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

PubMed

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

1996-01-01

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

The Log-Linear Cognitive Diagnostic Model (LCDM) as a Special Case of The General Diagnostic Model (GDM). Research Report. ETS RR-14-40

ERIC Educational Resources Information Center

von Davier, Matthias

2014-01-01

Diagnostic models combine multiple binary latent variables in an attempt to produce a latent structure that provides more information about test takers' performance than do unidimensional latent variable models. Recent developments in diagnostic modeling emphasize the possibility that multiple skills may interact in a conjunctive way within the…

Detecting multiple outliers in linear functional relationship model for circular variables using clustering technique

NASA Astrophysics Data System (ADS)

Mokhtar, Nurkhairany Amyra; Zubairi, Yong Zulina; Hussin, Abdul Ghapor

2017-05-01

Outlier detection has been used extensively in data analysis to detect anomalous observation in data and has important application in fraud detection and robust analysis. In this paper, we propose a method in detecting multiple outliers for circular variables in linear functional relationship model. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering procedure. With the use of tree diagram, we illustrate the graphical approach of the detection of outlier. A simulation study is done to verify the accuracy of the proposed method. Also, an illustration to a real data set is given to show its practical applicability.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys

NASA Technical Reports Server (NTRS)

Barrett, C. A.

1985-01-01

Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.

Estimation in Linear Systems Featuring Correlated Uncertain Observations Coming from Multiple Sensors

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2009-08-01

In this paper, the state least-squares linear estimation problem from correlated uncertain observations coming from multiple sensors is addressed. It is assumed that, at each sensor, the state is measured in the presence of additive white noise and that the uncertainty in the observations is characterized by a set of Bernoulli random variables which are only correlated at consecutive time instants. Assuming that the statistical properties of such variables are not necessarily the same for all the sensors, a recursive filtering algorithm is proposed, and the performance of the estimators is illustrated by a numerical simulation example wherein a signal is estimated from correlated uncertain observations coming from two sensors with different uncertainty characteristics.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Statistical Methods for Generalized Linear Models with Covariates Subject to Detection Limits.

PubMed

Bernhardt, Paul W; Wang, Huixia J; Zhang, Daowen

2015-05-01

Censored observations are a common occurrence in biomedical data sets. Although a large amount of research has been devoted to estimation and inference for data with censored responses, very little research has focused on proper statistical procedures when predictors are censored. In this paper, we consider statistical methods for dealing with multiple predictors subject to detection limits within the context of generalized linear models. We investigate and adapt several conventional methods and develop a new multiple imputation approach for analyzing data sets with predictors censored due to detection limits. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and suggest a computationally simple and consistent variance estimator. We also demonstrate that the conditional mean imputation method often leads to inconsistent estimates in generalized linear models, while several other methods are either computationally intensive or lead to parameter estimates that are biased or more variable compared to the proposed multiple imputation estimator. In an extensive simulation study, we assess the bias and variability of different approaches within the context of a logistic regression model and compare variance estimation methods for the proposed multiple imputation estimator. Lastly, we apply several methods to analyze the data set from a recently-conducted GenIMS study.

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

NASA Technical Reports Server (NTRS)

Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas

1996-01-01

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

Robust control of a parallel hybrid drivetrain with a CVT

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

Mayer, T.; Schroeder, D.

1996-09-01

In this paper the design of a robust control system for a parallel hybrid drivetrain is presented. The drivetrain is based on a continuously variable transmission (CVT) and is therefore a highly nonlinear multiple-input-multiple-output system (MIMO-System). Input-Output-Linearization offers the possibility of linearizing and of decoupling the system. Since for example the vehicle mass varies with the load and the efficiency of the gearbox depends strongly on the actual working point, an exact linearization of the plant will mostly fail. Therefore a robust control algorithm based on sliding mode is used to control the drivetrain.

Modeling non-linear growth responses to temperature and hydrology in wetland trees

NASA Astrophysics Data System (ADS)

Keim, R.; Allen, S. T.

2016-12-01

Growth responses of wetland trees to flooding and climate variations are difficult to model because they depend on multiple, apparently interacting factors, but are a critical link in hydrological control of wetland carbon budgets. To more generally understand tree growth to hydrological forcing, we modeled non-linear responses of tree ring growth to flooding and climate at sub-annual time steps, using Vaganov-Shashkin response functions. We calibrated the model to six baldcypress tree-ring chronologies from two hydrologically distinct sites in southern Louisiana, and tested several hypotheses of plasticity in wetlands tree responses to interacting environmental variables. The model outperformed traditional multiple linear regression. More importantly, optimized response parameters were generally similar among sites with varying hydrological conditions, suggesting generality to the functions. Model forms that included interacting responses to multiple forcing factors were more effective than were single response functions, indicating the principle of a single limiting factor is not correct in wetlands and both climatic and hydrological variables must be considered in predicting responses to hydrological or climate change.

The performance of integrated transconductance amplifiers as variable current sources for bio-electric impedance measurements.

PubMed

Smith, D N

1992-01-01

Multiple applied current impedance measurement systems require numbers of current sources which operate simultaneously at the same frequency and within the same phase but at variable amplitudes. Investigations into the performance of some integrated operational transconductance amplifiers as variable current sources are described. Measurements of breakthrough, non-linearity and common-mode output levels for LM13600, NE5517 and CA3280 were carried out. The effects of such errors on the overall performance and stability of multiple current systems when driving floating loads are considered.

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

Food insecurity and CD4% Among HIV+ children in Gaborone, Botswana.

PubMed

Mendoza, Jason A; Matshaba, Mogomotsi; Makhanda, Jeremiah; Liu, Yan; Boitshwarelo, Matshwenyego; Anabwani, Gabriel M

2014-08-01

We investigated the association between household food insecurity (HFI) and CD4% among 2-6-year old HIV+ outpatients (n = 78) at the Botswana-Baylor Children's Clinical Center of Excellence in Gaborone, Botswana. HFI was assessed by a validated survey. CD4% data were abstracted from the medical record. We used multiple linear regression with CD4% (dependent variable), HFI (independent variable), and controlled for sociodemographic and clinical covariates. Multiple linear regression showed a significant main effect for HFI [beta = -0.6, 95% confidence interval (CI): -1.0 to -0.1] and child gender (beta = 5.6, 95% CI: 1.3 to 9.8). Alleviating food insecurity may improve pediatric HIV outcomes in Botswana and similar Sub-Saharan settings.

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

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

Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data.

PubMed

Johnson, Brent A

2009-10-01

We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

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

Log-Multiplicative Association Models as Item Response Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Yu, Hsiu-Ting

2007-01-01

Log-multiplicative association (LMA) models, which are special cases of log-linear models, have interpretations in terms of latent continuous variables. Two theoretical derivations of LMA models based on item response theory (IRT) arguments are presented. First, we show that Anderson and colleagues (Anderson & Vermunt, 2000; Anderson & Bockenholt,…

Multiplicative Forests for Continuous-Time Processes

PubMed Central

Weiss, Jeremy C.; Natarajan, Sriraam; Page, David

2013-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability. PMID:25284967

Multiplicative Forests for Continuous-Time Processes.

PubMed

Weiss, Jeremy C; Natarajan, Sriraam; Page, David

2012-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability.

Advanced Statistics for Exotic Animal Practitioners.

PubMed

Hodsoll, John; Hellier, Jennifer M; Ryan, Elizabeth G

2017-09-01

Correlation and regression assess the association between 2 or more variables. This article reviews the core knowledge needed to understand these analyses, moving from visual analysis in scatter plots through correlation, simple and multiple linear regression, and logistic regression. Correlation estimates the strength and direction of a relationship between 2 variables. Regression can be considered more general and quantifies the numerical relationships between an outcome and 1 or multiple variables in terms of a best-fit line, allowing predictions to be made. Each technique is discussed with examples and the statistical assumptions underlying their correct application. Copyright © 2017 Elsevier Inc. All rights reserved.

Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

PubMed Central

Almedeij, Jaber

2012-01-01

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

A Kernel Embedding-Based Approach for Nonstationary Causal Model Inference.

PubMed

Hu, Shoubo; Chen, Zhitang; Chan, Laiwan

2018-05-01

Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods.

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

NASA Technical Reports Server (NTRS)

Davis, J. W.

1973-01-01

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

On Performance of Linear Multiuser Detectors for Wireless Multimedia Applications

NASA Astrophysics Data System (ADS)

Agarwal, Rekha; Reddy, B. V. R.; Bindu, E.; Nayak, Pinki

In this paper, performance of different multi-rate schemes in DS-CDMA system is evaluated. The analysis of multirate linear multiuser detectors with multiprocessing gain is analyzed for synchronous Code Division Multiple Access (CDMA) systems. Variable data rate is achieved by varying the processing gain. Our conclusion is that bit error rate for multirate and single rate systems can be made same with a tradeoff with number of users in linear multiuser detectors.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

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

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

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

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

PubMed

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

2006-02-15

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

Building "e-rater"® Scoring Models Using Machine Learning Methods. Research Report. ETS RR-16-04

ERIC Educational Resources Information Center

Chen, Jing; Fife, James H.; Bejar, Isaac I.; Rupp, André A.

2016-01-01

The "e-rater"® automated scoring engine used at Educational Testing Service (ETS) scores the writing quality of essays. In the current practice, e-rater scores are generated via a multiple linear regression (MLR) model as a linear combination of various features evaluated for each essay and human scores as the outcome variable. This…

Maximizing the Information and Validity of a Linear Composite in the Factor Analysis Model for Continuous Item Responses

ERIC Educational Resources Information Center

Ferrando, Pere J.

2008-01-01

This paper develops results and procedures for obtaining linear composites of factor scores that maximize: (a) test information, and (b) validity with respect to external variables in the multiple factor analysis (FA) model. I treat FA as a multidimensional item response theory model, and use Ackerman's multidimensional information approach based…

Modeling Signal-Noise Processes Supports Student Construction of a Hierarchical Image of Sample

ERIC Educational Resources Information Center

Lehrer, Richard

2017-01-01

Grade 6 (modal age 11) students invented and revised models of the variability generated as each measured the perimeter of a table in their classroom. To construct models, students represented variability as a linear composite of true measure (signal) and multiple sources of random error. Students revised models by developing sampling…

Evaluation of prestress cable strain in multiple beam configurations.

DOT National Transportation Integrated Search

1996-08-01

A system to measure prestress cable strain was fabricated, software written, and the unit calibrated. Strain measurements were made by attaching four Linear Variable Differential Transformers (LVDT) to prestress cable before they were stressed.

Estimation of streamflow, base flow, and nitrate-nitrogen loads in Iowa using multiple linear regression models

USGS Publications Warehouse

Schilling, K.E.; Wolter, C.F.

2005-01-01

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest. (JAWRA) (Copyright ?? 2005).

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

NASA Astrophysics Data System (ADS)

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali; Naderi, Mehdi; Dematte, Jose Alexandre M.; Kerry, Ruth

2016-11-01

The measurement of soil erodibility (K) in the field is tedious, time-consuming and expensive; therefore, its prediction through pedotransfer functions (PTFs) could be far less costly and time-consuming. The aim of this study was to develop new PTFs to estimate the K factor using multiple linear regression, Mamdani fuzzy inference systems, and artificial neural networks. For this purpose, K was measured in 40 erosion plots with natural rainfall. Various soil properties including the soil particle size distribution, calcium carbonate equivalent, organic matter, permeability, and wet-aggregate stability were measured. The results showed that the mean measured K was 0.014 t h MJ- 1 mm- 1 and 2.08 times less than the estimated mean K (0.030 t h MJ- 1 mm- 1) using the USLE model. Permeability, wet-aggregate stability, very fine sand, and calcium carbonate were selected as independent variables by forward stepwise regression in order to assess the ability of multiple linear regression, Mamdani fuzzy inference systems and artificial neural networks to predict K. The calcium carbonate equivalent, which is not accounted for in the USLE model, had a significant impact on K in multiple linear regression due to its strong influence on the stability of aggregates and soil permeability. Statistical indices in validation and calibration datasets determined that the artificial neural networks method with the highest R2, lowest RMSE, and lowest ME was the best model for estimating the K factor. A strong correlation (R2 = 0.81, n = 40, p < 0.05) between the estimated K from multiple linear regression and measured K indicates that the use of calcium carbonate equivalent as a predictor variable gives a better estimation of K in areas with calcareous soils.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies.

PubMed

Koerner, Tess K; Zhang, Yang

2017-02-27

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.

Normal forms for reduced stochastic climate models

PubMed Central

Majda, Andrew J.; Franzke, Christian; Crommelin, Daan

2009-01-01

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables. The use of a few Empirical Orthogonal Functions (EOFs) (also known as Principal Component Analysis, Karhunen–Loéve and Proper Orthogonal Decomposition) depending on observational data to span the low-frequency subspace requires the assessment of dyad interactions besides the more familiar triads in the interaction between the low- and high-frequency subspaces of the dynamics. It is shown below that the dyad and multiplicative triad interactions combine with the climatological linear operator interactions to simultaneously produce both strong nonlinear dissipation and Correlated Additive and Multiplicative (CAM) stochastic noise. For a single low-frequency variable the dyad interactions and climatological linear operator alone produce a normal form with CAM noise from advection of the large scales by the small scales and simultaneously strong cubic damping. These normal forms should prove useful for developing systematic strategies for the estimation of stochastic models from climate data. As an illustrative example the one-dimensional normal form is applied below to low-frequency patterns such as the North Atlantic Oscillation (NAO) in a climate model. The results here also illustrate the short comings of a recent linear scalar CAM noise model proposed elsewhere for low-frequency variability. PMID:19228943

Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea.

PubMed

Hasani Sangani, Mohammad; Jabbarian Amiri, Bahman; Alizadeh Shabani, Afshin; Sakieh, Yousef; Ashrafi, Sohrab

2015-04-01

Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.

Confounding Problems in Multifactor AOV When Using Several Organismic Variables of Limited Reliability

ERIC Educational Resources Information Center

Games, Paul A.

1975-01-01

A brief introduction is presented on how multiple regression and linear model techniques can handle data analysis situations that most educators and psychologists think of as appropriate for analysis of variance. (Author/BJG)

Multiple indicators, multiple causes measurement error models

DOE PAGES

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.; ...

2014-06-25

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methodsmore » for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.« less

Multiple Indicators, Multiple Causes Measurement Error Models

PubMed Central

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.; Carroll, Raymond J.

2014-01-01

Multiple Indicators, Multiple Causes Models (MIMIC) are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times however when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this paper are: (1) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model, (2) to develop likelihood based estimation methods for the MIMIC ME model, (3) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. As a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure. PMID:24962535

Multiple indicators, multiple causes measurement error models

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

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methodsmore » for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.« less

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2017-04-01

This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.

Study of process variables associated with manufacturing hermetically-sealed nickel-cadmium cells

NASA Technical Reports Server (NTRS)

Miller, L.; Doan, D. J.; Carr, E. S.

1971-01-01

A program to determine and study the critical process variables associated with the manufacture of aerospace, hermetically-sealed, nickel-cadmium cells is described. The determination and study of the process variables associated with the positive and negative plaque impregnation/polarization process are emphasized. The experimental data resulting from the implementation of fractional factorial design experiments are analyzed by means of a linear multiple regression analysis technique. This analysis permits the selection of preferred levels for certain process variables to achieve desirable impregnated plaque characteristics.

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

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

PubMed

Chang, Wen-Ruey; Chang, Chien-Chi

2018-04-01

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

Micro–Single-Photon Emission Computed Tomography Image Acquisition and Quantification of Sodium-Iodide Symporter–Mediated Radionuclide Accumulation in Mouse Thyroid and Salivary Glands

PubMed Central

Brandt, Michael P.; Kloos, Richard T.; Shen, Daniel H.; Zhang, Xiaoli; Liu, Yu-Yu

2012-01-01

Background Micro–single-photon emission computed tomography (SPECT) provides a noninvasive way to evaluate the effects of genetic and/or pharmacological modulation on sodium-iodide symporter (NIS)–mediated radionuclide accumulation in mouse thyroid and salivary glands. However, parameters affecting image acquisition and analysis of mouse thyroids and salivary glands have not been thoroughly investigated. In this study, we investigated the effects of region-of-interest (ROI) selection, collimation, scan time, and imaging orbit on image acquisition and quantification of thyroidal and salivary radionuclide accumulation in mice. Methods The effects of data window minima and maxima on thyroidal and salivary ROI selection using a visual boundary method were examined in SPECT images acquired from mice injected with 123I NaI. The effects of collimation, scan time, and imaging orbit on counting linearity and signal intensity were investigated using phantoms filled with various activities of 123I NaI or Tc-99m pertechnetate. Spatial resolution of target organs in whole-animal images was compared between circular orbit with parallel-hole collimation and spiral orbit with five-pinhole collimation. Lastly, the inter-experimental variability of the same mouse scanned multiple times was compared with the intra-experimental variability among different mice scanned at the same time. Results Thyroid ROI was separated from salivary glands by empirically increasing the data window maxima. Counting linearity within the range of 0.5–14.2 μCi was validated by phantom imaging using single- or multiple-pinhole collimators with circular or spiral imaging orbit. Scanning time could be shortened to 15 minutes per mouse without compromising counting linearity despite proportionally decreased signal intensity. Whole-animal imaging using a spiral orbit with five-pinhole collimators achieved a high spatial resolution and counting linearity. Finally, the extent of inter-experimental variability of NIS-mediated radionuclide accumulation in the thyroid and salivary glands by SPECT imaging in the same mouse was less than the magnitude of variability among the littermates. Conclusions The impacts of multiple variables and experimental designs on micro-SPECT imaging and quantification of radionuclide accumulation in mouse thyroid and salivary glands can be minimized. This platform will serve as an invaluable tool to screen for pharmacologic reagents that differentially modulate thyroidal and salivary radioiodine accumulation in preclinical mouse models. PMID:22540327

Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

NASA Astrophysics Data System (ADS)

Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried

2018-03-01

This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

Modulation of brain activity by multiple lexical and word form variables in visual word recognition: A parametric fMRI study.

PubMed

Hauk, Olaf; Davis, Matthew H; Pulvermüller, Friedemann

2008-09-01

Psycholinguistic research has documented a range of variables that influence visual word recognition performance. Many of these variables are highly intercorrelated. Most previous studies have used factorial designs, which do not exploit the full range of values available for continuous variables, and are prone to skewed stimulus selection as well as to effects of the baseline (e.g. when contrasting words with pseudowords). In our study, we used a parametric approach to study the effects of several psycholinguistic variables on brain activation. We focussed on the variable word frequency, which has been used in numerous previous behavioural, electrophysiological and neuroimaging studies, in order to investigate the neuronal network underlying visual word processing. Furthermore, we investigated the variable orthographic typicality as well as a combined variable for word length and orthographic neighbourhood size (N), for which neuroimaging results are still either scarce or inconsistent. Data were analysed using multiple linear regression analysis of event-related fMRI data acquired from 21 subjects in a silent reading paradigm. The frequency variable correlated negatively with activation in left fusiform gyrus, bilateral inferior frontal gyri and bilateral insulae, indicating that word frequency can affect multiple aspects of word processing. N correlated positively with brain activity in left and right middle temporal gyri as well as right inferior frontal gyrus. Thus, our analysis revealed multiple distinct brain areas involved in visual word processing within one data set.

Beyond triple collocation: Applications to satellite soil moisture

USDA-ARS?s Scientific Manuscript database

Triple collocation is now routinely used to resolve the exact (linear) relationships between multiple measurements and/or representations of a geophysical variable that are subject to errors. It has been utilized in the context of calibration, rescaling and error characterisation to allow comparison...

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

PubMed

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Variables associated with health-related quality of life in a Brazilian sample of patients from a tertiary outpatient clinic for depression and anxiety disorders.

PubMed

Schwab, Bianca; Daniel, Heloisa Silveira; Lutkemeyer, Carine; Neves, João Arthur Lange Lins; Zilli, Louise Nassif; Guarnieri, Ricardo; Diaz, Alexandre Paim; Michels, Ana Maria Maykot Prates

2015-01-01

Health-related quality of life (HRQOL) assessment tools have been broadly used in the medical context. These tools are used to measure the subjective impact of the disease on patients. The objective of this study was to evaluate the variables associated with HRQOL in a Brazilian sample of patients followed up in a tertiary outpatient clinic for depression and anxiety disorders. Cross-sectional study. Independent variables were those included in a sociodemographic questionnaire and the Hospital Anxiety and Depression Scale (HADS) scores. Dependent variables were those included in the short version of the World Health Organization Quality of Life (WHOQOL-BREF) and the scores for its subdomains (overall quality of life and general health, physical health, psychological health, social relationships, and environment). A multiple linear regression analysis was used to find the variables independently associated with each outcome. Seventy-five adult patients were evaluated. After multiple linear regression analysis, the HADS scores were associated with all outcomes, except social relationships (p = 0.08). Female gender was associated with poor total scores, as well as psychological health and environment. Unemployment was associated with poor physical health. Identifying the factors associated with HRQOL and recognizing that depression and anxiety are major factors are essential to improve the care of patients.

Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change

EPA Science Inventory

We applied a multiple linear regression model to understand the relationships of PM_2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM_2.5 to climate change. We used 2004-2008 PM_2.5 observations fro...

Variable structure control of spacecraft reorientation maneuvers

NASA Technical Reports Server (NTRS)

Sira-Ramirez, H.; Dwyer, T. A. W., III

1986-01-01

A Variable Structure Control (VSC) approach is presented for multi-axial spacecraft reorientation maneuvers. A nonlinear sliding surface is proposed which results in an asymptotically stable, ideal linear sliding motion of Cayley-Rodriques attitude parameters. By imposing a desired equivalent dynamics on the attitude parameters, the approach is devoid of optimal control considerations. The single axis case provides a design scheme for the multiple axes design problem. Illustrative examples are presented.

A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Li, Jun

2002-09-01

In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

PubMed Central

Koerner, Tess K.; Zhang, Yang

2017-01-01

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers. PMID:28264422

Exhaustive Search for Sparse Variable Selection in Linear Regression

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

2016-01-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

dos Santos, T. S.; Mendes, D.; Torres, R. R.

2015-08-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

Kinetics of human aging: I. Rates of senescence between ages 30 and 70 years in healthy people.

PubMed

Sehl, M E; Yates, F E

2001-05-01

A calculation of loss rates is reported for human structural and functional variables from a substantially larger data set than has been previously studied. Data were collected for healthy, nonsmoking human subjects of both sexes from a literature search of cross-sectional, longitudinal, and cross-sequential studies. The number of studies analyzed was 469, and the total number of subjects was 54,274. A linear model provided a fit of the data, for each variable, that was not significantly different from the best polynomial fit. Therefore, linear loss rates (as a percent decline per year from the reference value at age 30) were calculated for 445 variables from 13 organ systems, and additionally for 24 variables even more integrative, such as maximum oxygen consumption and exercise performance, that express effects of multiple contributing variables and systems. The frequency distribution of the 13 individual system linear loss rates (as percent loss per year) for a very healthy population has roughly a unimodal, right-skewed shape, with mean 0.65, median 0.5, and variance 0.32. (The actual underlying distribution could be a truncated Gaussian, an exponential, Poisson, gamma or some other). The linear estimates of loss rates were clustered between 0% and 2% per year for variables from most organ systems, with exceptions being the endocrine, thermoregulatory, and gastrointestinal systems, for which wider ranges (up to approximately 3% per year) of loss rates were found. We suggest that this set of linear losses over time, observed in healthy individuals between ages (approximately) 30 to 70 years, exposes the underlying kinetics of human senescence, independent of effects of substantial disease.

Nonlinear Dynamic Models in Advanced Life Support

NASA Technical Reports Server (NTRS)

Jones, Harry

2002-01-01

To facilitate analysis, ALS systems are often assumed to be linear and time invariant, but they usually have important nonlinear and dynamic aspects. Nonlinear dynamic behavior can be caused by time varying inputs, changes in system parameters, nonlinear system functions, closed loop feedback delays, and limits on buffer storage or processing rates. Dynamic models are usually cataloged according to the number of state variables. The simplest dynamic models are linear, using only integration, multiplication, addition, and subtraction of the state variables. A general linear model with only two state variables can produce all the possible dynamic behavior of linear systems with many state variables, including stability, oscillation, or exponential growth and decay. Linear systems can be described using mathematical analysis. Nonlinear dynamics can be fully explored only by computer simulations of models. Unexpected behavior is produced by simple models having only two or three state variables with simple mathematical relations between them. Closed loop feedback delays are a major source of system instability. Exceeding limits on buffer storage or processing rates forces systems to change operating mode. Different equilibrium points may be reached from different initial conditions. Instead of one stable equilibrium point, the system may have several equilibrium points, oscillate at different frequencies, or even behave chaotically, depending on the system inputs and initial conditions. The frequency spectrum of an output oscillation may contain harmonics and the sums and differences of input frequencies, but it may also contain a stable limit cycle oscillation not related to input frequencies. We must investigate the nonlinear dynamic aspects of advanced life support systems to understand and counter undesirable behavior.

Determination of water depth with high-resolution satellite imagery over variable bottom types

USGS Publications Warehouse

Stumpf, Richard P.; Holderied, Kristine; Sinclair, Mark

2003-01-01

A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms--the standard linear transform and the new ratio transform--were compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10-15 m. However, the linear transform does not distinguish depths >15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in >25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4-5 pixels) in water depths >15-20 m. In general, the ratio transform is more robust than the linear transform.

Understanding logistic regression analysis.

PubMed

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.

Forecasting on the total volumes of Malaysia's imports and exports by multiple linear regression

NASA Astrophysics Data System (ADS)

Beh, W. L.; Yong, M. K. Au

2017-04-01

This study is to give an insight on the doubt of the important of macroeconomic variables that affecting the total volumes of Malaysia's imports and exports by using multiple linear regression (MLR) analysis. The time frame for this study will be determined by using quarterly data of the total volumes of Malaysia's imports and exports covering the period between 2000-2015. The macroeconomic variables will be limited to eleven variables which are the exchange rate of US Dollar with Malaysia Ringgit (USD-MYR), exchange rate of China Yuan with Malaysia Ringgit (RMB-MYR), exchange rate of European Euro with Malaysia Ringgit (EUR-MYR), exchange rate of Singapore Dollar with Malaysia Ringgit (SGD-MYR), crude oil prices, gold prices, producer price index (PPI), interest rate, consumer price index (CPI), industrial production index (IPI) and gross domestic product (GDP). This study has applied the Johansen Co-integration test to investigate the relationship among the total volumes to Malaysia's imports and exports. The result shows that crude oil prices, RMB-MYR, EUR-MYR and IPI play important roles in the total volumes of Malaysia's imports. Meanwhile crude oil price, USD-MYR and GDP play important roles in the total volumes of Malaysia's exports.

A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study.

PubMed

De Silva, Anurika Priyanjali; Moreno-Betancur, Margarita; De Livera, Alysha Madhu; Lee, Katherine Jane; Simpson, Julie Anne

2017-07-25

Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation (MVNI)) treat repeated measurements of the same time-dependent variable as just another 'distinct' variable for imputation and therefore do not make the most of the longitudinal structure of the data. Only a few studies have explored extensions to the standard approaches to account for the temporal structure of longitudinal data. One suggestion is the two-fold fully conditional specification (two-fold FCS) algorithm, which restricts the imputation of a time-dependent variable to time blocks where the imputation model includes measurements taken at the specified and adjacent times. To date, no study has investigated the performance of two-fold FCS and standard MI methods for handling missing data in a time-varying covariate with a non-linear trajectory over time - a commonly encountered scenario in epidemiological studies. We simulated 1000 datasets of 5000 individuals based on the Longitudinal Study of Australian Children (LSAC). Three missing data mechanisms: missing completely at random (MCAR), and a weak and a strong missing at random (MAR) scenarios were used to impose missingness on body mass index (BMI) for age z-scores; a continuous time-varying exposure variable with a non-linear trajectory over time. We evaluated the performance of FCS, MVNI, and two-fold FCS for handling up to 50% of missing data when assessing the association between childhood obesity and sleep problems. The standard two-fold FCS produced slightly more biased and less precise estimates than FCS and MVNI. We observed slight improvements in bias and precision when using a time window width of two for the two-fold FCS algorithm compared to the standard width of one. We recommend the use of FCS or MVNI in a similar longitudinal setting, and when encountering convergence issues due to a large number of time points or variables with missing values, the two-fold FCS with exploration of a suitable time window.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

Efficacy of Social Media Adoption on Client Growth for Independent Management Consultants

DTIC Science & Technology

2017-02-01

design , a linear multiple regression with three predictor variables and one dependent variable per testing were used. Under those circumstances...regression test was used to compare the social media adoption of two groups on a single measure to determine if there was a statistical difference...number and types of social media platforms used and their influence on client growth was examined in this research design that used a descriptive

Predicting daily use of urban forest recreation sites

Treesearch

John F. Dwyer

1988-01-01

A multiple linear regression model explains 90% of the variance in daily use of an urban recreation site. Explanatory variables include season, day of the week, and weather. The results offer guides for recreation site planning and management as well as suggestions for improving the model.

On optimal control of linear systems in the presence of multiplicative noise

NASA Technical Reports Server (NTRS)

Joshi, S. M.

1976-01-01

This correspondence considers the problem of optimal regulator design for discrete time linear systems subjected to white state-dependent and control-dependent noise in addition to additive white noise in the input and the observations. A pseudo-deterministic problem is first defined in which multiplicative and additive input disturbances are present, but noise-free measurements of the complete state vector are available. This problem is solved via discrete dynamic programming. Next is formulated the problem in which the number of measurements is less than that of the state variables and the measurements are contaminated with state-dependent noise. The inseparability of control and estimation is brought into focus, and an 'enforced separation' solution is obtained via heuristic reasoning in which the control gains are shown to be the same as those in the pseudo-deterministic problem. An optimal linear state estimator is given in order to implement the controller.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

PubMed Central

Dai, James Y.; Chan, Kwun Chuen Gary; Hsu, Li

2014-01-01

Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerale work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent due to the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed. PMID:24863158

Light propagation in linearly perturbed ΛLTB models

NASA Astrophysics Data System (ADS)

Meyer, Sven; Bartelmann, Matthias

2017-11-01

We apply a generic formalism of light propagation to linearly perturbed spherically symmetric dust models including a cosmological constant. For a comoving observer on the central worldline, we derive the equation of geodesic deviation and perform a suitable spherical harmonic decomposition. This allows to map the abstract gauge-invariant perturbation variables to well-known quantities from weak gravitational lensing like convergence or cosmic shear. The resulting set of differential equations can effectively be solved by a Green's function approach leading to line-of-sight integrals sourced by the perturbation variables on the backward lightcone. The resulting spherical harmonic coefficients of the lensing observables are presented and the shear field is decomposed into its E- and B-modes. Results of this work are an essential tool to add information from linear structure formation to the analysis of spherically symmetric dust models with the purpose of testing the Copernican Principle with multiple cosmological probes.

Multiple imputation in the presence of non-normal data.

PubMed

Lee, Katherine J; Carlin, John B

2017-02-20

Multiple imputation (MI) is becoming increasingly popular for handling missing data. Standard approaches for MI assume normality for continuous variables (conditionally on the other variables in the imputation model). However, it is unclear how to impute non-normally distributed continuous variables. Using simulation and a case study, we compared various transformations applied prior to imputation, including a novel non-parametric transformation, to imputation on the raw scale and using predictive mean matching (PMM) when imputing non-normal data. We generated data from a range of non-normal distributions, and set 50% to missing completely at random or missing at random. We then imputed missing values on the raw scale, following a zero-skewness log, Box-Cox or non-parametric transformation and using PMM with both type 1 and 2 matching. We compared inferences regarding the marginal mean of the incomplete variable and the association with a fully observed outcome. We also compared results from these approaches in the analysis of depression and anxiety symptoms in parents of very preterm compared with term-born infants. The results provide novel empirical evidence that the decision regarding how to impute a non-normal variable should be based on the nature of the relationship between the variables of interest. If the relationship is linear in the untransformed scale, transformation can introduce bias irrespective of the transformation used. However, if the relationship is non-linear, it may be important to transform the variable to accurately capture this relationship. A useful alternative is to impute the variable using PMM with type 1 matching. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

New Zealand Management Students' Perceptions of Communication Technologies in Correspondence Education.

ERIC Educational Resources Information Center

Ostman, Ronald E.; Wagner, Graham A.

1987-01-01

Describes a survey of 724 management students in New Zealand's Technical Correspondence Institute which was conducted to determine whether the introduction of educational technologies could decrease the dropout rate. The multiple linear regression model that was used to analyze the questionnaire responses is presented, and predictor variables are…

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections.

PubMed

Chen, Chen; Xie, Yuanchang

2016-06-01

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

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

Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection.

PubMed

Fernandes, David Douglas Sousa; Gomes, Adriano A; Costa, Gean Bezerra da; Silva, Gildo William B da; Véras, Germano

2011-12-15

This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant. Copyright © 2011 Elsevier B.V. All rights reserved.

Trellis Coding of Non-coherent Multiple Symbol Full Response M-ary CPFSK with Modulation Index 1/M

NASA Technical Reports Server (NTRS)

Lee, H.; Divsalar, D.; Weber, C.

1994-01-01

This paper introduces a trellis coded modulation (TCM) scheme for non-coherent multiple full response M-ary CPFSK with modulation index 1/M. A proper branch metric for the trellis decoder is obtained by employing a simple approximation of the modified Bessel function for large signal to noise ratio (SNR). Pairwise error probability of coded sequences is evaluated by applying a linear approximation to the Rician random variable.

Correction of the significance level when attempting multiple transformations of an explanatory variable in generalized linear models

PubMed Central

2013-01-01

Background In statistical modeling, finding the most favorable coding for an exploratory quantitative variable involves many tests. This process involves multiple testing problems and requires the correction of the significance level. Methods For each coding, a test on the nullity of the coefficient associated with the new coded variable is computed. The selected coding corresponds to that associated with the largest statistical test (or equivalently the smallest pvalue). In the context of the Generalized Linear Model, Liquet and Commenges (Stat Probability Lett,71:33–38,2005) proposed an asymptotic correction of the significance level. This procedure, based on the score test, has been developed for dichotomous and Box-Cox transformations. In this paper, we suggest the use of resampling methods to estimate the significance level for categorical transformations with more than two levels and, by definition those that involve more than one parameter in the model. The categorical transformation is a more flexible way to explore the unknown shape of the effect between an explanatory and a dependent variable. Results The simulations we ran in this study showed good performances of the proposed methods. These methods were illustrated using the data from a study of the relationship between cholesterol and dementia. Conclusion The algorithms were implemented using R, and the associated CPMCGLM R package is available on the CRAN. PMID:23758852

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India.

PubMed

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K

2013-01-01

Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. CBRs were inversely related to literacy rates (slope parameter = -0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = -0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = -1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = -0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Female literacy is relatively highly important for both population stabilization and better infant health.

Hybrid Optimization Parallel Search PACKage

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

2009-11-10

HOPSPACK is open source software for solving optimization problems without derivatives. Application problems may have a fully nonlinear objective function, bound constraints, and linear and nonlinear constraints. Problem variables may be continuous, integer-valued, or a mixture of both. The software provides a framework that supports any derivative-free type of solver algorithm. Through the framework, solvers request parallel function evaluation, which may use MPI (multiple machines) or multithreading (multiple processors/cores on one machine). The framework provides a Cache and Pending Cache of saved evaluations that reduces execution time and facilitates restarts. Solvers can dynamically create other algorithms to solve subproblems, amore » useful technique for handling multiple start points and integer-valued variables. HOPSPACK ships with the Generating Set Search (GSS) algorithm, developed at Sandia as part of the APPSPACK open source software project.« less

Influence of age on the correlations of hematological and biochemical variables with the stability of erythrocyte membrane in relation to sodium dodecyl sulfate.

PubMed

de Freitas, Mariana V; Marquez-Bernardes, Liandra F; de Arvelos, Letícia R; Paraíso, Lara F; Gonçalves E Oliveira, Ana Flávia M; Mascarenhas Netto, Rita de C; Neto, Morun Bernardino; Garrote-Filho, Mario S; de Souza, Paulo César A; Penha-Silva, Nilson

2014-10-01

To evaluate the influence of age on the relationships between biochemical and hematological variables and stability of erythrocyte membrane in relation to the sodium dodecyl sulfate (SDS) in population of 105 female volunteers between 20 and 90 years. The stability of RBC membrane was determined by non-linear regression of the dependency of the absorbance of hemoglobin released as a function of SDS concentration, represented by the half-transition point of the curve (D50) and the variation in the concentration of the detergent to promote lysis (dD). There was an age-dependent increase in the membrane stability in relation to SDS. Analyses by multiple linear regression showed that this stability increase is significantly related to the hematological variable red cell distribution width (RDW) and the biochemical variables blood albumin and cholesterol. The positive association between erythrocyte stability and RDW may reflect one possible mechanism involved in the clinical meaning of this hematological index.

[Non-linear canonical correlation analysis between anthropometric indicators and multiple metabolic abnormalities].

PubMed

Fu, Xiaoli; Liu, Li; Ping, Zhiguang; Li, Linlin

2013-09-01

To define the general correlation between anthropometric indicators and multiple metabolic abnormalities, and to put forward some particular suggestions for the prevention of multiple metabolic abnormalities. A random cluster sampling was carried out in one county of Henan Province. Questionnaire, physical examination and biochemical tests were admitted to the adult inhabitants. Non-linear canonical correlation analysis (NLCCA) was applied with OVERALS of SPSS 13.0. The coefficients of canonical correlation and multiple correlation were calculated. The plot of centroids labeled by variables showed the correlation among various indicators. In total, 2,914 objects were investigated. It included 1,134 (38.9%) males and 1,780 (61.1%) females (60.0%). The average age was (50.58 +/- 13.70) years old. The fitting result of NLCCA were as follows: the loss of 0.577 accounting for 28.8% of the total variation was relatively small, and indicated that the two sets of variables of this study, namely sets of biochemical indicators (including serum total cholesterol, total triglyceride, high-density lipoprotein cholesterol, low density lipoprotein cholesterol and fasting plasma glucose) and sets of others (including gender, BMI and waist circumference) were closely related and often changed synchronously. Multivariate correlation coefficient showed that internal indicators of the above two sets were closely related respectively and often showed the multiple anomalies of the same set. The diagram of the center of gravity of the association of various indicators showed that the symptoms of metabolic abnormalities increased with age. Women were more liable to have metabolic abnormalities. Overweight and obese people often suffer multiple metabolic disorders. Waist circumference was positively correlated with metabolic abnormalities. (1) Biochemical indicators and anthropometric often change in combination. (2) Much attention should be paid to older people especially middle-aged or older men and older women in primary prevention. (3) Overweight and abdominal obesity can be considered the sensitive predictive indicator of multiple metabolic abnormalities. (4) Nonlinear canonical correlation and center of gravity Figure had the advantage of analyze the correlation between multiple sets of variables.

Exploring correlates of diabetes-related stress among adults with Type 1 diabetes in the T1D exchange clinic registry.

PubMed

Boden, Matthew Tyler; Gala, Sasha

2018-04-01

To explore relations between diabetes-related stress and multiple sociodemographic, diabetes health, other health, and treatment-related variables among a large sample of adults with Type 1 Diabetes (T1D). The sample consisted of 10,821 adults (over 18 years old) enrolled in the T1D Exchange Clinic Registry. The T1D Exchange clinic network consists of 67 diabetes clinical centers throughout the United States selected to broadly represent pediatric and adult patients with T1D. Variables were assessed through participant self-report and extraction of clinic chart data. Univariate and multiple linear regression (with simultaneous entry of all predictors) analyses were conducted. Robustly associated with increased diabetes-related stress across analyses were multiple sociodemographic (female [vs. male], native Hawaiian/other Pacific islander [vs. white/Caucasian], decreased age and diabetes duration), diabetes health (higher HbA1c), other health (lower general health, presence of major life stress and depression, less physical activity), and treatment related variables (use of injections/pen or combination injection/pen/pump [vs. pump], use of CGM, increased frequency of missing insulin doses and BG checking, decreased frequency of BG checking prior to bolus, receipt of mental health treatment). We replicated and extended research demonstrating that diabetes-related stress among people with T1D occurs at higher levels among those with particular sociodemographic characteristics and is associated with a range poorer diabetes health and other health variables, and multiple treatment-related variables. The strong incremental prediction of diabetes-related stress by multiple variables in our study suggests that a multi-variable, personalized approach may increase the effectiveness of treatments for diabetes-related stress. Published by Elsevier B.V.

Communicative participation restrictions in multiple sclerosis: associated variables and correlation with social functioning.

PubMed

Yorkston, Kathryn M; Baylor, Carolyn; Amtmann, Dagmar

2014-01-01

Individuals with multiple sclerosis (MS) are at risk for communication problems that may restrict their ability to take participation in important life roles such as maintenance of relationships, work, or household management. The aim of this project is to examine selected demographic and symptom-related variables that may contribute to participation restrictions. This examination is intended to aid clinicians in predicting who might be at risk for such restrictions and what variables may be targeted in interventions. Community-dwelling adults with MS (n=216) completed a survey either online or using paper forms. The survey included the 46-item version of the Communicative Participation Item Bank, demographics (age, sex, living situation, employment status, education, and time since onset of diagnosis of MS), and self-reported symptom-related variables (physical activity, emotional problems, fatigue, pain, speech severity, and cognitive/communication skills). In order to identify predictors of restrictions in communicative participation, these variables were entered into a backwards stepwise multiple linear regression analysis. Five variables (cognitive/communication skills, speech severity, speech usage, physical activity, and education) were statistically significant predictors of communication participation. In order to examine the relationship of communicative participation and social role variables, bivariate Spearman correlations were conducted. Results suggest only a fair to moderate relationship between communicative participation and measures of social roles. Communicative participation is a complex construct associated with a number of self-reported variables. Clinicians should be alert to risk factors for reduced communicative participation including reduced cognitive and speech skills, lower levels of speech usage, limitations in physical activities and higher levels of education. The reader will be able to: (a) describe the factors that may restrict participation in individuals with multiple sclerosis; (b) list measures of social functioning that may be pertinent in adults with multiple sclerosis; (c) discuss factors that can be used to predict communicative participation in multiple sclerosis. Copyright © 2014 Elsevier Inc. All rights reserved.

Normality of raw data in general linear models: The most widespread myth in statistics

USGS Publications Warehouse

Kery, Marc; Hatfield, Jeff S.

2003-01-01

In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.

The 11-year solar cycle in current reanalyses: a (non)linear attribution study of the middle atmosphere

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2015-06-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11-year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (support vector regression, neural networks) besides the multiple linear regression approach. The analysis was applied to several current reanalysis data sets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how these types of data resolve especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the tropical stratosphere were found to be in qualitative agreement with previous attribution studies, although the agreement with observational results was incomplete, especially for JRA-55. The analysis also pointed to the solar signal in the ozone data sets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. The results obtained by linear regression were confirmed by the nonlinear approach through all data sets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. The seasonal evolution of the solar response was also discussed in terms of dynamical causalities in the winter hemispheres. The hypothetical mechanism of a weaker Brewer-Dobson circulation at solar maxima was reviewed together with a discussion of polar vortex behaviour.

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.

PubMed

Mathotaarachchi, Sulantha; Wang, Seqian; Shin, Monica; Pascoal, Tharick A; Benedet, Andrea L; Kang, Min Su; Beaudry, Thomas; Fonov, Vladimir S; Gauthier, Serge; Labbe, Aurélie; Rosa-Neto, Pedro

2016-01-01

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

Cumulative risk effects for the development of behaviour difficulties in children and adolescents with special educational needs and disabilities.

PubMed

Oldfield, Jeremy; Humphrey, Neil; Hebron, Judith

2015-01-01

Research has identified multiple risk factors for the development of behaviour difficulties. What have been less explored are the cumulative effects of exposure to multiple risks on behavioural outcomes, with no study specifically investigating these effects within a population of young people with special educational needs and disabilities (SEND). Furthermore, it is unclear whether a threshold or linear risk model better fits the data for this population. The sample included 2660 children and 1628 adolescents with SEND. Risk factors associated with increases in behaviour difficulties over an 18-month period were summed to create a cumulative risk score, with this explanatory variable being added into a multi-level model. A quadratic term was then added to test the threshold model. There was evidence of a cumulative risk effect, suggesting that exposure to higher numbers of risk factors, regardless of their exact nature, resulted in increased behaviour difficulties. The relationship between risk and behaviour difficulties was non-linear, with exposure to increasing risk having a disproportionate and detrimental impact on behaviour difficulties in child and adolescent models. Interventions aimed at reducing behaviour difficulties need to consider the impact of multiple risk variables. Tailoring interventions towards those exposed to large numbers of risks would be advantageous. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Optimal Scaling of Interaction Effects in Generalized Linear Models

ERIC Educational Resources Information Center

van Rosmalen, Joost; Koning, Alex J.; Groenen, Patrick J. F.

2009-01-01

Multiplicative interaction models, such as Goodman's (1981) RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are suitable only for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of…

The Prediction of Achievement and Time Spent in Instruction in a Self-Paced Individualized Course.

ERIC Educational Resources Information Center

Franklin, Thomas E.

Multiple linear regressions were employed to determine the relative contributions of cognitive and affective variables accounting for variance in college students' achievement and amount of time taken to complete a self-paced, individualized course. Study habits and attitudes (SSHA) made greater relative contributions to explaining total course…

Electronic Resource Expenditure and the Decline in Reference Transaction Statistics in Academic Libraries

ERIC Educational Resources Information Center

Dubnjakovic, Ana

2012-01-01

The current study investigates factors influencing increase in reference transactions in a typical week in academic libraries across the United States of America. Employing multiple regression analysis and general linear modeling, variables of interest from the "Academic Library Survey (ALS) 2006" survey (sample size 3960 academic libraries) were…

The optimal hormonal replacement modality selection for multiple organ procurement from brain-dead organ donors

PubMed Central

Mi, Zhibao; Novitzky, Dimitri; Collins, Joseph F; Cooper, David KC

2015-01-01

The management of brain-dead organ donors is complex. The use of inotropic agents and replacement of depleted hormones (hormonal replacement therapy) is crucial for successful multiple organ procurement, yet the optimal hormonal replacement has not been identified, and the statistical adjustment to determine the best selection is not trivial. Traditional pair-wise comparisons between every pair of treatments, and multiple comparisons to all (MCA), are statistically conservative. Hsu’s multiple comparisons with the best (MCB) – adapted from the Dunnett’s multiple comparisons with control (MCC) – has been used for selecting the best treatment based on continuous variables. We selected the best hormonal replacement modality for successful multiple organ procurement using a two-step approach. First, we estimated the predicted margins by constructing generalized linear models (GLM) or generalized linear mixed models (GLMM), and then we applied the multiple comparison methods to identify the best hormonal replacement modality given that the testing of hormonal replacement modalities is independent. Based on 10-year data from the United Network for Organ Sharing (UNOS), among 16 hormonal replacement modalities, and using the 95% simultaneous confidence intervals, we found that the combination of thyroid hormone, a corticosteroid, antidiuretic hormone, and insulin was the best modality for multiple organ procurement for transplantation. PMID:25565890

Associations of blood lead, cadmium, and mercury with estimated glomerular filtration rate in the Korean general population: Analysis of 2008-2010 Korean National Health and Nutrition Examination Survey data

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

Kim, Yangho; Lee, Byung-Kook, E-mail: bklee@sch.ac.kr

Introduction: The objective of this study was to evaluate associations between blood lead, cadmium, and mercury levels with estimated glomerular filtration rate in a general population of South Korean adults. Methods: This was a cross-sectional study based on data obtained in the Korean National Health and Nutrition Examination Survey (KNHANES) (2008-2010). The final analytical sample consisted of 5924 participants. Estimated glomerular filtration rate (eGFR) was calculated using the MDRD Study equation as an indicator of glomerular function. Results: In multiple linear regression analysis of log2-transformed blood lead as a continuous variable on eGFR, after adjusting for covariates including cadmium andmore » mercury, the difference in eGFR levels associated with doubling of blood lead were -2.624 mL/min per 1.73 m Superscript-Two (95% CI: -3.803 to -1.445). In multiple linear regression analysis using quartiles of blood lead as the independent variable, the difference in eGFR levels comparing participants in the highest versus the lowest quartiles of blood lead was -3.835 mL/min per 1.73 m Superscript-Two (95% CI: -5.730 to -1.939). In a multiple linear regression analysis using blood cadmium and mercury, as continuous or categorical variables, as independent variables, neither metal was a significant predictor of eGFR. Odds ratios (ORs) and 95% CI values for reduced eGFR calculated for log2-transformed blood metals and quartiles of the three metals showed similar trends after adjustment for covariates. Discussion: In this large, representative sample of South Korean adults, elevated blood lead level was consistently associated with lower eGFR levels and with the prevalence of reduced eGFR even in blood lead levels below 10 {mu}g/dL. In conclusion, elevated blood lead level was associated with lower eGFR in a Korean general population, supporting the role of lead as a risk factor for chronic kidney disease.« less

Cortical Contribution to Linear, Non-linear and Frequency Components of Motor Variability Control during Standing.

PubMed

König Ignasiak, Niklas; Habermacher, Lars; Taylor, William R; Singh, Navrag B

2017-01-01

Motor variability is an inherent feature of all human movements and reflects the quality of functional task performance. Depending on the requirements of the motor task, the human sensory-motor system is thought to be able to flexibly govern the appropriate level of variability. However, it remains unclear which neurophysiological structures are responsible for the control of motor variability. In this study, we tested the contribution of cortical cognitive resources on the control of motor variability (in this case postural sway) using a dual-task paradigm and furthermore observed potential changes in control strategy by evaluating Ia-afferent integration (H-reflex). Twenty healthy subjects were instructed to stand relaxed on a force plate with eyes open and closed, as well as while trying to minimize sway magnitude and performing a "subtracting-sevens" cognitive task. In total 25 linear and non-linear parameters were used to evaluate postural sway, which were combined using a Principal Components procedure. Neurophysiological response of Ia-afferent reflex loop was quantified using the Hoffman reflex. In order to assess the contribution of the H-reflex on the sway outcome in the different standing conditions multiple mixed-model ANCOVAs were performed. The results suggest that subjects were unable to further minimize their sway, despite actively focusing to do so. The dual-task had a destabilizing effect on PS, which could partly (by 4%) be counter-balanced by increasing reliance on Ia-afferent information. The effect of the dual-task was larger than the protective mechanism of increasing Ia-afferent information. We, therefore, conclude that cortical structures, as compared to peripheral reflex loops, play a dominant role in the control of motor variability.

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

PubMed Central

Kim, Seong-Gil

2018-01-01

Background The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. Material/Methods This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. Results In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). Conclusions Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement. PMID:29760375

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

PubMed

Kim, Seong-Gil; Kim, Wan-Soo

2018-05-15

BACKGROUND The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. MATERIAL AND METHODS This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. RESULTS In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). CONCLUSIONS Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement.

Langevin dynamics for vector variables driven by multiplicative white noise: A functional formalism

NASA Astrophysics Data System (ADS)

Moreno, Miguel Vera; Arenas, Zochil González; Barci, Daniel G.

2015-04-01

We discuss general multidimensional stochastic processes driven by a system of Langevin equations with multiplicative white noise. In particular, we address the problem of how time reversal diffusion processes are affected by the variety of conventions available to deal with stochastic integrals. We present a functional formalism to build up the generating functional of correlation functions without any type of discretization of the Langevin equations at any intermediate step. The generating functional is characterized by a functional integration over two sets of commuting variables, as well as Grassmann variables. In this representation, time reversal transformation became a linear transformation in the extended variables, simplifying in this way the complexity introduced by the mixture of prescriptions and the associated calculus rules. The stochastic calculus is codified in our formalism in the structure of the Grassmann algebra. We study some examples such as higher order derivative Langevin equations and the functional representation of the micromagnetic stochastic Landau-Lifshitz-Gilbert equation.

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections

NASA Astrophysics Data System (ADS)

Wakazuki, Y.

2015-12-01

A dynamical downscaling method for probabilistic regional scale climate change projections was developed to cover an uncertainty of multiple general circulation model (GCM) climate simulations. The climatological increments (future minus present climate states) estimated by GCM simulation results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and were added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding to an objective analysis data. This data handling is regarded to be an advanced method of the pseudo-global-warming (PGW) method previously developed by Kimura and Kitoh (2007). The incremental handling for GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. Three values of a climatological variable simulated by RCMs for a mode were used to estimate the response to the perturbation of the mode. For the probabilistic analysis, climatological variables of RCMs were assumed to show linear response to the multiple modal perturbations, although the non-linearity was seen for local scale rainfall. Probability of temperature was able to be estimated within two modes perturbation simulations, where the number of RCM simulations for the future climate is five. On the other hand, local scale rainfalls needed four modes simulations, where the number of the RCM simulations is nine. The probabilistic method is expected to be used for regional scale climate change impact assessment in the future.

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

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

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

TiO2 dye sensitized solar cell (DSSC): linear relationship of maximum power point and anthocyanin concentration

NASA Astrophysics Data System (ADS)

Ahmadian, Radin

2010-09-01

This study investigated the relationship of anthocyanin concentration from different organic fruit species and output voltage and current in a TiO2 dye-sensitized solar cell (DSSC) and hypothesized that fruits with greater anthocyanin concentration produce higher maximum power point (MPP) which would lead to higher current and voltage. Anthocyanin dye solution was made with crushing of a group of fresh fruits with different anthocyanin content in 2 mL of de-ionized water and filtration. Using these test fruit dyes, multiple DSSCs were assembled such that light enters through the TiO2 side of the cell. The full current-voltage (I-V) co-variations were measured using a 500 Ω potentiometer as a variable load. Point-by point current and voltage data pairs were measured at various incremental resistance values. The maximum power point (MPP) generated by the solar cell was defined as a dependent variable and the anthocyanin concentration in the fruit used in the DSSC as the independent variable. A regression model was used to investigate the linear relationship between study variables. Regression analysis showed a significant linear relationship between MPP and anthocyanin concentration with a p-value of 0.007. Fruits like blueberry and black raspberry with the highest anthocyanin content generated higher MPP. In a DSSC, a linear model may predict MPP based on the anthocyanin concentration. This model is the first step to find organic anthocyanin sources in the nature with the highest dye concentration to generate energy.

Climate change but not unemployment explains the changing suicidality in Thessaloniki Greece (2000-2012).

PubMed

Fountoulakis, Konstantinos N; Savopoulos, Christos; Zannis, Prodromos; Apostolopoulou, Martha; Fountoukidis, Ilias; Kakaletsis, Nikolaos; Kanellos, Ilias; Dimellis, Dimos; Hyphantis, Thomas; Tsikerdekis, Athanasios; Pompili, Maurizio; Hatzitolios, Apostolos I

2016-03-15

Recently there was a debate concerning the etiology behind attempts and completed suicides. The aim of the current study was to search for possible correlations between the rates of attempted and completed suicide and climate variables and regional unemployment per year in the county of Thessaloniki, Macedonia, northern Greece, for the years 2000-12. The regional rates of suicide and attempted suicide as well as regional unemployment were available from previous publications of the authors. The climate variables were calculated from the daily E-OBS gridded dataset which is based on observational data Only the male suicide rates correlate significantly with high mean annual temperature but not with unemployment. The multiple linear regression analysis results suggest that temperature is the only variable that determines male suicides and explains 51% of their variance. Unemployment fails to contribute significantly to the model. There seems to be a seasonal distribution for attempts with mean rates being higher for the period from May to October and the rates clearly correlate with temperature. The highest mean rates were observed during May and August and the lowest during December and February. Multiple linear regression analysis suggests that temperature also determines the female attempts rate although the explained variable is significant but very low (3-5%) Climate variables and specifically high temperature correlate both with suicide and attempted suicide rates but with a different way between males and females. The climate effect was stronger than the effect of unemployment. Copyright © 2016 Elsevier B.V. All rights reserved.

What is the relationship between renal function and visit-to-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data.

PubMed

Lasserson, Daniel S; Scherpbier de Haan, Nynke; de Grauw, Wim; van der Wel, Mark; Wetzels, Jack F; O'Callaghan, Christopher A

2016-06-10

To determine the relationship between renal function and visit-to-visit blood pressure (BP) variability in a cohort of primary care patients. Retrospective cohort study from routinely collected healthcare data. Primary care in Nijmegen, the Netherlands, from 2007 to 2012. 19 175 patients who had a measure of renal function, and 7 separate visits with BP readings in the primary care record. Visit-to-visit variability in systolic BP, calculated from the first 7 office measurements, including SD, successive variation, absolute real variation and metrics of variability shown to be independent of mean. Multiple linear regression was used to analyse the influence of estimated glomerular filtration rate (eGFR) on BP variability measures with adjustment for age, sex, diabetes, mean BP, proteinuria, cardiovascular disease, time interval between measures and antihypertensive use. In the patient cohort, 57% were women, mean (SD) age was 65.5 (12.3) years, mean (SD) eGFR was 75.6 (18.0) mL/min/1.73m(2) and SD systolic BP 148.3 (21.4) mm Hg. All BP variability measures were negatively correlated with eGFR and positively correlated with age. However, multiple linear regressions demonstrated consistent, small magnitude negative relationships between eGFR and all measures of BP variability adjusting for confounding variables. Worsening renal function is associated with small increases in measures of visit-to-visit BP variability after adjustment for confounding factors. This is seen across the spectrum of renal function in the population, and provides a mechanism whereby chronic kidney disease may raise the risk of cardiovascular events. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Energy expenditure estimation during daily military routine with body-fixed sensors.

PubMed

Wyss, Thomas; Mäder, Urs

2011-05-01

The purpose of this study was to develop and validate an algorithm for estimating energy expenditure during the daily military routine on the basis of data collected using body-fixed sensors. First, 8 volunteers completed isolated physical activities according to an established protocol, and the resulting data were used to develop activity-class-specific multiple linear regressions for physical activity energy expenditure on the basis of hip acceleration, heart rate, and body mass as independent variables. Second, the validity of these linear regressions was tested during the daily military routine using indirect calorimetry (n = 12). Volunteers' mean estimated energy expenditure did not significantly differ from the energy expenditure measured with indirect calorimetry (p = 0.898, 95% confidence interval = -1.97 to 1.75 kJ/min). We conclude that the developed activity-class-specific multiple linear regressions applied to the acceleration and heart rate data allow estimation of energy expenditure in 1-minute intervals during daily military routine, with accuracy equal to indirect calorimetry.

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models.

PubMed

Wilson, Machelle D; Sethi, Sunjay; Lein, Pamela J; Keil, Kimberly P

2017-03-01

The Sholl technique is widely used to quantify dendritic morphology. Data from such studies, which typically sample multiple neurons per animal, are often analyzed using simple linear models. However, simple linear models fail to account for intra-class correlation that occurs with clustered data, which can lead to faulty inferences. Mixed effects models account for intra-class correlation that occurs with clustered data; thus, these models more accurately estimate the standard deviation of the parameter estimate, which produces more accurate p-values. While mixed models are not new, their use in neuroscience has lagged behind their use in other disciplines. A review of the published literature illustrates common mistakes in analyses of Sholl data. Analysis of Sholl data collected from Golgi-stained pyramidal neurons in the hippocampus of male and female mice using both simple linear and mixed effects models demonstrates that the p-values and standard deviations obtained using the simple linear models are biased downwards and lead to erroneous rejection of the null hypothesis in some analyses. The mixed effects approach more accurately models the true variability in the data set, which leads to correct inference. Mixed effects models avoid faulty inference in Sholl analysis of data sampled from multiple neurons per animal by accounting for intra-class correlation. Given the widespread practice in neuroscience of obtaining multiple measurements per subject, there is a critical need to apply mixed effects models more widely. Copyright © 2017 Elsevier B.V. All rights reserved.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

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.

Evaluating Feynman integrals by the hypergeometry

NASA Astrophysics Data System (ADS)

Feng, Tai-Fu; Chang, Chao-Hsi; Chen, Jian-Bin; Gu, Zhi-Hua; Zhang, Hai-Bin

2018-02-01

The hypergeometric function method naturally provides the analytic expressions of scalar integrals from concerned Feynman diagrams in some connected regions of independent kinematic variables, also presents the systems of homogeneous linear partial differential equations satisfied by the corresponding scalar integrals. Taking examples of the one-loop B0 and massless C0 functions, as well as the scalar integrals of two-loop vacuum and sunset diagrams, we verify our expressions coinciding with the well-known results of literatures. Based on the multiple hypergeometric functions of independent kinematic variables, the systems of homogeneous linear partial differential equations satisfied by the mentioned scalar integrals are established. Using the calculus of variations, one recognizes the system of linear partial differential equations as stationary conditions of a functional under some given restrictions, which is the cornerstone to perform the continuation of the scalar integrals to whole kinematic domains numerically with the finite element methods. In principle this method can be used to evaluate the scalar integrals of any Feynman diagrams.

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

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

2018-01-01

Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

PubMed

Van Looy, Stijn; Verplancke, Thierry; Benoit, Dominique; Hoste, Eric; Van Maele, Georges; De Turck, Filip; Decruyenaere, Johan

2007-01-01

Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR. Prediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.

Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Treesearch

Becky K. Kerns; Janet L. Ohmann

2004-01-01

We used data from regional forest inventories and research programs, coupled with mapped climatic and topographic information, to explore relationships and develop multiple linear regression (MLR) and regression tree models for total and deciduous shrub cover in the Oregon coastal province. Results from both types of models indicate that forest structure variables were...

Multiple linear regression analysis

NASA Technical Reports Server (NTRS)

Edwards, T. R.

1980-01-01

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

Ecological and Topographic Features of Volcanic Ash-Influenced Forest Soils

Treesearch

Mark Kimsey; Brian Gardner; Alan Busacca

2007-01-01

Volcanic ash distribution and thickness were determined for a forested region of north-central Idaho. Mean ash thickness and multiple linear regression analyses were used to model the effect of environmental variables on ash thickness. Slope and slope curvature relationships with volcanic ash thickness varied on a local spatial scale across the study area. Ash...

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India

PubMed Central

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K.

2013-01-01

Background: Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Materials and Methods: Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. Results: CBRs were inversely related to literacy rates (slope parameter = −0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = −0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = −1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = −0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Conclusion: Female literacy is relatively highly important for both population stabilization and better infant health. PMID:26664840

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images

NASA Astrophysics Data System (ADS)

Fernández-Manso, O.; Fernández-Manso, A.; Quintano, C.

2014-09-01

Aboveground biomass (AGB) estimation from optical satellite data is usually based on regression models of original or synthetic bands. To overcome the poor relation between AGB and spectral bands due to mixed-pixels when a medium spatial resolution sensor is considered, we propose to base the AGB estimation on fraction images from Linear Spectral Mixture Analysis (LSMA). Our study area is a managed Mediterranean pine woodland (Pinus pinaster Ait.) in central Spain. A total of 1033 circular field plots were used to estimate AGB from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) optical data. We applied Pearson correlation statistics and stepwise multiple regression to identify suitable predictors from the set of variables of original bands, fraction imagery, Normalized Difference Vegetation Index and Tasselled Cap components. Four linear models and one nonlinear model were tested. A linear combination of ASTER band 2 (red, 0.630-0.690 μm), band 8 (short wave infrared 5, 2.295-2.365 μm) and green vegetation fraction (from LSMA) was the best AGB predictor (Radj2=0.632, the root-mean-squared error of estimated AGB was 13.3 Mg ha-1 (or 37.7%), resulting from cross-validation), rather than other combinations of the above cited independent variables. Results indicated that using ASTER fraction images in regression models improves the AGB estimation in Mediterranean pine forests. The spatial distribution of the estimated AGB, based on a multiple linear regression model, may be used as baseline information for forest managers in future studies, such as quantifying the regional carbon budget, fuel accumulation or monitoring of management practices.

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Meteorological Contribution to Variability in Particulate Matter Concentrations

NASA Astrophysics Data System (ADS)

Woods, H. L.; Spak, S. N.; Holloway, T.

2006-12-01

Local concentrations of fine particulate matter (PM) are driven by a number of processes, including emissions of aerosols and gaseous precursors, atmospheric chemistry, and meteorology at local, regional, and global scales. We apply statistical downscaling methods, typically used for regional climate analysis, to estimate the contribution of regional scale meteorology to PM mass concentration variability at a range of sites in the Upper Midwestern U.S. Multiple years of daily PM10 and PM2.5 data, reported by the U.S. Environmental Protection Agency (EPA), are correlated with large-scale meteorology over the region from the National Centers for Environmental Prediction (NCEP) reanalysis data. We use two statistical downscaling methods (multiple linear regression, MLR, and analog) to identify which processes have the greatest impact on aerosol concentration variability. Empirical Orthogonal Functions of the NCEP meteorological data are correlated with PM timeseries at measurement sites. We examine which meteorological variables exert the greatest influence on PM variability, and which sites exhibit the greatest response to regional meteorology. To evaluate model performance, measurement data are withheld for limited periods, and compared with model results. Preliminary results suggest that regional meteorological processes account over 50% of aerosol concentration variability at study sites.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Technical Reports Server (NTRS)

Banse, Karl; Yong, Marina

1990-01-01

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Long-term forecasting of internet backbone traffic.

PubMed

Papagiannaki, Konstantina; Taft, Nina; Zhang, Zhi-Li; Diot, Christophe

2005-09-01

We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.

An Application of Robust Method in Multiple Linear Regression Model toward Credit Card Debt

NASA Astrophysics Data System (ADS)

Amira Azmi, Nur; Saifullah Rusiman, Mohd; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Salleh, Rohayu Mohd; Hamzah, Nur Shamsidah Amir

2018-04-01

Credit card is a convenient alternative replaced cash or cheque, and it is essential component for electronic and internet commerce. In this study, the researchers attempt to determine the relationship and significance variables between credit card debt and demographic variables such as age, household income, education level, years with current employer, years at current address, debt to income ratio and other debt. The provided data covers 850 customers information. There are three methods that applied to the credit card debt data which are multiple linear regression (MLR) models, MLR models with least quartile difference (LQD) method and MLR models with mean absolute deviation method. After comparing among three methods, it is found that MLR model with LQD method became the best model with the lowest value of mean square error (MSE). According to the final model, it shows that the years with current employer, years at current address, household income in thousands and debt to income ratio are positively associated with the amount of credit debt. Meanwhile variables for age, level of education and other debt are negatively associated with amount of credit debt. This study may serve as a reference for the bank company by using robust methods, so that they could better understand their options and choice that is best aligned with their goals for inference regarding to the credit card debt.

Fasting insulin levels and metabolic risk factors in type 2 diabetic patients at the first visit in Japan: a 10-year, nationwide, observational study (JDDM 28).

PubMed

Matsuba, Ikuro; Saito, Kazumi; Takai, Masahiko; Hirao, Koichi; Sone, Hirohito

2012-09-01

To investigate the relationship between fasting insulin levels and metabolic risk factors (MRFs) in type 2 diabetic patients at the first clinic/hospital visit in Japan over the years 2000 to 2009. In total, 4,798 drug-naive Japanese patients with type 2 diabetes were registered on their first clinic/hospital visits. Conventional clinical factors and fasting insulin levels were observed at baseline within the Japan Diabetes Clinical Data Management (JDDM) study between consecutive 2-year groups. Multiple linear regression analysis was performed using a model in which the dependent variable was fasting insulin values using various clinical explanatory variables. Fasting insulin levels were found to be decreasing from 2000 to 2009. Multiple linear regression analysis with the fasting insulin levels as the dependent variable showed that waist circumference (WC), BMI, mean blood pressure, triglycerides, and HDL cholesterol were significant, with WC and BMI as the main factors. ANCOVA after adjustment for age and fasting plasma glucose clearly shows the decreasing trend in fasting insulin levels and the increasing trend in BMI. During the 10-year observation period, the decreasing trend in fasting insulin was related to the slight increase in WC/BMI in type 2 diabetes. Low pancreatic β-cell reserve on top of a lifestyle background might be dependent on an increase in MRFs.

Fasting Insulin Levels and Metabolic Risk Factors in Type 2 Diabetic Patients at the First Visit in Japan

PubMed Central

Matsuba, Ikuro; Saito, Kazumi; Takai, Masahiko; Hirao, Koichi; Sone, Hirohito

2012-01-01

OBJECTIVE To investigate the relationship between fasting insulin levels and metabolic risk factors (MRFs) in type 2 diabetic patients at the first clinic/hospital visit in Japan over the years 2000 to 2009. RESEARCH DESIGN AND METHODS In total, 4,798 drug-naive Japanese patients with type 2 diabetes were registered on their first clinic/hospital visits. Conventional clinical factors and fasting insulin levels were observed at baseline within the Japan Diabetes Clinical Data Management (JDDM) study between consecutive 2-year groups. Multiple linear regression analysis was performed using a model in which the dependent variable was fasting insulin values using various clinical explanatory variables. RESULTS Fasting insulin levels were found to be decreasing from 2000 to 2009. Multiple linear regression analysis with the fasting insulin levels as the dependent variable showed that waist circumference (WC), BMI, mean blood pressure, triglycerides, and HDL cholesterol were significant, with WC and BMI as the main factors. ANCOVA after adjustment for age and fasting plasma glucose clearly shows the decreasing trend in fasting insulin levels and the increasing trend in BMI. CONCLUSIONS During the 10-year observation period, the decreasing trend in fasting insulin was related to the slight increase in WC/BMI in type 2 diabetes. Low pancreatic β-cell reserve on top of a lifestyle background might be dependent on an increase in MRFs. PMID:22665215

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

PubMed

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

2017-04-01

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

Examining the Influence of Selected Factors on Perceived Co-Op Work-Term Quality from a Student Perspective

ERIC Educational Resources Information Center

Drewery, David; Nevison, Colleen; Pretti, T. Judene; Cormier, Lauren; Barclay, Sage; Pennaforte, Antoine

2016-01-01

This study discusses and tests a conceptual model of co-op work-term quality from a student perspective. Drawing from an earlier exploration of co-op students' perceptions of work-term quality, variables related to role characteristics, interpersonal dynamics, and organizational elements were used in a multiple linear regression analysis to…

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

NASA Technical Reports Server (NTRS)

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.

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

The prediction of intelligence in preschool children using alternative models to regression.

PubMed

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Longitudinal change in physical activity and its correlates in relapsing-remitting multiple sclerosis.

PubMed

Motl, Robert W; McAuley, Edward; Sandroff, Brian M

2013-08-01

Physical activity is beneficial for people with multiple sclerosis (MS), but this population is largely inactive. There is minimal information on change in physical activity and its correlates for informing the development of behavioral interventions. This study examined change in physical activity and its symptomatic, social-cognitive, and ambulatory or disability correlates over a 2.5-year period of time in people with relapsing-remitting multiple sclerosis. On 6 occasions, each separated by 6 months, people (N=269) with relapsing-remitting multiple sclerosis completed assessments of symptoms, self-efficacy, walking impairment, disability, and physical activity. The participants wore an accelerometer for 7 days. The change in study variables over 6 time points was examined with unconditional latent growth curve modeling. The association among changes in study variables over time was examined using conditional latent growth curve modeling, and the associations were expressed as standardized path coefficients (β). There were significant linear changes in self-reported and objectively measured physical activity, self-efficacy, walking impairment, and disability over the 2.5-year period; there were no changes in fatigue, depression, and pain. The changes in self-reported and objective physical activity were associated with change in self-efficacy (β=.49 and β=.61, respectively), after controlling for other variables and confounders. The primary limitations of the study were the generalizability of results among those with progressive multiple sclerosis and inclusion of a single variable from social-cognitive theory. Researchers should consider designing interventions that target self-efficacy for the promotion and maintenance of physical activity in this population.

A model for the flux-r.m.s. correlation in blazar variability or the minijets-in-a-jet statistical model

NASA Astrophysics Data System (ADS)

Biteau, J.; Giebels, B.

2012-12-01

Very high energy gamma-ray variability of blazar emission remains of puzzling origin. Fast flux variations down to the minute time scale, as observed with H.E.S.S. during flares of the blazar PKS 2155-304, suggests that variability originates from the jet, where Doppler boosting can be invoked to relax causal constraints on the size of the emission region. The observation of log-normality in the flux distributions should rule out additive processes, such as those resulting from uncorrelated multiple-zone emission models, and favour an origin of the variability from multiplicative processes not unlike those observed in a broad class of accreting systems. We show, using a simple kinematic model, that Doppler boosting of randomly oriented emitting regions generates flux distributions following a Pareto law, that the linear flux-r.m.s. relation found for a single zone holds for a large number of emitting regions, and that the skewed distribution of the total flux is close to a log-normal, despite arising from an additive process.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

A General Accelerated Degradation Model Based on the Wiener Process.

PubMed

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-12-06

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.

A General Accelerated Degradation Model Based on the Wiener Process

PubMed Central

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-01-01

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses. PMID:28774107

Tornado outbreak variability follows Taylor's power law of fluctuation scaling and increases dramatically with severity.

PubMed

Tippett, Michael K; Cohen, Joel E

2016-02-29

Tornadoes cause loss of life and damage to property each year in the United States and around the world. The largest impacts come from 'outbreaks' consisting of multiple tornadoes closely spaced in time. Here we find an upward trend in the annual mean number of tornadoes per US tornado outbreak for the period 1954-2014. Moreover, the variance of this quantity is increasing more than four times as fast as the mean. The mean and variance of the number of tornadoes per outbreak vary according to Taylor's power law of fluctuation scaling (TL), with parameters that are consistent with multiplicative growth. Tornado-related atmospheric proxies show similar power-law scaling and multiplicative growth. Path-length-integrated tornado outbreak intensity also follows TL, but with parameters consistent with sampling variability. The observed TL power-law scaling of outbreak severity means that extreme outbreaks are more frequent than would be expected if mean and variance were independent or linearly related.

Tornado outbreak variability follows Taylor's power law of fluctuation scaling and increases dramatically with severity

PubMed Central

Tippett, Michael K.; Cohen, Joel E.

2016-01-01

Tornadoes cause loss of life and damage to property each year in the United States and around the world. The largest impacts come from ‘outbreaks' consisting of multiple tornadoes closely spaced in time. Here we find an upward trend in the annual mean number of tornadoes per US tornado outbreak for the period 1954–2014. Moreover, the variance of this quantity is increasing more than four times as fast as the mean. The mean and variance of the number of tornadoes per outbreak vary according to Taylor's power law of fluctuation scaling (TL), with parameters that are consistent with multiplicative growth. Tornado-related atmospheric proxies show similar power-law scaling and multiplicative growth. Path-length-integrated tornado outbreak intensity also follows TL, but with parameters consistent with sampling variability. The observed TL power-law scaling of outbreak severity means that extreme outbreaks are more frequent than would be expected if mean and variance were independent or linearly related. PMID:26923210

Tornado outbreak variability follows Taylor's power law of fluctuation scaling and increases dramatically with severity

NASA Astrophysics Data System (ADS)

Tippett, Michael K.; Cohen, Joel E.

2016-02-01

Tornadoes cause loss of life and damage to property each year in the United States and around the world. The largest impacts come from `outbreaks' consisting of multiple tornadoes closely spaced in time. Here we find an upward trend in the annual mean number of tornadoes per US tornado outbreak for the period 1954-2014. Moreover, the variance of this quantity is increasing more than four times as fast as the mean. The mean and variance of the number of tornadoes per outbreak vary according to Taylor's power law of fluctuation scaling (TL), with parameters that are consistent with multiplicative growth. Tornado-related atmospheric proxies show similar power-law scaling and multiplicative growth. Path-length-integrated tornado outbreak intensity also follows TL, but with parameters consistent with sampling variability. The observed TL power-law scaling of outbreak severity means that extreme outbreaks are more frequent than would be expected if mean and variance were independent or linearly related.

Log-normal distribution from a process that is not multiplicative but is additive.

PubMed

Mouri, Hideaki

2013-10-01

The central limit theorem ensures that a sum of random variables tends to a Gaussian distribution as their total number tends to infinity. However, for a class of positive random variables, we find that the sum tends faster to a log-normal distribution. Although the sum tends eventually to a Gaussian distribution, the distribution of the sum is always close to a log-normal distribution rather than to any Gaussian distribution if the summands are numerous enough. This is in contrast to the current consensus that any log-normal distribution is due to a product of random variables, i.e., a multiplicative process, or equivalently to nonlinearity of the system. In fact, the log-normal distribution is also observable for a sum, i.e., an additive process that is typical of linear systems. We show conditions for such a sum, an analytical example, and an application to random scalar fields such as those of turbulence.

Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period

PubMed Central

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

Abstract Objective: To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. Methods: This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95%, was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α <5%. Results: From 226 women included, 200 (88.5%) were 20-44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. Conclusions: This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. PMID:26100593

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

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

The authors report the results of an investigation into the possible association between air pollution and infant mortality from pneumonia in the Rio de Janeiro Metropolitan Area. This investigation employed multiple linear regression analysis (stepwise method) for infant mortality from pneumonia in 1980, including the study population's areas of residence, incomes, and pollution exposure as independent variables. With the income variable included in the regression, a statistically significant association was observed between the average annual level of particulates and infant mortality from pneumonia. While this finding should be accepted with caution, it does suggest a biological association between these variables.more » The authors' conclusion is that air quality indicators should be included in studies of acute respiratory infections in developing countries.« less

The isoform A of reticulon-4 (Nogo-A) in cerebrospinal fluid of primary brain tumor patients: influencing factors.

PubMed

Koper, Olga Martyna; Kamińska, Joanna; Milewska, Anna; Sawicki, Karol; Mariak, Zenon; Kemona, Halina; Matowicka-Karna, Joanna

2018-05-18

The influence of isoform A of reticulon-4 (Nogo-A), also known as neurite outgrowth inhibitor, on primary brain tumor development was reported. Therefore the aim was the evaluation of Nogo-A concentrations in cerebrospinal fluid (CSF) and serum of brain tumor patients compared with non-tumoral individuals. All serum results, except for two cases, obtained both in brain tumors and non-tumoral individuals, were below the lower limit of ELISA detection. Cerebrospinal fluid Nogo-A concentrations were significantly lower in primary brain tumor patients compared to non-tumoral individuals. The univariate linear regression analysis found that if white blood cell count increases by 1 × 10 3 /μL, the mean cerebrospinal fluid Nogo-A concentration value decreases 1.12 times. In the model of multiple linear regression analysis predictor variables influencing cerebrospinal fluid Nogo-A concentrations included: diagnosis, sex, and sodium level. The mean cerebrospinal fluid Nogo-A concentration value was 1.9 times higher for women in comparison to men. In the astrocytic brain tumor group higher sodium level occurs with lower cerebrospinal fluid Nogo-A concentrations. We found the opposite situation in non-tumoral individuals. Univariate linear regression analysis revealed, that cerebrospinal fluid Nogo-A concentrations change in relation to white blood cell count. In the created model of multiple linear regression analysis we found, that within predictor variables influencing CSF Nogo-A concentrations were diagnosis, sex, and sodium level. Results may be relevant to the search for cerebrospinal fluid biomarkers and potential therapeutic targets in primary brain tumor patients. Nogo-A concentrations were tested by means of enzyme-linked immunosorbent assay (ELISA).

Does weather shape rodents? Climate related changes in morphology of two heteromyid species

NASA Astrophysics Data System (ADS)

Wolf, Mosheh; Friggens, Michael; Salazar-Bravo, Jorge

2009-01-01

Geographical variation in morphometric characters in heteromyid rodents has often correlated with climate gradients. Here, we used the long-term database of rodents trapped in the Sevilleta National Wildlife Refuge in New Mexico, USA to test whether significant annual changes in external morphometric characters are observed in a region with large variations in temperature and precipitation. We looked at the relationships between multiple temperature and precipitation variables and a number of morphological traits (body mass, body, tail, hind leg, and ear length) for two heteromyid rodents, Dipodomys merriami and Perognathus flavescens. Because these rodents can live multiple years in the wild, the climate variables for the year of the capture and the previous 2 years were included in the analyses. Using multiple linear regressions, we found that all of our morphometric traits, with the exception of tail length in D. merriami, had a significant relationship with one or more of the climate variables used. Our results demonstrate that effects of climate change on morphological traits occur over short periods, even in noninsular mammal populations. It is unclear, though, whether these changes are the result of morphological plasticity or natural selection.

Compact characterization of liquid absorption and emission spectra using linear variable filters integrated with a CMOS imaging camera.

PubMed

Wan, Yuhang; Carlson, John A; Kesler, Benjamin A; Peng, Wang; Su, Patrick; Al-Mulla, Saoud A; Lim, Sung Jun; Smith, Andrew M; Dallesasse, John M; Cunningham, Brian T

2016-07-08

A compact analysis platform for detecting liquid absorption and emission spectra using a set of optical linear variable filters atop a CMOS image sensor is presented. The working spectral range of the analysis platform can be extended without a reduction in spectral resolution by utilizing multiple linear variable filters with different wavelength ranges on the same CMOS sensor. With optical setup reconfiguration, its capability to measure both absorption and fluorescence emission is demonstrated. Quantitative detection of fluorescence emission down to 0.28 nM for quantum dot dispersions and 32 ng/mL for near-infrared dyes has been demonstrated on a single platform over a wide spectral range, as well as an absorption-based water quality test, showing the versatility of the system across liquid solutions for different emission and absorption bands. Comparison with a commercially available portable spectrometer and an optical spectrum analyzer shows our system has an improved signal-to-noise ratio and acceptable spectral resolution for discrimination of emission spectra, and characterization of colored liquid's absorption characteristics generated by common biomolecular assays. This simple, compact, and versatile analysis platform demonstrates a path towards an integrated optical device that can be utilized for a wide variety of applications in point-of-use testing and point-of-care diagnostics.

Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

NASA Astrophysics Data System (ADS)

Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

2016-12-01

The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

A Novel Joint Problem of Routing, Scheduling, and Variable-Width Channel Allocation in WMNs

PubMed Central

Liu, Wan-Yu; Chou, Chun-Hung

2014-01-01

This paper investigates a novel joint problem of routing, scheduling, and channel allocation for single-radio multichannel wireless mesh networks in which multiple channel widths can be adjusted dynamically through a new software technology so that more concurrent transmissions and suppressed overlapping channel interference can be achieved. Although the previous works have studied this joint problem, their linear programming models for the problem were not incorporated with some delicate constraints. As a result, this paper first constructs a linear programming model with more practical concerns and then proposes a simulated annealing approach with a novel encoding mechanism, in which the configurations of multiple time slots are devised to characterize the dynamic transmission process. Experimental results show that our approach can find the same or similar solutions as the optimal solutions for smaller-scale problems and can efficiently find good-quality solutions for a variety of larger-scale problems. PMID:24982990

Identity effects dominate the impacts of multiple species extinctions on the functioning of complex food webs.

PubMed

Harvey, Eric; Séguin, Annie; Nozais, Christian; Archambault, Philippe; Gravel, Dominique

2013-01-01

Understanding the impacts of species extinctions on the functioning of food webs is a challenging task because of the complexity of ecological interactions. We report the impacts of experimental species extinctions on the functioning of two food webs of freshwater and marine systems. We used a linear model to partition the variance among the multiple components of the diversity effect (linear group richness, nonlinear group richness, and identity). The identity of each functional group was the best explaining variable of ecosystem functioning for both systems. We assessed the contribution of each functional group in multifunctional space and found that, although the effect of functional group varied across ecosystem functions, some functional groups shared common effects on functions. This study is the first experimental demonstration that functional identity dominates the effects of extinctions on ecosystem functioning, suggesting that generalizations are possible despite the inherent complexity of interactions.

Multiple causes of nonstationarity in the Weihe annual low-flow series

NASA Astrophysics Data System (ADS)

Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong-Yu; Li, Lingqi

2018-02-01

Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data

Treesearch

Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan

2006-01-01

We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived...

Parallel Implicit Runge-Kutta Methods Applied to Coupled Orbit/Attitude Propagation

NASA Astrophysics Data System (ADS)

Hatten, Noble; Russell, Ryan P.

2017-12-01

A variable-step Gauss-Legendre implicit Runge-Kutta (GLIRK) propagator is applied to coupled orbit/attitude propagation. Concepts previously shown to improve efficiency in 3DOF propagation are modified and extended to the 6DOF problem, including the use of variable-fidelity dynamics models. The impact of computing the stage dynamics of a single step in parallel is examined using up to 23 threads and 22 associated GLIRK stages; one thread is reserved for an extra dynamics function evaluation used in the estimation of the local truncation error. Efficiency is found to peak for typical examples when using approximately 8 to 12 stages for both serial and parallel implementations. Accuracy and efficiency compare favorably to explicit Runge-Kutta and linear-multistep solvers for representative scenarios. However, linear-multistep methods are found to be more efficient for some applications, particularly in a serial computing environment, or when parallelism can be applied across multiple trajectories.

Intrauterine Linear Echogenicities in the Gravid Uterus: What Radiologists Should Know.

PubMed

Jensen, Kyle K; Oh, Karen Y; Kennedy, Anne M; Sohaey, Roya

2018-01-01

Intrauterine linear echogenicity (ILE) is a common ultrasonographic finding in the gravid uterus and has variable causes and variable maternal and fetal outcomes. Correctly categorizing ILE during pregnancy is crucial for guiding surveillance and advanced imaging strategies. Common causes of ILE include membranes in multiple gestations, uterine synechiae with amniotic sheets, and uterine duplication anomalies. Less common causes include circumvallate placenta, chorioamniotic separation, and hemorrhage between membranes. Amniotic band syndrome is a rare but important diagnosis to consider, as it causes severe fetal defects. Imaging findings enable body stalk anomaly, a lethal defect, to be distinguished from amniotic bands, which although destructive are not necessarily lethal. This review describes the key imaging findings used to differentiate the various types of ILE in pregnancy, thus enabling accurate diagnosis and appropriate patient counseling. Online supplemental material is available for this article. © RSNA, 2018.

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

A composite measure to explore visual disability in primary progressive multiple sclerosis.

PubMed

Poretto, Valentina; Petracca, Maria; Saiote, Catarina; Mormina, Enricomaria; Howard, Jonathan; Miller, Aaron; Lublin, Fred D; Inglese, Matilde

2017-01-01

Optical coherence tomography (OCT) and magnetic resonance imaging (MRI) can provide complementary information on visual system damage in multiple sclerosis (MS). The objective of this paper is to determine whether a composite OCT/MRI score, reflecting cumulative damage along the entire visual pathway, can predict visual deficits in primary progressive multiple sclerosis (PPMS). Twenty-five PPMS patients and 20 age-matched controls underwent neuro-ophthalmologic evaluation, spectral-domain OCT, and 3T brain MRI. Differences between groups were assessed by univariate general linear model and principal component analysis (PCA) grouped instrumental variables into main components. Linear regression analysis was used to assess the relationship between low-contrast visual acuity (LCVA), OCT/MRI-derived metrics and PCA-derived composite scores. PCA identified four main components explaining 80.69% of data variance. Considering each variable independently, LCVA 1.25% was significantly predicted by ganglion cell-inner plexiform layer (GCIPL) thickness, thalamic volume and optic radiation (OR) lesion volume (adjusted R 2 0.328, p  = 0.00004; adjusted R 2 0.187, p  = 0.002 and adjusted R 2 0.180, p  = 0.002). The PCA composite score of global visual pathway damage independently predicted both LCVA 1.25% (adjusted R 2 value 0.361, p  = 0.00001) and LCVA 2.50% (adjusted R 2 value 0.323, p  = 0.00003). A multiparametric score represents a more comprehensive and effective tool to explain visual disability than a single instrumental metric in PPMS.

Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

PubMed

Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

2006-06-01

This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

Multi-Constraint Multi-Variable Optimization of Source-Driven Nuclear Systems

NASA Astrophysics Data System (ADS)

Watkins, Edward Francis

1995-01-01

A novel approach to the search for optimal designs of source-driven nuclear systems is investigated. Such systems include radiation shields, fusion reactor blankets and various neutron spectrum-shaping assemblies. The novel approach involves the replacement of the steepest-descents optimization algorithm incorporated in the code SWAN by a significantly more general and efficient sequential quadratic programming optimization algorithm provided by the code NPSOL. The resulting SWAN/NPSOL code system can be applied to more general, multi-variable, multi-constraint shield optimization problems. The constraints it accounts for may include simple bounds on variables, linear constraints, and smooth nonlinear constraints. It may also be applied to unconstrained, bound-constrained and linearly constrained optimization. The shield optimization capabilities of the SWAN/NPSOL code system is tested and verified in a variety of optimization problems: dose minimization at constant cost, cost minimization at constant dose, and multiple-nonlinear constraint optimization. The replacement of the optimization part of SWAN with NPSOL is found feasible and leads to a very substantial improvement in the complexity of optimization problems which can be efficiently handled.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Linear versus non-linear measures of temporal variability in finger tapping and their relation to performance on open- versus closed-loop motor tasks: comparing standard deviations to Lyapunov exponents.

PubMed

Christman, Stephen D; Weaver, Ryan

2008-05-01

The nature of temporal variability during speeded finger tapping was examined using linear (standard deviation) and non-linear (Lyapunov exponent) measures. Experiment 1 found that right hand tapping was characterised by lower amounts of both linear and non-linear measures of variability than left hand tapping, and that linear and non-linear measures of variability were often negatively correlated with one another. Experiment 2 found that increased non-linear variability was associated with relatively enhanced performance on a closed-loop motor task (mirror tracing) and relatively impaired performance on an open-loop motor task (pointing in a dark room), especially for left hand performance. The potential uses and significance of measures of non-linear variability are discussed.

Hierarchical multiple regression modelling on predictors of behavior and sexual practices at Takoradi Polytechnic, Ghana.

PubMed

Turkson, Anthony Joe; Otchey, James Eric

2015-01-14

Various psychosocial studies on health related lifestyles lay emphasis on the fact that the perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between eight independent variables and one dependent variable to isolate predictors which have significant influence on behavior and sexual practices. A Cross-sectional design was used for the study. Structured close-ended interviewer-administered questionnaire was used to collect primary data. Multistage stratified technique was used to sample views from 380 students from Takoradi Polytechnic, Ghana. A Hierarchical multiple regression model was used to ascertain the significance of certain predictors of sexual behavior and practices. The variables that were extracted from the multiple regression were; for the constant; Beta=14.202, t=2.279, p=0.023, variable is significant; for the marital status; Beta=0.092, t=1.996, p<0.05, variable is significant; for the knowledge on AIDs; Beta=0.090, t=1.996, p<0.05, variable is significant; for the attitude towards HIV/AIDs; =0.486, t=10.575, p<0.001, variable is highly significant. Thus, the best fitting model for predicting behavior and sexual practices was a linear combination of the constant, one's marital status, knowledge on HIV/AIDs and Attitude towards HIV/AIDs., Y(Behavior and sexual practies)= Beta0+Beta1(Marital status)+Beta2(Knowledge on HIV/AIDs issues)+Beta3(Attitude towards HIV/AIDs issues) Beta0, Beta1, Beta2 and Beta3 are respectively 14.201, 2.038, 0.148 and 0.486; the higher the better. Attitude and behavior change education on HIV/AIDs should be intensified in the institution so that students could adopt better lifestyles.

Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis

USGS Publications Warehouse

Granato, Gregory E.

2012-01-01

A nationwide study to better define triangular-hydrograph statistics for use with runoff-quality and flood-flow studies was done by the U.S. Geological Survey (USGS) in cooperation with the Federal Highway Administration. Although the triangular hydrograph is a simple linear approximation, the cumulative distribution of stormflow with a triangular hydrograph is a curvilinear S-curve that closely approximates the cumulative distribution of stormflows from measured data. The temporal distribution of flow within a runoff event can be estimated using the basin lagtime, (which is the time from the centroid of rainfall excess to the centroid of the corresponding runoff hydrograph) and the hydrograph recession ratio (which is the ratio of the duration of the falling limb to the rising limb of the hydrograph). This report documents results of the study, methods used to estimate the variables, and electronic files that facilitate calculation of variables. Ten viable multiple-linear regression equations were developed to estimate basin lagtimes from readily determined drainage basin properties using data published in 37 stormflow studies. Regression equations using the basin lag factor (BLF, which is a variable calculated as the main-channel length, in miles, divided by the square root of the main-channel slope in feet per mile) and two variables describing development in the drainage basin were selected as the best candidates, because each equation explains about 70 percent of the variability in the data. The variables describing development are the USGS basin development factor (BDF, which is a function of the amount of channel modifications, storm sewers, and curb-and-gutter streets in a basin) and the total impervious area variable (IMPERV) in the basin. Two datasets were used to develop regression equations. The primary dataset included data from 493 sites that have values for the BLF, BDF, and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and BDF variables. The secondary dataset included data from 896 sites that have values for the BLF and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and IMPERV variables. Analysis of hydrograph recession ratios and basin characteristics for 41 sites indicated that recession ratios are random variables. Thus, recession ratios cannot be estimated quantitatively using multiple linear regression equations developed using the data available for these sites. The minimums of recession ratios for different streamgages are well characterized by a value of one. The most probable values and maximum values of recession ratios for different streamgages are, however, more variable than the minimums. The most probable values of recession ratios for the 41 streamgages analyzed ranged from 1.0 to 3.52 and had a median of 1.85. The maximum values ranged from 2.66 to 11.3 and had a median of 4.36.

Relation of Environmental characteristics to the composition of aquatic assemblages along a gradient of urban land use in New Jersey, 1996-98

USGS Publications Warehouse

Kennen, Jonathan G.; Ayers, Mark A.

2002-01-01

Community data from 36 watersheds were used to evaluate the response of fish, invertebrate, and algal assemblages in New Jersey streams to environmental characteristics along a gradient of urban land use that ranged from 3 to 96 percent. Aquatic assemblages were sampled at 36 sites during 1996-98, and more than 400 environmental attributes at multiple spatial scales were summarized. Data matrices were reduced to 43, 170, and 103 species of fish, invertebrates, and algae, respectively, by means of a predetermined joint frequency and relative abundance approach. White sucker (Catostomus commersoni) and Tessellated darter (Etheostoma olmstedi) were the most abundant fishes, accounting for more than 20 and 17 percent, respectively, of the mean abundance. Net-spinning caddisflies (Hydropsychidae) were the most commonly occurring benthic invertebrates and were found at all but one of the 36 sampling sites. Blue-green (for example, Calothrix sp. and Oscillatoria sp.) and green (for example, Protoderma viride) algae were the most widely distrib-uted algae; however, more than 81 percent of the algal taxa collected were diatoms. Principal-component and correlation analyses were used to reduce the dimensionality of the environmental data. Multiple linear regression analysis of extracted ordination axes then was used to develop models that expressed effects of increasing urban land use on the structure of aquatic assemblages. Significant environmental variables identified by using multiple linear regression analysis then were included in a direct gradient analysis. Partial canonical correspondence analysis of relativized abundance data was used to restrict further the effects of residual natural variability, and to identify relations among the environmental variables and the structure of fish, invertebrate, and algal assemblages along an urban land-use gradient. Results of this approach, combined with the results of the multiple linear regression analyses, were used to identify human population density (311-37,594 persons/km2), amount and type of impervious surface cover (0.12-1,350 km2), nutrient concentrations (for example, 0.01-0.29 mg/L of phosphorus), hydrologic instability (for example, 100-8,955 ft3/s for 2-year peak flow), the amount of forest and wetlands in a basin (0.01-6.25 km2), and substrate quality (0-87 percent cobble substrate) as variables that are highly correlated with aquatic-assemblage structure. Species distributions in ordination space clearly indicate that tolerant species are more abundant in the streams impaired by urbanization and sensitive taxa are more closely associated with the least impaired basins. The distinct differences in aquatic assemblages along the urban land-use gradient demonstrate the deleterious effects of urbanization on assemblage structure and indicate that conserving landscape attributes that mitigate anthropogenic influences (for example, stormwater-management practices emphasizing infiltration and preservation of existing forests, wetlands, and riparian corridors) will help to maintain the relative abundance of sensitive taxa. Complementary multiple linear regression models indicate that aquatic community indices were correlated with many of the anthropogenic factors that were found to be significant along the urban land-use gradient. These indices appear to be effective in differentiating the moderately and severely impaired streams from the minimally impaired streams. Evaluation of disturbance thresholds for aquatic assemblages indicates that moderate to severe impairment is detectable in New Jersey streams when impervious surface cover in the drainage basin reaches approximately 18 percent.

Normality of Residuals Is a Continuous Variable, and Does Seem to Influence the Trustworthiness of Confidence Intervals: A Response to, and Appreciation of, Williams, Grajales, and Kurkiewicz (2013)

ERIC Educational Resources Information Center

Osborne, Jason W.

2013-01-01

Osborne and Waters (2002) focused on checking some of the assumptions of multiple linear regression. In a critique of that paper, Williams, Grajales, and Kurkiewicz correctly clarify that regression models estimated using ordinary least squares require the assumption of normally distributed errors, but not the assumption of normally distributed…

Catalog of Air Force Weather Technical Documents, 1941-2006

DTIC Science & Technology

2006-05-19

radiosondes in current use in USA. Elementary discussion of statistical terms and concepts used for expressing accuracy or error is discussed. AWS TR 105...Techniques, Appendix B: Vorticity—An Elementary Discussion of the Concept, August 1956, 27pp. Formerly AWSM 105– 50/1A. Provides the necessary back...steps involved in ordinary multiple linear regression. Conditional probability is calculated using transnormalized variables in the multivariate normal

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Serum Albumin and Disease Severity of Non-Cystic Fibrosis Bronchiectasis.

PubMed

Lee, Seung Jun; Kim, Hyo-Jung; Kim, Ju-Young; Ju, Sunmi; Lim, Sujin; Yoo, Jung Wan; Nam, Sung-Jin; Lee, Gi Dong; Cho, Hyun Seop; Kim, Rock Bum; Cho, Yu Ji; Jeong, Yi Yeong; Kim, Ho Cheol; Lee, Jong Deog

2017-08-01

A clinical classification system has been developed to define the severity and predict the prognosis of subjects with non-cystic fibrosis (CF) bronchiectasis. We aimed to identify laboratory parameters that are correlated with the bronchiectasis severity index (BSI) and FACED score. The medical records of 107 subjects with non-CF bronchiectasis for whom BSI and FACED scores could be calculated were retrospectively reviewed. The correlations between the laboratory parameters and BSI or FACED score were assessed, and multiple-linear regression analysis was performed to identify variables independently associated with BSI and FACED score. An additional subgroup analysis was performed according to sex. Among all of the enrolled subjects, 49 (45.8%) were male and 58 (54.2%) were female. The mean BSI and FACED scores were 9.43 ± 3.81 and 1.92 ± 1.59, respectively. The serum albumin level (r = -0.49), bilirubin level (r = -0.31), C-reactive protein level (r = 0.22), hemoglobin level (r = -0.2), and platelet/lymphocyte ratio (r = 0.31) were significantly correlated with BSI. Meanwhile, serum albumin (r = -0.37) and bilirubin level (r = -0.25) showed a significant correlation with the FACED score. Multiple-linear regression analysis showed that the serum bilirubin level was independently associated with BSI, and the serum albumin level was independently associated with both scoring systems. Subgroup analysis revealed that the level of uric acid was also a significant variable independently associated with the BSI in male bronchiectasis subjects. Several laboratory variables were identified as possible prognostic factors for non-CF bronchiectasis. Among them, the serum albumin level exhibited the strongest correlation and was identified as an independent variable associated with the BSI and FACED scores. Copyright © 2017 by Daedalus Enterprises.

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Neustadter, H. E.

1976-01-01

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

An Optimization Framework for Dynamic Hybrid Energy Systems

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

Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis

A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problemmore » takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.« less

Breakfast intake among adults with type 2 diabetes: is bigger better?

PubMed Central

Jarvandi, Soghra; Schootman, Mario; Racette, Susan B.

2015-01-01

Objective To assess the association between breakfast energy and total daily energy intake among individuals with type 2 diabetes. Design Cross-sectional study. Daily energy intake was computed from a 24-h dietary recall. Multiple regression models were used to estimate the association between daily energy intake (dependent variable) and quartiles of energy intake at breakfast (independent variable) expressed as either absolute or relative (% of total daily energy intake) terms. Orthogonal polynomial contrasts were used to test for linear and quadratic trends. Models were controlled for sex, age, race/ethnicity, body mass index, physical activity and smoking. In addition, we used separate multiple regression models to test the effect of quartiles of absolute and relative breakfast energy on intake at lunch, dinner, and snacks. Setting The 1999–2004 National Health and Nutrition Examination Survey (NHANES). Subjects Participants aged ≥ 30 years with self-reported history of diabetes (N = 1,146). Results Daily energy intake increased as absolute breakfast energy intake increased (linear trend, P < 0.0001; quadratic trend, P = 0.02), but decreased as relative breakfast energy intake increased (linear trend, P < 0.0001). In addition, while higher quartiles of absolute breakfast intake had no associations with energy intake at subsequent meals, higher quartiles of relative breakfast intake were associated with lower energy intake during all subsequent meals and snacks (P < 0.05). Conclusions Consuming a breakfast that provided less energy or comprised a greater proportion of daily energy intake was associated with lower total daily energy intake in adults with type 2 diabetes. PMID:25529061

[Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period].

PubMed

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado Junior, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95% was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α<5%. From 226 women included, 200 (88.5%) were 20 to 44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Simulation of non-linear acoustic field and thermal pattern of phased-array high-intensity focused ultrasound (HIFU).

PubMed

Wang, Mingjun; Zhou, Yufeng

2016-08-01

HIFU becomes an effective and non-invasive modality of solid tumour/cancer ablation. Simulation of the non-linear acoustic wave propagation using a phased-array transducer in multiple layered media using different focusing strategies and the consequent lesion formation are essential in HIFU planning in order to enhance the efficacy and efficiency of treatment. An angular spectrum approach with marching fractional steps was applied in the wave propagation from phased-array HIFU transducer, and diffraction, attenuation, and non-linearity effects were accounted for by a second-order operator splitting scheme. The simulated distributions of the first three harmonics along and transverse to the transducer axis were compared to the hydrophone measurements. The bioheat equation was used to simulate the subsequent temperature elevation using the deposited acoustic energy, and lesion formation was determined by the thermal dose. Better agreement was found between the measured harmonics distribution and simulation using the proposed algorithm than the Khokhlov-Zabozotskaya-Kuznetsov equation. Variable focusing of the phased-array transducer (geometric focusing, transverse shifting and the generation of multiple foci) can be simulated successfully. The shifting and splitting of focus was found to result in significantly less temperature elevation at the focus and the subsequently, the smaller lesion size, but the larger grating lobe grating lobe in the pre-focal region. The proposed algorithm could simulate the non-linear wave propagation from the source with arbitrary shape and distribution of excitation through multiple tissue layers in high computation accuracy. The performance of phased-array HIFU can be optimised in the treatment planning.

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

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

PubMed

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

2015-07-07

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

Compact characterization of liquid absorption and emission spectra using linear variable filters integrated with a CMOS imaging camera

PubMed Central

Wan, Yuhang; Carlson, John A.; Kesler, Benjamin A.; Peng, Wang; Su, Patrick; Al-Mulla, Saoud A.; Lim, Sung Jun; Smith, Andrew M.; Dallesasse, John M.; Cunningham, Brian T.

2016-01-01

A compact analysis platform for detecting liquid absorption and emission spectra using a set of optical linear variable filters atop a CMOS image sensor is presented. The working spectral range of the analysis platform can be extended without a reduction in spectral resolution by utilizing multiple linear variable filters with different wavelength ranges on the same CMOS sensor. With optical setup reconfiguration, its capability to measure both absorption and fluorescence emission is demonstrated. Quantitative detection of fluorescence emission down to 0.28 nM for quantum dot dispersions and 32 ng/mL for near-infrared dyes has been demonstrated on a single platform over a wide spectral range, as well as an absorption-based water quality test, showing the versatility of the system across liquid solutions for different emission and absorption bands. Comparison with a commercially available portable spectrometer and an optical spectrum analyzer shows our system has an improved signal-to-noise ratio and acceptable spectral resolution for discrimination of emission spectra, and characterization of colored liquid’s absorption characteristics generated by common biomolecular assays. This simple, compact, and versatile analysis platform demonstrates a path towards an integrated optical device that can be utilized for a wide variety of applications in point-of-use testing and point-of-care diagnostics. PMID:27389070

Compact characterization of liquid absorption and emission spectra using linear variable filters integrated with a CMOS imaging camera

NASA Astrophysics Data System (ADS)

Wan, Yuhang; Carlson, John A.; Kesler, Benjamin A.; Peng, Wang; Su, Patrick; Al-Mulla, Saoud A.; Lim, Sung Jun; Smith, Andrew M.; Dallesasse, John M.; Cunningham, Brian T.

2016-07-01

A compact analysis platform for detecting liquid absorption and emission spectra using a set of optical linear variable filters atop a CMOS image sensor is presented. The working spectral range of the analysis platform can be extended without a reduction in spectral resolution by utilizing multiple linear variable filters with different wavelength ranges on the same CMOS sensor. With optical setup reconfiguration, its capability to measure both absorption and fluorescence emission is demonstrated. Quantitative detection of fluorescence emission down to 0.28 nM for quantum dot dispersions and 32 ng/mL for near-infrared dyes has been demonstrated on a single platform over a wide spectral range, as well as an absorption-based water quality test, showing the versatility of the system across liquid solutions for different emission and absorption bands. Comparison with a commercially available portable spectrometer and an optical spectrum analyzer shows our system has an improved signal-to-noise ratio and acceptable spectral resolution for discrimination of emission spectra, and characterization of colored liquid’s absorption characteristics generated by common biomolecular assays. This simple, compact, and versatile analysis platform demonstrates a path towards an integrated optical device that can be utilized for a wide variety of applications in point-of-use testing and point-of-care diagnostics.

Linear mixed-effects modeling approach to FMRI group analysis

PubMed Central

Chen, Gang; Saad, Ziad S.; Britton, Jennifer C.; Pine, Daniel S.; Cox, Robert W.

2013-01-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance–covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance–covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. PMID:23376789

Linear mixed-effects modeling approach to FMRI group analysis.

PubMed

Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

2013-06-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. Published by Elsevier Inc.

Can Functional Cardiac Age be Predicted from ECG in a Normal Healthy Population

NASA Technical Reports Server (NTRS)

Schlegel, Todd; Starc, Vito; Leban, Manja; Sinigoj, Petra; Vrhovec, Milos

2011-01-01

In a normal healthy population, we desired to determine the most age-dependent conventional and advanced ECG parameters. We hypothesized that changes in several ECG parameters might correlate with age and together reliably characterize the functional age of the heart. Methods: An initial study population of 313 apparently healthy subjects was ultimately reduced to 148 subjects (74 men, 84 women, in the range from 10 to 75 years of age) after exclusion criteria. In all subjects, ECG recordings (resting 5-minute 12-lead high frequency ECG) were evaluated via custom software programs to calculate up to 85 different conventional and advanced ECG parameters including beat-to-beat QT and RR variability, waveform complexity, and signal-averaged, high-frequency and spatial/spatiotemporal ECG parameters. The prediction of functional age was evaluated by multiple linear regression analysis using the best 5 univariate predictors. Results: Ignoring what were ultimately small differences between males and females, the functional age was found to be predicted (R2= 0.69, P < 0.001) from a linear combination of 5 independent variables: QRS elevation in the frontal plane (p<0.001), a new repolarization parameter QTcorr (p<0.001), mean high frequency QRS amplitude (p=0.009), the variability parameter % VLF of RRV (p=0.021) and the P-wave width (p=0.10). Here, QTcorr represents the correlation between the calculated QT and the measured QT signal. Conclusions: In apparently healthy subjects with normal conventional ECGs, functional cardiac age can be estimated by multiple linear regression analysis of mostly advanced ECG results. Because some parameters in the regression formula, such as QTcorr, high frequency QRS amplitude and P-wave width also change with disease in the same direction as with increased age, increased functional age of the heart may reflect subtle age-related pathologies in cardiac electrical function that are usually hidden on conventional ECG.

Partitioning sources of variation in vertebrate species richness

USGS Publications Warehouse

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

High school science enrollment of black students

NASA Astrophysics Data System (ADS)

Goggins, Ellen O.; Lindbeck, Joy S.

How can the high school science enrollment of black students be increased? School and home counseling and classroom procedures could benefit from variables identified as predictors of science enrollment. The problem in this study was to identify a set of variables which characterize science course enrollment by black secondary students. The population consisted of a subsample of 3963 black high school seniors from The High School and Beyond 1980 Base-Year Survey. Using multiple linear regression, backward regression, and correlation analyses, the US Census regions and grades mostly As and Bs in English were found to be significant predictors of the number of science courses scheduled by black seniors.

Analysis of the thermal comfort model in an environment of metal mechanical branch.

PubMed

Pinto, N M; Xavier, A A P; do Amaral, Regiane T

2012-01-01

This study aims to identify the correlation between the Predicted Mean Vote (PMV) with the thermal sensation (S) of 55 employees, establishing a linear multiple regression equation. The measurement of environmental variables followed established standards. The survey was conducted in a metal industry located in Ponta Grossa of the State of Parana in Brazil. It was applied the physical model of thermal comfort to the environmental variables and also to the subjective data on the thermal sensations of employees. The survey was conducted from May to November, 2010, with 48 measurements. This study will serve as the basis for a dissertation consisting of 72 measurements.

An Index and Test of Linear Moderated Mediation.

PubMed

Hayes, Andrew F

2015-01-01

I describe a test of linear moderated mediation in path analysis based on an interval estimate of the parameter of a function linking the indirect effect to values of a moderator-a parameter that I call the index of moderated mediation. This test can be used for models that integrate moderation and mediation in which the relationship between the indirect effect and the moderator is estimated as linear, including many of the models described by Edwards and Lambert ( 2007 ) and Preacher, Rucker, and Hayes ( 2007 ) as well as extensions of these models to processes involving multiple mediators operating in parallel or in serial. Generalization of the method to latent variable models is straightforward. Three empirical examples describe the computation of the index and the test, and its implementation is illustrated using Mplus and the PROCESS macro for SPSS and SAS.

Exploring the Associations Among Nutrition, Science, and Mathematics Knowledge for an Integrative, Food-Based Curriculum.

PubMed

Stage, Virginia C; Kolasa, Kathryn M; Díaz, Sebastián R; Duffrin, Melani W

2018-01-01

Explore associations between nutrition, science, and mathematics knowledge to provide evidence that integrating food/nutrition education in the fourth-grade curriculum may support gains in academic knowledge. Secondary analysis of a quasi-experimental study. Sample included 438 students in 34 fourth-grade classrooms across North Carolina and Ohio; mean age 10 years old; gender (I = 53.2% female; C = 51.6% female). Dependent variable = post-test-nutrition knowledge; independent variables = baseline-nutrition knowledge, and post-test science and mathematics knowledge. Analyses included descriptive statistics and multiple linear regression. The hypothesized model predicted post-nutrition knowledge (F(437) = 149.4, p < .001; Adjusted R = .51). All independent variables were significant predictors with positive association. Science and mathematics knowledge were predictive of nutrition knowledge indicating use of an integrative science and mathematics curriculum to improve academic knowledge may also simultaneously improve nutrition knowledge among fourth-grade students. Teachers can benefit from integration by meeting multiple academic standards, efficiently using limited classroom time, and increasing nutrition education provided in the classroom. © 2018, American School Health Association.

Operational Planning for Multiple Heterogeneous Unmanned Aerial Vehicles in Three Dimensions

DTIC Science & Technology

2009-06-01

human input in the planning process. Two solution methods are presented: (1) a mixed-integer program, and (2) an algorithm that utilizes a metaheuristic ...and (2) an algorithm that utilizes a metaheuristic to generate composite variables for a linear program, called the Composite Operations Planning...that represent a path and an associated type of UAV. The reformulation is incorporated into an algorithm that uses a metaheuristic to generate the

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

PubMed

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

2018-02-27

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

Integrating models that depend on variable data

NASA Astrophysics Data System (ADS)

Banks, A. T.; Hill, M. C.

2016-12-01

Models of human-Earth systems are often developed with the goal of predicting the behavior of one or more dependent variables from multiple independent variables, processes, and parameters. Often dependent variable values range over many orders of magnitude, which complicates evaluation of the fit of the dependent variable values to observations. Many metrics and optimization methods have been proposed to address dependent variable variability, with little consensus being achieved. In this work, we evaluate two such methods: log transformation (based on the dependent variable being log-normally distributed with a constant variance) and error-based weighting (based on a multi-normal distribution with variances that tend to increase as the dependent variable value increases). Error-based weighting has the advantage of encouraging model users to carefully consider data errors, such as measurement and epistemic errors, while log-transformations can be a black box for typical users. Placing the log-transformation into the statistical perspective of error-based weighting has not formerly been considered, to the best of our knowledge. To make the evaluation as clear and reproducible as possible, we use multiple linear regression (MLR). Simulations are conducted with MatLab. The example represents stream transport of nitrogen with up to eight independent variables. The single dependent variable in our example has values that range over 4 orders of magnitude. Results are applicable to any problem for which individual or multiple data types produce a large range of dependent variable values. For this problem, the log transformation produced good model fit, while some formulations of error-based weighting worked poorly. Results support previous suggestions fthat error-based weighting derived from a constant coefficient of variation overemphasizes low values and degrades model fit to high values. Applying larger weights to the high values is inconsistent with the log-transformation. Greater consistency is obtained by imposing smaller (by up to a factor of 1/35) weights on the smaller dependent-variable values. From an error-based perspective, the small weights are consistent with large standard deviations. This work considers the consequences of these two common ways of addressing variable data.

No association of smoke-free ordinances with profits from bingo and charitable games in Massachusetts.

PubMed

Glantz, S A; Wilson-Loots, R

2003-12-01

Because it is widely played, claims that smoking restrictions will adversely affect bingo games is used as an argument against these policies. We used publicly available data from Massachusetts to assess the impact of 100% smoke-free ordinances on profits from bingo and other gambling sponsored by charitable organisations between 1985 and 2001. We conducted two analyses: (1) a general linear model implementation of a time series analysis with net profits (adjusted to 2001 dollars) as the dependent variable, and community (as a fixed effect), year, lagged net profits, and the length of time the ordinance had been in force as the independent variables; (2) multiple linear regression of total state profits against time, lagged profits, and the percentage of the entire state population in communities that allow charitable gaming but prohibit smoking. The general linear model analysis of data from individual communities showed that, while adjusted profits fell over time, this effect was not related to the presence of an ordinance. The analysis in terms of the fraction of the population living in communities with ordinances yielded the same result. Policymakers can implement smoke-free policies without concern that these policies will affect charitable gaming.

Developing a multipoint titration method with a variable dose implementation for anaerobic digestion monitoring.

PubMed

Salonen, K; Leisola, M; Eerikäinen, T

2009-01-01

Determination of metabolites from an anaerobic digester with an acid base titration is considered as superior method for many reasons. This paper describes a practical at line compatible multipoint titration method. The titration procedure was improved by speed and data quality. A simple and novel control algorithm for estimating a variable titrant dose was derived for this purpose. This non-linear PI-controller like algorithm does not require any preliminary information from sample. Performance of this controller is superior compared to traditional linear PI-controllers. In addition, simplification for presenting polyprotic acids as a sum of multiple monoprotic acids is introduced along with a mathematical error examination. A method for inclusion of the ionic strength effect with stepwise iteration is shown. The titration model is presented with matrix notations enabling simple computation of all concentration estimates. All methods and algorithms are illustrated in the experimental part. A linear correlation better than 0.999 was obtained for both acetate and phosphate used as model compounds with slopes of 0.98 and 1.00 and average standard deviations of 0.6% and 0.8%, respectively. Furthermore, insensitivity of the presented method for overlapping buffer capacity curves was shown.

Quality of search strategies reported in systematic reviews published in stereotactic radiosurgery.

PubMed

Faggion, Clovis M; Wu, Yun-Chun; Tu, Yu-Kang; Wasiak, Jason

2016-06-01

Systematic reviews require comprehensive literature search strategies to avoid publication bias. This study aimed to assess and evaluate the reporting quality of search strategies within systematic reviews published in the field of stereotactic radiosurgery (SRS). Three electronic databases (Ovid MEDLINE(®), Ovid EMBASE(®) and the Cochrane Library) were searched to identify systematic reviews addressing SRS interventions, with the last search performed in October 2014. Manual searches of the reference lists of included systematic reviews were conducted. The search strategies of the included systematic reviews were assessed using a standardized nine-question form based on the Cochrane Collaboration guidelines and Assessment of Multiple Systematic Reviews checklist. Multiple linear regression analyses were performed to identify the important predictors of search quality. A total of 85 systematic reviews were included. The median quality score of search strategies was 2 (interquartile range = 2). Whilst 89% of systematic reviews reported the use of search terms, only 14% of systematic reviews reported searching the grey literature. Multiple linear regression analyses identified publication year (continuous variable), meta-analysis performance and journal impact factor (continuous variable) as predictors of higher mean quality scores. This study identified the urgent need to improve the quality of search strategies within systematic reviews published in the field of SRS. This study is the first to address how authors performed searches to select clinical studies for inclusion in their systematic reviews. Comprehensive and well-implemented search strategies are pivotal to reduce the chance of publication bias and consequently generate more reliable systematic review findings.

Development of reaching during mid-childhood from a Developmental Systems perspective.

PubMed

Golenia, Laura; Schoemaker, Marina M; Otten, Egbert; Mouton, Leonora J; Bongers, Raoul M

2018-01-01

Inspired by the Developmental Systems perspective, we studied the development of reaching during mid-childhood (5-10 years of age) not just at the performance level (i.e., endpoint movements), as commonly done in earlier studies, but also at the joint angle level. Because the endpoint position (i.e., the tip of the index finger) at the reaching target can be achieved with multiple joint angle combinations, we partitioned variability in joint angles over trials into variability that does not (goal-equivalent variability, GEV) and that does (non-goal-equivalent variability, NGEV) influence the endpoint position, using the Uncontrolled Manifold method. Quantifying this structure in joint angle variability allowed us to examine whether and how spatial variability of the endpoint at the reaching target is related to variability in joint angles and how this changes over development. 6-, 8- and 10-year-old children and young adults performed reaching movements to a target with the index finger. Polynomial trend analysis revealed a linear and a quadratic decreasing trend for the variable error. Linear decreasing and cubic trends were found for joint angle standard deviations at movement end. GEV and NGEV decreased gradually with age, but interestingly, the decrease of GEV was steeper than the decrease of NGEV, showing that the different parts of the joint angle variability changed differently over age. We interpreted these changes in the structure of variability as indicating changes over age in exploration for synergies (a family of task solutions), a concept that links the performance level with the joint angle level. Our results suggest changes in the search for synergies during mid-childhood development.

Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

NASA Astrophysics Data System (ADS)

Wei, Jun; Jiang, Guo-Qing; Liu, Xin

2017-09-01

This study proposed three algorithms that can potentially be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based), and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a Weather Research and Forecasting model for the simulation of typhoon to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The TE-based algorithm I considers wind-induced ocean vertical mixing and upwelling processes only, and thus obtained a synoptic and relatively smooth sea surface temperature cooling. The TE-based algorithm II incorporates not only typhoon winds but also ocean information, and thus resolves more cooling features. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Sensitivity analysis indicated that the typhoon-induced ocean cooling is a nonlinear process involving interactions of multiple atmospheric and oceanic variables. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

PubMed

Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

2012-02-01

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Health related quality of life in parents of six to eight year old children with Down syndrome.

PubMed

Marchal, Jan Pieter; Maurice-Stam, Heleen; Hatzmann, Janneke; van Trotsenburg, A S Paul; Grootenhuis, Martha A

2013-11-01

Raising a child with Down syndrome (DS) has been found to be associated with lowered health related quality of life (HRQoL) in the domains cognitive functioning, social functioning, daily activities and vitality. We aimed to explore which socio-demographics, child functioning and psychosocial variables were related to these HRQoL domains in parents of children with DS. Parents of 98 children with DS completed the TNO-AZL adult quality of life questionnaire (TAAQOL) and a questionnaire assessing socio-demographic, child functioning and psychosocial predictors. Using multiple linear regression analyses for each category of predictors, we selected relevant predictors for the final models. The final multiple linear regression models revealed that cognitive functioning was best predicted by the sleep of the child (β=.29, p<.01) and by the parent having given up a hobby (β=-.29, p<.01), social functioning by the quality of the partner relation (β=.34, p<.001), daily activities by the parent having to care for an ill friend or family member (β=-.31, p<.01), and vitality by the parent having enough personal time (β=.32, p<.01). Overall, psychosocial variables rather than socio-demographics or child functioning showed most consistent and powerful relations to the HRQoL domains of cognitive functioning, social functioning, daily activities and vitality. These psychosocial variables mainly related to social support and time pressure. Systematic screening of parents to detect problems timely, and interventions targeting the supportive network and the demands in time are recommended. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

NASA Technical Reports Server (NTRS)

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

1996-01-01

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

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

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

A trait-based framework for stream algal communities.

PubMed

Lange, Katharina; Townsend, Colin Richard; Matthaei, Christoph David

2016-01-01

The use of trait-based approaches to detect effects of land use and climate change on terrestrial plant and aquatic phytoplankton communities is increasing, but such a framework is still needed for benthic stream algae. Here we present a conceptual framework of morphological, physiological, behavioural and life-history traits relating to resource acquisition and resistance to disturbance. We tested this approach by assessing the relationships between multiple anthropogenic stressors and algal traits at 43 stream sites. Our "natural experiment" was conducted along gradients of agricultural land-use intensity (0-95% of the catchment in high-producing pasture) and hydrological alteration (0-92% streamflow reduction resulting from water abstraction for irrigation) as well as related physicochemical variables (total nitrogen concentration and deposited fine sediment). Strategic choice of study sites meant that agricultural intensity and hydrological alteration were uncorrelated. We studied the relationships of seven traits (with 23 trait categories) to our environmental predictor variables using general linear models and an information-theoretic model-selection approach. Life form, nitrogen fixation and spore formation were key traits that showed the strongest relationships with environmental stressors. Overall, FI (farming intensity) exerted stronger effects on algal communities than hydrological alteration. The large-bodied, non-attached, filamentous algae that dominated under high farming intensities have limited dispersal abilities but may cope with unfavourable conditions through the formation of spores. Antagonistic interactions between FI and flow reduction were observed for some trait variables, whereas no interactions occurred for nitrogen concentration and fine sediment. Our conceptual framework was well supported by tests of ten specific hypotheses predicting effects of resource supply and disturbance on algal traits. Our study also shows that investigating a fairly comprehensive set of traits can help shed light on the drivers of algal community composition in situations where multiple stressors are operating. Further, to understand non-linear and non-additive effects of such drivers, communities need to be studied along multiple gradients of natural variation or anthropogenic stressors.

Predictors of the physical impact of Multiple Sclerosis following community-based, exercise trial.

PubMed

Kehoe, M; Saunders, J; Jakeman, P; Coote, S

2015-04-01

Studies evaluating exercise interventions in people with multiple sclerosis (PwMS) demonstrate small to medium positive effects and large variability on a number of outcome measures. No study to date has tried to explain this variability. This paper presents a novel exploration of data examining the predictors of outcome for PwMS with minimal gait impairment following a randomised, controlled trial evaluating community-based exercise interventions (N = 242). The primary variable was the physical component of the Multiple Sclerosis Impact Scale-29, version 2 (MSIS-29, v2) after a 10-week, controlled intervention period. Predictors were identified a priori and were measured at baseline. Multiple linear regression was conducted. Four models are presented lower MSIS-29, v2 scores after the intervention period were best predicted by a lower baseline MSIS-29,v2, a lower baseline Modified Fatigue Impact Score (physical subscale), randomisation to an exercise intervention, a longer baseline walking distance measured by the Six Minute Walk Test and female gender. This model explained 57.4% of the variance (F (5, 211) = 59.24, p < 0.01). These results suggest that fatigue and walking distance at baseline contribute significantly to predicting MSIS-29, v29 (physical component) after intervention, and thus should be the focus of intervention and assessment. Exercise is an important contributor to minimising the physical impact of MS, and gender-specific interventions may be warranted. © The Author(s), 2014.

A High-Linearity Low-Noise Amplifier with Variable Bandwidth for Neural Recoding Systems

NASA Astrophysics Data System (ADS)

Yoshida, Takeshi; Sueishi, Katsuya; Iwata, Atsushi; Matsushita, Kojiro; Hirata, Masayuki; Suzuki, Takafumi

2011-04-01

This paper describes a low-noise amplifier with multiple adjustable parameters for neural recording applications. An adjustable pseudo-resistor implemented by cascade metal-oxide-silicon field-effect transistors (MOSFETs) is proposed to achieve low-signal distortion and wide variable bandwidth range. The amplifier has been implemented in 0.18 µm standard complementary metal-oxide-semiconductor (CMOS) process and occupies 0.09 mm2 on chip. The amplifier achieved a selectable voltage gain of 28 and 40 dB, variable bandwidth from 0.04 to 2.6 Hz, total harmonic distortion (THD) of 0.2% with 200 mV output swing, input referred noise of 2.5 µVrms over 0.1-100 Hz and 18.7 µW power consumption at a supply voltage of 1.8 V.

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

PubMed

de Castro, John M

2002-01-01

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

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

PubMed

de Castro, J M

2001-09-01

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

A high-accuracy optical linear algebra processor for finite element applications

NASA Technical Reports Server (NTRS)

Casasent, D.; Taylor, B. K.

1984-01-01

Optical linear processors are computationally efficient computers for solving matrix-matrix and matrix-vector oriented problems. Optical system errors limit their dynamic range to 30-40 dB, which limits their accuray to 9-12 bits. Large problems, such as the finite element problem in structural mechanics (with tens or hundreds of thousands of variables) which can exploit the speed of optical processors, require the 32 bit accuracy obtainable from digital machines. To obtain this required 32 bit accuracy with an optical processor, the data can be digitally encoded, thereby reducing the dynamic range requirements of the optical system (i.e., decreasing the effect of optical errors on the data) while providing increased accuracy. This report describes a new digitally encoded optical linear algebra processor architecture for solving finite element and banded matrix-vector problems. A linear static plate bending case study is described which quantities the processor requirements. Multiplication by digital convolution is explained, and the digitally encoded optical processor architecture is advanced.

Unified treatment of microscopic boundary conditions and efficient algorithms for estimating tangent operators of the homogenized behavior in the computational homogenization method

NASA Astrophysics Data System (ADS)

Nguyen, Van-Dung; Wu, Ling; Noels, Ludovic

2017-03-01

This work provides a unified treatment of arbitrary kinds of microscopic boundary conditions usually considered in the multi-scale computational homogenization method for nonlinear multi-physics problems. An efficient procedure is developed to enforce the multi-point linear constraints arising from the microscopic boundary condition either by the direct constraint elimination or by the Lagrange multiplier elimination methods. The macroscopic tangent operators are computed in an efficient way from a multiple right hand sides linear system whose left hand side matrix is the stiffness matrix of the microscopic linearized system at the converged solution. The number of vectors at the right hand side is equal to the number of the macroscopic kinematic variables used to formulate the microscopic boundary condition. As the resolution of the microscopic linearized system often follows a direct factorization procedure, the computation of the macroscopic tangent operators is then performed using this factorized matrix at a reduced computational time.

A finite element solution algorithm for the Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Baker, A. J.

1974-01-01

A finite element solution algorithm is established for the two-dimensional Navier-Stokes equations governing the steady-state kinematics and thermodynamics of a variable viscosity, compressible multiple-species fluid. For an incompressible fluid, the motion may be transient as well. The primitive dependent variables are replaced by a vorticity-streamfunction description valid in domains spanned by rectangular, cylindrical and spherical coordinate systems. Use of derived variables provides a uniformly elliptic partial differential equation description for the Navier-Stokes system, and for which the finite element algorithm is established. Explicit non-linearity is accepted by the theory, since no psuedo-variational principles are employed, and there is no requirement for either computational mesh or solution domain closure regularity. Boundary condition constraints on the normal flux and tangential distribution of all computational variables, as well as velocity, are routinely piecewise enforceable on domain closure segments arbitrarily oriented with respect to a global reference frame.

Explanation of the variance in quality of life and activity capacity of patients with heart failure by laboratory data.

PubMed

Athanasopoulos, Leonidas V; Dritsas, Athanasios; Doll, Helen A; Cokkinos, Dennis V

2010-08-01

This study was conducted to explain the variance in quality of life (QoL) and activity capacity of patients with congestive heart failure from pathophysiological changes as estimated by laboratory data. Peak oxygen consumption (peak VO2) and ventilation (VE)/carbon dioxide output (VCO2) slope derived from cardiopulmonary exercise testing, plasma N-terminal prohormone of B-type natriuretic peptide (NT-proBNP), and echocardiographic markers [left atrium (LA), left ventricular ejection fraction (LVEF)] were measured in 62 patients with congestive heart failure, who also completed the Minnesota Living with Heart Failure Questionnaire and the Specific Activity Questionnaire. All regression models were adjusted for age and sex. On linear regression analysis, peak VO2 with P value less than 0.001, VE/VCO2 slope with P value less than 0.01, LVEF with P value less than 0.001, LA with P=0.001, and logNT-proBNP with P value less than 0.01 were found to be associated with QoL. On stepwise multiple linear regression, peak VO2 and LVEF continued to be predictive, accounting for 40% of the variability in Minnesota Living with Heart Failure Questionnaire score. On linear regression analysis, peak VO2 with P value less than 0.001, VE/VCO2 slope with P value less than 0.001, LVEF with P value less than 0.05, LA with P value less than 0.001, and logNT-proBNP with P value less than 0.001 were found to be associated with activity capacity. On stepwise multiple linear regression, peak VO2 and LA continued to be predictive, accounting for 53% of the variability in Specific Activity Questionnaire score. Peak VO2 is independently associated both with QoL and activity capacity. In addition to peak VO2, LVEF is independently associated with QoL, and LA with activity capacity.

Does Parental Control Work With Smartphone Addiction?: A Cross-Sectional Study of Children in South Korea.

PubMed

Lee, Eun Jee; Ogbolu, Yolanda

The purposes of this study were to (a) examine the relationship between personal characteristics (age, gender), psychological factors (depression), and physical factors (sleep time) on smartphone addiction in children and (b) determine whether parental control is associated with a lower incidence of smartphone addiction. Data were collected from children aged 10-12 years (N = 208) by a self-report questionnaire in two elementary schools and were analyzed using t test, one-way analysis of variance, correlation, and multiple linear regression. Most of the participants (73.3%) owned a smartphone, and the percentage of risky smartphone users was 12%. The multiple linear regression model explained 25.4% (adjusted R = .239) of the variance in the smartphone addiction score (SAS). Three variables were significantly associated with the SAS (age, depression, and parental control), and three variables were excluded (gender, geographic region, and parental control software). Teens, aged 10-12 years, with higher depression scores had higher SASs. The more parental control perceived by the student, the higher the SAS. There was no significant relationship between parental control software and smartphone addiction. This is one of the first studies to examine smartphone addiction in teens. Control-oriented managing by parents of children's smartphone use is not very effective and may exacerbate smartphone addiction. Future research should identify additional strategies, beyond parental control software, that have the potential to prevent, reduce, and eliminate smartphone addiction.

Prediction of erodibility in Oxisols using iron oxides, soil color and diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Arantes Camargo, Livia; Marques, José, Jr.

2015-04-01

The prediction of erodibility using indirect methods such as diffuse reflectance spectroscopy could facilitate the characterization of the spatial variability in large areas and optimize implementation of conservation practices. The aim of this study was to evaluate the prediction of interrill erodibility (Ki) and rill erodibility (Kr) by means of iron oxides content and soil color using multiple linear regression and diffuse reflectance spectroscopy (DRS) using regression analysis by least squares partial (PLSR). The soils were collected from three geomorphic surfaces and analyzed for chemical, physical and mineralogical properties, plus scanned in the spectral range from the visible and infrared. Maps of spatial distribution of Ki and Kr were built with the values calculated by the calibrated models that obtained the best accuracy using geostatistics. Interrill-rill erodibility presented negative correlation with iron extracted by dithionite-citrate-bicarbonate, hematite, and chroma, confirming the influence of iron oxides in soil structural stability. Hematite and hue were the attributes that most contributed in calibration models by multiple linear regression for the prediction of Ki (R2 = 0.55) and Kr (R2 = 0.53). The diffuse reflectance spectroscopy via PLSR allowed to predict Interrill-rill erodibility with high accuracy (R2adj = 0.76, 0.81 respectively and RPD> 2.0) in the range of the visible spectrum (380-800 nm) and the characterization of the spatial variability of these attributes by geostatistics.

Internal and external environmental factors affecting the performance of hospital-based home nursing care.

PubMed

Noh, J-W; Kwon, Y-D; Yoon, S-J; Hwang, J-I

2011-06-01

Numerous studies on HNC services have been carried out by signifying their needs, efficiency and effectiveness. However, no study has ever been performed to determine the critical factors associated with HNC's positive results despite the deluge of positive studies on the service. This study included all of the 89 training hospitals that were practising HNC service in Korea as of November 2006. The input factors affecting the performance were classified as either internal or external environmental factors. This analysis was conducted to understand the impact that the corresponding factors had on performance. Data were analysed by using multiple linear regressions. The internal and external environment variables affected the performance of HNC based on univariate analysis. The meaningful variables were internal environmental factors. Specifically, managerial resource (the number of operating beds and the outpatient/inpatient ratio) were meaningful when the multiple linear regression analysis was performed. Indeed, the importance of organizational culture (the passion of HNC nurses) was significant. This study, considering the limited market size of Korea, illustrates that the critical factor for the development of hospital-led HNC lies with internal environmental factors rather than external ones. Among the internal environmental factors, the hospitals' managerial resource-related factors (specifically, the passion of nurses) were the most important contributing element. © 2011 The Authors. International Nursing Review © 2011 International Council of Nurses.

Selection of higher order regression models in the analysis of multi-factorial transcription data.

PubMed

Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim

2014-01-01

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.

Trends in bullying victimization by gender among U.S. high school students.

PubMed

Pontes, Nancy M H; Ayres, Cynthia G; Lewandowski, Carla; Pontes, Manuel C F

2018-06-01

This research used four consecutive waves of data from the National Youth Risk Behavior Survey (YRBS) conducted by the Centers for Disease Control (CDC), to estimate linear time trends by gender in the prevalence of school and electronic bullying victimization among U.S. high school students (N = 61,042). Dependent variables were student self-reported school bullying victimization and electronic bullying victimization during the previous 12 months. Independent variables used to estimate multiple logistic regression models by gender were survey year, race/ethnicity, and grade level. Results showed the prevalence of school bullying increased significantly among females from 2009 (21.2%) to 2015 (24.8%), linear trend OR = 1.08 [1.04, 1.12]; and decreased significantly among males from 2009 (18.7%) to 2015 (15.8%), linear trend OR = 0.93 [0.89, 0.98]. Prevalence of electronic bullying was unchanged between 2011 to 2015 among both male and female students. Asian race, relative to White race, was associated with significantly lower rates of both school and electronic bullying victimization among females, but not males. The incidence of school and electronic bullying victimization was significantly lower among Black and Hispanic students, but not among multiple-race students, regardless of student gender. Healthy People 2020 set a goal to reduce school bullying victimization 10% by 2019. As of 2015, school bullying victimization decreased significantly among males (16% decrease); it significantly increased among females (17% increase). Future research should explore underlying factors related to these divergent trends, and develop effective strategies to reverse the alarming rise in female school bullying victimization. © 2018 Wiley Periodicals, Inc.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Variability in postural control with and without balance-based torso- weighting in people with multiple sclerosis and healthy controls.

PubMed

Hunt, Charlotte M; Widener, Gail; Allen, Diane D

2014-10-01

People with multiple sclerosis (MS) have diminished postural control, and center of pressure (COP) displacement varies more in this population than in healthy controls. Balance-based torso-weighting (BBTW) can improve clinical balance and mobility in people with MS, and exploration using both linear and nonlinear measures of COP may help determine whether BBTW optimizes movement variability. The aim of this study was to investigate the effects of BBTW on people with MS and healthy controls during quiet standing. This was a quasi-experimental study comparing COP variability between groups, between eye closure conditions, and between weighting conditions in the anterior-posterior and medial-lateral directions. Twenty participants with MS and 18 healthy controls stood on a forceplate in 4 conditions: eyes open and closed and with and without BBTW. Linear measures of COP displacement included range and root mean square (RMS). Nonlinear measures included approximate entropy (ApEn) and Lyapunov exponent (LyE). Three-way repeated-measures analyses of variance compared measures across groups and conditions. The association between weighting response and baseline nonlinear variables was examined. When significant associations were found, MS subgroups were created and compared. The MS and control groups had significantly different range, RMS, and ApEn values. The eyes-open and eyes-closed conditions had significantly different range and RMS values. Change with weighting correlated with LyE (r=-.70) and ApEn (r=-.59). Two MS subgroups, with low and high baseline LyE values, responded to BBTW in opposite directions, with a significant main effect for weighting condition for the LyE variable in the medial-lateral direction. The small samples and no identification of impairments related to LyE at baseline were limitations of the study. The LyE may help differentiate subgroups who respond differently to BBTW. In both subgroups, LyE values moved toward the average of healthy controls, suggesting that BBTW may help optimize movement variability in people with MS. © 2014 American Physical Therapy Association.

Variability in Postural Control With and Without Balance-Based Torso- Weighting in People With Multiple Sclerosis and Healthy Controls

PubMed Central

Widener, Gail; Allen, Diane D.

2014-01-01

Background People with multiple sclerosis (MS) have diminished postural control, and center of pressure (COP) displacement varies more in this population than in healthy controls. Balance-based torso-weighting (BBTW) can improve clinical balance and mobility in people with MS, and exploration using both linear and nonlinear measures of COP may help determine whether BBTW optimizes movement variability. Objective The aim of this study was to investigate the effects of BBTW on people with MS and healthy controls during quiet standing. Design This was a quasi-experimental study comparing COP variability between groups, between eye closure conditions, and between weighting conditions in the anterior-posterior and medial-lateral directions. Methods Twenty participants with MS and 18 healthy controls stood on a forceplate in 4 conditions: eyes open and closed and with and without BBTW. Linear measures of COP displacement included range and root mean square (RMS). Nonlinear measures included approximate entropy (ApEn) and Lyapunov exponent (LyE). Three-way repeated-measures analyses of variance compared measures across groups and conditions. The association between weighting response and baseline nonlinear variables was examined. When significant associations were found, MS subgroups were created and compared. Results The MS and control groups had significantly different range, RMS, and ApEn values. The eyes-open and eyes-closed conditions had significantly different range and RMS values. Change with weighting correlated with LyE (r=−.70) and ApEn (r=−.59). Two MS subgroups, with low and high baseline LyE values, responded to BBTW in opposite directions, with a significant main effect for weighting condition for the LyE variable in the medial-lateral direction. Limitations The small samples and no identification of impairments related to LyE at baseline were limitations of the study. Conclusions The LyE may help differentiate subgroups who respond differently to BBTW. In both subgroups, LyE values moved toward the average of healthy controls, suggesting that BBTW may help optimize movement variability in people with MS. PMID:24903118

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

PubMed

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

2015-01-01

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

Models of subjective response to in-flight motion data

NASA Technical Reports Server (NTRS)

Rudrapatna, A. N.; Jacobson, I. D.

1973-01-01

Mathematical relationships between subjective comfort and environmental variables in an air transportation system are investigated. As a first step in model building, only the motion variables are incorporated and sensitivities are obtained using stepwise multiple regression analysis. The data for these models have been collected from commercial passenger flights. Two models are considered. In the first, subjective comfort is assumed to depend on rms values of the six-degrees-of-freedom accelerations. The second assumes a Rustenburg type human response function in obtaining frequency weighted rms accelerations, which are used in a linear model. The form of the human response function is examined and the results yield a human response weighting function for different degrees of freedom.

Cooperation without culture? The null effect of generalized trust on intentional homicide: a cross-national panel analysis, 1995-2009.

PubMed

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation.

Association between masseter muscle activity levels recorded during sleep and signs and symptoms of temporomandibular disorders in healthy young adults.

PubMed

Baba, Kazuyoshi; Haketa, Tadasu; Sasaki, Yoshiyuki; Ohyama, Takashi; Clark, Glenn T

2005-01-01

To examine whether any signs and symptoms of temporomandibular disorders were significantly associated with masseter muscle activity levels during sleep. One hundred three healthy adult subjects (age range, 22 to 32 years) participated in the study. They were asked to fill out questionnaires, undergo a calibrated clinical examination of their jaws and teeth, and perform 6 consecutive nightly masseter electromyographic (EMG) recordings with a portable EMG recording system in their home. The EMG data were considered dependent variables, while the questionnaire and examination data were considered independent variables. Multiple stepwise linear regression analysis was utilized to assess possible associations between these variables. Both gender and joint sound scores were significantly related to the duration of EMG activity. None of the other independent variables were found to be related to any of the muscle activity variables. The results suggest that both gender and clicking are significantly related to duration of masseter EMG activity during sleep.

FAST TRACK PAPER: Non-iterative multiple-attenuation methods: linear inverse solutions to non-linear inverse problems - II. BMG approximation

NASA Astrophysics Data System (ADS)

Ikelle, Luc T.; Osen, Are; Amundsen, Lasse; Shen, Yunqing

2004-12-01

The classical linear solutions to the problem of multiple attenuation, like predictive deconvolution, τ-p filtering, or F-K filtering, are generally fast, stable, and robust compared to non-linear solutions, which are generally either iterative or in the form of a series with an infinite number of terms. These qualities have made the linear solutions more attractive to seismic data-processing practitioners. However, most linear solutions, including predictive deconvolution or F-K filtering, contain severe assumptions about the model of the subsurface and the class of free-surface multiples they can attenuate. These assumptions limit their usefulness. In a recent paper, we described an exception to this assertion for OBS data. We showed in that paper that a linear and non-iterative solution to the problem of attenuating free-surface multiples which is as accurate as iterative non-linear solutions can be constructed for OBS data. We here present a similar linear and non-iterative solution for attenuating free-surface multiples in towed-streamer data. For most practical purposes, this linear solution is as accurate as the non-linear ones.

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

New structures of power density spectra for four Kepler active galactic nuclei

NASA Astrophysics Data System (ADS)

Dobrotka, A.; Antonuccio-Delogu, V.; Bajčičáková, I.

2017-09-01

Many nearby active galactic nuclei display a significant short-term variability. In this work, we reanalyse photometric data of four active galactic nuclei observed by Kepler in order to study the flickering activity, with our main goal to search for multiple components in the power density spectra. We find that all four objects have similar characteristics, with two break frequencies at approximately log( f /Hz) = -5.2 and -4.7. We consider some physical phenomena whose characteristic time-scales are consistent with those observed, in particular mass accretion fluctuations in the inner geometrically thick disc (hot X-ray corona) and unstable relativistic Rayleigh-Taylor modes. The former is supported by detection of the same break frequencies in the Swift X-ray data of ZW229-15. We also discuss rms-flux relations, and we detect a possible typical linear trend at lower flux levels. Our findings support the hypothesis of a multiplicative character of variability, in agreement with the propagating accretion fluctuation model.

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

PubMed

Wolf, Lisa

2013-02-01

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

Theory and simulations of covariance mapping in multiple dimensions for data analysis in high-event-rate experiments

NASA Astrophysics Data System (ADS)

Zhaunerchyk, V.; Frasinski, L. J.; Eland, J. H. D.; Feifel, R.

2014-05-01

Multidimensional covariance analysis and its validity for correlation of processes leading to multiple products are investigated from a theoretical point of view. The need to correct for false correlations induced by experimental parameters which fluctuate from shot to shot, such as the intensity of self-amplified spontaneous emission x-ray free-electron laser pulses, is emphasized. Threefold covariance analysis based on simple extension of the two-variable formulation is shown to be valid for variables exhibiting Poisson statistics. In this case, false correlations arising from fluctuations in an unstable experimental parameter that scale linearly with signals can be eliminated by threefold partial covariance analysis, as defined here. Fourfold covariance based on the same simple extension is found to be invalid in general. Where fluctuations in an unstable parameter induce nonlinear signal variations, a technique of contingent covariance analysis is proposed here to suppress false correlations. In this paper we also show a method to eliminate false correlations associated with fluctuations of several unstable experimental parameters.

Birth dimensions in Melanesian and Indian Fijians and their relationships with maternal and socio-economic factors.

PubMed

Clegg, E J; Clegg, S D

1989-01-01

Fifty-nine Melanesian (MF) and 39 Indian (IF) Fijian full-term newborns were studied within 5 days of birth. Dimensions recorded included birthweight, length, crown-rump length, head circumference, upper limb length, bycondylar humeral and femoral diameters and four skinfolds (triceps, subscapular, suprailiac and thigh). Data from previous pregnancies of the presenting newborns' mothers were added to presenting birthweights, giving a total of 160 MF and 84 IF birthweights. In all birthweight and linear dimensions MFs were the bigger. Sex differences were significant in respect only of head circumference and the two bicondylar diameters. Multiple regression analysis showed dimensions in MF newborns to have few significant relationships with the maternal and socio-economic variables of age, parity, stature and years of education, but IFs had many more significant relationships. When covariance correction was made for the significant maternal and socio-economic variables (maternal age and parity) little effect on racial differences was seen. All linear dimensions except length could be subsumed into birthweight. MFs had greater triceps and subscapular skinfold thicknesses than IFs, a difference which was not much changed by covariance correction for significant maternal and socio-economic variables (maternal stature and years of education). Measurements of shape, expressed as ratios of linear dimensions, showed few racial differences but males had relatively broader limbs. For upper limb shape only, this difference was maintained after covariance correction for significant maternal and socio-economic variables (parity, stature and education). The greater size of MF infants at birth is associated with lower peri- and neonatal death rates. However this advantage is reversed during the remainder of the first year of life. It is suggested that better standards of infant care among IFs are responsible for this change.

Potential impacts of climate variability on dengue hemorrhagic fever in Honduras, 2010.

PubMed

Zambrano, L I; Sevilla, C; Reyes-García, S Z; Sierra, M; Kafati, R; Rodriguez-Morales, A J; Mattar, S

2012-12-01

Climate change and variability are affecting human health and disease direct or indirectly through many mechanisms. Dengue is one of those diseases that is strongly influenced by climate variability; however its study in Central America has been poorly approached. In this study, we assessed potential associations between macroclimatic and microclimatic variation and dengue hemorrhagic fever (DHF) cases in the main hospital of Honduras during 2010. In this year, 3,353 cases of DHF were reported in the Hospital Escuela, Tegucigalpa. Climatic periods marked a difference of 158% in the mean incidence of cases, from El Niño weeks (-99% of cases below the mean incidence) to La Niña months (+59% of cases above it) (p<0.01). Linear regression showed significantly higher dengue incidence with lower values of Oceanic Niño Index (p=0.0097), higher rain probability (p=0.0149), accumulated rain (p=0.0443) and higher relative humidity (p=0.0292). At a multiple linear regression model using those variables, ONI values shown to be the most important and significant factor found to be associated with the monthly occurrence of DHF cases (r²=0.649; βstandardized=-0.836; p=0.01). As has been shown herein, climate variability is an important element influencing the dengue epidemiology in Honduras. However, it is necessary to extend these studies in this and other countries in the Central America region, because these models can be applied for surveillance as well as for prediction of dengue.

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

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

[Association between hours of television watched, physical activity, sleep and excess weight among young adults].

PubMed

Martínez-Moyá, María; Navarrete-Muñoz, Eva M; García de la Hera, Manuela; Giménez-Monzo, Daniel; González-Palacios, Sandra; Valera-Gran, Desirée; Sempere-Orts, María; Vioque, Jesús

2014-01-01

To explore the association between excess weight or body mass index (BMI) and the time spent watching television, self-reported physical activity and sleep duration in a young adult population. We analyzed cross-sectional baseline data of 1,135 participants (17-35 years old) from the project Dieta, salud y antropometría en población universitaria (Diet, Health and Anthrompmetric Variables in Univeristy Students). Information about time spent watching television, sleep duration, self-reported physical activity and self-reported height and weight was provided by a baseline questionnaire. BMI was calculated as kg/m(2) and excess of weight was defined as ≥25. We used multiple logistic regression to explore the association between excess weight (no/yes) and independent variables, and multiple linear regression for BMI. The prevalence of excess weight was 13.7% (11.2% were overweight and 2.5% were obese). A significant positive association was found between excess weight and a greater amount of time spent watching television. Participants who reported watching television >2h a day had a higher risk of excess weight than those who watched television ≤1h a day (OR=2.13; 95%CI: 1.37-3.36; p-trend: 0.002). A lower level of physical activity was associated with an increased risk of excess weight, although the association was statistically significant only in multiple linear regression (p=0.037). No association was observed with sleep duration. A greater number of hours spent watching television and lower physical activity were significantly associated with a higher BMI in young adults. Both factors are potentially modifiable with preventive strategies. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Forecasting generation of urban solid waste in developing countries--a case study in Mexico.

PubMed

Buenrostro, O; Bocco, G; Vence, J

2001-01-01

Based on a study of the composition of urban solid waste (USW) and of socioeconomic variables in Morelia, Mexico, generation rates were estimated. In addition, the generation of residential solid waste (RSW) and nonresidential solid waste (NRSW) was forecasted by means of a multiple linear regression (MLR) analysis. For residential sources, the independent variables analyzed were monthly wages, persons per dwelling, age, and educational level of the heads of the household. For nonresidential sources, variables analyzed were number of employees, area of facilities, number of working days, and working hours per day. The forecasted values for residential waste were similar to those observed. This approach may be applied to areas in which available data are scarce, and in which there is an urgent need for the planning of adequate management of USW.

A Comparison of Variable Selection Criteria for Multiple Linear Regression: A Second Simulation Study

DTIC Science & Technology

1993-03-01

statistical mathe- matics, began in the late 1800’s when Sir Francis Galton first attempted to use practical mathematical techniques to investigate the...randomly collected (sampled) many pairs of parent/child height mea- surements (data), Galton observed that for a given parent- height average, the...ty only Maximum Adjusted R2 will be discussed. However, Maximum Adjusted R’ and Minimum MSE test exactly the same 2.thing. Adjusted R is related to R

Advanced Electrocardiographic Predictors of Sudden Death in Familial Dysautonomia

NASA Technical Reports Server (NTRS)

Solaimanzadeh, I.; Schlegel, T. T.; Greco, E. C.; DePalma, J. L.; Starc, V.; Marthol, H.; Tutaj, M.; Buechner, S.; Axelrod, F. B.; Hilz, M. J.

2007-01-01

To identify accurate predictors for the risk of sudden death in patients with familial dysautonomia (FD). Ten-minute resting high-fidelity 12-lead ECGs were obtained from 14 FD patients and 14 age/gender-matched healthy subjects. Multiple conventional and advanced ECG parameters were studied for their ability to predict sudden death in FD over a subsequent 4.5-year period, including multiple indices of linear and non-linear heart rate variability (HRV); QT variability; waveform complexity; high frequency QRS; and derived Frank-lead parameters. Four of the 14 FD patients died suddenly during the follow-up period, usually with concomitant pulmonary disorder. The presence of low vagally-mediated HRV was the ECG finding most predictive of sudden death. Concomitant left ventricular hypertrophy and other ECG abnormalities such as increased QTc and JTc intervals, spatial QRS-T angles, T-wave complexity, and QT variability were also present in FD patients, suggesting that structural heart disease is fairly common in FD. Although excessive or unopposed cardiac vagal (relative to sympathetic) activity has been postulated as a contributor to sudden death in FD, the presence of low vagally-mediated HRV was paradoxically the best predictor of sudden death. However, we suggest that low vagally-mediated HRV be construed not as a direct cause of sudden death in FD, but rather as an effect of concurrent pathological processes, especially hypoxia due to pulmonary disorders and sleep apnea, that themselves increase the risk of sudden death in FD and simultaneously diminish HRV. We speculate that adenosine may play a role in sudden death in FD, possibly independently of vagal activity, and that adenosine inhibitors such as theophylline might therefore be useful as prophylaxis in this disorder.

Intimate relationship quality, self-concept and illness acceptance in those with multiple sclerosis.

PubMed

Wright, Thomas M; Kiropoulos, Litza A

2017-02-01

Lower levels of Intimate Relationship Quality (IRQ) have been found in those with Multiple Sclerosis (MS) compared to the general population. This study examined an MS sample to see whether IRQ was positively associated with self-concept, whether IRQ was positively associated with MS illness acceptance and whether IRQ was predicted by self-concept and illness acceptance. In this cross-sectional study, 115 participants with MS who were in an intimate relationship completed an online survey advertised on MS related websites. The survey assessed demographic variables, MS illness variables and levels of IRQ, self-concept and illness acceptance. Results revealed that IRQ was significantly positively associated with self-concept and with illness acceptance. Multiple hierarchical linear regression analysis revealed that, after controlling for illness duration and level of disability, self-concept significantly predicted IRQ but illness acceptance did not significantly predict IRQ. This study addressed several gaps and methodological flaws in the literature and was the first known to assess predictors of IRQ in those with MS. The results suggest that self-concept could be a potential target for individual and couple psychological interventions to improve IRQ and contribute to improved outcomes for those with MS.

Female homicide in Rio Grande do Sul, Brazil.

PubMed

Leites, Gabriela Tomedi; Meneghel, Stela Nazareth; Hirakata, Vania Noemi

2014-01-01

This study aimed to assess the female homicide rate due to aggression in Rio Grande do Sul, Brazil, using this as a "proxy" of femicide. This was an ecological study which correlated the female homicide rate due to aggression in Rio Grande do Sul, according to the 35 microregions defined by the Brazilian Institute of Geography and Statistics (IBGE), with socioeconomic and demographic variables access and health indicators. Pearson's correlation test was performed with the selected variables. After this, multiple linear regressions were performed with variables with p < 0.20. The standardized average of female homicide rate due to aggression in the period from 2003 to 2007 was 3.1 obits per 100 thousand. After multiple regression analysis, the final model included male mortality due to aggression (p = 0.016), the percentage of hospital admissions for alcohol (p = 0.005) and the proportion of ill-defined deaths (p = 0.015). The model have an explanatory power of 39% (adjusted r2 = 0.391). The results are consistent with other studies and indicate a strong relationship between structural violence in society and violence against women, in addition to a higher incidence of female deaths in places with high alcohol hospitalization.

Habitat characteristics affecting fish assemblages on a Hawaiian coral reef

USGS Publications Warehouse

Friedlander, A.M.; Parrish, J.D.

1998-01-01

Habitat characteristics of a reef were examined as potential influences on fish assemblage structure, using underwater visual census to estimate numbers and biomass of all fishes visible on 42 benthic transects and making quantitative measurements of 13 variables of the corresponding physical habitat and sessile biota. Fish assemblages in the diverse set of benthic habitats were grouped by detrended correspondence analysis, and associated with six major habitat types. Statistical differences were shown between a number of these habitat types for various ensemble variables of the fish assemblages. Overall, both for complete assemblages and for component major trophic and mobility guilds, these variables tended to have higher values where reef substratum was more structurally or topographically complex, and closer to reef edges. When study sites were separately divided into five depth strata, the deeper strata tended to have statistically higher values of ensemble variables for the fish assemblages. Patterns with depth varied among the various trophic and mobility guilds. Multiple linear regression models indicated that for the complete assemblages and for most trophic and mobility guilds, a large part of the variability for most ensemble variables was explained by measures of holes in the substratum, with important contributions from measured substratum rugosity and depth. A strong linear relationship found by regression of mean fish length on mean volume of holes in the reef surface emphasized the importance of shelter for fish assemblages. Results of this study may have practical applications in designing reserve areas as well as theoretical value in helping to explain the organization of reef fish assemblages.

Neural correlates of gait variability in people with multiple sclerosis with fall history.

PubMed

Kalron, Alon; Allali, Gilles; Achiron, Anat

2018-05-28

Investigate the association between step time variability and related brain structures in accordance with fall status in people with multiple sclerosis (PwMS). The study included 225 PwMS. A whole-brain MRI was performed by a high-resolution 3.0-Telsa MR scanner in addition to volumetric analysis based on 3D T1-weighted images using the FreeSurfer image analysis suite. Step time variability was measured by an electronic walkway. Participants were defined as "fallers" (at least two falls during the previous year) and "non-fallers". One hundred and five PwMS were defined as fallers and had a greater step time variability compared to non-fallers (5.6% (S.D.=3.4) vs. 3.4% (S.D.=1.5); p=0.001). MS fallers exhibited a reduced volume in the left caudate and both cerebellum hemispheres compared to non-fallers. By using a linear regression analysis no association was found between gait variability and related brain structures in the total cohort and non-fallers group. However, the analysis found an association between the left hippocampus and left putamen volumes with step time variability in the faller group; p=0.031, 0.048, respectively, controlling for total cranial volume, walking speed, disability, age and gender. Nevertheless, according to the hierarchical regression model, the contribution of these brain measures to predict gait variability was relatively small compared to walking speed. An association between low left hippocampal, putamen volumes and step time variability was found in PwMS with a history of falls, suggesting brain structural characteristics may be related to falls and increased gait variability in PwMS. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Determining vehicle operating speed and lateral position along horizontal curves using linear mixed-effects models.

PubMed

Fitzsimmons, Eric J; Kvam, Vanessa; Souleyrette, Reginald R; Nambisan, Shashi S; Bonett, Douglas G

2013-01-01

Despite recent improvements in highway safety in the United States, serious crashes on curves remain a significant problem. To assist in better understanding causal factors leading to this problem, this article presents and demonstrates a methodology for collection and analysis of vehicle trajectory and speed data for rural and urban curves using Z-configured road tubes. For a large number of vehicle observations at 2 horizontal curves located in Dexter and Ames, Iowa, the article develops vehicle speed and lateral position prediction models for multiple points along these curves. Linear mixed-effects models were used to predict vehicle lateral position and speed along the curves as explained by operational, vehicle, and environmental variables. Behavior was visually represented for an identified subset of "risky" drivers. Linear mixed-effect regression models provided the means to predict vehicle speed and lateral position while taking into account repeated observations of the same vehicle along horizontal curves. Speed and lateral position at point of entry were observed to influence trajectory and speed profiles. Rural horizontal curve site models are presented that indicate that the following variables were significant and influenced both vehicle speed and lateral position: time of day, direction of travel (inside or outside lane), and type of vehicle.

No association of smoke-free ordinances with profits from bingo and charitable games in Massachusetts

PubMed Central

Glantz, S; Wilson-Loots, R

2003-01-01

Background: Because it is widely played, claims that smoking restrictions will adversely affect bingo games is used as an argument against these policies. We used publicly available data from Massachusetts to assess the impact of 100% smoke-free ordinances on profits from bingo and other gambling sponsored by charitable organisations between 1985 and 2001. Methods: We conducted two analyses: (1) a general linear model implementation of a time series analysis with net profits (adjusted to 2001 dollars) as the dependent variable, and community (as a fixed effect), year, lagged net profits, and the length of time the ordinance had been in force as the independent variables; (2) multiple linear regression of total state profits against time, lagged profits, and the percentage of the entire state population in communities that allow charitable gaming but prohibit smoking. Results: The general linear model analysis of data from individual communities showed that, while adjusted profits fell over time, this effect was not related to the presence of an ordinance. The analysis in terms of the fraction of the population living in communities with ordinances yielded the same result. Conclusion: Policymakers can implement smoke-free policies without concern that these policies will affect charitable gaming. PMID:14660778

Serum bilirubin levels are positively associated with glycemic variability in women with type 2 diabetes.

PubMed

Kim, Lee Kyung; Roh, Eun; Kim, Min Joo; Kim, Min Kyeong; Park, Kyeong Seon; Kwak, Soo Heon; Cho, Young Min; Park, Kyong Soo; Jang, Hak Chul; Jung, Hye Seung

2016-11-01

Glycemic variability is known to induce oxidative stress. We investigated the relationships between glycemic variability and serum bilirubin levels, an endogenous anti-oxidant, in patients with diabetes. A cross-sectional study was carried out with 77 patients with type 2 diabetes who had been recruited to two clinical studies from 2008 to 2014. There were no participants with diseases of the pancreas, liver, biliary tract and chronic renal insufficiency. Glycemic variation was calculated by a continuous glucose monitoring system, and correlation analyses were carried out to evaluate their association with bilirubin levels. Multiple linear regression was carried out to identify independent factors influencing bilirubin levels and glycemic variation. Among the participants, 42.3% were men. The mean (standard deviation) age was 61.5 years (10.4 years), body mass index was 24.2 kg/m 2 (2.8 kg/m 2 ), diabetes duration was 17.7 years (9.5 years), hemoglobin A 1c was 60.7 mmol/mol (7.1 mmol/mol; 7.7 [0.7]%) and bilirubin was 11.8 μmol/L (4.10 μmol/L). Serum bilirubin levels were not different according to age, body mass index and hemoglobin A 1c . However, the mean amplitude of glucose excursion was positively associated with bilirubin levels in women (r = 0.588, P < 0.001). After adjustment with duration of diabetes, serum albumin, liver enzymes, and mean glucose, the correlation between bilirubin and mean amplitude of glucose excursion remained significant (r = 0.566, P < 0.001). Multiple linear regression analyses showed that bilirubin was an independent determinant for the mean amplitude of glucose excursion in women. 1,5-Anhydroglucitol was also associated with bilirubin levels in women. Bilirubin level within the physiological range might be an independent predictor for glycemic variability in women with type 2 diabetes. © 2016 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Prediction of Ba, Mn and Zn for tropical soils using iron oxides and magnetic susceptibility

NASA Astrophysics Data System (ADS)

Marques Júnior, José; Arantes Camargo, Livia; Reynaldo Ferracciú Alleoni, Luís; Tadeu Pereira, Gener; De Bortoli Teixeira, Daniel; Santos Rabelo de Souza Bahia, Angelica

2017-04-01

Agricultural activity is an important source of potentially toxic elements (PTEs) in soil worldwide but particularly in heavily farmed areas. Spatial distribution characterization of PTE contents in farming areas is crucial to assess further environmental impacts caused by soil contamination. Designing prediction models become quite useful to characterize the spatial variability of continuous variables, as it allows prediction of soil attributes that might be difficult to attain in a large number of samples through conventional methods. This study aimed to evaluate, in three geomorphic surfaces of Oxisols, the capacity for predicting PTEs (Ba, Mn, Zn) and their spatial variability using iron oxides and magnetic susceptibility (MS). Soil samples were collected from three geomorphic surfaces and analyzed for chemical, physical, mineralogical properties, as well as magnetic susceptibility (MS). PTE prediction models were calibrated by multiple linear regression (MLR). MLR calibration accuracy was evaluated using the coefficient of determination (R2). PTE spatial distribution maps were built using the values calculated by the calibrated models that reached the best accuracy by means of geostatistics. The high correlations between the attributes clay, MS, hematite (Hm), iron oxides extracted by sodium dithionite-citrate-bicarbonate (Fed), and iron oxides extracted using acid ammonium oxalate (Feo) with the elements Ba, Mn, and Zn enabled them to be selected as predictors for PTEs. Stepwise multiple linear regression showed that MS and Fed were the best PTE predictors individually, as they promoted no significant increase in R2 when two or more attributes were considered together. The MS-calibrated models for Ba, Mn, and Zn prediction exhibited R2 values of 0.88, 0.66, and 0.55, respectively. These are promising results since MS is a fast, cheap, and non-destructive tool, allowing the prediction of a large number of samples, which in turn enables detailed mapping of large areas. MS predicted values enabled the characterization and the understanding of spatial variability of the studied PTEs.

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

PubMed

Joseph, Mini; Gupta, Riddhi Das; Prema, L; Inbakumari, Mercy; Thomas, Nihal

2017-01-01

The accuracy of existing predictive equations to determine the resting energy expenditure (REE) of professional weightlifters remains scarcely studied. Our study aimed at assessing the REE of male Asian Indian weightlifters with indirect calorimetry and to compare the measured REE (mREE) with published equations. A new equation using potential anthropometric variables to predict REE was also evaluated. REE was measured on 30 male professional weightlifters aged between 17 and 28 years using indirect calorimetry and compared with the eight formulas predicted by Harris-Benedicts, Mifflin-St. Jeor, FAO/WHO/UNU, ICMR, Cunninghams, Owen, Katch-McArdle, and Nelson. Pearson correlation coefficient, intraclass correlation coefficient, and multiple linear regression analysis were carried out to study the agreement between the different methods, association with anthropometric variables, and to formulate a new prediction equation for this population. Pearson correlation coefficients between mREE and the anthropometric variables showed positive significance with suprailiac skinfold thickness, lean body mass (LBM), waist circumference, hip circumference, bone mineral mass, and body mass. All eight predictive equations underestimated the REE of the weightlifters when compared with the mREE. The highest mean difference was 636 kcal/day (Owen, 1986) and the lowest difference was 375 kcal/day (Cunninghams, 1980). Multiple linear regression done stepwise showed that LBM was the only significant determinant of REE in this group of sportspersons. A new equation using LBM as the independent variable for calculating REE was computed. REE for weightlifters = -164.065 + 0.039 (LBM) (confidence interval -1122.984, 794.854]. This new equation reduced the mean difference with mREE by 2.36 + 369.15 kcal/day (standard error = 67.40). The significant finding of this study was that all the prediction equations underestimated the REE. The LBM was the sole determinant of REE in this population. In the absence of indirect calorimetry, the REE equation developed by us using LBM is a better predictor for calculating REE of professional male weightlifters of this region.

Supporting second grade lower secondary school students’ understanding of linear equation system in two variables using ethnomathematics

NASA Astrophysics Data System (ADS)

Nursyahidah, F.; Saputro, B. A.; Rubowo, M. R.

2018-03-01

The aim of this research is to know the students’ understanding of linear equation system in two variables using Ethnomathematics and to acquire learning trajectory of linear equation system in two variables for the second grade of lower secondary school students. This research used methodology of design research that consists of three phases, there are preliminary design, teaching experiment, and retrospective analysis. Subject of this study is 28 second grade students of Sekolah Menengah Pertama (SMP) 37 Semarang. The result of this research shows that the students’ understanding in linear equation system in two variables can be stimulated by using Ethnomathematics in selling buying tradition in Peterongan traditional market in Central Java as a context. All of strategies and model that was applied by students and also their result discussion shows how construction and contribution of students can help them to understand concept of linear equation system in two variables. All the activities that were done by students produce learning trajectory to gain the goal of learning. Each steps of learning trajectory of students have an important role in understanding the concept from informal to the formal level. Learning trajectory using Ethnomathematics that is produced consist of watching video of selling buying activity in Peterongan traditional market to construct linear equation in two variables, determine the solution of linear equation in two variables, construct model of linear equation system in two variables from contextual problem, and solving a contextual problem related to linear equation system in two variables.

An Integrated Method to Analyze Farm Vulnerability to Climatic and Economic Variability According to Farm Configurations and Farmers' Adaptations.

PubMed

Martin, Guillaume; Magne, Marie-Angélina; Cristobal, Magali San

2017-01-01

The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers' technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers' technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 2008-2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4-66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers' practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use…) were more consistent with the objective of developing the properties targeted (efficiency, robustness…). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations.

An Integrated Method to Analyze Farm Vulnerability to Climatic and Economic Variability According to Farm Configurations and Farmers’ Adaptations

PubMed Central

Martin, Guillaume; Magne, Marie-Angélina; Cristobal, Magali San

2017-01-01

The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers’ technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers’ technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 2008–2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4–66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers’ practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use…) were more consistent with the objective of developing the properties targeted (efficiency, robustness…). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations. PMID:28900435

A stochastic-geometric model of soil variation in Pleistocene patterned ground

NASA Astrophysics Data System (ADS)

Lark, Murray; Meerschman, Eef; Van Meirvenne, Marc

2013-04-01

In this paper we examine the spatial variability of soil in parent material with complex spatial structure which arises from complex non-linear geomorphic processes. We show that this variability can be better-modelled by a stochastic-geometric model than by a standard Gaussian random field. The benefits of the new model are seen in the reproduction of features of the target variable which influence processes like water movement and pollutant dispersal. Complex non-linear processes in the soil give rise to properties with non-Gaussian distributions. Even under a transformation to approximate marginal normality, such variables may have a more complex spatial structure than the Gaussian random field model of geostatistics can accommodate. In particular the extent to which extreme values of the variable are connected in spatially coherent regions may be misrepresented. As a result, for example, geostatistical simulation generally fails to reproduce the pathways for preferential flow in an environment where coarse infill of former fluvial channels or coarse alluvium of braided streams creates pathways for rapid movement of water. Multiple point geostatistics has been developed to deal with this problem. Multiple point methods proceed by sampling from a set of training images which can be assumed to reproduce the non-Gaussian behaviour of the target variable. The challenge is to identify appropriate sources of such images. In this paper we consider a mode of soil variation in which the soil varies continuously, exhibiting short-range lateral trends induced by local effects of the factors of soil formation which vary across the region of interest in an unpredictable way. The trends in soil variation are therefore only apparent locally, and the soil variation at regional scale appears random. We propose a stochastic-geometric model for this mode of soil variation called the Continuous Local Trend (CLT) model. We consider a case study of soil formed in relict patterned ground with pronounced lateral textural variations arising from the presence of infilled ice-wedges of Pleistocene origin. We show how knowledge of the pedogenetic processes in this environment, along with some simple descriptive statistics, can be used to select and fit a CLT model for the apparent electrical conductivity (ECa) of the soil. We use the model to simulate realizations of the CLT process, and compare these with realizations of a fitted Gaussian random field. We show how statistics that summarize the spatial coherence of regions with small values of ECa, which are expected to have coarse texture and so larger saturated hydraulic conductivity, are better reproduced by the CLT model than by the Gaussian random field. This suggests that the CLT model could be used to generate an unlimited supply of training images to allow multiple point geostatistical simulation or prediction of this or similar variables.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Quantile regression models of animal habitat relationships

USGS Publications Warehouse

Cade, Brian S.

2003-01-01

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n, greater number of parameters p, and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F-ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large (N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling (n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel (Macomona liliana) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

Solar cycle in current reanalyses: (non)linear attribution study

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2014-12-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11 year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (Support Vector Regression, Neural Networks) besides the traditional linear approach. The analysis was applied to several current reanalysis datasets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how this type of data resolves especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the lower and upper stratosphere were found to be sufficiently robust and in qualitative agreement with previous observational studies. The analysis also pointed to the solar signal in the ozone datasets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. Consequently the results obtained by linear regression were confirmed by the nonlinear approach through all datasets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. Furthermore, the seasonal dependence of the solar response was also discussed, mainly as a source of dynamical causalities in the wave propagation characteristics in the zonal wind and the induced meridional circulation in the winter hemispheres. The hypothetical mechanism of a weaker Brewer Dobson circulation was reviewed together with discussion of polar vortex stability.

Linear and non-linear contributions to oxygen transport and utilization during moderate random exercise in humans.

PubMed

Beltrame, T; Hughson, R L

2017-05-01

What is the central question of this study? The pulmonary oxygen uptake (pV̇O2) data used to study the muscle aerobic system dynamics during moderate-exercise transitions is classically described as a mono-exponential function controlled by a complex interaction of the oxygen delivery-utilization balance. This elevated complexity complicates the acquisition of relevant information regarding aerobic system dynamics based on pV̇O2 data during a varying exercise stimulus. What is the main finding and its importance? The elevated complexity of pV̇O2 dynamics is a consequence of a multiple-order interaction between muscle oxygen uptake and circulatory distortion. Our findings challenge the use of a first-order function to study the influences of the oxygen delivery-utilization balance over the pV̇O2 dynamics. The assumption of aerobic system linearity implies that the pulmonary oxygen uptake (pV̇O2) dynamics during exercise transitions present a first-order characteristic. The main objective of this study was to test the linearity of the oxygen delivery-utilization balance during random moderate exercise. The cardiac output (Q̇) and deoxygenated haemoglobin concentration ([HHb]) were measured to infer the central and local O 2 availability, respectively. Thirteen healthy men performed two consecutive pseudorandom binary sequence cycling exercises followed by an incremental protocol. The system input and the outputs pV̇O2, [HHb] and Q̇ were submitted to frequency-domain analysis. The linearity of the variables was tested by computing the ability of the response at a specific frequency to predict the response at another frequency. The predictability levels were assessed by the coefficient of determination. In a first-order system, a participant who presents faster dynamics at a specific frequency should also present faster dynamics at any other frequency. All experimentally obtained variables (pV̇O2, [HHb] and Q̇) presented a certainly degree of non-linearity. The local O 2 availability, evaluated by the ratio pV̇O2/[HHb], presented the most irregular behaviour. The overall [HHb] kinetics were faster than pV̇O2 and Q̇ kinetics. In conclusion, the oxygen delivery-utilization balance behaved as a non-linear phenomenon. Therefore, the elevated complexity of the pulmonary oxygen uptake dynamics is governed by a complex multiple-order interaction between the oxygen delivery and utilization systems. © 2017 The Authors. Experimental Physiology © 2017 The Physiological Society.

Older age, higher perceived disability and depressive symptoms predict the amount and severity of work-related difficulties in persons with multiple sclerosis.

PubMed

Raggi, Alberto; Giovannetti, Ambra Mara; Schiavolin, Silvia; Brambilla, Laura; Brenna, Greta; Confalonieri, Paolo Agostino; Cortese, Francesca; Frangiamore, Rita; Leonardi, Matilde; Mantegazza, Renato Emilio; Moscatelli, Marco; Ponzio, Michela; Torri Clerici, Valentina; Zaratin, Paola; De Torres, Laura

2018-04-16

This cross-sectional study aims to identify the predictors of work-related difficulties in a sample of employed persons with multiple sclerosis as addressed with the Multiple Sclerosis Questionnaire for Job Difficulties. Hierarchical linear regression analysis was conducted to identify predictors of work difficulties: predictors included demographic variables (age, formal education), disease duration and severity, perceived disability and psychological variables (cognitive dysfunction, depression and anxiety). The targets were the questionnaire's overall score and its six subscales. A total of 177 participants (108 females, aged 21-63) were recruited. Age, perceived disability and depression were direct and significant predictors of the questionnaire total score, and the final model explained 43.7% of its variation. The models built on the questionnaire's subscales show that perceived disability and depression were direct and significant predictors of most of its subscales. Our results show that, among patients with multiple sclerosis, those who were older, with higher perceived disability and higher depression symptoms have more and more severe work-related difficulties. The Multiple Sclerosis Questionnaire for Job Difficulties can be fruitfully exploited to plan tailored actions to limit the likelihood of near-future job loss in persons of working age with multiple sclerosis. Implications for rehabilitation Difficulties with work are common among people with multiple sclerosis and are usually addressed in terms of unemployment or job loss. The Multiple Sclerosis Questionnaire for Job Difficulties is a disease-specific questionnaire developed to address the amount and severity of work-related difficulties. We found that work-related difficulties were associated to older age, higher perceived disability and depressive symptoms. Mental health issues and perceived disability should be consistently included in future research targeting work-related difficulties.

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

PubMed

Fisz, Jacek J

2006-12-07

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

PubMed Central

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-01-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population. PMID:28722705

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses.

PubMed

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-10-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population.

Association of Alimentary Factors and Nutritional Status with Caries in Children of Leon, Mexico.

PubMed

Guizar, Juan Manuel; Muñoz, Nathalie; Amador, Norma; Garcia, Gabriela

To determine the association between types of food consumed, nutritional status (BMI) and caries in schoolchildren. A cross-sectional study was performed with 224 schoolchildren 6 to 12 years of age. DMFT/ dmft indices, level of oral hygiene, nutritional status as quantified by BMI and types of food consumed were determined in all participants. Data were analysed using multiple linear regression with significance set at p < 0.05. Caries prevalence was 36%. In the multiple linear regression analysis adjusted for BMI, variables related to a higher number of caries were younger age and lower intake of vitamin D, calcium and fiber, with higher consumption of phosphorous and carbohydrates (R2 = 0.30; p < 0.0001 for the model). Sweetened softdrinks and chewy candy were risk factors for higher caries prevalence, while consuming milk and carrots were protectors. Caries in schoolchildren is highly prevalent in this community and is related to younger age and lower intake of vitamin D, calcium and fiber, but a higher consumption of phosphorous and carbohydrates. No relationship was found between caries and nutritional status.

Using Modern C++ Idiom for the Discretisation of Sets of Coupled Transport Equations in Numerical Plasma Physics

NASA Astrophysics Data System (ADS)

van Dijk, Jan; Hartgers, Bart; van der Mullen, Joost

2006-10-01

Self-consistent modelling of plasma sources requires a simultaneous treatment of multiple physical phenomena. As a result plasma codes have a high degree of complexity. And with the growing interest in time-dependent modelling of non-equilibrium plasma in three dimensions, codes tend to become increasingly hard to explain-and-maintain. As a result of these trends there has been an increased interest in the software-engineering and implementation aspects of plasma modelling in our group at Eindhoven University of Technology. In this contribution we will present modern object-oriented techniques in C++ to solve an old problem: that of the discretisation of coupled linear(ized) equations involving multiple field variables on ortho-curvilinear meshes. The `LinSys' code has been tailored to the transport equations that occur in transport physics. The implementation has been made both efficient and user-friendly by using modern idiom like expression templates and template meta-programming. Live demonstrations will be given. The code is available to interested parties; please visit www.dischargemodelling.org.

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

PubMed

Golmohammadi, Hassan

2009-11-30

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

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

An efficient method for generalized linear multiplicative programming problem with multiplicative constraints.

PubMed

Zhao, Yingfeng; Liu, Sanyang

2016-01-01

We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient.

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

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

2013-10-01

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

Digital robust active control law synthesis for large order systems using constrained optimization

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

1987-01-01

This paper presents a direct digital control law synthesis procedure for a large order, sampled data, linear feedback system using constrained optimization techniques to meet multiple design requirements. A linear quadratic Gaussian type cost function is minimized while satisfying a set of constraints on the design loads and responses. General expressions for gradients of the cost function and constraints, with respect to the digital control law design variables are derived analytically and computed by solving a set of discrete Liapunov equations. The designer can choose the structure of the control law and the design variables, hence a stable classical control law as well as an estimator-based full or reduced order control law can be used as an initial starting point. Selected design responses can be treated as constraints instead of lumping them into the cost function. This feature can be used to modify a control law, to meet individual root mean square response limitations as well as minimum single value restrictions. Low order, robust digital control laws were synthesized for gust load alleviation of a flexible remotely piloted drone aircraft.

Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise.

PubMed

Ražanskas, Petras; Verikas, Antanas; Olsson, Charlotte; Viberg, Per-Arne

2015-08-19

This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from R(2) = 0:77 to R(2) = 0:98 (for blood lactate) and from R(2) = 0:81 to R(2) = 0:97 (for oxygen uptake) were obtained when using random forest regressors.

Regional investigations of soil and overburden analysis and plant uptake of metals

USGS Publications Warehouse

Gough, L.P.

1984-01-01

Regional studies on the bioavailability of metals at native and disturbed sites were conducted over the past seven years by the USGS. The work was concentrated in the Fort Union, Powder River, and Green River coal resource regions where measures of extractable metals in soils were found to have limited use in predicting metal levels in plants. Correlations between Cu, Fe, and Zn in plants and extractable (DTPA, EDTA, and oxalate) or total levels in native A- and C-horizons of soil were occasionally significant. A simple linear model is generally not adequate, however, in estimating element uptake by plants. Prediction capabilities were improved when a number of soil chemical and physical parameters were included as independent variables in a stepwise linear multiple regression analysis; however, never more than 54% of the total variability in the data was explained by the equations for these metals. Soil pH was the most important variable relating soil chemistry to plant chemistry. This relation was always positive and apparently a response to soil levels of metal carbonates and not Fe and Mn oxides. Studies that compared the metal uptake by rehabilitation species to extractable (DTPA) metal levels in mice soils produced similar results. ?? 1984 Science and Technology Letters.

The predictors of chemistry achievement of 12th grade students in secondary schools in the United Arab Emirates

NASA Astrophysics Data System (ADS)

Khalaf, Ali Khalfan

2000-10-01

The purpose of this study is to explore variables related to chemistry achievement of 12th grade science students in the United Arab Emirates (UAE). The focus is to identify student, teacher, and school variables that predict chemistry achievement. The analysis sample included 204 males and 252 females in 66 classes in 60 schools from 10 districts or bureaus of education in the UAE. Thirty-two male and 33 female chemistry teachers and 60 school principals were included. The Khalaf Chemistry Achievement Test, GALT, the Student Questionnaire, Teacher Questionnaire, and School Information Questionnaire were administered. Descriptive statistics, correlations, analyses of variance, factor analysis, and stepwise multiple linear regression analyses were done. The results indicate that demographic, home environment, prior knowledge, scholastic ability, attitudes and perceptions related to chemistry and science, and student perception of instructional practices variables correlated with student chemistry achievement. The amount of help teachers received from the supervisor, class size, and courses in geology were teacher variables that correlated with class chemistry achievement. Nine school variables involving school, division, and class sizes correlated with school chemistry achievement. Analyses of variance revealed significant interaction effects: district by school size and district by student gender. In two districts, students in small schools achieved better than those in large schools. Generally female students achieved equal to or better than males. Three factors from the factor analysis: School Size, Prior Student Achievement, and Student Perception of Teacher Effectiveness, correlated with school chemistry achievement. The results of the multiple linear regression indicated that the factors of Prior Student Achievement, Student Perception of Teacher Effectiveness, and Teacher Experience and Expertise accounted for 45% of the variance in school chemistry achievement. Results indicate that the strongest predictors of chemistry achievement are prior achievement in science, Arabic language, and mathematics; student perception of teacher effectiveness; and teacher experience and expertise. Females tend to achieve better in chemistry than males. No nationality differences were found and the relationship of school size to chemistry achievement was inconclusive. Recommendations related to chemistry and science are presented. These include curriculum, school practice, teacher professional development, and future research.

Interresponse Time Structures in Variable-Ratio and Variable-Interval Schedules

ERIC Educational Resources Information Center

Bowers, Matthew T.; Hill, Jade; Palya, William L.

2008-01-01

The interresponse-time structures of pigeon key pecking were examined under variable-ratio, variable-interval, and variable-interval plus linear feedback schedules. Whereas the variable-ratio and variable-interval plus linear feedback schedules generally resulted in a distinct group of short interresponse times and a broad distribution of longer…

White Blood Cells, Neutrophils, and Reactive Oxygen Metabolites among Asymptomatic Subjects.

PubMed

Kotani, Kazuhiko; Sakane, Naoki

2012-06-01

Chronic inflammation and oxidative stress are associated with health and the disease status. The objective of the present study was to investigate the association among white blood cell (WBC) counts, neutrophil counts as a WBC subpopulation, and diacron reactive oxygen metabolites (d-ROMs) levels in an asymptomatic population. The clinical data, including general cardiovascular risk variables and high-sensitivity C-reactive protein (hs-CRP), were collected from 100 female subjects (mean age, 62 years) in outpatient clinics. The correlation of the d-ROMs with hs-CRP, WBC, and neutrophil counts was examined. The mean/median levels were WBC counts 5.9 × 10(9)/L, neutrophil counts 3.6 × 10(9)/L, hs-CRP 0.06 mg/dL, and d-ROMs 359 CURR U. A simple correlation analysis showed a significant positive correlation of the d-ROMs with the WBC counts, neutrophil counts, or hs-CRP levels. The correlation between d-ROMs and neutrophil counts (β = 0.22, P < 0.05), as well as that between d-ROMs and hs-CRP (β = 0.28, P < 0.01), remained significant and independent in a multiple linear regression analysis adjusted for other variables. A multiple linear regression analysis showed that WBC counts had only a positive correlation tendency to the d-ROMs. Neutrophils may be slightly but more involved in the oxidative stress status, as assessed by d-ROMs, in comparison to the overall WBC. Further studies are needed to clarify the biologic mechanism(s) of the observed relationship.

Influence of Competitive-Anxiety on Heart Rate Variability in Swimmers.

PubMed

Fortes, Leonardo S; da Costa, Bruna D V; Paes, Pedro P; do Nascimento Júnior, José R A; Fiorese, Lenamar; Ferreira, Maria E C

2017-12-01

The aim of this study was to analyze the relationship between competitive anxiety and heart rate variability (HRV) in swimming athletes. A total of 66 volunteers (41 male and 27 female) who swam the 400-m freestyle in the Brazilian Swimming Championships participated. Thirty minutes before the 400-m freestyle event, the athletes answered the Competitive Anxiety Inventory (CSAI-2R) questionnaire, then underwent anthropometric (body weight, height, and skinfold thickness) and HRV measurements. Then, at a second meeting, held 3 h after the 400-m freestyle event, the athletes returned to the evaluation room for HRV measurement (Polar ® RS800cx, Kempele, Finland). Multiple linear regression was used to evaluate the relationship between competitive anxiety and HRV. The multiple linear regression was performed in three blocks (block 1: cognitive anxiety, block 2: somatic anxiety, and block 3: self-confidence), adopting the forward model. The results indicated a significant association between cognitive anxiety (p = 0.001) and HRV. An increased magnitude of the association was observed when somatic anxiety was inserted in the model (p = 0.001). In contrast, self-confidence showed, which was inserted in block 3, no relationship with HRV (p = 0.27). It was concluded that cognitive and somatic anxieties were associated with the HRV of swimmers. Athletes with a high magnitude of cognitive and/or somatic anxiety demonstrated more significant autonomic nervous system disturbance. Practically, psychological interventions are needed to improve anxiety states that are specific to perform well, and to improve HRV.

Inflammation, homocysteine and carotid intima-media thickness.

PubMed

Baptista, Alexandre P; Cacdocar, Sanjiva; Palmeiro, Hugo; Faísca, Marília; Carrasqueira, Herménio; Morgado, Elsa; Sampaio, Sandra; Cabrita, Ana; Silva, Ana Paula; Bernardo, Idalécio; Gome, Veloso; Neves, Pedro L

2008-01-01

Cardiovascular disease is the main cause of morbidity and mortality in chronic renal patients. Carotid intima-media thickness (CIMT) is one of the most accurate markers of atherosclerosis risk. In this study, the authors set out to evaluate a population of chronic renal patients to determine which factors are associated with an increase in intima-media thickness. We included 56 patients (F=22, M=34), with a mean age of 68.6 years, and an estimated glomerular filtration rate of 15.8 ml/min (calculated by the MDRD equation). Various laboratory and inflammatory parameters (hsCRP, IL-6 and TNF-alpha) were evaluated. All subjects underwent measurement of internal carotid artery intima-media thickness by high-resolution real-time B-mode ultrasonography using a 10 MHz linear transducer. Intima-media thickness was used as a dependent variable in a simple linear regression model, with the various laboratory parameters as independent variables. Only parameters showing a significant correlation with CIMT were evaluated in a multiple regression model: age (p=0.001), hemoglobin (p=00.3), logCRP (p=0.042), logIL-6 (p=0.004) and homocysteine (p=0.002). In the multiple regression model we found that age (p=0.001) and homocysteine (p=0.027) were independently correlated with CIMT. LogIL-6 did not reach statistical significance (p=0.057), probably due to the small population size. The authors conclude that age and homocysteine correlate with carotid intima-media thickness, and thus can be considered as markers/risk factors in chronic renal patients.

Direct Multiple Shooting Optimization with Variable Problem Parameters

NASA Technical Reports Server (NTRS)

Whitley, Ryan J.; Ocampo, Cesar A.

2009-01-01

Taking advantage of a novel approach to the design of the orbital transfer optimization problem and advanced non-linear programming algorithms, several optimal transfer trajectories are found for problems with and without known analytic solutions. This method treats the fixed known gravitational constants as optimization variables in order to reduce the need for an advanced initial guess. Complex periodic orbits are targeted with very simple guesses and the ability to find optimal transfers in spite of these bad guesses is successfully demonstrated. Impulsive transfers are considered for orbits in both the 2-body frame as well as the circular restricted three-body problem (CRTBP). The results with this new approach demonstrate the potential for increasing robustness for all types of orbit transfer problems.

Income analysis of goat farmers on the farmers group in district of Serdang Bedagai

NASA Astrophysics Data System (ADS)

Manurung, J. N.; Hasnudi; Supriana, T.

2018-02-01

The farmers group are expected to reduce the production cost of goat breeding and improve the income of farmers which impact on the welfare of goat farmers. This research aim to analyze the factors that influence the income of farmers group, in sub-district Dolok Masihul Pegajahan, and Dolok Merawan, Serdang Bedagai. The method used is survey method with 90 respondents. Data was analysed by multiple linear regression. The result showed, simultaneously goat cost, sale price of goat, fixed cost and variable cost had significant effect on income of goat farmers. Partially, goat cost, variable cost and sale price of goat had significant effect on income of goat farmers, while fixed cost had no significant effect.

An extended Kalman-Bucy filter for atmospheric temperature profile retrieval with a passive microwave sounder

NASA Technical Reports Server (NTRS)

Ledsham, W. H.; Staelin, D. H.

1978-01-01

An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.

Probabilistic lifetime strength of aerospace materials via computational simulation

NASA Technical Reports Server (NTRS)

Boyce, Lola; Keating, Jerome P.; Lovelace, Thomas B.; Bast, Callie C.

1991-01-01

The results of a second year effort of a research program are presented. The research included development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic phenomenological constitutive relationship, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects of primitive variables. These primitive variables often originate in the environment and may include stress from loading, temperature, chemical, or radiation attack. This multifactor interaction constitutive equation is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the constitutive equation using actual experimental materials data together with the multiple linear regression of that data.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

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

PubMed

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

2009-01-01

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

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

ERIC Educational Resources Information Center

Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

2009-01-01

This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

Cooperation without Culture? The Null Effect of Generalized Trust on Intentional Homicide: A Cross-National Panel Analysis, 1995–2009

PubMed Central

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation. PMID:23527211

Factors influencing the ablative efficiency of high intensity focused ultrasound (HIFU) treatment for adenomyosis: A retrospective study.

PubMed

Gong, Chunmei; Yang, Bin; Shi, Yarong; Liu, Zhongqiong; Wan, Lili; Zhang, Hong; Jiang, Denghua; Zhang, Lian

2016-08-01

Objectives The aim of this study was to investigate factors affecting ablative efficiency of high intensity focused ultrasound (HIFU) for adenomyosis. Materials and methods In all, 245 patients with adenomyosis who underwent ultrasound guided HIFU (USgHIFU) were retrospectively reviewed. All patients underwent dynamic contrast-enhanced magnetic resonance imaging (MRI) before and after HIFU treatment. The non-perfused volume (NPV) ratio, energy efficiency factor (EEF) and greyscale change were set as dependent variables, while the factors possibly affecting ablation efficiency were set as independent variables. These variables were used to build multiple regression models. Results A total of 245 patients with adenomyosis successfully completed HIFU treatment. Enhancement type on T1 weighted image (WI), abdominal wall thickness, volume of adenomyotic lesion, the number of hyperintense points, location of the uterus, and location of adenomyosis all had a linear relationship with the NPV ratio. Distance from skin to the adenomyotic lesion's ventral side, enhancement type on T1WI, volume of adenomyotic lesion, abdominal wall thickness, and signal intensity on T2WI all had a linear relationship with EEF. Location of the uterus and abdominal wall thickness also both had a linear relationship with greyscale change. Conclusion The enhancement type on T1WI, signal intensity on T2WI, volume of adenomyosis, location of the uterus and adenomyosis, number of hyperintense points, abdominal wall thickness, and distance from the skin to the adenomyotic lesion's ventral side can all be used as predictors of HIFU for adenomyosis.

Immobilized Metal Affinity Chromatography Coupled to Multiple Reaction Monitoring Enables Reproducible Quantification of Phospho-signaling*

PubMed Central

Kennedy, Jacob J.; Yan, Ping; Zhao, Lei; Ivey, Richard G.; Voytovich, Uliana J.; Moore, Heather D.; Lin, Chenwei; Pogosova-Agadjanyan, Era L.; Stirewalt, Derek L.; Reding, Kerryn W.; Whiteaker, Jeffrey R.; Paulovich, Amanda G.

2016-01-01

A major goal in cell signaling research is the quantification of phosphorylation pharmacodynamics following perturbations. Traditional methods of studying cellular phospho-signaling measure one analyte at a time with poor standardization, rendering them inadequate for interrogating network biology and contributing to the irreproducibility of preclinical research. In this study, we test the feasibility of circumventing these issues by coupling immobilized metal affinity chromatography (IMAC)-based enrichment of phosphopeptides with targeted, multiple reaction monitoring (MRM) mass spectrometry to achieve precise, specific, standardized, multiplex quantification of phospho-signaling responses. A multiplex immobilized metal affinity chromatography- multiple reaction monitoring assay targeting phospho-analytes responsive to DNA damage was configured, analytically characterized, and deployed to generate phospho-pharmacodynamic curves from primary and immortalized human cells experiencing genotoxic stress. The multiplexed assays demonstrated linear ranges of ≥3 orders of magnitude, median lower limit of quantification of 0.64 fmol on column, median intra-assay variability of 9.3%, median inter-assay variability of 12.7%, and median total CV of 16.0%. The multiplex immobilized metal affinity chromatography- multiple reaction monitoring assay enabled robust quantification of 107 DNA damage-responsive phosphosites from human cells following DNA damage. The assays have been made publicly available as a resource to the community. The approach is generally applicable, enabling wide interrogation of signaling networks. PMID:26621847

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

PubMed Central

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

2015-01-01

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

Introduction to the use of regression models in epidemiology.

PubMed

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.

Assessing Factors Contributing to Cyanobacteria Harmful Algal Blooms in U.S. Lakes

NASA Astrophysics Data System (ADS)

Salls, W. B.; Iiames, J. S., Jr.; Lunetta, R. S.; Mehaffey, M.; Schaeffer, B. A.

2017-12-01

Cyanobacteria Harmful Algal Blooms (CHABs) in inland lakes have emerged as a major threat to water quality from both ecological and public health standpoints. Understanding the factors and processes driving CHAB occurrence is important in order to properly manage ensuring more favorable water quality outcomes. High water temperatures and nutrient loadings are known drivers of CHABs; however, the contribution of landscape variables and their interactions with these drivers remains relatively unstudied at a regional or national scale. This study assesses upstream landscape variables that may contribute to or obstruct/delay nutrient loadings to freshwater systems in several hundred inland lakes in the Upper Mid-western and Northeastern United States. We employ multiple linear regression and random forest modeling to determine which variables contribute most strongly to CHAB occurrence. This lakeshed-based approach will rank the impact of each landscape variable on cyanobacteria levels derived from satellite remotely sensed data from the Medium Resolution Imaging Spectrometer (MERIS) sensor for the 2011 bloom season (July - October).

[Winter wheat yield gap between field blocks based on comparative performance analysis].

PubMed

Chen, Jian; Wang, Zhong-Yi; Li, Liang-Tao; Zhang, Ke-Feng; Yu, Zhen-Rong

2008-09-01

Based on a two-year household survey data, the yield gap of winter wheat in Quzhou County of Hebei Province, China in 2003-2004 was studied through comparative performance analysis (CPA). The results showed that there was a greater yield gap (from 4.2 to 7.9 t x hm(-2)) between field blocks, with a variation coefficient of 0.14. Through stepwise forward linear multiple regression, it was found that the yield model with 8 selected variables could explain 63% variability of winter wheat yield. Among the variables selected, soil salinity, soil fertility, and irrigation water quality were the most important limiting factors, accounting for 52% of the total yield gap. Crop variety was another important limiting factor, accounting for 14%; while planting date, fertilizer type, disease and pest, and water press accounted for 7%, 14%, 10%, and 3%, respectively. Therefore, besides soil and climate conditions, management practices occupied the majority of yield variability in Quzhou County, suggesting that the yield gap could be reduced significantly through optimum field management.

Relationship of negative self-schemas and attachment styles with appearance schemas.

PubMed

Ledoux, Tracey; Winterowd, Carrie; Richardson, Tamara; Clark, Julie Dorton

2010-06-01

The purpose was to test, among women, the relationship between negative self-schemas and styles of attachment with men and women and two types of appearance investment (Self-evaluative and Motivational Salience). Predominantly Caucasian undergraduate women (N=194) completed a modified version of the Relationship Questionnaire, the Young Schema Questionnaire-Short Form, and the Appearance Schemas Inventory-Revised. Linear multiple regression analyses were conducted with Motivational Salience and Self-evaluative Salience of appearance serving as dependent variables and relevant demographic variables, negative self-schemas, and styles of attachment to men serving as independent variables. Styles of attachment to women were not entered into these regression models because Pearson correlations indicated they were not related to either dependent variable. Self-evaluative Salience of appearance was related to impaired autonomy and performance negative self-schema and the preoccupation style of attachment with men, while Motivational Salience of appearance was related only to the preoccupation style of attachment with men. 2010 Elsevier Ltd. All rights reserved.

Testing the dose-response specification in epidemiology: public health and policy consequences for lead.

PubMed

Rothenberg, Stephen J; Rothenberg, Jesse C

2005-09-01

Statistical evaluation of the dose-response function in lead epidemiology is rarely attempted. Economic evaluation of health benefits of lead reduction usually assumes a linear dose-response function, regardless of the outcome measure used. We reanalyzed a previously published study, an international pooled data set combining data from seven prospective lead studies examining contemporaneous blood lead effect on IQ (intelligence quotient) of 7-year-old children (n = 1,333). We constructed alternative linear multiple regression models with linear blood lead terms (linear-linear dose response) and natural-log-transformed blood lead terms (log-linear dose response). We tested the two lead specifications for nonlinearity in the models, compared the two lead specifications for significantly better fit to the data, and examined the effects of possible residual confounding on the functional form of the dose-response relationship. We found that a log-linear lead-IQ relationship was a significantly better fit than was a linear-linear relationship for IQ (p = 0.009), with little evidence of residual confounding of included model variables. We substituted the log-linear lead-IQ effect in a previously published health benefits model and found that the economic savings due to U.S. population lead decrease between 1976 and 1999 (from 17.1 microg/dL to 2.0 microg/dL) was 2.2 times (319 billion dollars) that calculated using a linear-linear dose-response function (149 billion dollars). The Centers for Disease Control and Prevention action limit of 10 microg/dL for children fails to protect against most damage and economic cost attributable to lead exposure.

The influence of obstetric variables on school achievement, intelligence and neuropsychological development in a sample of Spanish twins at the age of six: a retrospective study.

PubMed

González-Mesa, Ernesto; Cazorla-Granados, Olga; González-Valenzuela, María José

2016-01-01

The increasing number of multiple pregnancies in recent years has raised a particular concern about the problems associated to these pregnancies. It still remains unclear whether twin deliveries, as currently conceived, provide the same future health chances to both fetuses. In this regard, it is worth mentioning that the effects of obstetric and neonatal care beyond the perinatal period have not often been evaluated. The main objective of this research was to analyze the impact of obstetric and perinatal variables on postnatal neuropsychological development, intelligence and school achievement of twin children. We conducted a cross-sectional and observational study on 62 pairs of 6-year-old twins, who were on their first year of primary education. All 124 children and their mother were individually assessed and perinatal clinical data were collected. A stratified multivariate analysis was performed using multiple linear regressions. The type of birth was the best predicting variable, so that the best results were achieved in children born in spontaneous vaginal deliveries. Comparatively, however, poorer scores were seen in males second twins born by the vaginal route in spatial structuring, non-verbal development and total development areas, especially in case of delivery before 37 weeks. Our study confirms the impact of some obstetric variables on school achievement and psychological development of twins.

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

NASA Astrophysics Data System (ADS)

Martínez-Fernández, J.; Chuvieco, E.; Koutsias, N.

2013-02-01

Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983-2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character.

Experimental and analytical investigations of longitudinal combustion instability in a continuously variable resonance combustor (CVRC)

NASA Astrophysics Data System (ADS)

Yu, Yen Ching

An analytical model based on linearized Euler equations (LEE) is developed and used in conjunction with a validating experiment to study combustion instability. The LEE model features mean flow effects, entropy waves, adaptability for more physically-realistic boundary conditions, and is generalized for multiple-domain conditions. The model calculates spatial modes, resonant frequencies and linear growth rates of the overall system. The predicted resonant frequencies and spatially-resolved mode shapes agree with the experimental data from a longitudinally-unstable model rocket combustor to within 7%. Different gaseous fuels (methane, ethylene, and hydrogen) were tested under fixed geometry. Tests with hydrogen were stable, whereas ethylene, methane, and JP-8 were increasingly unstable. A novel method for obtaining large amounts of stability data under variable resonance conditions in a single test was demonstrated. The continuously variable resonance combustor (CVRC) incorporates a traversing choked axial oxidizer inlet to vary the overall combustion system resonance. The CVRC experiment successfully demonstrates different level of instability, transitions between stability levels, and identifies the most stable and unstable geometric combination. Pressure oscillation amplitudes ranged from less than 10% of mean pressure to greater than 60%. At low amplitudes, measured resonant frequency changed with inlet location but at high amplitude the measured resonance frequency matched the frequency of the combustion chamber. As the system transitions from linear to non-linear instability, the higher harmonics of the fundamental resonant mode appear nearly simultaneously. Transient, high-amplitude, broadband noise, at lower frequencies (on the order of 200 Hz) are also observed. Conversely, as the system transitions back to a more linear stability regime, the higher harmonics disappear sequentially, led by the highest order. Good agreements between analytical and experimental results are attained by treating the experiment as quasi-stationary. The stability characteristics from the high frequency measurements are further analyzed using filtered pressure traces, spectrograms, power spectral density plots, and oscillation decrements. Future works recommended include: direct measurements, such as chemiluminescence or high-speed imaging to examine the unsteady combustion processes; three-way comparisons between the acoustic-based, linear Euler-based, and non-linear Euler/RANS model; use the high fidelity computation to investigate the forcing terms modeled in the acoustic-based model.

Galaxy Clustering, Photometric Redshifts and Diagnosis of Systematics in the DES Science Verification Data

DOE PAGES

Crocce, M.

2015-12-09

We study the clustering of galaxies detected at i < 22.5 in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using 2.3 × 106 galaxies over a contiguous 116 deg 2 region in five bins of photometric redshift width Δz = 0.2 in the range 0.2 < z < 1.2. The impact of photometric redshift errors is assessed by comparing results using a template-based photo-zalgorithm (BPZ) to a machine-learning algorithm (TPZ). A companion paper presents maps of several observational variables (e.g. seeing, sky brightness) which could modulate the galaxy density. Here we characterizemore » and mitigate systematic errors on the measured clustering which arise from these observational variables, in addition to others such as Galactic dust and stellar contamination. After correcting for systematic effects, we then measure galaxy bias over a broad range of linear scales relative to mass clustering predicted from the Planck Λ cold dark matter model, finding agreement with the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) measurements with χ 2 of 4.0 (8.7) with 5 degrees of freedom for the TPZ (BPZ) redshifts. Furthermore, we test a ‘linear bias’ model, in which the galaxy clustering is a fixed multiple of the predicted non-linear dark matter clustering. The precision of the data allows us to determine that the linear bias model describes the observed galaxy clustering to 2.5 percent accuracy down to scales at least 4–10 times smaller than those on which linear theory is expected to be sufficient.« less

Galaxy Clustering, Photometric Redshifts and Diagnosis of Systematics in the DES Science Verification Data

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

Crocce, M.

We study the clustering of galaxies detected at i < 22.5 in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using 2.3 × 106 galaxies over a contiguous 116 deg 2 region in five bins of photometric redshift width Δz = 0.2 in the range 0.2 < z < 1.2. The impact of photometric redshift errors is assessed by comparing results using a template-based photo-zalgorithm (BPZ) to a machine-learning algorithm (TPZ). A companion paper presents maps of several observational variables (e.g. seeing, sky brightness) which could modulate the galaxy density. Here we characterizemore » and mitigate systematic errors on the measured clustering which arise from these observational variables, in addition to others such as Galactic dust and stellar contamination. After correcting for systematic effects, we then measure galaxy bias over a broad range of linear scales relative to mass clustering predicted from the Planck Λ cold dark matter model, finding agreement with the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) measurements with χ 2 of 4.0 (8.7) with 5 degrees of freedom for the TPZ (BPZ) redshifts. Furthermore, we test a ‘linear bias’ model, in which the galaxy clustering is a fixed multiple of the predicted non-linear dark matter clustering. The precision of the data allows us to determine that the linear bias model describes the observed galaxy clustering to 2.5 percent accuracy down to scales at least 4–10 times smaller than those on which linear theory is expected to be sufficient.« less

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Race-ethnicity is a strong correlate of circulating fat-soluble nutrient concentrations in a representative sample of the U.S. population.

PubMed

Schleicher, Rosemary L; Sternberg, Maya R; Pfeiffer, Christine M

2013-06-01

Sociodemographic and lifestyle factors exert important influences on nutritional status; however, information on their association with biomarkers of fat-soluble nutrients is limited, particularly in a representative sample of adults. Serum or plasma concentrations of vitamin A, vitamin E, carotenes, xanthophylls, 25-hydroxyvitamin D [25(OH)D], SFAs, MUFAs, PUFAs, and total fatty acids (tFAs) were measured in adults (aged ≥ 20 y) during all or part of NHANES 2003-2006. Simple and multiple linear regression models were used to assess 5 sociodemographic variables (age, sex, race-ethnicity, education, and income) and 5 lifestyle behaviors (smoking, alcohol consumption, BMI, physical activity, and supplement use) and their relation to biomarker concentrations. Adjustment for total serum cholesterol and lipid-altering drug use was added to the full regression model. Adjustment for latitude and season was added to the full model for 25(OH)D. Based on simple linear regression, race-ethnicity, BMI, and supplement use were significantly related to all fat-soluble biomarkers. Sociodemographic variables as a group explained 5-17% of biomarker variability, whereas together, sociodemographic and lifestyle variables explained 22-23% [25(OH)D, vitamin E, xanthophylls], 17% (vitamin A), 15% (MUFAs), 10-11% (SFAs, carotenes, tFAs), and 6% (PUFAs) of biomarker variability. Although lipid adjustment explained additional variability for all biomarkers except for 25(OH)D, it appeared to be largely independent of sociodemographic and lifestyle variables. After adjusting for sociodemographic, lifestyle, and lipid-related variables, major differences in biomarkers were associated with race-ethnicity (from -44 to 57%), smoking (up to -25%), supplement use (up to 21%), and BMI (up to -15%). Latitude and season attenuated some race-ethnicity differences. Of the sociodemographic and lifestyle variables examined, with or without lipid adjustment, most fat-soluble nutrient biomarkers were significantly associated with race-ethnicity.

[Bioacoustic of the advertisement call of Ceratophrys cranwelli (Anura: Ceratophryidae)].

PubMed

Valetti, Julián Alonso; Salas, Nancy Edith; Martino, Adolfo Ludovico

2013-03-01

The advertisement call plays an important role in the life history of anuran amphibians, mainly during the breeding season. Call features represent an important character to discriminate species, and sound emissions are very effective to assure or reinforce genetic incompatibility, especially in the case of sibling species. Since frogs are ectotherms, acoustic properties of their calls will vary with temperature. In this study, we described the advertisement call of C. cranwelli, quantifying the temperature effect on its components. The acoustic emissions were recorded during 2007 using a DAT record Sony TCD-100 with stereo microphone ECM-MS907 Sony and tape TDK DAT-RGX 60. As males emit their calls floating in temporary ponds, water temperatures were registered after recording the advertisement calls with a digital thermometer TES 1300+/-0.1 degreeC. Altogether, 54 calls from 18 males were analyzed. The temporal variables of each advertisement call were measured using oscillograms and sonograms and the analyses of dominant frequency were performed using a spectrogram. Multiple correlation analysis was used to identify the temperature-dependent acoustic variables and the temperature effect on these variables was quantified using linear regression models. The advertisement call of C. cranwelli consists of a single pulse group. Call duration, Pulse duration and Pulse interval decreased with the temperature, whereas the Pulse rate increased with temperature. The temperature-dependent variables were standardized at 25 degreeC according to the linear regression model obtained. The acoustic variables that were correlated with the temperature are the variables which emissions depend on laryngeal muscles and the temperature constraints the contractile properties of muscles. Our results indicated that temperature explains an important fraction of the variability in some acoustic variables (79% in the Pulse rate), and demonstrated the importance of considering the effect of temperature in acoustic components. The results suggest that acoustic variables show geographic variation to compare data with previous works.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

Missing Data and Multiple Imputation: An Unbiased Approach

NASA Technical Reports Server (NTRS)

Foy, M.; VanBaalen, M.; Wear, M.; Mendez, C.; Mason, S.; Meyers, V.; Alexander, D.; Law, J.

2014-01-01

The default method of dealing with missing data in statistical analyses is to only use the complete observations (complete case analysis), which can lead to unexpected bias when data do not meet the assumption of missing completely at random (MCAR). For the assumption of MCAR to be met, missingness cannot be related to either the observed or unobserved variables. A less stringent assumption, missing at random (MAR), requires that missingness not be associated with the value of the missing variable itself, but can be associated with the other observed variables. When data are truly MAR as opposed to MCAR, the default complete case analysis method can lead to biased results. There are statistical options available to adjust for data that are MAR, including multiple imputation (MI) which is consistent and efficient at estimating effects. Multiple imputation uses informing variables to determine statistical distributions for each piece of missing data. Then multiple datasets are created by randomly drawing on the distributions for each piece of missing data. Since MI is efficient, only a limited number, usually less than 20, of imputed datasets are required to get stable estimates. Each imputed dataset is analyzed using standard statistical techniques, and then results are combined to get overall estimates of effect. A simulation study will be demonstrated to show the results of using the default complete case analysis, and MI in a linear regression of MCAR and MAR simulated data. Further, MI was successfully applied to the association study of CO2 levels and headaches when initial analysis showed there may be an underlying association between missing CO2 levels and reported headaches. Through MI, we were able to show that there is a strong association between average CO2 levels and the risk of headaches. Each unit increase in CO2 (mmHg) resulted in a doubling in the odds of reported headaches.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

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

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

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

Predictors of Sun-Protective Practices among Iranian Female College Students: Application of Protection Motivation Theory.

PubMed

Dehbari, Samaneh Rooshanpour; Dehdari, Tahereh; Dehdari, Laleh; Mahmoudi, Maryam

2015-01-01

Given the importance of sun protection in the prevention of skin cancer, this study was designed to determine predictors of sun-protective practices among a sample of Iranian female college students based on protection motivation theory (PMT) variables. In this cross-sectional study, a total of 201 female college students in Iran University of Medical Sciences were selected. Demographic and PMT variables were assessed with a 67-item questionnaire. Multiple linear regression was used to identify demographic and PMT variables that were associated with sun-protective practices and intention. one percent of participants always wore a hat with a brim, 3.5% gloves and 15.9% sunglasses while outdoors. Only 10.9% regularly had their skin checked by a doctor. Perceived rewards, response efficacy, fear, self-efficacy and marital status were the five variables which could predict 39% variance of participants intention to perform sun-protective practices. Also, intention and response cost explained 31% of the variance of sun-protective practices. These predictive variables may be used to develop theory-based education interventions interventions to prevent skin cancer among college students.

Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb

PubMed Central

Kahan, Anat; Ben-Shaul, Yoram

2016-01-01

For many animals, chemosensation is essential for guiding social behavior. However, because multiple factors can modulate levels of individual chemical cues, deriving information about other individuals via natural chemical stimuli involves considerable challenges. How social information is extracted despite these sources of variability is poorly understood. The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits. Here, we focus on two such traits: a female mouse’s strain and reproductive state. In particular, we measure stimulus-induced neuronal activity in the accessory olfactory bulb (AOB) in response to various dilutions of urine, vaginal secretions, and saliva, from estrus and non-estrus female mice from two different strains. We first show that all tested secretions provide information about a female’s receptivity and genotype. Next, we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability. We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability. However, simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units. Furthermore, we show that some traits are more efficiently detected than others, and that particular secretions may be optimized for conveying information about specific traits. Across all tested stimulus sources, discrimination between strains is more accurate than discrimination of receptivity, and detection of receptivity is more accurate with vaginal secretions than with urine. Our findings highlight the challenges of chemosensory processing of natural stimuli, and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons. PMID:26938460

Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb.

PubMed

Kahan, Anat; Ben-Shaul, Yoram

2016-03-01

For many animals, chemosensation is essential for guiding social behavior. However, because multiple factors can modulate levels of individual chemical cues, deriving information about other individuals via natural chemical stimuli involves considerable challenges. How social information is extracted despite these sources of variability is poorly understood. The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits. Here, we focus on two such traits: a female mouse's strain and reproductive state. In particular, we measure stimulus-induced neuronal activity in the accessory olfactory bulb (AOB) in response to various dilutions of urine, vaginal secretions, and saliva, from estrus and non-estrus female mice from two different strains. We first show that all tested secretions provide information about a female's receptivity and genotype. Next, we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability. We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability. However, simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units. Furthermore, we show that some traits are more efficiently detected than others, and that particular secretions may be optimized for conveying information about specific traits. Across all tested stimulus sources, discrimination between strains is more accurate than discrimination of receptivity, and detection of receptivity is more accurate with vaginal secretions than with urine. Our findings highlight the challenges of chemosensory processing of natural stimuli, and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons.

Additive effects prevail: The response of biota to multiple stressors in an intensively monitored watershed.

PubMed

Gieswein, Alexander; Hering, Daniel; Feld, Christian K

2017-09-01

Freshwater ecosystems are impacted by a range of stressors arising from diverse human-caused land and water uses. Identifying the relative importance of single stressors and understanding how multiple stressors interact and jointly affect biology is crucial for River Basin Management. This study addressed multiple human-induced stressors and their effects on the aquatic flora and fauna based on data from standard WFD monitoring schemes. For altogether 1095 sites within a mountainous catchment, we used 12 stressor variables covering three different stressor groups: riparian land use, physical habitat quality and nutrient enrichment. Twenty-one biological metrics calculated from taxa lists of three organism groups (fish, benthic invertebrates and aquatic macrophytes) served as response variables. Stressor and response variables were subjected to Boosted Regression Tree (BRT) analysis to identify stressor hierarchy and stressor interactions and subsequently to Generalised Linear Regression Modelling (GLM) to quantify the stressors standardised effect size. Our results show that riverine habitat degradation was the dominant stressor group for the river fauna, notably the bed physical habitat structure. Overall, the explained variation in benthic invertebrate metrics was higher than it was in fish and macrophyte metrics. In particular, general integrative (aggregate) metrics such as % Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa performed better than ecological traits (e.g. % feeding types). Overall, additive stressor effects dominated, while significant and meaningful stressor interactions were generally rare and weak. We concluded that given the type of stressor and ecological response variables addressed in this study, river basin managers do not need to bother much about complex stressor interactions, but can focus on the prevailing stressors according to the hierarchy identified. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of stream invertebrate response models for bioassessment metric

USGS Publications Warehouse

Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2012-01-01

We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.

Optimal experimental designs for estimating Henry's law constants via the method of phase ratio variation.

PubMed

Kapelner, Adam; Krieger, Abba; Blanford, William J

2016-10-14

When measuring Henry's law constants (k H ) using the phase ratio variation (PRV) method via headspace gas chromatography (G C ), the value of k H of the compound under investigation is calculated from the ratio of the slope to the intercept of a linear regression of the inverse G C response versus the ratio of gas to liquid volumes of a series of vials drawn from the same parent solution. Thus, an experimenter collects measurements consisting of the independent variable (the gas/liquid volume ratio) and dependent variable (the G C -1 peak area). A review of the literature found that the common design is a simple uniform spacing of liquid volumes. We present an optimal experimental design which estimates k H with minimum error and provides multiple means for building confidence intervals for such estimates. We illustrate performance improvements of our design with an example measuring the k H for Naphthalene in aqueous solution as well as simulations on previous studies. Our designs are most applicable after a trial run defines the linear G C response and the linear phase ratio to the G C -1 region (where the PRV method is suitable) after which a practitioner can collect measurements in bulk. The designs can be easily computed using our open source software optDesignSlopeInt, an R package on CRAN. Copyright © 2016 Elsevier B.V. All rights reserved.

Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors.

PubMed

Yu, Qingzhao; Medeiros, Kaelen L; Wu, Xiaocheng; Jensen, Roxanne E

2018-04-02

Mediation analysis allows the examination of effects of a third variable (mediator/confounder) in the causal pathway between an exposure and an outcome. The general multiple mediation analysis method (MMA), proposed by Yu et al., improves traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. Previous studies find that compared with non-Hispanic cancer survivors, Hispanic survivors are more likely to endure anxiety and depression after cancer diagnoses. In this paper, we applied MMA on MY-Health study to identify mediators/confounders and quantify the indirect effect of each identified mediator/confounder in explaining ethnic disparities in anxiety and depression among cancer survivors who enrolled in the study. We considered a number of socio-demographic variables, tumor characteristics, and treatment factors as potential mediators/confounders and found that most of the ethnic differences in anxiety or depression between Hispanic and non-Hispanic white cancer survivors were explained by younger diagnosis age, lower education level, lower proportions of employment, less likely of being born in the USA, less insurance, and less social support among Hispanic patients.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

A rotor optimization using regression analysis

NASA Technical Reports Server (NTRS)

Giansante, N.

1984-01-01

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin

USGS Publications Warehouse

McKenna, James E.

2005-01-01

Diversity and fish productivity are important measures of the health and status of aquatic systems. Being able to predict the values of these indices as a function of environmental variables would be valuable to management. Diversity and productivity have been related to environmental conditions by multiple linear regression and discriminant analysis, but such methods have several shortcomings. In an effort to predict fish species diversity and estimate salmonid production for streams in the eastern basin of Lake Ontario, I constructed neural networks and trained them on a data set containing abiotic information and either fish diversity or juvenile salmonid abundance. Twenty percent of the original data were retained as a test data set and used in the training. The ability to extend these neural networks to conditions throughout the streams was tested with data not involved in the network training. The resulting neural networks were able to predict the number of salmonids with more than 84% accuracy and diversity with more than 73% accuracy, which was far superior to the performance of multiple regression. The networks also identified the environmental variables with the greatest predictive power, namely, those describing water movement, stream size, and water chemistry. Thirteen input variables were used to predict diversity and 17 to predict salmonid abundance.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Predictors of psychological resilience amongst medical students following major earthquakes.

PubMed

Carter, Frances; Bell, Caroline; Ali, Anthony; McKenzie, Janice; Boden, Joseph M; Wilkinson, Timothy; Bell, Caroline

2016-05-06

To identify predictors of self-reported psychological resilience amongst medical students following major earthquakes in Canterbury in 2010 and 2011. Two hundred and fifty-three medical students from the Christchurch campus, University of Otago, were invited to participate in an electronic survey seven months following the most severe earthquake. Students completed the Connor-Davidson Resilience Scale, the Depression, Anxiety and Stress Scale, the Post-traumatic Disorder Checklist, the Work and Adjustment Scale, and the Eysenck Personality Questionnaire. Likert scales and other questions were also used to assess a range of variables including demographic and historical variables (eg, self-rated resilience prior to the earthquakes), plus the impacts of the earthquakes. The response rate was 78%. Univariate analyses identified multiple variables that were significantly associated with higher resilience. Multiple linear regression analyses produced a fitted model that was able to explain 35% of the variance in resilience scores. The best predictors of higher resilience were: retrospectively-rated personality prior to the earthquakes (higher extroversion and lower neuroticism); higher self-rated resilience prior to the earthquakes; not being exposed to the most severe earthquake; and less psychological distress following the earthquakes. Psychological resilience amongst medical students following major earthquakes was able to be predicted to a moderate extent.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Apical root resorption in orthodontically treated adults.

PubMed

Baumrind, S; Korn, E L; Boyd, R L

1996-09-01

This study analyzed the relationship in orthodontically treated adults between upper central incisor displacement measured on lateral cephalograms and apical root resorption measured on anterior periapical x-ray films. A multiple linear regression examined incisor displacements in four directions (retraction, advancement, intrusion, and extrusion) as independent variables, attempting to account for observed differences in the dependent variable, resorption. Mean apical resorption was 1.36 mm (sd +/- 1.46, n = 73). Mean horizontal displacement of the apex was -0.83 mm (sd +/- 1.74, n = 67); mean vertical displacement was 0.19 mm (sd +/- 1.48, n = 67). The regression coefficients for the intercept and for retraction were highly significant; those for extrusion, intrusion, and advancement were not. At the 95% confidence level, an average of 0.99 mm (se = +/- 0.34) of resorption was implied in the absence of root displacement and an average of 0.49 mm (se = +/- 0.14) of resorption was implied per millimeter of retraction. R2 for all four directional displacement variables (DDVs) taken together was only 0.20, which implied that only a relatively small portion of the observed apical resorption could be accounted for by tooth displacement alone. In a secondary set of univariate analyses, the associations between apical resorption and each of 14 additional treatment-related variables were examined. Only Gender, Elapsed Time, and Total Apical Displacement displayed statistically significant associations with apical resorption. Additional multiple regressions were then performed in which the data for each of these three statistically significant variables were considered separately, with the data for the four directional displacement variables. The addition of information on Elapsed Time or Total Apical Displacement did not explain a significant additional portion of the variability in apical resorption. On the other hand, the addition of information on Gender to the information on the four directional displacement variables yielded an R2 value of 0.35, which indicated that these variables taken together could account for approximately a third of the observed variability in apical resorption in this sample.

Output transformations and separation results for feedback linearisable delay systems

NASA Astrophysics Data System (ADS)

Cacace, F.; Conte, F.; Germani, A.

2018-04-01

The class of strict-feedback systems enjoys special properties that make it similar to linear systems. This paper proves that such a class is equivalent, under a change of coordinates, to the wider class of feedback linearisable systems with multiplicative input, when the multiplicative terms are functions of the measured variables only. We apply this result to the control problem of feedback linearisable nonlinear MIMO systems with input and/or output delays. In this way, we provide sufficient conditions under which a separation result holds for output feedback control and moreover a predictor-based controller exists. When these conditions are satisfied, we obtain that the existence of stabilising controllers for arbitrarily large delays in the input and/or the output can be proved for a wider class of systems than previously known.

Longitudinal association of short-term, metronome-paced heart rate variability and echocardiographically assessed cardiac structure at a 4-year follow-up: results from the prospective, population-based CARLA cohort.

PubMed

Medenwald, Daniel; Swenne, Cees A; Frantz, Stefan; Nuding, Sebastian; Kors, Jan A; Pietzner, Diana; Tiller, Daniel; Greiser, Karin H; Kluttig, Alexander; Haerting, Johannes

2017-12-01

To assess the value of cardiac structure/function in predicting heart rate variability (HRV) and the possibly predictive value of HRV on cardiac parameters. Baseline and 4-year follow-up data from the population-based CARLA cohort were used (790 men, 646 women, aged 45-83 years at baseline and 50-87 years at follow-up). Echocardiographic and HRV recordings were performed at baseline and at follow-up. Linear regression models with a quadratic term were used. Crude and covariate adjusted estimates were calculated. Missing values were imputed by means of multiple imputation. Heart rate variability measures taken into account consisted of linear time and frequency domain [standard deviation of normal-to-normal intervals (SDNN), high-frequency power (HF), low-frequency power (LF), LF/HF ratio] and non-linear measures [detrended fluctuation analysis (DFA1), SD1, SD2, SD1/SD2 ratio]. Echocardiographic parameters considered were ventricular mass index, diastolic interventricular septum thickness, left ventricular diastolic dimension, left atrial dimension systolic (LADS), and ejection fraction (Teichholz). A negative quadratic relation between baseline LADS and change in SDNN and HF was observed. The maximum HF and SDNN change (an increase of roughly 0.02%) was predicted at LADS of 3.72 and 3.57 cm, respectively, while the majority of subjects experienced a decrease in HRV. There was no association between further echocardiographic parameters and change in HRV, and there was no evidence of a predictive value of HRV in the prediction of changes in cardiac structure. In the general population, LADS predicts 4-year alteration in SDNN and HF non-linearly. Because of the novelty of the result, analyses should be replicated in other populations. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions please email: journals.permissions@oup.com.

Multiple long-term trends and trend reversals dominate environmental conditions in a man-made freshwater reservoir.

PubMed

Znachor, Petr; Nedoma, Jiří; Hejzlar, Josef; Seďa, Jaromír; Kopáček, Jiří; Boukal, David; Mrkvička, Tomáš

2018-05-15

Man-made reservoirs are common across the world and provide a wide range of ecological services. Environmental conditions in riverine reservoirs are affected by the changing climate, catchment-wide processes and manipulations with the water level, and water abstraction from the reservoir. Long-term trends of environmental conditions in reservoirs thus reflect a wider range of drivers in comparison to lakes, which makes the understanding of reservoir dynamics more challenging. We analysed a 32-year time series of 36 environmental variables characterising weather, land use in the catchment, reservoir hydrochemistry, hydrology and light availability in the small, canyon-shaped Římov Reservoir in the Czech Republic to detect underlying trends, trend reversals and regime shifts. To do so, we fitted linear and piecewise linear regression and a regime shift model to the time series of mean annual values of each variable and to principal components produced by Principal Component Analysis. Models were weighted and ranked using Akaike information criterion and the model selection approach. Most environmental variables exhibited temporal changes that included time-varying trends and trend reversals. For instance, dissolved organic carbon showed a linear increasing trend while nitrate concentration or conductivity exemplified trend reversal. All trend reversals and cessations of temporal trends in reservoir hydrochemistry (except total phosphorus concentrations) occurred in the late 1980s and during 1990s as a consequence of dramatic socioeconomic changes. After a series of heavy rains in the late 1990s, an administrative decision to increase the flood-retention volume of the reservoir resulted in a significant regime shift in reservoir hydraulic conditions in 1999. Our analyses also highlight the utility of the model selection framework, based on relatively simple extensions of linear regression, to describe temporal trends in reservoir characteristics. This approach can provide a solid basis for a better understanding of processes in freshwater reservoirs. Copyright © 2017 Elsevier B.V. All rights reserved.

Supporting Students' Understanding of Linear Equations with One Variable Using Algebra Tiles

ERIC Educational Resources Information Center

Saraswati, Sari; Putri, Ratu Ilma Indra; Somakim

2016-01-01

This research aimed to describe how algebra tiles can support students' understanding of linear equations with one variable. This article is a part of a larger research on learning design of linear equations with one variable using algebra tiles combined with balancing method. Therefore, it will merely discuss one activity focused on how students…

Race-ethnicity is a strong correlate of circulating fat-soluble nutrient concentrations in a representative sample of the US population1,2,3

PubMed Central

Schleicher, Rosemary L; Sternberg, Maya R; Pfeiffer, Christine M

2016-01-01

Sociodemographic and lifestyle factors exert important influences on nutritional status; however, information on their association with biomarkers of fat-soluble nutrients is limited, particularly in a representative sample of adults. Serum or plasma concentrations of vitamin A (VIA), vitamin E (VIE), carotenes (CAR), xanthophylls (XAN), 25-hydroxyvitamin D (25OHD), saturated- (SFA), monounsaturated- (MUFA), polyunsaturated- (PUFA) and total fatty acids (tFA) were measured in adults (≥20 y) during all or part of NHANES 2003–2006. Simple and multiple linear regression were used to assess 5 sociodemographic variables (age, sex, race-ethnicity, education, income) and 5 lifestyle behaviors (smoking, alcohol consumption, BMI, physical activity, supplement use) and their relation to biomarker concentrations. Adjustment for total serum cholesterol and lipid-altering drug use was added to the full regression model. Adjustment for latitude and season was added to the full model for 25OHD. Based on simple linear regression, race-ethnicity, BMI and supplement use were significantly related to all fat-soluble biomarkers. Sociodemographic variables as a groupexplained 5–17% of biomarker variability, whereas together, sociodemographic and lifestyle variables explained 22–23% (25OHD, VIE, XAN), 17% (VIA), 15% (MUFA), 10–11% (SFA, CAR, tFA) and 6% (PUFA). Although lipid adjustment explained additional variability for all biomarkers except 25OHD, it appeared to be largely independent of sociodemographic and lifestyle variables. After adjusting for sociodemographic, lifestyle and lipid-related variables, major differences in biomarkers were associated with race-ethnicity (from −44% to 57%); smoking (up to −25%); supplement use (up to 21%); and BMI (up to −15%). Latitude and season attenuated some race-ethnic differences. Of the sociodemographic and lifestyle variables examined, with or without lipid-adjustment, most fat-soluble nutrient biomarkers were significantly associated with race-ethnicity. PMID:23596163

Climate Variability and Dengue Fever in Warm and Humid Mexico

PubMed Central

Colón-González, Felipe J.; Lake, Iain R.; Bentham, Graham

2011-01-01

Multiple linear regression models were fitted to look for associations between changes in the incidence rate of dengue fever and climate variability in the warm and humid region of Mexico. Data were collected for 12 Mexican provinces over a 23-year period (January 1985 to December 2007). Our results show that the incidence rate or risk of infection is higher during El Niño events and in the warm and wet season. We provide evidence to show that dengue fever incidence was positively associated with the strength of El Niño and the minimum temperature, especially during the cool and dry season. Our study complements the understanding of dengue fever dynamics in the region and may be useful for the development of early warning systems. PMID:21540386

Climate variability and dengue fever in warm and humid Mexico.

PubMed

Colón-González, Felipe J; Lake, Iain R; Bentham, Graham

2011-05-01

Multiple linear regression models were fitted to look for associations between changes in the incidence rate of dengue fever and climate variability in the warm and humid region of Mexico. Data were collected for 12 Mexican provinces over a 23-year period (January 1985 to December 2007). Our results show that the incidence rate or risk of infection is higher during El Niño events and in the warm and wet season. We provide evidence to show that dengue fever incidence was positively associated with the strength of El Niño and the minimum temperature, especially during the cool and dry season. Our study complements the understanding of dengue fever dynamics in the region and may be useful for the development of early warning systems.

Refractive Status at Birth: Its Relation to Newborn Physical Parameters at Birth and Gestational Age

PubMed Central

Varghese, Raji Mathew; Sreenivas, Vishnubhatla; Puliyel, Jacob Mammen; Varughese, Sara

2009-01-01

Background Refractive status at birth is related to gestational age. Preterm babies have myopia which decreases as gestational age increases and term babies are known to be hypermetropic. This study looked at the correlation of refractive status with birth weight in term and preterm babies, and with physical indicators of intra-uterine growth such as the head circumference and length of the baby at birth. Methods All babies delivered at St. Stephens Hospital and admitted in the nursery were eligible for the study. Refraction was performed within the first week of life. 0.8% tropicamide with 0.5% phenylephrine was used to achieve cycloplegia and paralysis of accommodation. 599 newborn babies participated in the study. Data pertaining to the right eye is utilized for all the analyses except that for anisometropia where the two eyes were compared. Growth parameters were measured soon after birth. Simple linear regression analysis was performed to see the association of refractive status, (mean spherical equivalent (MSE), astigmatism and anisometropia) with each of the study variables, namely gestation, length, weight and head circumference. Subsequently, multiple linear regression was carried out to identify the independent predictors for each of the outcome parameters. Results Simple linear regression showed a significant relation between all 4 study variables and refractive error but in multiple regression only gestational age and weight were related to refractive error. The partial correlation of weight with MSE adjusted for gestation was 0.28 and that of gestation with MSE adjusted for weight was 0.10. Birth weight had a higher correlation to MSE than gestational age. Conclusion This is the first study to look at refractive error against all these growth parameters, in preterm and term babies at birth. It would appear from this study that birth weight rather than gestation should be used as criteria for screening for refractive error, especially in developing countries where the incidence of intrauterine malnutrition is higher. PMID:19214228

Work Ability Index (WAI) and its health-related determinants among Iranian farmers working in small farm enterprises.

PubMed

Rostamabadi, Akbar; Mazloumi, Adel; Rahimi Foroushani, Abbas

2014-01-01

This study aimed to determine the Work Ability Index (WAI) and examine the influence of health dimensions and demographic variables on the work ability of Iranian farmers working in small farm enterprises. A cross-sectional study was conducted among 294 male farmers. The WAI and SF-36 questionnaires were used to determine work ability and health status. The effect of demographics variables on the work ability index was investigated with the independent samples t-test and one-way ANOVA. Also, multiple linear regression analysis was used to test the association between the mean WAI score and the SF-36 scales. The mean WAI score was 35.1 (SD=10.6). One-way ANOVA revealed a significant relationship between the mean WAI and age. Multiple linear regression analysis showed that work ability was more influenced by physical scales of the health dimensions, such as physical function, role-physical, and general health, whereas a lower association was found for mental scales such as mental health. The average WAI was at a moderate work ability level for the sample population of farmers in this study. Based on the WAI guidelines, improvement of work ability and identification of factors affecting it should be considered a priority in interventional programs. Given the influence of health dimensions on WAI, any intervention program for preservation and promotion of work ability among the studied farmers should be based on balancing and optimizing the physical and psychosocial work environments, with a special focus on reducing physical work load.(J Occup Health 2014; 56: 478-484).

A multiple linear regression analysis of factors affecting the simulated Basic Life Support (BLS) performance with Automated External Defibrillator (AED) in Flemish lifeguards.

PubMed

Iserbyt, Peter; Schouppe, Gilles; Charlier, Nathalie

2015-04-01

Research investigating lifeguards' performance of Basic Life Support (BLS) with Automated External Defibrillator (AED) is limited. Assessing simulated BLS/AED performance in Flemish lifeguards and identifying factors affecting this performance. Six hundred and sixteen (217 female and 399 male) certified Flemish lifeguards (aged 16-71 years) performed BLS with an AED on a Laerdal ResusciAnne manikin simulating an adult victim of drowning. Stepwise multiple linear regression analysis was conducted with BLS/AED performance as outcome variable and demographic data as explanatory variables. Mean BLS/AED performance for all lifeguards was 66.5%. Compression rate and depth adhered closely to ERC 2010 guidelines. Ventilation volume and flow rate exceeded the guidelines. A significant regression model, F(6, 415)=25.61, p<.001, ES=.38, explained 27% of the variance in BLS performance (R2=.27). Significant predictors were age (beta=-.31, p<.001), years of certification (beta=-.41, p<.001), time on duty per year (beta=-.25, p<.001), practising BLS skills (beta=.11, p=.011), and being a professional lifeguard (beta=-.13, p=.029). 71% of lifeguards reported not practising BLS/AED. Being young, recently certified, few days of employment per year, practising BLS skills and not being a professional lifeguard are factors associated with higher BLS/AED performance. Measures should be taken to prevent BLS/AED performances from decaying with age and longer certification. Refresher courses could include a formal skills test and lifeguards should be encouraged to practise their BLS/AED skills. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Prevalence of Pterygia in Hawaii: Examining Cumulative Surfing Hours as a Risk Factor.

PubMed

Lin, Alexander D; Miles, Ku'ulei; Brinks, Mitchel V

2016-08-01

To examine the association between surfing and pterygium prevalence in Hawaii. A convenience sampling was performed at four beaches on the island of Oahu, Hawaii. A total of 169 individuals were interviewed and underwent penlight examination to assess grade and extent of pterygium. Of 169 participants aged 18-80 years, 88 non-surfers, 41 occasional surfers, 15 recreational surfers and 25 surfing enthusiasts were identified based on their lifetime surfing hours. Overall, 19 participants were found to have pterygia (28 pterygia total) including two non-surfers (2.3%), five occasional surfers (12.2%), three recreational surfers (20.0%), and nine enthusiast surfers (36.0%). Variables associated with pterygium prevalence were lifetime surfing hours (p < 0.0001), outdoor occupation (p = 0.04), Hawaiian residence (p = 0.009), and Hawaiian/Pacific Islander ethnicity (p = 0.002). Multiple logistic regression with the outcome of pterygium was performed, along with multiple linear regression for the continuous outcomes of corneal extent, chord length, and apex-visual axis gap, with lifetime surfing hours as the primary explanatory variable. After adjustment for confounders, a significant linear relationship was observed between chord length and lifetime surfing hours (p = 0.01). Surfing was associated with an increased pterygium prevalence and trend towards an association with increased pterygium severity. Increased risk of exposure to wind, particle irritation, and ultraviolet (UV) radiation while surfing may contribute to pterygium development. Implications for public health include promoting UV protective eyewear during surfing, in addition to raising awareness about the association of pterygia and the sport of surfing.

Evidence of a robust relationship between solar-induced chlorophyll fluorescence and gross primary productivity across dryland ecosystems of southwestern North America

NASA Astrophysics Data System (ADS)

Smith, W. K.; Biederman, J. A.; Scott, R. L.; Moore, D. J.; Kimball, J. S.; He, M.; Yan, D.; Hudson, A.; Barnes, M.; MacBean, N.; Fox, A. M.; Litvak, M. E.

2017-12-01

Satellite remote sensing provides unmatched spatiotemporal information on multiple facets of vegetation dynamics including seasonal to interannual total photosynthesis, termed gross primary productivity (GPP). Yet, our understanding of the relationship between GPP and remote sensing observations - and how this relationship changes with scale, biophysical constraint, vegetation type, etc. - remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have high spatial and temporal variability and are under-represented by long-term, continuous field measurements. Here, utilizing a new synthesis of eddy covariance flux tower data for southwestern North America, we present a first assessment of the ability of novel satellite remote sensing vegetation proxies to accurately capture seasonal to interannual GPP dynamics across the region. We evaluate the greenness-based Enhanced Vegetation Index (EVI) and emerging proxies linked to plant physiological function, Solar-Induced Fluorescence (SIF) and Photochemical Reflectivity Index (PRI). We find that SIF observations more consistently correlate with seasonal GPP dynamics (R = 0.90) compared to EVI (R = 0.85) and PRI (R = 0.78). More, we find that SIF observations are also more sensitive to interannual GPP variability (linear slope = 0.80) relative to EVI (linear slope = 0.63) and PRI (linear slope = 0.35). This is likely due to increased sensitivity of SIF to GPP during periods of decoupling between greenness and photosynthesis due to water-limitation / stomatal closure. Conversely, EVI and PRI observations better capture spatial GPP variability between flux tower sites. These results suggest that combinations of these independent vegetation growth proxies could yield synergistic improvements in satellite-based GPP estimates.

Assessment of unconstrained cerebrovascular reactivity marker for large age-range FMRI studies.

PubMed

Kannurpatti, Sridhar S; Motes, Michael A; Biswal, Bharat B; Rypma, Bart

2014-01-01

Breath hold (BH), a commonly used task to measure cerebrovascular reactivity (CVR) in fMRI studies varies in outcome among individuals due to subject-physiology and/or BH-inspiration/expiration differences (i.e., performance). In prior age-related fMRI studies, smaller task-related BOLD response variability is observed among younger than older individuals. Also, a linear CVR versus task relationship exists in younger individuals which maybe useful to test the accuracy of CVR responses in older groups. Hence we hypothesized that subject-related physiological and/or BH differences, if present, may compromise CVR versus task linearity in older individuals. To test the hypothesis, empirical BH versus task relationships from motor and cognitive areas were obtained in younger (mean age = 26 years) and older (mean age = 58 years) human subjects. BH versus task linearity was observed only in the younger group, confirming our hypothesis. Further analysis indicated BH responses and its variability to be similar in both younger and older groups, suggesting that BH may not accurately represent CVR in a large age range. Using the resting state fluctuation of amplitude (RSFA) as an unconstrained alternative to BH, subject-wise correspondence between BH and RSFA was tested. Correlation between BH versus RSFA was significant within the motor but was not significant in the cognitive areas in the younger and was completely disrupted in both areas in the older subjects indicating that BH responses are constrained by subject-related physiology and/or performance-related differences. Contrasting BH to task, RSFA-task relationships were independent of age accompanied by age-related increases in CVR variability as measured by RSFA, not observed with BH. Together the results obtained indicate that RSFA accurately represents CVR in any age range avoiding multiple and yet unknown physiologic and task-related pitfalls of BH.

Factors contributing to tooth loss among the elderly: A cross sectional study.

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alasqah, Mohammed; Papas, Athena

2014-12-01

The present study evaluates the influence of several demographic, health, personal, and clinical factors on the number of missing teeth in old age sample. The number of patients included was 259; they received a full mouth examination and answered a questionnaire provided by one examiner. All the variables related to teeth loss based on the literature were included. These variables focused on age, gender, race, marital status, clinical attachment level, pocket depth, year of smoking, number of cigarettes smoked per day, number of medications, root decay, coronal decay, health status, and year of education. Statistical analysis involved stepwise multivariate linear regression. Teeth loss was statistically associated with clinical attachment level (CAL)(p value 0.0001), pocket depth (PD) (0.0007) and education level (0.0048). When smoking was included in the model, age was significantly associated with teeth loss (0.0037). At least one of these four factors was also related to teeth loss in several specific groups such as diabetes mellitus, male, and White. The multiple linear regressions for all the proposed variables showed that they contributed to teeth loss by about 23%. It can be concluded that less education or increased clinical attachment level loss may increase number of missing teeth. Additionally, age may cause teeth loss in the presence of smoking. Copyright © 2014. Published by Elsevier B.V.

Do US metropolitan core counties have lower scope 1 and 2 CO2 emissions than less urbanized counties?

NASA Astrophysics Data System (ADS)

Tamayao, M. M.; Blackhurst, M. F.; Matthews, H. S.

2014-10-01

Recent sustainability research has focused on urban systems given their high share of environmental impacts and potential for centralized impact mitigation. Recent research emphasizes descriptive statistics from place-based case studies to argue for policy action. This limits the potential for general insights and decision support. Here, we implement generalized linear and multiple linear regression analyses to obtain more robust insights on the relationship between urbanization and greenhouse gas (GHG) emissions in the US We used consistently derived county-level scope 1 and scope 2 GHG inventories for our response variable while predictor variables included dummy-coded variables for county geographic type (central, outlying, and nonmetropolitan), median household income, population density, and climate indices (heating degree days (HDD) and cooling degree days (CDD)). We find that there is not enough statistical evidence indicating per capita scope 1 and 2 emissions differ by geographic type, ceteris paribus. These results are robust for different assumed electricity emissions factors. We do find statistically significant differences in per capita emissions by sector for different county types, with transportation and residential emissions highest in nonmetropolitan (rural) counties, transportation emissions lowest in central counties, and commercial sector emissions highest in central counties. These results indicate the importance of regional land use and transportation dynamics when planning local emissions mitigation measures.

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

PubMed

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

2018-02-01

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

Use of allele scores as instrumental variables for Mendelian randomization

PubMed Central

Burgess, Stephen; Thompson, Simon G

2013-01-01

Background An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome. Methods Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate ‘weak instrument’ bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed. Results Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases. Conclusions Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score. PMID:24062299

Handgrip fatiguing exercise can provide objective assessment of cancer-related fatigue: a pilot study.

PubMed

Veni, T; Boyas, S; Beaune, B; Bourgeois, H; Rahmani, A; Landry, S; Bochereau, A; Durand, S; Morel, B

2018-06-24

As a subjective symptom, cancer-related fatigue is assessed via patient-reported outcomes. Due to the inherent bias of such evaluation, screening and treatment for cancer-related fatigue remains suboptimal. The purpose is to evaluate whether objective cancer patients' hand muscle mechanical parameters (maximal force, critical force, force variability) extracted from a fatiguing handgrip exercise may be correlated to the different dimensions (physical, emotional, and cognitive) of cancer-related fatigue. Fourteen women with advanced breast cancer, still under or having previously received chemotherapy within the preceding 3 months, and 11 healthy women participated to the present study. Cancer-related fatigue was first assessed through the EORTC QLQ-30 and its fatigue module. Fatigability was then measured during 60 maximal repeated handgrip contractions. The maximum force, critical force (asymptote of the force-time evolution), and force variability (root mean square of the successive differences) were extracted. Multiple regression models were performed to investigate the influence of the force parameters on cancer-related fatigue's dimensions. The multiple linear regression analysis evidenced that physical fatigue was best explained by maximum force and critical force (r = 0.81; p = 0.029). The emotional fatigue was best explained by maximum force, critical force, and force variability (r = 0.83; p = 0.008). The cognitive fatigue was best explained by critical force and force variability (r = 0.62; p = 0.035). The handgrip maximal force, critical force, and force variability may offer objective measures of the different dimensions of cancer-related fatigue and could provide a complementary approach to the patient reported outcomes.

State-variable analysis of non-linear circuits with a desk computer

NASA Technical Reports Server (NTRS)

Cohen, E.

1981-01-01

State variable analysis was used to analyze the transient performance of non-linear circuits on a desk top computer. The non-linearities considered were not restricted to any circuit element. All that is required for analysis is the relationship defining each non-linearity be known in terms of points on a curve.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Language and hope in schizophrenia-spectrum disorders.

PubMed

Bonfils, Kelsey A; Luther, Lauren; Firmin, Ruth L; Lysaker, Paul H; Minor, Kyle S; Salyers, Michelle P

2016-11-30

Hope is integral to recovery for those with schizophrenia. Considering recent advancements in the examination of clients' lexical qualities, we were interested in how clients' words reflect hope. Using computerized lexical analysis, we examined social, emotion, and future words' relations to hope and its pathways and agency components. Forty-five clients provided detailed narratives about their life and mental illness. Transcripts were analyzed using the Linguistic Inquiry and Word Count program (LIWC), which assigns words to categories (e.g., "anxiety") based on a pre-existing dictionary. Correlations and linear multiple regression were used to examine relationships between lexical qualities and hope. Hope and its subcomponents had significant or trending bivariate correlations in expected directions with several emotion-related word categories (anger and sadness) but were not associated with expected categories such as social words, positive emotions, optimism, achievement, and future words. In linear multiple regressions, no LIWC variable significantly predicted hope agency, but anger words significantly predicted both total hope and hope pathways. Our findings indicate lexical analysis tools can be used to investigate recovery-oriented concepts such as hope, and results may inform clinical practice. Future research should aim to replicate our findings in larger samples. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Study of the Effect of the Front-End Styling of Sport Utility Vehicles on Pedestrian Head Injuries

PubMed Central

Qin, Qin; Chen, Zheng; Bai, Zhonghao; Cao, Libo

2018-01-01

Background The number of sport utility vehicles (SUVs) on China market is continuously increasing. It is necessary to investigate the relationships between the front-end styling features of SUVs and head injuries at the styling design stage for improving the pedestrian protection performance and product development efficiency. Methods Styling feature parameters were extracted from the SUV side contour line. And simplified finite element models were established based on the 78 SUV side contour lines. Pedestrian headform impact simulations were performed and validated. The head injury criterion of 15 ms (HIC15) at four wrap-around distances was obtained. A multiple linear regression analysis method was employed to describe the relationships between the styling feature parameters and the HIC15 at each impact point. Results The relationship between the selected styling features and the HIC15 showed reasonable correlations, and the regression models and the selected independent variables showed statistical significance. Conclusions The regression equations obtained by multiple linear regression can be used to assess the performance of SUV styling in protecting pedestrians' heads and provide styling designers with technical guidance regarding their artistic creations.

Rural-urban analyses of health-related quality of life among people with multiple sclerosis.

PubMed

Buchanan, Robert J; Zhu, Li; Schiffer, Randolph; Radin, Dagmar; James, Wesley

2008-01-01

Health-related quality of life (HRQOL) is a multi-dimensional construct including aspects of life quality or function that are affected by physical health and symptoms, psychosocial factors, and psychiatric conditions. HRQOL gives a broader measure of the burden of disease than physical impairment or disability levels. To identify factors associated with HRQOL among people with multiple sclerosis (MS) utilizing the SF-8 Health Survey. Data presented in this study were collected in a survey of 1,518 people with MS living in all 50 states. The survey sample was randomly selected from the database of the National Multiple Sclerosis Society, using ZIP codes to recruit the survey sample. A multiple linear regression model was employed to analyze the survey data, with the Physical Component Summary and the Mental Component Summary of the SF-8 the dependent variables. Independent variables were demographic characteristics, MS-disease characteristics, and health services utilized. People with MS in rural areas tended to report lower physically related HRQOL. Worsening MS symptoms were associated with reduced physical and mental dimensions of HRQOL. In addition, people with MS who received a diagnosis of depression tended to have reduced physical and mental dimensions of HRQOL. Receiving MS care at an MS clinic was associated with better physically related HRQOL, while having a neurologist as principal care physician was associated with better mental-related HRQOL. The challenge is to increase the access that people living with MS in rural areas have to MS-focused specialty care.

Combining multiple imputation and meta-analysis with individual participant data

PubMed Central

Burgess, Stephen; White, Ian R; Resche-Rigon, Matthieu; Wood, Angela M

2013-01-01

Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse-variance weighted meta-analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between-study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse-variance weighted meta-analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta-analysis, rather than meta-analyzing each of the multiple imputations and then combining the meta-analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration. PMID:23703895

Long-term disability progression in primary progressive multiple sclerosis: a 15-year study.

PubMed

Rocca, Maria A; Sormani, Maria Pia; Rovaris, Marco; Caputo, Domenico; Ghezzi, Angelo; Montanari, Enrico; Bertolotto, Antonio; Laroni, Alice; Bergamaschi, Roberto; Martinelli, Vittorio; Comi, Giancarlo; Filippi, Massimo

2017-11-01

Prognostic markers of primary progressive multiple sclerosis evolution are needed. We investigated the added value of magnetic resonance imaging measures of brain and cervical cord damage in predicting long-term clinical worsening of primary progressive multiple sclerosis compared to simple clinical assessment. In 54 patients, conventional and diffusion tensor brain scans and cervical cord T1-weighted scans were acquired at baseline and after 15 months. Clinical evaluation was performed after 5 and 15 years in 49 patients. Lesion load, brain and cord atrophy, mean diffusivity and fractional anisotropy values from the brain normal-appearing white matter and grey matter were obtained. Using linear regression models, we screened the clinical and imaging variables as independent predictors of 15-year disability change (measured on the expanded disability status scale). At 15 years, 90% of the patients had disability progression. Integrating clinical and imaging variables at 15 months predicted disability changes at 15 years better than clinical factors at 5 years (R2 = 61% versus R2 = 57%). The model predicted long-term disability change with a precision within one point in 38 of 49 patients (77.6%). Integration of clinical and imaging measures allows identification of primary progressive multiple sclerosis patients at risk of long-term disease progression 4 years earlier than when using clinical assessment alone. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

Financial burden is associated with worse health-related quality of life in adults with multiple endocrine neoplasia type 1.

PubMed

Peipert, Benjamin J; Goswami, Sneha; Helenowski, Irene; Yount, Susan E; Sturgeon, Cord

2017-12-01

Health-related quality of life and financial burden among patients with multiple endocrine neoplasia type 1 is poorly described. It is not known how financial burden influences health-related quality of life in this population. We hypothesized that the financial burden attributable to multiple endocrine neoplasia type 1 is associated with worse health-related quality of life. United States adults (≥18 years) with multiple endocrine neoplasia type 1 were recruited from the AMENSupport MEN online support group. Patient demographics, clinical characteristics, and financial burden were assessed via an online survey. The instrument Patient-Reported Outcomes Measurement Information System 29-item profile measure was used to assess health-related quality of life. Multivariable linear regression was used to identify significant variables in each Patient-Reported Outcomes Measurement Information System domain. Out of 1,378 members in AMENSupport, our survey link was accessed 449 times (33%). Of 153 US respondents who completed our survey, 84% reported financial burden attributable to multiple endocrine neoplasia type 1. The degree of financial burden had a linear relationship with worse health-related quality of life across all Patient-Reported Outcomes Measurement Information System domains (r = 0.36-0.55, P < .001); 63% reported experiencing ≥1 negative financial event(s). Borrowing money from friends/family (30%), unemployment (13%), and spending >$100/month out-of-pocket on prescription medications (46%) were associated consistently with impaired health-related quality of life (ß = 3.75-6.77, P < .05). Respondents were 3- and 34-times more likely to be unemployed and declare bankruptcy than the US population, respectively. This study characterizes the financial burden in patients with multiple endocrine neoplasia type 1. Individuals with multiple endocrine neoplasia type 1 report a high degree of financial burden, negative financial events, and unemployment. Each of these factors was associated with worse health-related quality of life. Copyright © 2017 Elsevier Inc. All rights reserved.

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.

PubMed

Brunton, Steven L; Brunton, Bingni W; Proctor, Joshua L; Kutz, J Nathan

2016-01-01

In this wIn this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control.ork, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control.

Multivariate predictors of music perception and appraisal by adult cochlear implant users.

PubMed

Gfeller, Kate; Oleson, Jacob; Knutson, John F; Breheny, Patrick; Driscoll, Virginia; Olszewski, Carol

2008-02-01

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music.

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

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

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

Functional Capacity Evaluation in Different Societal Contexts: Results of a Multicountry Study.

PubMed

Ansuategui Echeita, Jone; Bethge, Matthias; van Holland, Berry J; Gross, Douglas P; Kool, Jan; Oesch, Peter; Trippolini, Maurizio A; Chapman, Elizabeth; Cheng, Andy S K; Sellars, Robert; Spavins, Megan; Streibelt, Marco; van der Wurff, Peter; Reneman, Michiel F

2018-05-25

Purpose To examine factors associated with Functional Capacity Evaluation (FCE) results in patients with painful musculoskeletal conditions, with focus on social factors across multiple countries. Methods International cross-sectional study was performed within care as usual. Simple and multiple multilevel linear regression analyses which considered measurement's dependency within clinicians and country were conducted: FCE characteristics and biopsychosocial variables from patients and clinicians as independent variables; and FCE results (floor-to-waist lift, six-minute walk, and handgrip strength) as dependent variables. Results Data were collected for 372 patients, 54 clinicians, 18 facilities and 8 countries. Patients' height and reported pain intensity were consistently associated with every FCE result. Patients' sex, height, reported pain intensity, effort during FCE, social isolation, and disability, clinician's observed physical effort, and whether FCE test was prematurely ended were associated with lift. Patient's height, Body Mass Index, post-test heart-rate, reported pain intensity and effort during FCE, days off work, and whether FCE test was prematurely ended were associated with walk. Patient's age, sex, height, affected body area, reported pain intensity and catastrophizing, and physical work demands were associated with handgrip. Final regression models explained 38‒65% of total variance. Clinician and country random effects composed 1-39% of total residual variance in these models. Conclusion Biopsychosocial factors were associated with every FCE result across multiple countries; specifically, patients' height, reported pain intensity, clinician, and measurement country. Social factors, which had been under-researched, were consistently associated with FCE performances. Patients' FCE results should be considered from a biopsychosocial perspective, including different social contexts.

Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment

PubMed Central

2013-01-01

Background Next Generation Sequencing techniques are producing enormous amounts of biological sequence data and analysis becomes a major computational problem. Currently, most analysis, especially the identification of conserved regions, relies heavily on Multiple Sequence Alignment and its various heuristics such as progressive alignment, whose run time grows with the square of the number and the length of the aligned sequences and requires significant computational resources. In this work, we present a method to efficiently discover regions of high similarity across multiple sequences without performing expensive sequence alignment. The method is based on approximating edit distance between segments of sequences using p-mer frequency counts. Then, efficient high-throughput data stream clustering is used to group highly similar segments into so called quasi-alignments. Quasi-alignments have numerous applications such as identifying species and their taxonomic class from sequences, comparing sequences for similarities, and, as in this paper, discovering conserved regions across related sequences. Results In this paper, we show that quasi-alignments can be used to discover highly similar segments across multiple sequences from related or different genomes efficiently and accurately. Experiments on a large number of unaligned 16S rRNA sequences obtained from the Greengenes database show that the method is able to identify conserved regions which agree with known hypervariable regions in 16S rRNA. Furthermore, the experiments show that the proposed method scales well for large data sets with a run time that grows only linearly with the number and length of sequences, whereas for existing multiple sequence alignment heuristics the run time grows super-linearly. Conclusion Quasi-alignment-based algorithms can detect highly similar regions and conserved areas across multiple sequences. Since the run time is linear and the sequences are converted into a compact clustering model, we are able to identify conserved regions fast or even interactively using a standard PC. Our method has many potential applications such as finding characteristic signature sequences for families of organisms and studying conserved and variable regions in, for example, 16S rRNA. PMID:24564200

Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment.

PubMed

Nagar, Anurag; Hahsler, Michael

2013-01-01

Next Generation Sequencing techniques are producing enormous amounts of biological sequence data and analysis becomes a major computational problem. Currently, most analysis, especially the identification of conserved regions, relies heavily on Multiple Sequence Alignment and its various heuristics such as progressive alignment, whose run time grows with the square of the number and the length of the aligned sequences and requires significant computational resources. In this work, we present a method to efficiently discover regions of high similarity across multiple sequences without performing expensive sequence alignment. The method is based on approximating edit distance between segments of sequences using p-mer frequency counts. Then, efficient high-throughput data stream clustering is used to group highly similar segments into so called quasi-alignments. Quasi-alignments have numerous applications such as identifying species and their taxonomic class from sequences, comparing sequences for similarities, and, as in this paper, discovering conserved regions across related sequences. In this paper, we show that quasi-alignments can be used to discover highly similar segments across multiple sequences from related or different genomes efficiently and accurately. Experiments on a large number of unaligned 16S rRNA sequences obtained from the Greengenes database show that the method is able to identify conserved regions which agree with known hypervariable regions in 16S rRNA. Furthermore, the experiments show that the proposed method scales well for large data sets with a run time that grows only linearly with the number and length of sequences, whereas for existing multiple sequence alignment heuristics the run time grows super-linearly. Quasi-alignment-based algorithms can detect highly similar regions and conserved areas across multiple sequences. Since the run time is linear and the sequences are converted into a compact clustering model, we are able to identify conserved regions fast or even interactively using a standard PC. Our method has many potential applications such as finding characteristic signature sequences for families of organisms and studying conserved and variable regions in, for example, 16S rRNA.

Entropy measures detect increased movement variability in resistance training when elite rugby players use the ball.

PubMed

Moras, Gerard; Fernández-Valdés, Bruno; Vázquez-Guerrero, Jairo; Tous-Fajardo, Julio; Exel, Juliana; Sampaio, Jaime

2018-05-24

This study described the variability in acceleration during a resistance training task, performed in horizontal inertial flywheels without (NOBALL) or with the constraint of catching and throwing a rugby ball (BALL). Twelve elite rugby players (mean±SD: age 25.6±3.0years, height 1.82±0.07m, weight 94.0±9.9kg) performed a resistance training task in both conditions (NOBALL AND BALL). Players had five minutes of a standardized warm-up, followed by two series of six repetitions of both conditions: at the first three repetitions the intensity was progressively increased while the last three were performed at maximal voluntary effort. Thereafter, the participants performed two series of eight repetitions from each condition for two days and in a random order, with a minimum of 10min between series. The structure of variability was analysed using non-linear measures of entropy. Mean changes (%; ±90% CL) of 4.64; ±3.1g for mean acceleration and 39.48; ±36.63a.u. for sample entropy indicated likely and very likely increase when in BALL condition. Multiscale entropy also showed higher unpredictability of acceleration under the BALL condition, especially at higher time scales. The application of match specific constraints in resistance training for rugby players elicit different amount of variability of body acceleration across multiple physiological time scales. Understanding the non-linear process inherent to the manipulation of resistance training variables with constraints and its motor adaptations may help coaches and trainers to enhance the effectiveness of physical training and, ultimately, better understand and maximize sports performance. Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

NASA Astrophysics Data System (ADS)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Emergency multiple sclerosis hospital admissions attributable to chemical and acoustic pollution: Madrid (Spain), 2001-2009.

PubMed

Carmona, Rocío; Linares, Cristina; Recio, Alberto; Ortiz, Cristina; Díaz, Julio

2018-01-15

Multiple sclerosis (MS) is the most prevalent neurological disease among young adults in Spain. A number of recent studies have linked traffic-related pollution, both chemical and acoustic, to the aetiology and exacerbation of neurodegenerative diseases. To analyse the existence of a significant short-term association between daily emergency MS hospital admissions and chemical and acoustic pollution caused by traffic in Madrid. We conducted a longitudinal ecological time series study, in which the dependent variable was the number of daily emergency MS hospital admissions (ICD-9: 340) registered in Madrid from 1 January 2001 to 31 December 2009. The independent variables were daily mean concentrations (μg/m 3 ) of PM 2.5 , PM 10 , O 3 and NO 2 . Equivalent diurnal (Leqd), nocturnal (Leqn) and daily equivalent noise levels (Leq24) were also considered. In addition, we controlled for linear trends, seasonality and the autoregressive nature of the series itself. Day of the week was also added as a covariate. Significant environmental variables were determined using Poisson GLM models. Relative risk (RR) and attributable risk (AR) values were calculated for increases of 10μg/m 3 in the case of chemical pollutants and 1dB(A) in noise levels. While there was no association between chemical pollutants caused by traffic and MS admissions, such an association was in evidence for Leqd at lag zero. This association is linear without a threshold, with there being a level above 67dB(A) from which this effect is more pronounced. The RRs were as follows: for all Leqd values, 1.21 (95% CI: 1.16, 1.26); and for Leqd >67dB(A), 1.62 (95% CI: 1.24, 2.13). The above results indicate that traffic noise can exacerbate MS symptoms, leading to hospital admissions due to this cause. Copyright © 2017 Elsevier B.V. All rights reserved.

A Spanish-language patient safety questionnaire to measure medical and nursing students' attitudes and knowledge.

PubMed

Mira, José J; Navarro, Isabel M; Guilabert, Mercedes; Poblete, Rodrigo; Franco, Astolfo L; Jiménez, Pilar; Aquino, Margarita; Fernández-Trujillo, Francisco J; Lorenzo, Susana; Vitaller, Julián; de Valle, Yohana Díaz; Aibar, Carlos; Aranaz, Jesús M; De Pedro, José A

2015-08-01

To design and validate a questionnaire for assessing attitudes and knowledge about patient safety using a sample of medical and nursing students undergoing clinical training in Spain and four countries in Latin America. In this cross-sectional study, a literature review was carried out and total of 786 medical and nursing students were surveyed at eight universities from five countries (Chile, Colombia, El Salvador, Guatemala, and Spain) to develop and refine a Spanish-language questionnaire on knowledge and attitudes about patient safety. The scope of the questionnaire was based on five dimensions (factors) presented in studies related to patient safety culture found in PubMed and Scopus. Based on the five factors, 25 reactive items were developed. Composite reliability indexes and Cronbach's alpha statistics were estimated for each factor, and confirmatory factor analysis was conducted to assess validity. After a pilot test, the questionnaire was refined using confirmatory models, maximum-likelihood estimation, and the variance-covariance matrix (as input). Multiple linear regression models were used to confirm external validity, considering variables related to patient safety culture as dependent variables and the five factors as independent variables. The final instrument was a structured five-point Likert self-administered survey (the "Latino Student Patient Safety Questionnaire") consisting of 21 items grouped into five factors. Compound reliability indexes (Cronbach's alpha statistic) calculated for the five factors were about 0.7 or higher. The results of the multiple linear regression analyses indicated good model fit (goodness-of-fit index: 0.9). Item-total correlations were higher than 0.3 in all cases. The convergent-discriminant validity was adequate. The questionnaire designed and validated in this study assesses nursing and medical students' attitudes and knowledge about patient safety. This instrument could be used to indirectly evaluate whether or not students in health disciplines are acquiring and thus likely to put into practice the professional skills currently considered most appropriate for patient safety.

Aerobic Fitness Level Affects Cardiovascular and Salivary Alpha Amylase Responses to Acute Psychosocial Stress.

PubMed

Wyss, Thomas; Boesch, Maria; Roos, Lilian; Tschopp, Céline; Frei, Klaus M; Annen, Hubert; La Marca, Roberto

2016-12-01

Good physical fitness seems to help the individual to buffer the potential harmful impact of psychosocial stress on somatic and mental health. The aim of the present study is to investigate the role of physical fitness levels on the autonomic nervous system (ANS; i.e. heart rate and salivary alpha amylase) responses to acute psychosocial stress, while controlling for established factors influencing individual stress reactions. The Trier Social Stress Test for Groups (TSST-G) was executed with 302 male recruits during their first week of Swiss Army basic training. Heart rate was measured continuously, and salivary alpha amylase was measured twice, before and after the stress intervention. In the same week, all volunteers participated in a physical fitness test and they responded to questionnaires on lifestyle factors and personal traits. A multiple linear regression analysis was conducted to determine ANS responses to acute psychosocial stress from physical fitness test performances, controlling for personal traits, behavioural factors, and socioeconomic data. Multiple linear regression revealed three variables predicting 15 % of the variance in heart rate response (area under the individual heart rate response curve during TSST-G) and four variables predicting 12 % of the variance in salivary alpha amylase response (salivary alpha amylase level immediately after the TSST-G) to acute psychosocial stress. A strong performance at the progressive endurance run (high maximal oxygen consumption) was a significant predictor of ANS response in both models: low area under the heart rate response curve during TSST-G as well as low salivary alpha amylase level after TSST-G. Further, high muscle power, non-smoking, high extraversion, and low agreeableness were predictors of a favourable ANS response in either one of the two dependent variables. Good physical fitness, especially good aerobic endurance capacity, is an important protective factor against health-threatening reactions to acute psychosocial stress.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Multiple Isoforms of ANRIL in Melanoma Cells: Structural Complexity Suggests Variations in Processing.

PubMed

Sarkar, Debina; Oghabian, Ali; Bodiyabadu, Pasani K; Joseph, Wayne R; Leung, Euphemia Y; Finlay, Graeme J; Baguley, Bruce C; Askarian-Amiri, Marjan E

2017-06-27

The long non-coding RNA ANRIL , antisense to the CDKN2B locus, is transcribed from a gene that encompasses multiple disease-associated polymorphisms. Despite the identification of multiple isoforms of ANRIL , expression of certain transcripts has been found to be tissue-specific and the characterisation of ANRIL transcripts remains incomplete. Several functions have been associated with ANRIL . In our judgement, studies on ANRIL functionality are premature pending a more complete appreciation of the profusion of isoforms. We found differential expression of ANRIL exons, which indicates that multiple isoforms exist in melanoma cells. In addition to linear isoforms, we identified circular forms of ANRIL ( circANRIL ). Further characterisation of circANR IL in two patient-derived metastatic melanoma cell lines (NZM7 and NZM37) revealed the existence of a rich assortment of circular isoforms. Moreover, in the two melanoma cell lines investigated, the complements of circANRIL isoforms were almost completely different. Novel exons were also discovered. We also found the family of linear ANRIL was enriched in the nucleus, whilst the circular isoforms were enriched in the cytoplasm and they differed markedly in stability. With respect to the variable processing of circANRIL species, bioinformatic analysis indicated that intronic Arthrobacter luteus (Alu) restriction endonuclease inverted repeats and exon skipping were not involved in selection of back-spliced exon junctions. Based on our findings, we hypothesise that " ANRIL " has wholly distinct dual sets of functions in melanoma. This reveals the dynamic nature of the locus and constitutes a basis for investigating the functions of ANRIL in melanoma.

A duality approach for solving bounded linear programming problems with fuzzy variables based on ranking functions and its application in bounded transportation problems

NASA Astrophysics Data System (ADS)

Ebrahimnejad, Ali

2015-08-01

There are several methods, in the literature, for solving fuzzy variable linear programming problems (fuzzy linear programming in which the right-hand-side vectors and decision variables are represented by trapezoidal fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings a new method based on the bounded dual simplex method is proposed to determine the fuzzy optimal solution of that kind of fuzzy variable linear programming problems in which some or all variables are restricted to lie within lower and upper bounds. To illustrate the proposed method, an application example is solved and the obtained results are given. The advantages of the proposed method over existing methods are discussed. Also, one application of this algorithm in solving bounded transportation problems with fuzzy supplies and demands is dealt with. The proposed method is easy to understand and to apply for determining the fuzzy optimal solution of bounded fuzzy variable linear programming problems occurring in real-life situations.

Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams

USGS Publications Warehouse

Kronholm, Scott C.; Capel, Paul D.; Terziotti, Silvia

2016-01-01

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km2) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.

Comparative analysis on the probability of being a good payer

NASA Astrophysics Data System (ADS)

Mihova, V.; Pavlov, V.

2017-10-01

Credit risk assessment is crucial for the bank industry. The current practice uses various approaches for the calculation of credit risk. The core of these approaches is the use of multiple regression models, applied in order to assess the risk associated with the approval of people applying for certain products (loans, credit cards, etc.). Based on data from the past, these models try to predict what will happen in the future. Different data requires different type of models. This work studies the causal link between the conduct of an applicant upon payment of the loan and the data that he completed at the time of application. A database of 100 borrowers from a commercial bank is used for the purposes of the study. The available data includes information from the time of application and credit history while paying off the loan. Customers are divided into two groups, based on the credit history: Good and Bad payers. Linear and logistic regression are applied in parallel to the data in order to estimate the probability of being good for new borrowers. A variable, which contains value of 1 for Good borrowers and value of 0 for Bad candidates, is modeled as a dependent variable. To decide which of the variables listed in the database should be used in the modelling process (as independent variables), a correlation analysis is made. Due to the results of it, several combinations of independent variables are tested as initial models - both with linear and logistic regression. The best linear and logistic models are obtained after initial transformation of the data and following a set of standard and robust statistical criteria. A comparative analysis between the two final models is made and scorecards are obtained from both models to assess new customers at the time of application. A cut-off level of points, bellow which to reject the applications and above it - to accept them, has been suggested for both the models, applying the strategy to keep the same Accept Rate as in the current data.

Relation of pH and other soil variables to concentrations of Pb, Cu, Zn, Cd, and Se in earthworms

USGS Publications Warehouse

Beyer, W.N.; Hensler, G.L.; Moore, J.

1987-01-01

Various soil treatments (clay, composted peat, superphosphate, sulfur, calcium carbonate, calcium chloride, zinc chloride, selenous acid) were added to experimental field plots to test the effect of different soil variables on the concentrations of 5 elements in earthworms (Pb, Cu, Zn, Cd, Se). Concentrations of the 5 elements were related to 9 soil variables (soil Pb, soil Cu, soil Zn, pH, organic matter, P, K, Mg, and Ca) with linear multiple regression. Lead concentrations in earthworms were positively correlated with soil Pb and soil organic matter, and negatively correlated with soil pH and soil Mg, with an R2 of 64%. Se concentrations were higher in earthworms from plots amended with Se, and Zn concentrations were higher in earthworms from plots amended with Zn. However, none of the other soil variables had important effects on the concentrations of Cu, Zn, Cd and Se in earthworms. Although some significant statistical relations were demonstrated, the values of r2 of all relations (> 20%) were so low that they had little predictive value.

Motivation for change as a predictor of treatment response for dysthymia.

PubMed

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

On the interpretation of kernels - Computer simulation of responses to impulse pairs

NASA Technical Reports Server (NTRS)

Hung, G.; Stark, L.; Eykhoff, P.

1983-01-01

A method is presented for the use of a unit impulse response and responses to impulse pairs of variable separation in the calculation of the second-degree kernels of a quadratic system. A quadratic system may be built from simple linear terms of known dynamics and a multiplier. Computer simulation results on quadratic systems with building elements of various time constants indicate reasonably that the larger time constant term before multiplication dominates in the envelope of the off-diagonal kernel curves as these move perpendicular to and away from the main diagonal. The smaller time constant term before multiplication combines with the effect of the time constant after multiplication to dominate in the kernel curves in the direction of the second-degree impulse response, i.e., parallel to the main diagonal. Such types of insight may be helpful in recognizing essential aspects of (second-degree) kernels; they may be used in simplifying the model structure and, perhaps, add to the physical/physiological understanding of the underlying processes.

Weather and air pollutants have an impact on patients with respiratory diseases and breathing difficulties in Munich, Germany

NASA Astrophysics Data System (ADS)

Wanka, E. R.; Bayerstadler, A.; Heumann, C.; Nowak, D.; Jörres, R. A.; Fischer, R.

2014-03-01

This study determined the influence of various meteorological variables and air pollutants on airway disorders in general, and asthma and/or chronic obstructive pulmonary disease in particular, in Munich, Bavaria, during 2006 and 2007. This was achieved through an evaluation of the daily frequency of calls to medical and emergency call centres, ambulatory medical care visits at general practitioners, and prescriptions of antibiotics for respiratory diseases. Meteorological parameters were extracted from data supplied by the European Centre for Medium Range Weather Forecast. Data on air pollutant levels were extracted from the air quality database of the European Environmental Agency for different measurement sites. In addition to descriptive analyses, a backward elimination procedure was performed to identify variables associated with medical outcome variables. Afterwards, generalised additive models (GAM) were used to verify whether the selected variables had a linear or nonlinear impact on the medical outcomes. The analyses demonstrated associations between environmental parameters and daily frequencies of different medical outcomes, such as visits at GPs and air pressure (-27 % per 10 hPa change) or ozone (-24 % per 10 μg/m3 change). The results of the GAM indicated that the effects of some covariates, such as carbon monoxide on consultations at GPs, or humidity on medical calls in general, were nonlinear, while the type of association varied between medical outcomes. These data suggest that the multiple, complex effect of environmental factors on medical outcomes should not be assumed homogeneous or linear a priori and that different settings might be associated with different types of associations.

Prescription-induced jump distributions in multiplicative Poisson processes.

PubMed

Suweis, Samir; Porporato, Amilcare; Rinaldo, Andrea; Maritan, Amos

2011-06-01

Generalized Langevin equations (GLE) with multiplicative white Poisson noise pose the usual prescription dilemma leading to different evolution equations (master equations) for the probability distribution. Contrary to the case of multiplicative Gaussian white noise, the Stratonovich prescription does not correspond to the well-known midpoint (or any other intermediate) prescription. By introducing an inertial term in the GLE, we show that the Itô and Stratonovich prescriptions naturally arise depending on two time scales, one induced by the inertial term and the other determined by the jump event. We also show that, when the multiplicative noise is linear in the random variable, one prescription can be made equivalent to the other by a suitable transformation in the jump probability distribution. We apply these results to a recently proposed stochastic model describing the dynamics of primary soil salinization, in which the salt mass balance within the soil root zone requires the analysis of different prescriptions arising from the resulting stochastic differential equation forced by multiplicative white Poisson noise, the features of which are tailored to the characters of the daily precipitation. A method is finally suggested to infer the most appropriate prescription from the data.

Prescription-induced jump distributions in multiplicative Poisson processes

NASA Astrophysics Data System (ADS)

Suweis, Samir; Porporato, Amilcare; Rinaldo, Andrea; Maritan, Amos

2011-06-01

Generalized Langevin equations (GLE) with multiplicative white Poisson noise pose the usual prescription dilemma leading to different evolution equations (master equations) for the probability distribution. Contrary to the case of multiplicative Gaussian white noise, the Stratonovich prescription does not correspond to the well-known midpoint (or any other intermediate) prescription. By introducing an inertial term in the GLE, we show that the Itô and Stratonovich prescriptions naturally arise depending on two time scales, one induced by the inertial term and the other determined by the jump event. We also show that, when the multiplicative noise is linear in the random variable, one prescription can be made equivalent to the other by a suitable transformation in the jump probability distribution. We apply these results to a recently proposed stochastic model describing the dynamics of primary soil salinization, in which the salt mass balance within the soil root zone requires the analysis of different prescriptions arising from the resulting stochastic differential equation forced by multiplicative white Poisson noise, the features of which are tailored to the characters of the daily precipitation. A method is finally suggested to infer the most appropriate prescription from the data.

An operator calculus for surface and volume modeling

NASA Technical Reports Server (NTRS)

Gordon, W. J.

1984-01-01

The mathematical techniques which form the foundation for most of the surface and volume modeling techniques used in practice are briefly described. An outline of what may be termed an operator calculus for the approximation and interpolation of functions of more than one independent variable is presented. By considering the linear operators associated with bivariate and multivariate interpolation/approximation schemes, it is shown how they can be compounded by operator multiplication and Boolean addition to obtain a distributive lattice of approximation operators. It is then demonstrated via specific examples how this operator calculus leads to practical techniques for sculptured surface and volume modeling.

Reliable method for determination of the velocity of a sinker in a high-pressure falling body type viscometer

NASA Astrophysics Data System (ADS)

Dindar, Cigdem; Kiran, Erdogan

2002-10-01

We present a new sensor configuration and data reduction process to improve the accuracy and reliability of determining the terminal velocity of a falling sinker in falling body type viscometers. This procedure is based on the use of multiple linear variable differential transformer sensors and precise mapping of the sensor signal and position along with the time of fall which is then converted to distance versus fall time along the complete fall path. The method and its use in determination of high-pressure viscosity of n-pentane and carbon dioxide are described.

Digital robust active control law synthesis for large order flexible structure using parameter optimization

NASA Technical Reports Server (NTRS)

Mukhopadhyay, V.

1988-01-01

A generic procedure for the parameter optimization of a digital control law for a large-order flexible flight vehicle or large space structure modeled as a sampled data system is presented. A linear quadratic Guassian type cost function was minimized, while satisfying a set of constraints on the steady-state rms values of selected design responses, using a constrained optimization technique to meet multiple design requirements. Analytical expressions for the gradients of the cost function and the design constraints on mean square responses with respect to the control law design variables are presented.

Robust set-point regulation for ecological models with multiple management goals.

PubMed

Guiver, Chris; Mueller, Markus; Hodgson, Dave; Townley, Stuart

2016-05-01

Population managers will often have to deal with problems of meeting multiple goals, for example, keeping at specific levels both the total population and population abundances in given stage-classes of a stratified population. In control engineering, such set-point regulation problems are commonly tackled using multi-input, multi-output proportional and integral (PI) feedback controllers. Building on our recent results for population management with single goals, we develop a PI control approach in a context of multi-objective population management. We show that robust set-point regulation is achieved by using a modified PI controller with saturation and anti-windup elements, both described in the paper, and illustrate the theory with examples. Our results apply more generally to linear control systems with positive state variables, including a class of infinite-dimensional systems, and thus have broader appeal.

Numerical optimization techniques for bound circulation distribution for minimum induced drag of Nonplanar wings: Computer program documentation

NASA Technical Reports Server (NTRS)

Kuhlman, J. M.; Ku, T. J.

1981-01-01

A two dimensional advanced panel far-field potential flow model of the undistorted, interacting wakes of multiple lifting surfaces was developed which allows the determination of the spanwise bound circulation distribution required for minimum induced drag. This model was implemented in a FORTRAN computer program, the use of which is documented in this report. The nonplanar wakes are broken up into variable sized, flat panels, as chosen by the user. The wake vortex sheet strength is assumed to vary linearly over each of these panels, resulting in a quadratic variation of bound circulation. Panels are infinite in the streamwise direction. The theory is briefly summarized herein; sample results are given for multiple, nonplanar, lifting surfaces, and the use of the computer program is detailed in the appendixes.

A Necessary Condition for Coexistence of Autocatalytic Replicators in a Prebiotic Environment

PubMed Central

Hernandez, Andres F.; Grover, Martha A.

2013-01-01

A necessary, but not sufficient, mathematical condition for the coexistence of short replicating species is presented here. The mathematical condition is obtained for a prebiotic environment, simulated as a fed-batch reactor, which combines monomer recycling, variable reaction order and a fixed monomer inlet flow with two replicator types and two monomer types. An extensive exploration of the parameter space in the model validates the robustness and efficiency of the mathematical condition, with nearly 1.7% of parameter sets meeting the condition and half of those exhibiting sustained coexistence. The results show that it is possible to generate a condition of coexistence, where two replicators sustain a linear growth simultaneously for a wide variety of chemistries, under an appropriate environment. The presence of multiple monomer types is critical to sustaining the coexistence of multiple replicator types. PMID:25369813

A necessary condition for coexistence of autocatalytic replicators in a prebiotic environment.

PubMed

Hernandez, Andres F; Grover, Martha A

2013-07-24

A necessary, but not sufficient, mathematical condition for the coexistence of short replicating species is presented here. The mathematical condition is obtained for a prebiotic environment, simulated as a fed-batch reactor, which combines monomer recycling, variable reaction order and a fixed monomer inlet flow with two replicator types and two monomer types. An extensive exploration of the parameter space in the model validates the robustness and efficiency of the mathematical condition, with nearly 1.7% of parameter sets meeting the condition and half of those exhibiting sustained coexistence. The results show that it is possible to generate a condition of coexistence, where two replicators sustain a linear growth simultaneously for a wide variety of chemistries, under an appropriate environment. The presence of multiple monomer types is critical to sustaining the coexistence of multiple replicator types.

How Robust Is Linear Regression with Dummy Variables?

ERIC Educational Resources Information Center

Blankmeyer, Eric

2006-01-01

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

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Multivariable analysis of anesthetic factors associated with time to extubation in dogs.

PubMed

Kleine, Stephanie; Hofmeister, Erik; Egan, Katrina

2014-12-01

The purpose of this study was to identify factors that prolong the time to extubation in dogs. Anesthetic records of 900 dogs at a university teaching hospital were searched. Multiple linear regression was used to compare independent predictors (patient demographics, anesthetic and intraoperative variables) with the dependent variable (time to extubation). Induction with propofol (P < 0.025) was associated with a shorter time to extubation, while premedication with acepromazine (P = 0.000) was associated with a longer time to extubation. Time to extubation was increased by 0.311 minutes for every kilogram increase in body weight (P = 0.000), 5.924 minutes for every 1 °C loss in body temperature (P = 0.0000), and by 0.096 minutes for every 1 minute increase in anesthetic duration (P = 0.000). Anesthetic variables, which can be manipulated by the anesthetist, include choice of premedication and induction drugs, hypothermia, and duration of anesthesia. Published by Elsevier Ltd.

Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China

NASA Astrophysics Data System (ADS)

Zhu, Yun-Mei; Lu, X. X.; Zhou, Yue

2007-02-01

Artificial neural network (ANN) was used to model the monthly suspended sediment flux in the Longchuanjiang River, the Upper Yangtze Catchment, China. The suspended sediment flux was related to the average rainfall, temperature, rainfall intensity and water discharge. It is demonstrated that ANN is capable of modeling the monthly suspended sediment flux with fairly good accuracy when proper variables and their lag effect on the suspended sediment flux are used as inputs. Compared with multiple linear regression and power relation models, ANN can generate a better fit under the same data requirement. In addition, ANN can provide more reasonable predictions for extremely high or low values, because of the distributed information processing system and the nonlinear transformation involved. Compared with the ANNs that use the values of the dependent variable at previous time steps as inputs, the ANNs established in this research with only climate variables have an advantage because it can be used to assess hydrological responses to climate change.

An Exploration of the Associations Among Multiple Aspects of Religiousness, Body Image, Eating Pathology, and Appearance Investment.

PubMed

Goulet, Carol; Henrie, James; Szymanski, Lynda

2017-04-01

The purpose of this study was to investigate the influence of positive and negative aspects of religiousness on eating pathology, body satisfaction, and appearance investment beyond previously established variables (age, BMI, exercise frequency, weight stability, and self-esteem). Data collected from 168 adult females at a Catholic-affiliated university were analyzed using hierarchical linear regressions. As expected, some religiousness variables (spirituality and seeing one's body as having sacred qualities) were associated with eating pathology, body satisfaction, and appearance investment in potentially beneficial ways, and others (negative interaction with one's religious community) were associated in potentially harmful ways. Interestingly, greater religious meaning, or the importance of religion in one's life, was associated with greater eating pathology, and some variables (religious coping, participation in and support from one's religious community) expected to be associated with greater body satisfaction were unrelated. Results are discussed in terms of mechanisms through which the aspects of religiousness may influence body satisfaction, appearance investment, and eating pathology.

Potential impacts of climate variability on respiratory morbidity in children, infants, and adults.

PubMed

Souza, Amaury de; Fernandes, Widinei Alves; Pavão, Hamilton Germano; Lastoria, Giancarlo; Albrez, Edilce do Amaral

2012-01-01

To determine whether climate variability influences the number of hospitalizations for respiratory diseases in infants, children, and adults in the city of Campo Grande, Brazil. We used daily data on admissions for respiratory diseases, precipitation, air temperature, humidity, and wind speed for the 2004-2008 period. We calculated the thermal comfort index, effective temperature, and effective temperature with wind speed (wind-chill or heat index) using the meteorological data obtained. Generalized linear models, with Poisson multiple regression, were used in order to predict hospitalizations for respiratory disease. The variables studied were (collectively) found to show relatively high correlation coefficients in relation to hospital admission for pneumonia in children (R² = 68.4%), infants (R² = 71.8%), and adults (R² = 81.8%). Our results indicate a quantitative risk for an increase in the number of hospitalizations of children, infants, and adults, according to the increase or decrease in temperature, humidity, precipitation, wind speed, and thermal comfort index in the city under study.

Control Augmented Structural Synthesis

NASA Technical Reports Server (NTRS)

Lust, Robert V.; Schmit, Lucien A.

1988-01-01

A methodology for control augmented structural synthesis is proposed for a class of structures which can be modeled as an assemblage of frame and/or truss elements. It is assumed that both the plant (structure) and the active control system dynamics can be adequately represented with a linear model. The structural sizing variables, active control system feedback gains and nonstructural lumped masses are treated simultaneously as independent design variables. Design constraints are imposed on static and dynamic displacements, static stresses, actuator forces and natural frequencies to ensure acceptable system behavior. Multiple static and dynamic loading conditions are considered. Side constraints imposed on the design variables protect against the generation of unrealizable designs. While the proposed approach is fundamentally more general, here the methodology is developed and demonstrated for the case where: (1) the dynamic loading is harmonic and thus the steady state response is of primary interest; (2) direct output feedback is used for the control system model; and (3) the actuators and sensors are collocated.

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

PubMed

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

2017-09-05

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

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

Optimization of Bread Enriched with Garcinia mangostana Pericarp Powder

NASA Astrophysics Data System (ADS)

Ibrahim, U. K.; Salleh, R. Mohd; Maqsood-ul-Hague, S. N. S.; Hashib, S. Abd; Karim, S. F. Abd

2018-05-01

The aim of present work is to optimize the formulation of bread enhanced with Garcinia mangostana pericarp powder with the combination of baking process conditions. The independent variables used were baking time (15 - 30 minutes), baking temperature (180 - 220°C) and pericarp powder concentration (0.5 - 2.0%). The physical and chemical properties of bread sample such as antioxidant activity, phenolic content, moisture analysis and colour parameters were studied. Bread dough without fortification of pericarp powder was used as control. Data obtained were analyzed by multiple regressions and the significant model such as linear and quadratic with variables interactions were used. As a conclusion, the optimum baking conditions were found at 213°C baking temperature with 23 minutes baking time and addition of 0.87% for Garcinia mangostana pericarp powder to the bread formulation.

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

First-Order System Least Squares for the Stokes Equations, with Application to Linear Elasticity

NASA Technical Reports Server (NTRS)

Cai, Z.; Manteuffel, T. A.; McCormick, S. F.

1996-01-01

Following our earlier work on general second-order scalar equations, here we develop a least-squares functional for the two- and three-dimensional Stokes equations, generalized slightly by allowing a pressure term in the continuity equation. By introducing a velocity flux variable and associated curl and trace equations, we are able to establish ellipticity in an H(exp 1) product norm appropriately weighted by the Reynolds number. This immediately yields optimal discretization error estimates for finite element spaces in this norm and optimal algebraic convergence estimates for multiplicative and additive multigrid methods applied to the resulting discrete systems. Both estimates are uniform in the Reynolds number. Moreover, our pressure-perturbed form of the generalized Stokes equations allows us to develop an analogous result for the Dirichlet problem for linear elasticity with estimates that are uniform in the Lame constants.

Depressive symptoms in Chinese factory workers in Nagasaki, Japan.

PubMed

Date, Yutaka; Abe, Yasuyo; Aoyagi, Kiyoshi; Ye, Zhaojia; Takamura, Noboru; Tomita, Masato; Osaki, Makoto; Honda, Sumihisa

2009-08-01

The number of foreign workers in Japan, especially temporary workers, has been increasing recently. However, little is known about the mental health status of the foreign workers working temporarily in Japan. We examined the depressive symptoms in 81 Chinese factory workers and attempted to identify the determining factors. The subjects were requested to complete individual questionnaires on sociodemographic variables (sex, age group, and residence period in Japan), working condition variables (number of working days per week and working hours per day), health administration variables (health checkups and health education), a social support variable (interpreters at workplace), and health behavior variables (alcohol consumption, current smoking, and regular exercise). The 20-item Center for Epidemiologic Studies Depression scale (CES-D) was used to measure the depressive symptoms. Of all the subjects, 95% and 84% received health checkups and health education, respectively, at their workplaces. The results of multiple linear regression analysis showed that long working hours per day were significantly associated with high CES-D score. Further, we found that older age (30-49 yr) was marginally associated with high CES-D score. Health administration considering working time and age would be important for decreasing depressive symptoms among foreign workers.

Quantitatively measured tremor in hand-arm vibration-exposed workers.

PubMed

Edlund, Maria; Burström, Lage; Hagberg, Mats; Lundström, Ronnie; Nilsson, Tohr; Sandén, Helena; Wastensson, Gunilla

2015-04-01

The aim of the present study was to investigate the possible increase in hand tremor in relation to hand-arm vibration (HAV) exposure in a cohort of exposed and unexposed workers. Participants were 178 male workers with or without exposure to HAV. The study is cross-sectional regarding the outcome of tremor and has a longitudinal design with respect to exposure. The dose of HAV exposure was collected via questionnaires and measurements at several follow-ups. The CATSYS Tremor Pen(®) was used for measuring postural tremor. Multiple linear regression methods were used to analyze associations between different tremor variables and HAV exposure, along with predictor variables with biological relevance. There were no statistically significant associations between the different tremor variables and cumulative HAV or current exposure. Age was a statistically significant predictor of variation in tremor outcomes for three of the four tremor variables, whereas nicotine use was a statistically significant predictor of either left or right hand or both hands for all four tremor variables. In the present study, there was no evidence of an exposure-response association between HAV exposure and measured postural tremor. Increase in age and nicotine use appeared to be the strongest predictors of tremor.

Structural Equation Modeling: A Framework for Ocular and Other Medical Sciences Research

PubMed Central

Christ, Sharon L.; Lee, David J.; Lam, Byron L.; Diane, Zheng D.

2017-01-01

Structural equation modeling (SEM) is a modeling framework that encompasses many types of statistical models and can accommodate a variety of estimation and testing methods. SEM has been used primarily in social sciences but is increasingly used in epidemiology, public health, and the medical sciences. SEM provides many advantages for the analysis of survey and clinical data, including the ability to model latent constructs that may not be directly observable. Another major feature is simultaneous estimation of parameters in systems of equations that may include mediated relationships, correlated dependent variables, and in some instances feedback relationships. SEM allows for the specification of theoretically holistic models because multiple and varied relationships may be estimated together in the same model. SEM has recently expanded by adding generalized linear modeling capabilities that include the simultaneous estimation of parameters of different functional form for outcomes with different distributions in the same model. Therefore, mortality modeling and other relevant health outcomes may be evaluated. Random effects estimation using latent variables has been advanced in the SEM literature and software. In addition, SEM software has increased estimation options. Therefore, modern SEM is quite general and includes model types frequently used by health researchers, including generalized linear modeling, mixed effects linear modeling, and population average modeling. This article does not present any new information. It is meant as an introduction to SEM and its uses in ocular and other health research. PMID:24467557

Regression Analysis of Top of Descent Location for Idle-thrust Descents

NASA Technical Reports Server (NTRS)

Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg

2013-01-01

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

Hybrid finite element method for describing the electrical response of biological cells to applied fields.

PubMed

Ying, Wenjun; Henriquez, Craig S

2007-04-01

A novel hybrid finite element method (FEM) for modeling the response of passive and active biological membranes to external stimuli is presented. The method is based on the differential equations that describe the conservation of electric flux and membrane currents. By introducing the electric flux through the cell membrane as an additional variable, the algorithm decouples the linear partial differential equation part from the nonlinear ordinary differential equation part that defines the membrane dynamics of interest. This conveniently results in two subproblems: a linear interface problem and a nonlinear initial value problem. The linear interface problem is solved with a hybrid FEM. The initial value problem is integrated by a standard ordinary differential equation solver such as the Euler and Runge-Kutta methods. During time integration, these two subproblems are solved alternatively. The algorithm can be used to model the interaction of stimuli with multiple cells of almost arbitrary geometries and complex ion-channel gating at the plasma membrane. Numerical experiments are presented demonstrating the uses of the method for modeling field stimulation and action potential propagation.

Practical somewhat-secure quantum somewhat-homomorphic encryption with coherent states

NASA Astrophysics Data System (ADS)

Tan, Si-Hui; Ouyang, Yingkai; Rohde, Peter P.

2018-04-01

We present a scheme for implementing homomorphic encryption on coherent states encoded using phase-shift keys. The encryption operations require only rotations in phase space, which commute with computations in the code space performed via passive linear optics, and with generalized nonlinear phase operations that are polynomials of the photon-number operator in the code space. This encoding scheme can thus be applied to any computation with coherent-state inputs, and the computation proceeds via a combination of passive linear optics and generalized nonlinear phase operations. An example of such a computation is matrix multiplication, whereby a vector representing coherent-state amplitudes is multiplied by a matrix representing a linear optics network, yielding a new vector of coherent-state amplitudes. By finding an orthogonal partitioning of the support of our encoded states, we quantify the security of our scheme via the indistinguishability of the encrypted code words. While we focus on coherent-state encodings, we expect that this phase-key encoding technique could apply to any continuous-variable computation scheme where the phase-shift operator commutes with the computation.

Engineering challenges of BioNEMS: the integration of microfluidics, micro- and nanodevices, models and external control for systems biology.

PubMed

Wikswo, J P; Prokop, A; Baudenbacher, F; Cliffel, D; Csukas, B; Velkovsky, M

2006-08-01

Systems biology, i.e. quantitative, postgenomic, postproteomic, dynamic, multiscale physiology, addresses in an integrative, quantitative manner the shockwave of genetic and proteomic information using computer models that may eventually have 10(6) dynamic variables with non-linear interactions. Historically, single biological measurements are made over minutes, suggesting the challenge of specifying 10(6) model parameters. Except for fluorescence and micro-electrode recordings, most cellular measurements have inadequate bandwidth to discern the time course of critical intracellular biochemical events. Micro-array expression profiles of thousands of genes cannot determine quantitative dynamic cellular signalling and metabolic variables. Major gaps must be bridged between the computational vision and experimental reality. The analysis of cellular signalling dynamics and control requires, first, micro- and nano-instruments that measure simultaneously multiple extracellular and intracellular variables with sufficient bandwidth; secondly, the ability to open existing internal control and signalling loops; thirdly, external BioMEMS micro-actuators that provide high bandwidth feedback and externally addressable intracellular nano-actuators; and, fourthly, real-time, closed-loop, single-cell control algorithms. The unravelling of the nested and coupled nature of cellular control loops requires simultaneous recording of multiple single-cell signatures. Externally controlled nano-actuators, needed to effect changes in the biochemical, mechanical and electrical environment both outside and inside the cell, will provide a major impetus for nanoscience.

The association between subgingival periodontal pathogens and systemic inflammation.

PubMed

Winning, Lewis; Patterson, Christopher C; Cullen, Kathy M; Stevenson, Kathryn A; Lundy, Fionnuala T; Kee, Frank; Linden, Gerard J

2015-09-01

To investigate associations between periodontal disease pathogens and levels of systemic inflammation measured by C-reactive protein (CRP). A representative sample of dentate 60-70-year-old men in Northern Ireland had a comprehensive periodontal examination. Men taking statins were excluded. Subgingival plaque samples were analysed by quantitative real time PCR to identify the presence of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Treponema denticola and Tannerella forsythia. High-sensitivity CRP (mg/l) was measured from fasting blood samples. Multiple linear regression analysis was performed using log-transformed CRP concentration as the dependent variable, with the presence of each periodontal pathogen as predictor variables, with adjustment for various potential confounders. A total of 518 men (mean age 63.6 SD 3.0 years) were included in the analysis. Multiple regression analysis showed that body mass index (p < 0.001), current smoking (p < 0.01), the detectable presence of P. gingivalis (p < 0.01) and hypertension (p = 0.01), were independently associated with an increased CRP. The detectable presence of P. gingivalis was associated with a 20% (95% confidence interval 4-35%) increase in CRP (mg/l) after adjustment for all other predictor variables. In these 60-70-year-old dentate men, the presence of P. gingivalis in subgingival plaque was significantly associated with a raised level of C-reactive protein. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Linear quadratic optimization for positive LTI system

NASA Astrophysics Data System (ADS)

Muhafzan, Yenti, Syafrida Wirma; Zulakmal

2017-05-01

Nowaday the linear quadratic optimization subject to positive linear time invariant (LTI) system constitute an interesting study considering it can become a mathematical model of variety of real problem whose variables have to nonnegative and trajectories generated by these variables must be nonnegative. In this paper we propose a method to generate an optimal control of linear quadratic optimization subject to positive linear time invariant (LTI) system. A sufficient condition that guarantee the existence of such optimal control is discussed.

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Correlation and simple linear regression.

PubMed

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

2003-06-01

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

Linear Relationship between Resilience, Learning Approaches, and Coping Strategies to Predict Achievement in Undergraduate Students

PubMed Central

de la Fuente, Jesús; Fernández-Cabezas, María; Cambil, Matilde; Vera, Manuel M.; González-Torres, Maria Carmen; Artuch-Garde, Raquel

2017-01-01

The aim of the present research was to analyze the linear relationship between resilience (meta-motivational variable), learning approaches (meta-cognitive variables), strategies for coping with academic stress (meta-emotional variable) and academic achievement, necessary in the context of university academic stress. A total of 656 students from a southern university in Spain completed different questionnaires: a resiliency scale, a coping strategies scale, and a study process questionnaire. Correlations and structural modeling were used for data analyses. There was a positive and significant linear association showing a relationship of association and prediction of resilience to the deep learning approach, and problem-centered coping strategies. In a complementary way, these variables positively and significantly predicted the academic achievement of university students. These results enabled a linear relationship of association and consistent and differential prediction to be established among the variables studied. Implications for future research are set out. PMID:28713298

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

PubMed Central

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

2015-01-01

Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

PubMed

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A; Aguiló, Antoni

2015-01-01

Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation

PubMed Central

MANEL, STÉPHANIE; GUGERLI, FELIX; THUILLER, WILFRIED; ALVAREZ, NADIR; LEGENDRE, PIERRE; HOLDEREGGER, ROLF; GIELLY, LUDOVIC; TABERLET, PIERRE

2014-01-01

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran’s eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps. PMID:22680783

Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.

PubMed

Smyczynska, Joanna; Hilczer, Maciej; Smyczynska, Urszula; Stawerska, Renata; Tadeusiewicz, Ryszard; Lewinski, Andrzej

2015-01-01

The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness. Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH. For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability. ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.

Testing the Dose–Response Specification in Epidemiology: Public Health and Policy Consequences for Lead

PubMed Central

Rothenberg, Stephen J.; Rothenberg, Jesse C.

2005-01-01

Statistical evaluation of the dose–response function in lead epidemiology is rarely attempted. Economic evaluation of health benefits of lead reduction usually assumes a linear dose–response function, regardless of the outcome measure used. We reanalyzed a previously published study, an international pooled data set combining data from seven prospective lead studies examining contemporaneous blood lead effect on IQ (intelligence quotient) of 7-year-old children (n = 1,333). We constructed alternative linear multiple regression models with linear blood lead terms (linear–linear dose response) and natural-log–transformed blood lead terms (log-linear dose response). We tested the two lead specifications for nonlinearity in the models, compared the two lead specifications for significantly better fit to the data, and examined the effects of possible residual confounding on the functional form of the dose–response relationship. We found that a log-linear lead–IQ relationship was a significantly better fit than was a linear–linear relationship for IQ (p = 0.009), with little evidence of residual confounding of included model variables. We substituted the log-linear lead–IQ effect in a previously published health benefits model and found that the economic savings due to U.S. population lead decrease between 1976 and 1999 (from 17.1 μg/dL to 2.0 μg/dL) was 2.2 times ($319 billion) that calculated using a linear–linear dose–response function ($149 billion). The Centers for Disease Control and Prevention action limit of 10 μg/dL for children fails to protect against most damage and economic cost attributable to lead exposure. PMID:16140626

Computing anticipatory systems with incursion and hyperincursion

NASA Astrophysics Data System (ADS)

Dubois, Daniel M.

1998-07-01

An anticipatory system is a system which contains a model of itself and/or of its environment in view of computing its present state as a function of the prediction of the model. With the concepts of incursion and hyperincursion, anticipatory discrete systems can be modelled, simulated and controlled. By definition an incursion, an inclusive or implicit recursion, can be written as: x(t+1)=F[…,x(t-1),x(t),x(t+1),…] where the value of a variable x(t+1) at time t+1 is a function of this variable at past, present and future times. This is an extension of recursion. Hyperincursion is an incursion with multiple solutions. For example, chaos in the Pearl-Verhulst map model: x(t+1)=a.x(t).[1-x(t)] is controlled by the following anticipatory incursive model: x(t+1)=a.x(t).[1-x(t+1)] which corresponds to the differential anticipatory equation: dx(t)/dt=a.x(t).[1-x(t+1)]-x(t). The main part of this paper deals with the discretisation of differential equation systems of linear and non-linear oscillators. The non-linear oscillator is based on the Lotka-Volterra equations model. The discretisation is made by incursion. The incursive discrete equation system gives the same stability condition than the original differential equations without numerical instabilities. The linearisation of the incursive discrete non-linear Lotka-Volterra equation system gives rise to the classical harmonic oscillator. The incursive discretisation of the linear oscillator is similar to define backward and forward discrete derivatives. A generalized complex derivative is then considered and applied to the harmonic oscillator. Non-locality seems to be a property of anticipatory systems. With some mathematical assumption, the Schrödinger quantum equation is derived for a particle in a uniform potential. Finally an hyperincursive system is given in the case of a neural stack memory.

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools.

PubMed

Wöhling, Thomas; Geiges, Andreas; Nowak, Wolfgang

2016-11-01

Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data-worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south-western Germany, which has been established to monitor river-groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model-based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy-to-implement tools for an otherwise complex task and (2) yet to consider data-worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types. © 2016, National Ground Water Association.

Factors associated with added sugars intake among adolescents living in São Paulo, Brazil.

PubMed

Colucci, Ana Carolina A; Cesar, Chester L G; Marchioni, Dirce M L; Fisberg, Regina M

2012-08-01

To measure added sugars intake among adolescents and describe its demographic, socioeconomic, and nutritional status determinants. The study was conducted based on a household survey carried out between March and December 2003. Food intake was assessed through 24-hour food recalls, and an adjustment approach was applied using external variance estimates derived from 195 adolescents of the same age in 2007. Population-based cross-sectional study, city of São Paulo, Brazil. Seven hundred and ninety-three male (n = 410) and female (n = 383) adolescents aged 10-19 years. MEASURE OF OUTCOME: Foods with greater contributions toward the added sugars intake were identified. Multiple linear regression analysis was performed, with calories from added sugars as the dependent continuous variable and the remaining factors (socioeconomic, demographic, lifestyle, household condition, and food intake) as independent variables. The average contribution of added sugars to total energy value was 12.28% (95% confidence interval [CI]: 11.87-12.70) with no statistically significant sex difference (p > 0.05). Soft drinks were a major source of added sugars among the adolescents (34.2% among males and 32.0% among females), followed by sugars (sucrose and honey) and chocolate powder (around 11%). In the multiple linear regression analysis, the head of household's education level and calories from protein, fats, and carbohydrates other than sugars had an independent effect on added sugars intake. This study showed that the percentage contribution of added sugars to energy intake among adolescents in the city of São Paulo, Brazil, was above the current recommended levels. Socioeconomic condition (represented by the head of the household's education level) and macronutrient intake were shown to be determinants of sugars intake.

Ghrelin, leptin and insulin in cirrhotic children and adolescents: relationship with cirrhosis severity and nutritional status.

PubMed

Dornelles, Cristina T L; Goldani, Helena A S; Wilasco, Maria Inês A; Maurer, Rafael L; Kieling, Carlos O; Porowski, Marilene; Ferreira, Cristina T; Santos, Jorge L; Vieira, Sandra M G; Silveira, Themis R

2013-01-10

Ghrelin, leptin, and insulin concentrations are involved in the control of food intake and they seem to be associated with anorexia-cachexia in cirrhotic patients. The present study aimed to investigate the relationship between the nutritional status and fasting ghrelin, leptin and insulin concentrations in pediatric cirrhotic patients. Thirty-nine patients with cirrhosis and 39 healthy controls aged 0-15 years matched by sex and age were enrolled. Severity of liver disease was assessed by Child-Pugh classification, and Pediatric for End Stage Liver Disease (PELD) or Model for End-stage Liver Disease (MELD) scores. Blood samples were collected from patients and controls to assay total ghrelin, acyl ghrelin, leptin and insulin by using a commercial ELISA kit. Anthropometry parameters used were standard deviation score of height-for-age and triceps skinfold thickness-for-age ratio. A multiple linear regression analysis was used to determine the correlation between dependent and independent variables. Acyl ghrelin was significantly lower in cirrhotic patients than in controls [142 (93-278) pg/mL vs 275 (208-481) pg/mL, P=0.001]. After multiple linear regression analysis, total ghrelin and acyl ghrelin showed an inverse correlation with age; acyl ghrelin was associated with the severity of cirrhosis and des-acyl ghrelin with PELD or MELD scores ≥15. Leptin was positively correlated with gender and anthropometric parameters. Insulin was not associated with any variable. Low acyl ghrelin and high des-acyl ghrelin concentrations were associated with cirrhosis severity, whereas low leptin concentration was associated with undernourishment in children and adolescents with cirrhosis. Copyright © 2012 Elsevier B.V. All rights reserved.

Metabolic syndrome: An independent risk factor for erectile dysfunction

PubMed Central

Sanjay, Saran; Bharti, Gupta Sona; Manish, Gutch; Rajeev, Philip; Pankaj, Agrawal; Puspalata, Agroiya; Keshavkumar, Gupta

2015-01-01

Objective: The objective was to determine the role of various components of metabolic syndrome (MetS) as independent risk factor for erectile dysfunction (ED). Materials and Methods: A total of 113 subjects of MetS, as recommended by recent IDF and AHA/NHLBI joint interim statement were selected for study who presented for ED. After doing Anthropometric examination, fasting laboratory assay for fasting plasma glucose (FPG), fasting insulin, hemoglobin A1c, triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and 2 h oral glucose tolerance test (OGTT) was done. Erectile function was assessed by completing questions one through five of the International Index of Erectile Function (IIEF-5). A multiple linear regression analysis was carried out on 66 subjects with IIEF-5 score as dependent variable and components of MetS FPG, 2 h OGTT, TG, HDL, and waist circumference as independent variables. Results: Using a multiple linear regression analysis, we observed that presence of the various components of MetS was associated with ED and a decrease IIEF-5 score and this effect was greater than the effect associated with any of the individual components. Of the individual components of the MetS, HDL (B = 0.136; P = 0.004) and FPG (B = −0.069; P = 0.007) conferred the strongest effect on IIEF-5 score. However, overall age had most significant effect on IIEF-5 score. Conclusion: It is crucial to formulate strategies and implement them to prevent or control the epidemic of the MetS and its consequences. The early identification and treatment of risk factors might be helpful to prevent ED and secondary cardiovascular disease, including diet and lifestyle interventions. PMID:25729692

Metabolic syndrome: An independent risk factor for erectile dysfunction.

PubMed

Sanjay, Saran; Bharti, Gupta Sona; Manish, Gutch; Rajeev, Philip; Pankaj, Agrawal; Puspalata, Agroiya; Keshavkumar, Gupta

2015-01-01

The objective was to determine the role of various components of metabolic syndrome (MetS) as independent risk factor for erectile dysfunction (ED). A total of 113 subjects of MetS, as recommended by recent IDF and AHA/NHLBI joint interim statement were selected for study who presented for ED. After doing Anthropometric examination, fasting laboratory assay for fasting plasma glucose (FPG), fasting insulin, hemoglobin A1c, triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and 2 h oral glucose tolerance test (OGTT) was done. Erectile function was assessed by completing questions one through five of the International Index of Erectile Function (IIEF-5). A multiple linear regression analysis was carried out on 66 subjects with IIEF-5 score as dependent variable and components of MetS FPG, 2 h OGTT, TG, HDL, and waist circumference as independent variables. Using a multiple linear regression analysis, we observed that presence of the various components of MetS was associated with ED and a decrease IIEF-5 score and this effect was greater than the effect associated with any of the individual components. Of the individual components of the MetS, HDL (B = 0.136; P = 0.004) and FPG (B = -0.069; P = 0.007) conferred the strongest effect on IIEF-5 score. However, overall age had most significant effect on IIEF-5 score. It is crucial to formulate strategies and implement them to prevent or control the epidemic of the MetS and its consequences. The early identification and treatment of risk factors might be helpful to prevent ED and secondary cardiovascular disease, including diet and lifestyle interventions.

Use of cardiopulmonary exercise testing to assess early ventilatory changes related to occupational particulate matter

PubMed Central

Chao, T.P.; Sperandio, E.F.; Ostolin, T.L.V.P.; Almeida, V.R.; Romiti, M.; Gagliardi, A.R.T.; Arantes, R.L.; Dourado, V.Z.

2018-01-01

Spirometry has been used as the main strategy for assessing ventilatory changes related to occupational exposure to particulate matter (OEPM). However, in some cases, as one of its limitations, it may not be sensitive enough to show abnormalities before extensive damage, as seen in restrictive lung diseases. Therefore, we hypothesized that cardiopulmonary exercise testing (CPET) may be better than spirometry to detect early ventilatory impairment caused by OEPM. We selected 135 male workers with at least one year of exposure. After collection of self-reported socioeconomic status, educational level, and cardiovascular risk data, participants underwent spirometry, CPET, body composition assessment (bioelectrical impedance), and triaxial accelerometry (for level of physical activity in daily life). CPET was performed using a ramp protocol on a treadmill. Metabolic, cardiovascular, ventilatory, and submaximal relationships were measured. We compared 52 exposed to 83 non-exposed workers. Multiple linear regressions were developed using spirometry and CPET variables as outcomes and OEPM as the main predictor, and adjusted by the main covariates. Our results showed that OEPM was associated with significant reductions in peak minute ventilation, peak tidal volume, and breathing reserve index. Exposed participants presented shallower slope of ΔVT/ΔlnV̇E (breathing pattern), i.e., increased tachypneic breathing pattern. The OEPM explained 7.4% of the ΔVT/ΔlnV̇E variability. We found no significant influence of spirometric indices after multiple linear regressions. We conclude that CPET might be a more sensitive feature of assessing early pulmonary impairment related to OEPM. Our cross-sectional results suggested that CPET is a promising tool for the screening of asymptomatic male workers. PMID:29590255

Perceptions of Shared Decision Making Among Patients with Spinal Cord Injuries/Disorders.

PubMed

Locatelli, Sara M; Etingen, Bella; Heinemann, Allen; Neumann, Holly DeMark; Miskovic, Ana; Chen, David; LaVela, Sherri L

2016-01-01

Background: Individuals with spinal cord injuries/disorders (SCI/D) are interested in, and benefit from, shared decision making (SDM). Objective: To explore SDM among individuals with SCI/D and how demographics and health and SCI/D characteristics are related to SDM. Method: Individuals with SCI/D who were at least 1 year post injury, resided in the Chicago metropolitan area, and received SCI care at a Veterans Affairs (VA; n = 124) or an SCI Model Systems facility ( n = 326) completed a mailed survey measuring demographics, health and SCI/D characteristics, physical and mental health status, and perceptions of care, including SDM, using the Combined Outcome Measure for Risk Communication and Treatment Decision-Making Effectiveness (COMRADE) that assesses decision-making effectiveness (effectiveness) and risk communication (communication). Bivariate analyses and multiple linear regression were used to identify variables associated with SDM. Results: Participants were mostly male (83%) and White (70%) and were an average age of 54 years ( SD = 14.3). Most had traumatic etiology, 44% paraplegia, and 49% complete injury. Veteran/civilian status and demographics were unrelated to scores. Bivariate analyses showed that individuals with tetraplegia had better effectiveness scores than those with paraplegia. Better effectiveness was correlated with better physical and mental health; better communication was correlated with better mental health. Multiple linear regressions showed that tetraplegia, better physical health, and better mental health were associated with better effectiveness, and better mental health was associated with better communication. Conclusion: SCI/D and health characteristics were the only variables associated with SDM. Interventions to increase engagement in SDM and provider attention to SDM may be beneficial, especially for individuals with paraplegia or in poorer physical and mental health.

Remote sensing the sea surface CO2 of the Baltic Sea using the SOMLO methodology

NASA Astrophysics Data System (ADS)

Parard, G.; Charantonis, A. A.; Rutgerson, A.

2015-06-01

Studies of coastal seas in Europe have noted the high variability of the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes, complicates the accurate estimation of these mechanisms. This is particularly pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been characterized in as much detail as in the open oceans. In addition, the joint availability of in situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, we used the SOMLO (self-organizing multiple linear output; Sasse et al., 2013) methodology, which combines two existing methods (i.e. self-organizing maps and multiple linear regression) to estimate the ocean surface partial pressure of CO2 (pCO2) in the Baltic Sea from the remotely sensed sea surface temperature, chlorophyll, coloured dissolved organic matter, net primary production, and mixed-layer depth. The outputs of this research have a horizontal resolution of 4 km and cover the 1998-2011 period. These outputs give a monthly map of the Baltic Sea at a very fine spatial resolution. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in situ measurements and a root mean square error of 36 μatm. Removing any of the satellite parameters degraded this reconstructed CO2 flux, so we chose to supply any missing data using statistical imputation. The pCO2 maps produced using this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data, and we expect to be able to produce even more accurate reconstructions in coming years, given the predicted acquisition of new data.

Data-driven discovery of Koopman eigenfunctions using deep learning

NASA Astrophysics Data System (ADS)

Lusch, Bethany; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

Koopman operator theory transforms any autonomous non-linear dynamical system into an infinite-dimensional linear system. Since linear systems are well-understood, a mapping of non-linear dynamics to linear dynamics provides a powerful approach to understanding and controlling fluid flows. However, finding the correct change of variables remains an open challenge. We present a strategy to discover an approximate mapping using deep learning. Our neural networks find this change of variables, its inverse, and a finite-dimensional linear dynamical system defined on the new variables. Our method is completely data-driven and only requires measurements of the system, i.e. it does not require derivatives or knowledge of the governing equations. We find a minimal set of approximate Koopman eigenfunctions that are sufficient to reconstruct and advance the system to future states. We demonstrate the method on several dynamical systems.

Investigating the sources of variability in the dynamic response of built-up structures through a linear analytical model

NASA Astrophysics Data System (ADS)

Abolfathi, Ali; O'Boy, Dan J.; Walsh, Stephen J.; Fisher, Stephen A.

2017-01-01

It is well established that the dynamic response of a number of nominally identical built-up structures are often different and the variability increases with increasing complexity of the structure. Furthermore, the effects of the different parameters, for example the variation in joint locations or the range of the Young's modulus, on the dynamic response of the system are not the same. In this paper, the effects of different material and geometric parameters on the variability of a vibration transfer function are compared using an analytical model of a simple linear built-up structure that consist of two plates connected by a single mount. Similar results can be obtained if multiple mounts are used. The scope of this paper is limited to a low and medium frequency range where usually deterministic models are used for vibrational analysis. The effect of the mount position and also the global variation in the properties of the plate, such as modulus of elasticity or thickness, is higher on the variability of vibration transfer function than the effect of the mount properties. It is shown that the vibration transfer function between the plates is independent of the mount property if a stiff enough mount with a small mass is implemented. For a soft mount, there is a direct relationship between the mount impedance and the variation in the vibration transfer function. Furthermore, there are a range of mount stiffnesses between these two extreme cases at which the vibration transfer function is more sensitive to changes in the stiffness of the mount than when compared to a soft mount. It is found that the effect of variation in the mount damping and the mount mass on the variability is negligible. Similarly, the effect of the plate damping on the variability is not significant.

Using Log Linear Analysis for Categorical Family Variables.

ERIC Educational Resources Information Center

Moen, Phyllis

The Goodman technique of log linear analysis is ideal for family research, because it is designed for categorical (non-quantitative) variables. Variables are dichotomized (for example, married/divorced, childless/with children) or otherwise categorized (for example, level of permissiveness, life cycle stage). Contingency tables are then…

Prediction of rectal temperature using non-invasive physiologic variable measurements in hair pregnant ewes subjected to natural conditions of heat stress.

PubMed

Vicente-Pérez, Ricardo; Avendaño-Reyes, Leonel; Mejía-Vázquez, Ángel; Álvarez-Valenzuela, F Daniel; Correa-Calderón, Abelardo; Mellado, Miguel; Meza-Herrera, Cesar A; Guerra-Liera, Juan E; Robinson, P H; Macías-Cruz, Ulises

2016-01-01

Rectal temperature (RT) is the foremost physiological variable indicating if an animal is suffering hyperthermia. However, this variable is traditionally measured by invasive methods, which may compromise animal welfare. Models to predict RT have been developed for growing pigs and lactating dairy cows, but not for pregnant heat-stressed ewes. Our aim was to develop a prediction equation for RT using non-invasive physiological variables in pregnant ewes under heat stress. A total of 192 records of respiratory frequency (RF) and hair coat temperature in various body regions (i.e., head, rump, flank, shoulder, and belly) obtained from 24 Katahdin × Pelibuey pregnant multiparous ewes were collected during the last third of gestation (i.e., d 100 to lambing) with a 15 d sampling interval. Hair coat temperatures were taken using infrared thermal imaging technology. Initially, a Pearson correlation analysis examined the relationship among variables, and then multiple linear regression analysis was used to develop the prediction equations. All predictor variables were positively correlated (P<0.01; r=0.59-0.67) with RT. The adjusted equation which best predicted RT (P<0.01; Radj(2)=56.15%; CV=0.65%) included as predictors RF and head and belly temperatures. Comparison of predicted and observed values for RT indicates a suitable agreement (P<0.01) between them with moderate accuracy (Radj(2)=56.15%) when RT was calculated with the adjusted equation. In general, the final equation does not violate any assumption of multiple regression analysis. The RT in heat-stressed pregnant ewes can be predicted with an adequate accuracy using non-invasive physiologic variables, and the final equation was: RT=35.57+0.004 (RF)+0.067 (heat temperature)+0.028 (belly temperature). Copyright © 2015 Elsevier Ltd. All rights reserved.

Linear increases in carbon nanotube density through multiple transfer technique.

PubMed

Shulaker, Max M; Wei, Hai; Patil, Nishant; Provine, J; Chen, Hong-Yu; Wong, H-S P; Mitra, Subhasish

2011-05-11

We present a technique to increase carbon nanotube (CNT) density beyond the as-grown CNT density. We perform multiple transfers, whereby we transfer CNTs from several growth wafers onto the same target surface, thereby linearly increasing CNT density on the target substrate. This process, called transfer of nanotubes through multiple sacrificial layers, is highly scalable, and we demonstrate linear CNT density scaling up to 5 transfers. We also demonstrate that this linear CNT density increase results in an ideal linear increase in drain-source currents of carbon nanotube field effect transistors (CNFETs). Experimental results demonstrate that CNT density can be improved from 2 to 8 CNTs/μm, accompanied by an increase in drain-source CNFET current from 4.3 to 17.4 μA/μm.

Methodology for the development of normative data for Spanish-speaking pediatric populations.

PubMed

Rivera, D; Arango-Lasprilla, J C

2017-01-01

To describe the methodology utilized to calculate reliability and the generation of norms for 10 neuropsychological tests for children in Spanish-speaking countries. The study sample consisted of over 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Inclusion criteria for all countries were to have between 6 to 17 years of age, an Intelligence Quotient of≥80 on the Test of Non-Verbal Intelligence (TONI-2), and score of <19 on the Children's Depression Inventory. Participants completed 10 neuropsychological tests. Reliability and norms were calculated for all tests. Test-retest analysis showed excellent or good- reliability on all tests (r's>0.55; p's<0.001) except M-WCST perseverative errors whose coefficient magnitude was fair. All scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the models by country. The non-significant variables (p > 0.05) were removed and the analysis were run again. This is the largest Spanish-speaking children and adolescents normative study in the world. For the generation of normative data, the method based on linear regression models and the standard deviation of residual values was used. This method allows determination of the specific variables that predict test scores, helps identify and control for collinearity of predictive variables, and generates continuous and more reliable norms than those of traditional methods.

Climate patterns as predictors of amphibians species richness and indicators of potential stress

USGS Publications Warehouse

Battaglin, W.; Hay, L.; McCabe, G.; Nanjappa, P.; Gallant, Alisa L.

2005-01-01

Amphibians occupy a range of habitats throughout the world, but species richness is greatest in regions with moist, warm climates. We modeled the statistical relations of anuran and urodele species richness with mean annual climate for the conterminous United States, and compared the strength of these relations at national and regional levels. Model variables were calculated for county and subcounty mapping units, and included 40-year (1960-1999) annual mean and mean annual climate statistics, mapping unit average elevation, mapping unit land area, and estimates of anuran and urodele species richness. Climate data were derived from more than 7,500 first-order and cooperative meteorological stations and were interpolated to the mapping units using multiple linear regression models. Anuran and urodele species richness were calculated from the United States Geological Survey's Amphibian Research and Monitoring Initiative (ARMI) National Atlas for Amphibian Distributions. The national multivariate linear regression (MLR) model of anuran species richness had an adjusted coefficient of determination (R2) value of 0.64 and the national MLR model for urodele species richness had an R2 value of 0.45. Stratifying the United States by coarse-resolution ecological regions provided models for anUrans that ranged in R2 values from 0.15 to 0.78. Regional models for urodeles had R2 values. ranging from 0.27 to 0.74. In general, regional models for anurans were more strongly influenced by temperature variables, whereas precipitation variables had a larger influence on urodele models.

Model for predicting the injury severity score.

PubMed

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

2015-07-01

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

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

ERIC Educational Resources Information Center

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

2006-01-01

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

A FORTRAN technique for correlating a circular environmental variable with a linear physiological variable in the sugar maple.

PubMed

Pease, J M; Morselli, M F

1987-01-01

This paper deals with a computer program adapted to a statistical method for analyzing an unlimited quantity of binary recorded data of an independent circular variable (e.g. wind direction), and a linear variable (e.g. maple sap flow volume). Circular variables cannot be statistically analyzed with linear methods, unless they have been transformed. The program calculates a critical quantity, the acrophase angle (PHI, phi o). The technique is adapted from original mathematics [1] and is written in Fortran 77 for easier conversion between computer networks. Correlation analysis can be performed following the program or regression which, because of the circular nature of the independent variable, becomes periodic regression. The technique was tested on a file of approximately 4050 data pairs.

Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

PubMed Central

Wang, Zengjian; Zhang, Delong; Liang, Bishan; Chang, Song; Pan, Jinghua; Huang, Ruiwang; Liu, Ming

2016-01-01

Biological motion perception (BMP) refers to the ability to perceive the moving form of a human figure from a limited amount of stimuli, such as from a few point lights located on the joints of a moving body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants, for whom intrinsic brain networks were constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located in the Default Mode Network (DMN). Additionally, discrimination analysis showed that the local efficiency of certain regions such as the thalamus could be used to classify BMP performance across participants. Notably, the association pattern between network nodal efficiency and BMP was different from the association pattern of static directional/gender information perception. Overall, these findings show that intrinsic brain network efficiency may be considered a neural factor that explains BMP inter-individual variability. PMID:27853427

Serum alpha-fetoprotein in the three trimesters of pregnancy: effects of maternal characteristics and medical history.

PubMed

Bredaki, F E; Sciorio, C; Wright, A; Wright, D; Nicolaides, K H

2015-07-01

To define the contribution of maternal variables which influence the measured level of maternal serum alpha-fetoprotein (AFP) in screening for pregnancy complications. Maternal characteristics and medical history were recorded and serum AFP was measured in women with a singleton pregnancy attending for three routine hospital visits at 11 + 0 to 13 + 6, 19 + 0 to 24 + 6 and 30 + 0 to 34 + 6 weeks' gestation. For pregnancies delivering phenotypically normal live births or stillbirths ≥ 24 weeks' gestation, variables from maternal demographic characteristics and medical history that are important in the prediction of AFP were determined from a linear mixed-effects multiple regression. Serum AFP was measured in 17 071 cases in the first trimester, 8583 in the second trimester and 8607 in the third trimester. Significant independent contributions to serum AFP were provided by gestational age, maternal weight, racial origin, gestational age at delivery and birth-weight Z-score of the neonate of the previous pregnancy and interpregnancy interval. Cigarette smoking was found to significantly affect serum AFP in the first trimester only. The machine used to measure serum AFP was also found to have a significant effect. Random-effects multiple regression analysis was used to define the contribution of maternal variables that influence the measured level of serum AFP and express the values as multiples of the median (MoMs). The model was shown to provide an adequate fit of MoM values for all covariates, both in pregnancies that developed pre-eclampsia and in those without this pregnancy complication. A model was fitted to express measured serum AFP across the three trimesters of pregnancy as MoMs, after adjusting for variables from maternal characteristics and medical history that affect this measurement. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

Application of Local Linear Embedding to Nonlinear Exploratory Latent Structure Analysis

ERIC Educational Resources Information Center

Wang, Haonan; Iyer, Hari

2007-01-01

In this paper we discuss the use of a recent dimension reduction technique called Locally Linear Embedding, introduced by Roweis and Saul, for performing an exploratory latent structure analysis. The coordinate variables from the locally linear embedding describing the manifold on which the data reside serve as the latent variable scores. We…

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

Ecosystem response to climatic variables - air temperature and precipitation: How can these variables alter plant productions in C4-grass dominant ecosystem?

NASA Astrophysics Data System (ADS)

Jung, C. G.; Jiang, L.; Luo, Y.

2017-12-01

Understanding net primary production (NPP) response to the key climatic variables, temperature and precipitation, is essential since the response could be represented by one of future consequences from ecosystem responses. Under future climatic warming, fluctuating precipitation is expected. In addition, NPP solely could not explain whole ecosystem response; therefore, not only NPP, but also above- and below-ground NPP (ANPP and BNPP, respectively) need to be examined. This examination needs to include how the plant productions response along temperature and precipitation gradients. Several studies have examined the response of NPP against each of single climatic variable, but understanding the response of ANPP and BNPP to the multiple variables is notably poor. In this study, we used the plant productions data (NPP, ANPP, and BNPP) with climatic variables, i.e., air temperature and precipitation, from 1999 to 2015 under warming and clipping treatments (mimicking hay-harvesting) in C4-grass dominant ecosystem located in central Oklahoma, United States. Firstly, we examined the nonlinear relationships with the climatic variables for NPP, ANPP and BNPP; and then predicted possible responses in the temperature - precipitation space by using a linear mixed effect model. Nonlinearities of NPP, ANPP and BNPP to the climatic variables have been found to show unimodal curves, and nonlinear models have better goodness of fit as shown lower Akaike information criterion (AIC) than linear models. Optimum condition for NPP is represented at high temperature and precipitation level whereas BNPP is maximized at moderate precipitation levels while ANPP has same range of NPP's optimum condition. Clipping significantly reduced ANPP while there was no clipping effect on NPP and BNPP. Furthermore, inclining NPP and ANPP have shown in a range from moderate to high precipitation level with increasing temperature while inclining pattern for BNPP was observed in moderate precipitation level. Overall, the C4-grass dominant ecosystem has a potential for considerable increases in NPP in hotter and wetter conditions as shown a range from moderate to high temperature and precipitation levels; ANPP has peaked at the high temperature and precipitation level, but maximum BNPP needs moderate precipitation level and high temperature.

A Direct Method for Fuel Optimal Maneuvers of Distributed Spacecraft in Multiple Flight Regimes

NASA Technical Reports Server (NTRS)

Hughes, Steven P.; Cooley, D. S.; Guzman, Jose J.

2005-01-01

We present a method to solve the impulsive minimum fuel maneuver problem for a distributed set of spacecraft. We develop the method assuming a non-linear dynamics model and parameterize the problem to allow the method to be applicable to multiple flight regimes including low-Earth orbits, highly-elliptic orbits (HEO), Lagrange point orbits, and interplanetary trajectories. Furthermore, the approach is not limited by the inter-spacecraft separation distances and is applicable to both small formations as well as large constellations. Semianalytical derivatives are derived for the changes in the total AV with respect to changes in the independent variables. We also apply a set of constraints to ensure that the fuel expenditure is equalized over the spacecraft in formation. We conclude with several examples and present optimal maneuver sequences for both a HE0 and libration point formation.

Modeling brook trout presence and absence from landscape variables using four different analytical methods

USGS Publications Warehouse

Steen, Paul J.; Passino-Reader, Dora R.; Wiley, Michael J.

2006-01-01

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Empirical modeling of environment-enhanced fatigue crack propagation in structural alloys for component life prediction

NASA Technical Reports Server (NTRS)

Richey, Edward, III

1995-01-01

This research aims to develop the methods and understanding needed to incorporate time and loading variable dependent environmental effects on fatigue crack propagation (FCP) into computerized fatigue life prediction codes such as NASA FLAGRO (NASGRO). In particular, the effect of loading frequency on FCP rates in alpha + beta titanium alloys exposed to an aqueous chloride solution is investigated. The approach couples empirical modeling of environmental FCP with corrosion fatigue experiments. Three different computer models have been developed and incorporated in the DOS executable program. UVAFAS. A multiple power law model is available, and can fit a set of fatigue data to a multiple power law equation. A model has also been developed which implements the Wei and Landes linear superposition model, as well as an interpolative model which can be utilized to interpolate trends in fatigue behavior based on changes in loading characteristics (stress ratio, frequency, and hold times).

Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models

USGS Publications Warehouse

Linard, Joshua I.

2013-01-01

Mitigating the effects of salt and selenium on water quality in the Grand Valley and lower Gunnison River Basin in western Colorado is a major concern for land managers. Previous modeling indicated means to improve the models by including more detailed geospatial data and a more rigorous method for developing the models. After evaluating all possible combinations of geospatial variables, four multiple linear regression models resulted that could estimate irrigation-season salt yield, nonirrigation-season salt yield, irrigation-season selenium yield, and nonirrigation-season selenium yield. The adjusted r-squared and the residual standard error (in units of log-transformed yield) of the models were, respectively, 0.87 and 2.03 for the irrigation-season salt model, 0.90 and 1.25 for the nonirrigation-season salt model, 0.85 and 2.94 for the irrigation-season selenium model, and 0.93 and 1.75 for the nonirrigation-season selenium model. The four models were used to estimate yields and loads from contributing areas corresponding to 12-digit hydrologic unit codes in the lower Gunnison River Basin study area. Each of the 175 contributing areas was ranked according to its estimated mean seasonal yield of salt and selenium.

Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.

PubMed

Vsevolozhskaya, Olga A; Zaykin, Dmitri V; Barondess, David A; Tong, Xiaoren; Jadhav, Sneha; Lu, Qing

2016-04-01

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Reverse genetics in high throughput: rapid generation of complete negative strand RNA virus cDNA clones and recombinant viruses thereof.

PubMed

Nolden, T; Pfaff, F; Nemitz, S; Freuling, C M; Höper, D; Müller, T; Finke, Stefan

2016-04-05

Reverse genetics approaches are indispensable tools for proof of concepts in virus replication and pathogenesis. For negative strand RNA viruses (NSVs) the limited number of infectious cDNA clones represents a bottleneck as clones are often generated from cell culture adapted or attenuated viruses, with limited potential for pathogenesis research. We developed a system in which cDNA copies of complete NSV genomes were directly cloned into reverse genetics vectors by linear-to-linear RedE/T recombination. Rapid cloning of multiple rabies virus (RABV) full length genomes and identification of clones identical to field virus consensus sequence confirmed the approache's reliability. Recombinant viruses were recovered from field virus cDNA clones. Similar growth kinetics of parental and recombinant viruses, preservation of field virus characters in cell type specific replication and virulence in the mouse model were confirmed. Reduced titers after reporter gene insertion indicated that the low level of field virus replication is affected by gene insertions. The flexibility of the strategy was demonstrated by cloning multiple copies of an orthobunyavirus L genome segment. This important step in reverse genetics technology development opens novel avenues for the analysis of virus variability combined with phenotypical characterization of recombinant viruses at a clonal level.

Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors.

PubMed

Yamakado, Minoru; Tanaka, Takayuki; Nagao, Kenji; Imaizumi, Akira; Komatsu, Michiharu; Daimon, Takashi; Miyano, Hiroshi; Tani, Mizuki; Toda, Akiko; Yamamoto, Hiroshi; Horimoto, Katsuhisa; Ishizaka, Yuko

2017-11-03

Fatty liver disease (FLD) increases the risk of diabetes, cardiovascular disease, and steatohepatitis, which leads to fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, the early detection of FLD is necessary. We aimed to find a quantitative and feasible model for discriminating the FLD, based on plasma free amino acid (PFAA) profiles. We constructed models of the relationship between PFAA levels in 2,000 generally healthy Japanese subjects and the diagnosis of FLD by abdominal ultrasound scan by multiple logistic regression analysis with variable selection. The performance of these models for FLD discrimination was validated using an independent data set of 2,160 subjects. The generated PFAA-based model was able to identify FLD patients. The area under the receiver operating characteristic curve for the model was 0.83, which was higher than those of other existing liver function-associated markers ranging from 0.53 to 0.80. The value of the linear discriminant in the model yielded the adjusted odds ratio (with 95% confidence intervals) for a 1 standard deviation increase of 2.63 (2.14-3.25) in the multiple logistic regression analysis with known liver function-associated covariates. Interestingly, the linear discriminant values were significantly associated with the progression of FLD, and patients with nonalcoholic steatohepatitis also exhibited higher values.

Response of dissolved trace metals to land use/land cover and their source apportionment using a receptor model in a subtropic river, China.

PubMed

Li, Siyue; Zhang, Quanfa

2011-06-15

Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River. Our results revealed that land use was an important factor in water metals in the snow melt flow period and land use in the riparian zone was not a better predictor of metals than land use away from the river. Urbanization in a watershed and vegetation along river networks could better explain metals, and agriculture, regardless of its relative location, however slightly explained metal variables in the upper Han River. FA-MLR analysis identified five source types of metals, and mining, fossil fuel combustion, and vehicle exhaust were the dominant pollutions in the surface waters. The results demonstrated great impacts of human activities on metal concentrations in the subtropical river of China. Copyright © 2011 Elsevier B.V. All rights reserved.

Depressive mood mediates the influence of social support on health-related quality of life in elderly, multimorbid patients

PubMed Central

2014-01-01

Background It is not well established how psychosocial factors like social support and depression affect health-related quality of life in multimorbid and elderly patients. We investigated whether depressive mood mediates the influence of social support on health-related quality of life. Methods Cross-sectional data of 3,189 multimorbid patients from the baseline assessment of the German MultiCare cohort study were used. Mediation was tested using the approach described by Baron and Kenny based on multiple linear regression, and controlling for socioeconomic variables and burden of multimorbidity. Results Mediation analyses confirmed that depressive mood mediates the influence of social support on health-related quality of life (Sobel’s p < 0.001). Multiple linear regression showed that the influence of depressive mood (β = −0.341, p < 0.01) on health-related quality of life is greater than the influence of multimorbidity (β = −0.234, p < 0.01). Conclusion Social support influences health-related quality of life, but this association is strongly mediated by depressive mood. Depression should be taken into consideration in research on multimorbidity, and clinicians should be aware of its importance when caring for multimorbid patients. Trial registration ISRCTN89818205 PMID:24708815

Changes in aerobic power of men, ages 25-70 yr

NASA Technical Reports Server (NTRS)

Jackson, A. S.; Beard, E. F.; Wier, L. T.; Ross, R. M.; Stuteville, J. E.; Blair, S. N.

1995-01-01

This study quantified and compared the cross-sectional and longitudinal influence of age, self-report physical activity (SR-PA), and body composition (%fat) on the decline of maximal aerobic power (VO2peak). The cross-sectional sample consisted of 1,499 healthy men ages 25-70 yr. The 156 men of the longitudinal sample were from the same population and examined twice, the mean time between tests was 4.1 (+/- 1.2) yr. Peak oxygen uptake was determined by indirect calorimetry during a maximal treadmill exercise test. The zero-order correlations between VO2peak and %fat (r = -0.62) and SR-PA (r = 0.58) were significantly (P < 0.05) higher that the age correlation (r = -0.45). Linear regression defined the cross-sectional age-related decline in VO2peak at 0.46 ml.kg-1.min-1.yr-1. Multiple regression analysis (R = 0.79) showed that nearly 50% of this cross-sectional decline was due to %fat and SR-PA, adding these lifestyle variables to the multiple regression model reduced the age regression weight to -0.26 ml.kg-1.min-1.yr-1. Statistically controlling for time differences between tests, general linear models analysis showed that longitudinal changes in aerobic power were due to independent changes in %fat and SR-PA, confirming the cross-sectional results.

Multiple regression equations modelling of groundwater of Ajmer-Pushkar railway line region, Rajasthan (India).

PubMed

Mathur, Praveen; Sharma, Sarita; Soni, Bhupendra

2010-01-01

In the present work, an attempt is made to formulate multiple regression equations using all possible regressions method for groundwater quality assessment of Ajmer-Pushkar railway line region in pre- and post-monsoon seasons. Correlation studies revealed the existence of linear relationships (r 0.7) for electrical conductivity (EC), total hardness (TH) and total dissolved solids (TDS) with other water quality parameters. The highest correlation was found between EC and TDS (r = 0.973). EC showed highly significant positive correlation with Na, K, Cl, TDS and total solids (TS). TH showed highest correlation with Ca and Mg. TDS showed significant correlation with Na, K, SO4, PO4 and Cl. The study indicated that most of the contamination present was water soluble or ionic in nature. Mg was present as MgCl2; K mainly as KCl and K2SO4, and Na was present as the salts of Cl, SO4 and PO4. On the other hand, F and NO3 showed no significant correlations. The r2 values and F values (at 95% confidence limit, alpha = 0.05) for the modelled equations indicated high degree of linearity among independent and dependent variables. Also the error % between calculated and experimental values was contained within +/- 15% limit.

Global solutions to random 3D vorticity equations for small initial data

NASA Astrophysics Data System (ADS)

Barbu, Viorel; Röckner, Michael

2017-11-01

One proves the existence and uniqueness in (Lp (R3)) 3, 3/2 < p < 2, of a global mild solution to random vorticity equations associated to stochastic 3D Navier-Stokes equations with linear multiplicative Gaussian noise of convolution type, for sufficiently small initial vorticity. This resembles some earlier deterministic results of T. Kato [16] and are obtained by treating the equation in vorticity form and reducing the latter to a random nonlinear parabolic equation. The solution has maximal regularity in the spatial variables and is weakly continuous in (L3 ∩L 3p/4p - 6)3 with respect to the time variable. Furthermore, we obtain the pathwise continuous dependence of solutions with respect to the initial data. In particular, one gets a locally unique solution of 3D stochastic Navier-Stokes equation in vorticity form up to some explosion stopping time τ adapted to the Brownian motion.

Black Hole Variability in MHD: A Numerical Test of the Propagating Fluctuations Model

NASA Astrophysics Data System (ADS)

Hogg, J. Drew; Reynolds, Christopher S.

2017-08-01

The variability properties of accreting black hole systems offer a crucial probe of the accretion physics providing the angular momentum transport and enabling the mass accretion. A few of the most telling signatures are the characteristic log-normal flux distributions, linear RMS-flux relations, and frequency-dependent time lags between energy bands. These commonly observed properties are often interpreted as evidence of inward propagating mass accretion rate fluctuations where fluctuations in the accretion flow combine multiplicatively. We present recent results from a long, semi-global MHD simulation of a thin (h/r=0.1) accretion disk that naturally reproduces this phenomenology. This bolsters the theoretical underpinnings of the “propagating fluctuations” model and demonstrates the viability of this process manifesting in MHD turbulence driven by the magnetorotational instability. We find that a key ingredient to this model is the modulation of the effective α parameter by the magnetic dynamo.

NASA's X-Plane Database and Parametric Cost Model v 2.0

NASA Technical Reports Server (NTRS)

Sterk, Steve; Ogluin, Anthony; Greenberg, Marc

2016-01-01

The NASA Armstrong Cost Engineering Team with technical assistance from NASA HQ (SID)has gone through the full process in developing new CERs from Version #1 to Version #2 CERs. We took a step backward and reexamined all of the data collected, such as dependent and independent variables, cost, dry weight, length, wingspan, manned versus unmanned, altitude, Mach number, thrust, and skin. We used a well- known statistical analysis tool called CO$TAT instead of using "R" multiple linear or the "Regression" tool found in Microsoft Excel(TradeMark). We setup an "array of data" by adding 21" dummy variables;" we analyzed the standard error (SE) and then determined the "best fit." We have parametrically priced-out several future X-planes and compared our results to those of other resources. More work needs to be done in getting "accurate and traceable cost data" from historical X-plane records!

Dimension reduction techniques for the integrative analysis of multi-omics data

PubMed Central

Zeleznik, Oana A.; Thallinger, Gerhard G.; Kuster, Bernhard; Gholami, Amin M.

2016-01-01

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease. PMID:26969681

Non-fluent speech following stroke is caused by impaired efference copy.

PubMed

Feenaughty, Lynda; Basilakos, Alexandra; Bonilha, Leonardo; den Ouden, Dirk-Bart; Rorden, Chris; Stark, Brielle; Fridriksson, Julius

2017-09-01

Efference copy is a cognitive mechanism argued to be critical for initiating and monitoring speech: however, the extent to which breakdown of efference copy mechanisms impact speech production is unclear. This study examined the best mechanistic predictors of non-fluent speech among 88 stroke survivors. Objective speech fluency measures were subjected to a principal component analysis (PCA). The primary PCA factor was then entered into a multiple stepwise linear regression analysis as the dependent variable, with a set of independent mechanistic variables. Participants' ability to mimic audio-visual speech ("speech entrainment response") was the best independent predictor of non-fluent speech. We suggest that this "speech entrainment" factor reflects integrity of internal monitoring (i.e., efference copy) of speech production, which affects speech initiation and maintenance. Results support models of normal speech production and suggest that therapy focused on speech initiation and maintenance may improve speech fluency for individuals with chronic non-fluent aphasia post stroke.

POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS

PubMed Central

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2013-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models. PMID:23935262

Prediction of Psilocybin Response in Healthy Volunteers

PubMed Central

Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X.

2012-01-01

Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin. PMID:22363492

Prediction of psilocybin response in healthy volunteers.

PubMed

Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X

2012-01-01

Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin.

Factors associated with poor functional outcome in bipolar disorder: sociodemographic, clinical, and neurocognitive variables.

PubMed

Sanchez-Moreno, J; Bonnin, C M; González-Pinto, A; Amann, B L; Solé, B; Balanzá-Martinez, V; Arango, C; Jiménez, E; Tabarés-Seisdedos, R; Garcia-Portilla, M P; Ibáñez, A; Crespo, J M; Ayuso-Mateos, J L; Martinez-Aran, A; Torrent, C; Vieta, E

2018-05-03

The current investigation aimed at studying the sociodemographic, clinical, and neuropsychological variables related to functional outcome in a sample of euthymic patients with bipolar disorder(BD) presenting moderate-severe levels of functional impairment. Two-hundred and thirty-nine participants with BD disorders and with Functioning Assessment Short Test(FAST) scores equal or above 18 were administered a clinical and diagnostic interview, and the administration of mood measure scales and a comprehensive neuropsychological battery. Analyses involved preliminary Pearson bivariate correlations to identify sociodemographic and clinical variables associated with the FAST total score. Regarding neuropsychological variables, a principal component analysis (PCA) was performed to group the variables in orthogonal factors. Finally, a hierarchical multiple regression was run. The best fitting model for the variables associated with functioning was a linear combination of gender, age, estimated IQ, Hamilton Depression Rating Scale (HAM-D), number of previous manic episodes, Factor 1 and Factor 2 extracted from the PCA. The model, including all these previous variables, explained up to 29.4% of the observed variance. Male gender, older age, lower premorbid IQ, subdepressive symptoms, higher number of manic episodes, and lower performance in verbal memory, working memory, verbal fluency, and processing speed were associated with lower functioning in patients with BD. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Influence of rice straw cooking conditions in the soda-ethanol-water pulping on the mechanical properties of produced paper sheets.

PubMed

Navaee-Ardeh, S; Mohammadi-Rovshandeh, J; Pourjoozi, M

2004-03-01

A normalized design was used to examine the influence of independent variables (alcohol concentration, cooking time and temperature) in the catalytic soda-ethanol pulping of rice straw on various mechanical properties (breaking length, burst, tear index and folding endurance) of paper sheets obtained from each pulping process. An equation of each dependent variable as a function of cooking variables (independent variables) was obtained by multiple non-linear regression using the least square method by MATLAB software for developing of empirical models. The ranges of alcohol concentration, cooking time and temperature were 40-65% (w/w), 150-180 min and 195-210 degrees C, respectively. Three-dimensional graphs of dependent variables were also plotted versus independent variables. The optimum values of breaking length, burst and tear index and folding endurance were 4683.7 (m), 30.99 (kN/g), 376.93 (mN m2/g) and 27.31, respectively. However, short cooking time (150 min), high ethanol concentration (65%) and high temperature (210 degrees C) could be used to produce papers with suitable burst and tear index. However, for papers with best breaking length and folding endurance low temperature (195 degrees C) was desirable. Differences between optimum values of dependent variables obtained by normalized design and experimental data were less than 20%.

Multivariate Predictors of Music Perception and Appraisal by Adult Cochlear Implant Users

PubMed Central

Gfeller, Kate; Oleson, Jacob; Knutson, John F.; Breheny, Patrick; Driscoll, Virginia; Olszewski, Carol

2009-01-01

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music. PMID:18669126

The use of generalized linear models and generalized estimating equations in bioarchaeological studies.

PubMed

Nikita, Efthymia

2014-03-01

The current article explores whether the application of generalized linear models (GLM) and generalized estimating equations (GEE) can be used in place of conventional statistical analyses in the study of ordinal data that code an underlying continuous variable, like entheseal changes. The analysis of artificial data and ordinal data expressing entheseal changes in archaeological North African populations gave the following results. Parametric and nonparametric tests give convergent results particularly for P values <0.1, irrespective of whether the underlying variable is normally distributed or not under the condition that the samples involved in the tests exhibit approximately equal sizes. If this prerequisite is valid and provided that the samples are of equal variances, analysis of covariance may be adopted. GLM are not subject to constraints and give results that converge to those obtained from all nonparametric tests. Therefore, they can be used instead of traditional tests as they give the same amount of information as them, but with the advantage of allowing the study of the simultaneous impact of multiple predictors and their interactions and the modeling of the experimental data. However, GLM should be replaced by GEE for the study of bilateral asymmetry and in general when paired samples are tested, because GEE are appropriate for correlated data. Copyright © 2013 Wiley Periodicals, Inc.

Formalism for the solution of quadratic Hamiltonians with large cosine terms

NASA Astrophysics Data System (ADS)

Ganeshan, Sriram; Levin, Michael

2016-02-01

We consider quantum Hamiltonians of the form H =H0-U ∑jcos(Cj) , where H0 is a quadratic function of position and momentum variables {x1,p1,x2,p2,⋯} and the Cj's are linear in these variables. We allow H0 and Cj to be completely general with only two restrictions: we require that (1) the Cj's are linearly independent and (2) [Cj,Ck] is an integer multiple of 2 π i for all j ,k so that the different cosine terms commute with one another. Our main result is a recipe for solving these Hamiltonians and obtaining their exact low-energy spectrum in the limit U →∞ . This recipe involves constructing creation and annihilation operators and is similar in spirit to the procedure for diagonalizing quadratic Hamiltonians. In addition to our exact solution in the infinite U limit, we also discuss how to analyze these systems when U is large but finite. Our results are relevant to a number of different physical systems, but one of the most natural applications is to understanding the effects of electron scattering on quantum Hall edge modes. To demonstrate this application, we use our formalism to solve a toy model for a fractional quantum spin Hall edge with different types of impurities.

Realisation and optical engineering of linear variable bandpass filters in nanoporous anodic alumina photonic crystals.

PubMed

Sukarno; Law, Cheryl Suwen; Santos, Abel

2017-06-08

We present the first realisation of linear variable bandpass filters in nanoporous anodic alumina (NAA-LVBPFs) photonic crystal structures. NAA gradient-index filters (NAA-GIFs) are produced by sinusoidal pulse anodisation and used as photonic crystal platforms to generate NAA-LVBPFs. The anodisation period of NAA-GIFs is modified from 650 to 850 s to systematically tune the characteristic photonic stopband of these photonic crystals across the UV-visible-NIR spectrum. Then, the nanoporous structure of NAA-GIFs is gradually widened along the surface under controlled conditions by wet chemical etching using a dip coating approach aiming to create NAA-LVBPFs with finely engineered optical properties. We demonstrate that the characteristic photonic stopband and the iridescent interferometric colour displayed by these photonic crystals can be tuned with precision across the surface of NAA-LVBPFs by adjusting the fabrication and etching conditions. Here, we envisage for the first time the combination of the anodisation period and etching conditions as a cost-competitive, facile, and versatile nanofabrication approach that enables the generation of a broad range of unique LVBPFs covering the spectral regions. These photonic crystal structures open new opportunities for multiple applications, including adaptive optics, hyperspectral imaging, fluorescence diagnostics, spectroscopy, and sensing.

Influence of dietary intake during gestation on postpartum weight retention.

PubMed

Martins, Ana Paula Bortoletto; Benicio, Maria Helena D'Aquino

2011-10-01

To evaluate the influence of dietary intake during gestation on postpartum weight retention. A total of 82 healthy pregnant women who began prenatal care at public healthcare services in the Municipality of São Paulo (Southeastern Brazil) between April and June 2005 were followed up. Weight and height were measured in the first interview (up to 16 weeks of gestation) and the weight measure was repeated during a household visit 15 days after delivery. The 24-Hour Dietary Recall method was employed to evaluate dietary intake at the three trimesters of gestation. The mean ingestion of saturated fat, fibers, added sugar, soft drinks, processed foods, fruits and vegetables, as well as the dietary energy density were calculated. Weight retention was estimated by the difference between the measure of the postpartum weight and the first measured weight. The influence of dietary intake on postpartum weight retention was assessed by multiple linear regression analysis and the linear trend test was performed. The variables used to adjust the model were: body mass index at the beginning of gestation, height, per capita family income, smoking, age, and level of schooling. The mean body mass index at the beginning of gestation was 24 kg/m² and the mean weight retention was 1.9 kg. The increase in saturated fat intake (p=0.005) and processed foods ingestion (p=0.014) significantly increased postpartum weight retention, after adjustment by the control variables. The other dietary intake variables did not present a significant relationship to the outcome variable. The increased intake of unhealthy food, such as processed foods, and of saturated fat influences the increment of postpartum weight retention.

Task-switching cost and repetition priming: two overlooked confounds in the first-set procedure of the Sternberg paradigm and how they affect memory set-size effects.

PubMed

Jou, Jerwen

2014-10-01

Subjects performed Sternberg-type memory recognition tasks (Sternberg paradigm) in four experiments. Category-instance names were used as learning and testing materials. Sternberg's original experiments demonstrated a linear relation between reaction time (RT) and memory-set size (MSS). A few later studies found no relation, and other studies found a nonlinear relation (logarithmic) between the two variables. These deviations were used as evidence undermining Sternberg's serial scan theory. This study identified two confounding variables in the fixed-set procedure of the paradigm (where multiple probes are presented at test for a learned memory set) that could generate a MSS RT function that was either flat or logarithmic rather than linearly increasing. These two confounding variables were task-switching cost and repetition priming. The former factor worked against smaller memory sets and in favour of larger sets whereas the latter factor worked in the opposite way. Results demonstrated that a null or a logarithmic RT-to-MSS relation could be the artefact of the combined effects of these two variables. The Sternberg paradigm has been used widely in memory research, and a thorough understanding of the subtle methodological pitfalls is crucial. It is suggested that a varied-set procedure (where only one probe is presented at test for a learned memory set) is a more contamination-free procedure for measuring the MSS effects, and that if a fixed-set procedure is used, it is worthwhile examining the RT function of the very first trials across the MSSs, which are presumably relatively free of contamination by the subsequent trials.

Development of an effective and potentially scalable weather generator for temperature and growing degree days

NASA Astrophysics Data System (ADS)

Rahmani, Elham; Friederichs, Petra; Keller, Jan; Hense, Andreas

2016-05-01

The main purpose of this study is to develop an easy-to-use weather generator (WG) for the downscaling of gridded data to point measurements at regional scale. The WG is applied to daily averaged temperatures and annual growing degree days (GDD) of wheat. This particular choice of variables is motivated by future investigations on temperature impacts as the most important climate variable for wheat cultivation under irrigation in Iran. The proposed statistical downscaling relates large-scale ERA-40 reanalysis to local daily temperature and annual GDD. Long-term local observations in Iran are used at 16 synoptic stations from 1961 to 2001, which is the common period with ERA-40 data. We perform downscaling using two approaches: the first is a linear regression model that uses the ERA-40 fingerprints (FP) defined by the squared correlation with local variability, and the second employs a linear multiple regression (MR) analysis to relate the large-scale information at the neighboring grid points to the station data. Extending the usual downscaling, we implement a WG providing uncertainty information and realizations of the local temperatures and GDD by adding a Gaussian random noise. ERA-40 reanalysis well represents the local daily temperature as well as the annual GDD variability. For 2-m temperature, the FPs are more localized during the warm compared with the cold season. While MR is slightly superior for daily temperature time series, FP seems to perform best for annual GDD. We further assess the quality of the WGs applying probabilistic verification scores like the continuous ranked probability score (CRPS) and the respective skill score. They clearly demonstrate the superiority of WGs compared with a deterministic downscaling.

Describing rainfall in northern Australia using multiple climate indices

NASA Astrophysics Data System (ADS)

Wilks Rogers, Cassandra Denise; Beringer, Jason

2017-02-01

Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the effects on water and food availability and atmosphere-biosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas and, in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, annual and decadal timescales. Precipitation trends, climate index trends and wet season characteristics have also been investigated using linear statistical methods. In general, climate index-rainfall correlations were stronger in the north of the NATT where annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the Indian-Australian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas yearly variability was related to a greater number of climate indices, predominately the Tasman Sea and Indonesian sea surface temperature indices (both of which experienced a linear increase over the duration of the study) and the El Niño-Southern Oscillation indices. These findings highlight the importance of understanding the climatic processes driving variability and, subsequently, the importance of understanding the relationships between rainfall and climatic phenomena in the Northern Territory in order to project future rainfall patterns in the region.

Chronic low back pain in patients with systemic lupus erythematosus: prevalence and predictors of back muscle strength and its correlation with disability.

PubMed

Cezarino, Raíssa Sudré; Cardoso, Jefferson Rosa; Rodrigues, Kedma Neves; Magalhães, Yasmin Santana; Souza, Talita Yokoy de; Mota, Lícia Maria Henrique da; Bonini-Rocha, Ana Clara; McVeigh, Joseph; Martins, Wagner Rodrigues

To determine the prevalence of Chronic Low Back Pain and predictors of Back Muscle Strength in patients with Systemic Lupus Erythematosus. Cross-sectional study. Ninety-six ambulatory patients with lupus were selected by non-probability sampling and interviewed and tested during medical consultation. The outcomes measurements were: Point prevalence of chronic low back pain, Oswestry Disability Index, Tampa Scale of Kinesiophobia, Fatigue Severity Scale and maximal voluntary isometric contractions of handgrip and of the back muscles. Correlation coefficient and multiple linear regression were used in statistical analysis. Of the 96 individuals interviewed, 25 had chronic low back pain, indicating a point prevalence of 26% (92% women). The correlation between the Oswestry Index and maximal voluntary isometric contraction of the back muscles was r=-0.4, 95% CI [-0.68; -0.01] and between the maximal voluntary isometric contraction of handgrip and of the back muscles was r=0.72, 95% CI [0.51; 0.88]. The regression model presented the highest value of R 2 being observed when maximal voluntary isometric contraction of the back muscles was tested with five independent variables (63%). In this model handgrip strength was the only predictive variable (β=0.61, p=0.001). The prevalence of chronic low back pain in individuals with systemic lupus erythematosus was 26%. The maximal voluntary isometric contraction of the back muscles was 63% predicted by five variables of interest, however, only the handgrip strength was a statistically significant predictive variable. The maximal voluntary isometric contraction of the back muscles presented a linear relation directly proportional to handgrip and inversely proportional to Oswestry Index i.e. stronger back muscles are associated with lower disability scores. Copyright © 2017. Published by Elsevier Editora Ltda.

Regional Drought Monitoring Based on Multi-Sensor Remote Sensing

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Im, Jungho; Park, Seonyoung

2014-05-01

Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety of land cover types. Remote sensing data from the Tropical Rainfall Measuring Mission satellite (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) sensors were obtained for the period from 2000 to 2012, and observation data from 99 weather stations, 441 streamflow gauges, as well as the gridded observation data from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE) were obtained for validation. The objective blends of multiple indicators helped better assessment of various types of drought, and can be useful for drought early warning system. Since the improved SDCI is based on remotely sensed data, it can be easily applied to regions with limited or no observation data for drought assessment and monitoring.

Mapping extreme rainfall in the Northwest Portugal region: statistical analysis and spatial modelling

NASA Astrophysics Data System (ADS)

Santos, Monica; Fragoso, Marcelo

2010-05-01

Extreme precipitation events are one of the causes of natural hazards, such as floods and landslides, making its investigation so important, and this research aims to contribute to the study of the extreme rainfall patterns in a Portuguese mountainous area. The study area is centred on the Arcos de Valdevez county, located in the northwest region of Portugal, the rainiest of the country, with more than 3000 mm of annual rainfall at the Peneda-Gerês mountain system. This work focus on two main subjects related with the precipitation variability on the study area. First, a statistical analysis of several precipitation parameters is carried out, using daily data from 17 rain-gauges with a complete record for the 1960-1995 period. This approach aims to evaluate the main spatial contrasts regarding different aspects of the rainfall regime, described by ten parameters and indices of precipitation extremes (e.g. mean annual precipitation, the annual frequency of precipitation days, wet spells durations, maximum daily precipitation, maximum of precipitation in 30 days, number of days with rainfall exceeding 100 mm and estimated maximum daily rainfall for a return period of 100 years). The results show that the highest precipitation amounts (from annual to daily scales) and the higher frequency of very abundant rainfall events occur in the Serra da Peneda and Gerês mountains, opposing to the valleys of the Lima, Minho and Vez rivers, with lower precipitation amounts and less frequent heavy storms. The second purpose of this work is to find a method of mapping extreme rainfall in this mountainous region, investigating the complex influence of the relief (e.g. elevation, topography) on the precipitation patterns, as well others geographical variables (e.g. distance from coast, latitude), applying tested geo-statistical techniques (Goovaerts, 2000; Diodato, 2005). Models of linear regression were applied to evaluate the influence of different geographical variables (altitude, latitude, distance from sea and distance to the highest orographic barrier) on the rainfall behaviours described by the studied variables. The techniques of spatial interpolation evaluated include univariate and multivariate methods: cokriging, kriging, IDW (inverse distance weighted) and multiple linear regression. Validation procedures were used, assessing the estimated errors in the analysis of descriptive statistics of the models. Multiple linear regression models produced satisfactory results in relation to 70% of the rainfall parameters, suggested by lower average percentage of error. However, the results also demonstrates that there is no an unique and ideal model, depending on the rainfall parameter in consideration. Probably, the unsatisfactory results obtained in relation to some rainfall parameters was motivated by constraints as the spatial complexity of the precipitation patterns, as well as to the deficient spatial coverage of the territory by the rain-gauges network. References Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. Internacional Journal of Climatology, 25(3), 351-363. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113 - 129.

Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations

PubMed Central

Astrand, Elaine; Enel, Pierre; Ibos, Guilhem; Dominey, Peter Ford; Baraduc, Pierre; Ben Hamed, Suliann

2014-01-01

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders. PMID:24466019

Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants.

PubMed

González, Mari Feli; Facal, David; Juncos-Rabadán, Onésimo; Yanguas, Javier

2017-10-01

Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition. The total sample, drawn from the "Longitudinal Study Aging in Spain (ELES)" project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance. Education is the variable which best predicts cognitive performance in the 50-59, 60-69, and 70-79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model. Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.

LINEAR - DERIVATION AND DEFINITION OF A LINEAR AIRCRAFT MODEL

NASA Technical Reports Server (NTRS)

Duke, E. L.

1994-01-01

The Derivation and Definition of a Linear Model program, LINEAR, provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models. LINEAR was developed to provide a standard, documented, and verified tool to derive linear models for aircraft stability analysis and control law design. Linear system models define the aircraft system in the neighborhood of an analysis point and are determined by the linearization of the nonlinear equations defining vehicle dynamics and sensors. LINEAR numerically determines a linear system model using nonlinear equations of motion and a user supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. LINEAR is capable of extracting both linearized engine effects, such as net thrust, torque, and gyroscopic effects and including these effects in the linear system model. The point at which this linear model is defined is determined either by completely specifying the state and control variables, or by specifying an analysis point on a trajectory and directing the program to determine the control variables and the remaining state variables. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to provide easy selection of state, control, and observation variables to be used in a particular model. Thus, the order of the system model is completely under user control. Further, the program provides the flexibility of allowing alternate formulations of both the state and observation equations. Data describing the aircraft and the test case is input to the program through a terminal or formatted data files. All data can be modified interactively from case to case. The aerodynamic model can be defined in two ways: a set of nondimensional stability and control derivatives for the flight point of interest, or a full non-linear aerodynamic model as used in simulations. LINEAR is written in FORTRAN and has been implemented on a DEC VAX computer operating under VMS with a virtual memory requirement of approximately 296K of 8 bit bytes. Both an interactive and batch version are included. LINEAR was developed in 1988.

Variable-energy drift-tube linear accelerator

DOEpatents

Swenson, Donald A.; Boyd, Jr., Thomas J.; Potter, James M.; Stovall, James E.

1984-01-01

A linear accelerator system includes a plurality of post-coupled drift-tubes wherein each post coupler is bistably positionable to either of two positions which result in different field distributions. With binary control over a plurality of post couplers, a significant accumlative effect in the resulting field distribution is achieved yielding a variable-energy drift-tube linear accelerator.

Variable-energy drift-tube linear accelerator

DOEpatents

Swenson, D.A.; Boyd, T.J. Jr.; Potter, J.M.; Stovall, J.E.

A linear accelerator system includes a plurality of post-coupled drift-tubes wherein each post coupler is bistably positionable to either of two positions which result in different field distributions. With binary control over a plurality of post couplers, a significant accumlative effect in the resulting field distribution is achieved yielding a variable-energy drift-tube linear accelerator.

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

ERIC Educational Resources Information Center

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

Effects of multiple predator species on green treefrog (Hyla cinerea) tadpoles

USGS Publications Warehouse

Gunzburger, M.S.; Travis, J.

2005-01-01

Prey species that occur across a range of habitats may be exposed to variable communities of multiple predator species across habitats. Predicting the combined effects of multiple predators can be complex. Many experiments evaluating the effects of multiple predators on prey confound either variation in predator density with predator identity or variation in relative predator frequency with overall predation rates. We develop a new experimental design of factorial predator combinations that maintains a constant expected predation rate, under the null hypothesis of additive predator effects. We implement this design to evaluate the combined effects of three predator species (bass, aeshnid and libellulid odonate naiads) on mortality rate of a prey species, Hyla cinerea (Schneider, 1799) tadpoles, that occurs across a range of aquatic habitats. Two predator treatments (libellulid and aeshnid + libellulid) resulted in lower tadpole mortality than any of the other predator treatments. Variation in tadpole mortality across treatments was not related to coarse variation in microhabitat use, but was likely due to intraguild predation, which occurred in all predator treatments. Hyla cinerea tadpoles have constant, low survival values when exposed to many different combinations of predator species, and predation rate probably increases linearly with predator density.

A Comparison of Mathematical Models of Fish Mercury Concentration as a Function of Atmospheric Mercury Deposition Rate and Watershed Characteristics

NASA Astrophysics Data System (ADS)

Smith, R. A.; Moore, R. B.; Shanley, J. B.; Miller, E. K.; Kamman, N. C.; Nacci, D.

2009-12-01

Mercury (Hg) concentrations in fish and aquatic wildlife are complex functions of atmospheric Hg deposition rate, terrestrial and aquatic watershed characteristics that influence Hg methylation and export, and food chain characteristics determining Hg bioaccumulation. Because of the complexity and incomplete understanding of these processes, regional-scale models of fish tissue Hg concentration are necessarily empirical in nature, typically constructed through regression analysis of fish tissue Hg concentration data from many sampling locations on a set of potential explanatory variables. Unless the data sets are unusually long and show clear time trends, the empirical basis for model building must be based solely on spatial correlation. Predictive regional scale models are highly useful for improving understanding of the relevant biogeochemical processes, as well as for practical fish and wildlife management and human health protection. Mechanistically, the logical arrangement of explanatory variables is to multiply each of the individual Hg source terms (e.g. dry, wet, and gaseous deposition rates, and residual watershed Hg) for a given fish sampling location by source-specific terms pertaining to methylation, watershed transport, and biological uptake for that location (e.g. SO4 availability, hill slope, lake size). This mathematical form has the desirable property that predicted tissue concentration will approach zero as all individual source terms approach zero. One complication with this form, however, is that it is inconsistent with the standard linear multiple regression equation in which all terms (including those for sources and physical conditions) are additive. An important practical disadvantage of a model in which the Hg source terms are additive (rather than multiplicative) with their modifying factors is that predicted concentration is not zero when all sources are zero, making it unreliable for predicting the effects of large future reductions in Hg deposition. In this paper we compare the results of using several different linear and non-linear models in an analysis of watershed and fish Hg data for 450 New England lakes. The differences in model results pertain to both their utility in interpreting methylation and export processes as well as in fisheries management.

Water-quality trend analysis and sampling design for the Devils Lake Basin, North Dakota, January 1965 through September 2003

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.

2006-01-01

This report presents the results of a study conducted by the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, the Devils Lake Basin Joint Water Resource Board, and the Red River Joint Water Resource District, to analyze historical water-quality trends in three dissolved major ions, three nutrients, and one dissolved trace element for eight stations in the Devils Lake Basin in North Dakota and to develop an efficient sampling design to monitor the future trends. A multiple-regression model was used to detect and remove streamflow-related variability in constituent concentrations. To separate the natural variability in concentration as a result of variability in streamflow from the variability in concentration as a result of other factors, the base-10 logarithm of daily streamflow was divided into four components-a 5-year streamflow anomaly, an annual streamflow anomaly, a seasonal streamflow anomaly, and a daily streamflow anomaly. The constituent concentrations then were adjusted for streamflow-related variability by removing the 5-year, annual, seasonal, and daily variability. Constituents used for the water-quality trend analysis were evaluated for a step trend to examine the effect of Channel A on water quality in the basin and a linear trend to detect gradual changes with time from January 1980 through September 2003. The fitted upward linear trends for dissolved calcium concentrations during 1980-2003 for two stations were significant. The fitted step trends for dissolved sulfate concentrations for three stations were positive and similar in magnitude. Of the three upward trends, one was significant. The fitted step trends for dissolved chloride concentrations were positive but insignificant. The fitted linear trends for the upstream stations were small and insignificant, but three of the downward trends that occurred during 1980-2003 for the remaining stations were significant. The fitted upward linear trends for dissolved nitrite plus nitrate as nitrogen concentrations during 1987-2003 for two stations were significant. However, concentrations during recent years appear to be lower than those for the 1970s and early 1980s but higher than those for the late 1980s and early 1990s. The fitted downward linear trend for dissolved ammonia concentrations for one station was significant. The fitted linear trends for total phosphorus concentrations for two stations were significant. Upward trends for total phosphorus concentrations occurred from the late 1980s to 2003 for most stations, but a small and insignificant downward trend occurred for one station. Continued monitoring will be needed to determine if the recent trend toward higher dissolved nitrite plus nitrate as nitrogen and total phosphorus concentrations continues in the future. For continued monitoring of water-quality trends in the upper Devils Lake Basin, an efficient sampling design consists of five major-ion, nutrient, and trace-element samples per year at three existing stream stations and at three existing lake stations. This sampling design requires the collection of 15 stream samples and 15 lake samples per year rather than 16 stream samples and 20 lake samples per year as in the 1992-2003 program. Thus, the design would result in a program that is less costly and more efficient than the 1992-2003 program but that still would provide the data needed to monitor water-quality trends in the Devils Lake Basin.

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models

PubMed Central

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization. PMID:27243005

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models.

PubMed

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.

Dynamic Latent Trait Models with Mixed Hidden Markov Structure for Mixed Longitudinal Outcomes.

PubMed

Zhang, Yue; Berhane, Kiros

2016-01-01

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMM). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study (CHS) to jointly model questionnaire based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.

The bothersomeness of sciatica: patients' self-report of paresthesia, weakness and leg pain.

PubMed

Grøvle, Lars; Haugen, Anne Julsrud; Keller, Anne; Natvig, Bård; Brox, Jens Ivar; Grotle, Margreth

2010-02-01

The objective of the study was to investigate how patients with sciatica due to disc herniation rate the bothersomeness of paresthesia and weakness as compared to leg pain, and how these symptoms are associated with socio-demographic and clinical characteristics. A cross-sectional study was conducted on 411 patients with clinical signs of radiculopathy. Items from the Sciatica Bothersomeness Index (0 = none to 6 = extremely) were used to establish values for paresthesia, weakness and leg pain. Associations with socio-demographic and clinical variables were analyzed by multiple linear regression. Mean scores (SD) were 4.5 (1.5) for leg pain, 3.4 (1.8) for paresthesia and 2.6 (2.0) for weakness. Women reported higher levels of bothersomeness for all three symptoms with mean scores approximately 10% higher than men. In the multivariate models, more severe symptoms were associated with lower physical function and higher emotional distress. Muscular paresis explained 19% of the variability in self-reported weakness, sensory findings explained 10% of the variability in paresthesia, and straight leg raising test explained 9% of the variability in leg pain. In addition to leg pain, paresthesia and weakness should be assessed when measuring symptom severity in sciatica.

Health-Related Quality of Life Among Central Appalachian Residents in Mountaintop Mining Counties

PubMed Central

Hendryx, Michael

2011-01-01

Objectives. We examined the health-related quality of life of residents in mountaintop mining counties of Appalachia using the 2006 national Behavioral Risk Factor Surveillance System. Methods. Dependent variables included self-rated health; the number of poor physical, poor mental, and activity limitation days (in the past 30 days); and the Healthy Days Index. Independent variables included metropolitan status, primary care physician supply, and Behavioral Risk Factor Surveillance System behavioral and demographic variables. We compared dependent variables across 3 categories: mountaintop mining (yes or no), other coal mining (yes or no), and a referent nonmining group. We used SUDAAN MULTILOG and multiple linear regression models with post hoc least squares means to test mountaintop mining effects after adjusting for covariates. Results. Residents of mountaintop mining counties reported significantly more days of poor physical, mental, and activity limitation and poorer self-rated health (P < .01) compared with the other county groupings. Results were generally consistent in separate analyses by gender and age. Conclusions. Mountaintop mining areas are associated with the greatest reductions in health-related quality of life even when compared with counties with other forms of coal mining. PMID:21421943

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

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

PubMed

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

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

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Multi-Wavelength, Multi-Beam, and Polarization-Sensitive Laser Transmitter for Surface Mapping

NASA Technical Reports Server (NTRS)

Yu, Anthony W.; Ramos-Izquierdo, Luis; Harding, David; Huss, Tim

2011-01-01

A multi-beam, multi-color, polarized laser transmitter has been developed for mapping applications. It uses commercial off-the-shelf components for a lowcost approach for a ruggedized laser suitable for field deployment. The laser transmitter design is capable of delivering dual wavelengths, multiple beams on each wavelength with equal (or variable) intensities per beam, and a welldefined state of polarization. This laser transmitter has been flown on several airborne campaigns for the Slope Imaging Multi-Polarization Photon Counting Lidar (SIMPL) instrument, and at the time of this reporting is at a technology readiness level of between 5 and 6. The laser is a 1,064-nm microchip high-repetition-rate laser emitting energy of about 8 microjoules per pulse. The beam was frequency-doubled to 532 nm using a KTP (KTiOPO4) nonlinear crystal [other nonlinear crystals such as LBO (LiB3O5) or periodically poled lithium niobiate can be used as well, depending on the conversion efficiency requirements], and the conversion efficiency was approximately 30 percent. The KTP was under temperature control using a thermoelectric cooler and a feedback monitoring thermistor. The dual-wavelength beams were then spectrally separated and each color went through its own optical path, which consisted of a beam-shaping lens, quarterwave plate (QWP), and a birefringent crystal (in this case, a calcite crystal, but others such as vanadate can be used). The QWP and calcite crystal set was used to convert the laser beams from a linearly polarized state to circularly polarized light, which when injected into a calcite crystal, will spatially separate the circularly polarized light into the two linear polarized components. The spatial separation of the two linearly polarized components is determined by the length of the crystal. A second set of QWP and calcite then further separated the two beams into four. Additional sets of QWP and calcite can be used to further split the beams into multiple orders of two. The spatially separated beams had alternating linearly polarization states; a half-wave plate (HWP) array was then made to rotate the alternating states of A multi-beam, multi-color, polarized laser transmitter has been developed for mapping applications. It uses commercial off-the-shelf components for a lowcost approach for a ruggedized laser suitable for field deployment. The laser transmitter design is capable of delivering dual wavelengths, multiple beams on each wavelength with equal (or variable) intensities per beam, and a welldefined state of polarization. This laser transmitter has been flown on several airborne campaigns for the Slope Imaging Multi-Polarization Photon Counting Lidar (SIMPL) instrument, and at the time of this reporting is at a technology readiness level of between 5 and 6. The laser is a 1,064-nm microchip high-repetition-rate laser emitting energy of about 8 microjoules per pulse. The beam was frequency-doubled to 532 nm using a KTP (KTiOPO4) nonlinear crystal [other nonlinear crystals such as LBO (LiB3O5) or periodically poled lithium niobiate can be used as well, depending on the conversion efficiency requirements], and the conversion efficiency was approximately 30 percent. The KTP was under temperature control using a thermoelectric cooler and a feedback monitoring thermistor. The dual-wavelength beams were then spectrally separated and each color went through its own optical path, which consisted of a beam-shaping lens, quarterwave plate (QWP), and a birefringent crystal (in this case, a calcite crystal, but others such as vanadate can be used). The QWP and calcite crystal set was used to convert the laser beams from a linearly polarized state to circularly polarized light, which when injected into a calcite crystal, will spatially separate the circularly polarized light into the two linear polarized components. The spatial separation of the two linearly polarized components is determined by the length of the crystal. A cond set of QWP and calcite then further separated the two beams into four. Additional sets of QWP and calcite can be used to further split the beams into multiple orders of two. The spatially separated beams had alternating linearly polarization states; a half-wave plate (HWP) array was then made to rotate the alternating states of

The Ability of American Football Helmets to Manage Linear Acceleration With Repeated High-Energy Impacts.

PubMed

Cournoyer, Janie; Post, Andrew; Rousseau, Philippe; Hoshizaki, Blaine

2016-03-01

Football players can receive up to 1400 head impacts per season, averaging 6.3 impacts per practice and 14.3 impacts per game. A decrease in the capacity of a helmet to manage linear acceleration with multiple impacts could increase the risk of traumatic brain injury. To investigate the ability of football helmets to manage linear acceleration with multiple high-energy impacts. Descriptive laboratory study. Laboratory. We collected linear-acceleration data for 100 impacts at 6 locations on 4 helmets of different models currently used in football. Impacts 11 to 20 were compared with impacts 91 to 100 for each of the 6 locations. Linear acceleration was greater after multiple impacts (91-100) than after the first few impacts (11-20) for the front, front-boss, rear, and top locations. However, these differences are not clinically relevant as they do not affect the risk for head injury. American football helmet performance deteriorated with multiple impacts, but this is unlikely to be a factor in head-injury causation during a game or over a season.

Disentangling the drivers of taxonomic and phylogenetic beta diversities in disturbed and undisturbed subtropical forests

NASA Astrophysics Data System (ADS)

Liu, Jinliang; Qian, Hong; Jin, Yi; Wu, Chuping; Chen, Jianhua; Yu, Shuquan; Wei, Xinliang; Jin, Xiaofeng; Liu, Jiajia; Yu, Mingjian

2016-10-01

Understanding the relative importance of dispersal limitation and environmental filtering processes in structuring the beta diversities of subtropical forests in human disturbed landscapes is still limited. Here we used taxonomic (TBD) and phylogenetic (PBD), including terminal PBD (PBDt) and basal PBD (PBDb), beta diversity indices to quantify the taxonomic and phylogenetic turnovers at different depths of evolutionary history in disturbed and undisturbed subtropical forests. Multiple linear regression model and distance-based redundancy analysis were used to disentangle the relative importance of environmental and spatial variables. Environmental variables were significantly correlated with TBD and PBDt metrics. Temperature and precipitation were major environmental drivers of beta diversity patterns, which explained 7-27% of the variance in TBD and PBDt, whereas the spatial variables independently explained less than 1% of the variation for all forests. The relative importance of environmental and spatial variables differed between disturbed and undisturbed forests (e.g., when Bray-Curtis was used as a beta diversity metric, environmental variable had a significant effect on beta diversity for disturbed forests but had no effect on undisturbed forests). We conclude that environmental filtering plays a more important role than geographical limitation and disturbance history in driving taxonomic and terminal phylogenetic beta diversity.

Disentangling the drivers of taxonomic and phylogenetic beta diversities in disturbed and undisturbed subtropical forests

PubMed Central

Liu, Jinliang; Qian, Hong; Jin, Yi; Wu, Chuping; Chen, Jianhua; Yu, Shuquan; Wei, Xinliang; Jin, Xiaofeng; Liu, Jiajia; Yu, Mingjian

2016-01-01

Understanding the relative importance of dispersal limitation and environmental filtering processes in structuring the beta diversities of subtropical forests in human disturbed landscapes is still limited. Here we used taxonomic (TBD) and phylogenetic (PBD), including terminal PBD (PBDt) and basal PBD (PBDb), beta diversity indices to quantify the taxonomic and phylogenetic turnovers at different depths of evolutionary history in disturbed and undisturbed subtropical forests. Multiple linear regression model and distance-based redundancy analysis were used to disentangle the relative importance of environmental and spatial variables. Environmental variables were significantly correlated with TBD and PBDt metrics. Temperature and precipitation were major environmental drivers of beta diversity patterns, which explained 7–27% of the variance in TBD and PBDt, whereas the spatial variables independently explained less than 1% of the variation for all forests. The relative importance of environmental and spatial variables differed between disturbed and undisturbed forests (e.g., when Bray-Curtis was used as a beta diversity metric, environmental variable had a significant effect on beta diversity for disturbed forests but had no effect on undisturbed forests). We conclude that environmental filtering plays a more important role than geographical limitation and disturbance history in driving taxonomic and terminal phylogenetic beta diversity. PMID:27775021

Determination of variables in the prediction of strontium distribution coefficients for selected sediments

USGS Publications Warehouse

Pace, M.N.; Rosentreter, J.J.; Bartholomay, R.C.

2001-01-01

Idaho State University and the US Geological Survey, in cooperation with the US Department of Energy, conducted a study to determine and evaluate strontium distribution coefficients (Kds) of subsurface materials at the Idaho National Engineering and Environmental Laboratory (INEEL). The Kds were determined to aid in assessing the variability of strontium Kds and their effects on chemical transport of strontium-90 in the Snake River Plain aquifer system. Data from batch experiments done to determine strontium Kds of five sediment-infill samples and six standard reference material samples were analyzed by using multiple linear regression analysis and the stepwise variable-selection method in the statistical program, Statistical Product and Service Solutions, to derive an equation of variables that can be used to predict strontium Kds of sediment-infill samples. The sediment-infill samples were from basalt vesicles and fractures from a selected core at the INEEL; strontium Kds ranged from ???201 to 356 ml g-1. The standard material samples consisted of clay minerals and calcite. The statistical analyses of the batch-experiment results showed that the amount of strontium in the initial solution, the amount of manganese oxide in the sample material, and the amount of potassium in the initial solution are the most important variables in predicting strontium Kds of sediment-infill samples.

Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.

PubMed

Coull, Brent A; Bobb, Jennifer F; Wellenius, Gregory A; Kioumourtzoglou, Marianthi-Anna; Mittleman, Murray A; Koutrakis, Petros; Godleski, John J

2015-06-01

The United States Environmental Protection Agency (U.S. EPA*) currently regulates individual air pollutants on a pollutant-by-pollutant basis, adjusted for other pollutants and potential confounders. However, the National Academies of Science concluded that a multipollutant regulatory approach that takes into account the joint effects of multiple constituents is likely to be more protective of human health. Unfortunately, the large majority of existing research had focused on health effects of air pollution for one pollutant or for one pollutant with control for the independent effects of a small number of copollutants. Limitations in existing statistical methods are at least partially responsible for this lack of information on joint effects. The goal of this project was to fill this gap by developing flexible statistical methods to estimate the joint effects of multiple pollutants, while allowing for potential nonlinear or nonadditive associations between a given pollutant and the health outcome of interest. We proposed Bayesian kernel machine regression (BKMR) methods as a way to simultaneously achieve the multifaceted goals of variable selection, flexible estimation of the exposure-response relationship, and inference on the strength of the association between individual pollutants and health outcomes in a health effects analysis of mixtures. We first developed a BKMR variable-selection approach, which we call component-wise variable selection, to make estimating such a potentially complex exposure-response function possible by effectively using two types of penalization (or regularization) of the multivariate exposure-response surface. Next we developed an extension of this first variable-selection approach that incorporates knowledge about how pollutants might group together, such as multiple constituents of particulate matter that might represent a common pollution source category. This second grouped, or hierarchical, variable-selection procedure is applicable when groups of highly correlated pollutants are being studied. To investigate the properties of the proposed methods, we conducted three simulation studies designed to evaluate the ability of BKMR to estimate environmental mixtures responsible for health effects under potentially complex but plausible exposure-response relationships. An attractive feature of our simulation studies is that we used actual exposure data rather than simulated values. This real-data simulation approach allowed us to evaluate the performance of BKMR and several other models under realistic joint distributions of multipollutant exposure. The simulation studies compared the two proposed variable-selection approaches (component-wise and hierarchical variable selection) with each other and with existing frequentist treatments of kernel machine regression (KMR). After the simulation studies, we applied the newly developed methods to an epidemiologic data set and to a toxicologic data set. To illustrate the applicability of the proposed methods to human epidemiologic data, we estimated associations between short-term exposures to fine particulate matter constituents and blood pressure in the Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston study, a prospective cohort study of elderly subjects. To illustrate the applicability of these methods to animal toxicologic studies, we analyzed data on the associations between both blood pressure and heart rate in canines exposed to a composition of concentrated ambient particles (CAPs) in a study conducted at the Harvard T. H. Chan School of Public Health (the Harvard Chan School; formerly Harvard School of Public Health; Bartoli et al. 2009). We successfully developed the theory and computational tools required to apply the proposed methods to the motivating data sets. Collectively, the three simulation studies showed that component-wise variable selection can identify important pollutants within a mixture as long as the correlations among pollutant concentrations are low to moderate. The hierarchical variable-selection method was more effective in high-dimension, high-correlation settings. Variable selection in existing frequentist KMR models can incur inflated type I error rates, particularly when pollutants are highly correlated. The analyses of the MOBILIZE data yielded evidence of a linear and additive association of black carbon (BC) or Cu exposure with standing diastolic blood pressure (DBP), and a linear association of S exposure with standing systolic blood pressure (SBP). Cu is thought to be a marker of urban road dust associated with traffic; and S is a marker of power plant emissions or regional long-range transported air pollution or both. Therefore, these analyses of the MOBILIZE data set suggest that emissions from these three source categories were most strongly associated with hemodynamic responses in this cohort. In contrast, in the Harvard Chan School canine study, after controlling for an overall effect of CAPs exposure, we did not observe any associations between DBP or SBP and any elemental concentrations. Instead, we observed strong evidence of an association between Mn concentrations and heart rate in that heart rate increased linearly with increasing concentrations of Mn. According to the positive matrix factorization (PMF) source apportionment analyses of the multipollutant data set from the Harvard Chan School Boston Supersite, Mn loads on the two factors that represent the mobile and road dust source categories. The results of the BKMR analyses in both the MOBILIZE and canine studies were similar to those from existing linear mixed model analyses of the same multipollutant data because the effects have linear and additive forms that could also have been detected using standard methods. This work provides several contributions to the KMR literature. First, to our knowledge this is the first time KMR methods have been used to estimate the health effects of multipollutant mixtures. Second, we developed a novel hierarchical variable-selection approach within BKMR that is able to account for the structure of the mixture and systematically handle highly correlated exposures. The analyses of the epidemiologic and toxicologic data on associations between fine particulate matter constituents and blood pressure or heart rate demonstrated associations with constituents that are typically associated with traffic emissions, power plants, and long-range transported pollutants. The simulation studies showed that the BKMR methods proposed here work well for small to moderate data sets; more work is needed to develop computationally fast methods for large data sets. This will be a goal of future work.

Quantifying the Contribution of Wind-Driven Linear Response to the Seasonal and Interannual Variability of Amoc Volume Transports Across 26.5ºN

NASA Astrophysics Data System (ADS)

Shimizu, K.; von Storch, J. S.; Haak, H.; Nakayama, K.; Marotzke, J.

2014-12-01

Surface wind stress is considered to be an important forcing of the seasonal and interannual variability of Atlantic Meridional Overturning Circulation (AMOC) volume transports. A recent study showed that even linear response to wind forcing captures observed features of the mean seasonal cycle. However, the study did not assess the contribution of wind-driven linear response in realistic conditions against the RAPID/MOCHA array observation or Ocean General Circulation Model (OGCM) simulations, because it applied a linear two-layer model to the Atlantic assuming constant upper layer thickness and density difference across the interface. Here, we quantify the contribution of wind-driven linear response to the seasonal and interannual variability of AMOC transports by comparing wind-driven linear simulations under realistic continuous stratification against the RAPID observation and OCGM (MPI-OM) simulations with 0.4º resolution (TP04) and 0.1º resolution (STORM). All the linear and MPI-OM simulations capture more than 60% of the variance in the observed mean seasonal cycle of the Upper Mid-Ocean (UMO) and Florida Strait (FS) transports, two components of the upper branch of the AMOC. The linear and TP04 simulations also capture 25-40% of the variance in the observed transport time series between Apr 2004 and Oct 2012; the STORM simulation does not capture the observed variance because of the stochastic signal in both datasets. Comparison of half-overlapping 12-month-long segments reveals some periods when the linear and TP04 simulations capture 40-60% of the observed variance, as well as other periods when the simulations capture only 0-20% of the variance. These results show that wind-driven linear response is a major contributor to the seasonal and interannual variability of the UMO and FS transports, and that its contribution varies in an interannual timescale, probably due to the variability of stochastic processes.

Identification of biological traits associated with differences in residual energy intake among lactating Holstein cows.

PubMed

Fischer, A; Delagarde, R; Faverdin, P

2018-05-01

Residual feed intake, which is usually used to estimate individual variation of feed efficiency, requires frequent and accurate measurements of individual feed intake to be carried out. Developing a breeding scheme based on residual feed intake in dairy cows is therefore complicated, especially because feed intake is not measurable for a large population. Another solution could be to focus on biological determinants of feed efficiency, which could potentially be directly and broadband measurable on farm. Several phenotypes have been identified in literature as being associated with differences in feed efficiency. The present study therefore aims to identify which biological mechanisms are associated with residual energy intake (REI) differences among dairy cows. Several candidate phenotypes were recorded frequently and simultaneously throughout the first 238 d in milk for 60 Holstein cows fed on a constant diet based on maize silage. A multiple linear regression of the 238 d in milk average of net energy intake was fitted on the 238 d in milk averages for milk energy output, metabolic body weight, the sum over the 238 d in milk of both, body condition score loss and gain, and the residuals were defined as REI. A partial least square regression was fitted over all biological traits to explain REI variability. Linear multiple regression explained 93.6% of net energy intake phenotypic variation, with 65.5% associated with lactation requirement, 23.2% with maintenance, and 4.9% with body reserves change; the 6.4% residuals represented REI. Overall, measured biological traits contributed to 58.9% of REI phenotypic variability, which were mainly explained by activity (26.5%) and feeding behavior (21.3%). However, apparent confounding was observed between behavior, activity, digestibility, and rumen-temperature variables. Drawing a conclusion on biological traits that explain feed efficiency differences among dairy cows was not possible due to this apparent confounding between the measured variables. Further investigation is needed to validate these results and to characterize the causal relationship of feed efficiency with feeding behavior, digestibility, body reserves change, activity, and rumen temperature. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks

PubMed Central

Shi, Fanrong; Tuo, Xianguo; Yang, Simon X.; Li, Huailiang; Shi, Rui

2017-01-01

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring. PMID:28471418

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks.

PubMed

Shi, Fanrong; Tuo, Xianguo; Yang, Simon X; Li, Huailiang; Shi, Rui

2017-05-04

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.