficult criterion because of its nondifferentiability at equal variance designs. A methodology for obtaining MV-optimal designs where the design space is a compact interval [a, b] will be given for several standard weight functions. The methodology will allow us to build a user-friendly computer tool based on Mathematica to compute MV-optimal designs. Some illustrative examples will show a representation of MV-optimal designs in the Euclidean plane, taking a and b as the axes. The applet will be explained using two relevant models. In the first one the case of a weighted linear regression model is considered, where the weight function is directly chosen from a typical family. In the second example a binary response model is assumed, where the probability of the outcome is given by a typical probability distribution. Practitioners can use the provided applet to identify the solution and to know the exact support points and design weights. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  8. Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions.

    Science.gov (United States)

    Xia, Yin; Cai, Tianxi; Cai, T Tony

    2018-01-01

    Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates while controlling the false discovery rate and false discovery proportion. Theoretical justifications are provided to guarantee the validity of the proposed tests and optimality results are established under sparsity assumptions on the regression coefficients. The proposed testing procedures are easy to implement. Numerical properties of the procedures are investigated through simulation and data analysis. The results show that the proposed tests maintain the desired error rates under the null and have good power under the alternative at moderate sample sizes. The procedures are applied to the Framingham Offspring study to investigate the interactions between smoking and cardiovascular related genetic mutations important for an inflammation marker.

  9. Cool-season precipitation in the southwestern USA since AD 1000: comparison of linear and nonlinear techniques for reconstruction

    Science.gov (United States)

    Ni, Fenbiao; Cavazos, Tereza; Hughes, Malcolm K.; Comrie, Andrew C.; Funkhouser, Gary

    2002-11-01

    A 1000 year reconstruction of cool-season (November-April) precipitation was developed for each climate division in Arizona and New Mexico from a network of 19 tree-ring chronologies in the southwestern USA. Linear regression (LR) and artificial neural network (NN) models were used to identify the cool-season precipitation signal in tree rings. Using 1931-88 records, the stepwise LR model was cross-validated with a leave-one-out procedure and the NN was validated with a bootstrap technique. The final models were also independently validated using the 1896-1930 precipitation data. In most of the climate divisions, both techniques can successfully reconstruct dry and normal years, and the NN seems to capture large precipitation events and more variability better than the LR. In the 1000 year reconstructions the NN also produces more distinctive wet events and more variability, whereas the LR produces more distinctive dry events. The 1000 year reconstructed precipitation from the two models shows several sustained dry and wet periods comparable to the 1950s drought (e.g. 16th century mega drought) and to the post-1976 wet period (e.g. 1330s, 1610s). The impact of extreme periods on the environment may be stronger during sudden reversals from dry to wet, which were not uncommon throughout the millennium, such as the 1610s wet interval that followed the 16th century mega drought. The instrumental records suggest that strong dry to wet precipitation reversals in the past 1000 years might be linked to strong shifts from cold to warm El Niño-southern oscillation events and from a negative to positive Pacific decadal oscillation.

  10. The comparison of landslide ratio-based and general logistic regression landslide susceptibility models in the Chishan watershed after 2009 Typhoon Morakot

    Science.gov (United States)

    WU, Chunhung

    2015-04-01

    The research built the original logistic regression landslide susceptibility model (abbreviated as or-LRLSM) and landslide ratio-based ogistic regression landslide susceptibility model (abbreviated as lr-LRLSM), compared the performance and explained the error source of two models. The research assumes that the performance of the logistic regression model can be better if the distribution of landslide ratio and weighted value of each variable is similar. Landslide ratio is the ratio of landslide area to total area in the specific area and an useful index to evaluate the seriousness of landslide disaster in Taiwan. The research adopted the landside inventory induced by 2009 Typhoon Morakot in the Chishan watershed, which was the most serious disaster event in the last decade, in Taiwan. The research adopted the 20 m grid as the basic unit in building the LRLSM, and six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The six variables were divided as continuous variables, including elevation, slope, and accumulated rainfall, and categorical variables, including aspect, geological formation and bank erosion in building the or-LRLSM, while all variables, which were classified based on landslide ratio, were categorical variables in building the lr-LRLSM. Because the count of whole basic unit in the Chishan watershed was too much to calculate by using commercial software, the research took random sampling instead of the whole basic units. The research adopted equal proportions of landslide unit and not landslide unit in logistic regression analysis. The research took 10 times random sampling and selected the group with the best Cox & Snell R2 value and Nagelkerker R2 value as the database for the following analysis. Based on the best result from 10 random sampling groups, the or-LRLSM (lr-LRLSM) is significant at the 1% level with Cox & Snell R2 = 0.190 (0.196) and Nagelkerke R2

  11. Development of an ELISA and Immunochromatographic Strip for Highly Sensitive Detection of Microcystin-LR

    Directory of Open Access Journals (Sweden)

    Liqiang Liu

    2014-08-01

    Full Text Available A monoclonal antibody for microcystin–leucine–arginine (MC-LR was produced by cell fusion. The immunogen was synthesized in two steps. First, ovalbumin/ bovine serum albumin was conjugated with 6-acetylthiohexanoic acid using a carbodiimide EDC (1-ethyl-3-[3-dimethylaminopropyl]carbodiimide hydrochloride/ NHS (N-hydroxysulfosuccinimide reaction. After dialysis, the protein was reacted with MC-LR based on a free radical reaction under basic solution conditions. The protein conjugate was used for immunization based on low volume. The antibodies were identified by indirect competitive (icELISA and were subjected to tap water and lake water analysis. The concentration causing 50% inhibition of binding of MC-LR (IC50 by the competitive indirect ELISA was 0.27 ng/mL. Cross-reactivity to the MC-RR, MC-YR and MC-WR was good. The tap water and lake water matrices had no effect on the detection limit. The analytical recovery of MC-LR in the water samples in the icELISA was 94%–110%. Based on this antibody, an immunochromatographic biosensor was developed with a cut-off value of 1 ng/mL, which could satisfy the requirement of the World Health Organization for MC-LR detection in drinking water. This biosensor could be therefore be used as a fast screening tool in the field detection of MC-LR.

  12. A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection

    OpenAIRE

    Kwan, Johnny S. H.; Kung, Annie W. C.; Sham, Pak C.

    2011-01-01

    Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias. © The Author(s) 2011.

  13. Method validation using weighted linear regression models for quantification of UV filters in water samples.

    Science.gov (United States)

    da Silva, Claudia Pereira; Emídio, Elissandro Soares; de Marchi, Mary Rosa Rodrigues

    2015-01-01

    This paper describes the validation of a method consisting of solid-phase extraction followed by gas chromatography-tandem mass spectrometry for the analysis of the ultraviolet (UV) filters benzophenone-3, ethylhexyl salicylate, ethylhexyl methoxycinnamate and octocrylene. The method validation criteria included evaluation of selectivity, analytical curve, trueness, precision, limits of detection and limits of quantification. The non-weighted linear regression model has traditionally been used for calibration, but it is not necessarily the optimal model in all cases. Because the assumption of homoscedasticity was not met for the analytical data in this work, a weighted least squares linear regression was used for the calibration method. The evaluated analytical parameters were satisfactory for the analytes and showed recoveries at four fortification levels between 62% and 107%, with relative standard deviations less than 14%. The detection limits ranged from 7.6 to 24.1 ng L(-1). The proposed method was used to determine the amount of UV filters in water samples from water treatment plants in Araraquara and Jau in São Paulo, Brazil. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Linear and evolutionary polynomial regression models to forecast coastal dynamics: Comparison and reliability assessment

    Science.gov (United States)

    Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe

    2018-01-01

    In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.

  15. Consequences of Laughter Upon Trunk Compression and Cortical Activation: Linear and Polynomial Relations

    Science.gov (United States)

    Svebak, Sven

    2016-01-01

    Results from two studies of biological consequences of laughter are reported. A proposed inhibitory brain mechanism was tested in Study 1. It aims to protect against trunk compression that can cause health hazards during vigorous laughter. Compression may be maximal during moderate durations and, for protective reasons, moderate in enduring vigorous laughs. Twenty-five university students volunteered to see a candid camera film. Laughter responses (LR) and the superimposed ha-responses were operationally assessed by mercury-filled strain gauges strapped around the trunk. On average, the thorax compression amplitudes exceeded those of the abdomen, and greater amplitudes were seen in the males than in the females after correction for resting trunk circumference. Regression analyses supported polynomial relations because medium LR durations were associated with particularly high thorax amplitudes. In Study 2, power changes were computed in the beta and alpha EEG frequency bands of the parietal cortex from before to after exposure to the comedy “Dinner for one” in 56 university students. Highly significant linear relations were calculated between the number of laughs and post-exposure cortical activation (increase of beta, decrease of alpha) due to high activation after frequent laughter. The results from Study 1 supported the hypothesis of a protective brain mechanism that is activated during long LRs to reduce the risk of harm to vital organs in the trunk cavity. The results in Study 2 supported a linear cortical activation and, thus, provided evidence for a biological correlate to the subjective experience of mental refreshment after laughter. PMID:27547260

  16. Microcystin-LR induces anoikis resistance to the hepatocyte uptake transporter OATP1B3-expressing cell lines

    International Nuclear Information System (INIS)

    Takano, Hiroyuki; Takumi, Shota; Ikema, Satoshi; Mizoue, Nozomi; Hotta, Yuki; Shiozaki, Kazuhiro; Sugiyama, Yasumasa; Furukawa, Tatsuhiko; Komatsu, Masaharu

    2014-01-01

    Microcystin-LR is a cyclic peptide released by several bloom-forming cyanobacteria. Understanding the mechanism of microcystin-LR toxicity is important, because of the both potencies of its acute cytotoxicity and tumor-promoting activity in hepatocytes of animals and humans. Recently, we have reported that the expression of human hepatocyte uptake transporter OATP1B3 was critical for the selective uptake of microcystin-LR into hepatocytes and for induction of its fatal cytotoxicity. In this study, we demonstrated a novel function of microcystin-LR which induced bipotential changes including anoikis resistance and cytoskeleton reorganization to OATP1B3-transfected HEK293 cells (HEK293-OATP1B3). After exposure to microcystin-LR, HEK293-OATP1B3 cells were divided to the floating cells and remaining adherent cells. After collection and reseeding the floating cells into a fresh flask, cells were confluently proliferated (HEK293-OATP1B3-FL) under the microcystin-LR-free condition. Both the proliferated HEK293-OATP1B3-FL and remaining adherent HEK293-OATP1B3-AD cells changed the character with down- and up-regulation of E-cadherin, respectively. Additionally, these cells acquired resistance to microcystin-LR. These results suggest that microcystin-LR could be associated with not only tumor promotion, but also epithelial–mesenchymal transition-mediated cancer metastasis. Furthermore, microcystin-LR might induce the cytoskeleton reorganization be accompanied epithelial–mesenchymal transition

  17. Analysis of WRKY transcription factors and characterization of two Botrytis cinerea-responsive LrWRKY genes from Lilium regale.

    Science.gov (United States)

    Cui, Qi; Yan, Xiao; Gao, Xue; Zhang, Dong-Mei; He, Heng-Bin; Jia, Gui-Xia

    2018-06-01

    A major constraint in producing lilies is gray mold caused by Botrytis elliptica and B. cinerea. WRKY transcription factors play important roles in plant immune responses. However, limited information is available about the WRKY gene family in lily plants. In this study, 23 LrWRKY genes with complete WRKY domains were identified from the Botrytis-resistant species Lilium regale. The putative WRKY genes were divided into seven subgroups (Group I, IIa-e, and III) according to their structural features. Sequence alignment revealed that LrWRKY proteins have a highly conserved WRKYGQK domain and a variant, the WRKYGKK domain, and these proteins generally contained similar motif compositions throughout the same subgroup. Functional annotation predicted they might be involved in biological processes related to abiotic and biotic stresses. A qRT-PCR analysis confirmed that expression of six LrWRKY genes in L. regale or the susceptible Asian hybrid 'Yale' was induced by B. cinerea infection. Among these genes, LrWRKY4, LrWRKY8 and LrWRKY10 were expressed at a higher level in L. regale than 'Yale', while the expression of LrWRKY6 and LrWRKY12 was lower in L. regale. Furthermore, LrWRKY4 and LrWRKY12 genes, which also respond to salicylic acid (SA) and methyl jasmonate (MeJA) treatments, were isolated from L. regale. Subcellular localization analysis determined that they were targeted to the nucleus. Constitutive expression of LrWRKY4 and LrWRKY12 in Arabidopsis resulted in plants that were more resistant to B. cinerea than wild-type plants. This resistance was coupled with the transcriptional changes of SA and JA-responsive genes. Overall, our study provides valuable information about the structural and functional characterization of LrWRKY genes that will not only deepen our understanding of the molecular mechanisms underlying the defense of lily against B. cinerea but also offer potential targets for cultivar improvement via biotechnology. Copyright © 2018 Elsevier Masson

  18. Improving ASTER GDEM Accuracy Using Land Use-Based Linear Regression Methods: A Case Study of Lianyungang, East China

    Directory of Open Access Journals (Sweden)

    Xiaoyan Yang

    2018-04-01

    Full Text Available The Advanced Spaceborne Thermal-Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM is important to a wide range of geographical and environmental studies. Its accuracy, to some extent associated with land-use types reflecting topography, vegetation coverage, and human activities, impacts the results and conclusions of these studies. In order to improve the accuracy of ASTER GDEM prior to its application, we investigated ASTER GDEM errors based on individual land-use types and proposed two linear regression calibration methods, one considering only land use-specific errors and the other considering the impact of both land-use and topography. Our calibration methods were tested on the coastal prefectural city of Lianyungang in eastern China. Results indicate that (1 ASTER GDEM is highly accurate for rice, wheat, grass and mining lands but less accurate for scenic, garden, wood and bare lands; (2 despite improvements in ASTER GDEM2 accuracy, multiple linear regression calibration requires more data (topography and a relatively complex calibration process; (3 simple linear regression calibration proves a practicable and simplified means to systematically investigate and improve the impact of land-use on ASTER GDEM accuracy. Our method is applicable to areas with detailed land-use data based on highly accurate field-based point-elevation measurements.

  19. Estimating integrated variance in the presence of microstructure noise using linear regression

    Science.gov (United States)

    Holý, Vladimír

    2017-07-01

    Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

  20. Face Hallucination with Linear Regression Model in Semi-Orthogonal Multilinear PCA Method

    Science.gov (United States)

    Asavaskulkiet, Krissada

    2018-04-01

    In this paper, we propose a new face hallucination technique, face images reconstruction in HSV color space with a semi-orthogonal multilinear principal component analysis method. This novel hallucination technique can perform directly from tensors via tensor-to-vector projection by imposing the orthogonality constraint in only one mode. In our experiments, we use facial images from FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color faces. The experimental results assure clearly demonstrated that we can generate photorealistic color face images by using the SO-MPCA subspace with a linear regression model.

  1. To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory

    Science.gov (United States)

    Liu, Hongjie; Li, Tianhao; Zhan, Sha; Pan, Meilan; Ma, Zhiguo; Li, Chenghua

    2016-01-01

    Aims. To establish a logistic regression (LR) prediction model for hepatotoxicity of Chinese herbal medicines (HMs) based on traditional Chinese medicine (TCM) theory and to provide a statistical basis for predicting hepatotoxicity of HMs. Methods. The correlations of hepatotoxic and nonhepatotoxic Chinese HMs with four properties, five flavors, and channel tropism were analyzed with chi-square test for two-way unordered categorical data. LR prediction model was established and the accuracy of the prediction by this model was evaluated. Results. The hepatotoxic and nonhepatotoxic Chinese HMs were related with four properties (p 0.05). There were totally 12 variables from four properties and five flavors for the LR. Four variables, warm and neutral of the four properties and pungent and salty of five flavors, were selected to establish the LR prediction model, with the cutoff value being 0.204. Conclusions. Warm and neutral of the four properties and pungent and salty of five flavors were the variables to affect the hepatotoxicity. Based on such results, the established LR prediction model had some predictive power for hepatotoxicity of Chinese HMs. PMID:27656240

  2. Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

    Science.gov (United States)

    Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

    2014-01-01

    This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…

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

    Science.gov (United States)

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

    2014-11-06

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

  4. Chronic exposure to microcystin-LR affected mitochondrial DNA maintenance and caused pathological changes of lung tissue in mice

    International Nuclear Information System (INIS)

    Li, Xinxiu; Xu, Lizhi; Zhou, Wei; Zhao, Qingya; Wang, Yaping

    2016-01-01

    Microcystin-LR (MC-LR), an important variant of cyanotoxin family, was frequently encountered in the contaminated aquatic environment and taken as a potent hepatotoxin. However, a little was known on the association between the long-term MC-LR exposure and lung damage. In this study, we investigated the changes of the pulmonary histopathology, mitochondrial DNA (mtDNA) integrity and the expression of mtDNA encoded genes in the mice with chronic exposed to MC-LR at different concentrations (1, 5, 10, 20 and 40 μg/L) for 12 months. Our results showed that the long-term and persistent exposure to MC-LR disturbed the balance of redox system, influenced mtDNA stability, changed the expression of mitochondrial genes in the lung cells. Notably, MC-LR exposure influenced the level of inflammatory cytokines and resulted in thickening of the alveolar septa. In conclusion, chronic exposure to MC-LR affected mtDNA maintenance, and caused lung impairment in mice. - Highlights: • A simulated natural exposure to MC-LR caused the lung pathological changes. • The chronic exposure disturbed the redox system balance of lung tissue cells. • The chronic exposure impaired the mtDNA stability and mitochondria function. • The lung was one of the vulnerable organs to MC-LR exposure in mice. - Long-term exposure to MC-LR in drinking water disturbed the balance of redox system, affected mitochondrial DNA maintenance and caused lung impairment in mice.

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

    Science.gov (United States)

    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.

  6. U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

    Science.gov (United States)

    2017-10-01

    ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...distribution is unlimited. AD U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER Munitions Engineering Technology Center Picatinny...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

  7. Isotherms and thermodynamics by linear and non-linear regression analysis for the sorption of methylene blue onto activated carbon: Comparison of various error functions

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  8. Application of DNA RFLP procedures in interspecific gene transfer: The Lr19 translocation of wheat

    International Nuclear Information System (INIS)

    Prins, R.; Marais, G.F.; Marais, A.S.; Pretorius, Z.A.; Janse, B.J.H.

    1998-01-01

    Twenty-nine lines with deletions in the Lr19 ('Indis') translocated chromosome segment were used to physically map Thinopyrum Restriction Fragment Length Polymorphism (RFLP) loci as well as the Sr25 and Sdl loci. The relative distances between marker loci on the translocation were then calculated. The information was then used as an aid to characterize several recombined forms of the translocation. The data confirmed the reported homoeology between the Lr19 segment and chromosome arm 7DL of wheat. Also, it seems that the Lr19 translocation in 'Indis' is very similar to the Lr19 segment in the T4 source and that the former may not derive from Thinopyrum distichum. Near-isogenic lines of the recombined segments were derived and used to study their expression of leaf rust resistance. It became evident that only one potentially useful recombinant was obtained in an earlier attempt to induce allosyndetic pairing between the Lr19 translocation and 7DL of wheat. (author)

  9. Evaluation of the transfer and the accumulation of microcystins in tomato (Solanum lycopersicum cultivar MicroTom) tissues using a cyanobacterial extract containing microcystins and the radiolabeled microcystin-LR ("1"4C-MC-LR)

    International Nuclear Information System (INIS)

    Corbel, Sylvain; Mougin, Christian; Nélieu, Sylvie; Delarue, Ghislaine; Bouaïcha, Noureddine

    2016-01-01

    Microcystins are the most common cyanotoxins and may be expected wherever blooms of cyanobacteria occur in surface waters. Their persistence both in the irrigation water and in the soil can lead to their transfer and bioaccumulation into agricultural plants. The aim of this work was to investigate microcystin accumulation in Solanum lycopersicum cultivar MicroTom. The plant was exposed to either Microcystis aeruginosa crude extracts containing up to 100 μg eq. MC-LR L"−"1 in a soil–plant system for 90 days or pure radiolabeled "1"4C-MC-LR in a hydroponic condition for 48 h. Toxin bioaccumulation in the soil and different plant tissues was assessed both by the PP2A inhibition assay and by liquid chromatography-mass spectrometry (LC/MS/MS). After 90 days of exposure, microcystins persisted in the soil and their free extractable concentrations accumulated were very low varying between 1.6 and 3.9 μg eq. MC-LR kg"−"1 DW. Free MC-LR was detected only in roots and leaves with concentrations varying between 4.5 and 8.1 μg kg"−"1 DW and between 0.29 and 0.55 μg kg"−"1 DW, respectively. By using radioactivity ("1"4C-MC-LR), the results have reported a growing accumulation of toxins within the organs roots > leaves > stems and allowed them to confirm the absence of MC-LR in fruits after 48 h of exposure. The bioconcentration factor (BCF) was 13.6 in roots, 4.5 in leaves, and 1.4 in stems. On the other hand, the results highlight the presence of two radioactive fractions in different plant tissues. The non-extractable fraction of radioactivity, corresponding to the covalently bound MC-LR, was higher than that of the extractable fraction only in roots and leaves reaching 56% and 71% of the total accumulated toxin, respectively. Therefore, results raise that monitoring programs must monitor the presence of MCs in the irrigation water to avoid the transfer and accumulation of these toxins in crops. - Graphical abstract: Bioconcentration factors in organs of the

  10. Evaluation of the transfer and the accumulation of microcystins in tomato (Solanum lycopersicum cultivar MicroTom) tissues using a cyanobacterial extract containing microcystins and the radiolabeled microcystin-LR ({sup 14}C-MC-LR)

    Energy Technology Data Exchange (ETDEWEB)

    Corbel, Sylvain [INRA, UMR1402 ECOSYS, F-78850 Thiverval-Grignon (France); AgroParisTech, UMR1402 ECOSYS, F-78850 Thiverval-Grignon (France); Laboratoire Ecologie, Systématique et Evolution, UMR8079, Univ. Paris-Sud/CNRS/AgroParisTech, Université Paris-Sud, F-91405 Orsay (France); Mougin, Christian; Nélieu, Sylvie; Delarue, Ghislaine [INRA, UMR1402 ECOSYS, F-78850 Thiverval-Grignon (France); AgroParisTech, UMR1402 ECOSYS, F-78850 Thiverval-Grignon (France); Bouaïcha, Noureddine, E-mail: noureddine.bouaicha@u-psud.fr [Laboratoire Ecologie, Systématique et Evolution, UMR8079, Univ. Paris-Sud/CNRS/AgroParisTech, Université Paris-Sud, F-91405 Orsay (France)

    2016-01-15

    Microcystins are the most common cyanotoxins and may be expected wherever blooms of cyanobacteria occur in surface waters. Their persistence both in the irrigation water and in the soil can lead to their transfer and bioaccumulation into agricultural plants. The aim of this work was to investigate microcystin accumulation in Solanum lycopersicum cultivar MicroTom. The plant was exposed to either Microcystis aeruginosa crude extracts containing up to 100 μg eq. MC-LR L{sup −1} in a soil–plant system for 90 days or pure radiolabeled {sup 14}C-MC-LR in a hydroponic condition for 48 h. Toxin bioaccumulation in the soil and different plant tissues was assessed both by the PP2A inhibition assay and by liquid chromatography-mass spectrometry (LC/MS/MS). After 90 days of exposure, microcystins persisted in the soil and their free extractable concentrations accumulated were very low varying between 1.6 and 3.9 μg eq. MC-LR kg{sup −1} DW. Free MC-LR was detected only in roots and leaves with concentrations varying between 4.5 and 8.1 μg kg{sup −1} DW and between 0.29 and 0.55 μg kg{sup −1} DW, respectively. By using radioactivity ({sup 14}C-MC-LR), the results have reported a growing accumulation of toxins within the organs roots > leaves > stems and allowed them to confirm the absence of MC-LR in fruits after 48 h of exposure. The bioconcentration factor (BCF) was 13.6 in roots, 4.5 in leaves, and 1.4 in stems. On the other hand, the results highlight the presence of two radioactive fractions in different plant tissues. The non-extractable fraction of radioactivity, corresponding to the covalently bound MC-LR, was higher than that of the extractable fraction only in roots and leaves reaching 56% and 71% of the total accumulated toxin, respectively. Therefore, results raise that monitoring programs must monitor the presence of MCs in the irrigation water to avoid the transfer and accumulation of these toxins in crops. - Graphical abstract: Bioconcentration

  11. A Matlab program for stepwise regression

    Directory of Open Access Journals (Sweden)

    Yanhong Qi

    2016-03-01

    Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.

  12. Aspects of robust linear regression

    NARCIS (Netherlands)

    Davies, P.L.

    1993-01-01

    Section 1 of the paper contains a general discussion of robustness. In Section 2 the influence function of the Hampel-Rousseeuw least median of squares estimator is derived. Linearly invariant weak metrics are constructed in Section 3. It is shown in Section 4 that $S$-estimators satisfy an exact

  13. Quality assurance for environmental radon measurements by LR115 nuclear track detectors

    Energy Technology Data Exchange (ETDEWEB)

    Gomaa, M A [National Network of Radiation Physics, Atomic Energy Authority, Cairo (Egypt); Hafez, A F [Physics Department, Faculty of Science, Alexandria Univercity, Alexandria (Egypt); Hussein, A S [Radiation Protection Department, Nuclear Power Plants Authority, Cairo (Egypt)

    2007-06-15

    Passive radon dosimeters based on LR115 nuclear track detectors are very attractive for assessment of radon exposure. For developing countries wishing to undertake national radon survey the most appropriate techniques are those making use of LR115 detectors. These detectors are small, cheap, simple, and non-hazardous and provide an entirely adequate tool for large scale use in assessing levels of radon over several months because of the short - term fluctuations in radon concentrations. In this paper, the principles and philosophy in order to improve the quality and reliability of radon exposure under a quality assurance (QA) program are presented . Also examples of how a QA program of radon measurements by LR115 detectors using the can-techniques are well defined and applied.

  14. Quality assurance for environmental radon measurements by LR115 nuclear track detectors

    International Nuclear Information System (INIS)

    Gomaa, M.A.; Hafez, A.F.; Hussein, A.S.

    2007-01-01

    Passive radon dosimeters based on LR115 nuclear track detectors are very attractive for assessment of radon exposure. For developing countries wishing to undertake national radon survey the most appropriate techniques are those making use of LR115 detectors. These detectors are small, cheap, simple, and non-hazardous and provide an entirely adequate tool for large scale use in assessing levels of radon over several months because of the short - term fluctuations in radon concentrations. In this paper, the principles and philosophy in order to improve the quality and reliability of radon exposure under a quality assurance (QA) program are presented . Also examples of how a QA program of radon measurements by LR115 detectors using the can-techniques are well defined and applied

  15. A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

    Science.gov (United States)

    Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

    2006-08-01

    A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

  16. SLC and SLD: Experimental experience with a linear collider

    International Nuclear Information System (INIS)

    Breidenbach, M.

    1993-08-01

    The SLAC Linear Collider (SLC) is the prototype e + e - linear collider. This talk will consist of an introduction to SLC, a description of the strategy for luminosity, a description of the systems for the transport and measurement of the polarized electrons, and a description of the present performance of the SLC and planned upgrades. The detector, SLD, and the status of the polarization asymmetry measurement A LR will be described

  17. Characteristics and Properties of a Simple Linear Regression Model

    Directory of Open Access Journals (Sweden)

    Kowal Robert

    2016-12-01

    Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Despite the passage of time, it continues to raise interest both from the theoretical side as well as from the application side. One of the many fundamental questions in the model concerns determining derivative characteristics and studying the properties existing in their scope, referring to the first of these aspects. The literature of the subject provides several classic solutions in that regard. In the paper, a completely new design is proposed, based on the direct application of variance and its properties, resulting from the non-correlation of certain estimators with the mean, within the scope of which some fundamental dependencies of the model characteristics are obtained in a much more compact manner. The apparatus allows for a simple and uniform demonstration of multiple dependencies and fundamental properties in the model, and it does it in an intuitive manner. The results were obtained in a classic, traditional area, where everything, as it might seem, has already been thoroughly studied and discovered.

  18. Exhaustive Search for Sparse Variable Selection in Linear Regression

    Science.gov (United States)

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

    2018-04-01

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

  19. Soil moisture estimation using multi linear regression with terraSAR-X data

    Directory of Open Access Journals (Sweden)

    G. García

    2016-06-01

    Full Text Available The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil’s parameters. Indeed, active microwave sensors (Synthetic Aperture Radar - SAR offer the opportunity to monitor soil moisture (HS at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure, microwave characteristics and land use. Therefore, in this paper we aim at developing a multiple linear regression model to estimate HS on flat agricultural regions using TerraSAR-X satellite data and data from a ground weather station. The results show that the backscatter, the precipitation and the relative humidity are the explanatory variables of HS. The results obtained presented a RMSE of 5.4 and a R2  of about 0.6

  20. Linear Regression Based Real-Time Filtering

    Directory of Open Access Journals (Sweden)

    Misel Batmend

    2013-01-01

    Full Text Available This paper introduces real time filtering method based on linear least squares fitted line. Method can be used in case that a filtered signal is linear. This constraint narrows a band of potential applications. Advantage over Kalman filter is that it is computationally less expensive. The paper further deals with application of introduced method on filtering data used to evaluate a position of engraved material with respect to engraving machine. The filter was implemented to the CNC engraving machine control system. Experiments showing its performance are included.

  1. Theoretical spectroscopic study of the conjugate microcystin-LR-europium cryptate

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Julio G.; Dutra, Jose Diogo L.; Costa Junior, Nivan B. da; Freire, Ricardo O., E-mail: rfreire@ufs.br [Universidade Federal de Sergipe (UFS), Sao Cristovao, SE (Brazil). Departamento de Quimica; Alves Junior, Severino; Sa, Gilberto F. de [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Departamento de Quimica Fundamental

    2013-02-15

    In this work, theoretical tools were used to study spectroscopic properties of the conjugate microcystin-LR-europium cryptate. The Sparkle/AM1 model was applied to predict the geometry of the system and the INDO/S-CIS model was used to calculate the excited state energies. Based on the Judd-Ofelt theory, the intensity parameters were predicted and a theoretical model based on the theory of the 4f-4f transitions was applied to calculate energy transfer and backtransfer rates, radiative and non-radiative decay rates, quantum efficiency and quantum yield. A detailed study of the luminescent properties of the conjugate Microcystin-LR-europium cryptate was carried out. The results show that the theoretical quantum yield of luminescence of 23% is in good agreement with the experimental value published. This fact suggests that this theoretical protocol can be used to design new systems in order to improve their luminescence properties. The results suggest that this luminescent system may be a good conjugate for using in assay ELISA for detection by luminescence of the Microcystin-LR in water. (author)

  2. A unified framework for testing in the linear regression model under unknown order of fractional integration

    DEFF Research Database (Denmark)

    Christensen, Bent Jesper; Kruse, Robinson; Sibbertsen, Philipp

    We consider hypothesis testing in a general linear time series regression framework when the possibly fractional order of integration of the error term is unknown. We show that the approach suggested by Vogelsang (1998a) for the case of integer integration does not apply to the case of fractional...

  3. Use of multiple linear regression and logistic regression models to investigate changes in birthweight for term singleton infants in Scotland.

    Science.gov (United States)

    Bonellie, Sandra R

    2012-10-01

    To illustrate the use of regression and logistic regression models to investigate changes over time in size of babies particularly in relation to social deprivation, age of the mother and smoking. Mean birthweight has been found to be increasing in many countries in recent years, but there are still a group of babies who are born with low birthweights. Population-based retrospective cohort study. Multiple linear regression and logistic regression models are used to analyse data on term 'singleton births' from Scottish hospitals between 1994-2003. Mothers who smoke are shown to give birth to lighter babies on average, a difference of approximately 0.57 Standard deviations lower (95% confidence interval. 0.55-0.58) when adjusted for sex and parity. These mothers are also more likely to have babies that are low birthweight (odds ratio 3.46, 95% confidence interval 3.30-3.63) compared with non-smokers. Low birthweight is 30% more likely where the mother lives in the most deprived areas compared with the least deprived, (odds ratio 1.30, 95% confidence interval 1.21-1.40). Smoking during pregnancy is shown to have a detrimental effect on the size of infants at birth. This effect explains some, though not all, of the observed socioeconomic birthweight. It also explains much of the observed birthweight differences by the age of the mother.   Identifying mothers at greater risk of having a low birthweight baby as important implications for the care and advice this group receives. © 2012 Blackwell Publishing Ltd.

  4. Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

    Science.gov (United States)

    Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

    2016-01-01

    Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

  5. Credit Scoring Problem Based on Regression Analysis

    OpenAIRE

    Khassawneh, Bashar Suhil Jad Allah

    2014-01-01

    ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

  6. A Simple Linear Regression Method for Quantitative Trait Loci Linkage Analysis With Censored Observations

    OpenAIRE

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

    2006-01-01

    Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using...

  7. Observed to expected or logistic regression to identify hospitals with high or low 30-day mortality?

    Science.gov (United States)

    Helgeland, Jon; Clench-Aas, Jocelyne; Laake, Petter; Veierød, Marit B.

    2018-01-01

    Introduction A common quality indicator for monitoring and comparing hospitals is based on death within 30 days of admission. An important use is to determine whether a hospital has higher or lower mortality than other hospitals. Thus, the ability to identify such outliers correctly is essential. Two approaches for detection are: 1) calculating the ratio of observed to expected number of deaths (OE) per hospital and 2) including all hospitals in a logistic regression (LR) comparing each hospital to a form of average over all hospitals. The aim of this study was to compare OE and LR with respect to correctly identifying 30-day mortality outliers. Modifications of the methods, i.e., variance corrected approach of OE (OE-Faris), bias corrected LR (LR-Firth), and trimmed mean variants of LR and LR-Firth were also studied. Materials and methods To study the properties of OE and LR and their variants, we performed a simulation study by generating patient data from hospitals with known outlier status (low mortality, high mortality, non-outlier). Data from simulated scenarios with varying number of hospitals, hospital volume, and mortality outlier status, were analysed by the different methods and compared by level of significance (ability to falsely claim an outlier) and power (ability to reveal an outlier). Moreover, administrative data for patients with acute myocardial infarction (AMI), stroke, and hip fracture from Norwegian hospitals for 2012–2014 were analysed. Results None of the methods achieved the nominal (test) level of significance for both low and high mortality outliers. For low mortality outliers, the levels of significance were increased four- to fivefold for OE and OE-Faris. For high mortality outliers, OE and OE-Faris, LR 25% trimmed and LR-Firth 10% and 25% trimmed maintained approximately the nominal level. The methods agreed with respect to outlier status for 94.1% of the AMI hospitals, 98.0% of the stroke, and 97.8% of the hip fracture hospitals

  8. Combined toxicity of microcystin-LR and copper on lettuce (Lactuca sativa L.).

    Science.gov (United States)

    Cao, Qing; Steinman, Alan D; Wan, Xiang; Xie, Liqiang

    2018-05-10

    Microcystins and copper commonly co-exist in the natural environment, but their combined toxicity remains unclear, especially in terrestrial plants. The present study investigated the toxicity effects of microcystin-LR (0, 5, 50, 500, 1000 μg L -1 ) and copper (0, 50, 500, 1000, 2000 μg L -1 ), both individually and in mixture, on the germination, growth and oxidative response of lettuce. The bioaccumulation of microcystin-LR and copper was also evaluated. Results showed that the decrease in lettuce germination induced by copper alone was not significantly different from that induced by the mixture, and the combined toxicity assessment showed a simple additive effect. Lettuce growth was not significantly reduced by microcystin-LR alone, whereas it was significantly reduced by copper alone and the mixture when copper concentration was higher than 500 μg L -1 . High concentrations of microcystin-LR (1000 μg L -1 ) and copper (≥50 μg L -1 ),as well as their mixture (≥50 + 500 μg L -1 ), induced oxidative stress in lettuce. A synergistic effect on the growth and antioxidative system of lettuce was observed when exposed to low concentrations of the mixture (≤50 + 500 μg L -1 ), whereas an antagonistic effect was observed at high concentrations (≥1000 + 2000 μg L -1 ). Moreover, the interaction of microcystin-LR and copper can increase their accumulation in lettuce. Our results suggest that the toxicity effects of microcystin-LR and copper are exacerbated when they co-exist in the natural environment at low concentrations, which not only negatively affects plant growth but also poses a potential risk to human health via the food chain. Copyright © 2018. Published by Elsevier Ltd.

  9. Linear Regression between CIE-Lab Color Parameters and Organic Matter in Soils of Tea Plantations

    Science.gov (United States)

    Chen, Yonggen; Zhang, Min; Fan, Dongmei; Fan, Kai; Wang, Xiaochang

    2018-02-01

    To quantify the relationship between the soil organic matter and color parameters using the CIE-Lab system, 62 soil samples (0-10 cm, Ferralic Acrisols) from tea plantations were collected from southern China. After air-drying and sieving, numerical color information and reflectance spectra of soil samples were measured under laboratory conditions using an UltraScan VIS (HunterLab) spectrophotometer equipped with CIE-Lab color models. We found that soil total organic carbon (TOC) and nitrogen (TN) contents were negatively correlated with the L* value (lightness) ( r = -0.84 and -0.80, respectively), a* value (correlation coefficient r = -0.51 and -0.46, respectively) and b* value ( r = -0.76 and -0.70, respectively). There were also linear regressions between TOC and TN contents with the L* value and b* value. Results showed that color parameters from a spectrophotometer equipped with CIE-Lab color models can predict TOC contents well for soils in tea plantations. The linear regression model between color values and soil organic carbon contents showed it can be used as a rapid, cost-effective method to evaluate content of soil organic matter in Chinese tea plantations.

  10. Estimation of error components in a multi-error linear regression model, with an application to track fitting

    International Nuclear Information System (INIS)

    Fruehwirth, R.

    1993-01-01

    We present an estimation procedure of the error components in a linear regression model with multiple independent stochastic error contributions. After solving the general problem we apply the results to the estimation of the actual trajectory in track fitting with multiple scattering. (orig.)

  11. Calculation of U, Ra, Th and K contents in uranium ore by multiple linear regression method

    International Nuclear Information System (INIS)

    Lin Chao; Chen Yingqiang; Zhang Qingwen; Tan Fuwen; Peng Guanghui

    1991-01-01

    A multiple linear regression method was used to compute γ spectra of uranium ore samples and to calculate contents of U, Ra, Th, and K. In comparison with the inverse matrix method, its advantage is that no standard samples of pure U, Ra, Th and K are needed for obtaining response coefficients

  12. Reproduction impairment and endocrine disruption in female zebrafish after long-term exposure to MC-LR: A life cycle assessment

    International Nuclear Information System (INIS)

    Hou, Jie; Li, Li; Wu, Ning; Su, Yujing; Lin, Wang; Li, Guangyu; Gu, Zemao

    2016-01-01

    Microcystin-LR (MC-LR) has been found to cause reproductive and developmental impairments as well as to disrupt sex hormone homeostasis of fish during acute and sub-chronic toxic experiments. However, fish in natural environments are continuously exposed to MC-LR throughout their entire life cycle as opposed to short-term exposure. Here, we tested the hypothesis that the mechanism by which MC-LR harms female fish reproduction and development within natural water bodies is through interference of the reproductive endocrine system. In the present study, zebrafish hatchlings (5 d post-fertilization) were exposed to 0, 0.3, 3 and 30 μg/L MC-LR for 90 d until reaching sexual maturity. Female zebrafish were selected, and the changes in growth and developmental indicators, ovarian ultrastructure as well as the levels of gonadal steroid hormones and vitellogenin (VTG) were examined along with the transcription of related genes in the hypothalamic–pituitary–gonadal–liver axis (HPGL-axis). The results showed for the first time, a life cycle exposure to MC-LR caused growth inhibition, decreased ovary weight and ovarian ultra-pathological lesions. Decreased ovarian testosterone levels indicated that MC-LR disrupted sex steroid hormone balance. Significantly up-regulated transcription of brain FSHβ and LHβ along with ovarian ERα, FSHR and LHR suggested positive feedback regulation in the HPGL-axis was induced as a compensatory mechanism for MC-LR damage. It was also noted that ovarian VTG content and hepatic ERα and VTG1 expression were all down-regulated, which might be responsible for reduced vitellus storage noted in our histological observations. Our findings indicate that a life cycle exposure to MC-LR impairs the development and reproduction of female zebrafish by disrupting the transcription of related HPGL-axis genes, suggesting that MC-LR has potential adverse effects on fish reproduction and thus population dynamics in MCs-contaminated aquatic

  13. Logistic regression and multiple classification analyses to explore risk factors of under-5 mortality in bangladesh

    International Nuclear Information System (INIS)

    Bhowmik, K.R.; Islam, S.

    2016-01-01

    Logistic regression (LR) analysis is the most common statistical methodology to find out the determinants of childhood mortality. However, the significant predictors cannot be ranked according to their influence on the response variable. Multiple classification (MC) analysis can be applied to identify the significant predictors with a priority index which helps to rank the predictors. The main objective of the study is to find the socio-demographic determinants of childhood mortality at neonatal, post-neonatal, and post-infant period by fitting LR model as well as to rank those through MC analysis. The study is conducted using the data of Bangladesh Demographic and Health Survey 2007 where birth and death information of children were collected from their mothers. Three dichotomous response variables are constructed from children age at death to fit the LR and MC models. Socio-economic and demographic variables significantly associated with the response variables separately are considered in LR and MC analyses. Both the LR and MC models identified the same significant predictors for specific childhood mortality. For both the neonatal and child mortality, biological factors of children, regional settings, and parents socio-economic status are found as 1st, 2nd, and 3rd significant groups of predictors respectively. Mother education and household environment are detected as major significant predictors of post-neonatal mortality. This study shows that MC analysis with or without LR analysis can be applied to detect determinants with rank which help the policy makers taking initiatives on a priority basis. (author)

  14. Fuzzy multiple linear regression: A computational approach

    Science.gov (United States)

    Juang, C. H.; Huang, X. H.; Fleming, J. W.

    1992-01-01

    This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.

  15. Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI

    Energy Technology Data Exchange (ETDEWEB)

    Iyama, Yuji [Kumamoto Chuo Hospital, Department of Diagnostic Radiology, Kumamoto, Kumamoto (Japan); Kumamoto University, Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto, Kumamoto (Japan); Nakaura, Takeshi; Nagayama, Yasunori; Utsunomiya, Daisuke; Yamashita, Yasuyuki [Kumamoto University, Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto, Kumamoto (Japan); Katahira, Kazuhiro; Oda, Seitaro [Kumamoto Chuo Hospital, Department of Diagnostic Radiology, Kumamoto, Kumamoto (Japan); Iyama, Ayumi [National Hospital Organization Kumamoto Medical Center, Department of Diagnostic Radiology, Kumamoto, Kumamoto (Japan)

    2017-09-15

    To develop a prediction model to distinguish between transition zone (TZ) cancers and benign prostatic hyperplasia (BPH) on multi-parametric prostate magnetic resonance imaging (mp-MRI). This retrospective study enrolled 60 patients with either BPH or TZ cancer, who had undergone 3 T-MRI. We generated ten parameters for T2-weighted images (T2WI), diffusion-weighted images (DWI) and dynamic MRI. Using a t-test and multivariate logistic regression (LR) analysis to evaluate the parameters' accuracy, we developed LR models. We calculated the area under the receiver operating characteristic curve (ROC) of LR models by a leave-one-out cross-validation procedure, and the LR model's performance was compared with radiologists' performance with their opinion and with the Prostate Imaging Reporting and Data System (Pi-RADS v2) score. Multivariate LR analysis showed that only standardized T2WI signal and mean apparent diffusion coefficient (ADC) maintained their independent values (P < 0.001). The validation analysis showed that the AUC of the final LR model was comparable to that of board-certified radiologists, and superior to that of Pi-RADS scores. A standardized T2WI and mean ADC were independent factors for distinguishing between BPH and TZ cancer. The performance of the LR model was comparable to that of experienced radiologists. (orig.)

  16. Inhibition of Microcystis aeruginosa and microcystin- LR with one ...

    African Journals Online (AJOL)

    Yomi

    2State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150001,. China. ... industrial wastewater and domestic sewage. One of the .... LR detected by high performance liquid chromatography.

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

    Science.gov (United States)

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

    2018-02-01

    A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  18. Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

    KAUST Repository

    Abdul Jameel, Abdul Gani

    2016-09-14

    An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.

  19. 76 FR 60937 - Draft License Renewal Interim Staff Guidance LR-ISG-2011-02; Aging Management Program for Steam...

    Science.gov (United States)

    2011-09-30

    ...-2011-02; Aging Management Program for Steam Generators AGENCY: Nuclear Regulatory Commission. ACTION... License Renewal Interim Staff Guidance (LR-ISG), LR-ISG-2011-02, ``Aging Management Program for Steam... using Revision 3 of NEI 97-06 to manage steam generator aging. The Draft LR-ISG revises the NRC staff's...

  20. The interactive effects of microcystin-LR and cylindrospermopsin on the growth rate of the freshwater algae Chlorella vulgaris.

    Science.gov (United States)

    Pinheiro, Carlos; Azevedo, Joana; Campos, Alexandre; Vasconcelos, Vítor; Loureiro, Susana

    2016-05-01

    Microcystin-LR (MC-LR) and cylindrospermopsin (CYN) are the most representative cyanobacterial cyanotoxins. They have been simultaneously detected in aquatic systems, but their combined ecotoxicological effects to aquatic organisms, especially microalgae, is unknown. In this study, we examined the effects of these cyanotoxins individually and as a binary mixture on the growth rate of the freshwater algae Chlorella vulgaris. Using the MIXTOX tool, the reference model concentration addition (CA) was selected to evaluate the combined effects of MC-LR and CYN on the growth of the freshwater green algae due to its conservative prediction of mixture effect for putative similar or dissimilar acting chemicals. Deviations from the CA model such as synergism/antagonism, dose-ratio and dose-level dependency were also assessed. In single exposures, our results demonstrated that MC-LR and CYN had different impacts on the growth rates of C. vulgaris at the highest tested concentrations, being CYN the most toxic. In the mixture exposure trial, MC-LR and CYN showed a synergistic deviation from the conceptual model CA as the best descriptive model. MC-LR individually was not toxic even at high concentrations (37 mg L(-1)); however, the presence of MC-LR at much lower concentrations (0.4-16.7 mg L(-1)) increased the CYN toxicity. From these results, the combined exposure of MC-LR and CYN should be considered for risk assessment of mixtures as the toxicity may be underestimated when looking only at the single cyanotoxins and not their combination. This study also represents an important step to understand the interactions among MC-LR and CYN detected previously in aquatic systems.

  1. The uptake kinetics and immunotoxic effects of microcystin-LR in human and chicken peripheral blood lymphocytes in vitro

    International Nuclear Information System (INIS)

    Lankoff, Anna; Carmichael, Wayne W.; Grasman, Keith A.; Yuan, Moucun

    2004-01-01

    Microcystin-LR is a cyanobacterial heptapeptide that presents acute and chronic hazards to animal and human health. We investigated the influence of this toxin on human and chicken immune system modulation in vitro. Peripheral blood lymphocytes were treated with microcystin-LR at environmentally relevant doses of 1, 10 and 25 μg/ml for 12, 24, 48, 72 h (for proliferation assay cells were treated for 72 h). T-cell and B-cell proliferation as well as apoptosis and necrosis were determined in human and chicken samples. IL-2 and IL-6 production by human lymphocytes also was measured. In addition, uptake kinetics of microcystin-LR into human and chicken peripheral blood lymphocytes were calculated by Liquid Chromatography (LS) /Mass Spectrometry (MS) analysis. At the highest dose microcystin-LR decreased T-cell proliferation and all doses of microcystin-LR inhibited B-cell proliferation. The frequency of apoptotic and necrotic cells increased in a dose and time-dependent manner. Human lymphocytes responded to stimulation with microcystin-LR by increased production of IL-6 and decreased production of IL-2. Human lymphocytes were able to uptake from 0.014 to 1.663 μg/ml and chicken lymphocytes from 0.035 to 1.733 μg/ml of the microcystin-LR added to the cultures, depending on the treatment time and dose. In conclusion, microcystin-LR acted as an immunomodulator in cytokine production and down-regulated lymphocyte functions by induction of apoptosis and necrosis. However, further studies dealing with the influence of microcystin-LR on expression cytokine genes and transcription factors are necessary to confirm these hypotheses

  2. Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers.

    Science.gov (United States)

    Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H

    2018-01-01

    Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

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

    Science.gov (United States)

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

    2017-05-01

    The purpose of this study was to investigate which spatial and temporal parameters of the Timed Up and Go (TUG) test are associated with motor function in elderly individuals. This study included 99 community-dwelling women aged 72.9 ± 6.3 years. Step length, step width, single support time, variability of the aforementioned parameters, gait velocity, cadence, reaction time from starting signal to first step, and minimum distance between the foot and a marker placed to 3 in front of the chair were measured using our analysis system. The 10-m walk test, five times sit-to-stand (FTSTS) test, and one-leg standing (OLS) test were used to assess motor function. Stepwise multivariate linear regression analysis was used to determine which TUG test parameters were associated with each motor function test. Finally, we calculated a predictive model for each motor function test using each regression coefficient. In stepwise linear regression analysis, step length and cadence were significantly associated with the 10-m walk test, FTSTS and OLS test. Reaction time was associated with the FTSTS test, and step width was associated with the OLS test. Each predictive model showed a strong correlation with the 10-m walk test and OLS test (P motor function test. Moreover, the TUG test time regarded as the lower extremity function and mobility has strong predictive ability in each motor function test. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

  4. Main: 1LR4 [RPSD[Archive

    Lifescience Database Archive (English)

    Full Text Available 1LR4 トウモロコシ Corn Zea mays L. Casein Kinase Ii, Alpha Chain Name=Ack2; Zea Mays Mole...cule: Protein Kinase Ck2; Chain: A; Synonym: Casein Kinase Ii, Alpha Chain; Engineered: Yes Transferase 2.7....ray; A=2-326.|PDB; 1M2Q; X-ray; A=2-328.|PDB; 1M2R; X-ray; A=2-328.|PDB; 1OM1; X-ray; A=1-332.|Mai

  5. WWER-440 control assembly local power peaking investigation on LR-0 reactor

    International Nuclear Information System (INIS)

    Mikus, J.

    2002-01-01

    This paper presents information concerning the local power peaking problem induced by the WWER-440 control assembly and the investigation possibilities on the light water, zero power reactor LR-0 at the Nuclear Research Institute (NRI) Rez plc. A brief description is given about the disposable control assembly model, experimental arrangement and conditions on the LR-0 reactor with regard to the earlier performed investigations as well as to the relevant measurements to be realized in the near future.(abstract)

  6. A further study of the (CR-LR) difference technique for retrospective radon exposure assessment

    International Nuclear Information System (INIS)

    Nikezic, D.; Yip, C.W.Y.; Leung, S.Y.Y.; Leung, J.K.C.; Yu, K.N.

    2006-01-01

    The (CR-LR) difference technique, based on the CR-39 and LR 115 detectors, for the determination of implanted 210 Po in glass after deposition of short-lived radon progeny, was analyzed in details in this paper. The sensitivities of both detectors were calculated using the Monte Carlo method with V functions particularly derived in our previous works for the detectors used in the present experiments. The dependency of the sensitivity ratio on the removed layer of both detectors was determined and verified experimentally. The simulated sensitivity ratios correlate well with the experimental ones. A major finding of the present work is that the sensitivity ratio between the CR-39 and LR 115 detectors depends only weakly on the ratio between the 238 U and 232 Th concentrations in the glass samples. This is crucial for the application of the (CR-LR) difference technique for retrospective radon exposure assessments, since measurements of the 238 U and 232 Th concentrations in the relatively small real-life glass samples will make the retrospective radon exposure assessments impractical

  7. Intraperitoneal exposure of whitefish to microcystin-LR induces rapid liver injury followed by regeneration and resilience to subsequent exposures

    International Nuclear Information System (INIS)

    Woźny, Maciej; Lewczuk, Bogdan; Ziółkowska, Natalia; Gomułka, Piotr; Dobosz, Stefan; Łakomiak, Alicja; Florczyk, Maciej; Brzuzan, Paweł

    2016-01-01

    To date, there has been no systematic approach comprehensively describing the sequence of pathological changes in fish during prolonged exposure to microcystin-LR (MC-LR). Towards this aim, juvenile whitefish individuals received an intraperitoneal injection with pure MC-LR, and the injection was repeated every week to maintain continuous exposure for 28 days. During the exposure period, growth and condition of the fish were assessed based on biometric measurements. Additionally, selected biochemical markers were analysed in the fishes' blood, and their livers were carefully examined for morphological, ultrastructural, and molecular changes. The higher dose of MC-LR (100 μg·kg −1 ) caused severe liver injury at the beginning of the exposure period, whereas the lower dose (10 μg·kg −1 ) caused less, probably reversible injury, and its effects began to be observed later in the exposure period. These marked changes were accompanied by substantial MC-LR uptake by the liver. However, starting on the 7th day of exposure, cell debris began to be removed by phagocytes, then by 14th day, proliferation of liver cells had markedly increased, which led to reconstruction of the liver parenchyma at the end of the treatment. Surprisingly, despite weekly-repeated intraperitoneal injections, MC-LR did not accumulate over time of exposure which suggests its limited uptake in the later phase of exposure. In support, mRNA expression of the membrane transport protein oatp1d was decreased at the same time as the regenerative processes were observed. Our study shows that closing of active membrane transport may serve as one defence mechanism against further MC-LR intoxication. - Highlights: • The study presents pathological changes in whitefish during prolonged MC-LR exposure. • After early, severe injury, the damaged liver parenchyma of the fish regenerated. • Endoplasmic reticulum, cytoskeleton, and chromatin were the main targets for MC-LR. • MC-LR did not

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

    Science.gov (United States)

    Liu, Yan; Salvendy, Gavriel

    2009-05-01

    This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

  9. Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.

    Science.gov (United States)

    Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H

    2006-01-01

    Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.

  10. Bayesian linear regression with skew-symmetric error distributions with applications to survival analysis

    KAUST Repository

    Rubio, Francisco J.

    2016-02-09

    We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual assumption of normality of the errors in terms of heavy tails and asymmetry. We propose a general noninformative prior structure for these regression models and show that the corresponding posterior distribution is proper under mild conditions. We extend these propriety results to cases where the response variables are censored. The latter scenario is of interest in the context of accelerated failure time models, which are relevant in survival analysis. We present a simulation study that demonstrates good frequentist properties of the posterior credible intervals associated with the proposed priors. This study also sheds some light on the trade-off between increased model flexibility and the risk of over-fitting. We illustrate the performance of the proposed models with real data. Although we focus on models with univariate response variables, we also present some extensions to the multivariate case in the Supporting Information.

  11. Reduction of interferences in graphite furnace atomic absorption spectrometry by multiple linear regression modelling

    Science.gov (United States)

    Grotti, Marco; Abelmoschi, Maria Luisa; Soggia, Francesco; Tiberiade, Christian; Frache, Roberto

    2000-12-01

    The multivariate effects of Na, K, Mg and Ca as nitrates on the electrothermal atomisation of manganese, cadmium and iron were studied by multiple linear regression modelling. Since the models proved to efficiently predict the effects of the considered matrix elements in a wide range of concentrations, they were applied to correct the interferences occurring in the determination of trace elements in seawater after pre-concentration of the analytes. In order to obtain a statistically significant number of samples, a large volume of the certified seawater reference materials CASS-3 and NASS-3 was treated with Chelex-100 resin; then, the chelating resin was separated from the solution, divided into several sub-samples, each of them was eluted with nitric acid and analysed by electrothermal atomic absorption spectrometry (for trace element determinations) and inductively coupled plasma optical emission spectrometry (for matrix element determinations). To minimise any other systematic error besides that due to matrix effects, accuracy of the pre-concentration step and contamination levels of the procedure were checked by inductively coupled plasma mass spectrometric measurements. Analytical results obtained by applying the multiple linear regression models were compared with those obtained with other calibration methods, such as external calibration using acid-based standards, external calibration using matrix-matched standards and the analyte addition technique. Empirical models proved to efficiently reduce interferences occurring in the analysis of real samples, allowing an improvement of accuracy better than for other calibration methods.

  12. pKa prediction for acidic phosphorus-containing compounds using multiple linear regression with computational descriptors.

    Science.gov (United States)

    Yu, Donghai; Du, Ruobing; Xiao, Ji-Chang

    2016-07-05

    Ninety-six acidic phosphorus-containing molecules with pKa 1.88 to 6.26 were collected and divided into training and test sets by random sampling. Structural parameters were obtained by density functional theory calculation of the molecules. The relationship between the experimental pKa values and structural parameters was obtained by multiple linear regression fitting for the training set, and tested with the test set; the R(2) values were 0.974 and 0.966 for the training and test sets, respectively. This regression equation, which quantitatively describes the influence of structural parameters on pKa , and can be used to predict pKa values of similar structures, is significant for the design of new acidic phosphorus-containing extractants. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  13. Influence of management of variables, sampling zones and land units on LR analysis for landslide spatial prevision

    Directory of Open Access Journals (Sweden)

    R. Greco

    2013-09-01

    Full Text Available Several authors, according to different methodological approaches, have employed logistic Regression (LR, a multivariate statistical analysis adopted to assess the spatial probability of landslide, even though its fundamental principles have remained unaltered. This study aims at assessing the influence of some of these methodological approaches on the performance of LR, through a series of sensitivity analyses developed over a test area of about 300 km2 in Calabria (southern Italy. In particular, four types of sampling (1 – the whole study area; 2 – transects running parallel to the general slope direction of the study area with a total surface of about 1/3 of the whole study area; 3 – buffers surrounding the phenomena with a 1/1 ratio between the stable and the unstable area; 4 – buffers surrounding the phenomena with a 1/2 ratio between the stable and the unstable area, two variable coding modes (1 – grouped variables; 2 – binary variables, and two types of elementary land (1 – cells units; 2 – slope units units have been tested. The obtained results must be considered as statistically relevant in all cases (Aroc values > 70%, thus confirming the soundness of the LR analysis which maintains high predictive capacities notwithstanding the features of input data. As for the area under investigation, the best performing methodological choices are the following: (i transects produced the best results (0 P(y ≤ 93.4%; Aroc = 79.5%; (ii as for sampling modalities, binary variables (0 P(y ≤ 98.3%; Aroc = 80.7% provide better performance than ordinated variables; (iii as for the choice of elementary land units, slope units (0 P(y ≤ 100%; Aroc = 84.2% have obtained better results than cells matrix.

  14. Roles of piRNAs in microcystin-leucine-arginine (MC-LR) induced reproductive toxicity in testis on male offspring.

    Science.gov (United States)

    Zhang, Ling; Zhang, Hui; Zhang, Huan; Benson, Mikael; Han, Xiaodong; Li, Dongmei

    2017-07-01

    In the present study, we evaluated the toxic effects on the testis of the male offspring of MC-LR exposure during fetal and lactational periods. Pregnant females were distributed into two experimental groups: control group and MC-LR group which were exposed to 0 and 10 μg/L of MC-LR, respectively, through drinking water separately during fetal and lactational periods. At the age of 30 days after birth, the male offspring were euthanized. The body weight, testis index, and histomorphology change were observed and the global changes of piwi-interacting RNA (piRNA) expression were evaluated. The results revealed that MC-LR was found in the testis of male offspring, body weight and testis index decreased significantly, and testicular tissue structure was damaged in the MC-LR group. In addition, the exposure to MC-LR resulted in an altered piRNA expression profile and an increase of the cell apoptosis and a decrease of the cell proliferation in the testis of the male offspring. It was reasonable to speculate that the toxic effects on reproductive system of the male offspring in MC-LR group might be mediated by piRNAs through the regulation of the target genes. As far as we are aware, this is the first report showing that MC-LR could play a role in disorder of proliferative and cell apoptosis in the testis of the male offspring by the maternal transmission effect of toxicity. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  16. Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws.

    Science.gov (United States)

    Hsu, Ching-Chi; Lin, Jinn; Chao, Ching-Kong

    2011-12-01

    Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  17. Ecological risk assessment of microcystin-LR in the upstream section of the Haihe River based on a species sensitivity distribution model.

    Science.gov (United States)

    Niu, Zhiguang; Du, Lei; Li, Jiafu; Zhang, Ying; Lv, Zhiwei

    2018-02-01

    The eutrophication of surface water has been the main problem of water quality management in recent decades, and the ecological risk of microcystin-LR (MC-LR), which is the by-product of eutrophication, has drawn more attention worldwide. The aims of our study were to determine the predicted no effect concentration (PNEC) of MC-LR and to assess the ecological risk of MC-LR in the upstream section of the Haihe River. HC 5 (hazardous concentration for 5% of biological species) and PNEC were obtained from a species sensitivity distribution (SSD) model, which was constructed with the acute toxicity data of MC-LR on aquatic organisms. The concentrations of MC-LR in the upstream section of the Haihe River from April to August of 2015 were analysed, and the ecological risk characteristics of MC-LR were evaluated based on the SSD model. The results showed that the HC 5 of MC-LR in freshwater was 17.18 μg/L and PNEC was 5.73 μg/L. The concentrations of MC-LR ranged from 0.68 μg/L to 32.21 μg/L and were obviously higher in summer than in spring. The values of the risk quotient (RQ) ranged from 0.12 to 5.62, suggesting that the risk of MC-LR for aquatic organisms in the river was at a medium or high level during the study period. Compared with other waterbodies in the world, the pollution level of MC-LR in the Haihe River was at a moderate level. This research could promote the study of the ecological risk of MC-LR at the ecosystem level. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Testing for marginal linear effects in quantile regression

    KAUST Repository

    Wang, Huixia Judy

    2017-10-23

    The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

  19. Testing for marginal linear effects in quantile regression

    KAUST Repository

    Wang, Huixia Judy; McKeague, Ian W.; Qian, Min

    2017-01-01

    The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

  20. QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

    Directory of Open Access Journals (Sweden)

    Rachid Darnag

    2017-02-01

    Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

  1. Microcystin-LR induced reactive oxygen species mediate cytoskeletal disruption and apoptosis of hepatocytes in Cyprinus carpio L.

    Directory of Open Access Journals (Sweden)

    Jinlin Jiang

    Full Text Available Microcystins (MCs are a group of cyclic hepatotoxic peptides produced by cyanobacteria. Microcystin-LR (MC-LR contains Leucine (L and Arginine (R in the variable positions, and is one of the most common and potently toxic peptides. MC-LR can inhibit protein phosphatase type 1 and type 2A (PP1 and PP2A activities and induce excessive production of reactive oxygen species (ROS. The underlying mechanism of the inhibition of PP1 and PP2A has been extensively studied. The over-production of ROS is considered to be another main mechanism behind MC-LR toxicity; however, the detailed toxicological mechanism involved in over-production of ROS in carp (Cyprinus carpio L. remains largely unclear. In our present study, the hydroxyl radical (•OH was significantly induced in the liver of carp after a relatively short-term exposure to MC-LR. The elevated reactive oxygen species (ROS production may play an important role in the disruption of microtubule structure. Pre-injection of the antioxidant N-acetyl-cysteine (NAC provided significant protection to the cytoskeleton, however buthionine sulfoximine (BSO exacerbated cytoskeletal destruction. In addition, the elevated ROS formation induced the expression of apoptosis-related genes, including p38, JNKa, and bcl-2. A significant increase in apoptotic cells was observed at 12-48 hours. Our study further supports evidence that ROS are involved in MC-LR induced damage to liver cells in carp, and indicates the need for further study of the molecular mechanisms behind MC-LR toxicity.

  2. Microcystin-LR Induced Reactive Oxygen Species Mediate Cytoskeletal Disruption and Apoptosis of Hepatocytes in Cyprinus carpio L.

    Science.gov (United States)

    Jiang, Jinlin; Shan, Zhengjun; Xu, Weili; Wang, Xiaorong; Zhou, Junying; Kong, Deyang; Xu, Jing

    2013-01-01

    Microcystins (MCs) are a group of cyclic hepatotoxic peptides produced by cyanobacteria. Microcystin-LR (MC-LR) contains Leucine (L) and Arginine (R) in the variable positions, and is one of the most common and potently toxic peptides. MC-LR can inhibit protein phosphatase type 1 and type 2A (PP1 and PP2A) activities and induce excessive production of reactive oxygen species (ROS). The underlying mechanism of the inhibition of PP1 and PP2A has been extensively studied. The over-production of ROS is considered to be another main mechanism behind MC-LR toxicity; however, the detailed toxicological mechanism involved in over-production of ROS in carp (Cyprinus carpio L.) remains largely unclear. In our present study, the hydroxyl radical (•OH) was significantly induced in the liver of carp after a relatively short-term exposure to MC-LR. The elevated reactive oxygen species (ROS) production may play an important role in the disruption of microtubule structure. Pre-injection of the antioxidant N-acetyl-cysteine (NAC) provided significant protection to the cytoskeleton, however buthionine sulfoximine (BSO) exacerbated cytoskeletal destruction. In addition, the elevated ROS formation induced the expression of apoptosis-related genes, including p38, JNKa, and bcl-2. A significant increase in apoptotic cells was observed at 12 - 48 hours. Our study further supports evidence that ROS are involved in MC-LR induced damage to liver cells in carp, and indicates the need for further study of the molecular mechanisms behind MC-LR toxicity. PMID:24376844

  3. Dynamics of Total Microcystin LR Concentration in Three Subtropical Hydroelectric Generation Reservoirs in Uruguay, South America.

    Science.gov (United States)

    González-Piana, Mauricio; Fabián, Daniel; Piccardo, Andrea; Chalar, Guillermo

    2017-10-01

    This study analyzed the temporal dynamics of total microcystin LR concentrations between the years of 2012 and 2015 in the Bonete, Baygorria and Palmar hydroelectric generation reservoirs in the central region of the Negro River, Uruguay. The three reservoirs showed differents total microcystin LR concentration, with no significant differences among them. Over 20 sampling dates, the three reservoirs exhibited total microcystin LR concentrations on eight occasions that corresponded to a slight to moderate human health risk according to WHO guideline values for recreational waters. By determining the concentration of microcystin LR in cyanobacterial biomass, we identified cyanobacterial populations that occurred over time with varying degrees of toxin production (maximal 85.4 µg/mm 3 ). The microcystin LR concentration in Bonete was positively correlated with temperature (r = 0.587) and cyanobacterial biomass (r = 0.736), in Baygorria with cyanobacterial biomass (r = 0.521), and in Palmar with temperature (r = 0.500) and negatively correlated with ammonia (r = -0.492). Action is needed to reduce the presence of toxic cyanobacteria in these systems. A decrease in the use of agrochemicals and management changes in the reservoir basins could be successful long-term measures.

  4. Low-priced, time-saving, reliable and stable LR-115 counting system

    International Nuclear Information System (INIS)

    Tchorz-Trzeciakiewicz, D.E.

    2015-01-01

    Nuclear alpha particles leave etches (tracks) when they hit the surface of a LR-115 detector. The density of these tracks is used to measure radon concentration. Counting these tracks by human sense is tedious and time-consuming procedure and may introduce counting error, whereas most available automatic and semiautomatic counting systems are expensive or complex. An uncomplicated, robust, reliable and stable counting system using freely available on the Internet software as Digimizer™ and PhotoScape was developed and proposed. The effectiveness of the proposed procedure was evaluated by comparing the amount of tracks counted by software with the amount of tracks counted manually for 223 detectors. The percentage error for each analysed detector was obtained as a difference between automatic and manual counts divided by manual count. For more than 97% of detectors, the percentage errors oscillated between −3% and 3%. - Highlights: • Semiautomatic, uncomplicated procedure was proposed to count the amount of alpha tracks. • Freely available software on the Internet used as alpha tracks counting system for LR-115. • LR-115 detectors used to measure radon concentration and radon exhalation rate

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

    Science.gov (United States)

    Lorenzo-Seva, Urbano; Ferrando, Pere J

    2011-03-01

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

  6. An Introduction to the Hybrid Approach of Neural Networks and the Linear Regression Model : An Illustration in the Hedonic Pricing Model of Building Costs

    OpenAIRE

    浅野, 美代子; マーコ, ユー K.W.

    2007-01-01

    This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...

  7. Operations of the LR56 radioactive liquid cask transport system at U.S. Department of Energy sites

    International Nuclear Information System (INIS)

    Davidson, J.S.; Hornstra, D.J.; Sazawal, V.K.; Clement, G.

    1996-01-01

    The LR56 cask system is licensed for use in France under Certificate of Compliance F/309/B(U)F for transport of 4,000-liter volumes of radioactive liquids. Three LR56 cask systems (with modifications for use at Department of Energy (DOE) sites) have been purchased for delivery at the Hanford Site, Oak Ridge National Laboratory (ORNL), and Savannah River Site (SRS). The LR56 cask systems will be used for on-site transfers of Type B quantities of radioactive liquid waste. The ORNL unit will also be used as a Type A packaging for transfers of radioactive liquids between DOE sites. This paper discusses LR56 operating features and the use of the cask system at the three DOE sites

  8. Intraperitoneal exposure of whitefish to microcystin-LR induces rapid liver injury followed by regeneration and resilience to subsequent exposures

    Energy Technology Data Exchange (ETDEWEB)

    Woźny, Maciej, E-mail: maciej.wozny@uwm.edu.pl [Department of Environmental Biotechnology, Faculty of Environmental Sciences, University of Warmia and Mazury in Olsztyn, ul. Słoneczna 45G, 10-709 Olsztyn (Poland); Lewczuk, Bogdan; Ziółkowska, Natalia [Department of Histology and Embryology, Faculty of Veterinary Medicine, University of Warmia and Mazury in Olsztyn, ul. M. Oczapowskiego 13, 10-713 Olsztyn (Poland); Gomułka, Piotr [Department of Ichthyology, Faculty of Environmental Sciences, University of Warmia and Mazury in Olsztyn, ul. M. Oczapowskiego 5, 10-719 Olsztyn (Poland); Dobosz, Stefan [Department of the Salmonid Research in Rutki, Inland Fisheries Institute in Olsztyn, Rutki, 83-330 Żukowo (Poland); Łakomiak, Alicja; Florczyk, Maciej; Brzuzan, Paweł [Department of Environmental Biotechnology, Faculty of Environmental Sciences, University of Warmia and Mazury in Olsztyn, ul. Słoneczna 45G, 10-709 Olsztyn (Poland)

    2016-12-15

    To date, there has been no systematic approach comprehensively describing the sequence of pathological changes in fish during prolonged exposure to microcystin-LR (MC-LR). Towards this aim, juvenile whitefish individuals received an intraperitoneal injection with pure MC-LR, and the injection was repeated every week to maintain continuous exposure for 28 days. During the exposure period, growth and condition of the fish were assessed based on biometric measurements. Additionally, selected biochemical markers were analysed in the fishes' blood, and their livers were carefully examined for morphological, ultrastructural, and molecular changes. The higher dose of MC-LR (100 μg·kg{sup −1}) caused severe liver injury at the beginning of the exposure period, whereas the lower dose (10 μg·kg{sup −1}) caused less, probably reversible injury, and its effects began to be observed later in the exposure period. These marked changes were accompanied by substantial MC-LR uptake by the liver. However, starting on the 7th day of exposure, cell debris began to be removed by phagocytes, then by 14th day, proliferation of liver cells had markedly increased, which led to reconstruction of the liver parenchyma at the end of the treatment. Surprisingly, despite weekly-repeated intraperitoneal injections, MC-LR did not accumulate over time of exposure which suggests its limited uptake in the later phase of exposure. In support, mRNA expression of the membrane transport protein oatp1d was decreased at the same time as the regenerative processes were observed. Our study shows that closing of active membrane transport may serve as one defence mechanism against further MC-LR intoxication. - Highlights: • The study presents pathological changes in whitefish during prolonged MC-LR exposure. • After early, severe injury, the damaged liver parenchyma of the fish regenerated. • Endoplasmic reticulum, cytoskeleton, and chromatin were the main targets for MC-LR. • MC-LR did not

  9. Association of footprint measurements with plantar kinetics: a linear regression model.

    Science.gov (United States)

    Fascione, Jeanna M; Crews, Ryan T; Wrobel, James S

    2014-03-01

    The use of foot measurements to classify morphology and interpret foot function remains one of the focal concepts of lower-extremity biomechanics. However, only 27% to 55% of midfoot variance in foot pressures has been determined in the most comprehensive models. We investigated whether dynamic walking footprint measurements are associated with inter-individual foot loading variability. Thirty individuals (15 men and 15 women; mean ± SD age, 27.17 ± 2.21 years) walked at a self-selected speed over an electronic pedography platform using the midgait technique. Kinetic variables (contact time, peak pressure, pressure-time integral, and force-time integral) were collected for six masked regions. Footprints were digitized for area and linear boundaries using digital photo planimetry software. Six footprint measurements were determined: contact area, footprint index, arch index, truncated arch index, Chippaux-Smirak index, and Staheli index. Linear regression analysis with a Bonferroni adjustment was performed to determine the association between the footprint measurements and each of the kinetic variables. The findings demonstrate that a relationship exists between increased midfoot contact and increased kinetic values in respective locations. Many of these variables produced large effect sizes while describing 38% to 71% of the common variance of select plantar kinetic variables in the medial midfoot region. In addition, larger footprints were associated with larger kinetic values at the medial heel region and both masked forefoot regions. Dynamic footprint measurements are associated with dynamic plantar loading kinetics, with emphasis on the midfoot region.

  10. Describing Growth Pattern of Bali Cows Using Non-linear Regression Models

    Directory of Open Access Journals (Sweden)

    Mohd. Hafiz A.W

    2016-12-01

    Full Text Available The objective of this study was to evaluate the best fit non-linear regression model to describe the growth pattern of Bali cows. Estimates of asymptotic mature weight, rate of maturing and constant of integration were derived from Brody, von Bertalanffy, Gompertz and Logistic models which were fitted to cross-sectional data of body weight taken from 74 Bali cows raised in MARDI Research Station Muadzam Shah Pahang. Coefficient of determination (R2 and residual mean squares (MSE were used to determine the best fit model in describing the growth pattern of Bali cows. Von Bertalanffy model was the best model among the four growth functions evaluated to determine the mature weight of Bali cattle as shown by the highest R2 and lowest MSE values (0.973 and 601.9, respectively, followed by Gompertz (0.972 and 621.2, respectively, Logistic (0.971 and 648.4, respectively and Brody (0.932 and 660.5, respectively models. The correlation between rate of maturing and mature weight was found to be negative in the range of -0.170 to -0.929 for all models, indicating that animals of heavier mature weight had lower rate of maturing. The use of non-linear model could summarize the weight-age relationship into several biologically interpreted parameters compared to the entire lifespan weight-age data points that are difficult and time consuming to interpret.

  11. Relationships between the structure of wheat gluten and ACE inhibitory activity of hydrolysate: stepwise multiple linear regression analysis.

    Science.gov (United States)

    Zhang, Yanyan; Ma, Haile; Wang, Bei; Qu, Wenjuan; Wali, Asif; Zhou, Cunshan

    2016-08-01

    Ultrasound pretreatment of wheat gluten (WG) before enzymolysis can improve the angiotensin converting enzyme (ACE) inhibitory activity of the hydrolysates by alerting the structure of substrate proteins. Establishment of a relationship between the structure of WG and ACE inhibitory activity of the hydrolysates to judge the end point of the ultrasonic pretreatment is vital. The results of stepwise multiple linear regression (MLR) showed that the contents of free sulfhydryl, α-helix, disulfide bond, surface hydrophobicity and random coil were significantly correlated to ACE Inhibitory activity of the hydrolysate, with the standard partial regression coefficients were 3.729, -0.676, -0.252, 0.022 and 0.156, respectively. The R(2) of this model was 0.970. External validation showed that the stepwise MLR model could well predict the ACE inhibitory activity of hydrolysate based on the content of free sulfhydryl, α-helix, disulfide bond, surface hydrophobicity and random coil of WG before hydrolysis. A stepwise multiple linear regression model describing the quantitative relationships between the structure of WG and the ACE Inhibitory activity of the hydrolysates was established. This model can be used to predict the endpoint of the ultrasonic pretreatment. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

  12. Modified Regression Correlation Coefficient for Poisson Regression Model

    Science.gov (United States)

    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).

  13. Radioligand assays - methods and applications. IV. Uniform regression of hyperbolic and linear radioimmunoassay calibration curves

    Energy Technology Data Exchange (ETDEWEB)

    Keilacker, H; Becker, G; Ziegler, M; Gottschling, H D [Zentralinstitut fuer Diabetes, Karlsburg (German Democratic Republic)

    1980-10-01

    In order to handle all types of radioimmunoassay (RIA) calibration curves obtained in the authors' laboratory in the same way, they tried to find a non-linear expression for their regression which allows calibration curves with different degrees of curvature to be fitted. Considering the two boundary cases of the incubation protocol they derived a hyperbolic inverse regression function: x = a/sub 1/y + a/sub 0/ + asub(-1)y/sup -1/, where x is the total concentration of antigen, asub(i) are constants, and y is the specifically bound radioactivity. An RIA evaluation procedure based on this function is described providing a fitted inverse RIA calibration curve and some statistical quality parameters. The latter are of an order which is normal for RIA systems. There is an excellent agreement between fitted and experimentally obtained calibration curves having a different degree of curvature.

  14. LALR(1) : LL(1) = LR(0) : LL(0)

    NARCIS (Netherlands)

    Kruseman Aretz, F.E.J.; Emde Boas, van P.; Hemker, P.W.; Hoffman, W.; Houwen, van der P.J.; Pfluger, P.R.

    1992-01-01

    It is shown that LL(l) and LALR(l) acceptors can be obtained by restricting the action tables of the corresponding LL(O) and LR(O) acceptors to those entries that are accessed in at least one accepting sequence (of a sentence from the language). It follows that a number of properties that are easily

  15. Reproduction impairment and endocrine disruption in female zebrafish after long-term exposure to MC-LR: A life cycle assessment.

    Science.gov (United States)

    Hou, Jie; Li, Li; Wu, Ning; Su, Yujing; Lin, Wang; Li, Guangyu; Gu, Zemao

    2016-01-01

    Microcystin-LR (MC-LR) has been found to cause reproductive and developmental impairments as well as to disrupt sex hormone homeostasis of fish during acute and sub-chronic toxic experiments. However, fish in natural environments are continuously exposed to MC-LR throughout their entire life cycle as opposed to short-term exposure. Here, we tested the hypothesis that the mechanism by which MC-LR harms female fish reproduction and development within natural water bodies is through interference of the reproductive endocrine system. In the present study, zebrafish hatchlings (5 d post-fertilization) were exposed to 0, 0.3, 3 and 30 μg/L MC-LR for 90 d until reaching sexual maturity. Female zebrafish were selected, and the changes in growth and developmental indicators, ovarian ultrastructure as well as the levels of gonadal steroid hormones and vitellogenin (VTG) were examined along with the transcription of related genes in the hypothalamic-pituitary-gonadal-liver axis (HPGL-axis). The results showed for the first time, a life cycle exposure to MC-LR caused growth inhibition, decreased ovary weight and ovarian ultra-pathological lesions. Decreased ovarian testosterone levels indicated that MC-LR disrupted sex steroid hormone balance. Significantly up-regulated transcription of brain FSHβ and LHβ along with ovarian ERα, FSHR and LHR suggested positive feedback regulation in the HPGL-axis was induced as a compensatory mechanism for MC-LR damage. It was also noted that ovarian VTG content and hepatic ERα and VTG1 expression were all down-regulated, which might be responsible for reduced vitellus storage noted in our histological observations. Our findings indicate that a life cycle exposure to MC-LR impairs the development and reproduction of female zebrafish by disrupting the transcription of related HPGL-axis genes, suggesting that MC-LR has potential adverse effects on fish reproduction and thus population dynamics in MCs-contaminated aquatic environment

  16. Trend analysis by a piecewise linear regression model applied to surface air temperatures in Southeastern Spain (1973–2014)

    OpenAIRE

    Campra, Pablo; Morales, Maria

    2016-01-01

    The magnitude of the trends of environmental and climatic changes is mostly derived from the slopes of the linear trends using ordinary least-square fitting. An alternative flexible fitting model, piecewise regression, has been applied here to surface air temperature records in southeastern Spain for the recent warming period (1973–2014) to gain accuracy in the description of the inner structure of change, dividing the time series into linear segments with different slopes. Breakpoint y...

  17. A study on direct determination of uranium in ore by analyzing γ-ray spectrum with dual linear regression

    International Nuclear Information System (INIS)

    Liu Chunkui

    1996-01-01

    The method introduced is based on different energy of γ-ray emitted from radionuclide in the uranium-radium decay series in ore. The pulse counting rates of two spectra bands, i.e. N 1 (55∼193 keV) and N 2 (260∼1500 keV), are measured by portable type HYX-3 400-channel γ-ray spectrometer. On the other side, the uranium content (Q U ) is obtained by chemical analysis of channel sampling. Then the regression coefficients (b 0 , b 1 ,b 2 ) can be determined through dual linear regression by using Q U and N 1 , N 2 . The direct determination of uranium can be made with the regression equation Q U = b 0 + b 1 N 1 + b 2 N 2

  18. Microcystin-LR nanobody screening from an alpaca phage display nanobody library and its expression and application.

    Science.gov (United States)

    Xu, Chongxin; Yang, Ying; Liu, Liwen; Li, Jianhong; Liu, Xiaoqin; Zhang, Xiao; Liu, Yuan; Zhang, Cunzheng; Liu, Xianjin

    2018-04-30

    Microcystin-LR (MC-LR) is a type of biotoxin that pollutes the ecological environment and food. The study aimed to obtain new nanobodies from phage nanobody library for determination of MC-LR. The toxin was conjugated to keyhole limpet haemocyanin (KLH) and bovine serum albumin (BSA), respectively, then the conjugates were used as coated antigens for enrichment (coated MC-LR-KLH) and screening (coated MC-LR-BSA) of MC-LR phage nanobodies from an alpaca phage display nanobody library. The antigen-specific phage particles were enriched effectively with four rounds of biopanning. At the last round of enrichment, total 20 positive monoclonal phage nanobodies were obtained from the library, which were analyzed after monoclonal phage enzyme linked immunosorbent assay (ELISA), colony PCR and DNA sequencing. The most three positive nanobody genes, ANAb12, ANAb9 and ANAb7 were cloned into pET26b vector, then the nanobodies were expressed in Escherichia coli BL21 respectively. After being purified, the molecular weight (M.W.) of all nanobodies were approximate 15kDa with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The purified nanobodies, ANAb12, ANAb9 and ANAb7 were used to establish the indirect competitive ELISA (IC-ELISA) for MC-LR, and their half-maximum inhibition concentrations (IC 50 ) were 0.87, 1.17 and 1.47μg/L, their detection limits (IC 10 ) were 0.06, 0.08 and 0.12μg/L, respectively. All of them showed strong cross-reactivity (CRs) of 82.7-116.9% for MC-RR, MC-YR and MC-WR, and weak CRs of less than 4.56% for MC-LW, less than 0.1% for MC-LY and MC-LF. It was found that all the IC-ELISAs for MC-LR spiked in tap water samples detection were with good accuracy, stability and repeatability, their recoveries were 84.0-106.5%, coefficient of variations (CVs) were 3.4-10.6%. These results showed that IC-ELISA based on the nanobodies from the alpaca phage display antibody library were promising for high sensitive determination of multiple

  19. Multiple linear stepwise regression of liver lipid levels: proton MR spectroscopy study in vivo at 3.0 T

    International Nuclear Information System (INIS)

    Xu Li; Liang Changhong; Xiao Yuanqiu; Zhang Zhonglin

    2010-01-01

    Objective: To analyze the correlations between liver lipid level determined by liver 3.0 T 1 H-MRS in vivo and influencing factors using multiple linear stepwise regression. Methods: The prospective study of liver 1 H-MRS was performed with 3.0 T system and eight-channel torso phased-array coils using PRESS sequence. Forty-four volunteers were enrolled in this study. Liver spectra were collected with a TR of 1500 ms, TE of 30 ms, volume of interest of 2 cm×2 cm×2 cm, NSA of 64 times. The acquired raw proton MRS data were processed by using a software program SAGE. For each MRS measurement, using water as the internal reference, the amplitude of the lipid signal was normalized to the sum of the signal from lipid and water to obtain percentage lipid within the liver. The statistical description of height, weight, age and BMI, Line width and water suppression were recorded, and Pearson analysis was applied to test their relationships. Multiple linear stepwise regression was used to set the statistical model for the prediction of Liver lipid content. Results: Age (39.1±12.6) years, body weight (64.4±10.4) kg, BMI (23.3±3.1) kg/m 2 , linewidth (18.9±4.4) and the water suppression (90.7±6.5)% had significant correlation with liver lipid content (0.00 to 0.96%, median 0.02%), r were 0.11, 0.44, 0.40, 0.52, -0.73 respectively (P<0.05). But only age, BMI, line width, and the water suppression entered into the multiple linear regression equation. Liver lipid content prediction equation was as follows: Y= 1.395 - (0.021×water suppression) + (0.022×BMI) + (0.014×line width) - (0.004×age), and the coefficient of determination was 0. 613, corrected coefficient of determination was 0.59. Conclusion: The regression model fitted well, since the variables of age, BMI, width, and water suppression can explain about 60% of liver lipid content changes. (authors)

  20. Modeling the potential risk factors of bovine viral diarrhea prevalence in Egypt using univariable and multivariable logistic regression analyses

    Directory of Open Access Journals (Sweden)

    Abdelfattah M. Selim

    2018-03-01

    Full Text Available Aim: The present cross-sectional study was conducted to determine the seroprevalence and potential risk factors associated with Bovine viral diarrhea virus (BVDV disease in cattle and buffaloes in Egypt, to model the potential risk factors associated with the disease using logistic regression (LR models, and to fit the best predictive model for the current data. Materials and Methods: A total of 740 blood samples were collected within November 2012-March 2013 from animals aged between 6 months and 3 years. The potential risk factors studied were species, age, sex, and herd location. All serum samples were examined with indirect ELIZA test for antibody detection. Data were analyzed with different statistical approaches such as Chi-square test, odds ratios (OR, univariable, and multivariable LR models. Results: Results revealed a non-significant association between being seropositive with BVDV and all risk factors, except for species of animal. Seroprevalence percentages were 40% and 23% for cattle and buffaloes, respectively. OR for all categories were close to one with the highest OR for cattle relative to buffaloes, which was 2.237. Likelihood ratio tests showed a significant drop of the -2LL from univariable LR to multivariable LR models. Conclusion: There was an evidence of high seroprevalence of BVDV among cattle as compared with buffaloes with the possibility of infection in different age groups of animals. In addition, multivariable LR model was proved to provide more information for association and prediction purposes relative to univariable LR models and Chi-square tests if we have more than one predictor.

  1. Standardizing effect size from linear regression models with log-transformed variables for meta-analysis.

    Science.gov (United States)

    Rodríguez-Barranco, Miguel; Tobías, Aurelio; Redondo, Daniel; Molina-Portillo, Elena; Sánchez, María José

    2017-03-17

    Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables.

  2. Polynomial regression analysis and significance test of the regression function

    International Nuclear Information System (INIS)

    Gao Zhengming; Zhao Juan; He Shengping

    2012-01-01

    In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

  3. Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods

    Directory of Open Access Journals (Sweden)

    Dick Apronti

    2016-12-01

    Full Text Available Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost-effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies.

  4. Interpretation of commonly used statistical regression models.

    Science.gov (United States)

    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.

  5. Radon concentrations in some Egyptian dwellings using LR 115 detectors

    International Nuclear Information System (INIS)

    Hussein, A.S.

    2007-01-01

    Radon, a well-established risk factor for human lung cancer, is present at low concentrations in most homes. Consequently, many countries have established national guidelines for residential radon concentrations. This survey provides additional information about indoor radon concentrations in Egypt. Indoor radon survey of a total of 15 randomly selected houses in Qena city, Upper Egypt was carried out. LR 115 detectors were exposed for one year, covering all the seasons. The estimated indoor radon levels varied from 19 to 59 Bq m 3 with an average of 40 Bq m 3 . Using the bare and filtered LR 115 detectors, the average equilibrium factor F was assessed as 0.30 indoors. An average annual effective dose of 0.40 mSv has been estimated and was found to be lower than the ICRP-65

  6. A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.

    Science.gov (United States)

    Kim, Sun Mi; Han, Heon; Park, Jeong Mi; Choi, Yoon Jung; Yoon, Hoi Soo; Sohn, Jung Hee; Baek, Moon Hee; Kim, Yoon Nam; Chae, Young Moon; June, Jeon Jong; Lee, Jiwon; Jeon, Yong Hwan

    2012-10-01

    To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

  7. Detection of epistatic effects with logic regression and a classical linear regression model.

    Science.gov (United States)

    Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

    2014-02-01

    To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

  8. New neutral current effects at e+e- linear colliders

    International Nuclear Information System (INIS)

    Pankov, A.A.

    2002-01-01

    Four fermion contact interaction effects in the processes e + e - → μ + μ - , b-barb and c-barc at the e + e - linear colliders with √ s = 0.5 TeV and longitudinally polarized initial beams have been studied. Presented analysis has been performed by means of new integrated observables expressed in terms of the forward (σ F ) and backward (σ B ) polarized cross sections such that they give information on individual helicity cross sections. The helicity cross sections allow to perform a general model-independent analysis of four-fermion contact interactions and obtain the corresponding constraints on their parameters. It is also shows that the sensitivity of the new polarized observables to contact interactions is quite larger than that of the conventional observables (σ, A FB , A LR , A LR,FB ) [ru

  9. Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

    Directory of Open Access Journals (Sweden)

    Yunfeng Wu

    2014-01-01

    Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

  10. Lr67/Yr46 confers adult plant resistance to stem rust and powdery mildew in wheat.

    Science.gov (United States)

    Herrera-Foessel, Sybil A; Singh, Ravi P; Lillemo, Morten; Huerta-Espino, Julio; Bhavani, Sridhar; Singh, Sukhwinder; Lan, Caixia; Calvo-Salazar, Violeta; Lagudah, Evans S

    2014-04-01

    We demonstrate that Lr67/Yr46 has pleiotropic effect on stem rust and powdery mildew resistance and is associated with leaf tip necrosis. Genes are designated as Sr55, Pm46 and Ltn3 , respectively. Wheat (Triticum aestivum) accession RL6077, known to carry the pleiotropic slow rusting leaf and yellow rust resistance genes Lr67/Yr46 in Thatcher background, displayed significantly lower stem rust (P. graminis tritici; Pgt) and powdery mildew (Blumeria graminis tritici; Bgt) severities in Kenya and in Norway, respectively, compared to its recurrent parent Thatcher. We investigated the resistance of RL6077 to stem rust and powdery mildew using Avocet × RL6077 F6 recombinant inbred lines (RILs) derived from two photoperiod-insensitive F3 families segregating for Lr67/Yr46. Greenhouse seedling tests were conducted with Mexican Pgt race RTR. Field evaluations were conducted under artificially initiated stem rust epidemics with Pgt races RTR and TTKST (Ug99 + Sr24) at Ciudad Obregon (Mexico) and Njoro (Kenya) during 2010-2011; and under natural powdery mildew epiphytotic in Norway at Ås and Hamar during 2011 and 2012. In Mexico, a mean reduction of 41 % on stem rust severity was obtained for RILs carrying Lr67/Yr46, compared to RILs that lacked the gene, whereas in Kenya the difference was smaller (16 %) but significant. In Norway, leaf tip necrosis was associated with Lr67/Yr46 and RILs carrying Lr67/Yr46 showed a 20 % reduction in mean powdery mildew severity at both sites across the 2 years of evaluation. Our study demonstrates that Lr67/Yr46 confers partial resistance to stem rust and powdery mildew and is associated with leaf tip necrosis. The corresponding pleiotropic, or tightly linked, genes, designated as Sr55, Pm46, and Ltn3, can be utilized to provide broad-spectrum durable disease resistance in wheat.

  11. Effects of different algaecides on the photosynthetic capacity, cell integrity and microcystin-LR release of Microcystis aeruginosa

    International Nuclear Information System (INIS)

    Zhou, Shiqing; Shao, Yisheng; Gao, Naiyun; Deng, Yang; Qiao, Junlian; Ou, Huase; Deng, Jing

    2013-01-01

    Bench scale tests were conducted to study the effects of four common algaecides, including copper sulfate, hydrogen peroxide, diuron and ethyl 2-methylacetoacetate (EMA) on the photosynthetic capacity, cell integrity and microcystin-LR (MC-LR) release of Microcystis aeruginosa. The release of potassium (K + ) from cell membrane during algaecide exposure was also analyzed. The three typical photosynthetic parameters, including the effective quantum yield (φ e ), photosynthetic efficiency (α) and maximal electron transport rate (rETR max ), were measured by a pulse amplitude modulated (PAM) fluorometry. Results showed that the photosynthetic capacity was all inhibited by the four algaecides, to different degrees, by limiting the energy capture in photosynthesis, and blocking the electron transfer chain in primary reaction. For example, at high diuron concentration (7.5 mg L −1 ), φ e , α and rETR max decreased from 0.46 to 0.19 (p −2 s −1 /μmol photons m −2 s −1 , and from 160.7 to 0.1 (p −2 s −1 compared with the control group after 96 h of exposure, respectively. Furthermore, the increase of algaecide dose could lead to the cell lysis, as well as release of intracellular MC-LR that enhanced the accumulation of extracellular MC-LR. The order of MC-LR release potential for the four algaecides was CuSO 4 > H 2 O 2 > diuron > EMA. Highlights: • PAM was used to investigate the effects of algaecides on Microcystis aeruginosa. • We estimate the release of potassium (K + ) from cell membrane for cell lysis. • The risk of microcystin-LR release was evaluated after algaecides exposure. • The order of MC-LR release potential was copper sulfate > hydrogen peroxide > diuron > ethyl 2-methylacetoacetate

  12. Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2007-01-01

    This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

  13. SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wei, J [City College of New York, New York, NY (United States); Chao, M [The Mount Sinai Medical Center, New York, NY (United States)

    2016-06-15

    Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

  14. SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

    International Nuclear Information System (INIS)

    Wei, J; Chao, M

    2016-01-01

    Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

  15. A Multi-Walled Carbon Nanotube-based Biosensor for Monitoring Microcystin-LR in Sources of Drinking Water Supplies

    Science.gov (United States)

    A multi-walled carbon nanotube-based electrochemical biosensor is developed for monitoring microcystin-LR (MC-LR), a toxic cyanobacterial toxin, in sources of drinking water supplies. The biosensor electrodes are fabricated using dense, mm-long multi-walled CNT (MWCNT) arrays gro...

  16. Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure.

    Science.gov (United States)

    Yoo, Yun Joo; Sun, Lei; Poirier, Julia G; Paterson, Andrew D; Bull, Shelley B

    2017-02-01

    By jointly analyzing multiple variants within a gene, instead of one at a time, gene-based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster-specific effects in a quadratic sum of squares and cross-products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well-powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P-value, variance-component, and principal-component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene-specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome-wide analysis. The cluster construction of the MLC test statistics helps reveal within-gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations. © 2016 The Authors Genetic Epidemiology Published by Wiley Periodicals, Inc.

  17. Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

    Science.gov (United States)

    Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

    2017-10-01

    Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

  18. Radon concentrations in some Egyptian dwellings using LR 115 detectors

    Energy Technology Data Exchange (ETDEWEB)

    Hussein, A S [Radiation Protection Department, Nuclear Power Plants Authority, Cairo (Egypt)

    2007-06-15

    Radon, a well-established risk factor for human lung cancer, is present at low concentrations in most homes. Consequently, many countries have established national guidelines for residential radon concentrations. This survey provides additional information about indoor radon concentrations in Egypt. Indoor radon survey of a total of 15 randomly selected houses in Qena city, Upper Egypt was carried out. LR 115 detectors were exposed for one year, covering all the seasons. The estimated indoor radon levels varied from 19 to 59 Bq m{sup 3} with an average of 40 Bq m{sup 3}. Using the bare and filtered LR 115 detectors, the average equilibrium factor F was assessed as 0.30 indoors. An average annual effective dose of 0.40 mSv has been estimated and was found to be lower than the ICRP-65.

  19. Modeling the kinetics of essential oil hydrodistillation from juniper berries (Juniperus communis L. using non-linear regression

    Directory of Open Access Journals (Sweden)

    Radosavljević Dragana B.

    2017-01-01

    Full Text Available This paper presents kinetics modeling of essential oil hydrodistillation from juniper berries (Juniperus communis L. by using a non-linear regression methodology. The proposed model has the polynomial-logarithmic form. The initial equation of the proposed non-linear model is q = q∞•(a•(logt2 + b•logt + c and by substituting a1=q∞•a, b1 = q∞•b and c1 = q∞•c, the final equation is obtained as q = a1•(logt2 + b1•logt + c1. In this equation q is the quantity of the obtained oil at time t, while a1, b1 and c1 are parameters to be determined for each sample. From the final equation it can be seen that the key parameter q∞, which presents the maximal oil quantity obtained after infinite time, is already included in parameters a1, b1 and c1. In this way, experimental determination of this parameter is avoided. Using the proposed model with parameters obtained by regression, the values of oil hydrodistillation in time are calculated for each sample and compared to the experimental values. In addition, two kinetic models previously proposed in literature were applied to the same experimental results. The developed model provided better agreements with the experimental values than the two, generally accepted kinetic models of this process. The average values of error measures (RSS, RSE, AIC and MRPD obtained for our model (0.005; 0.017; –84.33; 1.65 were generally lower than the corresponding values of the other two models (0.025; 0.041; –53.20; 3.89 and (0.0035; 0.015; –86.83; 1.59. Also, parameter estimation for the proposed model was significantly simpler (maximum 2 iterations per sample using the non-linear regression than that for the existing models (maximum 9 iterations per sample. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR-35026

  20. Reliability of the Load-Velocity Relationship Obtained Through Linear and Polynomial Regression Models to Predict the One-Repetition Maximum Load.

    Science.gov (United States)

    Pestaña-Melero, Francisco Luis; Haff, G Gregory; Rojas, Francisco Javier; Pérez-Castilla, Alejandro; García-Ramos, Amador

    2017-12-18

    This study aimed to compare the between-session reliability of the load-velocity relationship between (1) linear vs. polynomial regression models, (2) concentric-only vs. eccentric-concentric bench press variants, as well as (3) the within-participants vs. the between-participants variability of the velocity attained at each percentage of the one-repetition maximum (%1RM). The load-velocity relationship of 30 men (age: 21.2±3.8 y; height: 1.78±0.07 m, body mass: 72.3±7.3 kg; bench press 1RM: 78.8±13.2 kg) were evaluated by means of linear and polynomial regression models in the concentric-only and eccentric-concentric bench press variants in a Smith Machine. Two sessions were performed with each bench press variant. The main findings were: (1) first-order-polynomials (CV: 4.39%-4.70%) provided the load-velocity relationship with higher reliability than second-order-polynomials (CV: 4.68%-5.04%); (2) the reliability of the load-velocity relationship did not differ between the concentric-only and eccentric-concentric bench press variants; (3) the within-participants variability of the velocity attained at each %1RM was markedly lower than the between-participants variability. Taken together, these results highlight that, regardless of the bench press variant considered, the individual determination of the load-velocity relationship by a linear regression model could be recommended to monitor and prescribe the relative load in the Smith machine bench press exercise.

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

    Science.gov (United States)

    Li, Yanming; Nan, Bin; Zhu, Ji

    2015-06-01

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

  2. Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

    Science.gov (United States)

    Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

    2018-02-01

    The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

  3. Allelopathic effects of microcystin-LR on the germination, growth and metabolism of five charophyte species and a submerged angiosperm.

    Science.gov (United States)

    Rojo, Carmen; Segura, Matilde; Cortés, Francisco; Rodrigo, María A

    2013-11-15

    Microcystins (MCs) are produced by cyanobacteria in aquatic environments and adversely affect macrophytes at very high concentrations. However, the effects of MC on macrophytes at concentrations of environmental relevance are largely unknown. The main objective of this study was to analyze the allelopathic effects of MC-LR at natural concentrations (1, 8 and 16 μg MC-LR/L) on five charophyte species (Chara aspera, C. baltica, C. hispida, C. vulgaris and Nitella hyalina) and the angiosperm Myriophyllum spicatum. Macrophyte specimens were obtained from a restored area located in Albufera de València Natural Park, a protected coastal Mediterranean wetland. Two different experiments were conducted involving (i) the addition of MC-LR to natural sediment to evaluate its effects on seed germination and (ii) the addition of MC-LR to water cultures of macrophytes to evaluate its effects on growth and metabolic functions. In water, the MC-LR concentration decreased by 84% in two weeks; the loss was not significant in sediment. The first seedlings (all C. hispida) emerged from the wetland sediment following a delay of a few days in the presence of MC-LR. The germination rates in 8 and 16 μg MC-LR/L treatments were 44% and 11% of that occurring in the absence of MC, but these differences disappeared over time. The final density was 6-7 germlings/dm(3). Final germling length was unaffected by MC-LR. Rotifers (Lecane spp.) emerging from the natural sediment during the experiment were favored by MC-LR; the opposite pattern was observed in the cladoceran Daphnia magna. The growth rates of C. vulgaris, C. baltica and N. hyalina were unaffected by MC exposure, whereas those of C. hispida and C. aspera were reduced in the MC treatments relative to the control treatment. The concentration of chlorophyll-a and the in vivo net photosynthetic rate were lower in the presence of MC-LR, even at the lowest concentration, for all of the characeans tested. M. spicatum was sensitive to the

  4. A STATISTICAL ANALYSIS OF GDP AND FINAL CONSUMPTION USING SIMPLE LINEAR REGRESSION. THE CASE OF ROMANIA 1990–2010

    OpenAIRE

    Aniela Balacescu; Marian Zaharia

    2011-01-01

    This paper aims to examine the causal relationship between GDP and final consumption. The authors used linear regression model in which GDP is considered variable results, and final consumption variable factor. In drafting article we used Excel software application that is a modern computing and statistical data analysis.

  5. Determination of microcystin-LR in drinking water using UPLC tandem mass spectrometry-matrix effects and measurement.

    Science.gov (United States)

    Li, Wei; Duan, Jinming; Niu, Chaoying; Qiang, Naichen; Mulcahy, Dennis

    2011-10-01

    A simple detection method using ultra-performance liquid chromatography electrospray ionisation tandem mass spectrometry (UPLC-ESI-MS-MS) coupled with the sample dilution method for determining trace microcystin-LR (MC-LR) in drinking water is presented. The limit of detection (LOD) was 0.04 µg/L and the limit of quantitation (LOQ) was 0.1 µg/L. Water matrix effects of ionic strength, dissolved organic carbon (DOC) and pH were examined. The results indicate that signal detection intensity for MC-LR was significantly suppressed as the ionic strength increased from ultrapure water condition, whereas it increased slightly with solution pH and DOC at low concentrations. However, addition of methanol (MeOH) into the sample was able to counter the signal suppression effects. In this study, dilution of the tap water sample by adding 4% MeOH (v/v) was observed to be adequate to compensate for the signal suppression. The recoveries of the samples fortified with MC-LR (0.2, 1, and 10 µg/L) for three different tap water samples ranged from 84.4% to 112.9%.

  6. Effective Surfactants Blend Concentration Determination for O/W Emulsion Stabilization by Two Nonionic Surfactants by Simple Linear Regression.

    Science.gov (United States)

    Hassan, A K

    2015-01-01

    In this work, O/W emulsion sets were prepared by using different concentrations of two nonionic surfactants. The two surfactants, tween 80(HLB=15.0) and span 80(HLB=4.3) were used in a fixed proportions equal to 0.55:0.45 respectively. HLB value of the surfactants blends were fixed at 10.185. The surfactants blend concentration is starting from 3% up to 19%. For each O/W emulsion set the conductivity was measured at room temperature (25±2°), 40, 50, 60, 70 and 80°. Applying the simple linear regression least squares method statistical analysis to the temperature-conductivity obtained data determines the effective surfactants blend concentration required for preparing the most stable O/W emulsion. These results were confirmed by applying the physical stability centrifugation testing and the phase inversion temperature range measurements. The results indicated that, the relation which represents the most stable O/W emulsion has the strongest direct linear relationship between temperature and conductivity. This relationship is linear up to 80°. This work proves that, the most stable O/W emulsion is determined via the determination of the maximum R² value by applying of the simple linear regression least squares method to the temperature-conductivity obtained data up to 80°, in addition to, the true maximum slope is represented by the equation which has the maximum R² value. Because the conditions would be changed in a more complex formulation, the method of the determination of the effective surfactants blend concentration was verified by applying it for more complex formulations of 2% O/W miconazole nitrate cream and the results indicate its reproducibility.

  7. Development of a Multiple Linear Regression Model to Forecast Facility Electrical Consumption at an Air Force Base.

    Science.gov (United States)

    1981-09-01

    corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John

  8. Causal correlation of foliar biochemical concentrations with AVIRIS spectra using forced entry linear regression

    Science.gov (United States)

    Dawson, Terence P.; Curran, Paul J.; Kupiec, John A.

    1995-01-01

    link between wavelengths chosen by stepwise regression and the biochemical of interest, and this in turn has cast doubts on the use of imaging spectrometry for the estimation of foliar biochemical concentrations at sites distant from the training sites. To investigate this problem, an analysis was conducted on the variation in canopy biochemical concentrations and reflectance spectra using forced entry linear regression.

  9. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy)

    Science.gov (United States)

    Conoscenti, Christian; Ciaccio, Marilena; Caraballo-Arias, Nathalie Almaru; Gómez-Gutiérrez, Álvaro; Rotigliano, Edoardo; Agnesi, Valerio

    2015-08-01

    In this paper, terrain susceptibility to earth-flow occurrence was evaluated by using geographic information systems (GIS) and two statistical methods: Logistic regression (LR) and multivariate adaptive regression splines (MARS). LR has been already demonstrated to provide reliable predictions of earth-flow occurrence, whereas MARS, as far as we know, has never been used to generate earth-flow susceptibility models. The experiment was carried out in a basin of western Sicily (Italy), which extends for 51 km2 and is severely affected by earth-flows. In total, we mapped 1376 earth-flows, covering an area of 4.59 km2. To explore the effect of pre-failure topography on earth-flow spatial distribution, we performed a reconstruction of topography before the landslide occurrence. This was achieved by preparing a digital terrain model (DTM) where altitude of areas hosting landslides was interpolated from the adjacent undisturbed land surface by using the algorithm topo-to-raster. This DTM was exploited to extract 15 morphological and hydrological variables that, in addition to outcropping lithology, were employed as explanatory variables of earth-flow spatial distribution. The predictive skill of the earth-flow susceptibility models and the robustness of the procedure were tested by preparing five datasets, each including a different subset of landslides and stable areas. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The results demonstrate that the overall accuracy of LR and MARS earth-flow susceptibility models is from excellent to outstanding. However, AUC values of the validation datasets attest to a higher predictive power of MARS-models (AUC between 0.881 and 0.912) with respect to LR-models (AUC between 0.823 and 0.870). The adopted procedure proved to be resistant to overfitting and stable when changes of the learning and validation samples are

  10. Implicit collinearity effect in linear regression: Application to basal ...

    African Journals Online (AJOL)

    Collinearity of predictor variables is a severe problem in the least square regression analysis. It contributes to the instability of regression coefficients and leads to a wrong prediction accuracy. Despite these problems, studies are conducted with a large number of observed and derived variables linked with a response ...

  11. Oxidation of microcystin-LR by the Fenton process : Kinetics, degradation intermediates, water quality and toxicity assessment

    NARCIS (Netherlands)

    Park, Jeong-Ann; Yang, Boram; Park, Chanhyuk; Choi, Jae-Woo; van Genuchten, Case M.|info:eu-repo/dai/nl/413489647; Lee, Sang-Hyup

    2017-01-01

    The Fenton process was assessed as a cost-effective technology for the removal of Microcystin-LR (MC-LR) among UV, UV/H2O2, and Fenton process according to efficiency and electrical energy per order (EE/O). The determined practical concentrations of the Fenton reagents were 5 mg/L Fe(II) and 5 mg/L

  12. The calibration of the solid state nuclear track detector LR 115 for radon measurements

    CERN Document Server

    Gericke, C; Jönsson, G; Freyer, K; Treutler, H C; Enge, W

    1999-01-01

    An experimental calibration of indoor room and outdoor soil detector devices which are based on LR 115 as sensitive element has taken place at the Swedish Radiation Protection Institute in Stockholm (Sweden) in 1994 and 1996, at the Physikalisch-Technischen Bundesanstalt in Braunschweig (Germany) in 1997 and at the Umweltforschungszentrum Leipzig-Halle (Germany) in 1997. Special properties of the used solid state nuclear track detector (SSNTD) material LR 115 have been measured to define the application of the experimental calibration.

  13. Effects of different algaecides on the photosynthetic capacity, cell integrity and microcystin-LR release of Microcystis aeruginosa

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Shiqing [State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092 (China); Shao, Yisheng, E-mail: yishengshao@163.com [State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092 (China); China Academy of Urban Planning and Design, Beijing 100037 (China); Gao, Naiyun [State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092 (China); Deng, Yang [Department of Earth and Environmental Studies, Montclair State University, Montclair NJ 07043 (United States); Qiao, Junlian; Ou, Huase; Deng, Jing [State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092 (China)

    2013-10-01

    Bench scale tests were conducted to study the effects of four common algaecides, including copper sulfate, hydrogen peroxide, diuron and ethyl 2-methylacetoacetate (EMA) on the photosynthetic capacity, cell integrity and microcystin-LR (MC-LR) release of Microcystis aeruginosa. The release of potassium (K{sup +}) from cell membrane during algaecide exposure was also analyzed. The three typical photosynthetic parameters, including the effective quantum yield (φ{sub e}), photosynthetic efficiency (α) and maximal electron transport rate (rETR{sub max}), were measured by a pulse amplitude modulated (PAM) fluorometry. Results showed that the photosynthetic capacity was all inhibited by the four algaecides, to different degrees, by limiting the energy capture in photosynthesis, and blocking the electron transfer chain in primary reaction. For example, at high diuron concentration (7.5 mg L{sup −1}), φ{sub e}, α and rETR{sub max} decreased from 0.46 to 0.19 (p < 0.01), from 0.20 to 0.01 (p < 0.01) μmol electrons m{sup −2} s{sup −1}/μmol photons m{sup −2} s{sup −1}, and from 160.7 to 0.1 (p < 0.001) μmol m{sup −2} s{sup −1} compared with the control group after 96 h of exposure, respectively. Furthermore, the increase of algaecide dose could lead to the cell lysis, as well as release of intracellular MC-LR that enhanced the accumulation of extracellular MC-LR. The order of MC-LR release potential for the four algaecides was CuSO{sub 4} > H{sub 2}O{sub 2} > diuron > EMA. Highlights: • PAM was used to investigate the effects of algaecides on Microcystis aeruginosa. • We estimate the release of potassium (K{sup +}) from cell membrane for cell lysis. • The risk of microcystin-LR release was evaluated after algaecides exposure. • The order of MC-LR release potential was copper sulfate > hydrogen peroxide > diuron > ethyl 2-methylacetoacetate.

  14. Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

    Science.gov (United States)

    Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried

    2018-03-01

    This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

  15. Performance of an Axisymmetric Rocket Based Combined Cycle Engine During Rocket Only Operation Using Linear Regression Analysis

    Science.gov (United States)

    Smith, Timothy D.; Steffen, Christopher J., Jr.; Yungster, Shaye; Keller, Dennis J.

    1998-01-01

    The all rocket mode of operation is shown to be a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. An axisymmetric RBCC engine was used to determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and multiple linear regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inlet diameter ratio. A perfect gas computational fluid dynamics analysis, using both the Spalart-Allmaras and k-omega turbulence models, was performed with the NPARC code to obtain values of vacuum specific impulse. Results from the multiple linear regression analysis showed that for both the full flow and gas generator configurations increasing mixer-ejector area ratio and rocket area ratio increase performance, while increasing mixer-ejector inlet area ratio and mixer-ejector length-to-diameter ratio decrease performance. Increasing injected secondary flow increased performance for the gas generator analysis, but was not statistically significant for the full flow analysis. Chamber pressure was found to be not statistically significant.

  16. Diagnostics on LALR(k) conflicts based on a method for LR(k) testing

    DEFF Research Database (Denmark)

    Kristensen, Bent Bruun; Madsen, Ole Lehrmann

    1981-01-01

    A user of an LALR(k) parser generator system may have difficulties in understanding how a given LALR(k) conflict is generated. This is especially difficult if the conflict does not correspond to an LR(k) conflict. A practical method for giving informative diagnostics on LALR(k) conflicts is prese......A user of an LALR(k) parser generator system may have difficulties in understanding how a given LALR(k) conflict is generated. This is especially difficult if the conflict does not correspond to an LR(k) conflict. A practical method for giving informative diagnostics on LALR(k) conflicts...

  17. Solar photo-Fenton treatment of microcystin-LR in aqueous environment: Transformation products and toxicity in different water matrices

    Science.gov (United States)

    Transformation products and toxicity patterns of microcystin-LR (MC-LR), a common cyanotoxin in freshwaters, during degradation by solar photo-Fenton process were studied in the absence and presence of two major water components, namely fulvic acid and alkalinity. The transformat...

  18. Linear regression models and k-means clustering for statistical analysis of fNIRS data.

    Science.gov (United States)

    Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

    2015-02-01

    We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.

  19. First successful ionization of Lr (Z = 103) by a surface-ionization technique

    Energy Technology Data Exchange (ETDEWEB)

    Sato, Tetsuya K., E-mail: sato.tetsuya@jaea.go.jp; Sato, Nozomi; Asai, Masato; Tsukada, Kazuaki; Toyoshima, Atsushi; Ooe, Kazuhiro; Miyashita, Sunao; Schädel, Matthias [Advanced Science Research Center, Japan Atomic Energy Agency (JAEA), 2-4 Shirakata-shirane, Tokai-mura, Ibaraki 319-1195 (Japan); Kaneya, Yusuke; Nagame, Yuichiro [Advanced Science Research Center, Japan Atomic Energy Agency (JAEA), 2-4 Shirakata-shirane, Tokai-mura, Ibaraki 319-1195 (Japan); Graduate School of Science and Engineering, Ibaraki University, 2-1-1, Bunkyo, Mito, Ibaraki 310-8512 (Japan); Osa, Akihiko [Department of Research Reactor and Tandem Accelerator, Japan Atomic Energy Agency (JAEA), 2-4 Shirakata shirane, Tokai-mura, Ibaraki 319-1195 (Japan); Ichikawa, Shin-ichi [Advanced Science Research Center, Japan Atomic Energy Agency (JAEA), 2-4 Shirakata-shirane, Tokai-mura, Ibaraki 319-1195 (Japan); Nishina Center for Accelerator-Based Science, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Stora, Thierry [ISOLDE, CERN, CH-1211 Geneva 23 (Switzerland); Kratz, Jens Volker [Institut für Kernchemie, Universität Mainz, D-55099 Mainz (Germany)

    2013-02-15

    We have developed a surface ionization ion-source as part of the JAEA-ISOL (Isotope Separator On-Line) setup, which is coupled to a He/CdI{sub 2} gas-jet transport system to determine the first ionization potential of the heaviest actinide lawrencium (Lr, Z = 103). The new ion-source is an improved version of the previous source that provided good ionization efficiencies for lanthanides. An additional filament was newly installed to give better control over its operation. We report, here, on the development of the new gas-jet coupled surface ion-source and on the first successful ionization and mass separation of 27-s {sup 256}Lr produced in the {sup 249}Cf + {sup 11}B reaction.

  20. Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery.

    Directory of Open Access Journals (Sweden)

    Hon-Yi Shi

    Full Text Available BACKGROUND: Since most published articles comparing the performance of artificial neural network (ANN models and logistic regression (LR models for predicting hepatocellular carcinoma (HCC outcomes used only a single dataset, the essential issue of internal validity (reproducibility of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC curves, Hosmer-Lemeshow (H-L statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

  1. The impact of meteorology on the occurrence of waterborne outbreaks of vero cytotoxin-producing Escherichia coli (VTEC): a logistic regression approach.

    Science.gov (United States)

    O'Dwyer, Jean; Morris Downes, Margaret; Adley, Catherine C

    2016-02-01

    This study analyses the relationship between meteorological phenomena and outbreaks of waterborne-transmitted vero cytotoxin-producing Escherichia coli (VTEC) in the Republic of Ireland over an 8-year period (2005-2012). Data pertaining to the notification of waterborne VTEC outbreaks were extracted from the Computerised Infectious Disease Reporting system, which is administered through the national Health Protection Surveillance Centre as part of the Health Service Executive. Rainfall and temperature data were obtained from the national meteorological office and categorised as cumulative rainfall, heavy rainfall events in the previous 7 days, and mean temperature. Regression analysis was performed using logistic regression (LR) analysis. The LR model was significant (p < 0.001), with all independent variables: cumulative rainfall, heavy rainfall and mean temperature making a statistically significant contribution to the model. The study has found that rainfall, particularly heavy rainfall in the preceding 7 days of an outbreak, is a strong statistical indicator of a waterborne outbreak and that temperature also impacts waterborne VTEC outbreak occurrence.

  2. The estimation and prediction of the inventories for the liquid and gaseous radwaste systems using the linear regression analysis

    International Nuclear Information System (INIS)

    Kim, J. Y.; Shin, C. H.; Kim, J. K.; Lee, J. K.; Park, Y. J.

    2003-01-01

    The variation transitions of the inventories for the liquid radwaste system and the radioactive gas have being released in containment, and their predictive values according to the operation histories of Yonggwang(YGN) 3 and 4 were analyzed by linear regression analysis methodology. The results show that the variation transitions of the inventories for those systems are linearly increasing according to the operation histories but the inventories released to the environment are considerably lower than the recommended values based on the FSAR suggestions. It is considered that some conservation were presented in the estimation methodology in preparing stage of FSAR

  3. Modeling of Soil Aggregate Stability using Support Vector Machines and Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Ali Asghar Besalatpour

    2016-02-01

    Full Text Available Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010. Various indicators have been proposed to characterize and quantify soil aggregate stability, for example percentage of water-stable aggregates (WSA, mean weight diameter (MWD, geometric mean diameter (GMD of aggregates, and water-dispersible clay (WDC content (Calero et al., 2008. Unfortunately, the experimental methods available to determine these indicators are laborious, time-consuming and difficult to standardize (Canasveras et al., 2010. Therefore, it would be advantageous if aggregate stability could be predicted indirectly from more easily available data (Besalatpour et al., 2014. The main objective of this study is to investigate the potential use of support vector machines (SVMs method for estimating soil aggregate stability (as quantified by GMD as compared to multiple linear regression approach. Materials and Methods: The study area was part of the Bazoft watershed (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E, which is located in the Northern part of the Karun river basin in central Iran. A total of 160 soil samples were collected from the top 5 cm of soil surface. Some easily available characteristics including topographic, vegetation, and soil properties were used as inputs. Soil organic matter (SOM content was determined by the Walkley-Black method (Nelson & Sommers, 1986. Particle size distribution in the soil samples (clay, silt, sand, fine sand, and very fine sand were measured using the procedure described by Gee & Bauder (1986 and calcium carbonate equivalent (CCE content was determined by the back-titration method (Nelson, 1982. The modified Kemper & Rosenau (1986 method was used to determine wet-aggregate stability (GMD. The topographic attributes of elevation, slope, and aspect were characterized using a 20-m

  4. Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R

    Directory of Open Access Journals (Sweden)

    Paulino Pérez

    2010-09-01

    Full Text Available The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require specialized computer programs, not always available to the end user and not implemented in standard statistical software yet. The R-package BLR (Bayesian Linear Regression implements several statistical procedures (e.g., Bayesian Ridge Regression, Bayesian LASSO in a unified framework that allows including marker genotypes and pedigree data jointly. This article describes the classes of models implemented in the BLR package and illustrates their use through examples. Some challenges faced when applying genomic-enabled selection, such as model choice, evaluation of predictive ability through cross-validation, and choice of hyper-parameters, are also addressed.

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

    Science.gov (United States)

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

    2017-09-29

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

  6. Monopole and dipole estimation for multi-frequency sky maps by linear regression

    Science.gov (United States)

    Wehus, I. K.; Fuskeland, U.; Eriksen, H. K.; Banday, A. J.; Dickinson, C.; Ghosh, T.; Górski, K. M.; Lawrence, C. R.; Leahy, J. P.; Maino, D.; Reich, P.; Reich, W.

    2017-01-01

    We describe a simple but efficient method for deriving a consistent set of monopole and dipole corrections for multi-frequency sky map data sets, allowing robust parametric component separation with the same data set. The computational core of this method is linear regression between pairs of frequency maps, often called T-T plots. Individual contributions from monopole and dipole terms are determined by performing the regression locally in patches on the sky, while the degeneracy between different frequencies is lifted whenever the dominant foreground component exhibits a significant spatial spectral index variation. Based on this method, we present two different, but each internally consistent, sets of monopole and dipole coefficients for the nine-year WMAP, Planck 2013, SFD 100 μm, Haslam 408 MHz and Reich & Reich 1420 MHz maps. The two sets have been derived with different analysis assumptions and data selection, and provide an estimate of residual systematic uncertainties. In general, our values are in good agreement with previously published results. Among the most notable results are a relative dipole between the WMAP and Planck experiments of 10-15μK (depending on frequency), an estimate of the 408 MHz map monopole of 8.9 ± 1.3 K, and a non-zero dipole in the 1420 MHz map of 0.15 ± 0.03 K pointing towards Galactic coordinates (l,b) = (308°,-36°) ± 14°. These values represent the sum of any instrumental and data processing offsets, as well as any Galactic or extra-Galactic component that is spectrally uniform over the full sky.

  7. Similar uptake profiles of microcystin-LR and -RR in an in vitro human intestinal model

    International Nuclear Information System (INIS)

    Zeller, P.; Clement, M.; Fessard, V.

    2011-01-01

    Highlights: → First description of in vitro cellular uptake of MCs into intestinal cells. → OATP 3A1 and OATP 4A1 are expressed in Caco-2 cell membranes. → MC-LR and MC-RR show similar uptake in Caco-2 cells. → MCs are probably excreted from Caco-2 cells by an active mechanism. -- Abstract: Microcystins (MCs) are cyclic hepatotoxins produced by various species of cyanobacteria. Their structure includes two variable amino acids (AA) leading to more than 80 MC variants. In this study, we focused on the most common variant, microcystin-LR (MC-LR), and microcystin-RR (MC-RR), a variant differing by only one AA. Despite their structural similarity, MC-LR elicits higher liver toxicity than MC-RR partly due to a discrepancy in their uptake by hepatic organic anion transporters (OATP 1B1 and 1B3). However, even though ingestion is the major pathway of human exposure to MCs, intestinal absorption of MCs has been poorly addressed. Consequently, we investigated the cellular uptake of the two MC variants in the human intestinal cell line Caco-2 by immunolocalization using an anti-MC antibody. Caco-2 cells were treated for 30 min to 24 h with several concentrations (1-50 μM) of both variants. We first confirmed the localization of OATP 3A1 and 4A1 at the cell membrane of Caco-2 cells. Our study also revealed a rapid uptake of both variants in less than 1 h. The uptake profiles of the two variants did not differ in our immunostaining study neither with respect to concentration nor the time of exposure. Furthermore, we have demonstrated for the first time the nuclear localization of MC-RR and confirmed that of MC-LR. Finally, our results suggest a facilitated uptake and an active excretion of MC-LR and MC-RR in Caco-2 cells. Further investigation on the role of OATP 3A1 and 4A1 in MC uptake should be useful to clarify the mechanism of intestinal absorption of MCs and contribute in risk assessment of cyanotoxin exposure.

  8. A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys

    Science.gov (United States)

    Barrett, C. A.

    1985-01-01

    Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.

  9. Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis.

    Science.gov (United States)

    Oguntunde, Philip G; Lischeid, Gunnar; Dietrich, Ottfried

    2018-03-01

    This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P  1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

  10. Quantile Regression Methods

    DEFF Research Database (Denmark)

    Fitzenberger, Bernd; Wilke, Ralf Andreas

    2015-01-01

    if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

  11. SR and LR Union Suture for the Treatment of Myopic Strabismus Fixus: Is Scleral Fixation Necessary?

    Directory of Open Access Journals (Sweden)

    Carol P. S. Lam

    2015-01-01

    Full Text Available Purpose. To evaluate and compare the effectiveness of scleral fixation SR and LR union suture and nonscleral fixation union suture for the treatment of myopic strabismus fixus. Methods. Retrospective review of 32 eyes of 22 patients with myopic strabismus fixus who had undergone union suture of superior rectus (SR and lateral rectus (LR with or without scleral fixation, and follow-up longer than 6 months at Hong Kong Eye Hospital from 2006 to 2013. Surgical techniques and outcomes in terms of ocular alignment are analyzed. Results. There is significant overall improvement both in postoperative angle of esodeviation (P0.05. Conclusions. Union suture of SR and LR is an effective procedure in correcting myopic strabismus fixus. Fixation of the union suture to the sclera does not improve surgical outcome.

  12. Dynamics of microcystins-LR and -RR in the phytoplanktivorous silver carp in a sub-chronic toxicity experiment

    International Nuclear Information System (INIS)

    Xie Liqiang; Xie Ping; Ozawa, Kazuhiko; Honma, Takamitsu; Yokoyama, Atsushi; Park, Ho-Dong

    2004-01-01

    A sub-chronic toxicity experiment was conducted to examine tissue distribution and depuration of two microcystins (microcystin-LR and microcystin -RR) in the phytoplanktivorous filter-feeding silver carp during a course of 80 days. Two large tanks (A, B) were used, and in Tank A, the fish were fed naturally with fresh Microcystis viridis cells (collected from a eutrophic pond) throughout the experiment, while in Tank B, the food of the fish were M. viridis cells for the first 40 days and then changed to artificial carp feed. High Performance Liquid Chromatography (HPLC) was used to measure MC-LR and MC-RR in the M. viridis cells, the seston, and the intestine, blood, liver and muscle tissue of silver carp at an interval of 20 days. MC-RR and MC-LR in the collected Microcystis cells varied between 268-580 and 110-292 μg g -1 DW, respectively. In Tank A, MC-RR and MC-LR varied between 41.5-99.5 and 6.9-15.8 μg g -1 DW in the seston, respectively. The maximum MC-RR in the blood, liver and muscle of the fish was 49.7, 17.8 and 1.77 μg g -1 DW, respectively. No MC-LR was detectable in the muscle and blood samples of the silver carp in spite of the abundant presence of this toxin in the intestines (for the liver, there was only one case when a relatively minor quantity was detected). These findings contrast with previous experimental results on rainbow trout. Perhaps silver carp has a mechanism to degrade MC-LR actively and to inhibit MC-LR transportation across the intestines. The depuration of MC-RR concentrations occurred slowly than uptakes in blood, liver and muscle, and the depuration rate was in the order of blood>liver>muscle. The grazing ability of silver carp on toxic cyanobacteria suggests an applicability of using phytoplanktivorous fish to counteract cyanotoxin contamination in eutrophic waters. - Silver carp are tolerant of cyanobacterial toxins, and might be used to control toxic algal blooms in highly eutrophic lakes

  13. Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.

    Science.gov (United States)

    Fragkaki, A G; Farmaki, E; Thomaidis, N; Tsantili-Kakoulidou, A; Angelis, Y S; Koupparis, M; Georgakopoulos, C

    2012-09-21

    The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

    Directory of Open Access Journals (Sweden)

    Adi Syahputra

    2014-03-01

    Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

  15. Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows.

    Science.gov (United States)

    Chandler, T L; Pralle, R S; Dórea, J R R; Poock, S E; Oetzel, G R; Fourdraine, R H; White, H M

    2018-03-01

    Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83

  16. 太冲穴古代文献应用分析%Application Analysis of Taichong(LR3) in the Ancient Literatures

    Institute of Scientific and Technical Information of China (English)

    王文琴; 张永臣; 贾红玲

    2013-01-01

    目的:通过对太冲古代文献的整理,总结太冲的应用规律。方法:以《中华医典》(第四版)收录的中医古籍1000部为检索范围,对太冲的主治病症、腧穴配伍、配伍主治病症、刺灸法、刺激量等文献进行系统整理,并建立数据库。结果:太冲应用符合纳入标准的有752条,治疗病症共有689条,涉及87部古籍。太冲配伍的使用频次共有1433次,与太冲穴配伍的经穴为167个。结论:太冲单穴适应于临床各科,尤善于治疗中医内科病症,前7位的内科病症为咳嗽、黄疸、痫病、呕吐、痹症、厥症、淋证;配伍后主治前10位的病症为痹症、胸痹、腹痛、血证、虚劳、黄疸、痉证、水肿、腰痛、中风;太冲的腧穴配伍以合谷、足三里、三阴交、行间、昆仑、照海、悬钟、中封、大敦、百会等经穴频数为较多,常用的配伍“穴对”为太冲与行间、太冲与合谷、太冲与三阴交;常用的刺灸法为灸法,针刺深度为针三分,针刺时间为十呼,灸法刺激量为三壮。%Objective:To sum up the application regularity of Taichong ( LR3) through studying the ancient lit-eratures about Taichong (LR3).Methods:We searched 1000 TCM Ancient Books in the “Encyclopedia of Tra-ditional Chinese”( the Fourth Edition ) and organized the information systematically including the primary disea-ses and symptoms of Taichong (LR3),the compatibility of acupionts, the primary diseases and symptoms of Tai-chong ( LR3 ) with compatible effects , the methods of needling and moxibustion , the stimulating quantity of nee-dling and moxibustion , with which we built a data base .Results:A total of 752 relevant articles of Taichong ( LR3) on the application and 689 primary diseases and symptoms were collected in accordance with the inclu-sive criteria in 87 TCM ancient books .The results also showed that the occurrence frequency of Taichong ( LR3

  17. A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet.

    Science.gov (United States)

    Brown, A M

    2001-06-01

    The objective of this present study was to introduce a simple, easily understood method for carrying out non-linear regression analysis based on user input functions. While it is relatively straightforward to fit data with simple functions such as linear or logarithmic functions, fitting data with more complicated non-linear functions is more difficult. Commercial specialist programmes are available that will carry out this analysis, but these programmes are expensive and are not intuitive to learn. An alternative method described here is to use the SOLVER function of the ubiquitous spreadsheet programme Microsoft Excel, which employs an iterative least squares fitting routine to produce the optimal goodness of fit between data and function. The intent of this paper is to lead the reader through an easily understood step-by-step guide to implementing this method, which can be applied to any function in the form y=f(x), and is well suited to fast, reliable analysis of data in all fields of biology.

  18. Omega-3 fatty acid docosahexaenoic acid increases SorLA/LR11, a sorting protein with reduced expression in sporadic Alzheimer's disease (AD): relevance to AD prevention.

    Science.gov (United States)

    Ma, Qiu-Lan; Teter, Bruce; Ubeda, Oliver J; Morihara, Takashi; Dhoot, Dilsher; Nyby, Michael D; Tuck, Michael L; Frautschy, Sally A; Cole, Greg M

    2007-12-26

    Environmental and genetic factors, notably ApoE4, contribute to the etiology of late-onset Alzheimer's disease (LOAD). Reduced mRNA and protein for an apolipoprotein E (ApoE) receptor family member, SorLA (LR11) has been found in LOAD but not early-onset AD, suggesting that LR11 loss is not secondary to pathology. LR11 is a neuronal sorting protein that reduces amyloid precursor protein (APP) trafficking to secretases that generate beta-amyloid (Abeta). Genetic polymorphisms that reduce LR11 expression are associated with increased AD risk. However these polymorphisms account for only a fraction of cases with LR11 deficits, suggesting involvement of environmental factors. Because lipoprotein receptors are typically lipid-regulated, we postulated that LR11 is regulated by docosahexaenoic acid (DHA), an essential omega-3 fatty acid related to reduced AD risk and reduced Abeta accumulation. In this study, we report that DHA significantly increases LR11 in multiple systems, including primary rat neurons, aged non-Tg mice and an aged DHA-depleted APPsw AD mouse model. DHA also increased LR11 in a human neuronal line. In vivo elevation of LR11 was also observed with dietary fish oil in young rats with insulin resistance, a model for type II diabetes, another AD risk factor. These data argue that DHA induction of LR11 does not require DHA-depleting diets and is not age dependent. Because reduced LR11 is known to increase Abeta production and may be a significant genetic cause of LOAD, our results indicate that DHA increases in SorLA/LR11 levels may play an important role in preventing LOAD.

  19. Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

    International Nuclear Information System (INIS)

    Jafri, Y.Z.; Kamal, L.

    2007-01-01

    Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

  20. A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

    Science.gov (United States)

    Brown, Angus M

    2006-04-01

    The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

  1. Photocatalytic degradation and mineralization of microcystin-LR under UV-A, solar and visible light using nanostructured nitrogen doped TiO2

    International Nuclear Information System (INIS)

    Triantis, T.M.; Fotiou, T.; Kaloudis, T.; Kontos, A.G.; Falaras, P.; Dionysiou, D.D.; Pelaez, M.; Hiskia, A.

    2012-01-01

    Highlights: ► N-TiO 2 exhibited effective degradation of MC-LR under UV-A, solar and visible light. ► Complete photocatalytic mineralization of MC-LR was achieved under UV-A and solar light. ► The organic nitrogen is mainly released as ammonium and nitrate ions. - Abstract: In an attempt to face serious environmental hazards, the degradation of microcystin-LR (MC-LR), one of the most common and more toxic water soluble cyanotoxin compounds released by cyanobacteria blooms, was investigated using nitrogen doped TiO 2 (N-TiO 2 ) photocatalyst, under UV-A, solar and visible light. Commercial Degussa P25 TiO 2 , Kronos and reference TiO 2 nanopowders were used for comparison. It was found that under UV-A irradiation, all photocatalysts were effective in toxin elimination. The higher MC-LR degradation (99%) was observed with Degussa P25 TiO 2 followed by N-TiO 2 with 96% toxin destruction after 20 min of illumination. Under solar light illumination, N-TiO 2 nanocatalyst exhibits similar photocatalytic activity with that of commercially available materials such as Degussa P25 and Kronos TiO 2 for the destruction of MC-LR. Upon irradiation with visible light Degussa P25 practically did not show any response, while the N-TiO 2 displayed remarkable photocatalytic efficiency. In addition, it has been shown that photodegradation products did not present any significant protein phosphatase inhibition activity, proving that toxicity is proportional only to the remaining MC-LR in solution. Finally, total organic carbon (TOC) and inorganic ions (NO 2 − , NO 3 − and NH 4 + ) determinations confirmed that complete photocatalytic mineralization of MC-LR was achieved under both UV-A and solar light.

  2. Inactivation Kinetics of the Cyanobacterial Toxin Microcystin-LR by Free Chlorine

    Science.gov (United States)

    Worldwide, the increasing occurrence of toxins produced by cyanobacteria in water bodies used as source waters for drinking water has become an important public health issue. Microcystin-LR is one of the most commonly found cyanotoxins. A detailed evaluation of the free chlorine ...

  3. Effect of Acupuncture at LR3 on Cerebral Glucose Metabolism in a Rat Model of Hypertension: A 18F-FDG-PET Study

    Directory of Open Access Journals (Sweden)

    Jing Li

    2018-01-01

    Full Text Available Our objective was to investigate the effect of acupuncture at LR3 on cerebral glucose metabolism in spontaneously hypertensive rats (SHRs. We used 18F-2-fluoro-deoxy-D-glucose positron emission tomography (18F-FDG-PET to examine the effects of acupuncture at LR3 on cerebral glucose metabolism in SHRs. SHRs were randomly allocated to receive no treatment (SHR group, needling at LR3 (SHR + LR3 group, or sham needling (SHR + sham group. Rats received 10 min acupuncture once per day for 7 days and were compared to normotensive Wistar Kyoto (WKY rats. Blood pressure (BP measurement and PET were performed after the first needling and the 7-day treatment period. BP was lower in the SHR + LR3 group compared to the other SHR groups between 30 and 60 min after the first needling and at 24 and 48 h after the 7-day treatment period. Glucose metabolism in the motor, sensory, and visual cortices was decreased in SHR group compared to WKY group. Needling at LR3 was associated with decreased glucose metabolism in the dorsal thalamus, thalamus, and hypothalamus and with increased metabolism in the cerebellar anterior and posterior lobes, medulla oblongata, and sensory cortex compared to the SHR group. These findings suggest that LR3 acupuncture improves hypertension through a mechanism involving altered brain activation in SHRs.

  4. Evaluation of Multiple Linear Regression-Based Limited Sampling Strategies for Enteric-Coated Mycophenolate Sodium in Adult Kidney Transplant Recipients.

    Science.gov (United States)

    Brooks, Emily K; Tett, Susan E; Isbel, Nicole M; McWhinney, Brett; Staatz, Christine E

    2018-04-01

    Although multiple linear regression-based limited sampling strategies (LSSs) have been published for enteric-coated mycophenolate sodium, none have been evaluated for the prediction of subsequent mycophenolic acid (MPA) exposure. This study aimed to examine the predictive performance of the published LSS for the estimation of future MPA area under the concentration-time curve from 0 to 12 hours (AUC0-12) in renal transplant recipients. Total MPA plasma concentrations were measured in 20 adult renal transplant patients on 2 occasions a week apart. All subjects received concomitant tacrolimus and were approximately 1 month after transplant. Samples were taken at 0, 0.33, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 6, and 8 hours and 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2, 3, 4, 6, 9, and 12 hours after dose on the first and second sampling occasion, respectively. Predicted MPA AUC0-12 was calculated using 19 published LSSs and data from the first or second sampling occasion for each patient and compared with the second occasion full MPA AUC0-12 calculated using the linear trapezoidal rule. Bias (median percentage prediction error) and imprecision (median absolute prediction error) were determined. Median percentage prediction error and median absolute prediction error for the prediction of full MPA AUC0-12 were multiple linear regression-based LSS was not possible without concentrations up to at least 8 hours after the dose.

  5. Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

    Science.gov (United States)

    Fisz, Jacek J

    2006-12-07

    The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear

  6. Comparative Studies on the pH Dependence of DOW of Microcystin-RR and -LR Using LC-MS

    Directory of Open Access Journals (Sweden)

    Gaodao Liang

    2011-01-01

    Full Text Available Microcystins (MCs are well known worldwide as hepatotoxins produced by cyanobacteria, but little is known about the physicochemical properties of these compounds. The dependence of the n-octanol/water distribution ratio (DOW of MC-RR and -LR to pH was measured by high-performance liquid chromatography combined with mass spectrometry (LC-MS. There was a remarkable difference in such relationships between MC-RR and -LR. The log DOW of MC-LR decreased from 1.63 at pH 1.0 to -1.26 at pH 6.5, and stabilized between -1.04 and -1.56 at a pH of 6.5~12.0; log DOW of MC-RR varied between -1.24 and -0.67 at a pH of 1.00~4.00, and stabilized between -1.20 and -1.54 at a pH of 4.00~12.00. The difference of hydrophobicity in acidic condition between MC-RR and -LR is important, not only for the analytical method of both toxins, but perhaps also for understanding the difference of toxicity to animals between the two toxins.

  7. Time series linear regression of half-hourly radon levels in a residence

    International Nuclear Information System (INIS)

    Hull, D.A.

    1990-01-01

    This paper uses time series linear regression modelling to assess the impact of temperature and pressure differences on the radon measured in the basement and in the basement drain of a research house in the Princeton area of New Jersey. The models examine half-hour averages of several climate and house parameters for several periods of up to 11 days. The drain radon concentrations follow a strong diurnal pattern that shifts 12 hours in phase between the summer and the fall seasons. This shift can be linked both to the change in temperature differences between seasons and to an experiment which involved sealing the connection between the drain and the basement. We have found that both the basement and the drain radon concentrations are correlated to basement-outdoor and soil-outdoor temperature differences (the coefficient of determination varies between 0.6 and 0.8). The statistical models for the summer periods clearly describe a physical system where the basement drain pumps radon in during the night and sucks radon out during the day

  8. An Ionospheric Index Model based on Linear Regression and Neural Network Approaches

    Science.gov (United States)

    Tshisaphungo, Mpho; McKinnell, Lee-Anne; Bosco Habarulema, John

    2017-04-01

    The ionosphere is well known to reflect radio wave signals in the high frequency (HF) band due to the present of electron and ions within the region. To optimise the use of long distance HF communications, it is important to understand the drivers of ionospheric storms and accurately predict the propagation conditions especially during disturbed days. This paper presents the development of an ionospheric storm-time index over the South African region for the application of HF communication users. The model will result into a valuable tool to measure the complex ionospheric behaviour in an operational space weather monitoring and forecasting environment. The development of an ionospheric storm-time index is based on a single ionosonde station data over Grahamstown (33.3°S,26.5°E), South Africa. Critical frequency of the F2 layer (foF2) measurements for a period 1996-2014 were considered for this study. The model was developed based on linear regression and neural network approaches. In this talk validation results for low, medium and high solar activity periods will be discussed to demonstrate model's performance.

  9. Development of statistical linear regression model for metals from transportation land uses.

    Science.gov (United States)

    Maniquiz, Marla C; Lee, Soyoung; Lee, Eunju; Kim, Lee-Hyung

    2009-01-01

    The transportation landuses possessing impervious surfaces such as highways, parking lots, roads, and bridges were recognized as the highly polluted non-point sources (NPSs) in the urban areas. Lots of pollutants from urban transportation are accumulating on the paved surfaces during dry periods and are washed-off during a storm. In Korea, the identification and monitoring of NPSs still represent a great challenge. Since 2004, the Ministry of Environment (MOE) has been engaged in several researches and monitoring to develop stormwater management policies and treatment systems for future implementation. The data over 131 storm events during May 2004 to September 2008 at eleven sites were analyzed to identify correlation relationships between particulates and metals, and to develop simple linear regression (SLR) model to estimate event mean concentration (EMC). Results indicate that there was no significant relationship between metals and TSS EMC. However, the SLR estimation models although not providing useful results are valuable indicators of high uncertainties that NPS pollution possess. Therefore, long term monitoring employing proper methods and precise statistical analysis of the data should be undertaken to eliminate these uncertainties.

  10. LR1: a candidate RNA virus of Leishmania.

    OpenAIRE

    Tarr, P I; Aline, R F; Smiley, B L; Scholler, J; Keithly, J; Stuart, K

    1988-01-01

    Although viruses are important biological agents and useful molecular tools, little is known about the viruses of parasites. We report here the discovery of a candidate for an RNA virus in a kinetoplastid parasite. This potential virus, which we term LR1, is present in the promastigote form of the human pathogen Leishmania braziliensis guyanensis CUMC1-1A but not in 11 other stocks of Leishmania that were examined nor in Trypanosoma brucei. The candidate viral RNA has a size of approximately ...

  11. Modelos de regressão não linear aplicados a grupos de acessos de alho

    OpenAIRE

    Reis, Renata M; Cecon, Paulo R; Puiatti, Mário; Finger, Fernando L; Nascimento, Moysés; Silva, Fabyano F; Carneiro, Antônio PS; Silva, Anderson R

    2014-01-01

    O principal objetivo deste estudo foi comparar modelos de regressão não linear aptos a descreverem o acúmulo de massa seca de diferentes partes da planta do alho ao longo do tempo (60, 90, 120 e 150 dias após plantio). Objetivou-se também identificar acessos semelhantes em relação às características avaliadas por meio de análises de agrupamento. Foram utilizados 20 acessos de alho pertencentes ao Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa (BGH/UFV). O teor de massa s...

  12. Generalized Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions

    DEFF Research Database (Denmark)

    Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf

    2017-01-01

    Large data sets that originate from administrative or operational activity are increasingly used for statistical analysis as they often contain very precise information and a large number of observations. But there is evidence that some variables can be subject to severe misclassification...... or contain missing values. Given the size of the data, a flexible semiparametric misclassification model would be good choice but their use in practise is scarce. To close this gap a semiparametric model for the probability of observing labour market transitions is estimated using a sample of 20 m...... observations from Germany. It is shown that estimated marginal effects of a number of covariates are sizeably affected by misclassification and missing values in the analysis data. The proposed generalized partially linear regression extends existing models by allowing a misclassified discrete covariate...

  13. hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression.

    Science.gov (United States)

    Wang, Pu; Ge, Ruiquan; Xiao, Xuan; Zhou, Manli; Zhou, Fengfeng

    2017-01-01

    Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label, and the multi-label classification problem remains to be a challenge for biomedical researchers. This study proposes a novel multi-label learning algorithm, hMuLab, by integrating both feature-based and neighbor-based similarity scores. The multiple linear regression modeling techniques make hMuLab capable of producing multiple label assignments for a query sample. The comparison results over six commonly-used multi-label performance measurements suggest that hMuLab performs accurately and stably for the biomedical datasets, and may serve as a complement to the existing literature.

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

    Science.gov (United States)

    Chen, Qingxia; Ibrahim, Joseph G

    2014-07-01

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

  15. Bulk etch rate of LR-115 cellulose nitrate film

    International Nuclear Information System (INIS)

    Harris, M.J.; Schlenker, R.A.

    1977-01-01

    Bulk etch rate (V/sub b/) of Kodak LR-115 cellulose nitrate film has been studied, and values for the parameter are presented. An interesting variability of V/sub b/ has been found which has implications for microdosimetry using this type of integrating nuclear track detector. Short-term and longer-term thickness changes have been observed which may increase the uncertainty in estimations of dose using this type of detector

  16. The Lr34 adult plant rust resistance gene provides seedling resistance in durum wheat without senescence.

    Science.gov (United States)

    Rinaldo, Amy; Gilbert, Brian; Boni, Rainer; Krattinger, Simon G; Singh, Davinder; Park, Robert F; Lagudah, Evans; Ayliffe, Michael

    2017-07-01

    The hexaploid wheat (Triticum aestivum) adult plant resistance gene, Lr34/Yr18/Sr57/Pm38/Ltn1, provides broad-spectrum resistance to wheat leaf rust (Lr34), stripe rust (Yr18), stem rust (Sr57) and powdery mildew (Pm38) pathogens, and has remained effective in wheat crops for many decades. The partial resistance provided by this gene is only apparent in adult plants and not effective in field-grown seedlings. Lr34 also causes leaf tip necrosis (Ltn1) in mature adult plant leaves when grown under field conditions. This D genome-encoded bread wheat gene was transferred to tetraploid durum wheat (T. turgidum) cultivar Stewart by transformation. Transgenic durum lines were produced with elevated gene expression levels when compared with the endogenous hexaploid gene. Unlike nontransgenic hexaploid and durum control lines, these transgenic plants showed robust seedling resistance to pathogens causing wheat leaf rust, stripe rust and powdery mildew disease. The effectiveness of seedling resistance against each pathogen correlated with the level of transgene expression. No evidence of accelerated leaf necrosis or up-regulation of senescence gene markers was apparent in these seedlings, suggesting senescence is not required for Lr34 resistance, although leaf tip necrosis occurred in mature plant flag leaves. Several abiotic stress-response genes were up-regulated in these seedlings in the absence of rust infection as previously observed in adult plant flag leaves of hexaploid wheat. Increasing day length significantly increased Lr34 seedling resistance. These data demonstrate that expression of a highly durable, broad-spectrum adult plant resistance gene can be modified to provide seedling resistance in durum wheat. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

  17. Cell density dependence of Microcystis aeruginosa responses to copper algaecide concentrations: Implications for microcystin-LR release.

    Science.gov (United States)

    Kinley, Ciera M; Iwinski, Kyla J; Hendrikse, Maas; Geer, Tyler D; Rodgers, John H

    2017-11-01

    Along with mechanistic models, predictions of exposure-response relationships for copper are often derived from laboratory toxicity experiments with standardized experimental exposures and conditions. For predictions of copper toxicity to algae, cell density is a critical factor often overlooked. For pulse exposures of copper-based algaecides in aquatic systems, cell density can significantly influence copper sorbed by the algal population, and consequent responses. A cyanobacterium, Microcystis aeruginosa, was exposed to a copper-based algaecide over a range of cell densities to model the density-dependence of exposures, and effects on microcystin-LR (MC-LR) release. Copper exposure concentrations were arrayed to result in a gradient of MC-LR release, and masses of copper sorbed to algal populations were measured following exposures. While copper exposure concentrations eliciting comparable MC-LR release ranged an order of magnitude (24-h EC50s 0.03-0.3mg Cu/L) among cell densities of 10 6 through 10 7 cells/mL, copper doses (mg Cu/mg algae) were similar (24-h EC50s 0.005-0.006mg Cu/mg algae). Comparisons of MC-LR release as a function of copper exposure concentrations and doses provided a metric of the density dependence of algal responses in the context of copper-based algaecide applications. Combined with estimates of other site-specific factors (e.g. water characteristics) and fate processes (e.g. dilution and dispersion, sorption to organic matter and sediments), measuring exposure-response relationships for specific cell densities can refine predictions for in situ exposures and algal responses. These measurements can in turn decrease the likelihood of amending unnecessary copper concentrations to aquatic systems, and minimize risks for non-target aquatic organisms. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Experimental study on the aging process of the LR 115 cellulose nitrate radon detector

    International Nuclear Information System (INIS)

    Siems, M.; Freyer, K.; Treutler, H.-C.; Joensson, G.; Enge, W.

    2001-01-01

    An experimental determination of the aging process of cellulose nitrate detector material was based on the examination of special properties of the LR 115 solid state nuclear track detectors (SSNTDs) of various ages up to 18 years. The examined relevant parameters are the bulk etching rate v b and the track etching rate v t . These parameters are responsible for the appearance, the size and the registration efficiency of tracks of α-particles from radon gas in the detector. To find a correlation between these material parameters and the detector sensitivity an experimental calibration of indoor room and outdoor soil detector devices based on LR 115 took place at the Umweltforschungszentrum Leipzig-Halle (Germany). To avoid routine calibration work in external radon exposure facilities a correction of the age dependent calibration factors with the material parameters measured in one's own laboratory was targeted. In this study a general age dependence, however, was not found. The following statements for practical applications can be made. (i) the bulk etching rate v b for detectors of the same batch has a depth dependence and this dependence is constant over 2 years (LR 115 September 1994). (ii) detectors of different batches older than 5 years and stored at room temperature show an odd v b behaviour when v b is used for describing track shapes. (iii) the calibration factor of detectors of different batches that were stored at about +4 deg. C is constant over 5 years (LR 115 September 1994 and February 1999, Table 2). The conclusion is that LR 115 detectors not older than 5 years and stored in a refrigerator at about +4 deg. C should be preferred for radon measurements. Furthermore these detectors should be recalibrated every year and the microscope work of this calibrations should be performed by the same person who performs the measurements. In addition, a phenomenon related to fundamental track formation mechanisms was found, that the time straggling of the

  19. A modified approach to estimating sample size for simple logistic regression with one continuous covariate.

    Science.gov (United States)

    Novikov, I; Fund, N; Freedman, L S

    2010-01-15

    Different methods for the calculation of sample size for simple logistic regression (LR) with one normally distributed continuous covariate give different results. Sometimes the difference can be large. Furthermore, some methods require the user to specify the prevalence of cases when the covariate equals its population mean, rather than the more natural population prevalence. We focus on two commonly used methods and show through simulations that the power for a given sample size may differ substantially from the nominal value for one method, especially when the covariate effect is large, while the other method performs poorly if the user provides the population prevalence instead of the required parameter. We propose a modification of the method of Hsieh et al. that requires specification of the population prevalence and that employs Schouten's sample size formula for a t-test with unequal variances and group sizes. This approach appears to increase the accuracy of the sample size estimates for LR with one continuous covariate.

  20. A Seemingly Unrelated Poisson Regression Model

    OpenAIRE

    King, Gary

    1989-01-01

    This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

  1. Detection of trace microcystin-LR on a 20 MHz QCM sensor coated with in situ self-assembled MIPs.

    Science.gov (United States)

    He, Hao; Zhou, Lianqun; Wang, Yi; Li, Chuanyu; Yao, Jia; Zhang, Wei; Zhang, Qingwen; Li, Mingyu; Li, Haiwen; Dong, Wen-fei

    2015-01-01

    A 20 MHz quartz crystal microbalance (QCM) sensor coated with in situ self-assembled molecularly imprinted polymers (MIPs) was presented for the detection of trace microcystin-LR (MC-LR) in drinking water. The sensor performance obtained using the in situ self-assembled MIPs was compared with traditionally synthesized MIPs on 20 MHz and normal 10 MHz QCM chip. The results show that the response increases by more than 60% when using the in situ self-assembly method compared using the traditionally method while the 20 MHz QCM chip provides four-fold higher response than the 10 MHz one. Therefore, the in situ self-assembled MIPs coated on a high frequency QCM chip was used in the sensor performance test to detect MC-LR in tap water. It showed a limit of detection (LOD) of 0.04 nM which is lower than the safety guideline level (1 nM MC-LR) of drinking water in China. The low sensor response to other analogs indicated the high specificity of the sensor to MC-LR. The sensor showed high stability and low signal variation less than 2.58% after regeneration. The lake water sample analysis shows the sensor is possible for practical use. The combination of the higher frequency QCM with the in situ self-assembled MIPs provides a good candidate for the detection of other small molecules. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. Degradation mechanisms of Microcystin-LR during UV-B photolysis and UV/H2O2 processes: Byproducts and pathways.

    Science.gov (United States)

    Moon, Bo-Ram; Kim, Tae-Kyoung; Kim, Moon-Kyung; Choi, Jaewon; Zoh, Kyung-Duk

    2017-10-01

    The removal and degradation pathways of microcystin-LR (MC-LR, [M+H] +  = 995.6) in UV-B photolysis and UV-B/H 2 O 2 processes were examined using liquid chromatography-tandem mass spectrometry. The UV/H 2 O 2 process was more efficient than UV-B photolysis for MC-LR removal. Eight by-products were newly identified in the UV-B photolysis ([M+H] +  = 414.3, 417.3, 709.6, 428.9, 608.6, 847.5, 807.4, and 823.6), and eleven by-products were identified in the UV-B/H 2 O 2 process ([M+H] +  = 707.4, 414.7, 429.3, 445.3, 608.6, 1052.0, 313.4, 823.6, 357.3, 245.2, and 805.7). Most of the MC-LR by-products had lower [M+H] + values than the MC-LR itself during both processes, except for the [M+H] + value of 1052.0 during UV-B photolysis. Based on identified by-products and peak area patterns, we proposed potential degradation pathways during the two processes. Bond cleavage and intramolecular electron rearrangement by electron pair in the nitrogen atom were the major reactions during UV-B photolysis and UV-B/H 2 O 2 processes, and hydroxylation by OH radical and the adduct formation reaction between the produced by-products were identified as additional pathways during the UV-B/H 2 O 2 process. Meanwhile, the degradation by-products identified from MC-LR during UV-B/H 2 O 2 process can be further degraded by increasing H 2 O 2 dose. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Photocatalytic degradation and mineralization of microcystin-LR under UV-A, solar and visible light using nanostructured nitrogen doped TiO{sub 2}

    Energy Technology Data Exchange (ETDEWEB)

    Triantis, T.M.; Fotiou, T. [Laboratory of Catalytic - Photocatalytic Processes (Solar Energy - Environment), Institute of Physical Chemistry, National Center for Scientific Research ' Demokritos' , Neapoleos 25, 15310 Agia Paraskevi, Attiki (Greece); Kaloudis, T. [Organic Micropollutants Laboratory, Athens Water Supply and Sewerage Company (EYDAP SA), WTP Aharnon, Menidi (Greece); Kontos, A.G.; Falaras, P. [Laboratory of Photo-redox Conversion and Storage of Solar Energy, Institute of Physical Chemistry, National Center for Scientific Research ' Demokritos' , Neapoleos 25, 15310 Agia Paraskevi, Attiki (Greece); Dionysiou, D.D.; Pelaez, M. [Environmental Engineering and Science Program, School of Energy, Environmental, Biological and Medical Engineering, University of Cincinnati, OH 45221-0012 (United States); Hiskia, A., E-mail: hiskia@chem.demokritos.gr [Laboratory of Catalytic - Photocatalytic Processes (Solar Energy - Environment), Institute of Physical Chemistry, National Center for Scientific Research ' Demokritos' , Neapoleos 25, 15310 Agia Paraskevi, Attiki (Greece)

    2012-04-15

    Highlights: Black-Right-Pointing-Pointer N-TiO{sub 2} exhibited effective degradation of MC-LR under UV-A, solar and visible light. Black-Right-Pointing-Pointer Complete photocatalytic mineralization of MC-LR was achieved under UV-A and solar light. Black-Right-Pointing-Pointer The organic nitrogen is mainly released as ammonium and nitrate ions. - Abstract: In an attempt to face serious environmental hazards, the degradation of microcystin-LR (MC-LR), one of the most common and more toxic water soluble cyanotoxin compounds released by cyanobacteria blooms, was investigated using nitrogen doped TiO{sub 2} (N-TiO{sub 2}) photocatalyst, under UV-A, solar and visible light. Commercial Degussa P25 TiO{sub 2}, Kronos and reference TiO{sub 2} nanopowders were used for comparison. It was found that under UV-A irradiation, all photocatalysts were effective in toxin elimination. The higher MC-LR degradation (99%) was observed with Degussa P25 TiO{sub 2} followed by N-TiO{sub 2} with 96% toxin destruction after 20 min of illumination. Under solar light illumination, N-TiO{sub 2} nanocatalyst exhibits similar photocatalytic activity with that of commercially available materials such as Degussa P25 and Kronos TiO{sub 2} for the destruction of MC-LR. Upon irradiation with visible light Degussa P25 practically did not show any response, while the N-TiO{sub 2} displayed remarkable photocatalytic efficiency. In addition, it has been shown that photodegradation products did not present any significant protein phosphatase inhibition activity, proving that toxicity is proportional only to the remaining MC-LR in solution. Finally, total organic carbon (TOC) and inorganic ions (NO{sub 2}{sup -}, NO{sub 3}{sup -} and NH{sub 4}{sup +}) determinations confirmed that complete photocatalytic mineralization of MC-LR was achieved under both UV-A and solar light.

  4. Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques

    Science.gov (United States)

    Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing

    2014-01-01

    Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas. PMID:24936948

  5. Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques

    Directory of Open Access Journals (Sweden)

    Wen-Yuan Chen

    2014-06-01

    Full Text Available Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1 we use a terrain drop compensation (TDC technique to solve the problem of the concavity of railway crossings; (2 we use a linear regression technique to predict the position and length of an object from image processing; (3 we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas.

  6. Weibull and lognormal Taguchi analysis using multiple linear regression

    International Nuclear Information System (INIS)

    Piña-Monarrez, Manuel R.; Ortiz-Yañez, Jesús F.

    2015-01-01

    The paper provides to reliability practitioners with a method (1) to estimate the robust Weibull family when the Taguchi method (TM) is applied, (2) to estimate the normal operational Weibull family in an accelerated life testing (ALT) analysis to give confidence to the extrapolation and (3) to perform the ANOVA analysis to both the robust and the normal operational Weibull family. On the other hand, because the Weibull distribution neither has the normal additive property nor has a direct relationship with the normal parameters (µ, σ), in this paper, the issues of estimating a Weibull family by using a design of experiment (DOE) are first addressed by using an L_9 (3"4) orthogonal array (OA) in both the TM and in the Weibull proportional hazard model approach (WPHM). Then, by using the Weibull/Gumbel and the lognormal/normal relationships and multiple linear regression, the direct relationships between the Weibull and the lifetime parameters are derived and used to formulate the proposed method. Moreover, since the derived direct relationships always hold, the method is generalized to the lognormal and ALT analysis. Finally, the method’s efficiency is shown through its application to the used OA and to a set of ALT data. - Highlights: • It gives the statistical relations and steps to use the Taguchi Method (TM) to analyze Weibull data. • It gives the steps to determine the unknown Weibull family to both the robust TM setting and the normal ALT level. • It gives a method to determine the expected lifetimes and to perform its ANOVA analysis in TM and ALT analysis. • It gives a method to give confidence to the extrapolation in an ALT analysis by using the Weibull family of the normal level.

  7. Introduction to generalized linear models

    CERN Document Server

    Dobson, Annette J

    2008-01-01

    Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Sampling Distribution for the Deviance Hypothesis Testing Normal Linear Models Introduction Basic Results Multiple Linear Regression Analysis of Variance Analysis of Covariance General Linear Models Binary Variables and Logistic Regression Probability Distributions ...

  8. Linear regression models for quantitative assessment of left ...

    African Journals Online (AJOL)

    Changes in left ventricular structures and function have been reported in cardiomyopathies. No prediction models have been established in this environment. This study established regression models for prediction of left ventricular structures in normal subjects. A sample of normal subjects was drawn from a large urban ...

  9. Using multiple linear regression techniques to quantify carbon ...

    African Journals Online (AJOL)

    Fallow ecosystems provide a significant carbon stock that can be quantified for inclusion in the accounts of global carbon budgets. Process and statistical models of productivity, though useful, are often technically rigid as the conditions for their application are not easy to satisfy. Multiple regression techniques have been ...

  10. Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study.

    Science.gov (United States)

    Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf

    2015-10-01

    The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200-400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset. © The Author 2014. Published by Oxford University Press.

  11. Direct integral linear least square regression method for kinetic evaluation of hepatobiliary scintigraphy

    International Nuclear Information System (INIS)

    Shuke, Noriyuki

    1991-01-01

    In hepatobiliary scintigraphy, kinetic model analysis, which provides kinetic parameters like hepatic extraction or excretion rate, have been done for quantitative evaluation of liver function. In this analysis, unknown model parameters are usually determined using nonlinear least square regression method (NLS method) where iterative calculation and initial estimate for unknown parameters are required. As a simple alternative to NLS method, direct integral linear least square regression method (DILS method), which can determine model parameters by a simple calculation without initial estimate, is proposed, and tested the applicability to analysis of hepatobiliary scintigraphy. In order to see whether DILS method could determine model parameters as good as NLS method, or to determine appropriate weight for DILS method, simulated theoretical data based on prefixed parameters were fitted to 1 compartment model using both DILS method with various weightings and NLS method. The parameter values obtained were then compared with prefixed values which were used for data generation. The effect of various weights on the error of parameter estimate was examined, and inverse of time was found to be the best weight to make the error minimum. When using this weight, DILS method could give parameter values close to those obtained by NLS method and both parameter values were very close to prefixed values. With appropriate weighting, the DILS method could provide reliable parameter estimate which is relatively insensitive to the data noise. In conclusion, the DILS method could be used as a simple alternative to NLS method, providing reliable parameter estimate. (author)

  12. Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model

    Science.gov (United States)

    Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei

    2014-10-01

    Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

  13. Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

    Science.gov (United States)

    Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

    2010-05-01

    This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

  14. Standards for Standardized Logistic Regression Coefficients

    Science.gov (United States)

    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…

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

    Science.gov (United States)

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

    2018-02-01

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

  16. Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

    Directory of Open Access Journals (Sweden)

    SASSAN MOHAMMADY

    2013-01-01

    Full Text Available Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR is equal to 88.91%. Percent Correct Match (PCM and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively.

  17. Effects of new neutral currents at linear electron-positron colliders

    International Nuclear Information System (INIS)

    Pankov, A.A.

    2002-01-01

    Effects that are induced by contact four-fermion interactions in the processes e + e - → μ + μ - , b-barb, and c-barc at √(s) = 0.5 TeV linear electron-positron colliders are investigated for the case of longitudinally polarized initial beams. This analysis employs new integrated observables constructed from the polarized cross sections for the scattering of final fermions into the forward (σ F ) and the backward (σ B ) hemisphere in such a way that they single out the helicity cross sections for the processes in question. This property of the observables makes it possible to perform, in the most general form, a model-independent analysis of contact four-fermion interactions and to set constraints on their parameters. It is also shown that the sensitivity of new polarization observables to contact interactions is noticeably higher than the corresponding sensitivity of canonical observables like σ, A FB , A LR , and A LR,FB

  18. Understanding logistic regression analysis

    OpenAIRE

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

  19. Time-adaptive quantile regression

    DEFF Research Database (Denmark)

    Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

    2008-01-01

    and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

  20. Application of single-step genomic best linear unbiased prediction with a multiple-lactation random regression test-day model for Japanese Holsteins.

    Science.gov (United States)

    Baba, Toshimi; Gotoh, Yusaku; Yamaguchi, Satoshi; Nakagawa, Satoshi; Abe, Hayato; Masuda, Yutaka; Kawahara, Takayoshi

    2017-08-01

    This study aimed to evaluate a validation reliability of single-step genomic best linear unbiased prediction (ssGBLUP) with a multiple-lactation random regression test-day model and investigate an effect of adding genotyped cows on the reliability. Two data sets for test-day records from the first three lactations were used: full data from February 1975 to December 2015 (60 850 534 records from 2 853 810 cows) and reduced data cut off in 2011 (53 091 066 records from 2 502 307 cows). We used marker genotypes of 4480 bulls and 608 cows. Genomic enhanced breeding values (GEBV) of 305-day milk yield in all the lactations were estimated for at least 535 young bulls using two marker data sets: bull genotypes only and both bulls and cows genotypes. The realized reliability (R 2 ) from linear regression analysis was used as an indicator of validation reliability. Using only genotyped bulls, R 2 was ranged from 0.41 to 0.46 and it was always higher than parent averages. The very similar R 2 were observed when genotyped cows were added. An application of ssGBLUP to a multiple-lactation random regression model is feasible and adding a limited number of genotyped cows has no significant effect on reliability of GEBV for genotyped bulls. © 2016 Japanese Society of Animal Science.

  1. Influence of the datasets size on the stability of the LR in the lower region of the within source distribution

    NARCIS (Netherlands)

    Haraksim, Rudolf; Meuwly, Didier

    2013-01-01

    This article focuses on the statistical evaluation of the fingermark evidence using the likelihood ratio (LR) approach. It studies the influence of the quantity of data used to model the within (WS) and between (BS) source variability. The LR system built for the experiment uses an Automated

  2. Identification of leaf rust resistant gene Lr10 in Pakistani wheat ...

    African Journals Online (AJOL)

    Leaf (brown) rust is the major disease of wheat in Pakistan and other countries. The disease is more effectively controlled when several rust resistance genes are pyramided into a single line. Molecular survey was conducted to screen 25 Pakistan wheat germplasm for the presence of leaf rust resistance gene Lr10 using ...

  3. Comparison of height-diameter models based on geographically weighted regressions and linear mixed modelling applied to large scale forest inventory data

    Energy Technology Data Exchange (ETDEWEB)

    Quirós Segovia, M.; Condés Ruiz, S.; Drápela, K.

    2016-07-01

    Aim of the study: The main objective of this study was to test Geographically Weighted Regression (GWR) for developing height-diameter curves for forests on a large scale and to compare it with Linear Mixed Models (LMM). Area of study: Monospecific stands of Pinus halepensis Mill. located in the region of Murcia (Southeast Spain). Materials and Methods: The dataset consisted of 230 sample plots (2582 trees) from the Third Spanish National Forest Inventory (SNFI) randomly split into training data (152 plots) and validation data (78 plots). Two different methodologies were used for modelling local (Petterson) and generalized height-diameter relationships (Cañadas I): GWR, with different bandwidths, and linear mixed models. Finally, the quality of the estimated models was compared throughout statistical analysis. Main results: In general, both LMM and GWR provide better prediction capability when applied to a generalized height-diameter function than when applied to a local one, with R2 values increasing from around 0.6 to 0.7 in the model validation. Bias and RMSE were also lower for the generalized function. However, error analysis showed that there were no large differences between these two methodologies, evidencing that GWR provides results which are as good as the more frequently used LMM methodology, at least when no additional measurements are available for calibrating. Research highlights: GWR is a type of spatial analysis for exploring spatially heterogeneous processes. GWR can model spatial variation in tree height-diameter relationship and its regression quality is comparable to LMM. The advantage of GWR over LMM is the possibility to determine the spatial location of every parameter without additional measurements. Abbreviations: GWR (Geographically Weighted Regression); LMM (Linear Mixed Model); SNFI (Spanish National Forest Inventory). (Author)

  4. A primer on linear models

    CERN Document Server

    Monahan, John F

    2008-01-01

    Preface Examples of the General Linear Model Introduction One-Sample Problem Simple Linear Regression Multiple Regression One-Way ANOVA First Discussion The Two-Way Nested Model Two-Way Crossed Model Analysis of Covariance Autoregression Discussion The Linear Least Squares Problem The Normal Equations The Geometry of Least Squares Reparameterization Gram-Schmidt Orthonormalization Estimability and Least Squares Estimators Assumptions for the Linear Mean Model Confounding, Identifiability, and Estimability Estimability and Least Squares Estimators F

  5. Vectors, a tool in statistical regression theory

    NARCIS (Netherlands)

    Corsten, L.C.A.

    1958-01-01

    Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding

  6. alpha-particle radioactivity from LR 115 by two methods of analysis

    CERN Document Server

    Azkour, K; Adloff, J C; Pape, A

    1999-01-01

    LR115 track detectors were exposed to samples of Moroccan phosphate and phosphogypsum to measure their alpha-particle radioactivity. Then two formalisms were used for the dosimetry: simulation by a Monte Carlo method and determination of concentrations from a numerically integrated track registration equation. The results were compared with those deduced gamma-ray spectrometry.

  7. Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2014-08-27

    State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study. Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.

  8. WWER safety investigations on LR-0 reactor

    International Nuclear Information System (INIS)

    Mikus, J.

    2001-01-01

    A set of the measurement needed for the WWER-440 and WWER-1000 reactor lifetime assessment, verification of the methods, codes and input cross section libraries for the WWER reactor pressure vessel exposure evaluation has been performed on the LR-0 experimental reactor. The WWER Mock-ups (engineering benchmarks) has been carried out on the reactor, with the aim to investigate differential neutron spectra for reactor dosimetry purposes. Critical experiments have also been performed to determine the perturbation of the fission density distribution caused by the WWER-440 control assembly. Such assembly, partially inserted in the core, has significant influence on the space power distribution. A wide research program for sub-criticality investigations of the spent nuclear fuel storage has been realized on the LR-0 reactor. A benchmark experiment is realized on the reactor in corresponding geometry for CASTOR 440/84 container for storage and transportation of spent fuel. Critical experiments with new fuel assemblies including various burnable absorbers and different enrichments are performed. A set of critical experiments is performed using the fuel assemblies with 3,6% and 4,4% enrichment, arranged in the WWER-440 type cores with various lattice pitch. The critical high of the moderator level and the moderator level coefficient of reactivity are measured and the effect of the fuel assembly, placed in a hexagonal tube of stainless steel containing boron absorber (ATABOR - STANDARD) is investigated. The obtained results are used for the validation of the codes (MCNP, KENO and SCALE) in the frame of the contract 'Burn-up credit implementation for the storage and transport containers of the spent fuel'. Combined neutron-gamma spectra measurements in the WWER-1000 Mock-up are carried out during 2001

  9. A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

    Science.gov (United States)

    Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

    2016-10-01

    We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms ( 13 C 2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C 2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C 2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  10. Aortic and Hepatic Contrast Enhancement During Hepatic-Arterial and Portal Venous Phase Computed Tomography Scanning: Multivariate Linear Regression Analysis Using Age, Sex, Total Body Weight, Height, and Cardiac Output.

    Science.gov (United States)

    Masuda, Takanori; Nakaura, Takeshi; Funama, Yoshinori; Higaki, Toru; Kiguchi, Masao; Imada, Naoyuki; Sato, Tomoyasu; Awai, Kazuo

    We evaluated the effect of the age, sex, total body weight (TBW), height (HT) and cardiac output (CO) of patients on aortic and hepatic contrast enhancement during hepatic-arterial phase (HAP) and portal venous phase (PVP) computed tomography (CT) scanning. This prospective study received institutional review board approval; prior informed consent to participate was obtained from all 168 patients. All were examined using our routine protocol; the contrast material was 600 mg/kg iodine. Cardiac output was measured with a portable electrical velocimeter within 5 minutes of starting the CT scan. We calculated contrast enhancement (per gram of iodine: [INCREMENT]HU/gI) of the abdominal aorta during the HAP and of the liver parenchyma during the PVP. We performed univariate and multivariate linear regression analysis between all patient characteristics and the [INCREMENT]HU/gI of aortic- and liver parenchymal enhancement. Univariate linear regression analysis demonstrated statistically significant correlations between the [INCREMENT]HU/gI and the age, sex, TBW, HT, and CO (all P linear regression analysis showed that only the TBW and CO were of independent predictive value (P linear regression analysis only the TBW and CO were significantly correlated with aortic and liver parenchymal enhancement; the age, sex, and HT were not. The CO was the only independent factor affecting aortic and liver parenchymal enhancement at hepatic CT when the protocol was adjusted for the TBW.

  11. Least-Squares Linear Regression and Schrodinger's Cat: Perspectives on the Analysis of Regression Residuals.

    Science.gov (United States)

    Hecht, Jeffrey B.

    The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…

  12. Electricity consumption forecasting in Italy using linear regression models

    Energy Technology Data Exchange (ETDEWEB)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)

    2009-09-15

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  13. Electricity consumption forecasting in Italy using linear regression models

    International Nuclear Information System (INIS)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

    2009-01-01

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  14. Lettuce (Lactuca sativa L.) leaf-proteome profiles after exposure to cylindrospermopsin and a microcystin-LR/cylindrospermopsin mixture: a concentration-dependent response.

    Science.gov (United States)

    Freitas, Marisa; Campos, Alexandre; Azevedo, Joana; Barreiro, Aldo; Planchon, Sébastien; Renaut, Jenny; Vasconcelos, Vitor

    2015-02-01

    The intensification of agricultural productivity is an important challenge worldwide. However, environmental stressors can provide challenges to this intensification. The progressive occurrence of the cyanotoxins cylindrospermopsin (CYN) and microcystin-LR (MC-LR) as a potential consequence of eutrophication and climate change is of increasing concern in the agricultural sector because it has been reported that these cyanotoxins exert harmful effects in crop plants. A proteomic-based approach has been shown to be a suitable tool for the detection and identification of the primary responses of organisms exposed to cyanotoxins. The aim of this study was to compare the leaf-proteome profiles of lettuce plants exposed to environmentally relevant concentrations of CYN and a MC-LR/CYN mixture. Lettuce plants were exposed to 1, 10, and 100 μg/l CYN and a MC-LR/CYN mixture for five days. The proteins of lettuce leaves were separated by two-dimensional electrophoresis (2-DE), and those that were differentially abundant were then identified by matrix-assisted laser desorption/ionization time of flight-mass spectrometry (MALDI-TOF/TOF MS). The biological functions of the proteins that were most represented in both experiments were photosynthesis and carbon metabolism and stress/defense response. Proteins involved in protein synthesis and signal transduction were also highly observed in the MC-LR/CYN experiment. Although distinct protein abundance patterns were observed in both experiments, the effects appear to be concentration-dependent, and the effects of the mixture were clearly stronger than those of CYN alone. The obtained results highlight the putative tolerance of lettuce to CYN at concentrations up to 100 μg/l. Furthermore, the combination of CYN with MC-LR at low concentrations (1 μg/l) stimulated a significant increase in the fresh weight (fr. wt) of lettuce leaves and at the proteomic level resulted in the increase in abundance of a high number of proteins. In

  15. TNF-α-induced NF-κB activation promotes myofibroblast differentiation of LR-MSCs and exacerbates bleomycin-induced pulmonary fibrosis.

    Science.gov (United States)

    Hou, Jiwei; Ma, Tan; Cao, Honghui; Chen, Yabing; Wang, Cong; Chen, Xiang; Xiang, Zou; Han, Xiaodong

    2018-03-01

    Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible lung disease of unknown cause. It has been reported that both lung resident mesenchymal stem cells (LR-MSCs) and tumor necrosis factor-α (TNF-α) play important roles in the development of pulmonary fibrosis. However, the underlying connections between LR-MSCs and TNF-α in the pathogenesis of pulmonary fibrosis are still elusive. In this study, we found that the pro-inflammatory cytokine TNF-α and the transcription factor nuclear factor kappa B (NF-κB) p65 subunit were both upregulated in bleomycin-induced fibrotic lung tissue. In addition, we discovered that TNF-α promotes myofibroblast differentiation of LR-MSCs through activating NF-κB signaling. Interestingly, we also found that TNF-α promotes the expression of β-catenin. Moreover, we demonstrated that suppression of the NF-κB signaling could attenuate myofibroblast differentiation of LR-MSCs and bleomycin-induced pulmonary fibrosis which were accompanied with decreased expression of β-catenin. Our data implicates that inhibition of the NF-κB signaling pathway may provide a therapeutic strategy for pulmonary fibrosis, a disease that warrants more effective treatment approaches. © 2017 Wiley Periodicals, Inc.

  16. Application of semi-empirical modeling and non-linear regression to unfolding fast neutron spectra from integral reaction rate data

    International Nuclear Information System (INIS)

    Harker, Y.D.

    1976-01-01

    A semi-empirical analytical expression representing a fast reactor neutron spectrum has been developed. This expression was used in a non-linear regression computer routine to obtain from measured multiple foil integral reaction data the neutron spectrum inside the Coupled Fast Reactivity Measurement Facility. In this application six parameters in the analytical expression for neutron spectrum were adjusted in the non-linear fitting process to maximize consistency between calculated and measured integral reaction rates for a set of 15 dosimetry detector foils. In two-thirds of the observations the calculated integral agreed with its respective measured value to within the experimental standard deviation, and in all but one case agreement within two standard deviations was obtained. Based on this quality of fit the estimated 70 to 75 percent confidence intervals for the derived spectrum are 10 to 20 percent for the energy range 100 eV to 1 MeV, 10 to 50 percent for 1 MeV to 10 MeV and 50 to 90 percent for 10 MeV to 18 MeV. The analytical model has demonstrated a flexibility to describe salient features of neutron spectra of the fast reactor type. The use of regression analysis with this model has produced a stable method to derive neutron spectra from a limited amount of integral data

  17. Alternative regression models to assess increase in childhood BMI

    OpenAIRE

    Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

    2008-01-01

    Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...

  18. Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

    Science.gov (United States)

    van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

    2017-03-21

    Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Two-Stage Method Based on Local Polynomial Fitting for a Linear Heteroscedastic Regression Model and Its Application in Economics

    Directory of Open Access Journals (Sweden)

    Liyun Su

    2012-01-01

    Full Text Available We introduce the extension of local polynomial fitting to the 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 nonparametric technique of local polynomial estimation, we do not need to know the heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we focus on comparison of parameters and reach an optimal fitting. Besides, we verify the asymptotic normality of parameters based on numerical simulations. Finally, this approach is applied to a case of economics, and it indicates that our method is surely effective in finite-sample situations.

  20. Determinants of LSIL Regression in Women from a Colombian Cohort

    International Nuclear Information System (INIS)

    Molano, Monica; Gonzalez, Mauricio; Gamboa, Oscar; Ortiz, Natasha; Luna, Joaquin; Hernandez, Gustavo; Posso, Hector; Murillo, Raul; Munoz, Nubia

    2010-01-01

    Objective: To analyze the role of Human Papillomavirus (HPV) and other risk factors in the regression of cervical lesions in women from the Bogota Cohort. Methods: 200 HPV positive women with abnormal cytology were included for regression analysis. The time of lesion regression was modeled using methods for interval censored survival time data. Median duration of total follow-up was 9 years. Results: 80 (40%) women were diagnosed with Atypical Squamous Cells of Undetermined Significance (ASCUS) or Atypical Glandular Cells of Undetermined Significance (AGUS) while 120 (60%) were diagnosed with Low Grade Squamous Intra-epithelial Lesions (LSIL). Globally, 40% of the lesions were still present at first year of follow up, while 1.5% was still present at 5 year check-up. The multivariate model showed similar regression rates for lesions in women with ASCUS/AGUS and women with LSIL (HR= 0.82, 95% CI 0.59-1.12). Women infected with HR HPV types and those with mixed infections had lower regression rates for lesions than did women infected with LR types (HR=0.526, 95% CI 0.33-0.84, for HR types and HR=0.378, 95% CI 0.20-0.69, for mixed infections). Furthermore, women over 30 years had a higher lesion regression rate than did women under 30 years (HR1.53, 95% CI 1.03-2.27). The study showed that the median time for lesion regression was 9 months while the median time for HPV clearance was 12 months. Conclusions: In the studied population, the type of infection and the age of the women are critical factors for the regression of cervical lesions.