Fossati, Andrea; Widiger, Thomas A; Borroni, Serena; Maffei, Cesare; Somma, Antonella
2017-06-01
To extend the evidence on the reliability and construct validity of the Five-Factor Model Rating Form (FFMRF) in its self-report version, two independent samples of Italian participants, which were composed of 510 adolescent high school students and 457 community-dwelling adults, respectively, were administered the FFMRF in its Italian translation. Adolescent participants were also administered the Italian translation of the Borderline Personality Features Scale for Children-11 (BPFSC-11), whereas adult participants were administered the Italian translation of the Triarchic Psychopathy Measure (TriPM). Cronbach α values were consistent with previous findings; in both samples, average interitem r values indicated acceptable internal consistency for all FFMRF scales. A multidimensional graded item response theory model indicated that the majority of FFMRF items had adequate discrimination parameters; information indices supported the reliability of the FFMRF scales. Both categorical (i.e., item-level) and scale-level regression analyses suggested that the FFMRF scores may predict a nonnegligible amount of variance in the BPFSC-11 total score in adolescent participants, and in the TriPM scale scores in adult participants.
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.
Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg
2009-11-01
G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
Applications of MIDAS regression in analysing trends in water quality
Penev, Spiridon; Leonte, Daniela; Lazarov, Zdravetz; Mann, Rob A.
2014-04-01
We discuss novel statistical methods in analysing trends in water quality. Such analysis uses complex data sets of different classes of variables, including water quality, hydrological and meteorological. We analyse the effect of rainfall and flow on trends in water quality utilising a flexible model called Mixed Data Sampling (MIDAS). This model arises because of the mixed frequency in the data collection. Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily. The advantage of using MIDAS regression is in the flexible and parsimonious modelling of the influence of the rain and flow on trends in water quality variables. We discuss the model and its implementation on a data set from the Shoalhaven Supply System and Catchments in the state of New South Wales, Australia. Information criteria indicate that MIDAS modelling improves upon simplistic approaches that do not utilise the mixed data sampling nature of the data.
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.
Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H
2016-04-01
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.
Statistical and regression analyses of detected extrasolar systems
Pintr, Pavel; Peřinová, V.; Lukš, A.; Pathak, A.
2013-01-01
Roč. 75, č. 1 (2013), s. 37-45 ISSN 0032-0633 Institutional support: RVO:61389021 Keywords : Exoplanets * Kepler candidates * Regression analysis Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 1.630, year: 2013 http://www.sciencedirect.com/science/article/pii/S0032063312003066
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies
Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.
2016-01-01
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epide...
Analysing inequalities in Germany a structured additive distributional regression approach
Silbersdorff, Alexander
2017-01-01
This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators. This new approach allows the observed discrepancy between the individuals’ realities and the abstract representation of those realities to be explicitly taken into consideration using the arithmetic mean alone. In turn, the method is applied to the question of economic inequality in Germany.
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
On logistic regression analysis of dichotomized responses.
Lu, Kaifeng
2017-01-01
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
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
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)
Karami, K; Zerehdaran, S; Barzanooni, B; Lotfi, E
2017-12-01
1. The aim of the present study was to estimate genetic parameters for average egg weight (EW) and egg number (EN) at different ages in Japanese quail using multi-trait random regression (MTRR) models. 2. A total of 8534 records from 900 quail, hatched between 2014 and 2015, were used in the study. Average weekly egg weights and egg numbers were measured from second until sixth week of egg production. 3. Nine random regression models were compared to identify the best order of the Legendre polynomials (LP). The most optimal model was identified by the Bayesian Information Criterion. A model with second order of LP for fixed effects, second order of LP for additive genetic effects and third order of LP for permanent environmental effects (MTRR23) was found to be the best. 4. According to the MTRR23 model, direct heritability for EW increased from 0.26 in the second week to 0.53 in the sixth week of egg production, whereas the ratio of permanent environment to phenotypic variance decreased from 0.48 to 0.1. Direct heritability for EN was low, whereas the ratio of permanent environment to phenotypic variance decreased from 0.57 to 0.15 during the production period. 5. For each trait, estimated genetic correlations among weeks of egg production were high (from 0.85 to 0.98). Genetic correlations between EW and EN were low and negative for the first two weeks, but they were low and positive for the rest of the egg production period. 6. In conclusion, random regression models can be used effectively for analysing egg production traits in Japanese quail. Response to selection for increased egg weight would be higher at older ages because of its higher heritability and such a breeding program would have no negative genetic impact on egg production.
USE OF THE SIMPLE LINEAR REGRESSION MODEL IN MACRO-ECONOMICAL ANALYSES
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.
The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...
Regression analysis of growth responses to water depth in three wetland plant species
Sorrell, Brian K; Tanner, Chris C; Brix, Hans
2012-01-01
depths from 0 – 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth...
Multiple Response Regression for Gaussian Mixture Models with Known Labels.
Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng
2012-12-01
Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.
Approximate median regression for complex survey data with skewed response.
Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett M; Pan, Yi
2016-12-01
The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and weighting. In this article, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS)'based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. © 2016, The International Biometric Society.
Response surface use in safety analyses
Prosek, A.
1999-01-01
When thousands of complex computer code runs related to nuclear safety are needed for statistical analysis, the response surface is used to replace the computer code. The main purpose of the study was to develop and demonstrate a tool called optimal statistical estimator (OSE) intended for response surface generation of complex and non-linear phenomena. The performance of optimal statistical estimator was tested by the results of 59 different RELAP5/MOD3.2 code calculations of the small-break loss-of-coolant accident in a two loop pressurized water reactor. The results showed that OSE adequately predicted the response surface for the peak cladding temperature. Some good characteristic of the OSE like monotonic function between two neighbor points and independence on the number of output parameters suggest that OSE can be used for response surface generation of any safety or system parameter in the thermal-hydraulic safety analyses.(author)
Esther Leushuis
2016-12-01
Full Text Available Background: Standardization of the semen analysis may improve reproducibility. We assessed variability between laboratories in semen analyses and evaluated whether a transformation using Z scores and regression statistics was able to reduce this variability. Materials and Methods: We performed a retrospective cohort study. We calculated between-laboratory coefficients of variation (CVB for sperm concentration and for morphology. Subsequently, we standardized the semen analysis results by calculating laboratory specific Z scores, and by using regression. We used analysis of variance for four semen parameters to assess systematic differences between laboratories before and after the transformations, both in the circulation samples and in the samples obtained in the prospective cohort study in the Netherlands between January 2002 and February 2004. Results: The mean CVB was 7% for sperm concentration (range 3 to 13% and 32% for sperm morphology (range 18 to 51%. The differences between the laboratories were statistically significant for all semen parameters (all P<0.001. Standardization using Z scores did not reduce the differences in semen analysis results between the laboratories (all P<0.001. Conclusion: There exists large between-laboratory variability for sperm morphology and small, but statistically significant, between-laboratory variation for sperm concentration. Standardization using Z scores does not eliminate between-laboratory variability.
Analyses of demand response in Denmark
Moeller Andersen, F.; Grenaa Jensen, S.; Larsen, Helge V.; Meibom, P.; Ravn, H.; Skytte, K.; Togeby, M.
2006-10-01
Due to characteristics of the power system, costs of producing electricity vary considerably over short time intervals. Yet, many consumers do not experience corresponding variations in the price they pay for consuming electricity. The topic of this report is: are consumers willing and able to respond to short-term variations in electricity prices, and if so, what is the social benefit of consumers doing so? Taking Denmark and the Nord Pool market as a case, the report focuses on what is known as short-term consumer flexibility or demand response in the electricity market. With focus on market efficiency, efficient allocation of resources and security of supply, the report describes demand response from a micro-economic perspective and provides empirical observations and case studies. The report aims at evaluating benefits from demand response. However, only elements contributing to an overall value are presented. In addition, the analyses are limited to benefits for society, and costs of obtaining demand response are not considered. (au)
Online Statistical Modeling (Regression Analysis) for Independent Responses
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Kevin D. Cashman
2017-05-01
Full Text Available Dietary Reference Values (DRVs for vitamin D have a key role in the prevention of vitamin D deficiency. However, despite adopting similar risk assessment protocols, estimates from authoritative agencies over the last 6 years have been diverse. This may have arisen from diverse approaches to data analysis. Modelling strategies for pooling of individual subject data from cognate vitamin D randomized controlled trials (RCTs are likely to provide the most appropriate DRV estimates. Thus, the objective of the present work was to undertake the first-ever individual participant data (IPD-level meta-regression, which is increasingly recognized as best practice, from seven winter-based RCTs (with 882 participants ranging in age from 4 to 90 years of the vitamin D intake–serum 25-hydroxyvitamin D (25(OHD dose-response. Our IPD-derived estimates of vitamin D intakes required to maintain 97.5% of 25(OHD concentrations >25, 30, and 50 nmol/L across the population are 10, 13, and 26 µg/day, respectively. In contrast, standard meta-regression analyses with aggregate data (as used by several agencies in recent years from the same RCTs estimated that a vitamin D intake requirement of 14 µg/day would maintain 97.5% of 25(OHD >50 nmol/L. These first IPD-derived estimates offer improved dietary recommendations for vitamin D because the underpinning modeling captures the between-person variability in response of serum 25(OHD to vitamin D intake.
Practical Aspects of Log-ratio Coordinate Representations in Regression with Compositional Response
Fišerová Eva
2016-10-01
Full Text Available Regression analysis with compositional response, observations carrying relative information, is an appropriate tool for statistical modelling in many scientific areas (e.g. medicine, geochemistry, geology, economics. Even though this technique has been recently intensively studied, there are still some practical aspects that deserve to be further analysed. Here we discuss the issue related to the coordinate representation of compositional data. It is shown that linear relation between particular orthonormal coordinates and centred log-ratio coordinates can be utilized to simplify the computation concerning regression parameters estimation and hypothesis testing. To enhance interpretation of regression parameters, the orthogonal coordinates and their relation with orthonormal and centred log-ratio coordinates are presented. Further we discuss the quality of prediction in different coordinate system. It is shown that the mean squared error (MSE for orthonormal coordinates is less or equal to the MSE for log-transformed data. Finally, an illustrative real-world example from geology is presented.
The number of subjects per variable required in linear regression analyses
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
The number of subjects per variable required in linear regression analyses.
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.
Cashman, Kevin D.; Ritz, Christian; Kiely, Mairead
2017-01-01
Dietary Reference Values (DRVs) for vitamin D have a key role in the prevention of vitamin D deficiency. However, despite adopting similar risk assessment protocols, estimates from authoritative agencies over the last 6 years have been diverse. This may have arisen from diverse approaches to data analysis. Modelling strategies for pooling of individual subject data from cognate vitamin D randomized controlled trials (RCTs) are likely to provide the most appropriate DRV estimates. Thus, the objective of the present work was to undertake the first-ever individual participant data (IPD)-level meta-regression, which is increasingly recognized as best practice, from seven winter-based RCTs (with 882 participants ranging in age from 4 to 90 years) of the vitamin D intake–serum 25-hydroxyvitamin D (25(OH)D) dose-response. Our IPD-derived estimates of vitamin D intakes required to maintain 97.5% of 25(OH)D concentrations >25, 30, and 50 nmol/L across the population are 10, 13, and 26 µg/day, respectively. In contrast, standard meta-regression analyses with aggregate data (as used by several agencies in recent years) from the same RCTs estimated that a vitamin D intake requirement of 14 µg/day would maintain 97.5% of 25(OH)D >50 nmol/L. These first IPD-derived estimates offer improved dietary recommendations for vitamin D because the underpinning modeling captures the between-person variability in response of serum 25(OH)D to vitamin D intake. PMID:28481259
Kromhout, D.
2009-01-01
Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements of the
Li, Spencer D.
2011-01-01
Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…
Wu, Dane W.
2002-01-01
The year 2000 US presidential election between Al Gore and George Bush has been the most intriguing and controversial one in American history. The state of Florida was the trigger for the controversy, mainly, due to the use of the misleading "butterfly ballot". Using prediction (or confidence) intervals for least squares regression lines…
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.
Check-all-that-apply data analysed by Partial Least Squares regression
Rinnan, Åsmund; Giacalone, Davide; Frøst, Michael Bom
2015-01-01
are analysed by multivariate techniques. CATA data can be analysed both by setting the CATA as the X and the Y. The former is the PLS-Discriminant Analysis (PLS-DA) version, while the latter is the ANOVA-PLS (A-PLS) version. We investigated the difference between these two approaches, concluding...
Analyses of Aircraft Responses to Atmospheric Turbulence
Van Staveren, W.H.J.J.
2003-01-01
The response of aircraft to stochastic atmospheric turbulence plays an important role in aircraft-design (load calculations), Flight Control System (FCS) design and flight-simulation (handling qualities research and pilot training). In order to simulate these aircraft responses, an accurate
Scott, Neil W; Fayers, Peter M; Aaronson, Neil K
2010-01-01
Differential item functioning (DIF) methods can be used to determine whether different subgroups respond differently to particular items within a health-related quality of life (HRQoL) subscale, after allowing for overall subgroup differences in that scale. This article reviews issues that arise ...... when testing for DIF in HRQoL instruments. We focus on logistic regression methods, which are often used because of their efficiency, simplicity and ease of application....
Analyses of Developmental Rate Isomorphy in Ectotherms: Introducing the Dirichlet Regression.
David S Boukal
Full Text Available Temperature drives development in insects and other ectotherms because their metabolic rate and growth depends directly on thermal conditions. However, relative durations of successive ontogenetic stages often remain nearly constant across a substantial range of temperatures. This pattern, termed 'developmental rate isomorphy' (DRI in insects, appears to be widespread and reported departures from DRI are generally very small. We show that these conclusions may be due to the caveats hidden in the statistical methods currently used to study DRI. Because the DRI concept is inherently based on proportional data, we propose that Dirichlet regression applied to individual-level data is an appropriate statistical method to critically assess DRI. As a case study we analyze data on five aquatic and four terrestrial insect species. We find that results obtained by Dirichlet regression are consistent with DRI violation in at least eight of the studied species, although standard analysis detects significant departure from DRI in only four of them. Moreover, the departures from DRI detected by Dirichlet regression are consistently much larger than previously reported. The proposed framework can also be used to infer whether observed departures from DRI reflect life history adaptations to size- or stage-dependent effects of varying temperature. Our results indicate that the concept of DRI in insects and other ectotherms should be critically re-evaluated and put in a wider context, including the concept of 'equiproportional development' developed for copepods.
Slutskaya, N.G.; Mosseh, I.B.
2006-01-01
Data about genetic mutations under radiation and chemical treatment for different types of cells have been analyzed with correlation and regression analyses. Linear correlation between different genetic effects in sex cells and somatic cells have found. The results may be extrapolated on sex cells of human and mammals. (authors)
Tybjærg-Hansen, Anne
2009-01-01
Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements...... of the risk factors are observed on a subsample. We extend the multivariate RC techniques to a meta-analysis framework where multiple studies provide independent repeat measurements and information on disease outcome. We consider the cases where some or all studies have repeat measurements, and compare study......-specific, averaged and empirical Bayes estimates of RC parameters. Additionally, we allow for binary covariates (e.g. smoking status) and for uncertainty and time trends in the measurement error corrections. Our methods are illustrated using a subset of individual participant data from prospective long-term studies...
Random Decrement and Regression Analysis of Traffic Responses of Bridges
Asmussen, J. C.; Ibrahim, S. R.; Brincker, Rune
1996-01-01
The topic of this paper is the estimation of modal parameters from ambient data by applying the Random Decrement technique. The data fro the Queensborough Bridge over the Fraser River in Vancouver, Canada have been applied. The loads producing the dynamic response are ambient, e. g. wind, traffic...
Random Decrement and Regression Analysis of Traffic Responses of Bridges
Asmussen, J. C.; Ibrahim, S. R.; Brincker, Rune
The topic of this paper is the estimation of modal parameters from ambient data by applying the Random Decrement technique. The data from the Queensborough Bridge over the Fraser River in Vancouver, Canada have been applied. The loads producing the dynamic response are ambient, e.g. wind, traffic...
Huang, Banglian; Yang, Yiming; Luo, Tingting; Wu, S.; Du, Xuezhu; Cai, Detian; Loo, van, E.N.; Huang Bangquan
2013-01-01
In the present study correlation, regression and path analyses were carried out to decide correlations among the agro- nomic traits and their contributions to seed yield per plant in Crambe abyssinica. Partial correlation analysis indicated that plant height (X1) was significantly correlated with branching height and the number of first branches (P <0.01); Branching height (X2) was significantly correlated with pod number of primary inflorescence (P <0.01) and number of secondary branch...
Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict
Michael X Cohen
2011-02-01
Full Text Available In most cognitive neuroscience experiments there are many behavioral and experimental dynamics, and many indices of brain activity, that vary from trial to trial. For example, in studies of response conflict, conflict is usually treated as a binary variable (i.e., response conflict exists or does not in any given trial, whereas some evidence and intuition suggests that conflict may vary in intensity from trial to trial. Here we demonstrate that single-trial multiple regression of time-frequency electrophysiological activity reveals neural mechanisms of cognitive control that are not apparent in cross-trial averages. We also introduce a novel extension to oscillation phase coherence and synchronization analyses, based on weighted phase modulation, that has advantages over standard coherence measures in terms of linking electrophysiological dynamics to trial-varying behavior and experimental variables. After replicating previous response conflict findings using trial-averaged data, we extend these findings using single trial analytic methods to provide novel evidence for the role of medial frontal-lateral prefrontal theta-band synchronization in conflict-induced response time dynamics, including a role for lateral prefrontal theta-band activity in biasing response times according to perceptual conflict. Given that these methods shed new light on the prefrontal mechanisms of response conflict, they are also likely to be useful for investigating other neurocognitive processes.
Analyses of non-fatal accidents in an opencast mine by logistic regression model - a case study.
Onder, Seyhan; Mutlu, Mert
2017-09-01
Accidents cause major damage for both workers and enterprises in the mining industry. To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. This study efficiently examines the Aegean Lignite Enterprise (ELI) of Turkish Coal Enterprises (TKI) in Soma between 2006 and 2011, and opencast coal mine occupational accident records were used for statistical analyses. A total of 231 occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days. The SPSS package program was used in this study for logistic regression analyses, which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling. Additionally, the model was tested for such reported accidents that occurred in 2012 for the ELI in Soma and estimated the probability of exposure to accidents with lost workdays correctly by 70%.
Luise A Seeker
Full Text Available Telomeres cap the ends of linear chromosomes and shorten with age in many organisms. In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length (TL to exploring TL change within individuals over time. Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1 characterize the change in bovine relative leukocyte TL (RLTL across the lifetime in Holstein Friesian dairy cattle, 2 estimate genetic parameters of RLTL over time and 3 investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age. The analyses were based on 1,328 repeated RLTL measurements of 308 female Holstein Friesian dairy cattle. A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life. The heritability of RLTL ranged from 0.36 to 0.47 (SE = 0.05-0.08 and remained statistically unchanged over time. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0.69, indicating that TL later in life might be regulated by different genes than TL early in life. Even though animals differed in their RLTL profiles significantly, those differences were not correlated with productive lifespan (p = 0.954.
Islamiyati, A.; Fatmawati; Chamidah, N.
2018-03-01
The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.
Corporate Social Responsibility and Financial Performance: A Two Least Regression Approach
Alexander Olawumi Dabor
2017-12-01
Full Text Available The objective of this study is to investigate the casuality between corporate social responsibility and firm financial performance. The study employed two least square regression approaches. Fifty-two firms were selected using the scientific method. The findings revealed that corporate social responsibility and firm performance in manufacturing sector are mutually related at 5%. The study recommended that management of manufacturing companies in Nigeria should expend on CSR to boost profitability and corporate image.
Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos
2017-06-01
We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Proteomic analyses of host and pathogen responses during bovine mastitis.
Boehmer, Jamie L
2011-12-01
The pursuit of biomarkers for use as clinical screening tools, measures for early detection, disease monitoring, and as a means for assessing therapeutic responses has steadily evolved in human and veterinary medicine over the past two decades. Concurrently, advances in mass spectrometry have markedly expanded proteomic capabilities for biomarker discovery. While initial mass spectrometric biomarker discovery endeavors focused primarily on the detection of modulated proteins in human tissues and fluids, recent efforts have shifted to include proteomic analyses of biological samples from food animal species. Mastitis continues to garner attention in veterinary research due mainly to affiliated financial losses and food safety concerns over antimicrobial use, but also because there are only a limited number of efficacious mastitis treatment options. Accordingly, comparative proteomic analyses of bovine milk have emerged in recent years. Efforts to prevent agricultural-related food-borne illness have likewise fueled an interest in the proteomic evaluation of several prominent strains of bacteria, including common mastitis pathogens. The interest in establishing biomarkers of the host and pathogen responses during bovine mastitis stems largely from the need to better characterize mechanisms of the disease, to identify reliable biomarkers for use as measures of early detection and drug efficacy, and to uncover potentially novel targets for the development of alternative therapeutics. The following review focuses primarily on comparative proteomic analyses conducted on healthy versus mastitic bovine milk. However, a comparison of the host defense proteome of human and bovine milk and the proteomic analysis of common veterinary pathogens are likewise introduced.
Željko V. Račić
2010-12-01
Full Text Available This paper aims to present the specifics of the application of multiple linear regression model. The economic (financial crisis is analyzed in terms of gross domestic product which is in a function of the foreign trade balance (on one hand and the credit cards, i.e. indebtedness of the population on this basis (on the other hand, in the USA (from 1999. to 2008. We used the extended application model which shows how the analyst should run the whole development process of regression model. This process began with simple statistical features and the application of regression procedures, and ended with residual analysis, intended for the study of compatibility of data and model settings. This paper also analyzes the values of some standard statistics used in the selection of appropriate regression model. Testing of the model is carried out with the use of the Statistics PASW 17 program.
Cuffney, T.F.; Kashuba, R.; Qian, S.S.; Alameddine, I.; Cha, Y.K.; Lee, B.; Coles, J.F.; McMahon, G.
2011-01-01
Multilevel hierarchical regression was used to examine regional patterns in the responses of benthic macroinvertebrates and algae to urbanization across 9 metropolitan areas of the conterminous USA. Linear regressions established that responses (intercepts and slopes) to urbanization of invertebrates and algae varied among metropolitan areas. Multilevel hierarchical regression models were able to explain these differences on the basis of region-scale predictors. Regional differences in the type of land cover (agriculture or forest) being converted to urban and climatic factors (precipitation and air temperature) accounted for the differences in the response of macroinvertebrates to urbanization based on ordination scores, total richness, Ephemeroptera, Plecoptera, Trichoptera richness, and average tolerance. Regional differences in climate and antecedent agriculture also accounted for differences in the responses of salt-tolerant diatoms, but differences in the responses of other diatom metrics (% eutraphenic, % sensitive, and % silt tolerant) were best explained by regional differences in soils (mean % clay soils). The effects of urbanization were most readily detected in regions where forest lands were being converted to urban land because agricultural development significantly degraded assemblages before urbanization and made detection of urban effects difficult. The effects of climatic factors (temperature, precipitation) on background conditions (biogeographic differences) and rates of response to urbanization were most apparent after accounting for the effects of agricultural development. The effects of climate and land cover on responses to urbanization provide strong evidence that monitoring, mitigation, and restoration efforts must be tailored for specific regions and that attainment goals (background conditions) may not be possible in regions with high levels of prior disturbance (e.g., agricultural development). ?? 2011 by The North American
Christensen, Erik R.; Kusk, Kresten Ole; Nyholm, Niels
2009-01-01
We derive equations for the effective concentration giving 10% inhibition (EC10) with 95% confidence limits for probit (log-normal), Weibull, and logistic dose -responsemodels on the basis of experimentally derived median effective concentrations (EC50s) and the curve slope at the central point (50......% inhibition). For illustration, data from closed, freshwater algal assays are analyzed using the green alga Pseudokirchneriella subcapitata with growth rate as the response parameter. Dose-response regressions for four test chemicals (tetraethylammonium bromide, musculamine, benzonitrile, and 4...... regression program with variance weighting and proper inverse estimation. The Weibull model provides the best fit to the data for all four chemicals. Predicted EC10s (95% confidence limits) from our derived equations are quite accurate; for example, with 4-4-(trifluoromethyl)phenoxy-phenol and the probit...
Analyses of demand response in Denmark[Electricity market
Moeller Andersen, F.; Grenaa Jensen, S.; Larsen, Helge V.; Meibom, P.; Ravn, H.; Skytte, K.; Togeby, M.
2006-10-15
Due to characteristics of the power system, costs of producing electricity vary considerably over short time intervals. Yet, many consumers do not experience corresponding variations in the price they pay for consuming electricity. The topic of this report is: are consumers willing and able to respond to short-term variations in electricity prices, and if so, what is the social benefit of consumers doing so? Taking Denmark and the Nord Pool market as a case, the report focuses on what is known as short-term consumer flexibility or demand response in the electricity market. With focus on market efficiency, efficient allocation of resources and security of supply, the report describes demand response from a micro-economic perspective and provides empirical observations and case studies. The report aims at evaluating benefits from demand response. However, only elements contributing to an overall value are presented. In addition, the analyses are limited to benefits for society, and costs of obtaining demand response are not considered. (au)
Seismic response analyses for reactor facilities at Savannah River
Miller, C.A.; Costantino, C.J.; Xu, J.
1991-01-01
The reactor facilities at the Savannah River Plant (SRP) were designed during the 1950's. The original seismic criteria defining the input ground motion was 0.1 G with UBC [uniform building code] provisions used to evaluate structural seismic loads. Later ground motion criteria have defined the free field seismic motion with a 0.2 G ZPA [free field acceleration] and various spectral shapes. The spectral shapes have included the Housner spectra, a site specific spectra, and the US NRC [Nuclear Regulatory Commission] Reg. Guide 1.60 shape. The development of these free field seismic criteria are discussed in the paper. The more recent seismic analyses have been of the following type: fixed base response spectra, frequency independent lumped parameter soil/structure interaction (SSI), frequency dependent lumped parameter SSI, and current state of the art analyses using computer codes such as SASSI. The results from these computations consist of structural loads and floor response spectra (used for piping and equipment qualification). These results are compared in the paper and the methods used to validate the results are discussed. 14 refs., 11 figs
Kang, Seung-Wan; Byun, Gukdo; Park, Hun-Joon
2014-12-01
This paper presents empirical research into the relationship between leader-follower value congruence in social responsibility and the level of ethical satisfaction for employees in the workplace. 163 dyads were analyzed, each consisting of a team leader and an employee working at a large manufacturing company in South Korea. Following current methodological recommendations for congruence research, polynomial regression and response surface modeling methodologies were used to determine the effects of value congruence. Results indicate that leader-follower value congruence in social responsibility was positively related to the ethical satisfaction of employees. Furthermore, employees' ethical satisfaction was stronger when aligned with a leader with high social responsibility. The theoretical and practical implications are discussed.
Peluso, Marco E M; Munnia, Armelle; Ceppi, Marcello
2014-11-05
Exposures to bisphenol-A, a weak estrogenic chemical, largely used for the production of plastic containers, can affect the rodent behaviour. Thus, we examined the relationships between bisphenol-A and the anxiety-like behaviour, spatial skills, and aggressiveness, in 12 toxicity studies of rodent offspring from females orally exposed to bisphenol-A, while pregnant and/or lactating, by median and linear splines analyses. Subsequently, the meta-regression analysis was applied to quantify the behavioural changes. U-shaped, inverted U-shaped and J-shaped dose-response curves were found to describe the relationships between bisphenol-A with the behavioural outcomes. The occurrence of anxiogenic-like effects and spatial skill changes displayed U-shaped and inverted U-shaped curves, respectively, providing examples of effects that are observed at low-doses. Conversely, a J-dose-response relationship was observed for aggressiveness. When the proportion of rodents expressing certain traits or the time that they employed to manifest an attitude was analysed, the meta-regression indicated that a borderline significant increment of anxiogenic-like effects was present at low-doses regardless of sexes (β)=-0.8%, 95% C.I. -1.7/0.1, P=0.076, at ≤120 μg bisphenol-A. Whereas, only bisphenol-A-males exhibited a significant inhibition of spatial skills (β)=0.7%, 95% C.I. 0.2/1.2, P=0.004, at ≤100 μg/day. A significant increment of aggressiveness was observed in both the sexes (β)=67.9,C.I. 3.4, 172.5, P=0.038, at >4.0 μg. Then, bisphenol-A treatments significantly abrogated spatial learning and ability in males (Pbisphenol-A, e.g. ≤120 μg/day, were associated to behavioural aberrations in offspring. Copyright © 2014. Published by Elsevier Ireland Ltd.
Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D
2015-05-01
Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical
Reddy, T.A. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States)); Claridge, D.E. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States))
1994-01-01
Multiple regression modeling of monitored building energy use data is often faulted as a reliable means of predicting energy use on the grounds that multicollinearity between the regressor variables can lead both to improper interpretation of the relative importance of the various physical regressor parameters and to a model with unstable regressor coefficients. Principal component analysis (PCA) has the potential to overcome such drawbacks. While a few case studies have already attempted to apply this technique to building energy data, the objectives of this study were to make a broader evaluation of PCA and multiple regression analysis (MRA) and to establish guidelines under which one approach is preferable to the other. Four geographic locations in the US with different climatic conditions were selected and synthetic data sequence representative of daily energy use in large institutional buildings were generated in each location using a linear model with outdoor temperature, outdoor specific humidity and solar radiation as the three regression variables. MRA and PCA approaches were then applied to these data sets and their relative performances were compared. Conditions under which PCA seems to perform better than MRA were identified and preliminary recommendations on the use of either modeling approach formulated. (orig.)
Genome wide analyses of metal responsive genes in Caenorhabditis elegans
Michael eAschner
2012-04-01
Full Text Available Metals are major contaminants that influence human health. Many metals have physiologic roles, but excessive levels can be harmful. Advances in technology have made toxicogenomic analyses possible to characterize the effects of metal exposure on the entire genome. Much of what is known about cellular responses to metals has come from mammalian systems; however the use of non-mammalian species is gaining wider attention. Caenorhabditis elegans (C. elegans is a small round worm whose genome has been fully sequenced and its development from egg to adult is well characterized. It is an attractive model for high throughput screens due to its short lifespan, ease of genetic mutability, low cost and high homology with humans. Research performed in C. elegans has led to insights in apoptosis, gene expression and neurodegeneration, all of which can be altered by metal exposure. Additionally, by using worms one can potentially study how the mechanisms that underline differential responses to metals in nematodes and humans, allowing for identification of novel pathways and therapeutic targets. In this review, toxicogenomic studies performed in C. elegans exposed to various metals will be discussed, highlighting how this non-mammalian system can be utilized to study cellular processes and pathways induced by metals. Recent work focusing on neurodegeneration in Parkinson’s disease will be discussed as an example of the usefulness of genetic screens in C. elegans and the novel findings that can be produced.
Wang, Lu; Su, Steven W; Celler, Branko G; Chan, Gregory S H; Cheng, Teddy M; Savkin, Andrey V
2009-01-01
This study aims to quantitatively describe the steady-state relationships among percentage changes in key central cardiovascular variables (i.e. stroke volume, heart rate (HR), total peripheral resistance and cardiac output), measured using non-invasive means, in response to moderate exercise, and the oxygen uptake rate, using a new nonlinear regression approach—support vector regression. Ten untrained normal males exercised in an upright position on an electronically braked cycle ergometer with constant workloads ranging from 25 W to 125 W. Throughout the experiment, .VO 2 was determined breath by breath and the HR was monitored beat by beat. During the last minute of each exercise session, the cardiac output was measured beat by beat using a novel non-invasive ultrasound-based device and blood pressure was measured using a tonometric measurement device. Based on the analysis of experimental data, nonlinear steady-state relationships between key central cardiovascular variables and .VO 2 were qualitatively observed except for the HR which increased linearly as a function of increasing .VO 2 . Quantitative descriptions of these complex nonlinear behaviour were provided by nonparametric models which were obtained by using support vector regression
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 Â© 2016 Elsevier Ireland Ltd. All rights reserved.
Laszlo, Sarah; Federmeier, Kara D.
2010-01-01
Linking print with meaning tends to be divided into subprocesses, such as recognition of an input's lexical entry and subsequent access of semantics. However, recent results suggest that the set of semantic features activated by an input is broader than implied by a view wherein access serially follows recognition. EEG was collected from participants who viewed items varying in number and frequency of both orthographic neighbors and lexical associates. Regression analysis of single item ERPs replicated past findings, showing that N400 amplitudes are greater for items with more neighbors, and further revealed that N400 amplitudes increase for items with more lexical associates and with higher frequency neighbors or associates. Together, the data suggest that in the N400 time window semantic features of items broadly related to inputs are active, consistent with models in which semantic access takes place in parallel with stimulus recognition. PMID:20624252
Renata Pires Gonçalves
2012-02-01
. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.
Quadratic Regression-based Non-uniform Response Correction for Radiochromic Film Scanners
Jeong, Hae Sun; Kim, Chan Hyeong; Han, Young Yih; Kum, O Yeon
2009-01-01
In recent years, several types of radiochromic films have been extensively used for two-dimensional dose measurements such as dosimetry in radiotherapy as well as imaging and radiation protection applications. One of the critical aspects in radiochromic film dosimetry is the accurate readout of the scanner without dose distortion. However, most of charge-coupled device (CCD) scanners used for the optical density readout of the film employ a fluorescent lamp or a coldcathode lamp as a light source, which leads to a significant amount of light scattering on the active layer of the film. Due to the effect of the light scattering, dose distortions are produced with non-uniform responses, although the dose is uniformly irradiated to the film. In order to correct the distorted doses, a method based on correction factors (CF) has been reported and used. However, the prediction of the real incident doses is difficult when the indiscreet doses are delivered to the film, since the dose correction with the CF-based method is restrictively used in case that the incident doses are already known. In a previous study, therefore, a pixel-based algorithm with linear regression was developed to correct the dose distortion of a flatbed scanner, and to estimate the initial doses. The result, however, was not very good for some cases especially when the incident dose is under approximately 100 cGy. In the present study, the problem was addressed by replacing the linear regression with the quadratic regression. The corrected doses using this method were also compared with the results of other conventional methods
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.
Botha, J.; De Ridder, J.H.; Potgieter, J.C.; Steyn, H.S.; Malan, L.
2013-01-01
A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fa...
Felton, A. J.; Smith, M. D.
2016-12-01
Heightened climatic variability due to atmospheric warming is forecast to increase the frequency and severity of climate extremes. In particular, changes to interannual variability in precipitation, characterized by increases in extreme wet and dry years, are likely to impact virtually all terrestrial ecosystem processes. However, to date experimental approaches have yet to explicitly test how ecosystem processes respond to multiple levels of climatic extremity, limiting our understanding of how ecosystems will respond to forecast increases in the magnitude of climate extremes. Here we report the results of a replicated regression experimental approach, in which we imposed 9 and 11 levels of growing season precipitation amount and extremity in mesic grassland during 2015 and 2016, respectively. Each level corresponded to a specific percentile of the long-term record, which produced a large gradient of soil moisture conditions that ranged from extreme wet to extreme dry. In both 2015 and 2016, asymptotic responses to water availability were observed for soil respiration. This asymmetry was driven in part by transitions between soil moisture versus temperature constraints on respiration as conditions became increasingly dry versus increasingly wet. In 2015, aboveground net primary production (ANPP) exhibited asymmetric responses to precipitation that largely mirrored those of soil respiration. In total, our results suggest that in this mesic ecosystem, these two carbon cycle processes were more sensitive to extreme drought than to extreme wet years. Future work will assess ANPP responses for 2016, soil nutrient supply and physiological responses of the dominant plant species. Future efforts are needed to compare our findings across a diverse array of ecosystem types, and in particular how the timing and magnitude of precipitation events may modify the response of ecosystem processes to increasing magnitudes of precipitation extremes.
Sharif, Behzad; Makowski, David; Plauborg, Finn
2017-01-01
Statistical regression models represent alternatives to process-based dynamic models for predicting the response of crop yields to variation in climatic conditions. Regression models can be used to quantify the effect of change in temperature and precipitation on yields. However, it is difficult ...
Hopper, D.A.; Hammer, P.A.
1991-01-01
A central composite rotatable design was used to estimate quadratic equations describing the relationship of irradiance, as measured by photosynthetic photon flux (PPF), and day (DT) and night (NT) temperatures to the growth and development of Rosa hybrida L. in controlled environments. Plants were subjected to 15 treatment combinations of the PPF, DT, and NT according to the coding of the design matrix. Day and night length were each 12 hours. Environmental factor ranges were chosen to include conditions representative of winter and spring commercial greenhouse production environments in the midwestern United States. After an initial hard pinch, 11 plant growth characteristics were measured every 10 days and at flowering. Four plant characteristics were recorded to describe flower bud development. Response surface equations were displayed as three-dimensional plots, with DT and NT as the base axes and the plant character on the z-axis while PPF was held constant. Response surfaces illustrated the plant response to interactions of DT and NT, while comparisons between plots at different PPF showed the overall effect of PPF. Canonical analysis of all regression models revealed the stationary point and general shape of the response surface. All stationary points of the significant models were located outside the original design space, and all but one surface was a saddle shape. Both the plots and analysis showed greater stem diameter, as well as higher fresh and dry weights of stems, leaves, and flower buds to occur at flowering under combinations of low DT (less than or equal to 17C) and low NT (less than or equal to 14C). However, low DT and NT delayed both visible bud formation and development to flowering. Increased PPF increased overall flower stem quality by increasing stem diameter and the fresh and dry weights of all plant parts at flowering, as well as decreased time until visible bud formation and flowering. These results summarize measured development at
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.
Engman, Mikael; Varghese, Suby; Lagerstedt Robinson, Kristina; Malmgren, Helena; Hammarsjö, Anna; Byström, Birgitta; L Lalitkumar, Parameswaran Grace; Gemzell-Danielsson, Kristina
2013-01-01
Progesterone receptor modulators, such as mifepristone are useful and well tolerated in reducing leiomyoma volume although with large individual variation. The objective of this study was to investigate the molecular basis for the observed leiomyoma volume reduction, in response to mifepristone treatment and explore a possible molecular marker for the selective usage of mifepristone in leiomyoma patients. Premenopausal women (N = 14) were treated with mifepristone 50 mg, every other day for 12 weeks prior to surgery. Women were arbitrarily sub-grouped as good (N = 4), poor (N = 4) responders to treatment or intermediate respondents (N = 3). Total RNA was extracted from leiomyoma tissue, after surgical removal of the tumour and the differential expression of genes were analysed by microarray. The results were analysed using Ingenuity Pathway Analysis software. The glutathione pathway was the most significantly altered canonical pathway in which the glutathione-s transferase mu 1 (GSTM1) gene was significantly over expressed (+8.03 folds) among the good responders compared to non responders. This was further confirmed by Real time PCR (p = 0.024). Correlation of immunoreactive scores (IRS) for GSTM1 accumulation in leiomyoma tissue was seen with base line volume change of leiomyoma R = −0.8 (p = 0.011). Furthermore the accumulation of protein GSTM1 analysed by Western Blot correlated significantly with the percentual leiomyoma volume change R = −0.82 (p = 0.004). Deletion of the GSTM1 gene in leiomyoma biopsies was found in 50% of the mifepristone treated cases, with lower presence of the GSTM1 protein. The findings support a significant role for GSTM1 in leiomyoma volume reduction induced by mifepristone and explain the observed individual variation in this response. Furthermore the finding could be useful to further explore GSTM1 as a biomarker for tailoring medical treatment of uterine leiomyomas for optimizing the response
Zhou, Pei-pei; Shan, Jin-feng; Jiang, Jian-lan
2015-12-01
To optimize the optimal microwave-assisted extraction method of curcuminoids from Curcuma longa. On the base of single factor experiment, the ethanol concentration, the ratio of liquid to solid and the microwave time were selected for further optimization. Support Vector Regression (SVR) and Central Composite Design-Response Surface Methodology (CCD) algorithm were utilized to design and establish models respectively, while Particle Swarm Optimization (PSO) was introduced to optimize the parameters of SVR models and to search optimal points of models. The evaluation indicator, the sum of curcumin, demethoxycurcumin and bisdemethoxycurcumin by HPLC, were used. The optimal parameters of microwave-assisted extraction were as follows: ethanol concentration of 69%, ratio of liquid to solid of 21 : 1, microwave time of 55 s. On those conditions, the sum of three curcuminoids was 28.97 mg/g (per gram of rhizomes powder). Both the CCD model and the SVR model were credible, for they have predicted the similar process condition and the deviation of yield were less than 1.2%.
Dyer, Betsey D.; Kahn, Michael J.; LeBlanc, Mark D.
2008-01-01
Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results. PMID:19054742
Statistical methods for analysing responses of wildlife to human disturbance.
Haiganoush K. Preisler; Alan A. Ager; Michael J. Wisdom
2006-01-01
1. Off-road recreation is increasing rapidly in many areas of the world, and effects on wildlife can be highly detrimental. Consequently, we have developed methods for studying wildlife responses to off-road recreation with the use of new technologies that allow frequent and accurate monitoring of human-wildlife interactions. To illustrate these methods, we studied the...
Vegetable parenting practices scale: Item response modeling analyses
Our objective was to evaluate the psychometric properties of a vegetable parenting practices scale using multidimensional polytomous item response modeling which enables assessing item fit to latent variables and the distributional characteristics of the items in comparison to the respondents. We al...
Analyses of transient plant response under emergency situations. 2
Koyama, Kazuya; Hishida, Masahiko
2000-03-01
In order to support development of the dynamic reliability analysis program DYANA, analyses were made on the event sequences anticipated under emergency situations using the plant dynamics simulation computer code Super-COPD. In this work 9 sequences were analyzed and integrated into an input file for preparing the functions for DYANA using the analytical model and input data which developed for Super-COPD in the previous work. These sequences could not analyze in the previous work, which were categorized into the PLOHS (Protected Loss of Heat Sink) event. (author)
Bowden, Jack; Del Greco M, Fabiola; Minelli, Cosetta; Davey Smith, George; Sheehan, Nuala A; Thompson, John R
2016-12-01
: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We
Analyses of transient plant response under emergency situations
Koyama, Kazuya [Advanced Reactor Technology, Co. Ltd., Engineering Department, Tokyo (Japan); Shimakawa, Yoshio; Hishida, Masahiko [Mitsubishi Heavy Industry, Ltd., Reactor Core Engineering and Safety Engineering Department, Tokyo (Japan)
1999-03-01
In order to support development of the dynamic reliability analysis program DYANA, analyses were made on the event sequences anticipated under emergency situations using the plant dynamics simulation computer code Super-COPD. The analytical models were developed for Super-COPD such as the guard vessel, the maintenance cooling system, the sodium overflow and makeup system, etc. in order to apply the code to the simulation of the emergency situations. The input data were prepared for the analyses. About 70 sequences were analyzed, which are categorized into the following events: (1) PLOHS (Protected Loss of Heat Sink), (2) LORL (Loss of Reactor Level)-J: failure of sodium makeup by the primary sodium overflow and makeup system, (3) LORL-G : failure of primary coolant pump trip, (4) LORL-I: failure of the argon cover gas isolation, and (5) heat removal only using the ventilation system of the primary cooling system rooms. The results were integrated into an input file for preparing the functions for the neural network simulation. (author)
Experimental benchmark for piping system dynamic response analyses
Schott, G.A.; Mallett, R.H.
1981-01-01
The scope and status of a piping system dynamics test program are described. A 0.20-m nominal diameter test piping specimen is designed to be representative of main heat transport system piping of LMFBR plants. Attention is given to representing piping restraints. Applied loadings consider component-induced vibration as well as seismic excitation. The principal objective of the program is to provide a benchmark for verification of piping design methods by correlation of predicted and measured responses. Pre-test analysis results and correlation methods are discussed. 3 refs
Experimental benchmark for piping system dynamic-response analyses
1981-01-01
This paper describes the scope and status of a piping system dynamics test program. A 0.20 m(8 in.) nominal diameter test piping specimen is designed to be representative of main heat transport system piping of LMFBR plants. Particular attention is given to representing piping restraints. Applied loadings consider component-induced vibration as well as seismic excitation. The principal objective of the program is to provide a benchmark for verification of piping design methods by correlation of predicted and measured responses. Pre-test analysis results and correlation methods are discussed
Seismic response Analyses of Hanaro in-chimney bracket structures
Lee, Jae Han; Ryu, J.S.; Cho, Y.G.; Lee, H.Y.; Kim, J.B.
1999-05-01
The in-chimney bracket will be installed in the upper part of chimney, which holds the capsule extension pipes in upper one-third of length. For evaluating the effects on the capsules and related reactor structures, ANSYS finite element analysis model is developed and the dynamic characteristics are analyzed. The seismic response anlayses of in-chimney bracket and related reactor structures of HANARO under the design earthquake response spectrum loads of OBE (0.1 g) and SSE (0.2 g) are performed. The maximum horizontal displacements of the flow tubes are within the minimum half gaps between close flow tubes, it is expected that these displacement will not produce any contact between neighbor flow tubes. The stress values in main points of reactor structures and in-chimney bracket for the seismic loads are also within the ASME Code limits. It is also confirmed that the fatigue usage factor is much less than 1.0. So, any damage on structural integrity is not expected when an in-chimney bracket is installed to upper part of the reactor chimney. (author). 12 refs., 24 tabs., 37 figs
Xie, Heping; Wang, Fuxing; Hao, Yanbin; Chen, Jiaxue; An, Jing; Wang, Yuxin; Liu, Huashan
2017-01-01
Cueing facilitates retention and transfer of multimedia learning. From the perspective of cognitive load theory (CLT), cueing has a positive effect on learning outcomes because of the reduction in total cognitive load and avoidance of cognitive overload. However, this has not been systematically evaluated. Moreover, what remains ambiguous is the direct relationship between the cue-related cognitive load and learning outcomes. A meta-analysis and two subsequent meta-regression analyses were conducted to explore these issues. Subjective total cognitive load (SCL) and scores on a retention test and transfer test were selected as dependent variables. Through a systematic literature search, 32 eligible articles encompassing 3,597 participants were included in the SCL-related meta-analysis. Among them, 25 articles containing 2,910 participants were included in the retention-related meta-analysis and the following retention-related meta-regression, while there were 29 articles containing 3,204 participants included in the transfer-related meta-analysis and the transfer-related meta-regression. The meta-analysis revealed a statistically significant cueing effect on subjective ratings of cognitive load (d = -0.11, 95% CI = [-0.19, -0.02], p < 0.05), retention performance (d = 0.27, 95% CI = [0.08, 0.46], p < 0.01), and transfer performance (d = 0.34, 95% CI = [0.12, 0.56], p < 0.01). The subsequent meta-regression analyses showed that dSCL for cueing significantly predicted dretention for cueing (β = -0.70, 95% CI = [-1.02, -0.38], p < 0.001), as well as dtransfer for cueing (β = -0.60, 95% CI = [-0.92, -0.28], p < 0.001). Thus in line with CLT, adding cues in multimedia materials can indeed reduce SCL and promote learning outcomes, and the more SCL is reduced by cues, the better retention and transfer of multimedia learning.
Thermal Response Analyses of Spherical LPG Storage Tank
Chen, Hsijen.; Lin, Mannhsing.; Chao, Fuyuan
1999-02-01
Liquefied petroleum gas (LPG) is a very important fuel and chemical feed stock as well; however, the hydrocarbon has been involved in many major fires and explosions. One of these accidents is boiling-liquid, expanding-vapor explosion (BLEVE). It is a phenomenon that results from the sudden release form confinement of a liquid at a temperature above its atmospheric-pressure boiling point. The sudden decrease in pressure results in the explosive vaporization of a fraction of the liquid and a cloud of vapor and mist with the accompanying blast effects. Most BLEVEs involve flammable liquids, and most BELEVE releases are ignited by a surrounding fire and result in a fireball. The primary objective of this paper is to develop a computer model in order to determine the thermal response of a spherical LPG tank involved in fire engulfment accidents. The assessment of the safety spacing between tanks was also discussed. (author)
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...
Shared dosimetry error in epidemiological dose-response analyses
Stram, Daniel O.; Preston, Dale L.; Sokolnikov, Mikhail; Napier, Bruce; Kopecky, Kenneth J.; Boice, John; Beck, Harold; Till, John; Bouville, Andre; Zeeb, Hajo
2015-01-01
Radiation dose reconstruction systems for large-scale epidemiological studies are sophisticated both in providing estimates of dose and in representing dosimetry uncertainty. For example, a computer program was used by the Hanford Thyroid Disease Study to provide 100 realizations of possible dose to study participants. The variation in realizations reflected the range of possible dose for each cohort member consistent with the data on dose determinates in the cohort. Another example is the Mayak Worker Dosimetry System 2013 which estimates both external and internal exposures and provides multiple realizations of 'possible' dose history to workers given dose determinants. This paper takes up the problem of dealing with complex dosimetry systems that provide multiple realizations of dose in an epidemiologic analysis. In this paper we derive expected scores and the information matrix for a model used widely in radiation epidemiology, namely the linear excess relative risk (ERR) model that allows for a linear dose response (risk in relation to radiation) and distinguishes between modifiers of background rates and of the excess risk due to exposure. We show that treating the mean dose for each individual (calculated by averaging over the realizations) as if it was true dose (ignoring both shared and unshared dosimetry errors) gives asymptotically unbiased estimates (i.e. the score has expectation zero) and valid tests of the null hypothesis that the ERR slope β is zero. Although the score is unbiased the information matrix (and hence the standard errors of the estimate of β) is biased for β≠0 when ignoring errors in dose estimates, and we show how to adjust the information matrix to remove this bias, using the multiple realizations of dose. The use of these methods in the context of several studies including, the Mayak Worker Cohort, and the U.S. Atomic Veterans Study, is discussed
Hutton, Eileen K; Simioni, Julia C; Thabane, Lehana
2017-08-01
Among women with a fetus with a non-cephalic presentation, external cephalic version (ECV) has been shown to reduce the rate of breech presentation at birth and cesarean birth. Compared with ECV at term, beginning ECV prior to 37 weeks' gestation decreases the number of infants in a non-cephalic presentation at birth. The purpose of this secondary analysis was to investigate factors associated with a successful ECV procedure and to present this in a clinically useful format. Data were collected as part of the Early ECV Pilot and Early ECV2 Trials, which randomized 1776 women with a fetus in breech presentation to either early ECV (34-36 weeks' gestation) or delayed ECV (at or after 37 weeks). The outcome of interest was successful ECV, defined as the fetus being in a cephalic presentation immediately following the procedure, as well as at the time of birth. The importance of several factors in predicting successful ECV was investigated using two statistical methods: logistic regression and classification and regression tree (CART) analyses. Among nulliparas, non-engagement of the presenting part and an easily palpable fetal head were independently associated with success. Among multiparas, non-engagement of the presenting part, gestation less than 37 weeks and an easily palpable fetal head were found to be independent predictors of success. These findings were consistent with results of the CART analyses. Regardless of parity, descent of the presenting part was the most discriminating factor in predicting successful ECV and cephalic presentation at birth. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.
Flexible meta-regression to assess the shape of the benzene-leukemia exposure-response curve.
Vlaanderen, J.J.|info:eu-repo/dai/nl/31403160X; Portengen, L.|info:eu-repo/dai/nl/269224742; Rothman, N.; Lan, Q.; Kromhout, H.|info:eu-repo/dai/nl/074385224; Vermeulen, R.|info:eu-repo/dai/nl/216532620
2010-01-01
BACKGROUND: Previous evaluations of the shape of the benzene-leukemia exposure-response curve (ERC) were based on a single set or on small sets of human occupational studies. Integrating evidence from all available studies that are of sufficient quality combined with flexible meta-regression models
Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O
2017-03-05
The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (K b ), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated K b and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10 -7 M for anthracene and 3.48×10 -8 M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized
Dose-response curves with semiochemicals are reported in many articles in insect chemical ecology regarding neurophysiology and behavioral bioassays. Most such curves are shown in figures where the x-axis has order of magnitude increases in dosages versus responses on the y-axis represented by point...
Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict
Cohen, M.X.; Cavanagh, J.F.
2011-01-01
In most cognitive neuroscience experiments there are many behavioral and experimental dynamics, and many indices of brain activity, that vary from trial to trial. For example, in studies of response conflict, conflict is usually treated as a binary variable (i.e., response conflict exists or does
Johansen, Mette; Bahrt, Henriette; Altman, Roy D; Bartels, Else M; Juhl, Carsten B; Bliddal, Henning; Lund, Hans; Christensen, Robin
2016-08-01
The aim was to identify factors explaining inconsistent observations concerning the efficacy of intra-articular hyaluronic acid compared to intra-articular sham/control, or non-intervention control, in patients with symptomatic osteoarthritis, based on randomized clinical trials (RCTs). A systematic review and meta-regression analyses of available randomized trials were conducted. The outcome, pain, was assessed according to a pre-specified hierarchy of potentially available outcomes. Hedges׳s standardized mean difference [SMD (95% CI)] served as effect size. REstricted Maximum Likelihood (REML) mixed-effects models were used to combine study results, and heterogeneity was calculated and interpreted as Tau-squared and I-squared, respectively. Overall, 99 studies (14,804 patients) met the inclusion criteria: Of these, only 71 studies (72%), including 85 comparisons (11,216 patients), had adequate data available for inclusion in the primary meta-analysis. Overall, compared with placebo, intra-articular hyaluronic acid reduced pain with an effect size of -0.39 [-0.47 to -0.31; P hyaluronic acid. Based on available trial data, intra-articular hyaluronic acid showed a better effect than intra-articular saline on pain reduction in osteoarthritis. Publication bias and the risk of selective outcome reporting suggest only small clinical effect compared to saline. Copyright © 2016 Elsevier Inc. All rights reserved.
Hall, Matthew D. [Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California (United States); Schultheiss, Timothy E., E-mail: schultheiss@coh.org [Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California (United States); Smith, David D. [Division of Biostatistics, City of Hope National Medical Center, Duarte, California (United States); Nguyen, Khanh H. [Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California (United States); Department of Radiation Oncology, Bayhealth Cancer Center, Dover, Delaware (United States); Wong, Jeffrey Y.C. [Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California (United States)
2015-01-01
Purpose/Objective(s): To perform a meta-regression on published data and to model the 5-year probability of cataract development after hematopoietic stem cell transplantation (HSCT) with and without total body irradiation (TBI). Methods and Materials: Eligible studies reporting cataract incidence after HSCT with TBI were identified by a PubMed search. Seventeen publications provided complete information on radiation dose schedule, fractionation, dose rate, and actuarial cataract incidence. Chemotherapy-only regimens were included as zero radiation dose regimens. Multivariate meta-regression with a weighted generalized linear model was used to model the 5-year cataract incidence and contributory factors. Results: Data from 1386 patients in 21 series were included for analysis. TBI was administered to a total dose of 0 to 15.75 Gy with single or fractionated schedules with a dose rate of 0.04 to 0.16 Gy/min. Factors significantly associated with 5-year cataract incidence were dose, dose times dose per fraction (D•dpf), pediatric versus adult status, and the absence of an ophthalmologist as an author. Dose rate, graft versus host disease, steroid use, hyperfractionation, and number of fractions were not significant. Five-fold internal cross-validation showed a model validity of 83% ± 8%. Regression diagnostics showed no evidence of lack-of-fit and no patterns in the studentized residuals. The α/β ratio from the linear quadratic model, estimated as the ratio of the coefficients for dose and D•dpf, was 0.76 Gy (95% confidence interval [CI], 0.05-1.55). The odds ratio for pediatric patients was 2.8 (95% CI, 1.7-4.6) relative to adults. Conclusions: Dose, D•dpf, pediatric status, and regimented follow-up care by an ophthalmologist were predictive of 5-year cataract incidence after HSCT. The low α/β ratio indicates the importance of fractionation in reducing cataracts. Dose rate effects have been observed in single institution studies but not in the
Hall, Matthew D.; Schultheiss, Timothy E.; Smith, David D.; Nguyen, Khanh H.; Wong, Jeffrey Y.C.
2015-01-01
Purpose/Objective(s): To perform a meta-regression on published data and to model the 5-year probability of cataract development after hematopoietic stem cell transplantation (HSCT) with and without total body irradiation (TBI). Methods and Materials: Eligible studies reporting cataract incidence after HSCT with TBI were identified by a PubMed search. Seventeen publications provided complete information on radiation dose schedule, fractionation, dose rate, and actuarial cataract incidence. Chemotherapy-only regimens were included as zero radiation dose regimens. Multivariate meta-regression with a weighted generalized linear model was used to model the 5-year cataract incidence and contributory factors. Results: Data from 1386 patients in 21 series were included for analysis. TBI was administered to a total dose of 0 to 15.75 Gy with single or fractionated schedules with a dose rate of 0.04 to 0.16 Gy/min. Factors significantly associated with 5-year cataract incidence were dose, dose times dose per fraction (D•dpf), pediatric versus adult status, and the absence of an ophthalmologist as an author. Dose rate, graft versus host disease, steroid use, hyperfractionation, and number of fractions were not significant. Five-fold internal cross-validation showed a model validity of 83% ± 8%. Regression diagnostics showed no evidence of lack-of-fit and no patterns in the studentized residuals. The α/β ratio from the linear quadratic model, estimated as the ratio of the coefficients for dose and D•dpf, was 0.76 Gy (95% confidence interval [CI], 0.05-1.55). The odds ratio for pediatric patients was 2.8 (95% CI, 1.7-4.6) relative to adults. Conclusions: Dose, D•dpf, pediatric status, and regimented follow-up care by an ophthalmologist were predictive of 5-year cataract incidence after HSCT. The low α/β ratio indicates the importance of fractionation in reducing cataracts. Dose rate effects have been observed in single institution studies but not in the
Heterogeneous global crop yield response to biochar: a meta-regression analysis
Crane-Droesch, Andrew; Torn, Margaret S; Abiven, Samuel; Jeffery, Simon
2013-01-01
Biochar may contribute to climate change mitigation at negative cost by sequestering photosynthetically fixed carbon in soil while increasing crop yields. The magnitude of biochar’s potential in this regard will depend on crop yield benefits, which have not been well-characterized across different soils and biochars. Using data from 84 studies, we employ meta-analytical, missing data, and semiparametric statistical methods to explain heterogeneity in crop yield responses across different soils, biochars, and agricultural management factors, and then estimate potential changes in yield across different soil environments globally. We find that soil cation exchange capacity and organic carbon were strong predictors of yield response, with low cation exchange and low carbon associated with positive response. We also find that yield response increases over time since initial application, compared to non-biochar controls. High reported soil clay content and low soil pH were weaker predictors of higher yield response. No biochar parameters in our dataset—biochar pH, percentage carbon content, or temperature of pyrolysis—were significant predictors of yield impacts. Projecting our fitted model onto a global soil database, we find the largest potential increases in areas with highly weathered soils, such as those characterizing much of the humid tropics. Richer soils characterizing much of the world’s important agricultural areas appear to be less likely to benefit from biochar. (letter)
Lo, Ching F.
1999-01-01
The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.
Samdal, Gro Beate; Eide, Geir Egil; Barth, Tom; Williams, Geoffrey; Meland, Eivind
2017-03-28
This systematic review aims to explain the heterogeneity in results of interventions to promote physical activity and healthy eating for overweight and obese adults, by exploring the differential effects of behaviour change techniques (BCTs) and other intervention characteristics. The inclusion criteria specified RCTs with ≥ 12 weeks' duration, from January 2007 to October 2014, for adults (mean age ≥ 40 years, mean BMI ≥ 30). Primary outcomes were measures of healthy diet or physical activity. Two reviewers rated study quality, coded the BCTs, and collected outcome results at short (≤6 months) and long term (≥12 months). Meta-analyses and meta-regressions were used to estimate effect sizes (ES), heterogeneity indices (I 2 ) and regression coefficients. We included 48 studies containing a total of 82 outcome reports. The 32 long term reports had an overall ES = 0.24 with 95% confidence interval (CI): 0.15 to 0.33 and I 2 = 59.4%. The 50 short term reports had an ES = 0.37 with 95% CI: 0.26 to 0.48, and I 2 = 71.3%. The number of BCTs unique to the intervention group, and the BCTs goal setting and self-monitoring of behaviour predicted the effect at short and long term. The total number of BCTs in both intervention arms and using the BCTs goal setting of outcome, feedback on outcome of behaviour, implementing graded tasks, and adding objects to the environment, e.g. using a step counter, significantly predicted the effect at long term. Setting a goal for change; and the presence of reporting bias independently explained 58.8% of inter-study variation at short term. Autonomy supportive and person-centred methods as in Motivational Interviewing, the BCTs goal setting of behaviour, and receiving feedback on the outcome of behaviour, explained all of the between study variations in effects at long term. There are similarities, but also differences in effective BCTs promoting change in healthy eating and physical activity and
Mayr, Nina A.; Wang, Jian Z.; Lo, Simon S.; Zhang Dongqing; Grecula, John C.; Lu Lanchun; Montebello, Joseph F.; Fowler, Jeffrey M.; Yuh, William T.C.
2010-01-01
Purpose: To assess individual volumetric tumor regression pattern in cervical cancer during therapy using serial four-dimensional MRI and to define the regression parameters' prognostic value validated with local control and survival correlation. Methods and Materials: One hundred and fifteen patients with Stage IB 2 -IVA cervical cancer treated with radiation therapy (RT) underwent serial MRI before (MRI 1) and during RT, at 2-2.5 weeks (MRI 2, at 20-25 Gy), and at 4-5 weeks (MRI 3, at 40-50 Gy). Eighty patients had a fourth MRI 1-2 months post-RT. Mean follow-up was 5.3 years. Tumor volume was measured by MRI-based three-dimensional volumetry, and plotted as dose(time)/volume regression curves. Volume regression parameters were correlated with local control, disease-specific, and overall survival. Results: Residual tumor volume, slope, and area under the regression curve correlated significantly with local control and survival. Residual volumes ≥20% at 40-50 Gy were independently associated with inferior 5-year local control (53% vs. 97%, p <0.001) and disease-specific survival rates (50% vs. 72%, p = 0.009) than smaller volumes. Patients with post-RT residual volumes ≥10% had 0% local control and 17% disease-specific survival, compared with 91% and 72% for <10% volume (p <0.001). Conclusion: Using more accurate four-dimensional volumetric regression analysis, tumor response can now be directly translated into individual patients' outcome for clinical application. Our results define two temporal thresholds critically influencing local control and survival. In patients with ≥20% residual volume at 40-50 Gy and ≥10% post-RT, the risk for local failure and death are so high that aggressive intervention may be warranted.
Added value of pharmacogenetic testing in predicting statin response: Results from the REGRESS trial
Van Der Baan, F.H.; Knol, M.J.; Maitland-Van Der Zee, A.H.; Regieli, J.J.; Van Iperen, E.P.A.; Egberts, A.C.G.; Klungel, O.H.; Grobbee, D.E.; Jukema, J.W.
2013-01-01
It was investigated whether pharmacogenetic factors, both as single polymorphism and as gene-gene interactions, have an added value over non-genetic factors in predicting statin response. Five common polymorphisms were selected in apolipoprotein E, angiotensin-converting enzyme, hepatic lipase and
Snedden, Gregg A.; Steyer, Gregory D.
2013-01-01
Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.
Snedden, Gregg A.; Steyer, Gregory D.
2013-02-01
Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.
Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo
2018-05-10
Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.
D'Ambrosio, Roberta; Aghemo, Alessio; Rumi, Maria Grazia; Degasperi, Elisabetta; Sangiovanni, Angelo; Maggioni, Marco; Fraquelli, Mirella; Perbellini, Riccardo; Rosenberg, William; Bedossa, Pierre; Colombo, Massimo; Lampertico, Pietro
2018-01-27
In patients with HCV-related cirrhosis, a sustained virological response may lead to cirrhosis regression. Whether histological changes translate into prevention of long-term complications, particularly hepatocellular carcinoma is still unknown. This was investigated in a cohort of histological cirrhotics who had been prospectively followed-up for 10 years after the achievement of a sustained virological response to IFN. In all, 38 sustained virological response cirrhotics who underwent a liver biopsy 5 years post-SVR were prospectively followed to assess the impact of cirrhosis regression on clinical endpoints. During a follow-up of 86 (30-96) months from liver biopsy, no patients developed clinical decompensation, whilst 5 (13%) developed hepatocellular carcinoma after 79 (7-88) months. The 8-year cumulative probability of hepatocellular carcinoma was 17%, without differences between patients with or without cirrhosis regression (19% [95% CI 6%-50%] vs 14% [95% CI 4%-44%], P = .88). Patients who developed or did not an hepatocellular carcinoma had similar rates of residual cirrhosis (P = 1.0), collagen content (P = .48), METAVIR activity (P = .34), portal inflammation (P = .06) and steatosis (P = .17). At baseline, patients who developed an hepatocellular carcinoma had higher γGT (HR 1.03, 95% CI 1.00-1.06; P = .014) and glucose (HR 1.02, 95% CI 1.00-1.02; P = .012) values; moreover, they had increased Forns Score (HR 12.8, 95% CI 1.14-143.9; P = .039), Lok Index (HR 6.24, 95% CI 1.03-37.6; P = .046) and PLF (HR 19.3, 95% CI 1.72-217.6; P = .016) values. One regressor died of lung cancer. The 8-year cumulative survival probability was 97%, independently on cirrhosis regression (96% vs 100%, P = 1.0) or hepatocellular carcinoma (100% vs 97%, P = 1.0). Post-SVR cirrhosis regression does not prevent hepatocellular carcinoma occurrence. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Susanne Unverzagt
Full Text Available This study is an in-depth-analysis to explain statistical heterogeneity in a systematic review of implementation strategies to improve guideline adherence of primary care physicians in the treatment of patients with cardiovascular diseases. The systematic review included randomized controlled trials from a systematic search in MEDLINE, EMBASE, CENTRAL, conference proceedings and registers of ongoing studies. Implementation strategies were shown to be effective with substantial heterogeneity of treatment effects across all investigated strategies. Primary aim of this study was to explain different effects of eligible trials and to identify methodological and clinical effect modifiers. Random effects meta-regression models were used to simultaneously assess the influence of multimodal implementation strategies and effect modifiers on physician adherence. Effect modifiers included the staff responsible for implementation, level of prevention and definition pf the primary outcome, unit of randomization, duration of follow-up and risk of bias. Six clinical and methodological factors were investigated as potential effect modifiers of the efficacy of different implementation strategies on guideline adherence in primary care practices on the basis of information from 75 eligible trials. Five effect modifiers were able to explain a substantial amount of statistical heterogeneity. Physician adherence was improved by 62% (95% confidence interval (95% CI 29 to 104% or 29% (95% CI 5 to 60% in trials where other non-medical professionals or nurses were included in the implementation process. Improvement of physician adherence was more successful in primary and secondary prevention of cardiovascular diseases by around 30% (30%; 95% CI -2 to 71% and 31%; 95% CI 9 to 57%, respectively compared to tertiary prevention. This study aimed to identify effect modifiers of implementation strategies on physician adherence. Especially the cooperation of different health
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
Simons, Monique; de Vet, Emely; Chinapaw, Mai Jm; de Boer, Michiel; Seidell, Jacob C; Brug, Johannes
2014-04-04
Playing video games contributes substantially to sedentary behavior in youth. A new generation of video games-active games-seems to be a promising alternative to sedentary games to promote physical activity and reduce sedentary behavior. At this time, little is known about correlates of active and non-active gaming among adolescents. The objective of this study was to examine potential personal, social, and game-related correlates of both active and non-active gaming in adolescents. A survey assessing game behavior and potential personal, social, and game-related correlates was conducted among adolescents (12-16 years, N=353) recruited via schools. Multivariable, multilevel logistic regression analyses, adjusted for demographics (age, sex and educational level of adolescents), were conducted to examine personal, social, and game-related correlates of active gaming ≥1 hour per week (h/wk) and non-active gaming >7 h/wk. Active gaming ≥1 h/wk was significantly associated with a more positive attitude toward active gaming (OR 5.3, CI 2.4-11.8; Pgames (OR 0.30, CI 0.1-0.6; P=.002), a higher score on habit strength regarding gaming (OR 1.9, CI 1.2-3.2; P=.008) and having brothers/sisters (OR 6.7, CI 2.6-17.1; Pgame engagement (OR 0.95, CI 0.91-0.997; P=.04). Non-active gaming >7 h/wk was significantly associated with a more positive attitude toward non-active gaming (OR 2.6, CI 1.1-6.3; P=.035), a stronger habit regarding gaming (OR 3.0, CI 1.7-5.3; P7 h/wk. Active gaming is most strongly (negatively) associated with attitude with respect to non-active games, followed by observed active game behavior of brothers and sisters and attitude with respect to active gaming (positive associations). On the other hand, non-active gaming is most strongly associated with observed non-active game behavior of friends, habit strength regarding gaming and attitude toward non-active gaming (positive associations). Habit strength was a correlate of both active and non-active gaming
de Vet, Emely; Chinapaw, Mai JM; de Boer, Michiel; Seidell, Jacob C; Brug, Johannes
2014-01-01
Background Playing video games contributes substantially to sedentary behavior in youth. A new generation of video games—active games—seems to be a promising alternative to sedentary games to promote physical activity and reduce sedentary behavior. At this time, little is known about correlates of active and non-active gaming among adolescents. Objective The objective of this study was to examine potential personal, social, and game-related correlates of both active and non-active gaming in adolescents. Methods A survey assessing game behavior and potential personal, social, and game-related correlates was conducted among adolescents (12-16 years, N=353) recruited via schools. Multivariable, multilevel logistic regression analyses, adjusted for demographics (age, sex and educational level of adolescents), were conducted to examine personal, social, and game-related correlates of active gaming ≥1 hour per week (h/wk) and non-active gaming >7 h/wk. Results Active gaming ≥1 h/wk was significantly associated with a more positive attitude toward active gaming (OR 5.3, CI 2.4-11.8; Pgames (OR 0.30, CI 0.1-0.6; P=.002), a higher score on habit strength regarding gaming (OR 1.9, CI 1.2-3.2; P=.008) and having brothers/sisters (OR 6.7, CI 2.6-17.1; Pgame engagement (OR 0.95, CI 0.91-0.997; P=.04). Non-active gaming >7 h/wk was significantly associated with a more positive attitude toward non-active gaming (OR 2.6, CI 1.1-6.3; P=.035), a stronger habit regarding gaming (OR 3.0, CI 1.7-5.3; P7 h/wk. Active gaming is most strongly (negatively) associated with attitude with respect to non-active games, followed by observed active game behavior of brothers and sisters and attitude with respect to active gaming (positive associations). On the other hand, non-active gaming is most strongly associated with observed non-active game behavior of friends, habit strength regarding gaming and attitude toward non-active gaming (positive associations). Habit strength was a
Seismic response analyses of turbine hall and electrical building of RBMK-1000 MW type NPP
Jordanov, M.J.; Karparov, K.T.
2003-01-01
This paper addresses results obtained during the study of turbine hall and electrical building of RBMK-1000 MW pair units at Leningradskaya NPP (LNPP) for seismic event. The study was performed in the frame of the Coordinated Research Program of the International Atomic Agency (IAEA) on Safety of RBMK type Nuclear Power Plants (NPP) in Relation of External Events. A 3-D finite element model of Main Building Complex was developed and seismic response analyses were performed taking into account the soil-structure interaction (SSI). The standard mode superposition method was used for evaluation of dynamic response of structure in time domain. The structure was assumed surface founded at the basemat level. Seismic response analyses were carried out considering shear wave propagation pattern for the input motion. The in-structure time histories and response spectra were generated in referenced locations. Conclusions are drawn for the reliability of the structural response evaluation considering the soil-structure interaction effects. (author)
Alexeeff, Stacey E; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A
2015-01-01
Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R(2) yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R(2) yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.
Lee, J. H.; Yoo, B.; Koo, K. H. [KAERI, Taejon (Korea, Republic of)
2001-05-01
The seismic response time history analyses for the lumped mass models of KALIMER reactor building with a horizontal and vertical seismic isolation are performed for Artificial Time History and Kobe earthquake. The vertical amplification by the horizontal isolation is reduced by a vertical isolation for both earthquakes. The 3% viscous damping and the vertical isolation frequency of 1.5Hz gives a reduced vertical response compared to the fixed base condition at reactor support, and the 9% viscous damping to Kobe earthquake is required to get an equivalent vertical response with a fixed base condition.
Lee, J. H.; Yoo, B.; Koo, K. H.
2001-01-01
The seismic response time history analyses for the lumped mass models of KALIMER reactor building with a horizontal and vertical seismic isolation are performed for Artificial Time History and Kobe earthquake. The vertical amplification by the horizontal isolation is reduced by a vertical isolation for both earthquakes. The 3% viscous damping and the vertical isolation frequency of 1.5Hz gives a reduced vertical response compared to the fixed base condition at reactor support, and the 9% viscous damping to Kobe earthquake is required to get an equivalent vertical response with a fixed base condition
Performing dynamic time history analyses by extension of the response spectrum method
Hulbert, G.M.
1983-01-01
A method is presented to calculate the dynamic time history response of finite-element models using results from response spectrum analyses. The proposed modified time history method does not represent a new mathamatical approach to dynamic analysis but suggests a more efficient ordering of the analytical equations and procedures. The modified time history method is considerably faster and less expensive to use than normal time hisory methods. This paper presents the theory and implementation of the modified time history approach along with comparisons of the modified and normal time history methods for a prototypic seismic piping design problem
Broman, Karolina; Bernholt, Sascha; Parchmann, Ilka
2015-05-01
Background:Context-based learning approaches are used to enhance students' interest in, and knowledge about, science. According to different empirical studies, students' interest is improved by applying these more non-conventional approaches, while effects on learning outcomes are less coherent. Hence, further insights are needed into the structure of context-based problems in comparison to traditional problems, and into students' problem-solving strategies. Therefore, a suitable framework is necessary, both for the analysis of tasks and strategies. Purpose:The aim of this paper is to explore traditional and context-based tasks as well as students' responses to exemplary tasks to identify a suitable framework for future design and analyses of context-based problems. The paper discusses different established frameworks and applies the Higher-Order Cognitive Skills/Lower-Order Cognitive Skills (HOCS/LOCS) taxonomy and the Model of Hierarchical Complexity in Chemistry (MHC-C) to analyse traditional tasks and students' responses. Sample:Upper secondary students (n=236) at the Natural Science Programme, i.e. possible future scientists, are investigated to explore learning outcomes when they solve chemistry tasks, both more conventional as well as context-based chemistry problems. Design and methods:A typical chemistry examination test has been analysed, first the test items in themselves (n=36), and thereafter 236 students' responses to one representative context-based problem. Content analysis using HOCS/LOCS and MHC-C frameworks has been applied to analyse both quantitative and qualitative data, allowing us to describe different problem-solving strategies. Results:The empirical results show that both frameworks are suitable to identify students' strategies, mainly focusing on recall of memorized facts when solving chemistry test items. Almost all test items were also assessing lower order thinking. The combination of frameworks with the chemistry syllabus has been
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Dai, Huanping; Micheyl, Christophe
2012-11-01
Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.
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.
Wang, Hui; Sui, Weiguo; Xue, Wen; Wu, Junyong; Chen, Jiejing; Dai, Yong
2014-09-01
Immunoglobulin A nephropathy (IgAN) is a complex trait regulated by the interaction among multiple physiologic regulatory systems and probably involving numerous genes, which leads to inconsistent findings in genetic studies. One possibility of failure to replicate some single-locus results is that the underlying genetics of IgAN nephropathy is based on multiple genes with minor effects. To learn the association between 23 single nucleotide polymorphisms (SNPs) in 14 genes predisposing to chronic glomerular diseases and IgAN in Han males, the 23 SNPs genotypes of 21 Han males were detected and analyzed with a BaiO gene chip, and their associations were analyzed with univariate analysis and multiple linear regression analysis. Analysis showed that CTLA4 rs231726 and CR2 rs1048971 revealed a significant association with IgAN. These findings support the multi-gene nature of the etiology of IgAN and propose a potential gene-gene interactive model for future studies.
Duda, David P.; Minnis, Patrick
2009-01-01
Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.
Better Autologistic Regression
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Rafael Fonseca Benevenuto
Full Text Available Some genetically modified (GM plants have transgenes that confer tolerance to abiotic stressors. Meanwhile, other transgenes may interact with abiotic stressors, causing pleiotropic effects that will affect the plant physiology. Thus, physiological alteration might have an impact on the product safety. However, routine risk assessment (RA analyses do not evaluate the response of GM plants exposed to different environmental conditions. Therefore, we here present a proteome profile of herbicide-tolerant maize, including the levels of phytohormones and related compounds, compared to its near-isogenic non-GM variety under drought and herbicide stresses. Twenty differentially abundant proteins were detected between GM and non-GM hybrids under different water deficiency conditions and herbicide sprays. Pathway enrichment analysis showed that most of these proteins are assigned to energetic/carbohydrate metabolic processes. Among phytohormones and related compounds, different levels of ABA, CA, JA, MeJA and SA were detected in the maize varieties and stress conditions analysed. In pathway and proteome analyses, environment was found to be the major source of variation followed by the genetic transformation factor. Nonetheless, differences were detected in the levels of JA, MeJA and CA and in the abundance of 11 proteins when comparing the GM plant and its non-GM near-isogenic variety under the same environmental conditions. Thus, these findings do support molecular studies in GM plants Risk Assessment analyses.
Emergency response guide-B ECCS guideline evaluation analyses for N reactor
Chapman, J.C.; Callow, R.A.
1989-07-01
INEL conducted two ECCS analyses for Westinghouse Hanford. Both analyses will assist in the evaluation of proposed changes to the N Reactor Emergency Response Guide-B (ERG-B) Emergency Core System (ECCS) guideline. The analyses were a sensitivity study for reduced-ECCS flow rates and a mechanistically determined confinement steam source for a delayed-ECCS LOCA sequence. The reduced-ECCS sensitivity study established the maximum allowable reduction in ECCS flow as a function of time after core refill for a large break loss-of-coolant accident (LOCA) sequence in the N Reactor. The maximum allowable ECCS flow reduction is defined as the maximum flow reduction for which ECCS continues to provide adequate core cooling. The delayed-ECCS analysis established the liquid and steam break flows and enthalpies during the reflood of a hot core following a delayed ECCS injection LOCA sequence. A simulation of a large, hot leg manifold break with a seven-minute ECCS injection delay was used as a representative LOCA sequence. Both analyses were perform using the RELAP5/MOD2.5 transient computer code. 13 refs., 17 figs., 3 tabs
Crown, William H
2014-02-01
This paper examines the use of propensity score matching in economic analyses of observational data. Several excellent papers have previously reviewed practical aspects of propensity score estimation and other aspects of the propensity score literature. The purpose of this paper is to compare the conceptual foundation of propensity score models with alternative estimators of treatment effects. References are provided to empirical comparisons among methods that have appeared in the literature. These comparisons are available for a subset of the methods considered in this paper. However, in some cases, no pairwise comparisons of particular methods are yet available, and there are no examples of comparisons across all of the methods surveyed here. Irrespective of the availability of empirical comparisons, the goal of this paper is to provide some intuition about the relative merits of alternative estimators in health economic evaluations where nonlinearity, sample size, availability of pre/post data, heterogeneity, and missing variables can have important implications for choice of methodology. Also considered is the potential combination of propensity score matching with alternative methods such as differences-in-differences and decomposition methods that have not yet appeared in the empirical literature.
Duda, David P.; Minnis, Patrick
2009-01-01
Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.
Seismic response and damage detection analyses of an instrumented steel moment-framed building
Rodgers, J.E.; Celebi, M.
2006-01-01
The seismic performance of steel moment-framed buildings has been of particular interest since brittle fractures were discovered at the beam-column connections in a number of buildings following the M 6.7 Northridge earthquake of January 17, 1994. A case study of the seismic behavior of an extensively instrumented 13-story steel moment frame building located in the greater Los Angeles area of California is described herein. Response studies using frequency domain, joint time-frequency, system identification, and simple damage detection analyses are performed using an extensive strong motion dataset dating from 1971 to the present, supported by engineering drawings and results of postearthquake inspections. These studies show that the building's response is more complex than would be expected from its highly symmetrical geometry. The response is characterized by low damping in the fundamental mode, larger accelerations in the middle and lower stories than at the roof and base, extended periods of vibration after the cessation of strong input shaking, beating in the response, elliptical particle motion, and significant torsion during strong shaking at the top of the concrete piers which extend from the basement to the second floor. The analyses conducted indicate that the response of the structure was elastic in all recorded earthquakes to date, including Northridge. Also, several simple damage detection methods employed did not indicate any structural damage or connection fractures. The combination of a large, real structure and low instrumentation density precluded the application of many recently proposed advanced damage detection methods in this case study. Overall, however, the findings of this study are consistent with the limited code-compliant postearthquake intrusive inspections conducted after the Northridge earthquake, which found no connection fractures or other structural damage. ?? ASCE.
Sunando Roy
2009-10-01
Full Text Available Feline immunodeficiency virus (FIV and human immunodeficiency virus (HIV are recently identified lentiviruses that cause progressive immune decline and ultimately death in infected cats and humans. It is of great interest to understand how to prevent immune system collapse caused by these lentiviruses. We recently described that disease caused by a virulent FIV strain in cats can be attenuated if animals are first infected with a feline immunodeficiency virus derived from a wild cougar. The detailed temporal tracking of cat immunological parameters in response to two viral infections resulted in high-dimensional datasets containing variables that exhibit strong co-variation. Initial analyses of these complex data using univariate statistical techniques did not account for interactions among immunological response variables and therefore potentially obscured significant effects between infection state and immunological parameters.Here, we apply a suite of multivariate statistical tools, including Principal Component Analysis, MANOVA and Linear Discriminant Analysis, to temporal immunological data resulting from FIV superinfection in domestic cats. We investigated the co-variation among immunological responses, the differences in immune parameters among four groups of five cats each (uninfected, single and dual infected animals, and the "immune profiles" that discriminate among them over the first four weeks following superinfection. Dual infected cats mount an immune response by 24 days post superinfection that is characterized by elevated levels of CD8 and CD25 cells and increased expression of IL4 and IFNgamma, and FAS. This profile discriminates dual infected cats from cats infected with FIV alone, which show high IL-10 and lower numbers of CD8 and CD25 cells.Multivariate statistical analyses demonstrate both the dynamic nature of the immune response to FIV single and dual infection and the development of a unique immunological profile in dual
Exposure-response analyses of liraglutide 3.0 mg for weight management.
Wilding, J P H; Overgaard, R V; Jacobsen, L V; Jensen, C B; le Roux, C W
2016-05-01
Liraglutide 3.0 mg, an acylated GLP-1 analogue approved for weight management, lowers body weight through decreased energy intake. We conducted exposure-response analyses to provide important information on individual responses to given drug doses, reflecting inter-individual variations in drug metabolism, absorption and excretion. We report efficacy and safety responses across a wide range of exposure levels, using data from one phase II (liraglutide doses 1.2, 1.8, 2.4 and 3.0 mg), and two phase IIIa [SCALE Obesity and Prediabetes (3.0 mg); SCALE Diabetes (1.8; 3.0 mg)] randomized, placebo-controlled trials (n = 4372). There was a clear exposure-weight loss response. Weight loss increased with greater exposure and appeared to level off at the highest exposures associated with liraglutide 3.0 mg in most individuals, but did not fully plateau in men. In individuals with overweight/obesity and comorbid type 2 diabetes, there was a clear exposure-glycated haemoglobin (HbA1c) relationship. HbA1c reduction increased with higher plasma liraglutide concentration (plateauing at ∼21 nM); however, for individuals with baseline HbA1c >8.5%, HbA1c reduction did not fully plateau. No exposure-response relationship was identified for any safety outcome, with the exception of gastrointestinal adverse events (AEs). Individuals with gallbladder AEs, acute pancreatitis or malignant/breast/benign colorectal neoplasms did not have higher liraglutide exposure compared with the overall population. These analyses support the use of liraglutide 3.0 mg for weight management in all subgroups investigated; weight loss increased with higher drug exposure, with no concomitant deterioration in safety/tolerability besides previously known gastrointestinal side effects. © 2016 John Wiley & Sons Ltd.
Exposure–response analyses of liraglutide 3.0 mg for weight management
Overgaard, R. V.; Jacobsen, L. V.; Jensen, C. B.; le Roux, C. W.
2016-01-01
Aims Liraglutide 3.0 mg, an acylated GLP‐1 analogue approved for weight management, lowers body weight through decreased energy intake. We conducted exposure‐response analyses to provide important information on individual responses to given drug doses, reflecting inter‐individual variations in drug metabolism, absorption and excretion. Methods We report efficacy and safety responses across a wide range of exposure levels, using data from one phase II (liraglutide doses 1.2, 1.8, 2.4 and 3.0 mg), and two phase IIIa [SCALE Obesity and Prediabetes (3.0 mg); SCALE Diabetes (1.8; 3.0 mg)] randomized, placebo‐controlled trials (n = 4372). Results There was a clear exposure–weight loss response. Weight loss increased with greater exposure and appeared to level off at the highest exposures associated with liraglutide 3.0 mg in most individuals, but did not fully plateau in men. In individuals with overweight/obesity and comorbid type 2 diabetes, there was a clear exposure–glycated haemoglobin (HbA1c) relationship. HbA1c reduction increased with higher plasma liraglutide concentration (plateauing at ∼21 nM); however, for individuals with baseline HbA1c >8.5%, HbA1c reduction did not fully plateau. No exposure–response relationship was identified for any safety outcome, with the exception of gastrointestinal adverse events (AEs). Individuals with gallbladder AEs, acute pancreatitis or malignant/breast/benign colorectal neoplasms did not have higher liraglutide exposure compared with the overall population. Conclusions These analyses support the use of liraglutide 3.0 mg for weight management in all subgroups investigated; weight loss increased with higher drug exposure, with no concomitant deterioration in safety/tolerability besides previously known gastrointestinal side effects. PMID:26833744
Mazeron, Renaud; Castelnau-Marchand, Pauline; Escande, Alexandre; Rivin Del Campo, Eleonor; Maroun, Pierre; Lefkopoulos, Dimitri; Chargari, Cyrus; Haie-Meder, Christine
2016-01-01
Image-guided adaptive brachytherapy is a high precision technique that allows dose escalation and adaptation to tumor response. Two monocentric studies reported continuous dose-volume response relationships, however, burdened by large confidence intervals. The aim was to refine these estimations by performing a meta-regression analysis based on published series. Eligibility was limited to series reporting dosimetric parameters according to the Groupe Européen de Curiethérapie-European SocieTy for Radiation Oncology recommendations. The local control rates reported at 2-3 years were confronted to the mean D90 clinical target volume (CTV) in 2-Gy equivalent using the probit model. The impact of each series on the relationships was pondered according to the number of patients reported. An exhaustive literature search retrieved 13 series reporting on 1299 patients. D90 high-risk CTV ranged from 70.9 to 93.1 Gy. The probit model showed a significant correlation between the D90 and the probability of achieving local control (p < 0.0001). The D90 associated to a 90% probability of achieving local control was 81.4 Gy (78.3-83.8 Gy). The planning aim of 90 Gy corresponded to a 95.0% probability (92.8-96.3%). For the intermediate-risk CTV, less data were available, with 873 patients from eight institutions. Reported mean D90 intermediate-risk CTV ranged from 61.7 to 69.1 Gy. A significant dose-volume effect was observed (p = 0.009). The D90 of 60 Gy was associated to a 79.4% (60.2-86.0%) local control probability. Based on published data from a high number of patients, significant dose-volume effect relationships were confirmed and refined between the D90 of both CTV and the probability of achieving local control. Further studies based on individual data are required to develop nomograms including nondosimetric prognostic criteria. Copyright © 2016 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.
Chuang, Shu-Chun; Rota, Matteo; Gunter, Marc J; Zeleniuch-Jacquotte, Anne; Eussen, Simone J P M; Vollset, Stein Emil; Ueland, Per Magne; Norat, Teresa; Ziegler, Regina G; Vineis, Paolo
2013-10-01
Most epidemiologic studies on folate intake suggest that folate may be protective against colorectal cancer, but the results on circulating (plasma or serum) folate are mostly inconclusive. We conducted a meta-analysis of case-control studies nested within prospective studies on circulating folate and colorectal cancer risk by using flexible meta-regression models to test the linear and nonlinear dose-response relationships. A total of 8 publications (10 cohorts, representing 3,477 cases and 7,039 controls) were included in the meta-analysis. The linear and nonlinear models corresponded to relative risks of 0.96 (95% confidence interval (CI): 0.91, 1.02) and 0.99 (95% CI: 0.96, 1.02), respectively, per 10 nmol/L of circulating folate in contrast to the reference value. The pooled relative risks when comparing the highest with the lowest category were 0.80 (95% CI: 0.61, 0.99) for radioimmunoassay and 1.03 (95% CI: 0.83, 1.22) for microbiological assay. Overall, our analyses suggest a null association between circulating folate and colorectal cancer risk. The stronger association for the radioimmunoassay-based studies could reflect differences in cohorts and study designs rather than assay performance. Further investigations need to integrate more accurate measurements and flexible modeling to explore the effects of folate in the presence of genetic, lifestyle, dietary, and hormone-related factors.
Trenholm, Susan; Ferlie, Ewan
2013-09-01
We employ complexity theory to analyse the English National Health Service (NHS)'s organisational response to resurgent tuberculosis across London. Tennison (2002) suggests that complexity theory could fruitfully explore a healthcare system's response to this complex and emergent phenomenon: we explore this claim here. We also bring in established New Public Management principles to enhance our empirical analysis, which is based on data collected between late 2009 and mid-2011. We find that the operation of complexity theory based features, especially self-organisation, are significantly impacted by the macro context of a New Public Management-based regime which values control, measurement and risk management more than innovation, flexibility and lateral system building. We finally explore limitations and suggest perspectives for further research. Copyright © 2012 Elsevier Ltd. All rights reserved.
The simulation of man-machine interaction in NPPs: the system response analyser project
Cacciabue, P.C.
1990-01-01
In this paper, the ongoing research at Joint Research Centre-Ispra on the simulation of man-machine interaction is reviewed with reference to the past experience of system modelling and to the advances of the technological world. These require the coalescence of mixed disciplines covering the fields of engineering, psychology and sociology. In particular, the complexity of man-machine systems with respect to safety analysis is depicted. The developments and issues in modelling humans and machines are discussed: the possibility of combining them through the System Response Analyser methodology is presented as a balanced to be applied when the objective is the study of safety of systems during abnormal sequences. The three analytical tools which constitute the body of system response analysis namely a quasi-classical simulation of the actual plant, a cognitive model of the operator activities and a driver model, are described. (author)
Paul M. Graham, DO
2018-05-01
Full Text Available We report a case of histologically confirmed primary cutaneous diffuse large B-cell lymphoma, leg type (PCDLBCL-LT that subsequently underwent spontaneous regression in the absence of systemic treatment. The case showed an atypical lymphoid infiltrate that was CD20+ and MUM-1+ and CD10–. A subsequent biopsy of the spontaneously regressed lesion showed fibrosis associated with a lymphocytic infiltrate comprising reactive T cells. PCDLBCL-LT is a cutaneous B-cell lymphoma with a poor prognosis, which is usually treated with chemotherapy. We describe a case of clinical and histologic spontaneous regression in a patient with PCDLBCL-LT who had a negative systemic workup but a recurrence over a year after his initial presentation. Key words: B cell, lymphoma, primary cutaneous diffuse large B-cell lymphoma, leg type, regression
Item Response Theory Analyses of the Cambridge Face Memory Test (CFMT)
Cho, Sun-Joo; Wilmer, Jeremy; Herzmann, Grit; McGugin, Rankin; Fiset, Daniel; Van Gulick, Ana E.; Ryan, Katie; Gauthier, Isabel
2014-01-01
We evaluated the psychometric properties of the Cambridge face memory test (CFMT; Duchaine & Nakayama, 2006). First, we assessed the dimensionality of the test with a bi-factor exploratory factor analysis (EFA). This EFA analysis revealed a general factor and three specific factors clustered by targets of CFMT. However, the three specific factors appeared to be minor factors that can be ignored. Second, we fit a unidimensional item response model. This item response model showed that the CFMT items could discriminate individuals at different ability levels and covered a wide range of the ability continuum. We found the CFMT to be particularly precise for a wide range of ability levels. Third, we implemented item response theory (IRT) differential item functioning (DIF) analyses for each gender group and two age groups (Age ≤ 20 versus Age > 21). This DIF analysis suggested little evidence of consequential differential functioning on the CFMT for these groups, supporting the use of the test to compare older to younger, or male to female, individuals. Fourth, we tested for a gender difference on the latent facial recognition ability with an explanatory item response model. We found a significant but small gender difference on the latent ability for face recognition, which was higher for women than men by 0.184, at age mean 23.2, controlling for linear and quadratic age effects. Finally, we discuss the practical considerations of the use of total scores versus IRT scale scores in applications of the CFMT. PMID:25642930
Wong, C.C.
1988-11-01
The HECTR (Hydrogen Event: Containment Transient Response) computer code has been developed at Sandia National Laboratories to predict the transient pressure and temperature responses within reactor containments for hypothetical accidents involving the transport and combustion of hydrogen. Although HECTR was designed primarily to investigate these phenomena in LWRs, it may also be used to analyze hydrogen transport and combustion experiments as well. It is in this manner that HECTR is assessed and empirical correlations, such as the combustion completeness and flame speed correlations for the hydrogen combustion model, if necessary, are upgraded. In this report, we present HECTR analyses of the large-scale premixed hydrogen combustion experiments at the Nevada Test Site (NTS) and comparison with the test results. The existing correlations in HECTR version 1.0, under certain conditions, have difficulty in predicting accurately the combustion completeness and burn time for the NTS experiments. By combining the combustion data obtained from the NTS experiments with other experimental data (FITS, VGES, ACUREX, and Whiteshell), a set of new and better combustion correlations was generated. HECTR prediction of the containment responses, using a single-compartment model and EPRI-provided combustion completeness and burn time, compares reasonably well against the test results. However, HECTR prediction of the containment responses using a multicompartment model does not compare well with the test results. This discrepancy shows the deficiency of the homogeneous burning model used in HECTR. To overcome this deficiency, a flame propagation model is highly recommended. 16 refs., 84 figs., 5 tabs
He, Z.; Zhou, A.; Baidoo, E.; He, Q.; Joachimiak, M. P.; Benke, P.; Phan, R.; Mukhopadhyay, A.; Hemme, C.L.; Huang, K.; Alm, E.J.; Fields, M.W.; Wall, J.; Stahl, D.; Hazen, T.C.; Keasling, J.D.; Arkin, A.P.; Zhou, J.
2009-12-01
The response of Desulfovibrio vulgaris Hildenborough to salt adaptation (long-term NaCl exposure) was examined by physiological, global transcriptional, and metabolite analyses. The growth of D. vulgaris was inhibited by high levels of NaCl, and the growth inhibition could be relieved by the addition of exogenous amino acids (e.g., glutamate, alanine, tryptophan) or yeast extract. Salt adaptation induced the expression of genes involved in amino acid biosynthesis and transport, electron transfer, hydrogen oxidation, and general stress responses (e.g., heat shock proteins, phage shock proteins, and oxidative stress response proteins). Genes involved in carbon metabolism, cell motility, and phage structures were repressed. Comparison of transcriptomic profiles of D. vulgaris responses to salt adaptation with those of salt shock (short-term NaCl exposure) showed some similarity as well as a significant difference. Metabolite assays showed that glutamate and alanine were accumulated under salt adaptation, suggesting that they may be used as osmoprotectants in D. vulgaris. A conceptual model is proposed to link the observed results to currently available knowledge for further understanding the mechanisms of D. vulgaris adaptation to elevated NaCl.
Longitudinal analyses of correlated response efficiencies of fillet traits in Nile tilapia.
Turra, E M; Fernandes, A F A; de Alvarenga, E R; Teixeira, E A; Alves, G F O; Manduca, L G; Murphy, T W; Silva, M A
2018-03-01
Recent studies with Nile tilapia have shown divergent results regarding the possibility of selecting on morphometric measurements to promote indirect genetic gains in fillet yield (FY). The use of indirect selection for fillet traits is important as these traits are only measurable after harvesting. Random regression models are a powerful tool in association studies to identify the best time point to measure and select animals. Random regression models can also be applied in a multiple trait approach to analyze indirect response to selection, which would avoid the need to sacrifice candidate fish. Therefore, the aim of this study was to investigate the genetic relationships between several body measurements, weight and fillet traits throughout the growth period and to evaluate the possibility of indirect selection for fillet traits in Nile tilapia. Data were collected from 2042 fish and was divided into two subsets. The first subset was used to estimate genetic parameters, including the permanent environmental effect for BW and body measurements (8758 records for each body measurement, as each fish was individually weighed and measured a maximum of six times). The second subset (2042 records for each trait) was used to estimate genetic correlations and heritabilities, which enabled the calculation of correlated response efficiencies between body measurements and the fillet traits. Heritability estimates across ages ranged from 0.05 to 0.5 for height, 0.02 to 0.48 for corrected length (CL), 0.05 to 0.68 for width, 0.08 to 0.57 for fillet weight (FW) and 0.12 to 0.42 for FY. All genetic correlation estimates between body measurements and FW were positive and strong (0.64 to 0.98). The estimates of genetic correlation between body measurements and FY were positive (except for CL at some ages), but weak to moderate (-0.08 to 0.68). These estimates resulted in strong and favorable correlated response efficiencies for FW and positive, but moderate for FY. These results
Karaaslan, Ozcan; Mahoney, Gerald
2015-01-01
Mediational analyses were conducted with data from two small randomized control trials of the Responsive Teaching (RT) parent-mediated developmental intervention which used nearly identical intervention and control procedures. The purpose of these analyses was to determine whether or how the changes in maternal responsiveness and children's…
Mazurana, Dyan; Benelli, Prisca; Walker, Peter
2013-07-01
Humanitarian aid remains largely driven by anecdote rather than by evidence. The contemporary humanitarian system has significant weaknesses with regard to data collection, analysis, and action at all stages of response to crises involving armed conflict or natural disaster. This paper argues that humanitarian actors can best determine and respond to vulnerabilities and needs if they use sex- and age-disaggregated data (SADD) and gender and generational analyses to help shape their assessments of crises-affected populations. Through case studies, the paper shows how gaps in information on sex and age limit the effectiveness of humanitarian response in all phases of a crisis. The case studies serve to show how proper collection, use, and analysis of SADD enable operational agencies to deliver assistance more effectively and efficiently. The evidence suggests that the employment of SADD and gender and generational analyses assists in saving lives and livelihoods in a crisis. © 2013 The Author(s). Journal compilation © Overseas Development Institute, 2013.
Understanding logistic regression analysis
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...
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Cohen, Michael X; Gulbinaite, Rasa
2017-02-15
Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to multiple electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response, as well as several other linear spatial filters. We also discuss the potential confound of overfitting, whereby the filter captures noise in absence of a signal. Matlab scripts are available to replicate and extend our simulations and methods. We conclude with some practical advice for optimizing SSEP data analyses and interpreting the results. Copyright © 2016 Elsevier Inc. All rights reserved.
Freund, Rudolf J; Sa, Ping
2006-01-01
The book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design
Van Der Meer, D.; Hoekstra, P. J.; Van Donkelaar, M.; Bralten, J.; Oosterlaan, J.; Heslenfeld, D.; Faraone, S. V.; Franke, B.; Buitelaar, J. K.; Hartman, C. A.
2017-01-01
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression
van der Meer, D.; Hoekstra, P. J.; van Donkelaar, Marjolein M. J.; Bralten, Janita; Oosterlaan, J; Heslenfeld, Dirk J.; Faraone, S. V.; Franke, B.; Buitelaar, J. K.; Hartman, C. A.
2017-01-01
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression
A multichannel frequency response analyser for impedance spectroscopy on power sources
DANIEL J. L. BRETT
2013-06-01
Full Text Available A low-cost multi-channel frequency response analyser (FRA has been developed based on a DAQ (data acquisition/LabVIEW interface. The system has been tested for electric and electrochemical impedance measurements. This novel association of hardware and software demonstrated performance comparable to a commercial potentiostat / FRA for passive electric circuits. The software has multichannel capabilities with minimal phase shift for 5 channels when operated below 3 kHz. When applied in active (galvanostatic mode in conjunction with a commercial electronic load (by discharging a lead acid battery at 1.5 A the performance was fit for purpose, providing electrochemical information to characterize the performance of the power source.
Site response - a critical problem in soil-structure interaction analyses for embedded structures
Seed, H.B.; Lysmer, J.
1986-01-01
Soil-structure interaction analyses for embedded structures must necessarily be based on a knowledge of the manner in which the soil would behave in the absence of any structure - that is on a knowledge and understanding of the spatial distribution of motions in the ground within the depth of embedment of the structure. The nature of these spatial variations is discussed and illustrated by examples of recorded motions. It is shown that both the amplitude of peak acceleration and the form of the acceleration response spectrum for earthquake motions will necessarily vary with depth and failure to take these variations into account may introduce an unwarranted degree of conservatism into the soil-structure interaction analysis procedure
Holmes, Tyson H; He, Xiao-Song
2016-10-01
Small, wide data sets are commonplace in human immunophenotyping research. As defined here, a small, wide data set is constructed by sampling a small to modest quantity n,1small, wide data sets. These prescriptions are distinctive in their especially heavy emphasis on minimizing the use of out-of-sample information for conducting statistical inference. This allows the working immunologist to proceed without being encumbered by imposed and often untestable statistical assumptions. Problems of unmeasured confounders, confidence-interval coverage, feature selection, and shrinkage/denoising are defined clearly and treated in detail. We propose an extension of an existing nonparametric technique for improved small-sample confidence-interval tail coverage from the univariate case (single immune feature) to the multivariate (many, possibly correlated immune features). An important role for derived features in the immunological interpretation of regression analyses is stressed. Areas of further research are discussed. Presented principles and methods are illustrated through application to a small, wide data set of adults spanning a wide range in ages and multiple immunophenotypes that were assayed before and after immunization with inactivated influenza vaccine (IIV). Our regression modeling prescriptions identify some potentially important topics for future immunological research. 1) Immunologists may wish to distinguish age-related differences in immune features from changes in immune features caused by aging. 2) A form of the bootstrap that employs linear extrapolation may prove to be an invaluable analytic tool because it allows the working immunologist to obtain accurate estimates of the stability of immune parameter estimates with a bare minimum of imposed assumptions. 3) Liberal inclusion of immune features in phenotyping panels can facilitate accurate separation of biological signal of interest from noise. In addition, through a combination of denoising and
Alecia J Carter
Full Text Available Animal personality, repeatable behaviour through time and across contexts, is ecologically and evolutionarily important as it can account for the exhibition of sub-optimal behaviours. Interspecific comparisons have been suggested as important for understanding the evolution of animal personality; however, these are seldom accomplished due, in part, to the lack of statistical tools for quantifying differences and similarities in behaviour between groups of individuals. We used nine species of closely-related coral reef fishes to investigate the usefulness of ecological community analyses for the analysis of between-species behavioural differences and behavioural heterogeneity. We first documented behavioural carryover across species by observing the fishes' behaviour and measuring their response to a threatening stimulus to quantify boldness. Bold fish spent more time away from the reef and fed more than shy fish. We then used ecological community analysis tools (canonical variate analysis, multi-response permutation procedure, and permutational analysis of multivariate dispersion and identified four 'clusters' of behaviourally similar fishes, and found that the species differ in the behavioural variation expressed; some species are more behaviourally heterogeneous than others. We found that ecological community analysis tools are easily and fruitfully applied to comparative studies of personality and encourage their use by future studies.
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.
Warton, David I; Thibaut, Loïc; Wang, Yi Alice
2017-01-01
Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.
Yntze van der Hoek
Full Text Available BACKGROUND: Identifying persistence and extinction thresholds in species-habitat relationships is a major focal point of ecological research and conservation. However, one major concern regarding the incorporation of threshold analyses in conservation is the lack of knowledge on the generality and transferability of results across species and regions. We present a multi-region, multi-species approach of modeling threshold responses, which we use to investigate whether threshold effects are similar across species and regions. METHODOLOGY/PRINCIPAL FINDINGS: We modeled local persistence and extinction dynamics of 25 forest-associated breeding birds based on detection/non-detection data, which were derived from repeated breeding bird atlases for the state of Vermont. We did not find threshold responses to be particularly well-supported, with 9 species supporting extinction thresholds and 5 supporting persistence thresholds. This contrasts with a previous study based on breeding bird atlas data from adjacent New York State, which showed that most species support persistence and extinction threshold models (15 and 22 of 25 study species respectively. In addition, species that supported a threshold model in both states had associated average threshold estimates of 61.41% (SE = 6.11, persistence and 66.45% (SE = 9.15, extinction in New York, compared to 51.08% (SE = 10.60, persistence and 73.67% (SE = 5.70, extinction in Vermont. Across species, thresholds were found at 19.45-87.96% forest cover for persistence and 50.82-91.02% for extinction dynamics. CONCLUSIONS/SIGNIFICANCE: Through an approach that allows for broad-scale comparisons of threshold responses, we show that species vary in their threshold responses with regard to habitat amount, and that differences between even nearby regions can be pronounced. We present both ecological and methodological factors that may contribute to the different model results, but propose that
van der Hoek, Yntze; Renfrew, Rosalind; Manne, Lisa L
2013-01-01
Identifying persistence and extinction thresholds in species-habitat relationships is a major focal point of ecological research and conservation. However, one major concern regarding the incorporation of threshold analyses in conservation is the lack of knowledge on the generality and transferability of results across species and regions. We present a multi-region, multi-species approach of modeling threshold responses, which we use to investigate whether threshold effects are similar across species and regions. We modeled local persistence and extinction dynamics of 25 forest-associated breeding birds based on detection/non-detection data, which were derived from repeated breeding bird atlases for the state of Vermont. We did not find threshold responses to be particularly well-supported, with 9 species supporting extinction thresholds and 5 supporting persistence thresholds. This contrasts with a previous study based on breeding bird atlas data from adjacent New York State, which showed that most species support persistence and extinction threshold models (15 and 22 of 25 study species respectively). In addition, species that supported a threshold model in both states had associated average threshold estimates of 61.41% (SE = 6.11, persistence) and 66.45% (SE = 9.15, extinction) in New York, compared to 51.08% (SE = 10.60, persistence) and 73.67% (SE = 5.70, extinction) in Vermont. Across species, thresholds were found at 19.45-87.96% forest cover for persistence and 50.82-91.02% for extinction dynamics. Through an approach that allows for broad-scale comparisons of threshold responses, we show that species vary in their threshold responses with regard to habitat amount, and that differences between even nearby regions can be pronounced. We present both ecological and methodological factors that may contribute to the different model results, but propose that regardless of the reasons behind these differences, our results merit a warning that
Bartoli, Francesco; Clerici, Massimo; Di Brita, Carmen; Riboldi, Ilaria; Crocamo, Cristina; Carrà, Giuseppe
2018-04-01
Randomised placebo-controlled trials investigating treatments for bipolar disorder have been hampered by wide variations of active drugs and placebo clinical response rates. It is important to estimate whether the active drug or placebo response has a greater influence in determining the relative efficacy of drugs for psychosis (antipsychotics) and relapse prevention (mood stabilisers) for bipolar depression and mania. We identified 53 randomised, placebo-controlled trials assessing antipsychotic or mood stabiliser monotherapy ('active drugs') for bipolar depression or mania. We carried out random-effects meta-regressions, estimating the influence of active drugs and placebo response rates on treatment relative efficacy. Meta-regressions showed that treatment relative efficacy for bipolar mania was influenced by the magnitude of clinical response to active drugs ( p=0.002), but not to placebo ( p=0.60). On the other hand, treatment relative efficacy for bipolar depression was influenced by response to placebo ( p=0.047), but not to active drugs ( p=0.98). Despite several limitations, our unexpected findings showed that antipsychotics / mood stabilisers relative efficacy for bipolar depression seems unrelated to active drugs response rates, depending only on clinical response to placebo. Future research should explore strategies to reduce placebo-related issues in randomised, placebo-controlled trials for bipolar depression.
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Mazloom, M.
2008-01-01
The idea of safe room has been developed for decreasing the earthquake casualties in masonry buildings. The information obtained from the previous ground motions occurring in seismic zones expresses the lack of enough safety of these buildings against earthquakes. For this reason, an attempt has been made to create some safe areas inside the existing masonry buildings, which are called safe rooms. The practical method for making these safe areas is to install some prefabricated steel frames in some parts of the existing structure. These frames do not carry any service loads before an earthquake. However, if a devastating earthquake happens and the load bearing walls of the building are destroyed, some parts of the floors, which are in the safe areas, will fall on the roof of the installed frames and the occupants who have sheltered there will survive. This paper presents the performance of these frames located in a destroying three storey masonry building with favorable conclusions. In fact, the experimental pushover diagram of the safe room located at the ground-floor level of this building is compared with the analytical results and it is concluded that pushover analysis is a good method for seismic performance evaluation of safe rooms. For time history analysis the 1940 El Centro, the 2003 Bam, and the 1990 Manjil earthquake records with the maximum peak accelerations of 0.35g were utilized. Also the design spectrum of Iranian Standard No. 2800-05 for the ground kind 2 is used for response spectrum analysis. The results of time history, response spectrum and pushover analyses show that the strength and displacement capacity of the steel frames are adequate to accommodate the distortions generated by seismic loads and aftershocks properly
Herlea-Pana, Oana; Yao, Longbiao; Heuser-Baker, Janet; Wang, Qiongxin; Wang, Qilong; Georgescu, Constantin; Zou, Ming-Hui; Barlic-Dicen, Jana
2015-01-01
Aims Atherosclerosis manifests itself as arterial plaques, which lead to heart attacks or stroke. Treatments supporting plaque regression are therefore aggressively pursued. Studies conducted in models in which hypercholesterolaemia is reversible, such as the Reversa mouse model we have employed in the current studies, will be instrumental for the development of such interventions. Using this model, we have shown that advanced atherosclerosis regression occurs when lipid lowering is used in combination with bone-marrow endothelial progenitor cell (EPC) treatment. However, it remains unclear how EPCs home to regressing plaques and how they augment atherosclerosis reversal. Here we identify molecules that support functional responses of EPCs during plaque resolution. Methods and results Chemokines CXCL1 and CX3CL1 were detected in the vascular wall of atheroregressing Reversa mice, and their cognate receptors CXCR2 and CX3CR1 were observed on adoptively transferred EPCs in circulation. We tested whether CXCL1–CXCR2 and CX3CL1–CX3CR1 axes regulate functional responses of EPCs during plaque reversal. We show that pharmacological inhibition of CXCR2 or CX3CR1, or genetic inactivation of these two chemokine receptors interfered with EPC-mediated advanced atherosclerosis regression. We also demonstrate that CXCR2 directs EPCs to regressing plaques while CX3CR1 controls a paracrine function(s) of these cells. Conclusion CXCR2 and CX3CR1 differentially regulate EPC functional responses during atheroregression. Our study improves understanding of how chemokines and chemokine receptors regulate plaque resolution, which could determine the effectiveness of interventions reducing complications of atherosclerosis. PMID:25765938
Jorjani, E.; Poorali, H.A.; Sam, A.; Chelgani, S.C.; Mesroghli, S.; Shayestehfar, M.R. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering
2009-10-15
In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) in (ash), volatile matter and moisture (b) in (ash), in (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R{sup 2}) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.
Piyanuch Piyatrakul
Full Text Available The AP2/ERF superfamily encodes transcription factors that play a key role in plant development and responses to abiotic and biotic stress. In Hevea brasiliensis, ERF genes have been identified by RNA sequencing. This study set out to validate the number of HbERF genes, and identify ERF genes involved in the regulation of latex cell metabolism. A comprehensive Hevea transcriptome was improved using additional RNA reads from reproductive tissues. Newly assembled contigs were annotated in the Gene Ontology database and were assigned to 3 main categories. The AP2/ERF superfamily is the third most represented compared with other transcription factor families. A comparison with genomic scaffolds led to an estimation of 114 AP2/ERF genes and 1 soloist in Hevea brasiliensis. Based on a phylogenetic analysis, functions were predicted for 26 HbERF genes. A relative transcript abundance analysis was performed by real-time RT-PCR in various tissues. Transcripts of ERFs from group I and VIII were very abundant in all tissues while those of group VII were highly accumulated in latex cells. Seven of the thirty-five ERF expression marker genes were highly expressed in latex. Subcellular localization and transactivation analyses suggested that HbERF-VII candidate genes encoded functional transcription factors.
Kim, Jeong-Soon; Sagaram, Uma Shankar; Burns, Jacqueline K; Li, Jian-Liang; Wang, Nian
2009-01-01
Citrus greening or huanglongbing (HLB) is a devastating disease of citrus. HLB is associated with the phloem-limited fastidious prokaryotic alpha-proteobacterium 'Candidatus Liberibacter spp.' In this report, we used sweet orange (Citrus sinensis) leaf tissue infected with 'Ca. Liberibacter asiaticus' and compared this with healthy controls. Investigation of the host response was examined with citrus microarray hybridization based on 33,879 expressed sequence tag sequences from several citrus species and hybrids. The microarray analysis indicated that HLB infection significantly affected expression of 624 genes whose encoded proteins were categorized according to function. The categories included genes associated with sugar metabolism, plant defense, phytohormone, and cell wall metabolism, as well as 14 other gene categories. The anatomical analyses indicated that HLB bacterium infection caused phloem disruption, sucrose accumulation, and plugged sieve pores. The up-regulation of three key starch biosynthetic genes including ADP-glucose pyrophosphorylase, starch synthase, granule-bound starch synthase and starch debranching enzyme likely contributed to accumulation of starch in HLB-affected leaves. The HLB-associated phloem blockage resulted from the plugged sieve pores rather than the HLB bacterial aggregates since 'Ca. Liberibacter asiaticus' does not form aggregate in citrus. The up-regulation of pp2 gene is related to callose deposition to plug the sieve pores in HLB-affected plants.
Understanding logistic regression analysis.
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 examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Langut, Yael; Talhami, Alaa; Mamidi, Samarasimhareddy; Shir, Alexei; Zigler, Maya; Joubran, Salim; Sagalov, Anna; Flashner-Abramson, Efrat; Edinger, Nufar; Klein, Shoshana; Levitzki, Alexander
2017-12-26
There is an urgent need for an effective treatment for metastatic prostate cancer (PC). Prostate tumors invariably overexpress prostate surface membrane antigen (PSMA). We designed a nonviral vector, PEI-PEG-DUPA (PPD), comprising polyethylenimine-polyethyleneglycol (PEI-PEG) tethered to the PSMA ligand, 2-[3-(1, 3-dicarboxy propyl)ureido] pentanedioic acid (DUPA), to treat PC. The purpose of PEI is to bind polyinosinic/polycytosinic acid (polyIC) and allow endosomal release, while DUPA targets PC cells. PolyIC activates multiple pathways that lead to tumor cell death and to the activation of bystander effects that harness the immune system against the tumor, attacking nontargeted neighboring tumor cells and reducing the probability of acquired resistance and disease recurrence. Targeting polyIC directly to tumor cells avoids the toxicity associated with systemic delivery. PPD selectively delivered polyIC into PSMA-overexpressing PC cells, inducing apoptosis, cytokine secretion, and the recruitment of human peripheral blood mononuclear cells (PBMCs). PSMA-overexpressing tumors in nonobese diabetic/severe combined immunodeficiency (NOD/SCID) mice with partially reconstituted immune systems were significantly shrunken following PPD/polyIC treatment, in all cases. Half of the tumors showed complete regression. PPD/polyIC invokes antitumor immunity, but unlike many immunotherapies does not need to be personalized for each patient. The potent antitumor effects of PPD/polyIC should spur its development for clinical use.
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.
A semi-parametric within-subject mixture approach to the analyses of responses and response times.
Molenaar, Dylan; Bolsinova, Maria; Vermunt, Jeroen K
2018-05-01
In item response theory, modelling the item response times in addition to the item responses may improve the detection of possible between- and within-subject differences in the process that resulted in the responses. For instance, if respondents rely on rapid guessing on some items but not on all, the joint distribution of the responses and response times will be a multivariate within-subject mixture distribution. Suitable parametric methods to detect these within-subject differences have been proposed. In these approaches, a distribution needs to be assumed for the within-class response times. In this paper, it is demonstrated that these parametric within-subject approaches may produce false positives and biased parameter estimates if the assumption concerning the response time distribution is violated. A semi-parametric approach is proposed which resorts to categorized response times. This approach is shown to hardly produce false positives and parameter bias. In addition, the semi-parametric approach results in approximately the same power as the parametric approach. © 2017 The British Psychological Society.
Gupta, S.; Kustu, O.; Jhaveri, D.P.; Blume, J.A.
1983-01-01
The paper presents the conclusions of a comprehensive study that investigated the relative conservatisms represented by various combination techniques. Two approaches were taken for the study, producing mutually consistent results. In the first, 20 representative nuclear piping systems were systematically analyzed using the response spectrum method. The total response was obtained using nine different combination methods. One procedure, using the SRSS method for combining spatial components of response and the 10% method for combining the responses of different modes (which is currently acceptable to the U.S. NRC), was the standard for comparison. Responses computed by the other methods were normalized to this standard method. These response ratios were then used to develop cumulative frequency-distribution curves, which were used to establish the relative conservatism of the methods in a probabilistic sense. In the second approach, 30 single-degree-of-freedom (SDOF) systems that represent different modes of hypothetical piping systems and have natural frequencies varying from 1 Hz to 30 Hz, were analyzed for 276 sets of three-component recorded ground motion. A set of hypothetical systems assuming a variety of modes and frequency ranges was developed. The responses of these systems were computed from the responses of the SDOF systems by combining the spatial response components by algebraic summation and the individual mode responses by the Navy method, or combining both spatial and modal response components using the SRSS method. Probability density functions and cumulative distribution functions were developed for the ratio of the responses obtained by both methods. (orig./HP)
A response-modeling alternative to surrogate models for support in computational analyses
Rutherford, Brian
2006-01-01
Often, the objectives in a computational analysis involve characterization of system performance based on some function of the computed response. In general, this characterization includes (at least) an estimate or prediction for some performance measure and an estimate of the associated uncertainty. Surrogate models can be used to approximate the response in regions where simulations were not performed. For most surrogate modeling approaches, however (1) estimates are based on smoothing of available data and (2) uncertainty in the response is specified in a point-wise (in the input space) fashion. These aspects of the surrogate model construction might limit their capabilities. One alternative is to construct a probability measure, G(r), for the computer response, r, based on available data. This 'response-modeling' approach will permit probability estimation for an arbitrary event, E(r), based on the computer response. In this general setting, event probabilities can be computed: prob(E)=∫ r I(E(r))dG(r) where I is the indicator function. Furthermore, one can use G(r) to calculate an induced distribution on a performance measure, pm. For prediction problems where the performance measure is a scalar, its distribution F pm is determined by: F pm (z)=∫ r I(pm(r)≤z)dG(r). We introduce response models for scalar computer output and then generalize the approach to more complicated responses that utilize multiple response models
Scarpace, F. L.; Voss, A. W.
1973-01-01
Dye densities of multi-layered films are determined by applying a regression analysis to the spectral response of the composite transparency. The amount of dye in each layer is determined by fitting the sum of the individual dye layer densities to the measured dye densities. From this, dye content constants are calculated. Methods of calculating equivalent exposures are discussed. Equivalent exposures are a constant amount of energy over a limited band-width that will give the same dye content constants as the real incident energy. Methods of using these equivalent exposures for analysis of photographic data are presented.
Rannik, Ü.; Haapanala, S.; Shurpali, N. J.; Mammarella, I.; Lind, S.; Hyvönen, N.; Peltola, O.; Zahniser, M.; Martikainen, P. J.; Vesala, T.
2015-01-01
Four gas analysers capable of measuring nitrous oxide (N2O) concentration at a response time necessary for eddy covariance flux measurements were operated from spring until winter 2011 over a field cultivated with reed canary grass (RCG, Phalaris arundinacea, L.), a perennial bioenergy crop in eastern Finland. The instruments were TGA100A (Campbell Scientific Inc.), CW-TILDAS-CS (Aerodyne Research Inc.), N2O / CO-23d (Los Gatos Research Inc.) and QC-TILDAS-76-CS (Aerodyne Research Inc.). The period with high emissions, lasting for about 2 weeks after fertilization in late May, was characterized by an up to 2 orders of magnitude higher emission, whereas during the rest of the campaign the N2O fluxes were small, from 0.01 to 1 nmol m-2 s-1. Two instruments, CW-TILDAS-CS and N2O / CO-23d, determined the N2O exchange with minor systematic difference throughout the campaign, when operated simultaneously. TGA100A produced the cumulatively highest N2O estimates (with 29% higher values during the period when all instruments were operational). QC-TILDAS-76-CS obtained 36% lower fluxes than CW-TILDAS-CS during the first period, including the emission episode, whereas the correspondence with other instruments during the rest of the campaign was good. The reasons for systematic differences were not identified, suggesting further need for detailed evaluation of instrument performance under field conditions with emphasis on stability, calibration and any other factors that can systematically affect the accuracy of flux measurements. The instrument CW-TILDAS-CS was characterized by the lowest noise level (with a standard deviation of around 0.12 ppb at 10 Hz sampling rate) as compared to N2O / CO-23d and QC-TILDAS-76-CS (around 0.50 ppb) and TGA100A (around 2 ppb). We identified that for all instruments except CW-TILDAS-CS the random error due to instrumental noise was an important source of uncertainty at the 30 min averaging level and the total stochastic error was frequently
Cohen, M.S.; Gulbinaite, R.
2017-01-01
Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency
Sy, Jolene R.; Borrero, John C.; Borrero, Carrie S. W.
2010-01-01
We assessed problem and appropriate behavior in the natural environment from a matching perspective. Problem and appropriate behavior were conceptualized as concurrently available responses, the occurrence of which was thought to be determined by the relative rates or durations of reinforcement. We also assessed whether response allocation could…
Nakamura, Teruya; Toda, Koichi; Kuratani, Toru; Miyagawa, Shigeru; Yoshikawa, Yasushi; Fukushima, Satsuki; Saito, Shunsuke; Sawa, Yoshiki
2017-06-01
We examined the impact of advanced age on left ventricular mass regression and the change in the diastolic function after aortic valve replacement in patients with aortic stenosis. The present study included 129 patients who underwent either surgical or transcatheter aortic valve replacement and 1-year postoperative echocardiography. The patient characteristics and echocardiographic findings were compared between patients who were regression was significantly greater (p = 0.02) and diastolic dysfunction was less prevalent in group Y (p = 0.02) in comparison to group O. The change in E/e' was significantly correlated with the left ventricular mass regression in group Y (p = 0.02), but not in Group O (p = 0.21). The patients in group O were less susceptible to improvements in myocardial remodeling and the diastolic function in comparison to those in group Y. The altered physiological response to aortic valve replacement might help to determine the appropriate timing of surgery in elderly patients.
Liang, He-Yue; Huang, Ya-Qin; Yang, Zhao-Xia; Ying-Ding; Zeng, Meng-Su; Rao, Sheng-Xiang
2016-07-01
To determine if magnetic resonance imaging (MRI) histogram analyses can help predict response to chemotherapy in patients with colorectal hepatic metastases by using response evaluation criteria in solid tumours (RECIST1.1) as the reference standard. Standard MRI including diffusion-weighted imaging (b=0, 500 s/mm(2)) was performed before chemotherapy in 53 patients with colorectal hepatic metastases. Histograms were performed for apparent diffusion coefficient (ADC) maps, arterial, and portal venous phase images; thereafter, mean, percentiles (1st, 10th, 50th, 90th, 99th), skewness, kurtosis, and variance were generated. Quantitative histogram parameters were compared between responders (partial and complete response, n=15) and non-responders (progressive and stable disease, n=38). Receiver operator characteristics (ROC) analyses were further analyzed for the significant parameters. The mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, 99th percentile of the ADC maps were significantly lower in responding group than that in non-responding group (p=0.000-0.002) with area under the ROC curve (AUCs) of 0.76-0.82. The histogram parameters of arterial and portal venous phase showed no significant difference (p>0.05) between the two groups. Histogram-derived parameters for ADC maps seem to be a promising tool for predicting response to chemotherapy in patients with colorectal hepatic metastases. • ADC histogram analyses can potentially predict chemotherapy response in colorectal liver metastases. • Lower histogram-derived parameters (mean, percentiles) for ADC tend to have good response. • MR enhancement histogram analyses are not reliable to predict response.
van Gestel, Natasja; Jan van Groenigen, Kees; Osenberg, Craig; Dukes, Jeffrey; Dijkstra, Paul
2018-03-20
This project examined the sensitivity of carbon in land ecosystems to environmental change, focusing on carbon contained in soil, and the role of carbon-nitrogen interactions in regulating ecosystem carbon storage. The project used a combination of empirical measurements, mathematical models, and statistics to partition effects of climate change on soil into processes enhancing soil carbon and processes through which it decomposes. By synthesizing results from experiments around the world, the work provided novel insight on ecological controls and responses across broad spatial and temporal scales. The project developed new approaches in meta-analysis using principles of element mass balance and large datasets to derive metrics of ecosystem responses to environmental change. The project used meta-analysis to test how nutrients regulate responses of ecosystems to elevated CO2 and warming, in particular responses of nitrogen fixation, critical for regulating long-term C balance.
Cohen, M.S.; Gulbinaite, R.
2017-01-01
Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differen...
Transcriptome-wide analyses indicate mitochondrial responses to particulate air pollution exposure
Winckelmans, Ellen; Nawrot, Tim S.; Tsamou, Maria
2017-01-01
validation cohort (n = 169, 55.6% women). Results: Overrepresentation analyses revealed significant pathways (p-value transport chain (ETC) for medium-term exposure in women. For men, medium-term PM10....... Conclusions: In this exploratory study, we identified mitochondrial genes and pathways associated with particulate air pollution indicating upregulation of energy producing pathways as a potential mechanism to compensate for PM-induced mitochondrial damage....
Barker Bridget M
2012-02-01
Full Text Available Abstract Background Aspergillus fumigatus is a mold responsible for the majority of cases of aspergillosis in humans. To survive in the human body, A. fumigatus must adapt to microenvironments that are often characterized by low nutrient and oxygen availability. Recent research suggests that the ability of A. fumigatus and other pathogenic fungi to adapt to hypoxia contributes to their virulence. However, molecular mechanisms of A. fumigatus hypoxia adaptation are poorly understood. Thus, to better understand how A. fumigatus adapts to hypoxic microenvironments found in vivo during human fungal pathogenesis, the dynamic changes of the fungal transcriptome and proteome in hypoxia were investigated over a period of 24 hours utilizing an oxygen-controlled fermenter system. Results Significant increases in transcripts associated with iron and sterol metabolism, the cell wall, the GABA shunt, and transcriptional regulators were observed in response to hypoxia. A concomitant reduction in transcripts was observed with ribosome and terpenoid backbone biosynthesis, TCA cycle, amino acid metabolism and RNA degradation. Analysis of changes in transcription factor mRNA abundance shows that hypoxia induces significant positive and negative changes that may be important for regulating the hypoxia response in this pathogenic mold. Growth in hypoxia resulted in changes in the protein levels of several glycolytic enzymes, but these changes were not always reflected by the corresponding transcriptional profiling data. However, a good correlation overall (R2 = 0.2, p A. fumigatus. Conclusions Taken together, our data suggest a robust cellular response that is likely regulated both at the transcriptional and post-transcriptional level in response to hypoxia by the human pathogenic mold A. fumigatus. As with other pathogenic fungi, the induction of glycolysis and transcriptional down-regulation of the TCA cycle and oxidative phosphorylation appear to major
Yücel, Meryem A; Selb, Juliette; Aasted, Christopher M; Petkov, Mike P; Becerra, Lino; Borsook, David; Boas, David A
2015-07-01
Autonomic nervous system response is known to be highly task-dependent. The sensitivity of near-infrared spectroscopy (NIRS) measurements to superficial layers, particularly to the scalp, makes it highly susceptible to systemic physiological changes. Thus, one critical step in NIRS data processing is to remove the contribution of superficial layers to the NIRS signal and to obtain the actual brain response. This can be achieved using short separation channels that are sensitive only to the hemodynamics in the scalp. We investigated the contribution of hemodynamic fluctuations due to autonomous nervous system activation during various tasks. Our results provide clear demonstrations of the critical role of using short separation channels in NIRS measurements to disentangle differing autonomic responses from the brain activation signal of interest.
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
Tropical cyclones in two atmospheric (re)analyses and their response in two oceanic reanalyses
Jourdain, N.C.; Barnier, B.; Ferry, N.; Vialard, J.; Menkes, C.E.; Lengaigne, M.; Parent, L.
composites in Fig. 7). We focus our description of the mechanisms on strong TCs in GLORYS1 to emphasize the major issues in the ocean reanalyses, and GLORYS2 is not shown because it has a very similar temperature response to GLORYS1. In the top 30 m and over...
Response surfaces and sensitivity analyses for an environmental model of dose calculations
Iooss, Bertrand [CEA Cadarache, DEN/DER/SESI/LCFR, 13108 Saint Paul lez Durance, Cedex (France)]. E-mail: bertrand.iooss@cea.fr; Van Dorpe, Francois [CEA Cadarache, DEN/DTN/SMTM/LMTE, 13108 Saint Paul lez Durance, Cedex (France); Devictor, Nicolas [CEA Cadarache, DEN/DER/SESI/LCFR, 13108 Saint Paul lez Durance, Cedex (France)
2006-10-15
A parametric sensitivity analysis is carried out on GASCON, a radiological impact software describing the radionuclides transfer to the man following a chronic gas release of a nuclear facility. An effective dose received by age group can thus be calculated according to a specific radionuclide and to the duration of the release. In this study, we are concerned by 18 output variables, each depending of approximately 50 uncertain input parameters. First, the generation of 1000 Monte-Carlo simulations allows us to calculate correlation coefficients between input parameters and output variables, which give a first overview of important factors. Response surfaces are then constructed in polynomial form, and used to predict system responses at reduced computation time cost; this response surface will be very useful for global sensitivity analysis where thousands of runs are required. Using the response surfaces, we calculate the total sensitivity indices of Sobol by the Monte-Carlo method. We demonstrate the application of this method to one site of study and to one reference group near the nuclear research Center of Cadarache (France), for two radionuclides: iodine 129 and uranium 238. It is thus shown that the most influential parameters are all related to the food chain of the goat's milk, in decreasing order of importance: dose coefficient 'effective ingestion', goat's milk ration of the individuals of the reference group, grass ration of the goat, dry deposition velocity and transfer factor to the goat's milk.
Dignity and cost-effectiveness: analysing the responsibility for decisions in medical ethics.
Robertson, G S
1984-01-01
In the operation of a health care system, defining the limits of medical care is the joint responsibility of many parties including clinicians, patients, philosophers and politicians. It is suggested that changes in the potential for prolonging life make it necessary to give doctors guidance which may have to incorporate certain features of utilitarianism, individualism and patient-autonomy. PMID:6502644
Sarah A. Turner
2016-01-01
Full Text Available Much emphasis has been placed recently on the repair of degenerate discs using implanted cells, such as disc cells or bone marrow derived mesenchymal stem cells (MSCs. This study examines the temporal response of bovine and human nucleus pulposus (NP cells and MSCs cultured in monolayer following exposure to altered levels of glucose (0, 3.15, and 4.5 g/L and foetal bovine serum (0, 10, and 20% using an automated time-lapse imaging system. NP cells were also exposed to the cell death inducers, hydrogen peroxide and staurosporine, in comparison to serum starvation. We have demonstrated that human NP cells show an initial “shock” response to reduced nutrition (glucose. However, as time progresses, NP cells supplemented with serum recover with minimal evidence of cell death. Human NP cells show no evidence of proliferation in response to nutrient supplementation, whereas MSCs showed greater response to increased nutrition. When specifically inducing NP cell death with hydrogen peroxide and staurosporine, as expected, the cell number declined. These results support the concept that implanted NP cells or MSCs may be capable of survival in the nutrient-poor environment of the degenerate human disc, which has important clinical implications for the development of IVD cell therapies.
Van der Elst, Wim; Ouwehand, Carolijn; van Rijn, Peter; Lee, Nikki; Van Boxtel, Martin; Jolles, Jelle
2013-01-01
The purpose of the present study was to evaluate the psychometric properties of a shortened version of the Raven Standard Progressive Matrices (SPM) under an item response theory framework (the one- and two-parameter logistic models). The shortened Raven SPM was administered to N = 453 cognitively healthy adults aged between 24 and 83 years. The…
2012-01-01
Background Aspergillus fumigatus is a mold responsible for the majority of cases of aspergillosis in humans. To survive in the human body, A. fumigatus must adapt to microenvironments that are often characterized by low nutrient and oxygen availability. Recent research suggests that the ability of A. fumigatus and other pathogenic fungi to adapt to hypoxia contributes to their virulence. However, molecular mechanisms of A. fumigatus hypoxia adaptation are poorly understood. Thus, to better understand how A. fumigatus adapts to hypoxic microenvironments found in vivo during human fungal pathogenesis, the dynamic changes of the fungal transcriptome and proteome in hypoxia were investigated over a period of 24 hours utilizing an oxygen-controlled fermenter system. Results Significant increases in transcripts associated with iron and sterol metabolism, the cell wall, the GABA shunt, and transcriptional regulators were observed in response to hypoxia. A concomitant reduction in transcripts was observed with ribosome and terpenoid backbone biosynthesis, TCA cycle, amino acid metabolism and RNA degradation. Analysis of changes in transcription factor mRNA abundance shows that hypoxia induces significant positive and negative changes that may be important for regulating the hypoxia response in this pathogenic mold. Growth in hypoxia resulted in changes in the protein levels of several glycolytic enzymes, but these changes were not always reflected by the corresponding transcriptional profiling data. However, a good correlation overall (R2 = 0.2, p proteomics datasets for all time points. The lack of correlation between some transcript levels and their subsequent protein levels suggests another regulatory layer of the hypoxia response in A. fumigatus. Conclusions Taken together, our data suggest a robust cellular response that is likely regulated both at the transcriptional and post-transcriptional level in response to hypoxia by the human pathogenic mold A. fumigatus. As
Mulroy, Sara J; Winstein, Carolee J; Kulig, Kornelia; Beneck, George J; Fowler, Eileen G; DeMuth, Sharon K; Sullivan, Katherine J; Brown, David A; Lane, Christianne J
2011-12-01
Each of the 4 randomized clinical trials (RCTs) hosted by the Physical Therapy Clinical Research Network (PTClinResNet) targeted a different disability group (low back disorder in the Muscle-Specific Strength Training Effectiveness After Lumbar Microdiskectomy [MUSSEL] trial, chronic spinal cord injury in the Strengthening and Optimal Movements for Painful Shoulders in Chronic Spinal Cord Injury [STOMPS] trial, adult stroke in the Strength Training Effectiveness Post-Stroke [STEPS] trial, and pediatric cerebral palsy in the Pediatric Endurance and Limb Strengthening [PEDALS] trial for children with spastic diplegic cerebral palsy) and tested the effectiveness of a muscle-specific or functional activity-based intervention on primary outcomes that captured pain (STOMPS, MUSSEL) or locomotor function (STEPS, PEDALS). The focus of these secondary analyses was to determine causal relationships among outcomes across levels of the International Classification of Functioning, Disability and Health (ICF) framework for the 4 RCTs. With the database from PTClinResNet, we used 2 separate secondary statistical approaches-mediation analysis for the MUSSEL and STOMPS trials and regression analysis for the STEPS and PEDALS trials-to test relationships among muscle performance, primary outcomes (pain related and locomotor related), activity and participation measures, and overall quality of life. Predictive models were stronger for the 2 studies with pain-related primary outcomes. Change in muscle performance mediated or predicted reductions in pain for the MUSSEL and STOMPS trials and, to some extent, walking speed for the STEPS trial. Changes in primary outcome variables were significantly related to changes in activity and participation variables for all 4 trials. Improvement in activity and participation outcomes mediated or predicted increases in overall quality of life for the 3 trials with adult populations. Variables included in the statistical models were limited to those
Woda, Clemens; Bassinet, Céline; Trompier, François; Bortolin, Emanuela; Della Monaca, Sara; Fattibene, Paola
2009-01-01
The increasing risk of a mass casualty scenario following a large scale radiological accident or attack necessitates the development of appropriate dosimetric tools for emergency response. Luminescence dosimetry has been reliably applied for dose reconstruction in contaminated settlements for several decades and recent research into new materials carried close to the human body opens the possibility of estimating individual doses for accident and emergency dosimetry using the same technique. This paper reviews the luminescence research into materials useful for accident dosimetry and applications in retrospective dosimetry. The properties of the materials are critically discussed with regard to the requirements for population triage. It is concluded that electronic components found within portable electronic devices, such as e.g. mobile phones, are at present the most promising material to function as a fortuitous dosimeter in an emergency response.
Henshaw, P.; Nicell, J.; Sikdar, A.
2002-01-01
Odorous emission from stationary sources account for the majority of air pollution complaints to regulatory agencies. Sometimes regulators rely on nuisance provisions of common law to assess odour impact, which is highly subjective. The other commonly used approach, the dilution-to-threshold principle, assumes that an odour is a problem simply if detected, without regard to the fact that a segment of the population can detect the odour at concentrations below the threshold. The odour impact model (OIM) represents a significant improvement over current methods for quantifying odours by characterizing the dose-response relationship of the odour. Dispersion modelling can be used in conjunction with the OIM to estimate the probability of response in the surrounding vicinity, taking into account the local meteorological conditions. The objective of this research is to develop an objective method of assessing the impact of odorous airborne emissions. To this end, several metrics were developed to quantify the impact of an odorous stationary source on the surrounding community. These 'odour impact parameters' are: maximum concentration, maximum probability of response, footprint area, probability-weighted footprint area and the number of people responding to the odour. These impact parameters were calculated for a stationary odour source in Canada. Several remediation scenarios for reducing the odour impact were proposed and their effect on the impact parameters calculated. (author)
Wei Wang
2017-11-01
Full Text Available Pollination is a crucial stage in plant reproductive process. The self-compatibility (SC and self-incompatibility (SI mechanisms determined the plant genetic diversity and species survival. D. chrysanthum is a highly valued ornamental and traditional herbal orchid in Asia but has been declared endangered. The sexual reproduction in D. chrysanthum relies on the compatibility of pollination. To provide a better understanding of the mechanism of pollination, the differentially expressed proteins (DEP between the self-pollination (SP and cross-pollination (CP pistil of D. chrysanthum were investigated using proteomic approaches—two-dimensional electrophoresis (2-DE coupled with tandem mass spectrometry technique. A total of 54 DEP spots were identified in the two-dimensional electrophoresis (2-DE maps between the SP and CP. Gene ontology analysis revealed an array of proteins belonging to following different functional categories: metabolic process (8.94%, response to stimulus (5.69%, biosynthetic process (4.07%, protein folding (3.25% and transport (3.25%. Identification of these DEPs at the early response stage of pollination will hopefully provide new insights in the mechanism of pollination response and help for the conservation of the orchid species.
Physiological and proteomic analyses of salt stress response in the halophyte Halogeton glomeratus.
Wang, Juncheng; Meng, Yaxiong; Li, Baochun; Ma, Xiaole; Lai, Yong; Si, Erjing; Yang, Ke; Xu, Xianliang; Shang, Xunwu; Wang, Huajun; Wang, Di
2015-04-01
Very little is known about the adaptation mechanism of Chenopodiaceae Halogeton glomeratus, a succulent annual halophyte, under saline conditions. In this study, we investigated the morphological and physiological adaptation mechanisms of seedlings exposed to different concentrations of NaCl treatment for 21 d. Our results revealed that H. glomeratus has a robust ability to tolerate salt; its optimal growth occurs under approximately 100 mm NaCl conditions. Salt crystals were deposited in water-storage tissue under saline conditions. We speculate that osmotic adjustment may be the primary mechanism of salt tolerance in H. glomeratus, which transports toxic ions such as sodium into specific salt-storage cells and compartmentalizes them in large vacuoles to maintain the water content of tissues and the succulence of the leaves. To investigate the molecular response mechanisms to salt stress in H. glomeratus, we conducted a comparative proteomic analysis of seedling leaves that had been exposed to 200 mm NaCl for 24 h, 72 h and 7 d. Forty-nine protein spots, exhibiting significant changes in abundance after stress, were identified using matrix-assisted laser desorption ionization tandem time-of-flight mass spectrometry (MALDI-TOF/TOF MS/MS) and similarity searches across EST database of H. glomeratus. These stress-responsive proteins were categorized into nine functional groups, such as photosynthesis, carbohydrate and energy metabolism, and stress and defence response. © 2014 The Authors. Plant, Cell & Environment published by John Wiley & Sons Ltd.
Yinghui Yuan
2016-07-01
Full Text Available Soil salinity is a major environmental constraint that threatens agricultural productivity. Different strategies have been developed to improve crop salt tolerance, among which the effects of polyamines have been well reported. To gain a better understanding of the cucumber (Cucumis sativus L. responses to NaCl and unravel the underlying mechanism of exogenous putrescine (Put alleviating salt-induced damage, comparative proteomic analysis was conducted on cucumber roots treated with NaCl and/or Put for 7 days. The results showed that exogenous Put restored the root growth inhibited by NaCl. 62 differentially expressed proteins implicated in various biological processes were successfully identified by MALDI-TOF/TOF MS. The four largest categories included proteins involved in defense response (24.2%, protein metabolism (24.2%, carbohydrate metabolism (19.4% and amino acid metabolism (14.5%. Exogenous Put up-regulated most identified proteins involved in carbohydrate metabolism, implying an enhancement in energy generation. Proteins involved in defense response and protein metabolism were differently regulated by Put, which indicated the roles of Put in stress resistance and proteome rearrangement. Put also increased the abundance of proteins involved in amino acid metabolism. Meanwhile, physiological analysis showed that Put could further up-regulated the levels of free amino acids in salt stressed-roots. In addition, Put also improved endogenous polyamines contents by regulating the transcription levels of key enzymes in polyamine metabolism. Taken together, these results suggest that Put may alleviate NaCl-induced growth inhibition through degradation of misfolded/damaged proteins, activation of stress defense, and the promotion of carbohydrate metabolism to generate more energy.
Jafari, Peyman; Sharafi, Zahra; Bagheri, Zahra; Shalileh, Sara
2014-06-01
Measurement equivalence is a necessary assumption for meaningful comparison of pediatric quality of life rated by children and parents. In this study, differential item functioning (DIF) analysis is used to examine whether children and their parents respond consistently to the items in the KINDer Lebensqualitätsfragebogen (KINDL; in German, Children Quality of Life Questionnaire). Two DIF detection methods, graded response model (GRM) and ordinal logistic regression (OLR), were applied for comparability. The KINDL was completed by 1,086 school children and 1,061 of their parents. While the GRM revealed that 12 out of the 24 items were flagged with DIF, the OLR identified 14 out of the 24 items with DIF. Seven items with DIF and five items without DIF were common across the two methods, yielding a total agreement rate of 50 %. This study revealed that parent proxy-reports cannot be used as a substitute for a child's ratings in the KINDL.
Jahromi, Hamed Dehdashti; Mahmoodi, Ali; Sheikhi, Mohammad Hossein; Zarifkar, Abbas
2016-10-20
Reduction of dark current at high-temperature operation is a great challenge in conventional quantum dot infrared photodetectors, as the rate of thermal excitations resulting in the dark current increases exponentially with temperature. A resonant tunneling barrier is the best candidate for suppression of dark current, enhancement in signal-to-noise ratio, and selective extraction of different wavelength response. In this paper, we use a physical model developed by the authors recently to design a proper resonant tunneling barrier for quantum infrared photodetectors and to study and analyze the spectral response of these devices. The calculated transmission coefficient of electrons by this model and its dependency on bias voltage are in agreement with experimental results. Furthermore, based on the calculated transmission coefficient, the dark current of a quantum dot infrared photodetector with a resonant tunneling barrier is calculated and compared with the experimental data. The validity of our model is proven through this comparison. Theoretical dark current by our model shows better agreement with the experimental data and is more accurate than the previously developed model. Moreover, noise in the device is calculated. Finally, the effect of different parameters, such as temperature, size of quantum dots, and bias voltage, on the performance of the device is simulated and studied.
Ohara, Minami; Takahashi, Harumi; Lee, Ming Ta Michael; Wen, Ming-Shien; Lee, Tsong-Hai; Chuang, Hui-Ping; Luo, Chen-Hui; Arima, Aki; Onozuka, Akiko; Nagai, Rui; Shiomi, Mari; Mihara, Kiyoshi; Morita, Takashi; Chen, Yuan-Tsong
2014-01-01
To clarify pharmacokinetic-pharmacodynamic (PK-PD) factors associated with the over-anticoagulation response in Asians during warfarin induction therapy, population PK-PD analyses were conducted in an attempt to predict the time-courses of the plasma S-warfarin concentration, Cp(S), and coagulation and anti-coagulation (INR) responses. In 99 Chinese patients we analyzed the relationships between dose and Cp(S) to estimate the clearance of S-warfarin, CL(S), and that between Cp(S) and the normal prothrombin concentration (NPT) as a coagulation marker for estimation of IC50. We also analyzed the non-linear relationship between NPT inhibition and the increase in INR to derive the non-linear index λ. Population analyses accurately predicted the time-courses of Cp(S), NPT and INR. Multivariate analysis showed that CYP2C9*3 mutation and body surface area were predictors of CL(S), that VKORC1 and CYP4F2 polymorphisms were predictors of IC50, and that baseline NPT was a predictor of λ. CL(S) and λ were significantly lower in patients with INR≥4 than in those with INR<4 (190 mL/h vs 265 mL/h, P<0.01 and 3.2 vs 3.7, P<0.01, respectively). Finally, logistic regression analysis revealed that CL(S), ALT and hypertension contributed significantly to INR≥4. All these results indicate that factors associated with the reduced metabolic activity of warfarin represented by CL(S), might be critical determinants of the over-anticoagulation response during warfarin initiation in Asians. ClinicalTrials.gov NCT02065388.
Minami Ohara
Full Text Available To clarify pharmacokinetic-pharmacodynamic (PK-PD factors associated with the over-anticoagulation response in Asians during warfarin induction therapy, population PK-PD analyses were conducted in an attempt to predict the time-courses of the plasma S-warfarin concentration, Cp(S, and coagulation and anti-coagulation (INR responses. In 99 Chinese patients we analyzed the relationships between dose and Cp(S to estimate the clearance of S-warfarin, CL(S, and that between Cp(S and the normal prothrombin concentration (NPT as a coagulation marker for estimation of IC50. We also analyzed the non-linear relationship between NPT inhibition and the increase in INR to derive the non-linear index λ. Population analyses accurately predicted the time-courses of Cp(S, NPT and INR. Multivariate analysis showed that CYP2C9*3 mutation and body surface area were predictors of CL(S, that VKORC1 and CYP4F2 polymorphisms were predictors of IC50, and that baseline NPT was a predictor of λ. CL(S and λ were significantly lower in patients with INR≥4 than in those with INR<4 (190 mL/h vs 265 mL/h, P<0.01 and 3.2 vs 3.7, P<0.01, respectively. Finally, logistic regression analysis revealed that CL(S, ALT and hypertension contributed significantly to INR≥4. All these results indicate that factors associated with the reduced metabolic activity of warfarin represented by CL(S, might be critical determinants of the over-anticoagulation response during warfarin initiation in Asians.ClinicalTrials.gov NCT02065388.
Dimitrios - Georgios Kontopoulos
2018-02-01
Full Text Available There is currently unprecedented interest in quantifying variation in thermal physiology among organisms, especially in order to understand and predict the biological impacts of climate change. A key parameter in this quantification of thermal physiology is the performance or value of a rate, across individuals or species, at a common temperature (temperature normalisation. An increasingly popular model for fitting thermal performance curves to data—the Sharpe-Schoolfield equation—can yield strongly inflated estimates of temperature-normalised rate values. These deviations occur whenever a key thermodynamic assumption of the model is violated, i.e., when the enzyme governing the performance of the rate is not fully functional at the chosen reference temperature. Using data on 1,758 thermal performance curves across a wide range of species, we identify the conditions that exacerbate this inflation. We then demonstrate that these biases can compromise tests to detect metabolic cold adaptation, which requires comparison of fitness or rate performance of different species or genotypes at some fixed low temperature. Finally, we suggest alternative methods for obtaining unbiased estimates of temperature-normalised rate values for meta-analyses of thermal performance across species in climate change impact studies.
Wave response analyses of floating crane structure; Crane sen no jobu kozobutsu no haro oto
Nobukawa, H.; Takaki, M.; Kitamura, M.; Ahou, G. [Hiroshima University, Hiroshima (Japan). Faculty of Engineering; Higashimura, M. [Fukada Salvage and Marine Works Co. Ltd., Osaka (Japan)
1996-12-31
Identifying a dynamic load acting on a lifted load in a floating crane moving in waves is important for preparing an operation manual for the floating crane. Analyses were made on motions in waves of a floating crane with a lifting load of 3,600 tons, with considerations given to deformation of the crane structure. Discussions were given on a dynamic load acting on a lifted load. If a case that considers elastic deformation in the crane structure is compared with a case that does not consider same in calculating hull motions of the floating crane, the difference between them is small if wave length {lambda} to the ship length L is about 0.5. However, if {lambda}/L is 1.0 and 1.5, the difference grows very large. Therefore, the effect of deformation in the crane structure on hull motions of the floating crane cannot be ignored in these cases. A dynamic load acting on a lifted load that considers deformation in the crane structure is about 5% of lifted weight in a headsea condition in which the wave height is 2 m and {lambda}/L is 1.5. As opposed, an estimated value of a dynamic load when the crane structure is regarded as a rigid body is 13%, which is 2.6 times as great as the case that considers deformation of the crane structure. 3 refs., 17 figs., 1 tab.
Wave response analyses of floating crane structure; Crane sen no jobu kozobutsu no haro oto
Nobukawa, H; Takaki, M; Kitamura, M; Ahou, G [Hiroshima University, Hiroshima (Japan). Faculty of Engineering; Higashimura, M [Fukada Salvage and Marine Works Co. Ltd., Osaka (Japan)
1997-12-31
Identifying a dynamic load acting on a lifted load in a floating crane moving in waves is important for preparing an operation manual for the floating crane. Analyses were made on motions in waves of a floating crane with a lifting load of 3,600 tons, with considerations given to deformation of the crane structure. Discussions were given on a dynamic load acting on a lifted load. If a case that considers elastic deformation in the crane structure is compared with a case that does not consider same in calculating hull motions of the floating crane, the difference between them is small if wave length {lambda} to the ship length L is about 0.5. However, if {lambda}/L is 1.0 and 1.5, the difference grows very large. Therefore, the effect of deformation in the crane structure on hull motions of the floating crane cannot be ignored in these cases. A dynamic load acting on a lifted load that considers deformation in the crane structure is about 5% of lifted weight in a headsea condition in which the wave height is 2 m and {lambda}/L is 1.5. As opposed, an estimated value of a dynamic load when the crane structure is regarded as a rigid body is 13%, which is 2.6 times as great as the case that considers deformation of the crane structure. 3 refs., 17 figs., 1 tab.
Earthquake response analyses of soil-structure system considering kinematic interaction
Murakami, H.; Yokono, K.; Miura, S.; Ishii, K.
1985-01-01
Improvement of soil-structure interaction analysis has been one of major concerns in earthquake engineering field, especially in nuclear industries, to evaluate the safety of structure accurately under earthquake events. This research aims to develop a rational analytical tool which considers effect of the 'kinematic interaction' satisfactory with a proposed simple low-pass filter. In this paper, first the effect of the kinematic interaction is investigated based on earthquake response analysis of a reactor building using the practical design models: the spring-mass-dashpot system and the 'lattice model', in which a building and soil medium are modeled by a system of lumped masses. Next, the filter is developed based on parametrical studies with various sizes of depth and width of foundations embedded in two-layers soil, which represents more general soil condition in practical designs compared with a homogeneous soil medium. (orig.)
Structural Response of Submerged Air-Backed Plates by Experimental and Numerical Analyses
Lloyd Hammond
2000-01-01
Full Text Available This paper presents the results of a series of small-scale underwater shock experiments that measured the structural responses of submerged, fully clamped, air-backed, steel plates to a range of high explosive charge sizes. The experimental results were subsequently used to validate a series of simulations using the coupled LS-DYNA/USA finite element/boundary element codes. The modelling exercise was complicated by a significant amount of local cavitation occurring in the fluid adjacent to the plate and difficulties in modelling the boundary conditions of the test plates. The finite element model results satisfactorily predicted the displacement-time history of the plate over a range of shock loadings although a less satisfactory correlation was achieved for the peak velocities. It is expected that the predictive capability of the finite element model will be significantly improved once hydrostatic initialisation can be fully utilised with the LS-DYNA/USA software.
Wang, Xinghua; Peng, Yong; Yi, Shengen
2017-11-01
To investigate the differences of the head impact responses between bicyclists and motorcyclists in vehicle collisions. A series of vehicle-bicycle and vehicle-motorcycle lateral impact simulations on four vehicle types at seven vehicle speeds (30, 35, 40, 45, 50, 55 and 60 km/h) and three two-wheeler moving speeds (5, 7.5 and 10 km/h for bicycle, 10, 12.5 and 15 km/h for motorcycle) were established based on PC-Crash software. To further comprehensively explore the differences, additional impact scenes with other initial conditions, such as impact angle (0, π/3, 2π/3 and π) and impact position (left, middle and right part of vehicle front-end), also were supplemented. And then, extensive comparisons were accomplished with regard to average head peak linear acceleration, average head impact speed, average head peak angular acceleration, average head peak angular speed and head injury severity. The results showed there were prominent differences of kinematics and body postures for bicyclists and motorcyclists even under same impact conditions. The variations of bicyclist head impact responses with the changing of impact conditions were a far cry from that of motorcyclists. The average head peak linear acceleration, average head impact speed and average head peak angular acceleration values were higher for motorcyclists than for bicyclists in most cases, while the bicyclists received greater average head peak angular speed values. And the head injuries of motorcyclists worsened faster with increased vehicle speed. The results may provide even deeper understanding of two-wheeler safety and contribute to improve the public health affected by road traffic accidents.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Determination of the spatial response of neutron based analysers using a Monte Carlo based method
Tickner, James
2000-01-01
One of the principal advantages of using thermal neutron capture (TNC, also called prompt gamma neutron activation analysis or PGNAA) or neutron inelastic scattering (NIS) techniques for measuring elemental composition is the high penetrating power of both the incident neutrons and the resultant gamma-rays, which means that large sample volumes can be interrogated. Gauges based on these techniques are widely used in the mineral industry for on-line determination of the composition of bulk samples. However, attenuation of both neutrons and gamma-rays in the sample and geometric (source/detector distance) effects typically result in certain parts of the sample contributing more to the measured composition than others. In turn, this introduces errors in the determination of the composition of inhomogeneous samples. This paper discusses a combined Monte Carlo/analytical method for estimating the spatial response of a neutron gauge. Neutron propagation is handled using a Monte Carlo technique which allows an arbitrarily complex neutron source and gauge geometry to be specified. Gamma-ray production and detection is calculated analytically which leads to a dramatic increase in the efficiency of the method. As an example, the method is used to study ways of reducing the spatial sensitivity of on-belt composition measurements of cement raw meal
Impact analyses for negative flexural responses (hogging) in railway prestressed concrete sleepers
Kaewunruen, S; Ishida, T; Remennikov, AM
2016-01-01
By nature, ballast interacts with railway concrete sleepers in order to provide bearing support to track system. Most train-track dynamic models do not consider the degradation of ballast over time. In fact, the ballast degradation causes differential settlement and impact forces acting on partial and unsupported tracks. Furthermore, localised ballast breakages underneath railseat increase the likelihood of centrebound cracks in concrete sleepers due to the unbalanced support under sleepers. This paper presents a dynamic finite element model of a standard-gauge concrete sleeper in a track system, taking into account the tensionless nature of ballast support. The finite element model was calibrated using static and dynamic responses in the past. In this paper, the effects of centre-bound ballast support on the impact behaviours of sleepers are highlighted. In addition, it is the first to demonstrate the dynamic effects of sleeper length on the dynamic design deficiency in concrete sleepers. The outcome of this study will benefit the rail maintenance criteria of track resurfacing in order to restore ballast profile and appropriate sleeper/ballast interaction. (paper)
Item response theory analyses of the Delis-Kaplan Executive Function System card sorting subtest.
Spencer, Mercedes; Cho, Sun-Joo; Cutting, Laurie E
2018-02-02
In the current study, we examined the dimensionality of the 16-item Card Sorting subtest of the Delis-Kaplan Executive Functioning System assessment in a sample of 264 native English-speaking children between the ages of 9 and 15 years. We also tested for measurement invariance for these items across age and gender groups using item response theory (IRT). Results of the exploratory factor analysis indicated that a two-factor model that distinguished between verbal and perceptual items provided the best fit to the data. Although the items demonstrated measurement invariance across age groups, measurement invariance was violated for gender groups, with two items demonstrating differential item functioning for males and females. Multigroup analysis using all 16 items indicated that the items were more effective for individuals whose IRT scale scores were relatively high. A single-group explanatory IRT model using 14 non-differential item functioning items showed that for perceptual ability, females scored higher than males and that scores increased with age for both males and females; for verbal ability, the observed increase in scores across age differed for males and females. The implications of these findings are discussed.
Impact analyses for negative flexural responses (hogging) in railway prestressed concrete sleepers
Kaewunruen, S.; Ishida, T.; Remennikov, AM
2016-09-01
By nature, ballast interacts with railway concrete sleepers in order to provide bearing support to track system. Most train-track dynamic models do not consider the degradation of ballast over time. In fact, the ballast degradation causes differential settlement and impact forces acting on partial and unsupported tracks. Furthermore, localised ballast breakages underneath railseat increase the likelihood of centrebound cracks in concrete sleepers due to the unbalanced support under sleepers. This paper presents a dynamic finite element model of a standard-gauge concrete sleeper in a track system, taking into account the tensionless nature of ballast support. The finite element model was calibrated using static and dynamic responses in the past. In this paper, the effects of centre-bound ballast support on the impact behaviours of sleepers are highlighted. In addition, it is the first to demonstrate the dynamic effects of sleeper length on the dynamic design deficiency in concrete sleepers. The outcome of this study will benefit the rail maintenance criteria of track resurfacing in order to restore ballast profile and appropriate sleeper/ballast interaction.
Li Chuan
2012-05-01
Full Text Available Abstract Background Little is known about the potential of Brachypodium distachyon as a model for low temperature stress responses in Pooideae. The ice recrystallization inhibition protein (IRIP genes, fructosyltransferase (FST genes, and many C-repeat binding factor (CBF genes are Pooideae specific and important in low temperature responses. Here we used comparative analyses to study conservation and evolution of these gene families in B. distachyon to better understand its potential as a model species for agriculturally important temperate grasses. Results Brachypodium distachyon contains cold responsive IRIP genes which have evolved through Brachypodium specific gene family expansions. A large cold responsive CBF3 subfamily was identified in B. distachyon, while CBF4 homologs are absent from the genome. No B. distachyon FST gene homologs encode typical core Pooideae FST-motifs and low temperature induced fructan accumulation was dramatically different in B. distachyon compared to core Pooideae species. Conclusions We conclude that B. distachyon can serve as an interesting model for specific molecular mechanisms involved in low temperature responses in core Pooideae species. However, the evolutionary history of key genes involved in low temperature responses has been different in Brachypodium and core Pooideae species. These differences limit the use of B. distachyon as a model for holistic studies relevant for agricultural core Pooideae species.
Hidalgo, María D; López-Martínez, María D; Gómez-Benito, Juana; Guilera, Georgina
2016-01-01
Short scales are typically used in the social, behavioural and health sciences. This is relevant since test length can influence whether items showing DIF are correctly flagged. This paper compares the relative effectiveness of discriminant logistic regression (DLR) and IRTLRDIF for detecting DIF in polytomous short tests. A simulation study was designed. Test length, sample size, DIF amount and item response categories number were manipulated. Type I error and power were evaluated. IRTLRDIF and DLR yielded Type I error rates close to nominal level in no-DIF conditions. Under DIF conditions, Type I error rates were affected by test length DIF amount, degree of test contamination, sample size and number of item response categories. DLR showed a higher Type I error rate than did IRTLRDIF. Power rates were affected by DIF amount and sample size, but not by test length. DLR achieved higher power rates than did IRTLRDIF in very short tests, although the high Type I error rate involved means that this result cannot be taken into account. Test length had an important impact on the Type I error rate. IRTLRDIF and DLR showed a low power rate in short tests and with small sample sizes.
Faramarz Ashenai Ghasemi
Full Text Available This paper presents analytical and mathematical modeling and optimization of the dynamic behavior of the fiber metal laminates (FMLs subjected to low-velocity impact. The deflection to thickness (w/h ratio has been identified through the governing equations of the plate that are solved using the first-order shear deformation theory as well as the Fourier series method. With the help of a two degrees-of-freedom system, consisting of springs-masses, and the Choi's linearized Hertzian contact model the interaction between the impactor and the plate is modeled. Thirty-one experiments are conducted on samples of different layer sequences and volume fractions of Al plies in the composite Structures. A reliable fitness function in the form of a strict linear mathematical function constructed. Using an ordinary least square method, response regression coefficients estimated and a zero-one programming technique proposed to optimize the FML plate behavior subjected to any technological or cost restrictions. The results indicated that FML plate behavior is highly affected by layer sequences and volume fractions of Al plies. The results also showed that, embedding Al plies at outer layers of the structure significantly results in a better response of the structure under low-velocity impact, instead of embedding them in the middle or middle and outer layers of the structure.
Steca, Patrizia; Monzani, Dario; Greco, Andrea; Chiesi, Francesca; Primi, Caterina
2015-06-01
This study is aimed at testing the measurement properties of the Life Orientation Test-Revised (LOT-R) for the assessment of dispositional optimism by employing item response theory (IRT) analyses. The LOT-R was administered to a large sample of 2,862 Italian adults. First, confirmatory factor analyses demonstrated the theoretical conceptualization of the construct measured by the LOT-R as a single bipolar dimension. Subsequently, IRT analyses for polytomous, ordered response category data were applied to investigate the items' properties. The equivalence of the items across gender and age was assessed by analyzing differential item functioning. Discrimination and severity parameters indicated that all items were able to distinguish people with different levels of optimism and adequately covered the spectrum of the latent trait. Additionally, the LOT-R appears to be gender invariant and, with minor exceptions, age invariant. Results provided evidence that the LOT-R is a reliable and valid measure of dispositional optimism. © The Author(s) 2014.
Xin Fang
2016-11-01
Full Text Available The epidemiological evidence for a dose-response relationship between magnesium intake and risk of type 2 diabetes mellitus (T2D is sparse. The aim of the study was to summarize the evidence for the association of dietary magnesium intake with risk of T2D and evaluate the dose-response relationship. We conducted a systematic review and meta-analysis of prospective cohort studies that reported dietary magnesium intake and risk of incident T2D. We identified relevant studies by searching major scientific literature databases and grey literature resources from their inception to February 2016. We included cohort studies that provided risk ratios, i.e., relative risks (RRs, odds ratios (ORs or hazard ratios (HRs, for T2D. Linear dose-response relationships were assessed using random-effects meta-regression. Potential nonlinear associations were evaluated using restricted cubic splines. A total of 25 studies met the eligibility criteria. These studies comprised 637,922 individuals including 26,828 with a T2D diagnosis. Compared with the lowest magnesium consumption group in the population, the risk of T2D was reduced by 17% across all the studies; 19% in women and 16% in men. A statistically significant linear dose-response relationship was found between incremental magnesium intake and T2D risk. After adjusting for age and body mass index, the risk of T2D incidence was reduced by 8%–13% for per 100 mg/day increment in dietary magnesium intake. There was no evidence to support a nonlinear dose-response relationship between dietary magnesium intake and T2D risk. The combined data supports a role for magnesium in reducing risk of T2D, with a statistically significant linear dose-response pattern within the reference dose range of dietary intake among Asian and US populations. The evidence from Europe and black people is limited and more prospective studies are needed for the two subgroups.
Duffy, Frank H; Shankardass, Aditi; McAnulty, Gloria B; Eksioglu, Yaman Z; Coulter, David; Rotenberg, Alexander; Als, Heidelise
2014-05-15
Up to a third of children with Autism Spectrum Disorder (ASD) manifest regressive autism (R-ASD).They show normal early development followed by loss of language and social skills. Absent evidence-based therapies, anecdotal evidence suggests improvement following use of corticosteroids. This study examined the effects of corticosteroids for R-ASD children upon the 4 Hz frequency modulated evoked response (FMAER) arising from language cortex of the superior temporal gyrus (STG) and upon EEG background activity, language, and behavior. An untreated clinical convenience sample of ASD children served as control sample. Twenty steroid-treated R-ASD (STAR) and 24 not-treated ASD patients (NSA), aged 3 - 5 years, were retrospectively identified from a large database. All study participants had two sequential FMAER and EEG studies;Landau-Kleffner syndrome diagnosis was excluded. All subjects' records contained clinical receptive and expressive language ratings based upon a priori developed metrics. The STAR group additionally was scored behaviorally regarding symptom severity as based on the Diagnostic and Statistical Manual IV (DSM-IV) ASD criteria list. EEGs were visually scored for abnormalities. FMAER responses were assessed quantitatively by spectral analysis. Treated and untreated group means and standard deviations for the FMAER, EEG, language, and behavior, were compared by paired t-test and Fisher's exact tests. The STAR group showed a significant increase in the 4 Hz FMAER spectral response and a significant reduction in response distortion compared to the NSA group. Star group subjects' language ratings were significantly improved and more STAR than NSA group subjects showed significant language improvement. Most STAR group children showed significant behavioral improvement after treatment. STAR group language and behavior improvement was retained one year after treatment. Groups did not differ in terms of minor EEG abnormalities. Steroid treatment produced no
Siddiqui, M.R.S.; Gormly, K.L.; Bhoday, J.; Balyansikova, S.; Battersby, N.J.; Chand, M.; Rao, S.; Tekkis, P.; Abulafi, A.M.; Brown, G.
2016-01-01
Aim: To investigate whether the magnetic resonance imaging (MRI) tumour regression grading (mrTRG) scale can be taught effectively resulting in a clinically reasonable interobserver agreement (>0.4; moderate to near perfect agreement). Materials and methods: This study examines the interobserver agreement of mrTRG, between 35 radiologists and a central reviewer. Two workshops were organised for radiologists to assess regression of rectal cancers on MRI staging scans. A range of mrTRGs on 12 patient scans were used for assessment. Results: Kappa agreement ranged from 0.14–0.82 with a median value of 0.57 (95% CI: 0.37–0.77) indicating good overall agreement. Eight (26%) radiologists had very good/near perfect agreement (κ>0.8). Six (19%) radiologists had good agreement (0.8≥κ>0.6) and a further 12 (39%) had moderate agreement (0.6≥κ>0.4). Five (16%) radiologists had a fair agreement (0.4≥κ>0.2) and two had poor agreement (0.2>κ). There was a tendency towards good agreement (skewness: 0.92). In 65.9% and 90% of cases the radiologists were able to correctly highlight good and poor responders, respectively. Conclusions: The assessment of the response of rectal cancers to chemoradiation therapy may be performed effectively using mrTRG. Radiologists can be taught the mrTRG scale. Even with minimal training, good agreement with the central reviewer along with effective differentiation between good and intermediate/poor responders can be achieved. Focus should be on facilitating the identification of good responders. It is predicted that with more intensive interactive case-based learning a κ>0.8 is likely to be achieved. Testing and retesting is recommended. - Highlights: • Inter-observer agreement of radiologists was assessed using MRI rectal tumour regression scale. • Kappa agreement had a median value of 0.57 (95% CI: 0.37–0.77) indicating an overall good agreement. • In 65.9% and 90% of cases the radiologists were able to correctly highlight
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Weimer, Katja; Colloca, Luana; Enck, Paul
2015-01-01
Predictors of the placebo response (PR) in randomized controlled trials (RCT) have been searched for ever since RCT have become the standard for testing novel therapies and age and gender are routinely documented data in all trials irrespective of the drug tested, its indication, and the primary and secondary end points chosen. To evaluate whether age and gender have been found to be reliable predictors of the PR across medical subspecialties, we extracted 75 systematic reviews, meta-analyses, and meta-regressions performed in major medical areas (neurology, psychiatry, internal medicine) known for high PR rates. The literature database used contains approximately 2,500 papers on various aspects of the genuine PR. These ‘meta-analyses’ were screened for statistical predictors of the PR across multiple RCT, including age and gender, but also other patient-based and design-based predictors of higher PR rates. Retrieved papers were sorted for areas and disease categories. Only 15 of the 75 analyses noted an effect of younger age to be associated with higher PR, and this was predominantly in psychiatric conditions but not in depression, and internal medicine but not in gastroenterology. Female gender was associated with higher PR in only 3 analyses. Among the patient-based predictors, the most frequently noted factor was lower symptom severity at baseline, and among the design- based factors, it was a randomization ratio that selected more patients to drugs than to placebo, more frequent study visits, and more recent trials that were associated with higher PR rates. While younger age may contribute to the PR in some conditions, sex does not. There is currently no evidence that the PR is different in the elderly. PR are, however, markedly influenced by the symptom severity at baseline, and by the likelihood of receiving active treatment in placebo- controlled trials. © 2014 S. Karger AG, Basel.
Romberg, T.M.
1982-12-01
Industrial plant such as heat exchangers and nuclear and conventional boilers are prone to coolant flow oscillations which may not be detected. In this report, a hydrodynamic model is formulated in which the one-dimensional, non-linear, partial differential equations for the conservation of mass, energy and momentum are perturbed with respect to time, linearised, and Laplace-transformed into the s-domain for frequency response analysis. A computer program has been developed to integrate numerically the resulting non-linear ordinary differential equations by finite difference methods. A sample problem demonstrates how the computer code is used to analyse the frequency response and flow stability characteristics of a heated channel
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
Lopci, Egesta; Chiti, Arturo [Humanitas Research Hospital, Nuclear Medicine Department, Rozzano, Milan (Italy); Zucali, Paolo Andrea; Perrino, Matteo; Gianoncelli, Letizia; Lorenzi, Elena; Gemelli, Maria; Santoro, Armando [Humanitas Research Hospital, Oncology, Rozzano (Italy); Ceresoli, Giovanni Luca [Humanitas Gavazzeni, Oncology, Bergamo (Italy); Giordano, Laura [Humanitas Research Hospital, Biostatistics, Rozzano (Italy)
2015-04-01
Quantitative analyses on FDG PET for response assessment are increasingly used in clinical studies, particularly with respect to tumours in which radiological assessment is challenging and complete metabolic response is rarely achieved after treatment. A typical example is malignant pleural mesothelioma (MPM), an aggressive tumour originating from mesothelial cells of the pleura. We present our results concerning the use of semiquantitative and quantitative parameters, evaluated at the baseline and interim PET examinations, for the prediction of treatment response and disease outcome in patients with MPM. We retrospectively analysed data derived from 131 patients (88 men, 43 women; mean age 66 years) with MPM who were referred to our institution for treatment between May 2004 and July 2013. Patients were investigated using FDG PET at baseline and after two cycles of pemetrexed-based chemotherapy. Responses were determined using modified RECIST criteria based on the best CT response after treatment. Disease control rate, progression-free survival (PFS) and overall survival (OS) were calculated for the whole population and were correlated with semiquantitative and quantitative parameters evaluated at the baseline and interim PET examinations; these included SUV{sub max}, total lesion glycolysis (TLG), percentage change in SUV{sub max} (ΔSUV{sub max}) and percentage change in TLG (ΔTLG). Disease control was achieved in 84.7 % of the patients, and median PFS and OS for the entire cohort were 7.2 and 14.3 months, respectively. The log-rank test showed a statistically significant difference in PFS between patients with radiological progression and those with partial response (PR) or stable disease (SD) (1.8 vs. 8.6 months, p < 0.001). Baseline SUV{sub max} and TLG showed a statistically significant correlation with PFS and OS (p < 0.001). In the entire population, both ΔSUV{sub max} and ΔTLG were correlated with disease control based on best CT response (p < 0
Jääskeläinen, Markku; Lagerkvist, Andreas
2017-01-01
In this paper we investigate teaching with a classroom response system in introductory physics with emphasis on two issues. First, we discuss retention between question rounds and the reasons why students avoid answering the question a second time. A question with declining response rate was followed by a question addressing the student reasons for not answering. We find that there appear to be several reasons for the observed decline, and that the students need to be reminded. We argue that small drops are unimportant as the process appears to work despite the drops. Second, we discuss the dynamics of learning in a concept-sequence in electromagnetism, where a majority of the students, despite poor statistics in a first round, manage to answer a followup question correctly. In addition, we analyse the response times for both situations to connect with research on student reasoning on situations with misconception-like answers. From the combination of the answer flows and response time behaviours we find it plausible that conceptual learning occurred during the discussion phase. (paper)
Nakamura, Ryota; Suhrcke, Marc; Jebb, Susan A; Pechey, Rachel; Almiron-Roig, Eva; Marteau, Theresa M
2015-04-01
There is a growing concern, but limited evidence, that price promotions contribute to a poor diet and the social patterning of diet-related disease. We examined the following questions: 1) Are less-healthy foods more likely to be promoted than healthier foods? 2) Are consumers more responsive to promotions on less-healthy products? 3) Are there socioeconomic differences in food purchases in response to price promotions? With the use of hierarchical regression, we analyzed data on purchases of 11,323 products within 135 food and beverage categories from 26,986 households in Great Britain during 2010. Major supermarkets operated the same price promotions in all branches. The number of stores that offered price promotions on each product for each week was used to measure the frequency of price promotions. We assessed the healthiness of each product by using a nutrient profiling (NP) model. A total of 6788 products (60%) were in healthier categories and 4535 products (40%) were in less-healthy categories. There was no significant gap in the frequency of promotion by the healthiness of products neither within nor between categories. However, after we controlled for the reference price, price discount rate, and brand-specific effects, the sales uplift arising from price promotions was larger in less-healthy than in healthier categories; a 1-SD point increase in the category mean NP score, implying the category becomes less healthy, was associated with an additional 7.7-percentage point increase in sales (from 27.3% to 35.0%; P sales uplift from promotions was larger for higher-socioeconomic status (SES) groups than for lower ones (34.6% for the high-SES group, 28.1% for the middle-SES group, and 23.1% for the low-SES group). Finally, there was no significant SES gap in the absolute volume of purchases of less-healthy foods made on promotion. Attempts to limit promotions on less-healthy foods could improve the population diet but would be unlikely to reduce health
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
Regression analysis with categorized regression calibrated exposure: some interesting findings
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Tumor regression patterns in retinoblastoma
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany
2016-07-01
Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
GSHR, a Web-Based Platform Provides Gene Set-Level Analyses of Hormone Responses in Arabidopsis
Xiaojuan Ran
2018-01-01
Full Text Available Phytohormones regulate diverse aspects of plant growth and environmental responses. Recent high-throughput technologies have promoted a more comprehensive profiling of genes regulated by different hormones. However, these omics data generally result in large gene lists that make it challenging to interpret the data and extract insights into biological significance. With the rapid accumulation of theses large-scale experiments, especially the transcriptomic data available in public databases, a means of using this information to explore the transcriptional networks is needed. Different platforms have different architectures and designs, and even similar studies using the same platform may obtain data with large variances because of the highly dynamic and flexible effects of plant hormones; this makes it difficult to make comparisons across different studies and platforms. Here, we present a web server providing gene set-level analyses of Arabidopsis thaliana hormone responses. GSHR collected 333 RNA-seq and 1,205 microarray datasets from the Gene Expression Omnibus, characterizing transcriptomic changes in Arabidopsis in response to phytohormones including abscisic acid, auxin, brassinosteroids, cytokinins, ethylene, gibberellins, jasmonic acid, salicylic acid, and strigolactones. These data were further processed and organized into 1,368 gene sets regulated by different hormones or hormone-related factors. By comparing input gene lists to these gene sets, GSHR helped to identify gene sets from the input gene list regulated by different phytohormones or related factors. Together, GSHR links prior information regarding transcriptomic changes induced by hormones and related factors to newly generated data and facilities cross-study and cross-platform comparisons; this helps facilitate the mining of biologically significant information from large-scale datasets. The GSHR is freely available at http://bioinfo.sibs.ac.cn/GSHR/.
Yi, Jun; Yang, Wenhong; Sun, Wen-Hua; Nomura, Kotohiro; Hada, Masahiko
2017-11-30
The NMR chemical shifts of vanadium ( 51 V) in (imido)vanadium(V) dichloride complexes with imidazolin-2-iminato and imidazolidin-2-iminato ligands were calculated by the density functional theory (DFT) method with GIAO. The calculated 51 V NMR chemical shifts were analyzed by the multiple linear regression (MLR) analysis (MLRA) method with a series of calculated molecular properties. Some of calculated NMR chemical shifts were incorrect using the optimized molecular geometries of the X-ray structures. After the global minimum geometries of all of the molecules were determined, the trend of the observed chemical shifts was well reproduced by the present DFT method. The MLRA method was performed to investigate the correlation between the 51 V NMR chemical shift and the natural charge, band energy gap, and Wiberg bond index of the V═N bond. The 51 V NMR chemical shifts obtained with the present MLR model were well reproduced with a correlation coefficient of 0.97.
Yan Song
2015-01-01
Full Text Available Background: Vascular endothelial growth factor-targeted agents are standard treatments in advanced clear-cell renal cell carcinoma (ccRCC, but biomarkers of activity are lacking. The aim of this study was to investigate the association of Von Hippel-Lindau (VHL gene status, vascular endothelial growth factor receptor (VEGFR or stem cell factor receptor (KIT expression, and their relationships with characteristics and clinical outcome of advanced ccRCC. Methods: A total of 59 patients who received targeted treatment with sunitinib or pazopanib were evaluated for determination at Cancer Hospital and Institute, Chinese Academy of Medical Sciences between January 2010 and November 2012. Parafﬁn-embedded tumor samples were collected and status of the VHL gene and expression of VEGFR and KIT were determined by VHL sequence analysis and immunohistochemistry. Clinical-pathological features were collected and efficacy such as response rate and Median progression-free survival (PFS and overall survival (OS were calculated and then compared based on expression status. The Chi-square test, the Kaplan-Meier method, and the Lon-rank test were used for statistical analyses. Results: Of 59 patients, objective responses were observed in 28 patients (47.5%. The median PFS was 13.8 months and median OS was 39.9 months. There was an improved PFS in patients with the following clinical features: Male gender, number of metastatic sites 2 or less, VEGFR-2 positive or KIT positive. Eleven patients (18.6% had evidence of VHL mutation, with an objective response rate of 45.5%, which showed no difference with patients with no VHL mutation (47.9%. VHL mutation status did not correlate with either overall response rate (P = 0.938 or PFS (P = 0.277. The PFS was 17.6 months and 22.2 months in VEGFR-2 positive patients and KIT positive patients, respectively, which was significantly longer than that of VEGFR-2 or KIT negative patients (P = 0.026 and P = 0.043. Conclusion
Independent contrasts and PGLS regression estimators are equivalent.
Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary
2012-05-01
We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.
Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo
2009-01-01
We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Chen, Xiancheng; Lin, Xiaojuan; Li, Meng
2012-10-01
Progressive tumor-bearing patients deserve to benefit from more realistic approaches. Here, a study revealed the impact of modified periodic fasting and refeeding regimen on tumor progression or regression with little or no loss of food intake and body weight. Human A549 lung, HepG-2 liver, and SKOV-3 ovary progressive tumor-bearing mice were established and subjected to 4 wk of periodic fasting/refeeding cycles (PFRC), including periodic 1-d fasting/6-d refeeding weekly (protocol 1) and periodic 2-d fasting/5-d refeeding weekly (P2DF/5DR, protocol 2), with ad libitum (AL)-fed hosts as controls. Afterwards, PFRC groups exhibited tumor growth arrest with some tendency towards regression; especially, complete regression of progressive tumors and metastases comprised between 43.75 and 56.25% of tumor-challenged hosts in P2DF/5DR group (P fasting/6-d refeeding weekly groups survived a 4-month study period vs. only 31.25-37.5% in AL control group. Immunological assays and Luminex microarray revealed that tumor growth remission is mainly via natural killer cell (NK) reactivity and cross-regulation of IGF-binding protein-3, IGF/IGF-receptor, and megakaryocyte growth and development factor autocrine and paracrine loops. In vivo cellular and humoral assays indicated that tumor-regressive induction by PFRC protocols could be partly terminated by NK cell and IGF-binding protein-3 blockade or replenishment of IGF-I/-II and megakaryocyte growth and development factor. These findings offer a better understanding of comprehensive modulation of periodic fasting/refeeding strategy on the balance between tumor progression and regression.
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...
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
In order to control algal blooms, stressor-response relationships between water quality metrics, environmental variables, and algal growth should be understood and modeled. Machine-learning methods were suggested to express stressor-response relationships found by application of mechanistic water qu...
Introduction to regression graphics
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
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Bezler, P.; Wang, Y.K.; Reich, M.
1988-03-01
An evaluation of Independent Support Motion (ISM) response spectrum methods of analysis coupled with the Pressure Vessel Research Committee (PVRC) recommendation for damping, to compute the dynamic component of the seismic response of piping systems, was completed. Response estimates for five piping/structural systems were developed using fourteen variants of the ISM response spectrum method, the Uniform Support Motions response spectrum method and the ISM time history analysis method, all based on the PVRC recommendations for damping. The ISM/PVRC calculational procedures were found to exhibit orderly characteristics with levels of conservatism comparable to those obtained with the ISM/uniform damping procedures. Using the ISM/PVRC response spectrum method with absolute combination between group contributions provided consistently conservative results while using the ISM/PVRC response spectrum method with square root sum of squares combination between group contributions provided estimates of response which were deemed to be acceptable
K. Srujay Varma
2017-04-01
Full Text Available In this study, effect of machining process parameters viz. pulse-on time, pulse-off time, current and servo-voltage for machining High Carbon High Chromium Steel (HCHCr using copper electrode in wire EDM was investigated. High Carbon High Chromium Steel is a difficult to machine alloy, which has many applications in low temperature manufacturing, and copper is chosen as electrode as it has good electrical conductivity and most frequently used electrode all over the world. Tool making culture of copper has made many shops in Europe and Japan to used copper electrode. Experiments were conducted according to Taguchi’s technique by varying the machining process parameters at three levels. Taguchi’s method based on L9 orthogonal array was followed and number of experiments was limited to 9. Experimental cost and time consumption was reduced by following this statistical technique. Targeted output parameters are Material Removal Rate (MRR, Vickers Hardness (HV and Surface Roughness (SR. Analysis of Variance (ANOVA and Regression Analysis was performed using Minitab 17 software to optimize the parameters and draw relationship between input and output process parameters. Regression models were developed relating input and output parameters. It was observed that most influential factor for MRR, Hardness and SR are Ton, Toff and SV.
Christopher A. Moyer
2009-01-01
Full Text Available The most recent massage therapy (MT study by Hernandez-Reif et al. displays flaws persistent in this area of research that are attributable to MT researchers’ frequent mistake of using within-group analyses of dependent variables in studies that are purported to be randomized control trials. This practise violates the logic of using randomization to create treatment and control groups, and thereby fails to control for the validity threats of spontaneous remission, placebo effects, and statistical regression. The result is that a clear understanding of what MT can and cannot do is seriously hampered.
F.M.O. Borges
2003-12-01
que significou pouca influência da metodologia sobre essa medida. A FDN não mostrou ser melhor preditor de EM do que a FB.One experiment was run with broiler chickens, to obtain prediction equations for metabolizable energy (ME based on feedstuffs chemical analyses, and determined ME of wheat grain and its by-products, using four different methodologies. Seven wheat grain by-products were used in five treatments: wheat grain, wheat germ, white wheat flour, dark wheat flour, wheat bran for human use, wheat bran for animal use and rough wheat bran. Based on chemical analyses of crude fiber (CF, ether extract (EE, crude protein (CP, ash (AS and starch (ST of the feeds and the determined values of apparent energy (MEA, true energy (MEV, apparent corrected energy (MEAn and true energy corrected by nitrogen balance (MEVn in five treatments, prediction equations were obtained using the stepwise procedure. CF showed the best relationship with metabolizable energy values, however, this variable alone was not enough for a good estimate of the energy values (R² below 0.80. When EE and CP were included in the equations, R² increased to 0.90 or higher in most estimates. When the equations were calculated with all treatments, the equation for MEA were less precise and R² decreased. When ME data of the traditional or force-feeding methods were used separately, the precision of the equations increases (R² higher than 0.85. For MEV and MEVn values, the best multiple linear equations included CF, EE and CP (R²>0.90, independently of using all experimental data or separating by methodology. The estimates of MEVn values showed high precision and the linear coefficients (a of the equations were similar for all treatments or methodologies. Therefore, it explains the small influence of the different methodologies on this parameter. NDF was not a better predictor of ME than CF.
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
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
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-
Weed interference with crop growth is often attributed to water, nutrient, or light competition; however, specific physiological responses to these stresses are not well described. This study’s objective was to compare growth, yield, and gene expression responses of corn to nitrogen (N), low light (...
Item Response Theory Analyses of the Parent and Teacher Ratings of the DSM-IV ADHD Rating Scale
Gomez, Rapson
2008-01-01
The graded response model (GRM), which is based on item response theory (IRT), was used to evaluate the psychometric properties of the inattention and hyperactivity/impulsivity symptoms in an ADHD rating scale. To accomplish this, parents and teachers completed the DSM-IV ADHD Rating Scale (DARS; Gomez et al., "Journal of Child Psychology and…
Multilingual speaker age recognition: regression analyses on the Lwazi corpus
Feld, M
2009-12-01
Full Text Available Multilinguality represents an area of significant opportunities for automatic speech-processing systems: whereas multilingual societies are commonplace, the majority of speechprocessing systems are developed with a single language in mind. As a step...
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Regüés, David; Arnáez, José; Badía, David; Cerdà, Artemi; Echeverría, María Teresa; Gispert, María; Lana-Renault, Noemí; Lasanta, Teodoro; León, Javier; Nadal-Romero, Estela; Pardini, Giovanni
2014-05-01
Rainfall simulation experiments are being used by soil scientists, geomorphologists, and hydrologist to study runoff generation and erosion processes. The use of different apparatus with different rainfall intensities and size of the wetted area contribute to determine the most vulnerable soils and land uses (Cerdá, 1998; Cerdà et al., 2009; Nadal-Romero et al., 2011; Martínez-Murillo et al., 2013; León et al., 2014). This research aims to determine the land uses that yield more sediments and water and to know the factors that control the differences. The information from 152 experiments of rainfall simulation was jointly analysed. Experiments were done in 17 land uses (natural forest, tree plantation, burned forest, scrub, meadows, crops and badlands), with contrasted exposition (north-south), and vegetation cover variety and/or density. These situations were selected from four geographic contexts (NE of Catalonia, high and medium lands from the Ebro valley and Southern range of central Pyrenees) with significant altitude variations, between 90 and 1000 meters above sea level, which represent the heterogeneity of the Mediterranean climate. The use of similar rainfall simulation apparatus, with the same spray nozzle, spraying components and plot size, favours the comparison of the results. A wide spectrum of precipitation intensities was applied, in order to reach surface runoff generation in all cases. Results showed significant differences in runoff amounts and erosion rates, which were mainly associated with land uses, even more than precipitation differences. Runoff coefficient shows an inversed exponential relationship with rainfall intensity, which is the opposite what could be previously expected (Ziadat and Taimeh, 2013). This may be only justified by land use characteristics because a direct effect between runoff generation intensity and soil degradation conditions, with respect vegetation covers features and density, was observed. In fact, even though
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Murti Lestari
2010-05-01
Full Text Available This study analyzes the responses of performances of BankMandiri, Bank Danamon, and Bank Permata to merger strategy.This paper harnesses the quantitative approach with structuralbreak analysis method and impulse response function. Theplausible findings indicate that the merger of Bank Permataproduces a better performance response in comparison to theconsolidation of Bank Mandiri and the merger of Bank Danamon.The merger of Bank Permata does not result in performanceshocks, and the structural break does not prevail either. On theother hand, the consolidation of Bank Mandiri and the mergerof Bank Danamon result in structural breaks, particularly in thespread performance. In order to return to the stable position, themergers of Bank Mandiri and Bank Danamon require a longertime than does the merger of Bank Permata. This researchindicates that for large banks, the mergers and acquisitions(retaining one existing bank will deliver a better performanceresponse than will the consolidations (no existing bank. Keywords: impulse response function; merger; structural break
Reem, Nathan T; Chen, Han-Yi; Hur, Manhoi; Zhao, Xuefeng; Wurtele, Eve Syrkin; Li, Xu; Li, Ling; Zabotina, Olga
2018-03-01
This research provides new insights into plant response to cell wall perturbations through correlation of transcriptome and metabolome datasets obtained from transgenic plants expressing cell wall-modifying enzymes. Plants respond to changes in their cell walls in order to protect themselves from pathogens and other stresses. Cell wall modifications in Arabidopsis thaliana have profound effects on gene expression and defense response, but the cell signaling mechanisms underlying these responses are not well understood. Three transgenic Arabidopsis lines, two with reduced cell wall acetylation (AnAXE and AnRAE) and one with reduced feruloylation (AnFAE), were used in this study to investigate the plant responses to cell wall modifications. RNA-Seq in combination with untargeted metabolome was employed to assess differential gene expression and metabolite abundance. RNA-Seq results were correlated with metabolite abundances to determine the pathways involved in response to cell wall modifications introduced in each line. The resulting pathway enrichments revealed the deacetylation events in AnAXE and AnRAE plants induced similar responses, notably, upregulation of aromatic amino acid biosynthesis and changes in regulation of primary metabolic pathways that supply substrates to specialized metabolism, particularly those related to defense responses. In contrast, genes and metabolites of lipid biosynthetic pathways and peroxidases involved in lignin polymerization were downregulated in AnFAE plants. These results elucidate how primary metabolism responds to extracellular stimuli. Combining the transcriptomics and metabolomics datasets increased the power of pathway prediction, and demonstrated the complexity of pathways involved in cell wall-mediated signaling.
Ristić-Medić, Danijela; Dullemeijer, Carla; Tepsić, Jasna; Petrović-Oggiano, Gordana; Popović, Tamara; Arsić, Aleksandra; Glibetić, Marija; Souverein, Olga W; Collings, Rachel; Cavelaars, Adriënne; de Groot, Lisette; van't Veer, Pieter; Gurinović, Mirjana
2014-03-01
The objective of this systematic review was to identify studies investigating iodine intake and biomarkers of iodine status, to assess the data of the selected studies, and to estimate dose-response relationships using meta-analysis. All randomized controlled trials, prospective cohort studies, nested case-control studies, and cross-sectional studies that supplied or measured dietary iodine and measured iodine biomarkers were included. The overall pooled regression coefficient (β) and the standard error of β were calculated by random-effects meta-analysis on a double-log scale, using the calculated intake-status regression coefficient (β) for each individual study. The results of pooled randomized controlled trials indicated that the doubling of dietary iodine intake increased urinary iodine concentrations by 14% in children and adolescents, by 57% in adults and the elderly, and by 81% in pregnant women. The dose-response relationship between iodine intake and biomarkers of iodine status indicated a 12% decrease in thyroid-stimulating hormone and a 31% decrease in thyroglobulin in pregnant women. The model of dose-response quantification used to describe the relationship between iodine intake and biomarkers of iodine status may be useful for providing complementary evidence to support recommendations for iodine intake in different population groups.
Jo Vearey
2017-07-01
Full Text Available Abstract Johannesburg is home to a diverse migrant population and a range of urban health challenges. Locally informed and implemented responses to migration and health that are sensitive to the particular needs of diverse migrant groups are urgently required. In the absence of a coordinated response to migration and health in the city, the Johannesburg Migrant Health Forum (MHF – an unfunded informal working group of civil society actors – was established in 2008. We assess the impact, contributions and challenges of the MHF on the development of local-level responses to migration and urban health in Johannesburg to date. In this Commentary, we draw on data from participant observation in MHF meetings and activities, a review of core MHF documents, and semi-structured interviews conducted with 15 MHF members. The MHF is contributing to the development of local-level migration and health responses in Johannesburg in three key ways: (1 tracking poor quality or denial of public services to migrants; (2 diverse organisational membership linking the policy process with community experiences; and (3 improving service delivery to migrant clients through participation of diverse service providers and civil society organisations in the Forum. Our findings indicate that the MHF has a vital role to play in supporting the development of appropriate local responses to migration and health in a context of continued – and increasing – migration, and against the backdrop of rising anti-immigrant sentiments.
Dial, B.W.; Maxwell, D.E.
1986-12-01
Numerical studies of the far-field repository and near-field shaft response for a nuclear waste repository in bedded salt have been performed with the STEALTH computer code using the CAVS model for jointed rock. CAVS is a constitutive model that can simulate the slip and dilatancy of fracture planes in a jointed rock mass. The initiation and/or propagation of fractures can also be modeled when stress intensity criteria are met. The CAVS models are based on the joint models proposed with appropriate modifications for numerical simulations. The STEALTH/CAVS model has been previously used to model (1) explosive fracturing of a wellbore, (2) earthquake effects on tunnels in a generic nuclear waste repository, (3) horizontal emplacement for a nuclear waste repository in jointed granite, and (4) tunnel response in jointed rock. The use of CAVS to model far-field repository and near-field shaft response was different from previous approaches because it represented a spatially oriented approach to rock response and failure, rather than the traditional stress invariant formulation for yielding. In addition, CAVS tracked the response of the joint apertures to the time-dependent stress changes in the far-field repository and near-field shaft regions. 28 refs., 21 figs., 11 tabs
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
de Fiebre, C M; Collins, A C
1992-04-01
A classical (Mendelian) genetic analysis of responses to ethanol and nicotine was conducted in crosses derived from mouse lines which were selectively bred for differential duration of loss of the righting response (sleep-time) after ethanol. Dose-response curves for these mice, the long- and short-sleep mouse lines, as well as the derived F1, F2 and backcross (F1 x long-sleep and F1 x short-sleep) generations were generated for several measures of nicotine and ethanol sensitivity. Ethanol sensitivity was assessed using the sleep-time measure. Nicotine sensitivity was tested using a battery of behavioral and physiological tests which included measures of seizure activity, respiration rate, acoustic startle response, Y-maze activities (both crossing and rearing activities), heart rate and body temperature. The inheritance of sensitivities to both of these agents appears to be polygenic and inheritance can be explained primarily by additive genetic effects with some epistasis. Sensitivity to the ethanol sleep-time measure was genetically correlated with sensitivity to both nicotine-induced hypothermia and seizures; the correlation was greater between sleep-time and hypothermia. These data indicate that there is overlap in the genetic regulation of sensitivity to both ethanol and nicotine as measured by some, but not all, tests.
Scott, N. W.; Fayers, P. M.; Aaronson, N. K.; Bottomley, A.; de Graeff, A.; Groenvold, M.; Koller, M.; Petersen, M. A.; Sprangers, M. A. G.
2007-01-01
INTRODUCTION: The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 is a widely used health-related quality of life instrument. The main aim of this study is to investigate whether there are international differences in response to the questionnaire that can be explained by
Tay, Louis; Drasgow, Fritz
2012-01-01
Two Monte Carlo simulation studies investigated the effectiveness of the mean adjusted X[superscript 2]/df statistic proposed by Drasgow and colleagues and, because of problems with the method, a new approach for assessing the goodness of fit of an item response theory model was developed. It has been previously recommended that mean adjusted…
Elide Formentin
2018-03-01
Full Text Available Salinity tolerance has been extensively investigated in recent years due to its agricultural importance. Several features, such as the regulation of ionic transporters and metabolic adjustments, have been identified as salt tolerance hallmarks. Nevertheless, due to the complexity of the trait, the results achieved to date have met with limited success in improving the salt tolerance of rice plants when tested in the field, thus suggesting that a better understanding of the tolerance mechanisms is still required. In this work, differences between two varieties of rice with contrasting salt sensitivities were revealed by the imaging of photosynthetic parameters, ion content analysis and a transcriptomic approach. The transcriptomic analysis conducted on tolerant plants supported the setting up of an adaptive program consisting of sodium distribution preferentially limited to the roots and older leaves, and in the activation of regulatory mechanisms of photosynthesis in the new leaves. As a result, plants resumed grow even under prolonged saline stress. In contrast, in the sensitive variety, RNA-seq analysis revealed a misleading response, ending in senescence and cell death. The physiological response at the cellular level was investigated by measuring the intracellular profile of H2O2 in the roots, using a fluorescent probe. In the roots of tolerant plants, a quick response was observed with an increase in H2O2 production within 5 min after salt treatment. The expression analysis of some of the genes involved in perception, signal transduction and salt stress response confirmed their early induction in the roots of tolerant plants compared to sensitive ones. By inhibiting the synthesis of apoplastic H2O2, a reduction in the expression of these genes was detected. Our results indicate that quick H2O2 signaling in the roots is part of a coordinated response that leads to adaptation instead of senescence in salt-treated rice plants.
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Bo Molander
2011-12-01
Full Text Available In a study of the Emotional Skills and Competence Questionnaire instrument (ESCQ; Takšić, 1998 three samples of university students from Balkan countries (Croatia, Serbia, and Slovenia were contrasted with two samples of university students from Nordic countries (Finland and Sweden. In total, 1978 students participated. Effects of country and gender were obtained from the ESCQ total scores, as well as from the subscale scores. The subsequent analyses of item bias, that is, differential item functioning (DIF, revealed a number of DIF items in pair wise comparisons of the samples, thus creating doubts about the fairness in comparing mean scores. Further analyses of the DIF items showed, however, that most of the item curve functions were uniform, and that effect sizes were low. It was also shown that the number of DIF items depended on which countries were compared. Spearman correlations between measures of number of DIF items and cultural values as measured by World Value Survey data were very high. Implications of these findings for future cross-cultural studies of the ESCQ instrument are discussed.
Prediction, Regression and Critical Realism
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...
Kronsteiner, Barbara; Bassaganya-Riera, Josep; Philipson, Casandra; Viladomiu, Monica; Carbo, Adria; Abedi, Vida; Hontecillas, Raquel
2016-01-01
Abstract Helicobacter pylori is the dominant member of the gastric microbiota in over half of the human population of which 5–15% develop gastritis or gastric malignancies. Immune responses to H. pylori are characterized by mixed T helper cell, cytotoxic T cell and NK cell responses. The presence of Tregs is essential for the control of gastritis and together with regulatory CX3CR1+ mononuclear phagocytes and immune-evasion strategies they enable life-long persistence of H. pylori. This H. pylori-induced regulatory environment might contribute to its cross-protective effect in inflammatory bowel disease and obesity. Here we review host-microbe interactions, the development of pro- and anti-inflammatory immune responses and how the latter contribute to H. pylori's role as beneficial member of the gut microbiota. Furthermore, we present the integration of existing and new data into a computational/mathematical model and its use for the investigation of immunological mechanisms underlying initiation, progression and outcomes of H. pylori infection. PMID:26939848
Ling Pan
Full Text Available Drought is a major abiotic stress that impairs growth and productivity of Italian ryegrass. Comparative analysis of drought responsive proteins will provide insight into molecular mechanism in Lolium multiflorum drought tolerance. Using the iTRAQ-based approach, proteomic changes in tolerant and susceptible lines were examined in response to drought condition. A total of 950 differentially accumulated proteins was found to be involved in carbohydrate metabolism, amino acid metabolism, biosynthesis of secondary metabolites, and signal transduction pathway, such as β-D-xylosidase, β-D-glucan glucohydrolase, glycerate dehydrogenase, Cobalamin-independent methionine synthase, glutamine synthetase 1a, Farnesyl pyrophosphate synthase, diacylglycerol, and inositol 1, 4, 5-trisphosphate, which might contributed to enhance drought tolerance or adaption in Lolium multiflorum. Interestingly, the two specific metabolic pathways, arachidonic acid and inositol phosphate metabolism including differentially accumulated proteins, were observed only in the tolerant lines. Cysteine protease cathepsin B, Cysteine proteinase, lipid transfer protein and Aquaporin were observed as drought-regulated proteins participating in hydrolysis and transmembrane transport. The activities of phospholipid hydroperoxide glutathione peroxidase, peroxiredoxin, dehydroascorbate reductase, peroxisomal ascorbate peroxidase and monodehydroascorbate reductase associated with alleviating the accumulation of reactive oxygen species in stress inducing environments. Our results showed that drought-responsive proteins were closely related to metabolic processes including signal transduction, antioxidant defenses, hydrolysis, and transmembrane transport.
Wen Huang
Full Text Available The Pacific white shrimp (Litopenaeus vannamei is an important cultured crustacean species worldwide. However, little is known about the molecular mechanism of this species involved in the response to cold stress. In this study, four separate RNA-Seq libraries of L. vannamei were generated from 13°C stress and control temperature. Total 29,662 of Unigenes and overall of 19,619 annotated genes were obtained. Three comparisons were carried out among the four libraries, in which 72 of the top 20% of differentially-expressed genes were obtained, 15 GO and 5 KEGG temperature-sensitive pathways were fished out. Catalytic activity (GO: 0003824 and Metabolic pathways (ko01100 were the most annotated GO and KEGG pathways in response to cold stress, respectively. In addition, Calcium, MAPK cascade, Transcription factor and Serine/threonine-protein kinase signal pathway were picked out and clustered. Serine/threonine-protein kinase signal pathway might play more important roles in cold adaptation, while other three signal pathway were not widely transcribed. Our results had summarized the differentially-expressed genes and suggested the major important signaling pathways and related genes. These findings provide the first profile insight into the molecular basis of L. vannamei response to cold stress.
Multiple linear regression analysis
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.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
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.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
Biaoyang Lin
2009-08-01
Full Text Available The androgen receptor (AR plays important roles in the development of male phenotype and in different human diseases including prostate cancers. The AR can act either as a promoter or a tumor suppressor depending on cell types. The AR proliferative response program has been well studied, but its prohibitive response program has not yet been thoroughly studied.Previous studies found that PC3 cells expressing the wild-type AR inhibit growth and suppress invasion. We applied expression profiling to identify the response program of PC3 cells expressing the AR (PC3-AR under different growth conditions (i.e. with or without androgens and at different concentration of androgens and then applied the newly developed ChIP-seq technology to identify the AR binding regions in the PC3 cancer genome. A surprising finding was that the comparison of MOCK-transfected PC3 cells with AR-transfected cells identified 3,452 differentially expressed genes (two fold cutoff even without the addition of androgens (i.e. in ethanol control, suggesting that a ligand independent activation or extremely low-level androgen activation of the AR. ChIP-Seq analysis revealed 6,629 AR binding regions in the cancer genome of PC3 cells with an FDR (false discovery rate cut off of 0.05. About 22.4% (638 of 2,849 can be mapped to within 2 kb of the transcription start site (TSS. Three novel AR binding motifs were identified in the AR binding regions of PC3-AR cells, and two of them share a core consensus sequence CGAGCTCTTC, which together mapped to 27.3% of AR binding regions (1,808/6,629. In contrast, only about 2.9% (190/6,629 of AR binding sites contains the canonical AR matrix M00481, M00447 and M00962 (from the Transfac database, which is derived mostly from AR proliferative responsive genes in androgen dependent cells. In addition, we identified four top ranking co-occupancy transcription factors in the AR binding regions, which include TEF1 (Transcriptional enhancer factor
Maki Cristina Sayuri
2001-01-01
Full Text Available The growth of thirty-four Lentinula edodes strains submitted to different mycelial cultivation conditions (pH and temperature was evaluated and strain variability was assessed by RAPD molecular markers. The growth at three pH values (5, 6 and 7 and four different temperatures (16, 25, 28 and 37ºC was measured using the in vitro mycelial development rate and water retention as parameters. Mycelial cultivation was successful at all pH tested, while the ideal temperature for mycelial cultivation ranged between 25 and 28ºC. The water content was lower in strains grown at 37ºC. Among 20 OPA primers (Operon Technologies, Inc. used for the RAPD analyses, seventeen presented good polymorphism (OPA01 to OPA05, OPA07 to OPA14, OPA17 to OPA20. The clustering based on similarity coefficients allowed the separation of strain in two groups with different geographic origins.
Forconi, Francesco; King, Catherine A; Sahota, Surinder S; Kennaway, Christopher K; Russell, Nigel H; Stevenson, Freda K
2002-01-01
DNA vaccines induce immune responses against encoded proteins, and have clear potential for cancer vaccines. For B-cell tumours, idiotypic (Id) immunoglobulin encoded by the variable region genes provides a target antigen. When assembled as single chain Fv (scFv), and fused to an immunoenhancing sequence from tetanus toxin (TT), DNA fusion vaccines induce anti-Id antibodies. In lymphoma models, these antibodies have a critical role in mediating protection. For application to patients with lymphoma, two questions arise: first, whether pre-existing antibody against TT affects induction of anti-scFv antibodies; second, whether individual human scFv fusion sequences are able to fold consistently to generate antibodies able to recognize private conformational Id determinants expressed by tumour cells. Using xenogeneic vaccination with scFv sequences from four patients, we have shown that pre-existing anti-TT immunity slows, but does not prevent, anti-Id antibody responses. To determine folding, we have monitored the ability of nine DNAscFv–FrC patients' vaccines to induce xenogeneic anti-Id antibodies. Antibodies were induced in all cases, and were strikingly specific for each patient's immunoglobulin with little cross-reactivity between patients, even when similar VH or VL genes were involved. Blocking experiments with human serum confirmed reactivity against private determinants in 26–97% of total antibody. Both immunoglobulin G1 (IgG1) and IgG2a subclasses were present at 1·3 : 1–15 : 1 consistent with a T helper 2-dominated response. Xenogeneic vaccination provides a simple route for testing individual patients' DNAscFv–FrC fusion vaccines, and offers a strategy for production of anti-Id antibodies. The findings underpin the approach of DNA idiotypic fusion vaccination for patients with B-cell tumours. PMID:12225361
Yang, Xiaozhen; Li, Hao; Yang, Yongchao; Wang, Yongqi; Mo, Yanling; Zhang, Ruimin; Zhang, Yong; Ma, Jianxiang; Wei, Chunhua; Zhang, Xian
2018-01-01
Despite identification of WRKY family genes in numerous plant species, a little is known about WRKY genes in watermelon, one of the most economically important fruit crops around the world. Here, we identified a total of 63 putative WRKY genes in watermelon and classified them into three major groups (I-III) and five subgroups (IIa-IIe) in group II. The structure analysis indicated that ClWRKYs with different WRKY domains or motifs may play different roles by regulating respective target genes. The expressions of ClWRKYs in different tissues indicate that they are involved in various tissue growth and development. Furthermore, the diverse responses of ClWRKYs to drought, salt, or cold stress suggest that they positively or negatively affect plant tolerance to various abiotic stresses. In addition, the altered expression patterns of ClWRKYs in response to phytohormones such as, ABA, SA, MeJA, and ETH, imply the occurrence of complex cross-talks between ClWRKYs and plant hormone signals in regulating plant physiological and biological processes. Taken together, our findings provide valuable clues to further explore the function and regulatory mechanisms of ClWRKY genes in watermelon growth, development, and adaption to environmental stresses.
Xiaozhen Yang
Full Text Available Despite identification of WRKY family genes in numerous plant species, a little is known about WRKY genes in watermelon, one of the most economically important fruit crops around the world. Here, we identified a total of 63 putative WRKY genes in watermelon and classified them into three major groups (I-III and five subgroups (IIa-IIe in group II. The structure analysis indicated that ClWRKYs with different WRKY domains or motifs may play different roles by regulating respective target genes. The expressions of ClWRKYs in different tissues indicate that they are involved in various tissue growth and development. Furthermore, the diverse responses of ClWRKYs to drought, salt, or cold stress suggest that they positively or negatively affect plant tolerance to various abiotic stresses. In addition, the altered expression patterns of ClWRKYs in response to phytohormones such as, ABA, SA, MeJA, and ETH, imply the occurrence of complex cross-talks between ClWRKYs and plant hormone signals in regulating plant physiological and biological processes. Taken together, our findings provide valuable clues to further explore the function and regulatory mechanisms of ClWRKY genes in watermelon growth, development, and adaption to environmental stresses.
Harkaitz Eguiraun
2014-11-01
Full Text Available The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni’s FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax, two of which were similar (C1 control and C2 tagged fish and very different from the third (C3, tagged fish submerged in methylmercury contaminated water. The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry.
Dutilleul, Pierre; Han, Li Wen; Beaulieu, Jean
2014-06-01
Tree growth, as measured via the width of annual rings, is used for environmental impact assessment and climate back-forecasting. This fascinating natural process has been studied at various scales in the stem (from cell and fiber within a growth ring, to ring and entire stem) in one, two, and three dimensions. A new approach is presented to study tree growth in 3D from stem sections, at a scale sufficiently small to allow the delineation of reliable limits for annual rings and large enough to capture directional variation in growth rates. The technology applied is computed tomography scanning, which provides - for one stem section - millions of data (indirect measures of wood density) that can be mapped, together with a companion measure of dispersion and growth ring limits in filigree. Graphical and quantitative analyses are reported for white spruce trees with circular vs non-circular growth. Implications for dendroclimatological research are discussed. Copyright © 2014 Académie des sciences. Published by Elsevier SAS. All rights reserved.
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Thompson, J.D.; Manning, D.J.; Hogg, P.
2014-01-01
Observer performance methods maintain their place in radiology research, particularly in the assessment of the diagnostic accuracy of new and existing techniques, despite not being fully embraced by the wider audience in medical imaging. The receiver operating characteristic (ROC) paradigm has been widely used in research and the latest location sensitive methods allow an analysis that is closer to the clinical scenario. This paper discusses the underpinning theories behind observer performance assessment, exploring the potential sources of error and the development of the ROC method. The paper progresses by explaining the clinical relevance and statistical suitability of the free-response ROC (FROC) paradigm, and the methodological considerations for those wishing to perform an observer performance study
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Maloyan, Alina; Muralimanoharan, Sribalasubashini; Huffman, Steven; Cox, Laura A; Nathanielsz, Peter W; Myatt, Leslie; Nijland, Mark J
2013-10-01
Human and animal studies show that suboptimal intrauterine environments lead to fetal programming, predisposing offspring to disease in later life. Maternal obesity has been shown to program offspring for cardiovascular disease (CVD), diabetes, and obesity. MicroRNAs (miRNAs) are small, noncoding RNA molecules that act as key regulators of numerous cellular processes. Compelling evidence links miRNAs to the control of cardiac development and etiology of cardiac pathology; however, little is known about their role in the fetal cardiac response to maternal obesity. Our aim was to sequence and profile the cardiac miRNAs that are dysregulated in the hearts of baboon fetuses born to high fat/high fructose-diet (HFD) fed mothers for comparison with fetal hearts from mothers eating a regular diet. Eighty miRNAs were differentially expressed. Of those, 55 miRNAs were upregulated and 25 downregulated with HFD. Twenty-two miRNAs were mapped to human; 14 of these miRNAs were previously reported to be dysregulated in experimental or human CVD. We used an Ingenuity Pathway Analysis to integrate miRNA profiling and bioinformatics predictions to determine miRNA-regulated processes and genes potentially involved in fetal programming. We found a correlation between miRNA expression and putative gene targets involved in developmental disorders and CVD. Cellular death, growth, and proliferation were the most affected cellular functions in response to maternal obesity. Thus, the current study reveals significant alterations in cardiac miRNA expression in the fetus of obese baboons. The epigenetic modifications caused by adverse prenatal environment may represent one of the mechanisms underlying fetal programming of CVD.
Snider, D P; Underdown, B J
1986-04-01
We analyzed the appearance and level of Giardia muris-specific antibody of immunoglobulin A (IgA), IgG, and IgM isotypes, at weekly intervals, over the course of a 7-week infection in BALB/c and C57BL/6 mice. Using sensitive immunoradiometric assays, we observed that IgA antibody was the only detectable anti-G. muris antibody in intestinal secretions throughout the course of infection. No secreted IgG or IgM anti-G. muris antibody was detected even in concentrated intestinal secretions. The expulsion of G. muris by the mice was associated closely with the appearance and increasing levels of secreted anti-G. muris IgA antibody. Both IgG and IgA serum antibody to G. muris were detected, but no serum IgM antibody was detected. Serum IgA and IgG anti-G. muris antibody remained at high levels up to 10 weeks following clearance of the parasite. An interesting observation indicated that serum IgA antibody to G. muris developed more slowly in response to infection than secreted IgA antibody. An analysis of the molecular weight distribution of total serum IgA in infected mice determined that infection produced a transient but significant shift in serum IgA to high-molecular-weight (greater than or equal to dimeric IgA) forms. The results indicate that a substantial IgA antibody response occurs in sera and in gut secretions of G. muris-resistant mice and that IgA antibody is the dominant and possibly the only effector antibody active in intestinal secretions during G. muris infection in mice.
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...
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Blechingberg, Jenny; Luo, Yonglun; Bolund, Lars
2012-01-01
The FET family of proteins is composed of FUS/TLS, EWS/EWSR1, and TAF15 and possesses RNA- and DNA-binding capacities. The FET-proteins are involved in transcriptional regulation and RNA processing, and FET-gene deregulation is associated with development of cancer and protein granule formations...... in amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and trinucleotide repeat expansion diseases. We here describe a comparative characterization of FET-protein localization and gene regulatory functions. We show that FUS and TAF15 locate to cellular stress granules to a larger extend than EWS....... FET-proteins have no major importance for stress granule formation and cellular stress responses, indicating that FET-protein stress granule association most likely is a downstream response to cellular stress. Gene expression analyses showed that the cellular response towards FUS and TAF15 reduction...
Kuhla, Björn; Albrecht, Dirk; Bruckmaier, Rupert; Viergutz, Torsten; Nürnberg, Gerd; Metges, Cornelia C
2010-12-01
The hypothalamic-pituitary system controls homeostasis during feed energy reduction. In order to examine which pituitary proteins and hormone variants are potentially associated with metabolic adaptation, pituitary glands from ad libitum and energy restrictively fed dairy cows were characterized using RIA and 2-DE followed by MALDI-TOF-MS. We found 64 different spots of regulatory hormones: growth hormone (44), preprolactin (16), luteinizing hormone (LH) (1), thyrotropin (1), proopiomelanocortin (1) and its cleavage product lipotropin (1), but none of these did significantly differ between feeding groups. Quantification of total pituitary LH and prolactin concentrations by RIA confirmed the results obtained by proteome analysis. Also, feed energy restriction provoked increasing non-esterified fatty acid, decreasing prolactin, but unaltered glucose, LH and growth hormone plasma concentrations. Energy restriction decreased the expression of glial fibrillary acidic protein, triosephosphate isomerase, purine-rich element-binding protein A and elongation factor Tu, whereas it increased expression of proline synthetase co-transcribed homolog, peroxiredoxin III, β-tubulin and annexin A5 which is involved in the hormone secretion process. Our results indicate that in response to feed energy restriction the pituitary reservoir of all posttranslationally modified hormone forms remains constant. Changing plasma hormone concentrations are likely attributed to a regulated releasing process from the gland into the blood. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Kwon, In Ho; Bae, Youin; Yeo, Un-Cheol; Lee, Jin Yong; Kwon, Hyuck Hoon; Choi, Young Hee; Park, Gyeong-Hun
2018-02-01
The histologic responses to varied parameters of 1,927-nm fractional thulium fiber laser treatment have not yet been sufficiently elucidated. This study sought to evaluate histologic changes immediately after 1,927-nm fractional thulium fiber laser session at various parameters. The dorsal skin of Yucatan mini-pig was treated with 1,927-nm fractional thulium fiber laser at varied parameters, with or without skin drying. The immediate histologic changes were evaluated to determine the effects of varying laser parameters on the width and the depth of treated zones. The increase in the level of pulse energy widened the area of epidermal changes in the low power level, but increased the dermal penetration depth in the high power level. As the pulse energy level increased, the increase in the power level under the given pulse energy level more evidently made dermal penetration deeper and the treatment area smaller. Skin drying did not show significant effects on epidermal changes, but evidently increased the depth of dermal denaturation under both high and low levels of pulse energy. These results may provide important information to establish treatment parameters of the 1,927-nm fractional thulium fiber laser for various skin conditions.
Tarin Paz-Kagan
2014-08-01
Full Text Available Drought events cause changes in ecosystem function and structure by reducing the shrub abundance and expanding the biological soil crusts (biocrusts. This change increases the leakage of nutrient resources and water into the river streams in semi-arid areas. A common management solution for decreasing this loss of resources is to create a runoff-harvesting system (RHS. The objective of the current research is to apply geo-information techniques, including remote sensing and geographic information systems (GIS, on the watershed scale, to monitor and analyze the spatial and temporal changes in response to drought of two source-sink systems, the natural shrubland and the human-made RHSs in the semi-arid area of the northern Negev Desert, Israel. This was done by evaluating the changes in soil, vegetation and landscape cover. The spatial changes were evaluated by three spectral indices: Normalized Difference Vegetation Index (NDVI, Crust Index (CI and landscape classification change between 2003 and 2010. In addition, we examined the effects of environmental factors on NDVI, CI and their clustering after successive drought years. The results show that vegetation cover indicates a negative ∆NDVI change due to a reduction in the abundance of woody vegetation. On the other hand, the soil cover change data indicate a positive ∆CI change due to the expansion of the biocrusts. These two trends are evidence for degradation processes in terms of resource conservation and bio-production. A considerable part of the changed area (39% represents transitions between redistribution processes of resources, such as water, sediments, nutrients and seeds, on the watershed scale. In the pre-drought period, resource redistribution mainly occurred on the slope scale, while in the post-drought period, resource redistribution occurred on the whole watershed scale. However, the RHS management is effective in reducing leakage, since these systems are located on the
Thomas, Geethu E.; Geetha, Kiran A.; Augustine, Lesly; Mamiyil, Sabu; Thomas, George
2016-01-01
Mode of reproduction is generally considered to have long-range evolutionary implications on population survival. Because sexual reproduction produces genetically diverse genotypes, this mode of reproduction is predicted to positively influence the success potential of offspring in evolutionary arms race with parasites (Red queen) whereas, without segregation and recombination, the obligate asexual multiplication may push a species into extinction due to the steady accumulation of deleterious mutations (Muller’s ratchet). However, the extent of linearity between reproductive strategies, genetic diversity and population fitness, and the contributions of different breeding strategies to population fitness are yet to be understood clearly. Genus Zingiber belonging to the pan-tropic family Zingiberaceae represents a good system to study contributions of different breeding behavior on genetic diversity and population fitness, as this genus comprises species with contrasting breeding systems. In this study, we analyzed breeding behavior, amplified fragment length polymorphism diversity and response to the soft-rot pathogen Pythium aphanidermatum in 18 natural populations of three wild Zingiber spp.: Z. neesanum, Z. nimmonii, and Z. zerumbet, together with the obligately asexual cultivated congener, ginger (Z. officinale). Ginger showed an exceptionally narrow genetic base, and adding to this, all the tested cultivars were uniformly susceptible to soft-rot. Concordant with the postulates of Muller’s ratchet, the background selection may be continuously pushing ginger into the ancestral state, rendering it inefficient in host-pathogen coevolution. Z. neesanum and Z. nimmonii populations were sexual and genetically diverse; however, contrary to Red Queen expectations, the populations were highly susceptible to soft-rot. Z. zerumbet showed a hemiclonal breeding behavior. The populations inhabiting forest understory were large and continuous, sexual and genetically
Steganalysis using logistic regression
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.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Krivolutsky, Alexei A.; Nazarova, Margarita; Knyazeva, Galina
Solar activity influences on atmospheric photochemical system via its changebale electromag-netic flux with eleven-year period and also by energetic particles during solar proton event (SPE). Energetic particles penetrate mostly into polar regions and induce additional produc-tion of NOx and HOx chemical compounds, which can destroy ozone in photochemical catalytic cycles. Solar irradiance variations cause in-phase variability of ozone in accordance with photo-chemical theory. However, real ozone response caused by these two factors, which has different physical nature, is not so clear on long-term time scale. In order to understand the situation multiply linear regression statistical method was used. Three data series, which covered the period 1958-2006, have been used to realize such analysis: yearly averaged total ozone at dif-ferent latitudes (World Ozone Data Centre, Canada, WMO); yearly averaged proton fluxes with E¿ 10 MeV ( IMP, GOES, METEOR satellites); yearly averaged numbers of solar spots (Solar Data). Then, before the analysis, the data sets of ozone deviations from the mean values for whole period (1958-2006) at each latitudinal belt were prepared. The results of multiply regression analysis (two factors) revealed rather complicated time-dependent behavior of ozone response with clear negative peaks for the years of strong SPEs. The magnitudes of such peaks on annual mean basis are not greater than 10 DU. The unusual effect -positive response of ozone to solar proton activity near both poles-was discovered by statistical analysis. The pos-sible photochemical nature of found effect is discussed. This work was supported by Russian Science Foundation for Basic Research (grant 09-05-009949) and by the contract 1-6-08 under Russian Sub-Program "Research and Investigation of Antarctica".
Geethu Elizabath Thomas
2016-12-01
Full Text Available AbstractMode of reproduction is generally considered to have long-range evolutionary implications on population survival. Because sexual reproduction produces genetically diverse genotypes, this mode of reproduction is predicted to positively influence the success potential of offspring in evolutionary arms race with parasites (Red queen whereas, without segregation and recombination, the obligate asexual multiplication may push a species into extinction due to the steady accumulation of deleterious mutations (Muller’s ratchet. However, the extent of linearity between reproductive strategies, genetic diversity and population fitness, and the contributions of different breeding strategies to population fitness are yet to be understood clearly. Genus Zingiber belonging to the pan-tropic family Zingiberaceae represents a good system to study contributions of different breeding behaviour on genetic diversity and population fitness, as this genus comprises species with contrasting breeding systems. In this study, we analyzed breeding behaviour, amplified fragment length polymorphism (AFLP diversity and response to the soft-rot pathogen Pythium aphanidermatum in 18 natural populations of three wild Zingiber spp.: Z. neesanum, Z. nimmonii and Z. zerumbet, together with the obligately asexual cultivated congener, ginger (Z. officinale. Ginger showed an exceptionally narrow genetic base, and adding to this, all the tested cultivars were uniformly susceptible to soft-rot. Concordant with the postulates of Muller’s ratchet, the background selection may be continuously pushing ginger into the ancestral state, rendering it inefficient in host-pathogen coevolution. Z. neesanum and Z. nimmonii populations were sexual and genetically diverse; however, contrary to Red Queen expectations, the populations were highly susceptible to soft-rot. Z. zerumbet showed a hemiclonal breeding behaviour. The populations inhabiting forest understory were large and
Dai, Liang-Che, E-mail: lcdai@iner.gov.tw; Chen, Yen-Shu; Yuann, Yng-Ruey
2014-12-15
Highlights: • The GOTHIC code is used for the PWR dry containment pressure and temperature analysis. • Boundary conditions are hot standby and 102% power main steam line break accidents. • Containment pressure and temperature responses of GOTHIC are similar with FSAR. • The capability of the developed model to perform licensing calculation is assessed. - Abstract: Units 1 and 2 of the Maanshan nuclear power station are the typical Westinghouse three-loop PWR (pressurized water reactor) with large dry containments. In this study, the containment analysis program GOTHIC is adopted for the dry containment pressure and temperature analysis. Free air space and sump of the PWR dry containment are individually modeled as control volumes. The containment spray system and fan cooler unit are also considered in the GOTHIC model. The blowdown mass and energy data of the main steam line break (hot standby condition and various reactor thermal power levels) are tabulated in the Maanshan Final Safety Analysis Report (FSAR) 6.2 which could be used as the boundary conditions for the containment model. The calculated containment pressure and temperature behaviors of the selected cases are in good agreement with the FSAR results. In this study, hot standby and 102% reactor thermal power main steam line break accidents are selected. The calculated peak containment pressure is 323.50 kPag (46.92 psig) for hot standby MSLB, which is a little higher than the FSAR value of 311.92 kPag (45.24 psig). But it is still below the design value of 413.69 kPag (60 psig). The calculated peak vapor temperature inside the containment is 187.0 °C (368.59 F) for 102% reactor thermal power MSLB, which is lower than the FSAR result of 194.42 °C (381.95 F). The effects of the containment spray system and fan cooler units could be clearly observed in the GOTHIC analysis. The calculated containment pressure and temperature behaviors of the selected cases are in good agreement with the FSAR
Lee, Sangchul; Yeo, In-Young; Sadeghi, Ali M.; McCarty, Gregory W.; Hively, Wells; Lang, Megan W.; Sharifi, Amir
2018-01-01
Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to be exacerbated by climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluated the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using the Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitivity scenarios to assess the individual impacts of variations in CO2concentration (590 and 850 ppm), precipitation increase (11 and 21 %), and temperature increase (2.9 and 5.0 °C), based on regional general circulation model (GCM) projections. Further, we considered the ensemble of five GCM projections (2085–2098) under the Representative Concentration Pathway (RCP) 8.5 scenario to evaluate simultaneous changes in CO2, precipitation, and temperature. Using SWAT model simulations from 2001 to 2014 as a baseline scenario, predicted hydrologic outputs (water and nitrate budgets) and crop growth were analyzed. Compared to the baseline scenario, a precipitation increase of 21 % and elevated CO2 concentration of 850 ppm significantly increased streamflow and nitrate loads by 50 and 52 %, respectively, while a temperature increase of 5.0 °C reduced streamflow and nitrate loads by 12 and 13 %, respectively. Crop biomass increased with elevated CO2 concentrations due to enhanced radiation- and water-use efficiency, while it decreased with precipitation and temperature increases. Over the GCM ensemble mean, annual streamflow and nitrate loads showed an increase of ∼ 70 % relative to the baseline scenario, due to elevated CO2 concentrations and precipitation increase. Different hydrological responses to climate change were observed from the two watersheds, due to contrasting land use and soil characteristics. The watershed with a larger percent of croplands demonstrated a greater
Dai, Liang-Che; Chen, Yen-Shu; Yuann, Yng-Ruey
2014-01-01
Highlights: • The GOTHIC code is used for the PWR dry containment pressure and temperature analysis. • Boundary conditions are hot standby and 102% power main steam line break accidents. • Containment pressure and temperature responses of GOTHIC are similar with FSAR. • The capability of the developed model to perform licensing calculation is assessed. - Abstract: Units 1 and 2 of the Maanshan nuclear power station are the typical Westinghouse three-loop PWR (pressurized water reactor) with large dry containments. In this study, the containment analysis program GOTHIC is adopted for the dry containment pressure and temperature analysis. Free air space and sump of the PWR dry containment are individually modeled as control volumes. The containment spray system and fan cooler unit are also considered in the GOTHIC model. The blowdown mass and energy data of the main steam line break (hot standby condition and various reactor thermal power levels) are tabulated in the Maanshan Final Safety Analysis Report (FSAR) 6.2 which could be used as the boundary conditions for the containment model. The calculated containment pressure and temperature behaviors of the selected cases are in good agreement with the FSAR results. In this study, hot standby and 102% reactor thermal power main steam line break accidents are selected. The calculated peak containment pressure is 323.50 kPag (46.92 psig) for hot standby MSLB, which is a little higher than the FSAR value of 311.92 kPag (45.24 psig). But it is still below the design value of 413.69 kPag (60 psig). The calculated peak vapor temperature inside the containment is 187.0 °C (368.59 F) for 102% reactor thermal power MSLB, which is lower than the FSAR result of 194.42 °C (381.95 F). The effects of the containment spray system and fan cooler units could be clearly observed in the GOTHIC analysis. The calculated containment pressure and temperature behaviors of the selected cases are in good agreement with the FSAR
Lee, Sangchul; Yeo, In-Young; Sadeghi, Ali M.; McCarty, Gregory W.; Hively, Wells D.; Lang, Megan W.; Sharifi, Amir
2018-01-01
Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to be exacerbated by climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluated the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using the Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitivity scenarios to assess the individual impacts of variations in CO2 concentration (590 and 850 ppm), precipitation increase (11 and 21 %), and temperature increase (2.9 and 5.0 °C), based on regional general circulation model (GCM) projections. Further, we considered the ensemble of five GCM projections (2085-2098) under the Representative Concentration Pathway (RCP) 8.5 scenario to evaluate simultaneous changes in CO2, precipitation, and temperature. Using SWAT model simulations from 2001 to 2014 as a baseline scenario, predicted hydrologic outputs (water and nitrate budgets) and crop growth were analyzed. Compared to the baseline scenario, a precipitation increase of 21 % and elevated CO2 concentration of 850 ppm significantly increased streamflow and nitrate loads by 50 and 52 %, respectively, while a temperature increase of 5.0 °C reduced streamflow and nitrate loads by 12 and 13 %, respectively. Crop biomass increased with elevated CO2 concentrations due to enhanced radiation- and water-use efficiency, while it decreased with precipitation and temperature increases. Over the GCM ensemble mean, annual streamflow and nitrate loads showed an increase of ˜ 70 % relative to the baseline scenario, due to elevated CO2 concentrations and precipitation increase. Different hydrological responses to climate change were observed from the two watersheds, due to contrasting land use and soil characteristics. The watershed with a larger percent of croplands demonstrated a greater increased rate of 5.2 kg N ha-1 in
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
Jurado, Maria Jose; Schleicher, Anja
2014-05-01
The objective of our research is a detailed characterization of structures on the basis of LWD oriented images and logs,and clay mineralogy of cuttings from Hole C0002F of the Nankai Trough accretionary prism. Our results show an integrated interpretation of structures derived from borehole images, petrophysical characterization on LWD logs and cuttings mineralogy. The geometry of the structure intersected at Hole C0002F has been characterized by the interpretation of oriented borehole resistivity images acquired during IODP Expedition 338. The characterization of structural features, faults and fracture zones is based on a detailed post-cruise interpretation of bedding and fractures on borehole images and also on the analysis of Logging While Drilling (LWD) log response (gamma radioactivity, resistivity and sonic logs). The interpretation and complete characterization of structures (fractures, fracture zones, fault zones, folds) was achieved after detailed shorebased reprocessing of resistivity images, which allowed to enhance bedding and fracture's imaging for geometry and orientation interpretation. In order to characterize distinctive petrophysical properties based on LWD log response, it could be compared with compositional changes derived from cuttings analyses. Cuttings analyses were used to calibrate and to characterize log response and to verify interpretations in terms of changes in composition and texture at fractures and fault zones defined on borehole images. Cuttings were taken routinely every 5 m during Expedition 338, indicating a clay-dominated lithology of silty claystone with interbeds of weakly consolidated, fine sandstones. The main mineralogical components are clay minerals, quartz, feldspar and calcite. Selected cuttings were taken from areas of interest as defined on LWD logs and images. The clay mineralogy was investigated on the LWD) data allowed us to characterize structural, petrophysical and mineralogical properties at fracture and
Sekar, Krithiga; Findley, William M.; Llinás, Rodolfo R.
2014-01-01
Whether consciousness is an all-or-none or graded phenomenon is an area of inquiry that has received considerable interest in neuroscience and is as of yet, still debated. In this magnetoencephalography (MEG) study we used a single stimulus paradigm with sub-threshold, threshold and supra-threshold duration inputs to assess whether stimulus perception is continuous with or abruptly differentiated from unconscious stimulus processing in the brain. By grouping epochs according to stimulus identification accuracy and exposure duration, we were able to investigate whether a high-amplitude perception-related cortical event was (1) only evoked for conditions where perception was most probable (2) had invariant amplitude once evoked and (3) was largely absent for conditions where perception was least probable (criteria satisfying an all-on-none hypothesis). We found that averaged evoked responses showed a gradual increase in amplitude with increasing perceptual strength. However, single trial analyses demonstrated that stimulus perception was correlated with an all-or-none response, the temporal precision of which increased systematically as perception transitioned from ambiguous to robust states. Due to poor signal-to-noise resolution of single trial data, whether perception-related responses, whenever present, were invariant in amplitude could not be unambiguously demonstrated. However, our findings strongly suggest that visual perception of simple stimuli is associated with an all-or-none cortical evoked response the temporal precision of which varies as a function of perceptual strength. PMID:22020091
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Modified Regression Correlation Coefficient for Poisson Regression Model
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).
Measurement Error in Education and Growth Regressions
Portela, M.; Teulings, C.N.; Alessie, R.
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Measurement error in education and growth regressions
Portela, Miguel; Teulings, Coen; Alessie, R.
2004-01-01
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Panel data specifications in nonparametric kernel regression
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Du, Jing; Guo, Shirong; Sun, Jin; Shu, Sheng
2018-05-01
The mechanism of exogenous Spd-induced Ca(NO 3 ) 2 stress tolerance in cucumber was studied by proteomics and physiological analyses. Protein-protein interaction network revealed 13 key proteins involved in Spd-induced Ca(NO 3 ) 2 stress resistance. Ca(NO 3 ) 2 stress is one of the major reasons for secondary salinization that limits cucumber plant development in greenhouse. The conferred protective role of exogenous Spd on cucumber in response to Ca(NO 3 ) 2 stress cues involves changes at the cellular and physiological levels. To investigate the molecular foundation of exogenous Spd in Ca(NO 3 ) 2 stress tolerance, a proteomic approach was performed in our work. After a 9 days period of Ca(NO 3 ) 2 stress and/or exogenous Spd, 71 differential protein spots were confidently identified. The resulting proteins were enriched in seven different categories of biological processes, including protein metabolism, carbohydrate and energy metabolism, ROS homeostasis and stress defense, cell wall related, transcription, others and unknown. Protein metabolism (31.2%), carbohydrate and energy metabolism (15.6%), ROS homeostasis and stress defense (32.5%) were the three largest functional categories in cucumber root and most of them were significantly increased by exogenous Spd. The Spd-responsive protein interaction network revealed 13 key proteins, whose accumulation changes could be critical for Spd-induced resistance; all 13 proteins were upregulated by Spd at transcriptional and protein levels in response to Ca(NO 3 ) 2 stress. Furthermore, accumulation of antioxidant enzymes, non-enzymatic antioxidant and polyamines, along with reduction of H 2 O 2 and MDA, were detected after exogenous Spd application during Ca(NO 3 ) 2 stress. The results of these proteomic and physiological analyses in cucumber root may facilitate a better understanding of the underlying mechanism of Ca(NO 3 ) 2 stress tolerance mediated by exogenous Spd.
Liu, Jianxia; Wang, Runmei; Liu, Wenying; Zhang, Hongli; Guo, Yaodong; Wen, Riyu
2018-01-23
Heat-shock proteins (HSPs) are ubiquitous proteins with important roles in response to biotic and abiotic stress. The 70-kDa heat-shock genes ( Hsp70s ) encode a group of conserved chaperone proteins that play central roles in cellular networks of molecular chaperones and folding catalysts across all the studied organisms including bacteria, plants and animals. Several Hsp70s involved in drought tolerance have been well characterized in various plants, whereas no research on Chenopodium quinoa HSPs has been completed. Here, we analyzed the genome of C. quinoa and identified sixteen Hsp70 members in quinoa genome. Phylogenetic analysis revealed the independent origination of those Hsp70 members, with eight paralogous pairs comprising the Hsp70 family in quinoa. While the gene structure and motif analysis showed high conservation of those paralogous pairs, the synteny analysis of those paralogous pairs provided evidence for expansion coming from the polyploidy event. With several subcellular localization signals detected in CqHSP70 protein paralogous pairs, some of the paralogous proteins lost the localization information, indicating the diversity of both subcellular localizations and potential functionalities of those HSP70s. Further gene expression analyses revealed by quantitative polymerase chain reaction (qPCR) analysis illustrated the significant variations of Cqhsp70s in response to drought stress. In conclusion, the sixteen Cqhsp70 s undergo lineage-specific expansions and might play important and varied roles in response to drought stress.
Çelik, Ali Kemal; Oktay, Erkan; Çebi, Kübranur
2017-09-01
The main objective of this article is to determine key factors that may have a significant effect on the verbal abuse, emotional abuse and physical assault of health care workers in north-eastern Turkey. A self-administered survey was completed by 450 health care workers in three well-established hospitals in Erzurum, Turkey. Because of the discrete and ordered nature of the dependent variable of the survey, the data were analysed using four distinctive ordered response models. Results revealed that several key variables were found to be a significant determinant of workplace violence, such as the type of health institution, occupational position, weekly working hours, weekly shift hours, number of daily patient contacts, age group of the respondents, experience in the health sector, training against workplace violence and current policies of the hospitals and the Turkish Ministry of Health.
Polynomial regression analysis and significance test of the regression function
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)
Panouillères, M; Anota, A; Nguyen, T V; Brédart, A; Bosset, J F; Monnier, A; Mercier, M; Hardouin, J B
2014-09-01
The present study investigates the properties of the French version of the OUT-PATSAT35 questionnaire, which evaluates the outpatients' satisfaction with care in oncology using classical analysis (CTT) and item response theory (IRT). This cross-sectional multicenter study includes 692 patients who completed the questionnaire at the end of their ambulatory treatment. CTT analyses tested the main psychometric properties (convergent and divergent validity, and internal consistency). IRT analyses were conducted separately for each OUT-PATSAT35 domain (the doctors, the nurses or the radiation therapists and the services/organization) by models from the Rasch family. We examined the fit of the data to the model expectations and tested whether the model assumptions of unidimensionality, monotonicity and local independence were respected. A total of 605 (87.4%) respondents were analyzed with a mean age of 64 years (range 29-88). Internal consistency for all scales separately and for the three main domains was good (Cronbach's α 0.74-0.98). IRT analyses were performed with the partial credit model. No disordered thresholds of polytomous items were found. Each domain showed high reliability but fitted poorly to the Rasch models. Three items in particular, the item about "promptness" in the doctors' domain and the items about "accessibility" and "environment" in the services/organization domain, presented the highest default of fit. A correct fit of the Rasch model can be obtained by dropping these items. Most of the local dependence concerned items about "information provided" in each domain. A major deviation of unidimensionality was found in the nurses' domain. CTT showed good psychometric properties of the OUT-PATSAT35. However, the Rasch analysis revealed some misfitting and redundant items. Taking the above problems into consideration, it could be interesting to refine the questionnaire in a future study.
Zhang, Yidan; Zhou, Zhi; Wang, Lingui; Huang, Bo
2018-02-12
Coral bleaching occurs worldwide with increasing frequencies and intensities, which is caused by the stress response of stony coral to environmental change, especially increased sea surface temperature. In the present study, transcriptome, expression, and activity analyses were employed to illustrate the underlying molecular mechanisms of heat shock protein 70 (HSP70) in the stress response of coral to environmental changes. The domain analyses of assembled transcripts revealed 30 HSP70 gene contigs in stony coral Pocillopora damicornis. One crucial HSP70 (PdHSP70) was observed, whose expressions were induced by both elevated temperature and ammonium after expression difference analysis. The complete complementary DNA (cDNA) sequence of PdHSP70 was identified, which encoded a polypeptide of 650 amino acids with a molecular weight of 71.93 kDa. The deduced amino acid sequence of PdHSP70 contained a HSP70 domain (from Pro8 to Gly616), and it shared the highest similarity (95%) with HSP70 from Stylophora pistillata. The expression level of PdHSP70 gene increased significantly at 12 h, and returned to the initial level at 24 h after the stress of high temperature (32 °C). The cDNA fragment encoding the mature peptide of PdHSP70 was recombined and expressed in the prokaryotic expression system. The ATPase activity of recombinant PdHSP70 protein was determined, and it did not change significantly in a wide range of temperature from 25 to 40 °C. These results collectively suggested that PdHSP70 was a vital heat shock protein 70 in the stony coral P. damicornis, whose mRNA expression could be induced by diverse environmental stress and whose activity could remain stable under heat stress. PdHSP70 might be involved in the regulation of the bleaching owing to heat stress in the stony coral P. damicornis.
Recursive Algorithm For Linear Regression
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.
Combining Alphas via Bounded Regression
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Tse, L A; Yu, I T S; Leung, C C; Tam, W; Wong, T W
2007-01-01
Objectives To examine the exposure–response relationships between various indices of exposure to silica dust and the mortality from non‐malignant respiratory diseases (NMRDs) or chronic obstructive pulmonary diseases (COPDs) among a cohort of workers with silicosis in Hong Kong. Methods The concentrations of respirable silica dust were assigned to each industry and job task according to historical industrial hygiene measurements documented previously in Hong Kong. Exposure indices included cumulative dust exposure (CDE) and mean dust concentration (MDC). Penalised smoothing spline models were used as a preliminary step to detect outliers and guide further analyses. Multiple Cox's proportional hazard models were used to estimate the dust effects on the risk of mortality from NMRDs or COPDs after truncating the highest exposures. Results 371 of the 853 (43.49%) deaths occurring among 2789 workers with silicosis during 1981–99 were from NMRDs, and 101 (27.22%) NMRDs were COPDs. Multiple Cox's proportional hazard models showed that CDE (p = 0.009) and MDC (pcaisson workers and among those ever employed in other occupations with high exposure to silica dust. No exposure–response relationship was observed for surface construction workers with low exposures. A clear upward trend for both NMRDs and COPDs mortality was found with increasing severity of radiological silicosis. Conclusion This study documented an exposure–response relationship between exposure to silica dust and the risk of death from NMRDs or COPDs among workers with silicosis, except for surface construction workers with low exposures. The risk of mortality from NMRDs increased significantly with the progression of International Labor Organization categories, independent of dust effects. PMID:16973737
Tse, L A; Yu, I T S; Leung, C C; Tam, W; Wong, T W
2007-02-01
To examine the exposure-response relationships between various indices of exposure to silica dust and the mortality from non-malignant respiratory diseases (NMRDs) or chronic obstructive pulmonary diseases (COPDs) among a cohort of workers with silicosis in Hong Kong. The concentrations of respirable silica dust were assigned to each industry and job task according to historical industrial hygiene measurements documented previously in Hong Kong. Exposure indices included cumulative dust exposure (CDE) and mean dust concentration (MDC). Penalised smoothing spline models were used as a preliminary step to detect outliers and guide further analyses. Multiple Cox's proportional hazard models were used to estimate the dust effects on the risk of mortality from NMRDs or COPDs after truncating the highest exposures. 371 of the 853 (43.49%) deaths occurring among 2789 workers with silicosis during 1981-99 were from NMRDs, and 101 (27.22%) NMRDs were COPDs. Multiple Cox's proportional hazard models showed that CDE (p = 0.009) and MDC (pcaisson workers and among those ever employed in other occupations with high exposure to silica dust. No exposure-response relationship was observed for surface construction workers with low exposures. A clear upward trend for both NMRDs and COPDs mortality was found with increasing severity of radiological silicosis. This study documented an exposure-response relationship between exposure to silica dust and the risk of death from NMRDs or COPDs among workers with silicosis, except for surface construction workers with low exposures. The risk of mortality from NMRDs increased significantly with the progression of International Labor Organization categories, independent of dust effects.
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
Linear regression in astronomy. I
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.
Advanced statistics: linear regression, part I: simple linear regression.
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.
Asres Berhan
Full Text Available The development of tipranavir and darunavir, second generation non-peptidic HIV protease inhibitors, with marked improved resistance profiles, has opened a new perspective on the treatment of antiretroviral therapy (ART experienced HIV patients with poor viral load control. The aim of this study was to determine the virologic response in ART experienced patients to tipranavir-ritonavir and darunavir-ritonavir based regimens.A computer based literature search was conducted in the databases of HINARI (Health InterNetwork Access to Research Initiative, Medline and Cochrane library. Meta-analysis was performed by including randomized controlled studies that were conducted in ART experienced patients with plasma viral load above 1,000 copies HIV RNA/ml. The odds ratios and 95% confidence intervals (CI for viral loads of <50 copies and <400 copies HIV RNA/ml at the end of the intervention were determined by the random effects model. Meta-regression, sensitivity analysis and funnel plots were done. The number of HIV-1 patients who were on either a tipranavir-ritonavir or darunavir-ritonavir based regimen and achieved viral load less than 50 copies HIV RNA/ml was significantly higher (overall OR = 3.4; 95% CI, 2.61-4.52 than the number of HIV-1 patients who were on investigator selected boosted comparator HIV-1 protease inhibitors (CPIs-ritonavir. Similarly, the number of patients with viral load less than 400 copies HIV RNA/ml was significantly higher in either the tipranavir-ritonavir or darunavir-ritonavir based regimen treated group (overall OR = 3.0; 95% CI, 2.15-4.11. Meta-regression showed that the viral load reduction was independent of baseline viral load, baseline CD4 count and duration of tipranavir-ritonavir or darunavir-ritonavir based regimen.Tipranavir and darunavir based regimens were more effective in patients who were ART experienced and had poor viral load control. Further studies are required to determine their consistent
National Oceanic and Atmospheric Administration, Department of Commerce — Chemical and laboratory analyses oceanographic data were collected aboard the Wes Bordelon in the Gulf of Mexico from 2010-08-18 to 2010-08-22 in response to the...
Linear regression in astronomy. II
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.
Time-adaptive quantile regression
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....
He, Peng; Zhao, Peng; Wang, Limin; Zhang, Yuzhou; Wang, Xiaosi; Xiao, Hui; Yu, Jianing; Xiao, Guanghui
2017-07-03
Cell elongation and expansion are significant contributors to plant growth and morphogenesis, and are often regulated by environmental cues and endogenous hormones. Auxin is one of the most important phytohormones involved in the regulation of plant growth and development and plays key roles in plant cell expansion and elongation. Cotton fiber cells are a model system for studying cell elongation due to their large size. Cotton is also the world's most utilized crop for the production of natural fibers for textile and garment industries, and targeted expression of the IAA biosynthetic gene iaaM increased cotton fiber initiation. Polar auxin transport, mediated by PIN and AUX/LAX proteins, plays a central role in the control of auxin distribution. However, very limited information about PIN-FORMED (PIN) efflux carriers in cotton is known. In this study, 17 PIN-FORMED (PIN) efflux carrier family members were identified in the Gossypium hirsutum (G. hirsutum) genome. We found that PIN1-3 and PIN2 genes originated from the At subgenome were highly expressed in roots. Additionally, evaluation of gene expression patterns indicated that PIN genes are differentially induced by various abiotic stresses. Furthermore, we found that the majority of cotton PIN genes contained auxin (AuxREs) and salicylic acid (SA) responsive elements in their promoter regions were significantly up-regulated by exogenous hormone treatment. Our results provide a comprehensive analysis of the PIN gene family in G. hirsutum, including phylogenetic relationships, chromosomal locations, and gene expression and gene duplication analyses. This study sheds light on the precise roles of PIN genes in cotton root development and in adaption to stress responses.
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Jong Wan Hu
2014-02-01
Full Text Available In this paper, the superelastic shape memory alloy (SMA slit damper system as an alternative design approach for steel structures is intended to be evaluated with respect to inelastic behavior simulated by refined finite element (FE analyses. Although the steel slit dampers conventionally used for aseismic design are able to dissipate a considerable amount of energy generated by the plastic yielding of the base materials, large permanent deformation may occur in the entire structure. After strong seismic events, extra damage repair costs are required to restore the original configuration and to replace defective devices with new ones. Innovative slit dampers fabricated by superelastic SMAs that automatically recover their initial conditions only by the removal of stresses without heat treatment are introduced with a view toward mitigating the problem of permanent deformation. The cyclically tested FE models are calibrated to experimental results for the purpose of predicting accurate behavior. This study also focuses on the material constitutive model that is able to reproduce the inherent behavior of superelastic SMA materials by taking phase transformation between austenite and martensite into consideration. The responses of SMA slit dampers are compared to those of steel slit dampers. Axial stress and strain components are also investigated on the FE models under cyclic loading in an effort to validate the adequacy of FE modeling and then to compare between two slit damper systems. It can be shown that SMA slit dampers exhibit many structural advantages in terms of ultimate strength, moderate energy dissipation and recentering capability.
Kellerman, Adam; Shprits, Yuri; Makarevich, Roman; Donovan, Eric; Zhu, Hui
2017-04-01
Riometers are low-cost passive radiowave instruments located in both northern and southern hemispheres that capable of operating during quiet and disturbed conditions. Many instruments have been operating continuously for multiple solar cycles, making them a useful tool for long-term statistical studies and for real-time analysis and forecasting of space weather. Here we present recent and new analyses of the relationship between the riometer-measured cosmic noise absorption and electron precipitation into the D-region and lower E-region ionosphere. We utilize two techniques: a drift-time analysis in realistic electric and magnetic field models, where a particle is traced from one location to another, and the energy determined by the time delay between similar observations; and a conjunction analysis, where we directly compare precipitated fluxes from THEMIS and Van Allen Probes with the riometer absorption. In both cases we present a statistical analysis of the response of riometer absorption to electron precipitation as a function of MLAT, MLT, and geomagnetic conditions.
Piecewise linear regression splines with hyperbolic covariates
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)
Nakamura, Ryota; Suhrcke, Marc; Jebb, Susan A; Pechey, Rachel; Almiron-Roig, Eva; Marteau, Theresa M
2015-01-01
Background: There is a growing concern, but limited evidence, that price promotions contribute to a poor diet and the social patterning of diet-related disease. Objective: We examined the following questions: 1) Are less-healthy foods more likely to be promoted than healthier foods? 2) Are consumers more responsive to promotions on less-healthy products? 3) Are there socioeconomic differences in food purchases in response to price promotions? Design: With the use of hierarchical regression, we analyzed data on purchases of 11,323 products within 135 food and beverage categories from 26,986 households in Great Britain during 2010. Major supermarkets operated the same price promotions in all branches. The number of stores that offered price promotions on each product for each week was used to measure the frequency of price promotions. We assessed the healthiness of each product by using a nutrient profiling (NP) model. Results: A total of 6788 products (60%) were in healthier categories and 4535 products (40%) were in less-healthy categories. There was no significant gap in the frequency of promotion by the healthiness of products neither within nor between categories. However, after we controlled for the reference price, price discount rate, and brand-specific effects, the sales uplift arising from price promotions was larger in less-healthy than in healthier categories; a 1-SD point increase in the category mean NP score, implying the category becomes less healthy, was associated with an additional 7.7–percentage point increase in sales (from 27.3% to 35.0%; P sales uplift from promotions was larger for higher–socioeconomic status (SES) groups than for lower ones (34.6% for the high-SES group, 28.1% for the middle-SES group, and 23.1% for the low-SES group). Finally, there was no significant SES gap in the absolute volume of purchases of less-healthy foods made on promotion. Conclusion: Attempts to limit promotions on less-healthy foods could improve the
Logistic regression applied to natural hazards: rare event logistic regression with replications
M. Guns
2012-06-01
Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, V.
2012-06-01
Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Panel Smooth Transition Regression Models
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
Regression to Causality : Regression-style presentation influences causal attribution
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Srisapoome, Prapansak; Klongklaew, Nawanith; Areechon, Nontawith; Wongpanya, Ratree
2018-06-15
obtained in this study, novel ALF genes were clearly identified. Analyses of their responses under pathogenic and temperature stresses demonstrated the binding and antimicrobial activities of these ALFs and the consequent physiological effects, indicating their crucial functional roles in the prawn immune system. Copyright © 2018 Elsevier Ltd. All rights reserved.
Augmenting Data with Published Results in Bayesian Linear Regression
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…
Predicting Word Reading Ability: A Quantile Regression Study
McIlraith, Autumn L.
2018-01-01
Predictors of early word reading are well established. However, it is unclear if these predictors hold for readers across a range of word reading abilities. This study used quantile regression to investigate predictive relationships at different points in the distribution of word reading. Quantile regression analyses used preschool and…
Rampin, Olivier; Jerôme, Nathalie; Saint-Albin, Audrey; Ouali, Christian; Boué, Frank; Meunier, Nicolas; Nielsen, Birte L
2018-02-02
TMT (2,5-dihydro-2,4,5-trimethylthiazoline) is known as a component of fox feces inducing fear in rodents. However, no recent chemical analyses of fox feces are available, and few studies make direct comparisons between TMT and fox feces. Fox feces from 3 individuals were used to prepare 24 samples to be analyzed for the presence of TMT using gas chromatography-mass spectrometry (GC-MS). When TMT was added in low amounts (50-2000 nmol/g), TMT was detected in 10 out of 11 samples. When no TMT was added, TMT was detected in only 1 out of 13 samples. In a second experiment, we tested the behavioral response of male Brown Norway (BN) and Wistar rats to either fox feces, a low amount of TMT (0.6 nmol) or 1-hexanol. TMT induced freezing in the rats, but fox feces induced significantly more freezing episodes and longer total duration of freezing in both rat strains. In experiment 3, male BN rats were exposed over several days to fox feces, rat feces, 1-hexanol, cadaverine, 2-phenylethylamine, and TMT, one odor at a time. Fox feces induced significantly more freezing episodes of a longer total duration than any of the other odors, with rat feces and 1-hexanol giving rise to the lowest amount of freezing. This finding, together with our inability to verify the presence of TMT in fox feces, indicates that the concentration of TMT in our fox feces samples was below 50 nmol/g. It may also be that other compounds in fox feces play a role in its fear-inducing properties. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Advanced statistics: linear regression, part II: multiple linear regression.
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.
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
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
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
From Rasch scores to regression
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Testing Heteroscedasticity in Robust Regression
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Sparse reduced-rank regression with covariance estimation
Chen, Lisha
2014-12-08
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Sparse reduced-rank regression with covariance estimation
Chen, Lisha; Huang, Jianhua Z.
2014-01-01
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Producing The New Regressive Left
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
A Matlab program for stepwise regression
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.
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Regression filter for signal resolution
Matthes, W.
1975-01-01
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Aaro, Leif E.; Breivik, Kyrre; Klepp, Knut-Inge; Kaaya, Sylvia; Onya, Hans E.; Wubs, Annegreet; Helleve, Arnfinn; Flisher, Alan J.
2011-01-01
A 14-item human immunodeficiency virus/acquired immunodeficiency syndrome knowledge scale was used among school students in 80 schools in 3 sites in Sub-Saharan Africa (Cape Town and Mankweng, South Africa, and Dar es Salaam, Tanzania). For each item, an incorrect or don't know response was coded as 0 and correct response as 1. Exploratory factor…
Implicit collinearity effect in linear regression: Application to basal ...
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 ...
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Regression Models for Repairable Systems
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
CATREG SOFTWARE FOR CATEGORICAL REGRESSION ANALYSIS
CatReg is a computer program, written in the R (r-project.org">http://cran.r-project.org) programming language, to support the conduct of exposure-response analyses by toxicologists and health scientists. CatReg can be used to perform categorical regressi...
Stochastic development regression using method of moments
Kühnel, Line; Sommer, Stefan Horst
2017-01-01
This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
Linear regression and the normality assumption.
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.
Ridderinkhof, K.R.; Scheres, A.; Oosterlaan, J.; Sergeant, J.A.
2005-01-01
The authors highlight the utility of distribution-analytical techniques in the study of individual differences and clinical disorders. Cognitive deficits associated with attention-deficit/hyperactivity disorder (AD/HD) were examined by using delta-plot analyses of performance data (reaction time and
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Kalapos, S.; Dobrev, Petre; Nagy, T.; Vítámvás, P.; Gyorgyey, J.; Kocsy, G.; Marincs, F.; Galiba, G.
2016-01-01
Roč. 253, DEC (2016), s. 86-97 ISSN 0168-9452 Institutional support: RVO:61389030 Keywords : complex phytohormone responses * abscisic-acid biosynthesis * frost-resistance * stress responses * gene-expression * chromosome 5a * triticum-monococcum * regulatory network * basal resistance * abiotic stresses * ABA-Signalling * Carbon metabolism * Freezing-tolerance * Gene ontology * Plant hormones * Short-term cold-shock * Triticum aestivum Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.437, year: 2016
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directional quantile regression in R
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
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.
Polylinear regression analysis in radiochemistry
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
de Fiebre, C M; Marley, R J; Miner, L L; de Fiebre, N E; Wehner, J M; Collins, A C
1992-06-01
A classical (Mendelian) genetic analysis of responses to eight sedative-hypnotic compounds (ethanol, urethane, trifluoroethanol, chloral hydrate, barbital, paraldehyde, methyprylon, pentobarbital) was conducted in crosses derived from mouse lines that were selectively bred for differential duration of anesthesia following ethanol. The sleep-time responses of these mice, the long-sleep (LS) and short-sleep (SS) mouse lines, as well as the F1, F2 and backcross (F1 x LS, F1 x SS) generations were measured. Generally, differences in responses among the generations were greater for water soluble compounds than were differences for more lipid soluble compounds. Also, the inheritance of responses to water soluble compounds could be explained primarily by additive effects of alleles while the inheritance patterns for more lipid soluble compounds were more complex. Genetic correlation with ethanol response decreased with increasing lipophilicity. These results suggest that the selection of the LS-SS mouse lines was specific for water soluble anesthetic agents. Because several of these agents are known to act at GABA receptors, examination of the interactions of compounds which differ in lipid solubility at GABA receptors from LS and SS mice may prove useful in elucidating the mechanism of the anesthetic actions of ethanol and other drugs.
den Hoed, M; Smeets, A J P G; Veldhorst, M A B; Nieuwenhuizen, A G; Bouwman, F G; Heidema, A G; Mariman, E C M; Westerterp-Plantenga, M S; Westerterp, K R
2008-12-01
The postprandial responses in (an)orexigenic hormones and feelings of hunger are characterized by large inter-individual differences. Food intake regulation was shown earlier to be partly under genetic control. This study aimed to determine whether the postprandial responses in (an)orexigenic hormones and parameters of food intake regulation are associated with single nucleotide polymorphisms (SNPs) in genes encoding for satiety hormones and their receptors. Peptide YY (PYY), glucagon-like peptide 1 and ghrelin levels, as well as feelings of hunger and satiety, were determined pre- and postprandially in 62 women and 41 men (age 31+/-14 years; body mass index 25.0+/-3.1 kg/m(2)). Dietary restraint, disinhibition and perceived hunger were determined using the three-factor eating questionnaire. SNPs were determined in the GHRL, GHSR, LEP, LEPR, PYY, NPY, NPY2R and CART genes. The postprandial response in plasma ghrelin levels was associated with SNPs in PYY (215G>C, PG and 688A>G, PGHRL (-501A>C, PA, PG and 585T>C, PA, PA and 204T>C, P<0.05). Part of the inter-individual variability in postprandial responses in (an)orexigenic hormones can be explained by genetic variation. These postprandial responses represent either long-term physiological adaptations to facilitate homeostasis or reinforce direct genetic effects.
Spectral density regression for bivariate extremes
Castro Camilo, Daniela
2016-05-11
We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
Folta, A.
2015-01-01
Living organisms have to deal with changing environmental conditions during their whole life cycle. In contrast to animals, plants are sessile organisms. Therefore they have evolved multiple regulatory mechanisms that help them to cope with changing conditions. One of the first responses to
Eskesen, T G; Wetterslev, M; Perner, A
2016-01-01
PURPOSE: Central venous pressure (CVP) has been shown to have poor predictive value for fluid responsiveness in critically ill patients. We aimed to re-evaluate this in a larger sample subgrouped by baseline CVP values. METHODS: In April 2015, we systematically searched and included all clinical...
Hall Moran, V.; Skinner, A.L.; Warthon Medina, M.; Patel, S.; Dykes, F.; Souverein, O.W.; Dullemeijer, C.; Lowe, N.M.M.
2012-01-01
Recommendations for zinc intake during pregnancy and lactation vary widely across Europe. Using data on zinc intake and biomarkers of zinc status reported in randomized controlled trials (RCTs) and observational studies can provide estimates of dose–response relationships that may be used for
Harvald, Eva Bang; Sprenger, Richard R; Dall, Kathrine Brændgaard
2017-01-01
Starvation causes comprehensive metabolic changes, which are still not fully understood. Here, we used quantitative proteomics and RNA sequencing to examine the temporal starvation responses in wild-type Caenorhabditis elegans and animals lacking the transcription factor HLH-30. Our findings show...
Ristic-Medic, D.; Dullemeijer, C.; Tepsic, J.; Petrovic-Oggiano, G.; Popovic, Z.; Arsic, A.; Glibetic, M.; Souverein, O.W.; Collings, R.; Cavelaars, A.J.E.M.; Groot, de C.P.G.M.; Veer, van 't P.; Gurinovic, M.
2014-01-01
The objective of this systematic review was to identify studies investigating iodine intake and biomarkers of iodine status, to assess the data of the selected studies, and to estimate dose-response relationships using meta-analysis. All randomized controlled trials, prospective cohort studies,
Tutorial on Using Regression Models with Count Outcomes Using R
A. Alexander Beaujean
2016-02-01
Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
A gentle introduction to quantile regression for ecologists
Cade, B.S.; Noon, B.R.
2003-01-01
Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak; Ghosh, Malay; Mallick, Bani K.
2012-01-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik's ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Bellingeri, Michele; Lu, Zhe-Ming; Cassi, Davide; Scotognella, Francesco
2018-02-01
Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight.
Vaidyanathan, Karthik
2017-01-01
Business continuity management is often thought of as a proactive planning process for minimising impact from large-scale incidents and disasters. While this is true, and it is critical to plan for the worst, consistently validating plan effectiveness against smaller disruptions can enable an organisation to gain key insights about its business continuity readiness, drive programme improvements, reduce costs and provide an opportunity to quantitatively demonstrate the value of the programme to management. This paper describes a post mortem framework which is used as a continuous improvement mechanism for tracking, reviewing and learning from real-world events at Microsoft Customer Service & Support. This approach was developed and adopted because conducting regular business continuity exercises proved difficult and expensive in a complex and distributed operations environment with high availability requirements. Using a quantitative approach to measure response to incidents, and categorising outcomes based on such responses, enables business continuity teams to provide data-driven insights to leadership, change perceptions of incident root cause, and instil a higher level of confidence towards disaster response readiness and incident management. The scope of the framework discussed here is specific to reviewing and driving improvements from operational incidents. However, the concept can be extended to learning and evolving readiness plans for other types of incidents.
Spontaneous regression of pulmonary bullae
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Quantum algorithm for linear regression
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.
Lisa C Lindesmith
2015-03-01
Full Text Available Human noroviruses (NoVs are the primary cause of acute gastroenteritis and are characterized by antigenic variation between genogroups and genotypes and antigenic drift of strains within the predominant GII.4 genotype. In the context of this diversity, an effective NoV vaccine must elicit broadly protective immunity. We used an antibody (Ab binding blockade assay to measure the potential cross-strain protection provided by a multivalent NoV virus-like particle (VLP candidate vaccine in human volunteers.Sera from ten human volunteers immunized with a multivalent NoV VLP vaccine (genotypes GI.1/GII.4 were analyzed for IgG and Ab blockade of VLP interaction with carbohydrate ligand, a potential correlate of protective immunity to NoV infection and illness. Immunization resulted in rapid rises in IgG and blockade Ab titers against both vaccine components and additional VLPs representing diverse strains and genotypes not represented in the vaccine. Importantly, vaccination induced blockade Ab to two novel GII.4 strains not in circulation at the time of vaccination or sample collection. GII.4 cross-reactive blockade Ab titers were more potent than responses against non-GII.4 VLPs, suggesting that previous exposure history to this dominant circulating genotype may impact the vaccine Ab response. Further, antigenic cartography indicated that vaccination preferentially activated preexisting Ab responses to epitopes associated with GII.4.1997. Study interpretations may be limited by the relevance of the surrogate neutralization assay and the number of immunized participants evaluated.Vaccination with a multivalent NoV VLP vaccine induces a broadly blocking Ab response to multiple epitopes within vaccine and non-vaccine NoV strains and to novel antigenic variants not yet circulating at the time of vaccination. These data reveal new information about complex NoV immune responses to both natural exposure and to vaccination, and support the potential
Wenxiu eWang
2016-03-01
Full Text Available Cryptochromes (CRY are blue-light photoreceptors that mediate various light responses in plants and animals. It has long been demonstrated that Arabidopsis CRY (CRY1 and CRY2 C termini (CCT1 and CCT2 mediate light signaling through direct interaction with COP1. Most recently, CRY1 N terminus (CNT1 has been found to be involved in CRY1 signaling independent of CCT1, and implicated in the inhibition of gibberellin acids (GA/brassinosteroids (BR/auxin-responsive gene expression. Here, we performed RNA-Seq assay using transgenic plants expressing CCT1 fused to β-glucuronidase (GUS-CCT1, abbreviated as CCT1, which exhibit a constitutively photomorphogenic phenotype, and compared the results with those obtained previously from cry1cry2 mutant and the transgenic plants expressing CNT1 fused to nuclear localization signal sequence (NLS-tagged YFP (CNT1-NLS-YFP, abbreviated as CNT1, which display enhanced responsiveness to blue light. We found that 2,903 (67.85％ of the CRY-regulated genes are regulated by CCT1 and that 1,095 of these CCT1-regulated genes are also regulated by CNT1. After annotating the gene functions, we found that CCT1 is involved in mediating CRY1 regulation of phytohormone-responsive genes, like CNT1, and that about half of the up-regulated genes by GA/BR/auxin are down-regulated by CCT1 and CNT1, consistent with the antagonistic role for CRY1 and these phytohormones in regulating hypocotyl elongation. Physiological studies showed that both CCT1 and CNT1 are likely involved in mediating CRY1 reduction of seedlings sensitivity to GA under blue light. Furthermore, protein expression studies demonstrate that the inhibition of GA promotion of HY5 degradation by CRY1 is likely mediated by CCT1, but not by CNT1. These results give genome-wide transcriptome information concerning the signaling mechanism of CRY1, unraveling possible involvement of its C and N termini in its regulation of response of GA and likely other phytohormones.
Interpretation of commonly used statistical regression models.
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.
Keith, Timothy Z
2014-01-01
Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.
Bennett, Bradley C; Husby, Chad E
2008-03-28
Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.
Knol Dirk L
2011-09-01
Full Text Available Abstract Background For the Low Vision Quality Of Life questionnaire (LVQOL it is unknown whether the psychometric properties are satisfactory when an item response theory (IRT perspective is considered. This study evaluates some essential psychometric properties of the LVQOL questionnaire in an IRT model, and investigates differential item functioning (DIF. Methods Cross-sectional data were used from an observational study among visually-impaired patients (n = 296. Calibration was performed for every dimension of the LVQOL in the graded response model. Item goodness-of-fit was assessed with the S-X2-test. DIF was assessed on relevant background variables (i.e. age, gender, visual acuity, eye condition, rehabilitation type and administration type with likelihood-ratio tests for DIF. The magnitude of DIF was interpreted by assessing the largest difference in expected scores between subgroups. Measurement precision was assessed by presenting test information curves; reliability with the index of subject separation. Results All items of the LVQOL dimensions fitted the model. There was significant DIF on several items. For two items the maximum difference between expected scores exceeded one point, and DIF was found on multiple relevant background variables. Item 1 'Vision in general' from the "Adjustment" dimension and item 24 'Using tools' from the "Reading and fine work" dimension were removed. Test information was highest for the "Reading and fine work" dimension. Indices for subject separation ranged from 0.83 to 0.94. Conclusions The items of the LVQOL showed satisfactory item fit to the graded response model; however, two items were removed because of DIF. The adapted LVQOL with 21 items is DIF-free and therefore seems highly appropriate for use in heterogeneous populations of visually impaired patients.
Malone, Kerri M.; Rue-Albrecht, Kévin; Magee, David A.; Conlon, Kevin; Schubert, Olga T.; Nalpas, Nicolas C.; Browne, John A.; Smyth, Alicia; Gormley, Eamonn; Aebersold, Ruedi; MacHugh, David E.; Gordon, Stephen V.
2018-01-01
Members of the Mycobacterium tuberculosis complex (MTBC) are the causative agents of tuberculosis in a range of mammals, including humans. A key feature of MTBC pathogens is their high degree of genetic identity yet distinct host tropism. Notably, while Mycobacterium bovis is highly virulent and pathogenic for cattle, the human pathogen M. tuberculosis is attenuated in cattle. Previous research also suggests that host preference amongst MTBC members has a basis in host innate immune responses. To explore MTBC host tropism, we present in-depth profiling of the MTBC reference strains M. bovis AF2122/97 and M. tuberculosis H37Rv at both the global transcriptional and the translational level via RNA-sequencing and SWATH MS. Furthermore, a bovine alveolar macrophage infection time course model was used to investigate the shared and divergent host transcriptomic response to infection with M. tuberculosis H37Rv or M. bovis AF2122/97. Significant differential expression of virulence-associated pathways between the two bacilli was revealed, including the ESX-1 secretion system. A divergent transcriptional response was observed between M. tuberculosis H37Rv and M. bovis AF2122/97 infection of bovine alveolar macrophages, in particular cytosolic DNA-sensing pathways at 48 h post-infection, and highlights a distinct engagement of M. bovis with the bovine innate immune system. The work presented here therefore provides a basis for the identification of host innate immune mechanisms subverted by virulent host-adapted mycobacteria to promote their survival during the early stages of infection. PMID:29557774
Mitze, Timo Friedel; Burgard, Claudia; Alecke, Björn
2015-01-01
students. Second, changes in migration behaviour are sensitive to geographical distance. Finally, comparing different types of higher education institutions, we find that the migration effect is larger for universities compared to technical colleges and colleges of arts or music....... variation in tuition fee regimes as a result of a Federal Constitutional Court decision. Our empirical results show that the introduction of tuition fees had a particular impact on student migration. We observe three effects: first, male students show a stronger migration response compared to female...
meiying li
2016-09-01
Full Text Available Plant 14-3-3 proteins act as critical components of various cellular signaling processes and play an important role in regulating multiple physiological processes. However, less information is known about the 14-3-3 gene family in banana. In this study, 25 14-3-3 genes were identified from the banana genome. Based on the evolutionary analysis, banana 14-3-3 proteins were clustered into ε and non-ε groups. Conserved motif analysis showed that all identified banana 14-3-3 genes had the typical 14-3-3 motif. The gene structure of banana 14-3-3 genes showed distinct class-specific divergence between the ε group and the non-ε group. Most banana 14-3-3 genes showed strong transcript accumulation changes during fruit development and postharvest ripening in two banana varieties, indicating that they might be involved in regulating fruit development and ripening. Moreover, some 14-3-3 genes also showed great changes after osmotic, cold, and salt treatments in two banana varieties, suggested their potential role in regulating banana response to abiotic stress. Taken together, this systemic analysis reveals the involvement of banana 14-3-3 genes in fruit development, postharvest ripening, and response to abiotic stress and provides useful information for understanding the functions of 14-3-3 genes in banana.
Deng, Liting; Ng, Lindsay; Ozawa, Tatsuya; Stella, Nephi
2017-01-01
Evidence suggests that the nonpsychotropic cannabis-derived compound, cannabidiol (CBD), has antineoplastic activity in multiple types of cancers, including glioblastoma multiforme (GBM). DNA-damaging agents remain the main standard of care treatment available for patients diagnosed with GBM. Here we studied the antiproliferative and cell-killing activity of CBD alone and in combination with DNA-damaging agents (temozolomide, carmustine, or cisplatin) in several human GBM cell lines and in mouse primary GBM cells in cultures. This activity was also studied in mouse neural progenitor cells (NPCs) in culture to assess for potential central nervous system toxicity. We found that CBD induced a dose-dependent reduction of both proliferation and viability of all cells with similar potencies, suggesting no preferential activity for cancer cells. Hill plot analysis indicates an allosteric mechanism of action triggered by CBD in all cells. Cotreatment regimens combining CBD and DNA-damaging agents produced synergistic antiproliferating and cell-killing responses over a limited range of concentrations in all human GBM cell lines and mouse GBM cells as well as in mouse NPCs. Remarkably, antagonistic responses occurred at low concentrations in select human GBM cell lines and in mouse GBM cells. Our study suggests limited synergistic activity when combining CBD and DNA-damaging agents in treating GBM cells, along with little to no therapeutic window when considering NPCs. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.
On Weighted Support Vector Regression
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Rini, B I; Melichar, B; Fishman, M N; Oya, M; Pithavala, Y K; Chen, Y; Bair, A H; Grünwald, V
2015-07-01
In a randomized, double-blind phase II trial in patients with metastatic renal cell carcinoma (mRCC), axitinib versus placebo titration yielded a significantly higher objective response rate. We evaluated pharmacokinetic and blood pressure (BP) data from this study to elucidate relationships among axitinib exposure, BP change, and efficacy. Patients received axitinib 5 mg twice daily during a lead-in period. Patients who met dose-titration criteria were randomized 1:1 to stepwise dose increases with axitinib or placebo. Patients ineligible for randomization continued without dose increases. Serial 6-h and sparse pharmacokinetic sampling were carried out; BP was measured at clinic visits and at home in all patients, and by 24-h ambulatory BP monitoring (ABPM) in a subset of patients. Area under the plasma concentration-time curve from 0 to 24 h throughout the course of treatment (AUCstudy) was higher in patients with complete or partial responses than those with stable or progressive disease in the axitinib-titration arm, but comparable between these groups in the placebo-titration and nonrandomized arms. In the overall population, AUCstudy and efficacy outcomes were not strongly correlated. Mean BP across the population was similar when measured in clinic, at home, or by 24-h ABPM. Weak correlations were observed between axitinib steady-state exposure and diastolic BP. When grouped by change in diastolic BP from baseline, patients in the ≥10 and ≥15 mmHg groups had longer progression-free survival. Optimal axitinib exposure may differ among patients with mRCC. Pharmacokinetic or BP measurements cannot be used exclusively to guide axitinib dosing. Individualization of treatment with vascular endothelial growth factor receptor tyrosine kinase inhibitors, including axitinib, is thus more complex than anticipated and cannot be limited to a single clinical factor. © The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical
Brown, Margaret; Robinson, Craig; Davies, Ian M.; Moffat, Colin F.; Redshaw, John; Craft, John A.
2004-01-01
Suppression subtractive hybridisation (SSH) was used to generate cDNA libraries representing genes differentially-expressed in liver from male plaice (Pleuronectes platessa) exposed to ethynyl oestradiol (EE2). BLAST analysis and alignments of the clones with database sequence suggested at least three vitellogenin (VTG) genes and three zona radiata protein (ZRP) genes were represented. Clones with unique sequence (62 up-, 13 down-regulated) were arrayed as probes on nylon membranes to investigate temporal expression of oestrogen-responsive genes in experimental animals. Arrays were hybridised with radiolabelled cDNAs prepared from hepatic mRNA from animals treated with EE2 for various times upto 21 days and from treated animals transferred to clean water for upto a further 31 days. By day 21 of treatment 11 out of 17 probes from unidentified genes, 21/22 VTG, 13/14 ZRP, 2/2 liver aspartic proteinase (LAP) and 8/10 other gene sequences were induced by EE2 exposure. Of the down-regulated sequences, only three showed significant, decreased expression and these encode cytochrome b and two with cryptic functions. Based on the pattern of temporal response the up-regulated probes fell into two classes. Pattern A reached maximum expression by day 16 of exposure and then declined prior to removal of EE2 at 21 days. Pattern B genes reached maximal expression between day 16 and 22, declining only after removal of EE2. Independent investigation of the expression patterns of selected probes using quantitative Real-Time PCR reproduced the distinctive patterns. The results indicate a previously unrecognised mechanism for oestrogenic toxicity in which there is a selective down-regulation of some egg proteins, potentially diminishing the quality of eggs and this may contribute to reproductive failure described elsewhere
Normani, M Z; Hussain, S S; Lim, J K; Albrink, M J; Gunnells, C K; Davis, G K
1981-10-01
Two experiments were conducted in the rat to determine the relationships of serum cholesterol (SC, mg/dl), apparent digestibility of dry matter (DDM, %), and digested energy intake (DE, kcal/day) at suboptimal level of energy. The energies in diet and feces were determined by calorimetry. DE as percentage of the National Research Council requirement (DE%) was suboptimal (70 to 85%). The experiments had four to five isofibrous diets, and no fiber diets, supplemented with 0.2% crystalline cholesterol (CChol). Animals in experiment 1 were fed varying amounts of feed with 18% coconut oil in the diets where as these in experiment 2 were given fixed amounts of feed with either 6 or 18% oil. The following regressions (p less than 0.001) for SC were found: experiment 1: -1157.7 -5.97 DDM +105.5 CCI -1.48 CCI2 (r2 0.35), where CCI = CChol, mg/day; -1888.4 -2.66 DE +120.97 CCI -1.62 CCI2 (r2 0.37). Experiment 2: 762.99 -6.15 DDM -0.8 fat cal % -0.87DE% (r2 0.31), where fat cal % = fat calories % of DE. Data indicate that at suboptimal energy intake, SC was inversely related to (1) DDM, (2) fat cal, and (3) total energy intake. Liver cholesterol lowering effect of the dietary fiber was also observed. The above findings help to elucidate various conflicting reports related to diet and blood cholesterol.
Rie Nishiyama
Full Text Available Soil destruction by abiotic environmental conditions, such as high salinity, has resulted in dramatic losses of arable land, giving rise to the need of studying mechanisms of plant adaptation to salt stress aimed at creating salt-tolerant plants. Recently, it has been reported that cytokinins (CKs regulate plant environmental stress responses through two-component systems. A decrease in endogenous CK levels could enhance salt and drought stress tolerance. Here, we have investigated the global transcriptional change caused by a reduction in endogenous CK content under both normal and salt stress conditions. Ten-day-old Arabidopsis thaliana wild-type (WT and CK-deficient ipt1,3,5,7 plants were transferred to agar plates containing either 0 mM (control or 200 mM NaCl and maintained at normal growth conditions for 24 h. Our experimental design allowed us to compare transcriptome changes under four conditions: WT-200 mM vs. WT-0 mM, ipt1,3,5,7-0 mM vs. WT-0 mM, ipt1,3,5,7-200 mM vs. ipt1,3,5,7-0 mM and ipt1,3,5,7-200 mM vs. WT-200 mM NaCl. Our results indicated that the expression of more than 10% of all of the annotated Arabidopsis genes was altered by CK deficiency under either normal or salt stress conditions when compared to WT. We found that upregulated expression of many genes encoding either regulatory proteins, such as NAC, DREB and ZFHD transcription factors and the calcium sensor SOS3, or functional proteins, such as late embryogenesis-abundant proteins, xyloglucan endo-transglycosylases, glycosyltransferases, glycoside hydrolases, defensins and glyoxalase I family proteins, may contribute to improved salt tolerance of CK-deficient plants. We also demonstrated that the downregulation of photosynthesis-related genes and the upregulation of several NAC genes may cause the altered morphological phenotype of CK-deficient plants. This study highlights the impact of CK regulation on the well-known stress-responsive signaling pathways, which
Saikia, Ruprekha; Baruah, Bhargav; Kalita, Dipankar; Pant, Kamal K; Gogoi, Nirmali; Kataki, Rupam
2018-04-01
The objective of the present investigation was to optimize the pyrolysis condition of an abundantly available and low cost perennial grass of north-east India Saccharum ravannae L. (S. ravannae) using response surface methodology based on central composite design. Kinetic study of the biomass was conducted at four different heating rates of 10, 20, 40 and 60 °C min -1 and results were interpreted by Friedman, Kissinger Akira Sunnose and Flynn-Wall-Ozawa methods. Average activation energy 151.45 kJ mol -1 was used for evaluation of reaction mechanism following Criado master plot. Maximum bio-oil yield of 38.1 wt% was obtained at pyrolysis temperature of 550 °C, heating rate of 20 °C min -1 and nitrogen flow rate of 226 mL min -1 . Study on bio-oil quality revealed higher content of hydrocarbon, antioxidant property, total phenolic content and metal chelating capacity. These opened up probable applications of S. ravannae bio-oil in different fields including fuel, food industry and biomedical domain. Copyright © 2018 Elsevier Ltd. All rights reserved.
Shi, Haitao; Ye, Tiantian; Chan, Zhulong
2013-11-01
Polyamines conferred enhanced abiotic stress tolerance in multiple plant species. However, the effect of polyamines on abiotic stress and physiological change in bermudagrass, the most widely used warm-season turfgrasses, are unknown. In this study, pretreatment of exogenous polyamine conferred increased salt and drought tolerances in bermudagrass. Comparative proteomic analysis was performed to further investigate polyamines mediated responses, and 36 commonly regulated proteins by at least two types of polyamines in bermudagrass were successfully identified, including 12 proteins with increased level, 20 proteins with decreased level and other 4 specifically expressed proteins. Among them, proteins involved in electron transport and energy pathways were largely enriched, and nucleoside diphosphate kinase (NDPK) and three antioxidant enzymes were extensively regulated by polyamines. Dissection of reactive oxygen species (ROS) levels indicated that polyamine-derived H2O2 production might play dual roles under abiotic stress conditions. Moreover, accumulation of osmolytes was also observed after application of exogenous polyamines, which is consistent with proteomics results that several proteins involved in carbon fixation pathway were mediated commonly by polyamines pretreatment. Taken together, we proposed that polyamines could activate multiple pathways that enhance bermudagrass adaption to salt and drought stresses. These findings might be applicable for genetically engineering of grasses and crops to improve stress tolerance.
Zhen-Yu Zhang
Full Text Available Medulloblastoma (MB is one of the most common primary central nervous system tumors in children. Data is lacking of a large cohort of medulloblastoma patients in China. Also, our knowledge on the sensitivity of different molecular subgroups of MB to adjuvant radiation therapy (RT or chemotherapy (CHT is still limited. The authors performed a retrospective study of 173 medulloblastoma patients treated at two institutions from 2002 to 2011. Formalin-fixed paraffin embedded (FFPE tissues were available in all the cases and sections were stained to classify histological and molecular subgroups. Univariate and multivariate analyses were used to investigate prognostic factors. Of 173 patients, there were 118 children and 55 adults, 112 males and 61 females. Estimated 5-year overall survival (OS rates for all patients, children and adults were 52%, 48% and 63%, respectively. After multivariate analysis, postoperative primary radiation therapy (RT and chemotherapy (CHT were revealed as favorable prognostic factors influencing OS and EFS. Postoperative primary chemotherapy (CHT was found significantly improving the survival of children (p<0.001 while it was not a significant prognostic factor for adult patients. Moreover, patients in WNT subtype had better OS (p = 0.028 than others (SHH and Non-SHH/WNT subtypes given postoperative adjuvant therapies. Postoperative primary RT was found to be a strong prognostic factor influencing the survival in all histological and molecular subgroups (p<0.001. Postoperative primary CHT was found significantly to influence the survival of classic medulloblastoma (CMB (OS p<0.001, EFS p<0.001, SHH subgroup (OS p = 0.020, EFS p = 0.049 and WNT subgroup (OS p = 0.003, EFS p = 0.016 but not in desmoplastic/nodular medulloblastoma (DMB (OS p = 0.361, EFS p = 0.834 and Non-SHH/WNT subgroup (OS p = 0.127, EFS p = 0.055. Our study showed postoperative primary CHT significantly influence the
Simulation Experiments in Practice: Statistical Design and Regression Analysis
Kleijnen, J.P.C.
2007-01-01
In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...
Credit Scoring Problem Based on Regression Analysis
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....
Variable selection and model choice in geoadditive regression models.
Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard
2009-06-01
Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.
Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald
2006-11-01
We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.
An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy
Merlo, Juan; Wagner, Philippe; Ghith, Nermin
2016-01-01
BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting...
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance
Nathans, Laura L.; Oswald, Frederick L.; Nimon, Kim
2012-01-01
Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what…
Regularized Label Relaxation Linear Regression.
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.
Larsen, Klaus; Merlo, Juan
2005-01-01
The logistic regression model is frequently used in epidemiologic studies, yielding odds ratio or relative risk interpretations. Inspired by the theory of linear normal models, the logistic regression model has been extended to allow for correlated responses by introducing random effects. However......, the model does not inherit the interpretational features of the normal model. In this paper, the authors argue that the existing measures are unsatisfactory (and some of them are even improper) when quantifying results from multilevel logistic regression analyses. The authors suggest a measure...... of heterogeneity, the median odds ratio, that quantifies cluster heterogeneity and facilitates a direct comparison between covariate effects and the magnitude of heterogeneity in terms of well-known odds ratios. Quantifying cluster-level covariates in a meaningful way is a challenge in multilevel logistic...
Karimi, Mohammad, E-mail: m.karimi407@alumni.ut.ac.ir [School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box: 11365-4563, Tehran (Iran, Islamic Republic of); Milani, Saeid Alamdar [Nuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute, AEOI, P.O. Box: 14893-836, Tehran (Iran, Islamic Republic of); Abolgashemi, Hossein [School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box: 11365-4563, Tehran (Iran, Islamic Republic of)
2016-10-15
In this study, the ability and the adsorption capacity of magnetite/aminopropyltriethoxysilane/glutaraldehyde (Fe{sub 3}O{sub 4}/APTES/GA) adsorbent were evaluated for the adsorption of thorium (IV) ions from aqueous solutions. The influence of the several variables such as pH (1–5), Th (IV) initial concentration (50–300 mg L{sup −1}) and adsorbent concentration (1–5 g L{sup −1}) on the Th (IV) adsorption were investigated by response surface methodology (RSM). The results showed that the highest absorption capacity (q) was 107.23 mg g{sup −1} with respect to pH = 4.5, initial concentration of 250 mg L{sup −1} and adsorbent concentration of 1 g L{sup −1} for 90 min. Modeling equilibrium sorption data with the Langmuir, Freundlich and Dubinin–Radushkevich models pointed out that the results were in good agreement with Langmuir model. The experimental kinetic data were well fitted to pseudo-second-order equation with R{sup 2} = 0.9739. Also thermodynamic parameters (ΔG{sup o}, ΔH{sup o}, ΔS{sup o}) declared that the Th (IV) adsorption was endothermic and spontaneous. - Highlights: • Thorium ions were removed from aqueous solutions by modified magnetite nanoparticle. • The effects of process variables on adsorption capacity were investigated by RSM. • Thermodynamic parameters showed that the adsorption was endothermic and spontaneous. • The equilibrium data for the adsorption of Thorium followed the Langmuir isotherm. • The experimental kinetic data were described by the pseudo-second-order equation.
Cao, Lei; Zhang, Li; Zeng, Huawei; Wu, Ryan Ty; Wu, Tung-Lung; Cheng, Wen-Hsing
2017-10-01
Background: The hierarchies of tissue selenium distribution and selenotranscriptomes are thought to critically affect healthspan and longevity. Objective: We determined selenium status and selenotranscriptomes in response to long-term dietary selenium deficiency and age in tissues of male and female mice. Methods: Weanling telomerase RNA component knockout C57BL/6 mice were fed a selenium-deficient (0.03 mg Se/kg) Torula yeast-based AIN-93G diet or a diet supplemented with sodium selenate (0.15 mg Se/kg) until age 18 or 24 mo. Plasma, hearts, kidneys, livers, and testes were collected to assay for selenotranscriptomes, selected selenoproteins, and tissue selenium concentrations. Data were analyzed with the use of 2-factor ANOVA (diet × age) in both sexes. Results: Dietary selenium deficiency decreased ( P ≤ 0.05) selenium concentrations (65-72%) and glutathione peroxidase (GPX) 3 (82-94%) and selenoprotein P (SELENOP) (17-41%) levels in the plasma of both sexes of mice and mRNA levels (9-68%) of 4, 4, and 12 selenoproteins in the heart, kidney, and liver of males, respectively, and 5, 16, and 14 selenoproteins, respectively, in females. Age increased selenium concentrations and SELENOP levels (27% and 30%, respectively; P ≤ 0.05) in the plasma of males only but decreased (12-46%; P selenium deficiency and age in ≥1 tissue or sex, or both. Dietary selenium deficiency upregulated (40-160%; P ≤ 0.05) iodothyronine deiodinase 2 ( Dio2 ) and selenoprotein N ( Selenon ) in the kidneys of males. Age upregulated (11-44%; P selenium status and selenotranscriptomes because of dietary selenium deficiency and age. © 2017 American Society for Nutrition.
Dawoon Chung
2014-11-01
Full Text Available The Aspergillus fumigatus sterol regulatory element binding protein (SREBP SrbA belongs to the basic Helix-Loop-Helix (bHLH family of transcription factors and is crucial for antifungal drug resistance and virulence. The latter phenotype is especially striking, as loss of SrbA results in complete loss of virulence in murine models of invasive pulmonary aspergillosis (IPA. How fungal SREBPs mediate fungal virulence is unknown, though it has been suggested that lack of growth in hypoxic conditions accounts for the attenuated virulence. To further understand the role of SrbA in fungal infection site pathobiology, chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq was used to identify genes under direct SrbA transcriptional regulation in hypoxia. These results confirmed the direct regulation of ergosterol biosynthesis and iron uptake by SrbA in hypoxia and revealed new roles for SrbA in nitrate assimilation and heme biosynthesis. Moreover, functional characterization of an SrbA target gene with sequence similarity to SrbA identified a new transcriptional regulator of the fungal hypoxia response and virulence, SrbB. SrbB co-regulates genes involved in heme biosynthesis and demethylation of C4-sterols with SrbA in hypoxic conditions. However, SrbB also has regulatory functions independent of SrbA including regulation of carbohydrate metabolism. Loss of SrbB markedly attenuates A. fumigatus virulence, and loss of both SREBPs further reduces in vivo fungal growth. These data suggest that both A. fumigatus SREBPs are critical for hypoxia adaptation and virulence and reveal new insights into SREBPs' complex role in infection site adaptation and fungal virulence.
Estimating the exceedance probability of rain rate by logistic regression
Chiu, Long S.; Kedem, Benjamin
1990-01-01
Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Use of probabilistic weights to enhance linear regression myoelectric control.
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.
Mixed kernel function support vector regression for global sensitivity analysis
Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng
2017-11-01
Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.
Multilevel covariance regression with correlated random effects in the mean and variance structure.
Quintero, Adrian; Lesaffre, Emmanuel
2017-09-01
Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Comparing parametric and nonparametric regression methods for panel data
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...
Higgins, Chris
2012-01-01
This article presents the author's response to the reviews of his book, "The Good Life of Teaching: An Ethics of Professional Practice." He begins by highlighting some of the main concerns of his book. He then offers a brief response, doing his best to address the main criticisms of his argument and noting where the four reviewers (Charlene…
Robust mislabel logistic regression without modeling mislabel probabilities.
Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun
2018-03-01
Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.
Unbalanced Regressions and the Predictive Equation
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Semiparametric regression during 2003–2007
Ruppert, David; Wand, M.P.; Carroll, Raymond J.
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Regression analysis of sparse asynchronous longitudinal data.
Cao, Hongyuan; Zeng, Donglin; Fine, Jason P
2015-09-01
We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Standards for Standardized Logistic Regression Coefficients
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…
A Seemingly Unrelated Poisson Regression Model
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.
Research and analyze of physical health using multiple regression analysis
T. S. Kyi
2014-01-01
Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.
Xueyin Li
2016-11-01
Full Text Available Extensive studies in Arabidopsis and rice have demonstrated that Subgroup-A members of the bZIP transcription factor family play important roles in plant responses to multiple abiotic stresses. Although common wheat (Triticum aestivum is one of the most widely cultivated and consumed food crops in the world, there are limited investigations into Subgroup A of the bZIP family in wheat. In this study, we performed bioinformatic analyses of the 41 Subgroup-A members of the wheat bZIP family. Phylogenetic and conserved motif analyses showed that most of the Subgroup-A bZIP proteins involved in abiotic stress responses of wheat, Arabidopsis and rice clustered in Clade A1 of the phylogenetic tree, and shared a majority of conserved motifs, suggesting the potential importance of Clade-A1 members in abiotic stress responses. Gene structure analysis showed that TabZIP genes with close phylogenetic relationships tended to possess similar exon-intron compositions, and the positions of introns in the hinge regions of the bZIP domains were highly conserved, whereas introns in the leucine zipper regions were at variable positions. Additionally, eleven groups of homologs and two groups of tandem paralogs were also identified in Subgroup A of the wheat bZIP family. Expression profiling analysis indicated that most Subgroup-A TabZIP genes were responsive to abscisic acid and various abiotic stress treatments. TabZIP27, TabZIP74, TabZIP138 and TabZIP174 proteins were localized in the nucleus of wheat protoplasts, whereas TabZIP9-GFP fusion protein was simultaneously present in the nucleus, cytoplasm and cell membrane. Transgenic Arabidopsis overexpressing TabZIP174 displayed increased seed germination rates and primary root lengths under drought treatments. Overexpression of TabZIP174 in transgenic Arabidopsis conferred enhanced drought tolerance, and transgenic plants exhibited lower water loss rates, higher survival rates, higher proline, soluble sugar and leaf
Li, Xueyin; Feng, Biane; Zhang, Fengjie; Tang, Yimiao; Zhang, Liping; Ma, Lingjian; Zhao, Changping; Gao, Shiqing
2016-01-01
Extensive studies in Arabidopsis and rice have demonstrated that Subgroup-A members of the bZIP transcription factor family play important roles in plant responses to multiple abiotic stresses. Although common wheat (Triticum aestivum) is one of the most widely cultivated and consumed food crops in the world, there are limited investigations into Subgroup A of the bZIP family in wheat. In this study, we performed bioinformatic analyses of the 41 Subgroup-A members of the wheat bZIP family. Phylogenetic and conserved motif analyses showed that most of the Subgroup-A bZIP proteins involved in abiotic stress responses of wheat, Arabidopsis, and rice clustered in Clade A1 of the phylogenetic tree, and shared a majority of conserved motifs, suggesting the potential importance of Clade-A1 members in abiotic stress responses. Gene structure analysis showed that TabZIP genes with close phylogenetic relationships tended to possess similar exon–intron compositions, and the positions of introns in the hinge regions of the bZIP domains were highly conserved, whereas introns in the leucine zipper regions were at variable positions. Additionally, eleven groups of homologs and two groups of tandem paralogs were also identified in Subgroup A of the wheat bZIP family. Expression profiling analysis indicated that most Subgroup-A TabZIP genes were responsive to abscisic acid and various abiotic stress treatments. TabZIP27, TabZIP74, TabZIP138, and TabZIP174 proteins were localized in the nucleus of wheat protoplasts, whereas TabZIP9-GFP fusion protein was simultaneously present in the nucleus, cytoplasm, and cell membrane. Transgenic Arabidopsis overexpressing TabZIP174 displayed increased seed germination rates and primary root lengths under drought treatments. Overexpression of TabZIP174 in transgenic Arabidopsis conferred enhanced drought tolerance, and transgenic plants exhibited lower water loss rates, higher survival rates, higher proline, soluble sugar, and leaf chlorophyll
Regression with Sparse Approximations of Data
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Spontaneous regression of a congenital melanocytic nevus
Amiya Kumar Nath
2011-01-01
Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.
FBH1 Catalyzes Regression of Stalled Replication Forks
Kasper Fugger
2015-03-01
Full Text Available DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity, is required for early phosphorylation of ATM substrates such as CHK2 and CtIP as well as hyperphosphorylation of RPA. These phosphorylations occur prior to apparent DNA double-strand break formation. Furthermore, FBH1-dependent signaling promotes checkpoint control and preserves genome integrity. We propose a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks.
Multiple regression for physiological data analysis: the problem of multicollinearity.
Slinker, B K; Glantz, S A
1985-07-01
Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
Czekaj, Tomasz Gerard
and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...
Afanasyev Sergey
2011-03-01
Full Text Available Abstract Background The salmon louse (Lepeophtheirus salmonis Krøyer, an ectoparasitic copepod with a complex life cycle causes significant losses in salmon aquaculture. Pesticide treatments against the parasite raise environmental concerns and their efficacy is gradually decreasing. Improvement of fish resistance to lice, through biological control methods, needs better understanding of the protective mechanisms. We used a 21 k oligonucleotide microarray and RT-qPCR to examine the time-course of immune gene expression changes in salmon skin, spleen, and head kidney during the first 15 days after challenge, which encompassed the copepod and chalimus stages of lice development. Results Large scale and highly complex transcriptome responses were found already one day after infection (dpi. Many genes showed bi-phasic expression profiles with abrupt changes between 5 and 10 dpi (the copepod-chalimus transitions; the greatest fluctuations (up- and down-regulation were seen in a large group of secretory splenic proteases with unknown roles. Rapid sensing was witnessed with induction of genes involved in innate immunity including lectins and enzymes of eicosanoid metabolism in skin and acute phase proteins in spleen. Transient (1-5 dpi increase of T-cell receptor alpha, CD4-1, and possible regulators of lymphocyte differentiation suggested recruitment of T-cells of unidentified lineage to the skin. After 5 dpi the magnitude of transcriptomic responses decreased markedly in skin. Up-regulation of matrix metalloproteinases in all studied organs suggested establishment of a chronic inflammatory status. Up-regulation of putative lymphocyte G0/G1 switch proteins in spleen at 5 dpi, immunoglobulins at 15 dpi; and increase of IgM and IgT transcripts in skin indicated an onset of adaptive humoral immune responses, whereas MHCI appeared to be down-regulated. Conclusions Atlantic salmon develops rapid local and systemic reactions to L. salmonis, which, however
Logarithmic Transformations in Regression: Do You Transform Back Correctly?
Dambolena, Ismael G.; Eriksen, Steven E.; Kopcso, David P.
2009-01-01
The logarithmic transformation is often used in regression analysis for a variety of purposes such as the linearization of a nonlinear relationship between two or more variables. We have noticed that when this transformation is applied to the response variable, the computation of the point estimate of the conditional mean of the original response…
Analyses of developmental rate isomorphy in ectotherms: Introducing the dirichlet regression
Boukal S., David; Ditrich, Tomáš; Kutcherov, D.; Sroka, Pavel; Dudová, Pavla; Papáček, M.
2015-01-01
Roč. 10, č. 6 (2015), e0129341 E-ISSN 1932-6203 R&D Projects: GA ČR GAP505/10/0096 Grant - others:European Fund(CZ) PERG04-GA-2008-239543; GA JU(CZ) 145/2013/P Institutional support: RVO:60077344 Keywords : ectotherms Subject RIV: ED - Physiology Impact factor: 3.057, year: 2015 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129341
The benefits of using quantile regression for analysing the effect of weeds on organic winter wheat
Casagrande, M.; Makowski, D.; Jeuffroy, M.H.; Valantin-Morison, M.; David, C.
2010-01-01
P>In organic farming, weeds are one of the threats that limit crop yield. An early prediction of weed effect on yield loss and the size of late weed populations could help farmers and advisors to improve weed management. Numerous studies predicting the effect of weeds on yield have already been
Gilstrap, Donald L.
2013-01-01
In addition to qualitative methods presented in chaos and complexity theories in educational research, this article addresses quantitative methods that may show potential for future research studies. Although much in the social and behavioral sciences literature has focused on computer simulations, this article explores current chaos and…
Scott, Neil W.; Fayers, Peter M.; Aaronson, Neil K.
2010-01-01
Differential item functioning (DIF) methods can be used to determine whether different subgroups respond differently to particular items within a health-related quality of life (HRQoL) subscale, after allowing for overall subgroup differences in that scale. This article reviews issues that arise...
Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.
2018-01-01
In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.
Intermediate and advanced topics in multilevel logistic regression analysis.
Austin, Peter C; Merlo, Juan
2017-09-10
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Regression tools for CO2 inversions: application of a shrinkage estimator to process attribution
Shaby, Benjamin A.; Field, Christopher B.
2006-01-01
In this study we perform an atmospheric inversion based on a shrinkage estimator. This method is used to estimate surface fluxes of CO 2 , first partitioned according to constituent geographic regions, and then according to constituent processes that are responsible for the total flux. Our approach differs from previous approaches in two important ways. The first is that the technique of linear Bayesian inversion is recast as a regression problem. Seen as such, standard regression tools are employed to analyse and reduce errors in the resultant estimates. A shrinkage estimator, which combines standard ridge regression with the linear 'Bayesian inversion' model, is introduced. This method introduces additional bias into the model with the aim of reducing variance such that errors are decreased overall. Compared with standard linear Bayesian inversion, the ridge technique seems to reduce both flux estimation errors and prediction errors. The second divergence from previous studies is that instead of dividing the world into geographically distinct regions and estimating the CO 2 flux in each region, the flux space is divided conceptually into processes that contribute to the total global flux. Formulating the problem in this manner adds to the interpretability of the resultant estimates and attempts to shed light on the problem of attributing sources and sinks to their underlying mechanisms
Model-based Quantile Regression for Discrete Data
Padellini, Tullia
2018-04-10
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.
bayesQR: A Bayesian Approach to Quantile Regression
Dries F. Benoit
2017-01-01
Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Regression models of reactor diagnostic signals
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
[From clinical judgment to linear regression model.
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.
Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.
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.
Use of probabilistic weights to enhance linear regression myoelectric control
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.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Kostov, Marin
2000-01-01
In the last two decades there is increasing number of probabilistic seismic risk assessments performed. The basic ideas of the procedure for performing a Probabilistic Safety Analysis (PSA) of critical structures (NUREG/CR-2300, 1983) could be used also for normal industrial and residential buildings, dams or other structures. The general formulation of the risk assessment procedure applied in this investigation is presented in Franzini, et al., 1984. The probability of failure of a structure for an expected lifetime (for example 50 years) can be obtained from the annual frequency of failure, β E determined by the relation: β E ∫[d[β(x)]/dx]P(flx)dx. β(x) is the annual frequency of exceedance of load level x (for example, the variable x may be peak ground acceleration), P(fI x) is the conditional probability of structure failure at a given seismic load level x. The problem leads to the assessment of the seismic hazard β(x) and the fragility P(fl x). The seismic hazard curves are obtained by the probabilistic seismic hazard analysis. The fragility curves are obtained after the response of the structure is defined as probabilistic and its capacity and the associated uncertainties are assessed. Finally the fragility curves are combined with the seismic loading to estimate the frequency of failure for each critical scenario. The frequency of failure due to seismic event is presented by the scenario with the highest frequency. The tools usually applied for probabilistic safety analyses of critical structures could relatively easily be adopted to ordinary structures. The key problems are the seismic hazard definitions and the fragility analyses. The fragility could be derived either based on scaling procedures or on the base of generation. Both approaches have been presented in the paper. After the seismic risk (in terms of failure probability) is assessed there are several approaches for risk reduction. Generally the methods could be classified in two groups. The
Janssen, I.; Stebbings, J.H.
1990-01-01
In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs
RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,
This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Suppression Situations in Multiple Linear Regression
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…
Gibrat’s law and quantile regressions
Distante, Roberta; Petrella, Ivan; Santoro, Emiliano
2017-01-01
The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Repeated Results Analysis for Middleware Regression Benchmarking
Bulej, Lubomír; Kalibera, T.; Tůma, P.
2005-01-01
Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005
Principles of Quantile Regression and an Application
Chen, Fang; Chalhoub-Deville, Micheline
2014-01-01
Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…
ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES
RUSCHENDORF, L; DEVALK, [No Value
We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive
Abdul Ghafoor Memon
2014-03-01
Full Text Available In this study, thermodynamic and statistical analyses were performed on a gas turbine system, to assess the impact of some important operating parameters like CIT (Compressor Inlet Temperature, PR (Pressure Ratio and TIT (Turbine Inlet Temperature on its performance characteristics such as net power output, energy efficiency, exergy efficiency and fuel consumption. Each performance characteristic was enunciated as a function of operating parameters, followed by a parametric study and optimization. The results showed that the performance characteristics increase with an increase in the TIT and a decrease in the CIT, except fuel consumption which behaves oppositely. The net power output and efficiencies increase with the PR up to certain initial values and then start to decrease, whereas the fuel consumption always decreases with an increase in the PR. The results of exergy analysis showed the combustion chamber as a major contributor to the exergy destruction, followed by stack gas. Subsequently, multiple regression models were developed to correlate each of the response variables (performance characteristic with the predictor variables (operating parameters. The regression model equations showed a significant statistical relationship between the predictor and response variables.
Regression of environmental noise in LIGO data
Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G
2015-01-01
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)
Pathological assessment of liver fibrosis regression
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Vogt, Frank
2013-01-01
utilized such as literature values of concentration ranges, concentration ratios implied e.g. by stoichiometry, sum parameters to which multiple analytes need to amount to, and/or reasonable signal reconstructions. The core idea is to mitigate the regression principle's strive for the best possible explanation of measured signals toward the best possible explanation under the condition of chemical meaningfulness. As proof-of-principle application, quantitative analyses of selected compounds in microalgae cells have been chosen. After acquiring FTIR calibration spectra from concentration series of 28 analytes, an ex situ calibration model has been built via principal component regression (PCR). Since microalgae biomass is a very complex matrix, the prediction step based on such an incomplete calibration fails. However, after incorporating several regression constraints into PCR predictions, chemically impossible results are avoided as depicted in the graphical abstract. Equally important are enhancements in concentration reproducibility. For most samples in the chosen application, the errorbars were reduced by one order of magnitude. By means of this novel chemometric method, quantitative analyses have been improved so much that cell responses to chemical shifts in their culturing environment can be studied
Pfleiderer, Elaine M; Scroggins, Cheryl L; Manning, Carol A
2009-01-01
Two separate logistic regression analyses were conducted for low- and high-altitude sectors to determine whether a set of dynamic sector characteristics variables could reliably discriminate between operational error (OE...
Easy methods for extracting individual regression slopes: Comparing SPSS, R, and Excel
Roland Pfister
2013-10-01
Full Text Available Three different methods for extracting coefficientsof linear regression analyses are presented. The focus is on automatic and easy-to-use approaches for common statistical packages: SPSS, R, and MS Excel / LibreOffice Calc. Hands-on examples are included for each analysis, followed by a brief description of how a subsequent regression coefficient analysis is performed.
Zohreh Razzaghi
2011-07-01
Full Text Available Objectives: Vitamin D deficiency is one of the most important health problems of any society. It is more common in elderly even in those dwelling in rest homes. By now, several studies have been conducted on vitamin D deficiency using current statistical models. In this study, corresponding proportional odds and stereotype regression methods were used to identify threatening factors related to vitamin D deficiency in elderly living in rest homes and comparing them with those who live out of the mentioned places. Methods & Materials: In this case-control study, there were 140 older persons living in rest homes and 140 ones not dwelling in these centers. In the present study, 25(OHD serum level variable and age, sex, body mass index, duration of exposure to sunlight variables were regarded as response and predictive variables to vitamin D deficiency, respectively. The analyses were carried out using corresponding proportional odds and stereotype regression methods and estimating parameters of these two models. Deviation statistics (AIC was used to evaluate and compare the mentioned methods. Stata.9.1 software was elected to conduct the analyses. Results: Average serum level of 25(OHD was 16.10±16.65 ng/ml and 39.62±24.78 ng/ml in individuals living in rest homes and those not living there, respectively (P=0.001. Prevalence of vitamin D deficiency (less than 20 ng/ml was observed in 75% of members of the group consisting of those living in rest homes and 23.78% of members of another group. Using corresponding proportional odds and stereotype regression methods, age, sex, body mass index, duration of exposure to sunlight variables and whether they are member of rest home were fitted. In both models, variables of group and duration of exposure to sunlight were regarded as meaningful (P<0.001. Stereotype regression model included group variable (odd ratio for a group suffering from severe vitamin D deficiency was 42.85, 95%CI:9.93-185.67 and
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.
1985-01-01
Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque
Logistic Regression in the Identification of Hazards in Construction
Drozd, Wojciech
2017-10-01
The construction site and its elements create circumstances that are conducive to the formation of risks to safety during the execution of works. Analysis indicates the critical importance of these factors in the set of characteristics that describe the causes of accidents in the construction industry. This article attempts to analyse the characteristics related to the construction site, in order to indicate their importance in defining the circumstances of accidents at work. The study includes sites inspected in 2014 - 2016 by the employees of the District Labour Inspectorate in Krakow (Poland). The analysed set of detailed (disaggregated) data includes both quantitative and qualitative characteristics. The substantive task focused on classification modelling in the identification of hazards in construction and identifying those of the analysed characteristics that are important in an accident. In terms of methodology, resource data analysis using statistical classifiers, in the form of logistic regression, was the method used.
Testing for marginal linear effects in quantile regression
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.
Testing for marginal linear effects in quantile regression
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.
Variable and subset selection in PLS regression
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
Bonnefoi, H; Litière, S; Piccart, M; MacGrogan, G; Fumoleau, P; Brain, E; Petit, T; Rouanet, P; Jassem, J; Moldovan, C; Bodmer, A; Zaman, K; Cufer, T; Campone, M; Luporsi, E; Malmström, P; Werutsky, G; Bogaerts, J; Bergh, J; Cameron, D A
2014-06-01
Pathological complete response (pCR) following chemotherapy is strongly associated with both breast cancer subtype and long-term survival. Within a phase III neoadjuvant chemotherapy trial, we sought to determine whether the prognostic implications of pCR, TP53 status and treatment arm (taxane versus non-taxane) differed between intrinsic subtypes. Patients were randomized to receive either six cycles of anthracycline-based chemotherapy or three cycles of docetaxel then three cycles of eprirubicin/docetaxel (T-ET). pCR was defined as no evidence of residual invasive cancer (or very few scattered tumour cells) in primary tumour and lymph nodes. We used a simplified intrinsic subtypes classification, as suggested by the 2011 St Gallen consensus. Interactions between pCR, TP53 status, treatment arm and intrinsic subtype on event-free survival (EFS), distant metastasis-free survival (DMFS) and overall survival (OS) were studied using a landmark and a two-step approach multivariate analyses. Sufficient data for pCR analyses were available in 1212 (65%) of 1856 patients randomized. pCR occurred in 222 of 1212 (18%) patients: 37 of 496 (7.5%) luminal A, 22 of 147 (15%) luminal B/HER2 negative, 51 of 230 (22%) luminal B/HER2 positive, 43 of 118 (36%) HER2 positive/non-luminal, 69 of 221(31%) triple negative (TN). The prognostic effect of pCR on EFS did not differ between subtypes and was an independent predictor for better EFS [hazard ratio (HR) = 0.40, P analysis. EORTC 10994/BIG 1-00 Trial registration number NCT00017095. © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.